Package: a4
Version: 1.40.0
Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting
Suggests: MLP, nlcv, ALL, Cairo, Rgraphviz, GOstats
License: GPL-3
MD5sum: e2ac149c6780136ece005ede120035c3
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Umbrella Package
Description: Umbrella package is available for the entire Automated
Affymetrix Array Analysis suite of package.
biocViews: Microarray
Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud
[cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
git_url: https://git.bioconductor.org/packages/a4
git_branch: RELEASE_3_13
git_last_commit: f5e7837
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/a4_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/a4_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/a4_1.40.0.tgz
vignettes: vignettes/a4/inst/doc/a4vignette.pdf
vignetteTitles: a4vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/a4/inst/doc/a4vignette.R
dependencyCount: 81
Package: a4Base
Version: 1.40.0
Depends: a4Preproc, a4Core
Imports: methods, graphics, grid, Biobase, annaffy, mpm, genefilter,
limma, multtest, glmnet, gplots
Suggests: Cairo, ALL, hgu95av2.db, nlcv
Enhances: gridSVG, JavaGD
License: GPL-3
MD5sum: aa474675e93490a8847b794b6af33aa2
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Base Package
Description: Base utility functions are available for the Automated
Affymetrix Array Analysis set of packages.
biocViews: Microarray
Author: Willem Talloen [aut], Tine Casneuf [aut], An De Bondt [aut],
Steven Osselaer [aut], Hinrich Goehlmann [aut], Willem
Ligtenberg [aut], Tobias Verbeke [aut], Laure Cougnaud [cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
git_url: https://git.bioconductor.org/packages/a4Base
git_branch: RELEASE_3_13
git_last_commit: b2cd105
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/a4Base_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/a4Base_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/a4Base_1.40.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: a4
dependencyCount: 73
Package: a4Classif
Version: 1.40.0
Depends: a4Core, a4Preproc
Imports: methods, Biobase, ROCR, pamr, glmnet, varSelRF, utils,
graphics, stats
Suggests: ALL, hgu95av2.db, knitr, rmarkdown
License: GPL-3
MD5sum: 18794bef2876522232e1449fc9635c57
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Classification Package
Description: Functionalities for classification of Affymetrix
microarray data, integrating within the Automated Affymetrix
Array Analysis set of packages.
biocViews: Microarray, GeneExpression, Classification
Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud
[cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/a4Classif
git_branch: RELEASE_3_13
git_last_commit: 8fb3404
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/a4Classif_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/a4Classif_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/a4Classif_1.40.0.tgz
vignettes: vignettes/a4Classif/inst/doc/a4Classif-vignette.html
vignetteTitles: a4Classif package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/a4Classif/inst/doc/a4Classif-vignette.R
dependsOnMe: a4
dependencyCount: 30
Package: a4Core
Version: 1.40.0
Imports: Biobase, glmnet, methods, stats
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 8a703f3d78ecee9d5499d917140ab7c4
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Core Package
Description: Utility functions for the Automated Affymetrix Array
Analysis set of packages.
biocViews: Microarray, Classification
Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud
[cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/a4Core
git_branch: RELEASE_3_13
git_last_commit: e1ca087
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/a4Core_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/a4Core_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/a4Core_1.40.0.tgz
vignettes: vignettes/a4Core/inst/doc/a4Core-vignette.html
vignetteTitles: a4Core package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/a4Core/inst/doc/a4Core-vignette.R
dependsOnMe: a4, a4Base, a4Classif, nlcv
dependencyCount: 18
Package: a4Preproc
Version: 1.40.0
Imports: BiocGenerics, Biobase
Suggests: ALL, hgu95av2.db, knitr, rmarkdown
License: GPL-3
MD5sum: 4e2dbdacffcc1aac164978ff4eb0ff7c
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Preprocessing Package
Description: Utility functions to pre-process data for the Automated
Affymetrix Array Analysis set of packages.
biocViews: Microarray, Preprocessing
Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud
[cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/a4Preproc
git_branch: RELEASE_3_13
git_last_commit: 0fa3d10
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/a4Preproc_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/a4Preproc_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/a4Preproc_1.40.0.tgz
vignettes: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.html
vignetteTitles: a4Preproc package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.R
dependsOnMe: a4, a4Base, a4Classif
suggestsMe: graphite
dependencyCount: 7
Package: a4Reporting
Version: 1.40.0
Imports: methods, xtable
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: db9566798e7dca1875f68099ba7efba7
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Reporting Package
Description: Utility functions to facilitate the reporting of the
Automated Affymetrix Array Analysis Reporting set of packages.
biocViews: Microarray
Author: Tobias Verbeke [aut], Laure Cougnaud [cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/a4Reporting
git_branch: RELEASE_3_13
git_last_commit: 863239f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/a4Reporting_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/a4Reporting_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/a4Reporting_1.40.0.tgz
vignettes: vignettes/a4Reporting/inst/doc/a4reporting-vignette.html
vignetteTitles: a4Reporting package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/a4Reporting/inst/doc/a4reporting-vignette.R
dependsOnMe: a4
dependencyCount: 4
Package: ABAEnrichment
Version: 1.22.0
Depends: R (>= 3.4)
Imports: Rcpp (>= 0.11.5), gplots (>= 2.14.2), gtools (>= 3.5.0),
ABAData (>= 0.99.2), data.table (>= 1.10.4), GOfuncR (>=
1.1.2), grDevices, stats, graphics, utils
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, testthat
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 820a5bd18687e34d4385994c59fd836e
NeedsCompilation: yes
Title: Gene expression enrichment in human brain regions
Description: The package ABAEnrichment is designed to test for
enrichment of user defined candidate genes in the set of
expressed genes in different human brain regions. The core
function 'aba_enrich' integrates the expression of the
candidate gene set (averaged across donors) and the structural
information of the brain using an ontology, both provided by
the Allen Brain Atlas project. 'aba_enrich' interfaces the
ontology enrichment software FUNC to perform the statistical
analyses. Additional functions provided in this package like
'get_expression' and 'plot_expression' facilitate exploring the
expression data, and besides the standard candidate vs.
background gene set enrichment, also three additional tests are
implemented, e.g. for cases when genes are ranked instead of
divided into candidate and background.
biocViews: GeneSetEnrichment, GeneExpression
Author: Steffi Grote
Maintainer: Steffi Grote <steffi_grote@eva.mpg.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ABAEnrichment
git_branch: RELEASE_3_13
git_last_commit: fcc29c2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ABAEnrichment_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ABAEnrichment_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ABAEnrichment_1.22.0.tgz
vignettes: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.html
vignetteTitles: Introduction to ABAEnrichment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.R
suggestsMe: ABAData
dependencyCount: 60
Package: ABarray
Version: 1.60.0
Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk,
utils
Suggests: limma, LPE
License: GPL
MD5sum: bda82504584e53f0700375e698a4cd85
NeedsCompilation: no
Title: Microarray QA and statistical data analysis for Applied
Biosystems Genome Survey Microrarray (AB1700) gene expression
data.
Description: Automated pipline to perform gene expression analysis for
Applied Biosystems Genome Survey Microarray (AB1700) data
format. Functions include data preprocessing, filtering,
control probe analysis, statistical analysis in one single
function. A GUI interface is also provided. The raw data,
processed data, graphics output and statistical results are
organized into folders according to the analysis settings used.
biocViews: Microarray, OneChannel, Preprocessing
Author: Yongming Andrew Sun
Maintainer: Yongming Andrew Sun <sunya@appliedbiosystems.com>
git_url: https://git.bioconductor.org/packages/ABarray
git_branch: RELEASE_3_13
git_last_commit: 7e6ed61
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ABarray_1.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ABarray_1.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ABarray_1.60.0.tgz
vignettes: vignettes/ABarray/inst/doc/ABarray.pdf,
vignettes/ABarray/inst/doc/ABarrayGUI.pdf
vignetteTitles: ABarray gene expression, ABarray gene expression GUI
interface
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 17
Package: abseqR
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: ggplot2, RColorBrewer, circlize, reshape2, VennDiagram, plyr,
flexdashboard, BiocParallel (>= 1.1.25), png, grid, gridExtra,
rmarkdown, knitr, vegan, ggcorrplot, ggdendro, plotly,
BiocStyle, stringr, utils, methods, grDevices, stats, tools,
graphics
Suggests: testthat
License: GPL-3 | file LICENSE
MD5sum: c5b055b18ba9fb04532b7d5d19e5c91c
NeedsCompilation: no
Title: Reporting and data analysis functionalities for Rep-Seq datasets
of antibody libraries
Description: AbSeq is a comprehensive bioinformatic pipeline for the
analysis of sequencing datasets generated from antibody
libraries and abseqR is one of its packages. abseqR empowers
the users of abseqPy (https://github.com/malhamdoosh/abseqPy)
with plotting and reporting capabilities and allows them to
generate interactive HTML reports for the convenience of
viewing and sharing with other researchers. Additionally,
abseqR extends abseqPy to compare multiple repertoire analyses
and perform further downstream analysis on its output.
biocViews: Sequencing, Visualization, ReportWriting, QualityControl,
MultipleComparison
Author: JiaHong Fong [cre, aut], Monther Alhamdoosh [aut]
Maintainer: JiaHong Fong <jiahfong@gmail.com>
URL: https://github.com/malhamdoosh/abseqR
SystemRequirements: pandoc (>= 1.19.2.1)
VignetteBuilder: knitr
BugReports: https://github.com/malhamdoosh/abseqR/issues
git_url: https://git.bioconductor.org/packages/abseqR
git_branch: RELEASE_3_13
git_last_commit: 9ad3c88
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/abseqR_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/abseqR_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/abseqR_1.10.0.tgz
vignettes: vignettes/abseqR/inst/doc/abseqR.pdf
vignetteTitles: Introduction to abseqR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/abseqR/inst/doc/abseqR.R
dependencyCount: 109
Package: ABSSeq
Version: 1.46.0
Depends: R (>= 2.10), methods
Imports: locfit, limma
Suggests: edgeR
License: GPL (>= 3)
MD5sum: 0da0d9b8a382e84fa85baee07c00133a
NeedsCompilation: no
Title: ABSSeq: a new RNA-Seq analysis method based on modelling
absolute expression differences
Description: Inferring differential expression genes by absolute counts
difference between two groups, utilizing Negative binomial
distribution and moderating fold-change according to
heterogeneity of dispersion across expression level.
biocViews: DifferentialExpression
Author: Wentao Yang
Maintainer: Wentao Yang <wyang@zoologie.uni-kiel.de>
git_url: https://git.bioconductor.org/packages/ABSSeq
git_branch: RELEASE_3_13
git_last_commit: aae4a91
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ABSSeq_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ABSSeq_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ABSSeq_1.46.0.tgz
vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf
vignetteTitles: ABSSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ABSSeq/inst/doc/ABSSeq.R
importsMe: metaseqR2
dependencyCount: 9
Package: acde
Version: 1.22.0
Depends: R(>= 3.3), boot(>= 1.3)
Imports: stats, graphics
Suggests: BiocGenerics, RUnit
License: GPL-3
MD5sum: 862a91f5db1764982ffbe43c4bb44b42
NeedsCompilation: no
Title: Artificial Components Detection of Differentially Expressed
Genes
Description: This package provides a multivariate inferential analysis
method for detecting differentially expressed genes in gene
expression data. It uses artificial components, close to the
data's principal components but with an exact interpretation in
terms of differential genetic expression, to identify
differentially expressed genes while controlling the false
discovery rate (FDR). The methods on this package are described
in the vignette or in the article 'Multivariate Method for
Inferential Identification of Differentially Expressed Genes in
Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine
and S. Restrepo (2015, pending publication).
biocViews: DifferentialExpression, TimeCourse, PrincipalComponent,
GeneExpression, Microarray, mRNAMicroarray
Author: Juan Pablo Acosta, Liliana Lopez-Kleine
Maintainer: Juan Pablo Acosta <jpacostar@unal.edu.co>
git_url: https://git.bioconductor.org/packages/acde
git_branch: RELEASE_3_13
git_last_commit: db084f5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/acde_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/acde_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/acde_1.22.0.tgz
vignettes: vignettes/acde/inst/doc/acde.pdf
vignetteTitles: Identification of Differentially Expressed Genes with
Artificial Components
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/acde/inst/doc/acde.R
importsMe: coexnet
dependencyCount: 3
Package: ACE
Version: 1.10.0
Depends: R (>= 3.4)
Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods,
grDevices, GenomicRanges
Suggests: knitr, rmarkdown, BiocStyle
License: GPL-2
MD5sum: 736112617e52d5bbee29a269e28f6bbc
NeedsCompilation: no
Title: Absolute Copy Number Estimation from Low-coverage Whole Genome
Sequencing
Description: Uses segmented copy number data to estimate tumor cell
percentage and produce copy number plots displaying absolute
copy numbers.
biocViews: CopyNumberVariation, DNASeq, Coverage, WholeGenome,
Visualization, Sequencing
Author: Jos B Poell
Maintainer: Jos B Poell <j.poell@amsterdamumc.nl>
URL: https://github.com/tgac-vumc/ACE
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ACE
git_branch: RELEASE_3_13
git_last_commit: 791411a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ACE_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ACE_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ACE_1.10.0.tgz
vignettes: vignettes/ACE/inst/doc/ACE_vignette.html
vignetteTitles: ACE vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ACE/inst/doc/ACE_vignette.R
dependencyCount: 80
Package: aCGH
Version: 1.70.0
Depends: R (>= 2.10), cluster, survival, multtest
Imports: Biobase, grDevices, graphics, methods, stats, splines, utils
License: GPL-2
Archs: i386, x64
MD5sum: a8d7188a71f549c0428d5887040dd595
NeedsCompilation: yes
Title: Classes and functions for Array Comparative Genomic
Hybridization data
Description: Functions for reading aCGH data from image analysis output
files and clone information files, creation of aCGH S3 objects
for storing these data. Basic methods for accessing/replacing,
subsetting, printing and plotting aCGH objects.
biocViews: CopyNumberVariation, DataImport, Genetics
Author: Jane Fridlyand <jfridlyand@cc.ucsf.edu>, Peter Dimitrov
<dimitrov@stat.Berkeley.EDU>
Maintainer: Peter Dimitrov <dimitrov@stat.Berkeley.EDU>
git_url: https://git.bioconductor.org/packages/aCGH
git_branch: RELEASE_3_13
git_last_commit: e412576
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/aCGH_1.70.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/aCGH_1.70.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/aCGH_1.70.0.tgz
vignettes: vignettes/aCGH/inst/doc/aCGH.pdf
vignetteTitles: aCGH Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/aCGH/inst/doc/aCGH.R
dependsOnMe: CRImage
importsMe: ADaCGH2, snapCGH
suggestsMe: beadarraySNP
dependencyCount: 17
Package: ACME
Version: 2.48.0
Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics
Imports: graphics, stats
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 2c2356812f3def5c23ef7f11292859b0
NeedsCompilation: yes
Title: Algorithms for Calculating Microarray Enrichment (ACME)
Description: ACME (Algorithms for Calculating Microarray Enrichment) is
a set of tools for analysing tiling array ChIP/chip, DNAse
hypersensitivity, or other experiments that result in regions
of the genome showing "enrichment". It does not rely on a
specific array technology (although the array should be a
"tiling" array), is very general (can be applied in experiments
resulting in regions of enrichment), and is very insensitive to
array noise or normalization methods. It is also very fast and
can be applied on whole-genome tiling array experiments quite
easily with enough memory.
biocViews: Technology, Microarray, Normalization
Author: Sean Davis <sdavis2@mail.nih.gov>
Maintainer: Sean Davis <sdavis2@mail.nih.gov>
URL: http://watson.nci.nih.gov/~sdavis
git_url: https://git.bioconductor.org/packages/ACME
git_branch: RELEASE_3_13
git_last_commit: ae84fa6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ACME_2.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ACME_2.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ACME_2.48.0.tgz
vignettes: vignettes/ACME/inst/doc/ACME.pdf
vignetteTitles: ACME
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ACME/inst/doc/ACME.R
suggestsMe: oligo
dependencyCount: 7
Package: ADaCGH2
Version: 2.32.0
Depends: R (>= 3.2.0), parallel, ff, GLAD
Imports: bit, ffbase, DNAcopy, tilingArray, waveslim, cluster, aCGH,
snapCGH
Suggests: CGHregions, Cairo, limma
Enhances: Rmpi
License: GPL (>= 3)
Archs: i386, x64
MD5sum: 52ed5d30351b9daf10e1dc49ba8be8be
NeedsCompilation: yes
Title: Analysis of big data from aCGH experiments using parallel
computing and ff objects
Description: Analysis and plotting of array CGH data. Allows usage of
Circular Binary Segementation, wavelet-based smoothing (both as
in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM,
BioHMM, GLAD, CGHseg. Most computations are parallelized
(either via forking or with clusters, including MPI and sockets
clusters) and use ff for storing data.
biocViews: Microarray, CopyNumberVariants
Author: Ramon Diaz-Uriarte <rdiaz02@gmail.com> and Oscar M. Rueda
<rueda.om@gmail.com>. Wavelet-based aCGH smoothing code from Li
Hsu <lih@fhcrc.org> and Douglas Grove <dgrove@fhcrc.org>.
Imagemap code from Barry Rowlingson
<B.Rowlingson@lancaster.ac.uk>. HaarSeg code from Erez
Ben-Yaacov; downloaded from
<http://www.ee.technion.ac.il/people/YoninaEldar/Info/software/HaarSeg.htm>.
Maintainer: Ramon Diaz-Uriarte <rdiaz02@gmail.com>
URL: https://github.com/rdiaz02/adacgh2
git_url: https://git.bioconductor.org/packages/ADaCGH2
git_branch: RELEASE_3_13
git_last_commit: 43bdeb8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ADaCGH2_2.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ADaCGH2_2.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ADaCGH2_2.32.0.tgz
vignettes: vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf,
vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf,
vignettes/ADaCGH2/inst/doc/benchmarks.pdf
vignetteTitles: ADaCGH2-long-examples.pdf, ADaCGH2 Overview,
benchmarks.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ADaCGH2/inst/doc/ADaCGH2.R
dependencyCount: 101
Package: ADAM
Version: 1.8.0
Depends: R(>= 3.5), stats, utils, methods
Imports: Rcpp (>= 0.12.18), GO.db (>= 3.6.0), KEGGREST (>= 1.20.2),
knitr, pbapply (>= 1.3-4), dplyr (>= 0.7.6), DT (>= 0.4),
stringr (>= 1.3.1), SummarizedExperiment (>= 1.10.1)
LinkingTo: Rcpp
Suggests: testthat
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 6f1e08b7280d169f21c8205fe127d0a5
NeedsCompilation: yes
Title: ADAM: Activity and Diversity Analysis Module
Description: ADAM is a GSEA R package created to group a set of genes
from comparative samples (control versus experiment) belonging
to different species according to their respective functions
(Gene Ontology and KEGG pathways as default) and show their
significance by calculating p-values referring togene diversity
and activity. Each group of genes is called GFAG (Group of
Functionally Associated Genes).
biocViews: GeneSetEnrichment, Pathways, KEGG
Author: André Luiz Molan <andre.molan@unesp.br>, Giordano Bruno Sanches
Seco <giordano.bruno@unesp.br>, Agnes Alessandra Sekijima
Takeda <agnes.takeda@unesp.br>, Jose Luiz Rybarczyk Filho
<jose.luiz@unesp.br>
Maintainer: Jose Luiz Rybarczyk Filho <jose.luiz@unesp.br>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ADAM
git_branch: RELEASE_3_13
git_last_commit: e062ab5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ADAM_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ADAM_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ADAM_1.8.0.tgz
vignettes: vignettes/ADAM/inst/doc/ADAM.html
vignetteTitles: "Using ADAM"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ADAM/inst/doc/ADAM.R
dependsOnMe: ADAMgui
dependencyCount: 84
Package: ADAMgui
Version: 1.8.0
Depends: R(>= 3.6), stats, utils, methods, ADAM
Imports: GO.db (>= 3.5.0), dplyr (>= 0.7.6), shiny (>= 1.1.0), stringr
(>= 1.3.1), stringi (>= 1.2.4), varhandle (>= 2.0.3), ggplot2
(>= 3.0.0), ggrepel (>= 0.8.0), ggpubr (>= 0.1.8), ggsignif (>=
0.4.0), reshape2 (>= 1.4.3), RColorBrewer (>= 1.1-2),
colorRamps (>= 2.3), DT (>= 0.4), data.table (>= 1.11.4),
gridExtra (>= 2.3), shinyjs (>= 1.0), knitr, testthat
Suggests: BiocStyle
License: GPL (>= 2)
MD5sum: 9baf6690b94da51c92bbfad9edecdf18
NeedsCompilation: no
Title: Activity and Diversity Analysis Module Graphical User Interface
Description: ADAMgui is a Graphical User Interface for the ADAM
package. The ADAMgui package provides 2 shiny-based
applications that allows the user to study the output of the
ADAM package files through different plots. It's possible, for
example, to choose a specific GFAG and observe the gene
expression behavior with the plots created with the
GFAGtargetUi function. Features such as differential expression
and foldchange can be easily seen with aid of the plots made
with GFAGpathUi function.
biocViews: GeneSetEnrichment, Pathways, KEGG
Author: Giordano Bruno Sanches Seco <giordano.bruno@unesp.br>, André
Luiz Molan <andre.molan@unesp.br>, Agnes Alessandra Sekijima
Takeda <agnes.takeda@unesp.br>, Jose Luiz Rybarczyk Filho
<jose.luiz@unesp.br>
Maintainer: Jose Luiz Rybarczyk Filho <jose.luiz@unesp.br>
URL: TBA
VignetteBuilder: knitr
BugReports: https://github.com/jrybarczyk/ADAMgui/issues
git_url: https://git.bioconductor.org/packages/ADAMgui
git_branch: RELEASE_3_13
git_last_commit: 01ab40b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ADAMgui_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ADAMgui_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ADAMgui_1.8.0.tgz
vignettes: vignettes/ADAMgui/inst/doc/ADAMgui.html
vignetteTitles: "Using ADAMgui"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ADAMgui/inst/doc/ADAMgui.R
dependencyCount: 176
Package: adductomicsR
Version: 1.8.0
Depends: R (>= 3.6), adductData, ExperimentHub, AnnotationHub
Imports: parallel (>= 3.3.2), data.table (>= 1.10.4), OrgMassSpecR (>=
0.4.6), foreach (>= 1.4.3), mzR (>= 2.14.0), ade4 (>= 1.7.6),
rvest (>= 0.3.2), pastecs (>= 1.3.18), reshape2 (>= 1.4.2),
pracma (>= 2.0.4), DT (>= 0.2), fpc (>= 2.1.10), doSNOW (>=
1.0.14), fastcluster (>= 1.1.22), RcppEigen (>= 0.3.3.3.0),
bootstrap (>= 2017.2), smoother (>= 1.1), dplyr (>= 0.7.5), zoo
(>= 1.8), stats (>= 3.5.0), utils (>= 3.5.0), graphics (>=
3.5.0), grDevices (>= 3.5.0), methods (>= 3.5.0), datasets (>=
3.5.0)
Suggests: knitr (>= 1.15.1), rmarkdown (>= 1.5), Rdisop (>= 1.34.0),
testthat
License: Artistic-2.0
MD5sum: dde23d69cca629c593a45812a68c646b
NeedsCompilation: no
Title: Processing of adductomic mass spectral datasets
Description: Processes MS2 data to identify potentially adducted
peptides from spectra that has been corrected for mass drift
and retention time drift and quantifies MS1 level mass spectral
peaks.
biocViews:
MassSpectrometry,Metabolomics,Software,ThirdPartyClient,DataImport,
GUI
Author: Josie Hayes <jlhayes1982@gmail.com>
Maintainer: Josie Hayes <jlhayes1982@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/JosieLHayes/adductomicsR/issues
git_url: https://git.bioconductor.org/packages/adductomicsR
git_branch: RELEASE_3_13
git_last_commit: 778448c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/adductomicsR_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/adductomicsR_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/adductomicsR_1.8.0.tgz
vignettes: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.html
vignetteTitles: Adductomics workflow
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.R
dependencyCount: 137
Package: ADImpute
Version: 1.2.0
Depends: R (>= 4.0)
Imports: checkmate, BiocParallel, data.table, DrImpute, kernlab, MASS,
Matrix, methods, rsvd, S4Vectors, SAVER, SingleCellExperiment,
stats, SummarizedExperiment, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3 + file LICENSE
MD5sum: 422a4052353fce8d6e2b2778314043e9
NeedsCompilation: no
Title: Adaptive Dropout Imputer (ADImpute)
Description: Single-cell RNA sequencing (scRNA-seq) methods are
typically unable to quantify the expression levels of all genes
in a cell, creating a need for the computational prediction of
missing values (‘dropout imputation’). Most existing dropout
imputation methods are limited in the sense that they
exclusively use the scRNA-seq dataset at hand and do not
exploit external gene-gene relationship information. Here we
propose two novel methods: a gene regulatory network-based
approach using gene-gene relationships learnt from external
data and a baseline approach corresponding to a sample-wide
average. ADImpute can implement these novel methods and also
combine them with existing imputation methods (currently
supported: DrImpute, SAVER). ADImpute can learn the best
performing method per gene and combine the results from
different methods into an ensemble.
biocViews: GeneExpression, Network, Preprocessing, Sequencing,
SingleCell, Transcriptomics
Author: Ana Carolina Leote [cre, aut]
(<https://orcid.org/0000-0003-0879-328X>)
Maintainer: Ana Carolina Leote <anacarolinaleote@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/anacarolinaleote/ADImpute/issues
git_url: https://git.bioconductor.org/packages/ADImpute
git_branch: RELEASE_3_13
git_last_commit: 7c93ba7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ADImpute_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ADImpute_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ADImpute_1.2.0.tgz
vignettes: vignettes/ADImpute/inst/doc/ADImpute_tutorial.html
vignetteTitles: ADImpute tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ADImpute/inst/doc/ADImpute_tutorial.R
dependencyCount: 52
Package: adSplit
Version: 1.62.0
Depends: R (>= 2.1.0), methods (>= 2.1.0)
Imports: AnnotationDbi, Biobase (>= 1.5.12), cluster (>= 1.9.1), GO.db
(>= 1.8.1), graphics, grDevices, KEGGREST (>= 1.30.1), multtest
(>= 1.6.0), stats (>= 2.1.0)
Suggests: golubEsets (>= 1.0), vsn (>= 1.5.0), hu6800.db (>= 1.8.1)
License: GPL (>= 2)
Archs: i386, x64
MD5sum: ed4946aa72b1d5387b8947b7a3e77e65
NeedsCompilation: yes
Title: Annotation-Driven Clustering
Description: This package implements clustering of microarray gene
expression profiles according to functional annotations. For
each term genes are annotated to, splits into two subclasses
are computed and a significance of the supporting gene set is
determined.
biocViews: Microarray, Clustering
Author: Claudio Lottaz, Joern Toedling
Maintainer: Claudio Lottaz <Claudio.Lottaz@klinik.uni-regensburg.de>
URL: http://compdiag.molgen.mpg.de/software/adSplit.shtml
git_url: https://git.bioconductor.org/packages/adSplit
git_branch: RELEASE_3_13
git_last_commit: 2181a05
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/adSplit_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/adSplit_1.62.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/adSplit_1.62.0.tgz
vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf
vignetteTitles: Annotation-Driven Clustering
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/adSplit/inst/doc/tr_2005_02.R
dependencyCount: 55
Package: AffiXcan
Version: 1.10.0
Depends: R (>= 3.6), SummarizedExperiment
Imports: MultiAssayExperiment, BiocParallel, crayon
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 7b5434a4dad2cb8376ff074a9fd5607a
NeedsCompilation: no
Title: A Functional Approach To Impute Genetically Regulated Expression
Description: Impute a GReX (Genetically Regulated Expression) for a set
of genes in a sample of individuals, using a method based on
the Total Binding Affinity (TBA). Statistical models to impute
GReX can be trained with a training dataset where the real
total expression values are known.
biocViews: GeneExpression, Transcription, GeneRegulation,
DimensionReduction, Regression, PrincipalComponent
Author: Alessandro Lussana [aut, cre]
Maintainer: Alessandro Lussana <alessandro.lussana@protonmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AffiXcan
git_branch: RELEASE_3_13
git_last_commit: 80665cf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AffiXcan_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AffiXcan_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AffiXcan_1.10.0.tgz
vignettes: vignettes/AffiXcan/inst/doc/AffiXcan.html
vignetteTitles: AffiXcan
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AffiXcan/inst/doc/AffiXcan.R
dependencyCount: 54
Package: affxparser
Version: 1.64.1
Depends: R (>= 2.14.0)
Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles
License: LGPL (>= 2)
Archs: i386, x64
MD5sum: f78d57c891d7ac4d50a0183d57bf3f32
NeedsCompilation: yes
Title: Affymetrix File Parsing SDK
Description: Package for parsing Affymetrix files (CDF, CEL, CHP,
BPMAP, BAR). It provides methods for fast and memory efficient
parsing of Affymetrix files using the Affymetrix' Fusion SDK.
Both ASCII- and binary-based files are supported. Currently,
there are methods for reading chip definition file (CDF) and a
cell intensity file (CEL). These files can be read either in
full or in part. For example, probe signals from a few
probesets can be extracted very quickly from a set of CEL files
into a convenient list structure.
biocViews: Infrastructure, DataImport, Microarray,
ProprietaryPlatforms, OneChannel
Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman
[ctb], Kasper Daniel Hansen [aut, cre], Jim Hester [ctb],
Martin Morgan [ctb]
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
URL: https://github.com/HenrikBengtsson/affxparser
BugReports: https://github.com/HenrikBengtsson/affxparser/issues
git_url: https://git.bioconductor.org/packages/affxparser
git_branch: RELEASE_3_13
git_last_commit: 7863cf7
git_last_commit_date: 2021-09-09
Date/Publication: 2021-09-12
source.ver: src/contrib/affxparser_1.64.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/affxparser_1.64.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/affxparser_1.64.1.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: ITALICS, pdInfoBuilder
importsMe: affyILM, cn.farms, crossmeta, EventPointer, GCSscore,
GeneRegionScan, ITALICS, oligo
suggestsMe: TIN, aroma.affymetrix, aroma.apd
dependencyCount: 0
Package: affy
Version: 1.70.0
Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5)
Imports: affyio (>= 1.13.3), BiocManager, graphics, grDevices, methods,
preprocessCore, stats, utils, zlibbioc
LinkingTo: preprocessCore
Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools
License: LGPL (>= 2.0)
Archs: i386, x64
MD5sum: 82475b224f15947a29bb20f57dc754df
NeedsCompilation: yes
Title: Methods for Affymetrix Oligonucleotide Arrays
Description: The package contains functions for exploratory
oligonucleotide array analysis. The dependence on tkWidgets
only concerns few convenience functions. 'affy' is fully
functional without it.
biocViews: Microarray, OneChannel, Preprocessing
Author: Rafael A. Irizarry <rafa@ds.harvard.edu>, Laurent Gautier
<lgautier@gmail.com>, Benjamin Milo Bolstad
<bmb@bmbolstad.com>, and Crispin Miller
<cmiller@picr.man.ac.uk> with contributions from Magnus Astrand
<Magnus.Astrand@astrazeneca.com>, Leslie M. Cope
<cope@mts.jhu.edu>, Robert Gentleman, Jeff Gentry, Conrad
Halling <challing@agilixcorp.com>, Wolfgang Huber, James
MacDonald <jmacdon@u.washington.edu>, Benjamin I. P.
Rubinstein, Christopher Workman <workman@cbs.dtu.dk>, John
Zhang
Maintainer: Rafael A. Irizarry <rafa@ds.harvard.edu>
git_url: https://git.bioconductor.org/packages/affy
git_branch: RELEASE_3_13
git_last_commit: 9c32d61
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/affy_1.70.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/affy_1.70.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/affy_1.70.0.tgz
vignettes: vignettes/affy/inst/doc/affy.pdf,
vignettes/affy/inst/doc/builtinMethods.pdf,
vignettes/affy/inst/doc/customMethods.pdf,
vignettes/affy/inst/doc/vim.pdf
vignetteTitles: 1. Primer, 2. Built-in Processing Methods, 3. Custom
Processing Methods, 4. Import Methods
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affy/inst/doc/affy.R,
vignettes/affy/inst/doc/builtinMethods.R,
vignettes/affy/inst/doc/customMethods.R,
vignettes/affy/inst/doc/vim.R
dependsOnMe: affyContam, affyPara, affyPLM, AffyRNADegradation,
altcdfenvs, arrayMvout, bgx, Cormotif, DrugVsDisease, dualKS,
ExiMiR, farms, frmaTools, gcrma, logitT, maskBAD, panp, prebs,
qpcrNorm, RefPlus, Risa, RPA, SCAN.UPC, sscore, webbioc,
affydata, ALLMLL, AmpAffyExample, bronchialIL13, ccTutorial,
CLL, curatedBladderData, curatedOvarianData, ecoliLeucine,
Hiiragi2013, MAQCsubset, MAQCsubsetAFX, mvoutData,
PREDAsampledata, SpikeIn, SpikeInSubset, XhybCasneuf,
RobLoxBioC
importsMe: affycoretools, affyILM, affylmGUI, arrayQualityMetrics,
bnem, CAFE, ChIPXpress, coexnet, Cormotif, crossmeta, Doscheda,
farms, ffpe, frma, gcrma, GEOsubmission, Harshlight, HTqPCR,
iCheck, lumi, makecdfenv, mimager, MSnbase, PECA, plier, puma,
pvac, Rnits, STATegRa, tilingArray, TurboNorm, vsn,
rat2302frmavecs, DeSousa2013, signatureSearchData, bapred,
IsoGene
suggestsMe: AnnotationForge, ArrayExpress, autonomics, beadarray,
beadarraySNP, BiocGenerics, Biostrings, BufferedMatrixMethods,
categoryCompare, ecolitk, factDesign, GeneRegionScan, limma,
made4, piano, PREDA, qcmetrics, runibic, siggenes,
TCGAbiolinks, estrogen, ffpeExampleData, arrays,
aroma.affymetrix, hexbin, isatabr, maGUI
dependencyCount: 12
Package: affycomp
Version: 1.68.0
Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3)
Suggests: splines, affycompData
License: GPL (>= 2)
MD5sum: 9dfa85153c9f716d938d110bc297d2f2
NeedsCompilation: no
Title: Graphics Toolbox for Assessment of Affymetrix Expression
Measures
Description: The package contains functions that can be used to compare
expression measures for Affymetrix Oligonucleotide Arrays.
biocViews: OneChannel, Microarray, Preprocessing
Author: Rafael A. Irizarry <rafa@jhu.edu> and Zhijin Wu
<zwu@stat.brown.edu> with contributions from Simon Cawley
<simon_cawley@affymetrix.com>
Maintainer: Rafael A. Irizarry <rafa@jhu.edu>
git_url: https://git.bioconductor.org/packages/affycomp
git_branch: RELEASE_3_13
git_last_commit: 8b02704
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/affycomp_1.68.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/affycomp_1.68.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/affycomp_1.68.0.tgz
vignettes: vignettes/affycomp/inst/doc/affycomp.pdf
vignetteTitles: affycomp primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affycomp/inst/doc/affycomp.R
dependsOnMe: affycompData
dependencyCount: 7
Package: AffyCompatible
Version: 1.52.0
Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods
Imports: Biostrings
License: Artistic-2.0
MD5sum: 56ca6779f8f37b6bafef77cac749b726
NeedsCompilation: no
Title: Affymetrix GeneChip software compatibility
Description: This package provides an interface to Affymetrix chip
annotation and sample attribute files. The package allows an
easy way for users to download and manage local data bases of
Affynmetrix NetAffx annotation files. The package also provides
access to GeneChip Operating System (GCOS) and GeneChip Command
Console (AGCC)-compatible sample annotation files.
biocViews: Infrastructure, Microarray, OneChannel
Author: Martin Morgan, Robert Gentleman
Maintainer: Martin Morgan <martin.morgan@roswellpark.org>
git_url: https://git.bioconductor.org/packages/AffyCompatible
git_branch: RELEASE_3_13
git_last_commit: 7dc4c06
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AffyCompatible_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AffyCompatible_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AffyCompatible_1.52.0.tgz
vignettes: vignettes/AffyCompatible/inst/doc/MAGEAndARR.pdf,
vignettes/AffyCompatible/inst/doc/NetAffxResource.pdf
vignetteTitles: Retrieving MAGE and ARR sample attributes, Annotation
retrieval with NetAffxResource
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AffyCompatible/inst/doc/MAGEAndARR.R,
vignettes/AffyCompatible/inst/doc/NetAffxResource.R
dependencyCount: 20
Package: affyContam
Version: 1.50.0
Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata
Suggests: hgu95av2cdf
License: Artistic-2.0
MD5sum: 305d215ef67ef4b77cc061dbd81d3a8b
NeedsCompilation: no
Title: structured corruption of affymetrix cel file data
Description: structured corruption of cel file data to demonstrate QA
effectiveness
biocViews: Infrastructure
Author: V. Carey
Maintainer: V. Carey <stvjc@channing.harvard.edu>
git_url: https://git.bioconductor.org/packages/affyContam
git_branch: RELEASE_3_13
git_last_commit: bb3b0e7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/affyContam_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/affyContam_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/affyContam_1.50.0.tgz
vignettes: vignettes/affyContam/inst/doc/affyContam.pdf
vignetteTitles: affy contamination tools
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affyContam/inst/doc/affyContam.R
importsMe: arrayMvout
dependencyCount: 15
Package: affycoretools
Version: 1.64.0
Depends: Biobase, methods
Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi,
ggplot2, gplots, oligoClasses, ReportingTools, hwriter,
lattice, S4Vectors, edgeR, RSQLite, BiocGenerics, DBI, Glimma
Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl,
rmarkdown
License: Artistic-2.0
MD5sum: 158cfe006d0bd8f924a0d2dc7937699d
NeedsCompilation: no
Title: Functions useful for those doing repetitive analyses with
Affymetrix GeneChips
Description: Various wrapper functions that have been written to
streamline the more common analyses that a core Biostatistician
might see.
biocViews: ReportWriting, Microarray, OneChannel, GeneExpression
Author: James W. MacDonald
Maintainer: James W. MacDonald <jmacdon@u.washington.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/affycoretools
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git_last_commit: ed4f10c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
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win.binary.ver: bin/windows/contrib/4.1/affycoretools_1.64.0.zip
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vignettes:
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vignetteTitles: Creating annotated output with \Biocpkg{affycoretools}
and ReportingTools
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hasNEWS: TRUE
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Rfiles: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R
suggestsMe: EnMCB
dependencyCount: 188
Package: affyILM
Version: 1.44.0
Depends: R (>= 2.10.0), methods, gcrma
Imports: affxparser (>= 1.16.0), affy, graphics, Biobase
Suggests: AffymetrixDataTestFiles, hgfocusprobe
License: GPL-3
MD5sum: d3970dab4f20df85e8d724403d722f71
NeedsCompilation: no
Title: Linear Model of background subtraction and the Langmuir isotherm
Description: affyILM is a preprocessing tool which estimates gene
expression levels for Affymetrix Gene Chips. Input from
physical chemistry is employed to first background subtract
intensities before calculating concentrations on behalf of the
Langmuir model.
biocViews: Microarray, OneChannel, Preprocessing
Author: K. Myriam Kroll, Fabrice Berger, Gerard Barkema, Enrico Carlon
Maintainer: Myriam Kroll and Fabrice Berger <fabrice.berger@gmail.com>
git_url: https://git.bioconductor.org/packages/affyILM
git_branch: RELEASE_3_13
git_last_commit: 5d0305d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/affyILM_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/affyILM_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/affyILM_1.44.0.tgz
vignettes: vignettes/affyILM/inst/doc/affyILM.pdf
vignetteTitles: affyILM1.3.0
hasREADME: FALSE
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hasINSTALL: FALSE
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Rfiles: vignettes/affyILM/inst/doc/affyILM.R
dependencyCount: 27
Package: affyio
Version: 1.62.0
Depends: R (>= 2.6.0)
Imports: zlibbioc, methods
License: LGPL (>= 2)
Archs: i386, x64
MD5sum: 4bfadfb205c3b1473de05353aaffb5aa
NeedsCompilation: yes
Title: Tools for parsing Affymetrix data files
Description: Routines for parsing Affymetrix data files based upon file
format information. Primary focus is on accessing the CEL and
CDF file formats.
biocViews: Microarray, DataImport, Infrastructure
Author: Ben Bolstad <bmb@bmbolstad.com>
Maintainer: Ben Bolstad <bmb@bmbolstad.com>
URL: https://github.com/bmbolstad/affyio
git_url: https://git.bioconductor.org/packages/affyio
git_branch: RELEASE_3_13
git_last_commit: caa75be
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/affyio_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/affyio_1.62.0.zip
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hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: affyPara, makecdfenv, SCAN.UPC, sscore
importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses,
puma
suggestsMe: BufferedMatrixMethods
dependencyCount: 2
Package: affylmGUI
Version: 1.66.0
Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma,
affy, affyio, affyPLM, gcrma, BiocGenerics, AnnotationDbi,
BiocManager, R2HTML, xtable
License: GPL (>=2)
MD5sum: 40bafdc8dc4fe5b97d1d02c8e0bba2e8
NeedsCompilation: no
Title: GUI for limma Package with Affymetrix Microarrays
Description: A Graphical User Interface (GUI) for analysis of
Affymetrix microarray gene expression data using the affy and
limma packages.
biocViews: GUI, GeneExpression, Transcription, DifferentialExpression,
DataImport, Bayesian, Regression, TimeCourse, Microarray,
mRNAMicroarray, OneChannel, ProprietaryPlatforms, BatchEffect,
MultipleComparison, Normalization, Preprocessing,
QualityControl
Author: James Wettenhall [cre,aut], Gordon Smyth [aut], Ken Simpson
[aut], Keith Satterley [ctb]
Maintainer: Gordon Smyth <smyth@wehi.edu.au>
URL: http://bioinf.wehi.edu.au/affylmGUI/
git_url: https://git.bioconductor.org/packages/affylmGUI
git_branch: RELEASE_3_13
git_last_commit: ff6d6f1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/affylmGUI_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/affylmGUI_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/affylmGUI_1.66.0.tgz
vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf,
vignettes/affylmGUI/inst/doc/extract.pdf,
vignettes/affylmGUI/inst/doc/about.html,
vignettes/affylmGUI/inst/doc/CustMenu.html,
vignettes/affylmGUI/inst/doc/index.html,
vignettes/affylmGUI/inst/doc/windowsFocus.html
vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects
from affylmGUI files, about.html, CustMenu.html, index.html,
windowsFocus.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R
dependencyCount: 58
Package: affyPara
Version: 1.51.0
Depends: R (>= 2.5.0), methods, affy (>= 1.20.0), snow (>= 0.2-3), vsn
(>= 3.6.0), aplpack (>= 1.1.1), affyio
Suggests: affydata
Enhances: affy
License: GPL-3
MD5sum: b93cb65ed755bfad43699ee7c6080e7d
NeedsCompilation: no
Title: Parallelized preprocessing methods for Affymetrix
Oligonucleotide Arrays
Description: The package contains parallelized functions for
exploratory oligonucleotide array analysis. The package is
designed for large numbers of microarray data.
biocViews: Microarray, Preprocessing
Author: Markus Schmidberger <schmidb@ibe.med.uni-muenchen.de>,
Esmeralda Vicedo <e.vicedo@gmx.net>, Ulrich Mansmann
<mansmann@ibe.med.uni-muenchen.de>
Maintainer: Markus Schmidberger <MSchmidberger@freenet.de>
URL: http://www.ibe.med.uni-muenchen.de
git_url: https://git.bioconductor.org/packages/affyPara
git_branch: master
git_last_commit: a919225
git_last_commit_date: 2020-10-27
Date/Publication: 2021-03-19
source.ver: src/contrib/affyPara_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/affyPara_1.51.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/affyPara_1.51.0.tgz
vignettes: vignettes/affyPara/inst/doc/affyPara.pdf,
vignettes/affyPara/inst/doc/vsnStudy.pdf
vignetteTitles: Parallelized affy functions for preprocessing,
Simulation Study for VSN Add-On Normalization and Subsample
Size
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affyPara/inst/doc/affyPara.R,
vignettes/affyPara/inst/doc/vsnStudy.R
dependencyCount: 50
Package: affyPLM
Version: 1.68.0
Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0),
Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1)
Imports: zlibbioc, graphics, grDevices, methods
LinkingTo: preprocessCore
Suggests: affydata, MASS
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 8eea702e7fd9c62efe5c0c08017e5b15
NeedsCompilation: yes
Title: Methods for fitting probe-level models
Description: A package that extends and improves the functionality of
the base affy package. Routines that make heavy use of compiled
code for speed. Central focus is on implementation of methods
for fitting probe-level models and tools using these models.
PLM based quality assessment tools.
biocViews: Microarray, OneChannel, Preprocessing, QualityControl
Author: Ben Bolstad <bmb@bmbolstad.com>
Maintainer: Ben Bolstad <bmb@bmbolstad.com>
URL: https://github.com/bmbolstad/affyPLM
git_url: https://git.bioconductor.org/packages/affyPLM
git_branch: RELEASE_3_13
git_last_commit: 492a50a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/affyPLM_1.68.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/affyPLM_1.68.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/affyPLM_1.68.0.tgz
vignettes: vignettes/affyPLM/inst/doc/AffyExtensions.pdf,
vignettes/affyPLM/inst/doc/MAplots.pdf,
vignettes/affyPLM/inst/doc/QualityAssess.pdf,
vignettes/affyPLM/inst/doc/ThreeStep.pdf
vignetteTitles: affyPLM: Fitting Probe Level Models, affyPLM: Advanced
use of the MAplot function, affyPLM: Model Based QC Assessment
of Affymetrix GeneChips, affyPLM: the threestep function
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affyPLM/inst/doc/AffyExtensions.R,
vignettes/affyPLM/inst/doc/MAplots.R,
vignettes/affyPLM/inst/doc/QualityAssess.R,
vignettes/affyPLM/inst/doc/ThreeStep.R
dependsOnMe: RefPlus, bapred
importsMe: affylmGUI, arrayQualityMetrics, mimager
suggestsMe: arrayMvout, BiocGenerics, frmaTools, metahdep, piano,
aroma.affymetrix
dependencyCount: 26
Package: AffyRNADegradation
Version: 1.38.0
Depends: R (>= 2.9.0), methods, affy
Suggests: AmpAffyExample
License: GPL-2
MD5sum: bfab6a1ede8973a84e8eef7c9d3ed57c
NeedsCompilation: no
Title: Analyze and correct probe positional bias in microarray data due
to RNA degradation
Description: The package helps with the assessment and correction of
RNA degradation effects in Affymetrix 3' expression arrays. The
parameter d gives a robust and accurate measure of RNA
integrity. The correction removes the probe positional bias,
and thus improves comparability of samples that are affected by
RNA degradation.
biocViews: GeneExpression, Microarray, OneChannel, Preprocessing,
QualityControl
Author: Mario Fasold
Maintainer: Mario Fasold <fasold@izbi.uni-leipzig.de>
git_url: https://git.bioconductor.org/packages/AffyRNADegradation
git_branch: RELEASE_3_13
git_last_commit: fee09ea
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AffyRNADegradation_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AffyRNADegradation_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AffyRNADegradation_1.38.0.tgz
vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf
vignetteTitles: AffyRNADegradation Example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AffyRNADegradation/inst/doc/vignette.R
dependencyCount: 13
Package: AGDEX
Version: 1.40.0
Depends: R (>= 2.10), Biobase, GSEABase
Imports: stats
License: GPL Version 2 or later
MD5sum: 85f854216b2860d1f2e4e604846746f8
NeedsCompilation: no
Title: Agreement of Differential Expression Analysis
Description: A tool to evaluate agreement of differential expression
for cross-species genomics
biocViews: Microarray, Genetics, GeneExpression
Author: Stan Pounds <stanley.pounds@stjude.org>; Cuilan Lani Gao
<cuilan.gao@stjude.org>
Maintainer: Cuilan lani Gao <cuilan.gao@stjude.org>
git_url: https://git.bioconductor.org/packages/AGDEX
git_branch: RELEASE_3_13
git_last_commit: a2c2223
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AGDEX_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AGDEX_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AGDEX_1.40.0.tgz
vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf
vignetteTitles: AGDEX.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AGDEX/inst/doc/AGDEX.R
dependencyCount: 51
Package: aggregateBioVar
Version: 1.2.0
Depends: R (>= 4.0)
Imports: stats, methods, S4Vectors, SummarizedExperiment,
SingleCellExperiment, Matrix, tibble, rlang
Suggests: BiocStyle, magick, knitr, rmarkdown, testthat, BiocGenerics,
DESeq2, magrittr, dplyr, ggplot2, cowplot, ggtext,
RColorBrewer, pheatmap, viridis
License: GPL-3
MD5sum: d06c41b1c5251f6e548a17e4cac5e127
NeedsCompilation: no
Title: Differential Gene Expression Analysis for Multi-subject
scRNA-seq
Description: For single cell RNA-seq data collected from more than one
subject (e.g. biological sample or technical replicates), this
package contains tools to summarize single cell gene expression
profiles at the level of subject. A SingleCellExperiment object
is taken as input and converted to a list of
SummarizedExperiment objects, where each list element
corresponds to an assigned cell type. The SummarizedExperiment
objects contain aggregate gene-by-subject count matrices and
inter-subject column metadata for individual subjects that can
be processed using downstream bulk RNA-seq tools.
biocViews: Software, SingleCell, RNASeq, Transcriptomics,
Transcription, GeneExpression, DifferentialExpression
Author: Jason Ratcliff [aut, cre]
(<https://orcid.org/0000-0001-7079-334X>), Andrew Thurman
[aut], Michael Chimenti [ctb], Alejandro Pezzulo [ctb]
Maintainer: Jason Ratcliff <jason-ratcliff@uiowa.edu>
URL: https://github.com/jasonratcliff/aggregateBioVar
VignetteBuilder: knitr
BugReports: https://github.com/jasonratcliff/aggregateBioVar/issues
git_url: https://git.bioconductor.org/packages/aggregateBioVar
git_branch: RELEASE_3_13
git_last_commit: 70ba9dd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/aggregateBioVar_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/aggregateBioVar_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/aggregateBioVar_1.2.0.tgz
vignettes:
vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.html
vignetteTitles: Multi-subject scRNA-seq Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.R
dependencyCount: 40
Package: agilp
Version: 3.24.0
Depends: R (>= 2.14.0)
License: GPL-3
MD5sum: 59c0289df832bba9bee4264195f54f28
NeedsCompilation: no
Title: Agilent expression array processing package
Description: More about what it does (maybe more than one line)
Author: Benny Chain <b.chain@ucl.ac.uk>
Maintainer: Benny Chain <b.chain@ucl.ac.uk>
git_url: https://git.bioconductor.org/packages/agilp
git_branch: RELEASE_3_13
git_last_commit: 3346a25
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/agilp_3.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/agilp_3.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/agilp_3.24.0.tgz
vignettes: vignettes/agilp/inst/doc/agilp_manual.pdf
vignetteTitles: An R Package for processing expression microarray data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/agilp/inst/doc/agilp_manual.R
dependencyCount: 0
Package: AgiMicroRna
Version: 2.42.0
Depends: R (>= 2.10),methods,Biobase,limma,affy (>=
1.22),preprocessCore,affycoretools
Imports: Biobase
Suggests: geneplotter,marray,gplots,gtools,gdata,codelink
License: GPL-3
MD5sum: 57df1b9fccb0120161ddd90533e75a4b
NeedsCompilation: no
Title: Processing and Differential Expression Analysis of Agilent
microRNA chips
Description: Processing and Analysis of Agilent microRNA data
biocViews: Microarray, AgilentChip, OneChannel, Preprocessing,
DifferentialExpression
Author: Pedro Lopez-Romero <plopez@cnic.es>
Maintainer: Pedro Lopez-Romero <plopez@cnic.es>
git_url: https://git.bioconductor.org/packages/AgiMicroRna
git_branch: RELEASE_3_13
git_last_commit: 5f453f7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AgiMicroRna_2.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AgiMicroRna_2.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AgiMicroRna_2.42.0.tgz
vignettes: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.pdf
vignetteTitles: AgiMicroRna
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.R
dependencyCount: 189
Package: AIMS
Version: 1.24.0
Depends: R (>= 2.10), e1071, Biobase
Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: dca02f8473ed734a4913b69646b96698
NeedsCompilation: no
Title: AIMS : Absolute Assignment of Breast Cancer Intrinsic Molecular
Subtype
Description: This package contains the AIMS implementation. It contains
necessary functions to assign the five intrinsic molecular
subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like,
Normal-like). Assignments could be done on individual samples
as well as on dataset of gene expression data.
biocViews: ImmunoOncology, Classification, RNASeq, Microarray,
Software, GeneExpression
Author: Eric R. Paquet, Michael T. Hallett
Maintainer: Eric R Paquet <eric.r.paquet@gmail.com>
URL: http://www.bci.mcgill.ca/AIMS
git_url: https://git.bioconductor.org/packages/AIMS
git_branch: RELEASE_3_13
git_last_commit: 78a7be5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AIMS_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AIMS_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AIMS_1.24.0.tgz
vignettes: vignettes/AIMS/inst/doc/AIMS.pdf
vignetteTitles: AIMS An Introduction (HowTo)
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AIMS/inst/doc/AIMS.R
dependsOnMe: genefu
dependencyCount: 12
Package: airpart
Version: 1.0.1
Depends: R (>= 4.0)
Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, scater,
stats, smurf, apeglm (>= 1.13.3), emdbook, mclust, clue,
dynamicTreeCut, matrixStats, dplyr, plyr, ggplot2,
ComplexHeatmap, forestplot, RColorBrewer, rlang, lpSolve, grid,
grDevices, graphics, utils, pbapply
Suggests: knitr, rmarkdown, roxygen2 (>= 6.0.0), testthat (>= 3.0.0),
gplots
License: GPL-2
MD5sum: ecb0ae32c2e2f9f9bed84ff1a6c42c66
NeedsCompilation: no
Title: Differential cell-type-specific allelic imbalance
Description: Airpart identifies sets of genes displaying differential
cell-type-specific allelic imbalance across cell types or
states, utilizing single-cell allelic counts. It makes use of a
generalized fused lasso with binomial observations of allelic
counts to partition cell types by their allelic imbalance.
Alternatively, a nonparametric method for partitioning cell
types is offered. The package includes a number of
visualizations and quality control functions for examining
single cell allelic imbalance datasets.
biocViews: SingleCell, RNASeq, ATACSeq, ChIPSeq, Sequencing,
GeneRegulation, GeneExpression, Transcription,
TranscriptomeVariant, CellBiology, FunctionalGenomics,
DifferentialExpression, GraphAndNetwork, Regression,
Clustering, QualityControl
Author: Wancen Mu [aut, cre] (<https://orcid.org/0000-0002-5061-7581>),
Michael Love [aut, ctb]
(<https://orcid.org/0000-0001-8401-0545>)
Maintainer: Wancen Mu <wancen@live.unc.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/airpart
git_branch: RELEASE_3_13
git_last_commit: 39ff0e5
git_last_commit_date: 2021-08-23
Date/Publication: 2021-08-24
source.ver: src/contrib/airpart_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/airpart_1.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/airpart_1.0.1.tgz
vignettes: vignettes/airpart/inst/doc/airpart.html
vignetteTitles: Differential allelic imbalance with airpart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/airpart/inst/doc/airpart.R
dependencyCount: 123
Package: ALDEx2
Version: 1.24.0
Depends: methods, stats, zCompositions, Rfast
Imports: BiocParallel, GenomicRanges, IRanges, S4Vectors,
SummarizedExperiment, multtest
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: file LICENSE
MD5sum: 23f3608fbd47f707dbc4c13cbb1a5a06
NeedsCompilation: no
Title: Analysis Of Differential Abundance Taking Sample Variation Into
Account
Description: A differential abundance analysis for the comparison of
two or more conditions. Useful for analyzing data from standard
RNA-seq or meta-RNA-seq assays as well as selected and
unselected values from in-vitro sequence selections. Uses a
Dirichlet-multinomial model to infer abundance from counts,
optimized for three or more experimental replicates. The method
infers biological and sampling variation to calculate the
expected false discovery rate, given the variation, based on a
Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a
Kruskal-Wallis test (via aldex.kw), a generalized linear model
(via aldex.glm), or a correlation test (via aldex.corr). All
tests report p-values and Benjamini-Hochberg corrected
p-values.
biocViews: DifferentialExpression, RNASeq, Transcriptomics,
GeneExpression, DNASeq, ChIPSeq, Bayesian, Sequencing,
Software, Microbiome, Metagenomics, ImmunoOncology
Author: Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert,
Matt Links, Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon
Lieng
Maintainer: Greg Gloor <ggloor@uwo.ca>
URL: https://github.com/ggloor/ALDEx_bioc
VignetteBuilder: knitr
BugReports: https://github.com/ggloor/ALDEx_bioc/issues
git_url: https://git.bioconductor.org/packages/ALDEx2
git_branch: RELEASE_3_13
git_last_commit: a41b778
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ALDEx2_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ALDEx2_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ALDEx2_1.24.0.tgz
vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html
vignetteTitles: ANOVA-Like Differential Expression tool for high
throughput sequencing data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R
dependsOnMe: omicplotR
suggestsMe: propr
dependencyCount: 45
Package: alevinQC
Version: 1.8.0
Depends: R (>= 4.0)
Imports: rmarkdown (>= 2.5), tools, methods, ggplot2, GGally, dplyr,
rjson, shiny, shinydashboard, DT, stats, utils, tximport (>=
1.17.4), cowplot, rlang
Suggests: knitr, BiocStyle, testthat
License: MIT + file LICENSE
MD5sum: 3bad4dc843ed3b31c0e6bb43bd1a6700
NeedsCompilation: no
Title: Generate QC Reports For Alevin Output
Description: Generate QC reports summarizing the output from an alevin
run. Reports can be generated as html or pdf files, or as shiny
applications.
biocViews: QualityControl, SingleCell
Author: Charlotte Soneson [aut, cre]
(<https://orcid.org/0000-0003-3833-2169>), Avi Srivastava [aut]
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/csoneson/alevinQC
VignetteBuilder: knitr
BugReports: https://github.com/csoneson/alevinQC/issues
git_url: https://git.bioconductor.org/packages/alevinQC
git_branch: RELEASE_3_13
git_last_commit: b6880ce
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/alevinQC_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/alevinQC_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/alevinQC_1.8.0.tgz
vignettes: vignettes/alevinQC/inst/doc/alevinqc.html
vignetteTitles: alevinQC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alevinQC/inst/doc/alevinqc.R
dependencyCount: 89
Package: AllelicImbalance
Version: 1.30.0
Depends: R (>= 4.0.0), grid, GenomicRanges (>= 1.31.8),
SummarizedExperiment (>= 0.2.0), GenomicAlignments (>= 1.15.6)
Imports: methods, BiocGenerics, AnnotationDbi, BSgenome (>= 1.47.3),
VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6),
S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Rsamtools (>=
1.99.3), GenomicFeatures (>= 1.31.3), Gviz, lattice,
latticeExtra, gridExtra, seqinr, GenomeInfoDb, nlme
Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene,
SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: ddd690215d718ecdc98117d4aba6f0d0
NeedsCompilation: no
Title: Investigates Allele Specific Expression
Description: Provides a framework for allelic specific expression
investigation using RNA-seq data.
biocViews: Genetics, Infrastructure, Sequencing
Author: Jesper R Gadin, Lasse Folkersen
Maintainer: Jesper R Gadin <j.r.gadin@gmail.com>
URL: https://github.com/pappewaio/AllelicImbalance
VignetteBuilder: knitr
BugReports: https://github.com/pappewaio/AllelicImbalance/issues
git_url: https://git.bioconductor.org/packages/AllelicImbalance
git_branch: RELEASE_3_13
git_last_commit: 5e00a1f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AllelicImbalance_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AllelicImbalance_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AllelicImbalance_1.30.0.tgz
vignettes:
vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf
vignetteTitles: AllelicImbalance Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.R
dependencyCount: 147
Package: AlphaBeta
Version: 1.6.1
Depends: R (>= 3.6.0)
Imports: dplyr (>= 0.7), data.table (>= 1.10), stringr (>= 1.3), utils
(>= 3.6.0), gtools (>= 3.8.0), optimx (>= 2018-7.10), expm (>=
0.999-4), stats (>= 3.6), BiocParallel (>= 1.18), igraph (>=
1.2.4), graphics (>= 3.6), ggplot2 (>= 3.2), grDevices (>=
3.6), plotly (>= 4.9)
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: a3d911a6feb7e12eb7f90193d1752ccb
NeedsCompilation: no
Title: Computational inference of epimutation rates and spectra from
high-throughput DNA methylation data in plants
Description: AlphaBeta is a computational method for estimating
epimutation rates and spectra from high-throughput DNA
methylation data in plants. The method has been specifically
designed to: 1. analyze 'germline' epimutations in the context
of multi-generational mutation accumulation lines (MA-lines).
2. analyze 'somatic' epimutations in the context of plant
development and aging.
biocViews: Epigenetics, FunctionalGenomics, Genetics,
MathematicalBiology
Author: Yadollah Shahryary Dizaji [cre, aut], Frank Johannes [aut],
Rashmi Hazarika [aut]
Maintainer: Yadollah Shahryary Dizaji <shahryary@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AlphaBeta
git_branch: RELEASE_3_13
git_last_commit: cdfa5f6
git_last_commit_date: 2021-07-28
Date/Publication: 2021-07-29
source.ver: src/contrib/AlphaBeta_1.6.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AlphaBeta_1.6.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/AlphaBeta_1.6.1.tgz
vignettes: vignettes/AlphaBeta/inst/doc/AlphaBeta.pdf
vignetteTitles: AlphaBeta
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/AlphaBeta/inst/doc/AlphaBeta.R
dependencyCount: 79
Package: alpine
Version: 1.18.0
Depends: R (>= 3.3)
Imports: Biostrings, IRanges, GenomicRanges, GenomicAlignments,
Rsamtools, SummarizedExperiment, GenomicFeatures, speedglm,
splines, graph, RBGL, stringr, stats, methods, graphics,
GenomeInfoDb, S4Vectors
Suggests: knitr, testthat, alpineData, rtracklayer, ensembldb,
BSgenome.Hsapiens.NCBI.GRCh38, RColorBrewer
License: GPL (>=2)
MD5sum: 2929e9cb8297f544b46d60d323a49089
NeedsCompilation: no
Title: alpine
Description: Fragment sequence bias modeling and correction for RNA-seq
transcript abundance estimation.
biocViews: Sequencing, RNASeq, AlternativeSplicing,
DifferentialSplicing, GeneExpression, Transcription, Coverage,
BatchEffect, Normalization, Visualization, QualityControl
Author: Michael Love, Rafael Irizarry
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alpine
git_branch: RELEASE_3_13
git_last_commit: 06257d2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/alpine_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/alpine_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/alpine_1.18.0.tgz
vignettes: vignettes/alpine/inst/doc/alpine.html
vignetteTitles: alpine
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/alpine/inst/doc/alpine.R
dependencyCount: 101
Package: ALPS
Version: 1.5.0
Depends: R (>= 3.6)
Imports: assertthat, BiocParallel, ChIPseeker, corrplot, data.table,
dplyr, GenomicRanges, GGally, genefilter, gghalves, ggplot2,
ggseqlogo, Gviz, magrittr, org.Hs.eg.db, plyr, reshape2,
rtracklayer, stats, stringr, tibble, tidyr,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene, utils
Suggests: knitr, rmarkdown, ComplexHeatmap, circlize, testthat
License: MIT + file LICENSE
MD5sum: fb71399778ec443bed72deaf8a54b4b1
NeedsCompilation: no
Title: AnaLysis routines for ePigenomicS data
Description: The package provides analysis and publication quality
visualization routines for genome-wide epigenomics data such as
histone modification or transcription factor ChIP-seq,
ATAC-seq, DNase-seq etc. The functions in the package can be
used with any type of data that can be represented with bigwig
files at any resolution. The goal of the ALPS is to provide
analysis tools for most downstream analysis without leaving the
R environment and most tools in the package require a minimal
input that can be prepared with basic R, unix or excel skills.
biocViews: Epigenetics, Sequencing, ChIPSeq, ATACSeq, Visualization,
Transcription, HistoneModification
Author: Venu Thatikonda, Natalie Jäger
Maintainer: Venu Thatikonda <thatikonda92@gmail.com>
URL: https://github.com/itsvenu/ALPS
VignetteBuilder: knitr
BugReports: https://github.com/itsvenu/ALPS/issues
git_url: https://git.bioconductor.org/packages/ALPS
git_branch: master
git_last_commit: 6ea885b
git_last_commit_date: 2020-10-27
Date/Publication: 2021-03-19
source.ver: src/contrib/ALPS_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ALPS_1.5.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ALPS_1.5.0.tgz
vignettes: vignettes/ALPS/inst/doc/ALPS-vignette.html
vignetteTitles: ALPS-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ALPS/inst/doc/ALPS-vignette.R
dependencyCount: 196
Package: AlpsNMR
Version: 3.2.3
Depends: R (>= 4.0), dplyr (>= 0.7.5), future (>= 1.10.0), magrittr (>=
1.5)
Imports: utils, graphics, stats, grDevices, signal (>= 0.7-6),
assertthat (>= 0.2.0), rlang (>= 0.3.0.1), stringr (>= 1.3.1),
tibble(>= 1.3.4), tidyr (>= 1.0.0), readxl (>= 1.1.0), plyr (>=
1.8.4), purrr (>= 0.2.5), glue (>= 1.2.0), reshape2 (>= 1.4.3),
GGally (>= 1.4.0), mixOmics (>= 6.3.2), matrixStats (>=
0.54.0), writexl (>= 1.0), fs (>= 1.2.6), rmarkdown (>= 1.10),
speaq (>= 2.4.0), htmltools (>= 0.3.6), ggrepel (>= 0.8.0),
pcaPP (>= 1.9-73), furrr (>= 0.1.0), ggplot2 (>= 3.1.0),
baseline (>= 1.2-1), zip (>= 2.0.4), tidyselect (>= 0.2.5),
vctrs (>= 0.3.0), BiocParallel, SummarizedExperiment, S4Vectors
Suggests: DT (>= 0.5), testthat (>= 2.0.0), plotly (>= 4.7.1),
ChemoSpec, knitr
License: MIT + file LICENSE
MD5sum: 0cda75eee828f0e1c09342ff4552c349
NeedsCompilation: no
Title: Automated spectraL Processing System for NMR
Description: Reads Bruker NMR data directories both zipped and
unzipped. It provides automated and efficient signal processing
for untargeted NMR metabolomics. It is able to interpolate the
samples, detect outliers, exclude regions, normalize, detect
peaks, align the spectra, integrate peaks, manage metadata and
visualize the spectra. After spectra proccessing, it can apply
multivariate analysis on extracted data. Efficient plotting
with 1-D data is also available. Basic reading of 1D ACD/Labs
exported JDX samples is also available.
biocViews: Software, Preprocessing, Visualization, Classification,
Cheminformatics, Metabolomics, DataImport
Author: Ivan Montoliu Roura [aut], Sergio Oller Moreno [aut]
(<https://orcid.org/0000-0002-8994-1549>), Francisco Madrid
Gambin [aut] (<https://orcid.org/0000-0001-9333-0014>), Luis
Fernandez [aut, cre] (<https://orcid.org/0000-0001-9790-6287>),
Héctor Gracia Cabrera [aut], Santiago Marco Colás [aut]
(<https://orcid.org/0000-0003-2663-2965>), Nestlé Institute of
Health Sciences [cph], Institute for Bioengineering of
Catalonia [cph]
Maintainer: Luis Fernandez <lfernandez@ibecbarcelona.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AlpsNMR
git_branch: RELEASE_3_13
git_last_commit: 92db448
git_last_commit_date: 2021-09-16
Date/Publication: 2021-09-19
source.ver: src/contrib/AlpsNMR_3.2.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AlpsNMR_3.2.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/AlpsNMR_3.2.3.tgz
vignettes: vignettes/AlpsNMR/inst/doc/introduction-to-alpsnmr.html
vignetteTitles: Introduction to AlpsNMR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/AlpsNMR/inst/doc/introduction-to-alpsnmr.R
dependencyCount: 150
Package: alsace
Version: 1.28.0
Depends: R (>= 2.10), ALS, ptw (>= 1.0.6)
Suggests: lattice, knitr
License: GPL (>= 2)
MD5sum: 8b86ccfc02ccd7edf39b355d4811e9a0
NeedsCompilation: no
Title: ALS for the Automatic Chemical Exploration of mixtures
Description: Alternating Least Squares (or Multivariate Curve
Resolution) for analytical chemical data, in particular
hyphenated data where the first direction is a retention time
axis, and the second a spectral axis. Package builds on the
basic als function from the ALS package and adds functionality
for high-throughput analysis, including definition of time
windows, clustering of profiles, retention time correction,
etcetera.
Author: Ron Wehrens
Maintainer: Ron Wehrens <ron.wehrens@gmail.com>
URL: https://github.com/rwehrens/alsace
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alsace
git_branch: RELEASE_3_13
git_last_commit: 03344a1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/alsace_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/alsace_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/alsace_1.28.0.tgz
vignettes: vignettes/alsace/inst/doc/alsace.pdf
vignetteTitles: alsace
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
importsMe: tofsims
dependencyCount: 8
Package: altcdfenvs
Version: 2.54.0
Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), S4Vectors (>=
0.9.25), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings,
hypergraph
Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db,
hgu133acdf, Rgraphviz, RColorBrewer
License: GPL (>= 2)
MD5sum: 5c5b04cff87de11467bc04488b36ae2a
NeedsCompilation: no
Title: alternative CDF environments (aka probeset mappings)
Description: Convenience data structures and functions to handle
cdfenvs
biocViews: Microarray, OneChannel, QualityControl, Preprocessing,
Annotation, ProprietaryPlatforms, Transcription
Author: Laurent Gautier <lgautier@gmail.com>
Maintainer: Laurent Gautier <lgautier@gmail.com>
git_url: https://git.bioconductor.org/packages/altcdfenvs
git_branch: RELEASE_3_13
git_last_commit: fbe5728
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/altcdfenvs_2.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/altcdfenvs_2.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/altcdfenvs_2.54.0.tgz
vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf,
vignettes/altcdfenvs/inst/doc/modify.pdf,
vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf
vignetteTitles: altcdfenvs, Modifying existing CDF environments to make
alternative CDF environments, Alternative CDF environments for
2(or more)-genomes chips
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/altcdfenvs/inst/doc/altcdfenvs.R,
vignettes/altcdfenvs/inst/doc/modify.R,
vignettes/altcdfenvs/inst/doc/ngenomeschips.R
importsMe: Harshlight
dependencyCount: 27
Package: AMARETTO
Version: 1.8.0
Depends: R (>= 3.6), impute, doParallel, grDevices, dplyr, methods,
ComplexHeatmap
Imports: callr (>= 3.0.0.9001), Matrix, Rcpp, BiocFileCache, DT,
MultiAssayExperiment, circlize, curatedTCGAData, foreach,
glmnet, httr, limma, matrixStats, readr, reshape2, tibble,
rmarkdown, graphics, grid, parallel, stats, knitr, ggplot2,
gridExtra, utils
LinkingTo: Rcpp
Suggests: testthat, MASS, knitr
License: Apache License (== 2.0) + file LICENSE
MD5sum: 508c18b6d4b6cfe066c8577d345484fd
NeedsCompilation: no
Title: Regulatory Network Inference and Driver Gene Evaluation using
Integrative Multi-Omics Analysis and Penalized Regression
Description: Integrating an increasing number of available multi-omics
cancer data remains one of the main challenges to improve our
understanding of cancer. One of the main challenges is using
multi-omics data for identifying novel cancer driver genes. We
have developed an algorithm, called AMARETTO, that integrates
copy number, DNA methylation and gene expression data to
identify a set of driver genes by analyzing cancer samples and
connects them to clusters of co-expressed genes, which we
define as modules. We applied AMARETTO in a pancancer setting
to identify cancer driver genes and their modules on multiple
cancer sites. AMARETTO captures modules enriched in
angiogenesis, cell cycle and EMT, and modules that accurately
predict survival and molecular subtypes. This allows AMARETTO
to identify novel cancer driver genes directing canonical
cancer pathways.
biocViews:
StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,Transcription,Preprocessing,BatchEffect,DataImport,mRNAMicroarray,MicroRNAArray,Regression,Clustering,RNASeq,CopyNumberVariation,Sequencing,Microarray,Normalization,Network,Bayesian,ExonArray,OneChannel,TwoChannel,ProprietaryPlatforms,AlternativeSplicing,DifferentialExpression,DifferentialSplicing,GeneSetEnrichment,MultipleComparison,QualityControl,TimeCourse
Author: Jayendra Shinde, Celine Everaert, Shaimaa Bakr, Mohsen Nabian,
Jishu Xu, Vincent Carey, Nathalie Pochet and Olivier Gevaert
Maintainer: Olivier Gevaert <olivier.gevaert@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AMARETTO
git_branch: RELEASE_3_13
git_last_commit: 3a94300
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AMARETTO_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AMARETTO_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AMARETTO_1.8.0.tgz
vignettes: vignettes/AMARETTO/inst/doc/amaretto.html
vignetteTitles: "1. Introduction"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/AMARETTO/inst/doc/amaretto.R
dependencyCount: 156
Package: AMOUNTAIN
Version: 1.18.0
Depends: R (>= 3.3.0)
Imports: stats
Suggests: BiocStyle, qgraph, knitr, rmarkdown
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 80fcf1be99e98f0142e53b7b0948474a
NeedsCompilation: yes
Title: Active modules for multilayer weighted gene co-expression
networks: a continuous optimization approach
Description: A pure data-driven gene network, weighted gene
co-expression network (WGCN) could be constructed only from
expression profile. Different layers in such networks may
represent different time points, multiple conditions or various
species. AMOUNTAIN aims to search active modules in multi-layer
WGCN using a continuous optimization approach.
biocViews: GeneExpression, Microarray, DifferentialExpression, Network
Author: Dong Li, Shan He, Zhisong Pan and Guyu Hu
Maintainer: Dong Li <dxl466@cs.bham.ac.uk>
SystemRequirements: gsl
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AMOUNTAIN
git_branch: RELEASE_3_13
git_last_commit: b3cec6e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AMOUNTAIN_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AMOUNTAIN_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AMOUNTAIN_1.18.0.tgz
vignettes: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.R
importsMe: MODA
dependencyCount: 1
Package: amplican
Version: 1.14.0
Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.22.0), Biostrings
(>= 2.44.2), data.table (>= 1.10.4-3)
Imports: Rcpp, utils (>= 3.4.1), S4Vectors (>= 0.14.3), ShortRead (>=
1.34.0), IRanges (>= 2.10.2), GenomicRanges (>= 1.28.4),
GenomeInfoDb (>= 1.12.2), BiocParallel (>= 1.10.1), gtable (>=
0.2.0), gridExtra (>= 2.2.1), ggplot2 (>= 2.2.0), ggthemes (>=
3.4.0), waffle (>= 0.7.0), stringr (>= 1.2.0), stats (>=
3.4.1), matrixStats (>= 0.52.2), Matrix (>= 1.2-10), dplyr (>=
0.7.2), rmarkdown (>= 1.6), knitr (>= 1.16), clusterCrit (>=
1.2.7)
LinkingTo: Rcpp
Suggests: testthat, BiocStyle, GenomicAlignments
License: GPL-3
Archs: i386, x64
MD5sum: e870d031eee9696c3b1fa9e81700d09c
NeedsCompilation: yes
Title: Automated analysis of CRISPR experiments
Description: `amplican` performs alignment of the amplicon reads,
normalizes gathered data, calculates multiple statistics (e.g.
cut rates, frameshifts) and presents results in form of
aggregated reports. Data and statistics can be broken down by
experiments, barcodes, user defined groups, guides and
amplicons allowing for quick identification of potential
problems.
biocViews: ImmunoOncology, Technology, Alignment, qPCR, CRISPR
Author: Kornel Labun [aut], Eivind Valen [cph, cre]
Maintainer: Eivind Valen <eivind.valen@gmail.com>
URL: https://github.com/valenlab/amplican
VignetteBuilder: knitr
BugReports: https://github.com/valenlab/amplican/issues
git_url: https://git.bioconductor.org/packages/amplican
git_branch: RELEASE_3_13
git_last_commit: e994104
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/amplican_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/amplican_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/amplican_1.14.0.tgz
vignettes: vignettes/amplican/inst/doc/amplicanFAQ.html,
vignettes/amplican/inst/doc/amplicanOverview.html,
vignettes/amplican/inst/doc/example_amplicon_report.html,
vignettes/amplican/inst/doc/example_barcode_report.html,
vignettes/amplican/inst/doc/example_group_report.html,
vignettes/amplican/inst/doc/example_guide_report.html,
vignettes/amplican/inst/doc/example_id_report.html,
vignettes/amplican/inst/doc/example_index.html
vignetteTitles: amplican FAQ, amplican overview, example
amplicon_report report, example barcode_report report, example
group_report report, example guide_report report, example
id_report report, example index report
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/amplican/inst/doc/amplicanOverview.R,
vignettes/amplican/inst/doc/example_amplicon_report.R,
vignettes/amplican/inst/doc/example_barcode_report.R,
vignettes/amplican/inst/doc/example_group_report.R,
vignettes/amplican/inst/doc/example_guide_report.R,
vignettes/amplican/inst/doc/example_id_report.R,
vignettes/amplican/inst/doc/example_index.R
dependencyCount: 99
Package: Anaquin
Version: 2.16.0
Depends: R (>= 3.3), ggplot2 (>= 2.2.0)
Imports: ggplot2, ROCR, knitr, qvalue, locfit, methods, stats, utils,
plyr, DESeq2
Suggests: RUnit, rmarkdown
License: BSD_3_clause + file LICENSE
MD5sum: 1f29ca74d362bf16c74b231efb2666af
NeedsCompilation: no
Title: Statistical analysis of sequins
Description: The project is intended to support the use of sequins
(synthetic sequencing spike-in controls) owned and made
available by the Garvan Institute of Medical Research. The goal
is to provide a standard open source library for quantitative
analysis, modelling and visualization of spike-in controls.
biocViews: ImmunoOncology, DifferentialExpression, Preprocessing,
RNASeq, GeneExpression, Software
Author: Ted Wong
Maintainer: Ted Wong <t.wong@garvan.org.au>
URL: www.sequin.xyz
VignetteBuilder: knitr
BugReports: https://github.com/student-t/RAnaquin/issues
git_url: https://git.bioconductor.org/packages/Anaquin
git_branch: RELEASE_3_13
git_last_commit: a2406fb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Anaquin_2.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Anaquin_2.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Anaquin_2.16.0.tgz
vignettes: vignettes/Anaquin/inst/doc/Anaquin.pdf
vignetteTitles: Anaquin - Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Anaquin/inst/doc/Anaquin.R
dependencyCount: 108
Package: ANCOMBC
Version: 1.2.2
Imports: stats, MASS, nloptr, Rdpack, phyloseq, microbiome
Suggests: knitr, tidyverse, testthat, DT, magrittr, qwraps2 (>= 0.5.0),
rmarkdown
License: Artistic-2.0
MD5sum: 52be737254459224c2d72509f1fce9d4
NeedsCompilation: no
Title: Analysis of compositions of microbiomes with bias correction
Description: ANCOMBC is a package for normalizing the microbial
observed abundance data due to unequal sampling fractions
across samples, and identifying taxa (e.g. phyla, families,
genera, species, etc.) that are differentially abundant with
respect to the covariate of interest (e.g. study groups)
between two or more groups of multiple samples.
biocViews: DifferentialExpression, Microbiome, Normalization,
Sequencing, Software
Author: Huang Lin [cre, aut] (<https://orcid.org/0000-0002-4892-7871>),
Shyamal Das Peddada [aut]
(<https://orcid.org/0000-0002-5014-6513>)
Maintainer: Huang Lin <HUL40@pitt.edu>
URL: https://github.com/FrederickHuangLin/ANCOMBC
VignetteBuilder: knitr
BugReports: https://github.com/FrederickHuangLin/ANCOMBC/issues
git_url: https://git.bioconductor.org/packages/ANCOMBC
git_branch: RELEASE_3_13
git_last_commit: fa2dd53
git_last_commit_date: 2021-08-13
Date/Publication: 2021-08-15
source.ver: src/contrib/ANCOMBC_1.2.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ANCOMBC_1.2.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/ANCOMBC_1.2.2.tgz
vignettes: vignettes/ANCOMBC/inst/doc/ANCOMBC.html
vignetteTitles: ANCOMBC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ANCOMBC/inst/doc/ANCOMBC.R
dependencyCount: 88
Package: AneuFinder
Version: 1.20.0
Depends: R (>= 3.5), GenomicRanges, ggplot2, cowplot, AneuFinderData
Imports: methods, utils, grDevices, graphics, stats, foreach,
doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb,
IRanges, Rsamtools, bamsignals, DNAcopy, ecp, Biostrings,
GenomicAlignments, reshape2, ggdendro, ggrepel, ReorderCluster,
mclust
Suggests: knitr, BiocStyle, testthat, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm10
License: Artistic-2.0
Archs: i386, x64
MD5sum: 53ca2dbbb874c91f4a140c8061405c25
NeedsCompilation: yes
Title: Analysis of Copy Number Variation in Single-Cell-Sequencing Data
Description: AneuFinder implements functions for copy-number detection,
breakpoint detection, and karyotype and heterogeneity analysis
in single-cell whole genome sequencing and strand-seq data.
biocViews: ImmunoOncology, Software, Sequencing, SingleCell,
CopyNumberVariation, GenomicVariation, HiddenMarkovModel,
WholeGenome
Author: Aaron Taudt, Bjorn Bakker, David Porubsky
Maintainer: Aaron Taudt <aaron.taudt@gmail.com>
URL: https://github.com/ataudt/aneufinder.git
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AneuFinder
git_branch: RELEASE_3_13
git_last_commit: 1c7aed6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AneuFinder_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AneuFinder_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AneuFinder_1.20.0.tgz
vignettes: vignettes/AneuFinder/inst/doc/AneuFinder.pdf
vignetteTitles: A quick introduction to AneuFinder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AneuFinder/inst/doc/AneuFinder.R
dependencyCount: 89
Package: ANF
Version: 1.14.0
Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer
Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat
License: GPL-3
MD5sum: 82aa86db8efa639ca7b01fc3a4aed243
NeedsCompilation: no
Title: Affinity Network Fusion for Complex Patient Clustering
Description: This package is used for complex patient clustering by
integrating multi-omic data through affinity network fusion.
biocViews: Clustering, GraphAndNetwork, Network
Author: Tianle Ma, Aidong Zhang
Maintainer: Tianle Ma <tianlema@buffalo.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ANF
git_branch: RELEASE_3_13
git_last_commit: cec6e3e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-06-13
source.ver: src/contrib/ANF_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ANF_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ANF_1.14.0.tgz
vignettes: vignettes/ANF/inst/doc/ANF.html
vignetteTitles: Cancer Patient Clustering with ANF
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ANF/inst/doc/ANF.R
suggestsMe: HarmonizedTCGAData
dependencyCount: 18
Package: animalcules
Version: 1.8.1
Depends: R (>= 4.0.0)
Imports: assertthat, shiny, shinyjs, DESeq2, caret, plotly, ggplot2,
rentrez, reshape2, covr, ape, vegan, dplyr, magrittr,
MultiAssayExperiment, SummarizedExperiment, S4Vectors (>=
0.23.19), XML, forcats, scales, lattice, glmnet, tsne, plotROC,
DT, reactable, utils, limma, methods, stats, tibble,
biomformat, umap, Matrix, GUniFrac
Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis
License: Artistic-2.0
MD5sum: cb593b25ad849989fadd66b0e01e8738
NeedsCompilation: no
Title: Interactive microbiome analysis toolkit
Description: animalcules is an R package for utilizing up-to-date data
analytics, visualization methods, and machine learning models
to provide users an easy-to-use interactive microbiome analysis
framework. It can be used as a standalone software package or
users can explore their data with the accompanying interactive
R Shiny application. Traditional microbiome analysis such as
alpha/beta diversity and differential abundance analysis are
enhanced, while new methods like biomarker identification are
introduced by animalcules. Powerful interactive and dynamic
figures generated by animalcules enable users to understand
their data better and discover new insights.
biocViews: Microbiome, Metagenomics, Coverage, Visualization
Author: Yue Zhao [aut, cre] (<https://orcid.org/0000-0001-5257-5103>),
Anthony Federico [aut]
(<https://orcid.org/0000-0002-9200-1689>), W. Evan Johnson
[aut] (<https://orcid.org/0000-0002-6247-6595>)
Maintainer: Yue Zhao <yuezh@bu.edu>
URL: https://github.com/compbiomed/animalcules
VignetteBuilder: knitr
BugReports: https://github.com/compbiomed/animalcules/issues
git_url: https://git.bioconductor.org/packages/animalcules
git_branch: RELEASE_3_13
git_last_commit: 5a3dc66
git_last_commit_date: 2021-06-02
Date/Publication: 2021-06-03
source.ver: src/contrib/animalcules_1.8.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/animalcules_1.8.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/animalcules_1.8.1.tgz
vignettes: vignettes/animalcules/inst/doc/animalcules.html
vignetteTitles: animalcules
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/animalcules/inst/doc/animalcules.R
dependencyCount: 184
Package: annaffy
Version: 1.64.2
Depends: R (>= 2.5.0), methods, Biobase, BiocManager, GO.db
Imports: AnnotationDbi (>= 0.1.15), DBI
Suggests: hgu95av2.db, multtest, tcltk
License: LGPL
MD5sum: 89b99be5e6bb3f1bc439a541285727df
NeedsCompilation: no
Title: Annotation tools for Affymetrix biological metadata
Description: Functions for handling data from Bioconductor Affymetrix
annotation data packages. Produces compact HTML and text
reports including experimental data and URL links to many
online databases. Allows searching biological metadata using
various criteria.
biocViews: OneChannel, Microarray, Annotation, GO, Pathways,
ReportWriting
Author: Colin A. Smith <colin@colinsmith.org>
Maintainer: Colin A. Smith <colin@colinsmith.org>
git_url: https://git.bioconductor.org/packages/annaffy
git_branch: RELEASE_3_13
git_last_commit: e4b2445
git_last_commit_date: 2021-06-19
Date/Publication: 2021-06-20
source.ver: src/contrib/annaffy_1.64.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/annaffy_1.64.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/annaffy_1.64.2.tgz
vignettes: vignettes/annaffy/inst/doc/annaffy.pdf
vignetteTitles: annaffy Primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/annaffy/inst/doc/annaffy.R
dependsOnMe: webbioc
importsMe: a4Base
suggestsMe: metaMA
dependencyCount: 48
Package: annmap
Version: 1.34.0
Depends: R (>= 2.15.0), methods, GenomicRanges
Imports: DBI, RMySQL (>= 0.6-0), digest, Biobase, grid, lattice,
Rsamtools, genefilter, IRanges, BiocGenerics
Suggests: RUnit, rjson, Gviz
License: GPL-2
MD5sum: b6cafcdb5d1153daecdc95030f9398c4
NeedsCompilation: no
Title: Genome annotation and visualisation package pertaining to
Affymetrix arrays and NGS analysis.
Description: annmap provides annotation mappings for Affymetrix exon
arrays and coordinate based queries to support deep sequencing
data analysis. Database access is hidden behind the API which
provides a set of functions such as genesInRange(),
geneToExon(), exonDetails(), etc. Functions to plot gene
architecture and BAM file data are also provided. Underlying
data are from Ensembl.
biocViews: Annotation, Microarray, OneChannel, ReportWriting,
Transcription, Visualization
Author: Tim Yates <Tim.Yates@cruk.manchester.ac.uk>
Maintainer: Chris Wirth <Christopher.Wirth@cruk.manchester.ac.uk>
URL: http://annmap.cruk.manchester.ac.uk
git_url: https://git.bioconductor.org/packages/annmap
git_branch: RELEASE_3_13
git_last_commit: c79bf2e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/annmap_1.34.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/annmap_1.34.0.tgz
vignettes: vignettes/annmap/inst/doc/annmap.pdf,
vignettes/annmap/inst/doc/cookbook.pdf,
vignettes/annmap/inst/doc/INSTALL.pdf
vignetteTitles: annmap primer, The Annmap Cookbook, annmap installation
instruction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 67
Package: annotate
Version: 1.70.0
Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML
Imports: Biobase, DBI, xtable, graphics, utils, stats, methods,
BiocGenerics (>= 0.13.8), httr
Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges,
rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db,
org.Mm.eg.db, humanCHRLOC, Rgraphviz, RUnit,
License: Artistic-2.0
MD5sum: 1e911d1dce3a13cc46f89b020be4bedf
NeedsCompilation: no
Title: Annotation for microarrays
Description: Using R enviroments for annotation.
biocViews: Annotation, Pathways, GO
Author: R. Gentleman
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/annotate
git_branch: RELEASE_3_13
git_last_commit: 9c8cb9d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
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win.binary.ver: bin/windows/contrib/4.1/annotate_1.70.0.zip
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vignettes/annotate/inst/doc/GOusage.pdf,
vignettes/annotate/inst/doc/prettyOutput.pdf,
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vignettes/annotate/inst/doc/useDataPkgs.pdf,
vignettes/annotate/inst/doc/useProbeInfo.pdf
vignetteTitles: Annotation Overview, HowTo: use chromosomal
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the online query tools, Using Data Packages, Using Affymetrix
Probe Level Data
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Package: AnnotationDbi
Version: 1.54.1
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License: Artistic-2.0
MD5sum: d4dbc895bd2de2dca145ad86d96a28c9
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Title: Manipulation of SQLite-based annotations in Bioconductor
Description: Implements a user-friendly interface for querying
SQLite-based annotation data packages.
biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation
Author: Hervé Pagès, Marc Carlson, Seth Falcon, Nianhua Li
Maintainer: Bioconductor Package Maintainer
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URL: https://bioconductor.org/packages/AnnotationDbi
VignetteBuilder: knitr
Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik
BugReports: https://github.com/Bioconductor/AnnotationDbi/issues
git_url: https://git.bioconductor.org/packages/AnnotationDbi
git_branch: RELEASE_3_13
git_last_commit: 0040118
git_last_commit_date: 2021-06-07
Date/Publication: 2021-06-08
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vignetteTitles: 2. (Deprecated) How to use bimaps from the ".db"
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hgu133b.db, hgu133bprobe, hgu133plus2.db, hgu133plus2probe,
hgu219.db, hgu219probe, hgu95a.db, hgu95aprobe, hgu95av2.db,
hgu95av2probe, hgu95b.db, hgu95bprobe, hgu95c.db, hgu95cprobe,
hgu95d.db, hgu95dprobe, hgu95e.db, hgu95eprobe, hguatlas13k.db,
hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db,
hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db,
hguqiagenv3.db, hi16cod.db, Homo.sapiens, hs25kresogen.db,
Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db,
hta20transcriptcluster.db, hthgu133a.db, hthgu133aprobe,
hthgu133b.db, hthgu133bprobe, hthgu133plusa.db,
hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmprobe,
htmg430a.db, htmg430aprobe, htmg430b.db, htmg430bprobe,
htmg430pm.db, htmg430pmprobe, htrat230pm.db, htrat230pmprobe,
htratfocus.db, htratfocusprobe, hu35ksuba.db, hu35ksubaprobe,
hu35ksubb.db, hu35ksubbprobe, hu35ksubc.db, hu35ksubcprobe,
hu35ksubd.db, hu35ksubdprobe, hu6800.db, hu6800probe,
huex10stprobeset.db, huex10sttranscriptcluster.db,
HuExExonProbesetLocation, HuExExonProbesetLocationHg18,
HuExExonProbesetLocationHg19, hugene10stprobeset.db,
hugene10sttranscriptcluster.db, hugene10stv1probe,
hugene11stprobeset.db, hugene11sttranscriptcluster.db,
hugene20stprobeset.db, hugene20sttranscriptcluster.db,
hugene21stprobeset.db, hugene21sttranscriptcluster.db,
human.db0, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db,
IlluminaHumanMethylation450kprobe, illuminaHumanv1.db,
illuminaHumanv2.db, illuminaHumanv2BeadID.db,
illuminaHumanv3.db, illuminaHumanv4.db,
illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db,
illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db,
illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db,
lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db,
lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping,
m10kcod.db, m20kcod.db, maizeprobe, malaria.db0, medicagoprobe,
mgu74a.db, mgu74aprobe, mgu74av2.db, mgu74av2probe, mgu74b.db,
mgu74bprobe, mgu74bv2.db, mgu74bv2probe, mgu74c.db,
mgu74cprobe, mgu74cv2.db, mgu74cv2probe, mguatlas5k.db,
mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db,
mi16cod.db, mirbase.db, mirna10probe, mm24kresogen.db,
MmAgilentDesign026655.db, moe430a.db, moe430aprobe, moe430b.db,
moe430bprobe, moex10stprobeset.db,
moex10sttranscriptcluster.db, MoExExonProbesetLocation,
mogene10stprobeset.db, mogene10sttranscriptcluster.db,
mogene10stv1probe, mogene11stprobeset.db,
mogene11sttranscriptcluster.db, mogene20stprobeset.db,
mogene20sttranscriptcluster.db, mogene21stprobeset.db,
mogene21sttranscriptcluster.db, mouse.db0, mouse4302.db,
mouse4302probe, mouse430a2.db, mouse430a2probe, mpedbarray.db,
mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db,
mu11ksubaprobe, mu11ksubb.db, mu11ksubbprobe, Mu15v1.db,
mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db,
Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db,
nugohs1a520180probe, nugomm1a520177.db, nugomm1a520177probe,
OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db,
org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db,
org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db,
org.Mm.eg.db, org.Mmu.eg.db, org.Mxanthus.db, org.Pf.plasmo.db,
org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db,
org.Xl.eg.db, Orthology.eg.db, paeg1aprobe,
PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, PFAM.db,
pig.db0, plasmodiumanophelesprobe, POCRCannotation.db,
poplarprobe, porcine.db, porcineprobe, primeviewprobe,
r10kcod.db, rae230a.db, rae230aprobe, rae230b.db, rae230bprobe,
raex10stprobeset.db, raex10sttranscriptcluster.db,
RaExExonProbesetLocation, ragene10stprobeset.db,
ragene10sttranscriptcluster.db, ragene10stv1probe,
ragene11stprobeset.db, ragene11sttranscriptcluster.db,
ragene20stprobeset.db, ragene20sttranscriptcluster.db,
ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat.db0,
rat2302.db, rat2302probe, rattoxfxprobe, Rattus.norvegicus,
reactome.db, rgu34a.db, rgu34aprobe, rgu34b.db, rgu34bprobe,
rgu34c.db, rgu34cprobe, rguatlas4k.db, rgug4105a.db,
rgug4130a.db, rgug4131a.db, rhesus.db0, rhesusprobe,
ri16cod.db, riceprobe, RnAgilentDesign028282.db, rnu34.db,
rnu34probe, Roberts2005Annotation.db, rta10probeset.db,
rta10transcriptcluster.db, rtu34.db, rtu34probe, rwgcod.db,
saureusprobe, SHDZ.db, soybeanprobe, sugarcaneprobe,
targetscan.Hs.eg.db, targetscan.Mm.eg.db, test3probe,
tomatoprobe, u133x3p.db, u133x3pprobe, vitisviniferaprobe,
wheatprobe, worm.db0, xenopus.db0, xenopuslaevisprobe,
xlaevis.db, xlaevis2probe, xtropicalisprobe, yeast.db0,
yeast2.db, yeast2probe, ygs98.db, ygs98probe, zebrafish.db,
zebrafish.db0, zebrafishprobe, tinesath1probe, rnaseqGene,
convertid
importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance,
annaffy, AnnotationHub, AnnotationHubData, annotatr, artMS,
beadarray, bioCancer, BiocSet, biomaRt, BioNet, biovizBase,
bumphunter, BUSpaRse, categoryCompare, ccmap, cellity,
chimeraviz, chipenrich, ChIPpeakAnno, ChIPseeker,
clusterProfiler, CoCiteStats, compEpiTools, conclus,
consensusDE, cosmosR, crisprseekplus, CrispRVariants,
crossmeta, debrowser, derfinder, DominoEffect, DOSE, EDASeq,
eegc, EnrichmentBrowser, ensembldb, erma, esATAC, FRASER,
GA4GHshiny, gage, GAPGOM, genefilter, geneplotter, GeneTonic,
geneXtendeR, GenVisR, ggbio, GlobalAncova, globaltest, GmicR,
GOfuncR, GOpro, GOSemSim, goseq, GOSim, goSTAG, GOstats,
goTools, gpart, graphite, GSEABase, GSEABenchmarkeR, Gviz,
gwascat, ideal, IMAS, InPAS, interactiveDisplay, IRISFGM,
isomiRs, IVAS, karyoploteR, LRBaseDbi, lumi, mAPKL, MCbiclust,
MeSHDbi, meshes, MesKit, MetaboSignal, methyAnalysis,
methylGSA, methylumi, MIGSA, MineICA, MiRaGE, mirIntegrator,
miRNAmeConverter, missMethyl, MLP, MSEADbi, MSnID, multiGSEA,
multiMiR, NanoMethViz, NanoStringQCPro, nanotatoR, NetSAM,
ontoProc, ORFik, Organism.dplyr, PADOG, pathview, pcaExplorer,
phantasus, phenoTest, proActiv, psichomics, pwOmics, qpgraph,
QuasR, ReactomePA, REDseq, regutools, restfulSE, rgsepd,
ribosomeProfilingQC, RNAAgeCalc, RpsiXML, rrvgo, rTRM,
SBGNview, ScISI, scPipe, scruff, scTensor, SGSeq,
signatureSearch, simplifyEnrichment, singleCellTK, SLGI, SMITE,
SpidermiR, StarBioTrek, SubCellBarCode, TCGAutils, tenXplore,
tigre, trackViewer, trena, tricycle, tximeta, Ularcirc,
UniProt.ws, VariantAnnotation, VariantFiltering, ViSEAGO,
adme16cod.db, ag.db, agcdf, anopheles.db0, arabidopsis.db0,
ath1121501.db, ath1121501cdf, barley1cdf, bovine.db,
bovine.db0, bovinecdf, bsubtiliscdf, canine.db, canine.db0,
canine2.db, canine2cdf, caninecdf, celegans.db, celeganscdf,
chicken.db, chicken.db0, chickencdf, chimp.db0, citruscdf,
clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db,
clariomshumanhttranscriptcluster.db,
clariomshumantranscriptcluster.db,
clariomsmousehttranscriptcluster.db,
clariomsmousetranscriptcluster.db,
clariomsrathttranscriptcluster.db,
clariomsrattranscriptcluster.db, cottoncdf, cyp450cdf, DO.db,
drosgenome1.db, drosgenome1cdf, drosophila2.db, drosophila2cdf,
ecoli2.db, ecoli2cdf, ecoliasv2cdf, ecolicdf, ecoliK12.db0,
ecoliSakai.db0, FDb.FANTOM4.promoters.hg19,
FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19,
FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19,
FDb.UCSC.tRNAs, fly.db0, GenomicState,
GGHumanMethCancerPanelv1.db, GO.db, gp53cdf, h10kcod.db,
h20kcod.db, hcg110.db, hcg110cdf, hgfocus.db, hgfocuscdf,
hgu133a.db, hgu133a2.db, hgu133a2cdf, hgu133acdf,
hgu133atagcdf, hgu133b.db, hgu133bcdf, hgu133plus2.db,
hgu133plus2cdf, hgu219.db, hgu219cdf, hgu95a.db, hgu95acdf,
hgu95av2.db, hgu95av2cdf, hgu95b.db, hgu95bcdf, hgu95c.db,
hgu95ccdf, hgu95d.db, hgu95dcdf, hgu95e.db, hgu95ecdf,
hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db,
hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db,
hgug4845a.db, hguqiagenv3.db, hi16cod.db, hivprtplus2cdf,
Homo.sapiens, hs25kresogen.db, Hs6UG171.db,
HsAgilentDesign026652.db, Hspec, hspeccdf, hta20probeset.db,
hta20transcriptcluster.db, hthgu133a.db, hthgu133acdf,
hthgu133b.db, hthgu133bcdf, hthgu133plusa.db, hthgu133plusb.db,
hthgu133pluspm.db, hthgu133pluspmcdf, htmg430a.db, htmg430acdf,
htmg430b.db, htmg430bcdf, htmg430pm.db, htmg430pmcdf,
htrat230pm.db, htrat230pmcdf, htratfocus.db, htratfocuscdf,
hu35ksuba.db, hu35ksubacdf, hu35ksubb.db, hu35ksubbcdf,
hu35ksubc.db, hu35ksubccdf, hu35ksubd.db, hu35ksubdcdf,
hu6800.db, hu6800cdf, hu6800subacdf, hu6800subbcdf,
hu6800subccdf, hu6800subdcdf, huex10stprobeset.db,
huex10sttranscriptcluster.db, hugene10stprobeset.db,
hugene10sttranscriptcluster.db, hugene10stv1cdf,
hugene11stprobeset.db, hugene11sttranscriptcluster.db,
hugene20stprobeset.db, hugene20sttranscriptcluster.db,
hugene21stprobeset.db, hugene21sttranscriptcluster.db,
human.db0, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db,
illuminaHumanv1.db, illuminaHumanv2.db,
illuminaHumanv2BeadID.db, illuminaHumanv3.db,
illuminaHumanv4.db, illuminaHumanWGDASLv3.db,
illuminaHumanWGDASLv4.db, illuminaMousev1.db,
illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db,
indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db,
lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping,
lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db,
maizecdf, malaria.db0, medicagocdf, mgu74a.db, mgu74acdf,
mgu74av2.db, mgu74av2cdf, mgu74b.db, mgu74bcdf, mgu74bv2.db,
mgu74bv2cdf, mgu74c.db, mgu74ccdf, mgu74cv2.db, mgu74cv2cdf,
mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db,
mgug4122a.db, mi16cod.db, mirbase.db, miRBaseVersions.db,
mirna102xgaincdf, mirna10cdf, mirna20cdf, miRNAtap.db,
mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db,
moe430acdf, moe430b.db, moe430bcdf, moex10stprobeset.db,
moex10sttranscriptcluster.db, mogene10stprobeset.db,
mogene10sttranscriptcluster.db, mogene10stv1cdf,
mogene11stprobeset.db, mogene11sttranscriptcluster.db,
mogene20stprobeset.db, mogene20sttranscriptcluster.db,
mogene21stprobeset.db, mogene21sttranscriptcluster.db,
mouse.db0, mouse4302.db, mouse4302cdf, mouse430a2.db,
mouse430a2cdf, mpedbarray.db, mta10probeset.db,
mta10transcriptcluster.db, mu11ksuba.db, mu11ksubacdf,
mu11ksubb.db, mu11ksubbcdf, Mu15v1.db, mu19ksuba.db,
mu19ksubacdf, mu19ksubb.db, mu19ksubbcdf, mu19ksubc.db,
mu19ksubccdf, Mu22v3.db, mu6500subacdf, mu6500subbcdf,
mu6500subccdf, mu6500subdcdf, Mus.musculus, mwgcod.db,
Norway981.db, nugohs1a520180.db, nugohs1a520180cdf,
nugomm1a520177.db, nugomm1a520177cdf, OperonHumanV3.db,
org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db,
org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db,
org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db,
org.Mmu.eg.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db,
org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db,
paeg1acdf, PartheenMetaData.db, pedbarrayv10.db,
pedbarrayv9.db, PFAM.db, pig.db0, plasmodiumanophelescdf,
POCRCannotation.db, PolyPhen.Hsapiens.dbSNP131, poplarcdf,
porcine.db, porcinecdf, primeviewcdf, r10kcod.db, rae230a.db,
rae230acdf, rae230b.db, rae230bcdf, raex10stprobeset.db,
raex10sttranscriptcluster.db, ragene10stprobeset.db,
ragene10sttranscriptcluster.db, ragene10stv1cdf,
ragene11stprobeset.db, ragene11sttranscriptcluster.db,
ragene20stprobeset.db, ragene20sttranscriptcluster.db,
ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat.db0,
rat2302.db, rat2302cdf, rattoxfxcdf, Rattus.norvegicus,
reactome.db, rgu34a.db, rgu34acdf, rgu34b.db, rgu34bcdf,
rgu34c.db, rgu34ccdf, rguatlas4k.db, rgug4105a.db,
rgug4130a.db, rgug4131a.db, rhesus.db0, rhesuscdf, ri16cod.db,
ricecdf, RmiR.Hs.miRNA, RmiR.hsa, RnAgilentDesign028282.db,
rnu34.db, rnu34cdf, Roberts2005Annotation.db, rta10probeset.db,
rta10transcriptcluster.db, rtu34.db, rtu34cdf, rwgcod.db,
saureuscdf, SHDZ.db, SIFT.Hsapiens.dbSNP132,
SIFT.Hsapiens.dbSNP137, soybeancdf, sugarcanecdf,
targetscan.Hs.eg.db, targetscan.Mm.eg.db, test1cdf, test2cdf,
test3cdf, tomatocdf, TxDb.Athaliana.BioMart.plantsmart22,
TxDb.Athaliana.BioMart.plantsmart25,
TxDb.Athaliana.BioMart.plantsmart28,
TxDb.Btaurus.UCSC.bosTau8.refGene,
TxDb.Btaurus.UCSC.bosTau9.refGene,
TxDb.Celegans.UCSC.ce11.ensGene,
TxDb.Celegans.UCSC.ce11.refGene,
TxDb.Celegans.UCSC.ce6.ensGene,
TxDb.Cfamiliaris.UCSC.canFam3.refGene,
TxDb.Dmelanogaster.UCSC.dm3.ensGene,
TxDb.Dmelanogaster.UCSC.dm6.ensGene,
TxDb.Drerio.UCSC.danRer10.refGene,
TxDb.Drerio.UCSC.danRer11.refGene,
TxDb.Ggallus.UCSC.galGal4.refGene,
TxDb.Ggallus.UCSC.galGal5.refGene,
TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis,
TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Hsapiens.UCSC.hg38.refGene,
TxDb.Mmulatta.UCSC.rheMac10.refGene,
TxDb.Mmulatta.UCSC.rheMac3.refGene,
TxDb.Mmulatta.UCSC.rheMac8.refGene,
TxDb.Mmusculus.UCSC.mm10.ensGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Mmusculus.UCSC.mm39.refGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,
TxDb.Ptroglodytes.UCSC.panTro4.refGene,
TxDb.Ptroglodytes.UCSC.panTro5.refGene,
TxDb.Ptroglodytes.UCSC.panTro6.refGene,
TxDb.Rnorvegicus.BioMart.igis,
TxDb.Rnorvegicus.UCSC.rn4.ensGene,
TxDb.Rnorvegicus.UCSC.rn5.refGene,
TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq,
TxDb.Rnorvegicus.UCSC.rn6.refGene,
TxDb.Scerevisiae.UCSC.sacCer2.sgdGene,
TxDb.Scerevisiae.UCSC.sacCer3.sgdGene,
TxDb.Sscrofa.UCSC.susScr11.refGene,
TxDb.Sscrofa.UCSC.susScr3.refGene, u133aaofav2cdf, u133x3p.db,
u133x3pcdf, vitisviniferacdf, wheatcdf, worm.db0, xenopus.db0,
xenopuslaeviscdf, xlaevis.db, xlaevis2cdf, xtropicaliscdf,
ye6100subacdf, ye6100subbcdf, ye6100subccdf, ye6100subdcdf,
yeast.db0, yeast2.db, yeast2cdf, ygs98.db, ygs98cdf,
zebrafish.db, zebrafish.db0, zebrafishcdf, celldex,
chipenrich.data, DeSousa2013, msigdb, ppiData, scRNAseq,
ExpHunterSuite, aliases2entrez, BiSEp, DIscBIO, jetset,
MetaIntegrator, netgsa, pathfindR, prioGene, pulseTD,
RobLoxBioC, WGCNA
suggestsMe: APAlyzer, autonomics, bambu, BiocGenerics, BiocOncoTK,
CellTrails, cicero, cola, csaw, DEGreport, edgeR, eisaR,
enrichplot, esetVis, FELLA, FGNet, fgsea, GA4GHclient,
gCrisprTools, GeneRegionScan, GenomicRanges, iSEEu, limma,
MutationalPatterns, oligo, OUTRIDER, piano, Pigengene, pRoloc,
quantiseqr, R3CPET, recount, RGalaxy, sigPathway,
SummarizedExperiment, TFutils, tidybulk, topconfects, weitrix,
wiggleplotr, BloodCancerMultiOmics2017, curatedAdipoChIP,
RforProteomics, CALANGO, conos, cRegulome, DGCA, easylabel,
pagoda2, rliger
dependencyCount: 45
Package: AnnotationFilter
Version: 1.16.0
Depends: R (>= 3.4.0)
Imports: utils, methods, GenomicRanges, lazyeval
Suggests: BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db
License: Artistic-2.0
MD5sum: 8fa71403018268d536fcbc0c52e54a03
NeedsCompilation: no
Title: Facilities for Filtering Bioconductor Annotation Resources
Description: This package provides class and other infrastructure to
implement filters for manipulating Bioconductor annotation
resources. The filters will be used by ensembldb,
Organism.dplyr, and other packages.
biocViews: Annotation, Infrastructure, Software
Author: Martin Morgan [aut], Johannes Rainer [aut], Joachim Bargsten
[ctb], Daniel Van Twisk [ctb], Bioconductor Package Maintainer
[cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://github.com/Bioconductor/AnnotationFilter
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/AnnotationFilter/issues
git_url: https://git.bioconductor.org/packages/AnnotationFilter
git_branch: RELEASE_3_13
git_last_commit: e4e4425
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AnnotationFilter_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AnnotationFilter_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AnnotationFilter_1.16.0.tgz
vignettes: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.html
vignetteTitles: Facilities for Filtering Bioconductor Annotation
resources
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.R
dependsOnMe: chimeraviz, ensembldb, Organism.dplyr
importsMe: biovizBase, BUSpaRse, drugTargetInteractions, ggbio,
QFeatures, TVTB, GenomicDistributionsData, utr.annotation
suggestsMe: GenomicDistributions, TFutils, wiggleplotr
dependencyCount: 18
Package: AnnotationForge
Version: 1.34.1
Depends: R (>= 3.5.0), methods, utils, BiocGenerics (>= 0.15.10),
Biobase (>= 1.17.0), AnnotationDbi (>= 1.33.14)
Imports: DBI, RSQLite, XML, S4Vectors, RCurl
Suggests: biomaRt, httr, GenomeInfoDb (>= 1.17.1), Biostrings, affy,
hgu95av2.db, human.db0, org.Hs.eg.db, Homo.sapiens, GO.db,
markdown, BiocStyle, knitr, BiocManager, BiocFileCache
License: Artistic-2.0
MD5sum: f32a32e3bb7e148639efe09a8f7d6ff9
NeedsCompilation: no
Title: Tools for building SQLite-based annotation data packages
Description: Provides code for generating Annotation packages and their
databases. Packages produced are intended to be used with
AnnotationDbi.
biocViews: Annotation, Infrastructure
Author: Marc Carlson, Hervé Pagès
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/AnnotationForge
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/AnnotationForge/issues
git_url: https://git.bioconductor.org/packages/AnnotationForge
git_branch: RELEASE_3_13
git_last_commit: a2fccf4
git_last_commit_date: 2021-10-11
Date/Publication: 2021-10-12
source.ver: src/contrib/AnnotationForge_1.34.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AnnotationForge_1.34.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/AnnotationForge_1.34.1.tgz
vignettes: vignettes/AnnotationForge/inst/doc/makeProbePackage.pdf,
vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf,
vignettes/AnnotationForge/inst/doc/SQLForge.pdf,
vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html
vignetteTitles: Creating probe packages, AnnotationForge: Creating
select Interfaces for custom Annotation resources, SQLForge: An
easy way to create a new annotation package with a standard
database schema., Making New Organism Packages
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnnotationForge/inst/doc/makeProbePackage.R,
vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.R,
vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.R,
vignettes/AnnotationForge/inst/doc/SQLForge.R
importsMe: AnnotationHubData, GOstats, ViSEAGO,
GGHumanMethCancerPanelv1.db
suggestsMe: AnnotationDbi, AnnotationHub
dependencyCount: 47
Package: AnnotationHub
Version: 3.0.2
Depends: BiocGenerics (>= 0.15.10), BiocFileCache (>= 1.5.1)
Imports: utils, methods, grDevices, RSQLite, BiocManager, BiocVersion,
curl, rappdirs, AnnotationDbi (>= 1.31.19), S4Vectors,
interactiveDisplayBase, httr, yaml, dplyr
Suggests: IRanges, GenomicRanges, GenomeInfoDb, VariantAnnotation,
Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge,
rBiopaxParser, RUnit, GenomicFeatures, MSnbase, mzR,
Biostrings, SummarizedExperiment, ExperimentHub, gdsfmt,
rmarkdown
Enhances: AnnotationHubData
License: Artistic-2.0
MD5sum: 1723427f6cad829e707fd3fb1f4a437a
NeedsCompilation: yes
Title: Client to access AnnotationHub resources
Description: This package provides a client for the Bioconductor
AnnotationHub web resource. The AnnotationHub web resource
provides a central location where genomic files (e.g., VCF,
bed, wig) and other resources from standard locations (e.g.,
UCSC, Ensembl) can be discovered. The resource includes
metadata about each resource, e.g., a textual description,
tags, and date of modification. The client creates and manages
a local cache of files retrieved by the user, helping with
quick and reproducible access.
biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient
Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut],
Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb],
Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd
[aut]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/AnnotationHub/issues
git_url: https://git.bioconductor.org/packages/AnnotationHub
git_branch: RELEASE_3_13
git_last_commit: cdca7b7
git_last_commit_date: 2021-10-13
Date/Publication: 2021-10-14
source.ver: src/contrib/AnnotationHub_3.0.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AnnotationHub_3.0.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/AnnotationHub_3.0.2.tgz
vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html,
vignettes/AnnotationHub/inst/doc/AnnotationHub.html,
vignettes/AnnotationHub/inst/doc/CreateAHubPackage.html,
vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.html
vignetteTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub:
Access the AnnotationHub Web Service, Creating A Hub Package:
ExperimentHub or AnnotationHub, Troubleshooting The Hubs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.R,
vignettes/AnnotationHub/inst/doc/AnnotationHub.R,
vignettes/AnnotationHub/inst/doc/CreateAHubPackage.R,
vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.R
dependsOnMe: adductomicsR, AnnotationHubData, ExperimentHub, hipathia,
ipdDb, LRcell, ProteomicsAnnotationHubData, EpiTxDb.Hs.hg38,
EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB, GenomicState,
org.Mxanthus.db, phastCons30way.UCSC.hg38, MetaGxBreast,
MetaGxOvarian, NestLink, sesameData, tartare, annotation,
sequencing, OSCA.advanced, OSCA.basic, OSCA.workflows
importsMe: annotatr, circRNAprofiler, customCMPdb, dmrseq, EWCE,
GenomicScores, GSEABenchmarkeR, gwascat, MACSr, MSnID,
psichomics, pwOmics, regutools, REMP, restfulSE, scmeth,
scTensor, TSRchitect, tximeta, Ularcirc, AHLRBaseDbs,
AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs,
alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38,
grasp2db, metaboliteIDmapping, adductData, alpineData,
biscuiteerData, celldex, chipseqDBData, curatedMetagenomicData,
curatedTCGAData, depmap, DropletTestFiles, FieldEffectCrc,
GenomicDistributionsData, HCAData, HMP16SData, HMP2Data,
mcsurvdata, MetaGxPancreas, scpdata, scRNAseq,
SingleCellMultiModal, spatialLIBD, TENxBrainData, TENxBUSData,
TENxPBMCData, TCGAWorkflow, utr.annotation
suggestsMe: BgeeCall, Chicago, ChIPpeakAnno, CINdex, clusterProfiler,
CNVRanger, COCOA, DNAshapeR, dupRadar, ensembldb, epiNEM,
EpiTxDb, epivizrChart, epivizrData, GenomicRanges, GOSemSim,
maser, MIRA, MSnbase, multicrispr, OrganismDbi,
recountmethylation, satuRn, VariantAnnotation, AHEnsDbs,
ENCODExplorerData, gwascatData, HarmonizedTCGAData, SingleRBook
dependencyCount: 86
Package: AnnotationHubData
Version: 1.22.0
Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges
(>= 2.3.23), GenomicRanges, AnnotationHub (>= 2.15.15)
Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics,
jsonlite, BiocManager, biocViews, BiocCheck, graph,
AnnotationDbi, Biobase, Biostrings, DBI, GenomeInfoDb (>=
1.15.4), OrganismDbi, RSQLite, AnnotationForge, futile.logger
(>= 1.3.0), XML, RCurl
Suggests: RUnit, knitr, BiocStyle, grasp2db, GenomeInfoDbData,
rmarkdown
License: Artistic-2.0
MD5sum: 645494a68c78ef6557d677cd7c16a701
NeedsCompilation: no
Title: Transform public data resources into Bioconductor Data
Structures
Description: These recipes convert a wide variety and a growing number
of public bioinformatic data sets into easily-used standard
Bioconductor data structures.
biocViews: DataImport
Author: Martin Morgan [ctb], Marc Carlson [ctb], Dan Tenenbaum [ctb],
Sonali Arora [ctb], Paul Shannon [ctb], Lori Shepherd [ctb],
Bioconductor Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AnnotationHubData
git_branch: RELEASE_3_13
git_last_commit: c2a76fb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AnnotationHubData_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AnnotationHubData_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AnnotationHubData_1.22.0.tgz
vignettes:
vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.html
vignetteTitles: Introduction to AnnotationHubData
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: ExperimentHubData
importsMe: AHEnsDbs, EuPathDB
suggestsMe: HubPub, GenomicState
dependencyCount: 133
Package: annotationTools
Version: 1.66.0
Imports: Biobase, stats
Suggests: BiocStyle
License: GPL
MD5sum: 3495f307472834d2c1ff60b277ec7a15
NeedsCompilation: no
Title: Annotate microarrays and perform cross-species gene expression
analyses using flat file databases
Description: Functions to annotate microarrays, find orthologs, and
integrate heterogeneous gene expression profiles using
annotation and other molecular biology information available as
flat file database (plain text files).
biocViews: Microarray, Annotation
Author: Alexandre Kuhn <alexandre.m.kuhn@gmail.com>
Maintainer: Alexandre Kuhn <alexandre.m.kuhn@gmail.com>
git_url: https://git.bioconductor.org/packages/annotationTools
git_branch: RELEASE_3_13
git_last_commit: 34aad15
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/annotationTools_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/annotationTools_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/annotationTools_1.66.0.tgz
vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf
vignetteTitles: annotationTools: Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R
dependencyCount: 7
Package: annotatr
Version: 1.18.1
Depends: R (>= 3.4.0)
Imports: AnnotationDbi, AnnotationHub, dplyr, GenomicFeatures,
GenomicRanges, GenomeInfoDb (>= 1.10.3), ggplot2, IRanges,
methods, readr, regioneR, reshape2, rtracklayer, S4Vectors (>=
0.23.10), stats, utils
Suggests: BiocStyle, devtools, knitr, org.Dm.eg.db, org.Gg.eg.db,
org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, rmarkdown, roxygen2,
testthat, TxDb.Dmelanogaster.UCSC.dm3.ensGene,
TxDb.Dmelanogaster.UCSC.dm6.ensGene,
TxDb.Ggallus.UCSC.galGal5.refGene,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Rnorvegicus.UCSC.rn4.ensGene,
TxDb.Rnorvegicus.UCSC.rn5.refGene,
TxDb.Rnorvegicus.UCSC.rn6.refGene
License: GPL-3
MD5sum: 1e64c5197247dc95d69669dbab02bccb
NeedsCompilation: no
Title: Annotation of Genomic Regions to Genomic Annotations
Description: Given a set of genomic sites/regions (e.g. ChIP-seq peaks,
CpGs, differentially methylated CpGs or regions, SNPs, etc.) it
is often of interest to investigate the intersecting genomic
annotations. Such annotations include those relating to gene
models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs
(CpG islands, CpG shores, CpG shelves), or regulatory sequences
such as enhancers. The annotatr package provides an easy way to
summarize and visualize the intersection of genomic
sites/regions with genomic annotations.
biocViews: Software, Annotation, GenomeAnnotation, FunctionalGenomics,
Visualization
Author: Raymond G. Cavalcante [aut, cre], Maureen A. Sartor [ths]
Maintainer: Raymond G. Cavalcante <rcavalca@umich.edu>
VignetteBuilder: knitr
BugReports: https://www.github.com/rcavalcante/annotatr/issues
git_url: https://git.bioconductor.org/packages/annotatr
git_branch: RELEASE_3_13
git_last_commit: 0066d80
git_last_commit_date: 2021-07-12
Date/Publication: 2021-07-13
source.ver: src/contrib/annotatr_1.18.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/annotatr_1.18.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/annotatr_1.18.1.tgz
vignettes: vignettes/annotatr/inst/doc/annotatr-vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R
importsMe: dmrseq, scmeth
suggestsMe: ramr
dependencyCount: 141
Package: anota
Version: 1.40.0
Depends: qvalue
Imports: multtest, qvalue
License: GPL-3
MD5sum: 0eea6a1fc6403ae1c87f69e917243cb8
NeedsCompilation: no
Title: ANalysis Of Translational Activity (ANOTA).
Description: Genome wide studies of translational control is emerging
as a tool to study verious biological conditions. The output
from such analysis is both the mRNA level (e.g. cytosolic mRNA
level) and the levl of mRNA actively involved in translation
(the actively translating mRNA level) for each mRNA. The
standard analysis of such data strives towards identifying
differential translational between two or more sample classes -
i.e. differences in actively translated mRNA levels that are
independent of underlying differences in cytosolic mRNA levels.
This package allows for such analysis using partial variances
and the random variance model. As 10s of thousands of mRNAs are
analyzed in parallell the library performs a number of tests to
assure that the data set is suitable for such analysis.
biocViews: GeneExpression, DifferentialExpression, Microarray,
Sequencing
Author: Ola Larsson <ola.larsson@ki.se>, Nahum Sonenberg
<nahum.sonenberg@mcgill.ca>, Robert Nadon
<robert.nadon@mcgill.ca>
Maintainer: Ola Larsson <ola.larsson@ki.se>
git_url: https://git.bioconductor.org/packages/anota
git_branch: RELEASE_3_13
git_last_commit: ef88b0a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/anota_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/anota_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/anota_1.40.0.tgz
vignettes: vignettes/anota/inst/doc/anota.pdf
vignetteTitles: ANalysis Of Translational Activity (anota)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/anota/inst/doc/anota.R
dependsOnMe: tRanslatome
dependencyCount: 51
Package: anota2seq
Version: 1.14.0
Depends: R (>= 3.4.0), methods
Imports: multtest,qvalue,limma,DESeq2,edgeR,RColorBrewer, grDevices,
graphics, stats, utils, SummarizedExperiment
Suggests: BiocStyle,knitr
License: GPL-3
MD5sum: 6c045e2428867c986bc6005ab1d15cbc
NeedsCompilation: no
Title: Generally applicable transcriptome-wide analysis of
translational efficiency using anota2seq
Description: anota2seq provides analysis of translational efficiency
and differential expression analysis for polysome-profiling and
ribosome-profiling studies (two or more sample classes)
quantified by RNA sequencing or DNA-microarray.
Polysome-profiling and ribosome-profiling typically generate
data for two RNA sources; translated mRNA and total mRNA.
Analysis of differential expression is used to estimate changes
within each RNA source (i.e. translated mRNA or total mRNA).
Analysis of translational efficiency aims to identify changes
in translation efficiency leading to altered protein levels
that are independent of total mRNA levels (i.e. changes in
translated mRNA that are independent of levels of total mRNA)
or buffering, a mechanism regulating translational efficiency
so that protein levels remain constant despite fluctuating
total mRNA levels (i.e. changes in total mRNA that are
independent of levels of translated mRNA). anota2seq applies
analysis of partial variance and the random variance model to
fulfill these tasks.
biocViews: ImmunoOncology, GeneExpression, DifferentialExpression,
Microarray,GenomeWideAssociation, BatchEffect, Normalization,
RNASeq, Sequencing, GeneRegulation, Regression
Author: Christian Oertlin <christian.oertlin@ki.se>, Julie Lorent
<julie.lorent@ki.se>, Ola Larsson <ola.larsson@ki.se>
Maintainer: Christian Oertlin <christian.oertlin@ki.se>, Julie Lorent
<julie.lorent@ki.se>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/anota2seq
git_branch: RELEASE_3_13
git_last_commit: d5aad21
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/anota2seq_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/anota2seq_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/anota2seq_1.14.0.tgz
vignettes: vignettes/anota2seq/inst/doc/anota2seq.pdf
vignetteTitles: Generally applicable transcriptome-wide analysis of
translational efficiency using anota2seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/anota2seq/inst/doc/anota2seq.R
dependencyCount: 101
Package: antiProfiles
Version: 1.32.0
Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit
(>= 1.5)
Suggests: antiProfilesData, RColorBrewer
License: Artistic-2.0
MD5sum: f2ea29f1a395958e04d62e14057ee127
NeedsCompilation: no
Title: Implementation of gene expression anti-profiles
Description: Implements gene expression anti-profiles as described in
Corrada Bravo et al., BMC Bioinformatics 2012, 13:272
doi:10.1186/1471-2105-13-272.
biocViews: GeneExpression,Classification
Author: Hector Corrada Bravo, Rafael A. Irizarry and Jeffrey T. Leek
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
URL: https://github.com/HCBravoLab/antiProfiles
git_url: https://git.bioconductor.org/packages/antiProfiles
git_branch: RELEASE_3_13
git_last_commit: 9e83e6d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/antiProfiles_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/antiProfiles_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/antiProfiles_1.32.0.tgz
vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf
vignetteTitles: Introduction to antiProfiles
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R
dependencyCount: 9
Package: AnVIL
Version: 1.4.1
Depends: R (>= 3.6), dplyr
Imports: stats, utils, methods, futile.logger, jsonlite, httr,
rapiclient (>= 0.1.3), tibble, tidyselect, tidyr, rlang,
BiocManager
Suggests: knitr, rmarkdown, testthat, withr, readr
License: Artistic-2.0
MD5sum: b6703f54d31d850f2dd7045a48af2ff9
NeedsCompilation: no
Title: Bioconductor on the AnVIL compute environment
Description: The AnVIL is a cloud computing resource developed in part
by the National Human Genome Research Institute. The AnVIL
package provides end-user and developer functionality. For the
end-user, AnVIL provides fast binary package installation,
utitlities for working with Terra / AnVIL table and data
resources, and convenient functions for file movement to and
from Google cloud storage. For developers, AnVIL provides
programatic access to the Terra, Leonardo, Rawls, Dockstore,
and Gen3 RESTful programming interface, including helper
functions to transform JSON responses to formats more amenable
to manipulation in R.
biocViews: Infrastructure
Author: Martin Morgan [aut, cre]
(<https://orcid.org/0000-0002-5874-8148>), Nitesh Turaga [aut],
BJ Stubbs [ctb], Vincent Carey [ctb], Marcel Ramos [ctb],
Sehyun Oh [ctb], Sweta Gopaulakrishnan [ctb], Valerie Obenchain
[ctb]
Maintainer: Martin Morgan <mtmorgan.bioc@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AnVIL
git_branch: RELEASE_3_13
git_last_commit: d4bcc97
git_last_commit_date: 2021-06-21
Date/Publication: 2021-06-22
source.ver: src/contrib/AnVIL_1.4.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AnVIL_1.4.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/AnVIL_1.4.1.tgz
vignettes: vignettes/AnVIL/inst/doc/BiocDockstore.html,
vignettes/AnVIL/inst/doc/Introduction.html
vignetteTitles: Dockstore and Bioconductor for AnVIL, Introduction to
the AnVIL package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnVIL/inst/doc/BiocDockstore.R,
vignettes/AnVIL/inst/doc/Introduction.R
dependsOnMe: cBioPortalData
importsMe: AnVILPublish
dependencyCount: 39
Package: AnVILBilling
Version: 1.2.0
Depends: R (>= 4.1)
Imports: methods, DT, shiny, bigrquery, shinytoastr, DBI, magrittr,
dplyr, lubridate, plotly, ggplot2
Suggests: testthat, knitr, BiocStyle
License: Artistic-2.0
MD5sum: 127e9f4bbb5291f41756c02e84acfb59
NeedsCompilation: no
Title: Provide functions to retrieve and report on usage expenses in
NHGRI AnVIL (anvilproject.org).
Description: AnVILBilling helps monitor AnVIL-related costs in R, using
queries to a BigQuery table to which costs are exported daily.
Functions are defined to help categorize tasks and associated
expenditures, and to visualize and explore expense profiles
over time. This package will be expanded to help users estimate
costs for specific task sets.
biocViews: Infrastructure, Software
Author: BJ Stubbs [aut], Vince Carey [aut, cre]
Maintainer: Vince Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
BugReports: https://github.com/vjcitn/AnVILBilling/issues
git_url: https://git.bioconductor.org/packages/AnVILBilling
git_branch: RELEASE_3_13
git_last_commit: c339a21
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AnVILBilling_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AnVILBilling_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AnVILBilling_1.2.0.tgz
vignettes: vignettes/AnVILBilling/inst/doc/billing.html
vignetteTitles: Software for reckoning AnVIL/terra usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnVILBilling/inst/doc/billing.R
dependencyCount: 90
Package: AnVILPublish
Version: 1.2.0
Imports: AnVIL, httr, jsonlite, rmarkdown, whisker, tools, utils,
stats,
Suggests: knitr, BiocStyle, BiocManager
License: Artistic-2.0
MD5sum: 3e5a75448fb176065d6c3ab0cba833ff
NeedsCompilation: no
Title: Publish Packages and Other Resources to AnVIL Workspaces
Description: Use this package to create or update AnVIL workspaces from
resources such as R / Bioconductor packages. The metadata about
the package (e.g., select information from the package
DESCRIPTION file and from vignette YAML headings) are used to
populate the 'DASHBOARD'. Vignettes are translated to python
notebooks ready for evaluation in AnVIL.
biocViews: Infrastructure, Software
Author: Martin Morgan [aut, cre]
(<https://orcid.org/0000-0002-5874-8148>), Vincent Carey [ctb]
(<https://orcid.org/0000-0003-4046-0063>)
Maintainer: Martin Morgan <mtmorgan.bioc@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AnVILPublish
git_branch: RELEASE_3_13
git_last_commit: ca7c985
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AnVILPublish_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AnVILPublish_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AnVILPublish_1.2.0.tgz
vignettes: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.html
vignetteTitles: Publishing R / Bioconductor packages to AnVIL
Workspaces
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.R
dependencyCount: 54
Package: APAlyzer
Version: 1.6.0
Depends: R (>= 3.5.0)
Imports: GenomicRanges, GenomicFeatures, GenomicAlignments, DESeq2,
ggrepel, SummarizedExperiment, Rsubread, stats, ggplot2,
methods, rtracklayer, ensembldb, VariantAnnotation, dplyr,
tidyr, repmis, Rsamtools
Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, AnnotationDbi,
TBX20BamSubset, testthat, pasillaBamSubset
License: LGPL-3
MD5sum: 10a8883cf6002eae64909ac9224fab04
NeedsCompilation: no
Title: A toolkit for APA analysis using RNA-seq data
Description: Perform 3'UTR APA, Intronic APA and gene expression
analysis using RNA-seq data.
biocViews: Sequencing, RNASeq, DifferentialExpression, GeneExpression,
GeneRegulation, Annotation, DataImport, Software
Author: Ruijia Wang [cre, aut]
(<https://orcid.org/0000-0002-4211-5207>), Bin Tian [aut],
Chuwei Zhong [aut]
Maintainer: Ruijia Wang <rjwang.bioinfo@gmail.com>
URL: https://github.com/RJWANGbioinfo/APAlyzer/
VignetteBuilder: knitr
BugReports: https://github.com/RJWANGbioinfo/APAlyzer/issues
git_url: https://git.bioconductor.org/packages/APAlyzer
git_branch: RELEASE_3_13
git_last_commit: e2978dc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/APAlyzer_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/APAlyzer_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/APAlyzer_1.6.0.tgz
vignettes: vignettes/APAlyzer/inst/doc/APAlyzer.html
vignetteTitles: APAlyzer: toolkit for RNA-seq APA analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/APAlyzer/inst/doc/APAlyzer.R
dependencyCount: 135
Package: apComplex
Version: 2.58.0
Depends: R (>= 2.10), graph, RBGL
Imports: Rgraphviz, stats, org.Sc.sgd.db
License: LGPL
MD5sum: bf99032cb2c13c8724fd7ad7641cbca1
NeedsCompilation: no
Title: Estimate protein complex membership using AP-MS protein data
Description: Functions to estimate a bipartite graph of protein complex
membership using AP-MS data.
biocViews: ImmunoOncology, NetworkInference, MassSpectrometry,
GraphAndNetwork
Author: Denise Scholtens <dscholtens@northwestern.edu>
Maintainer: Denise Scholtens <dscholtens@northwestern.edu>
git_url: https://git.bioconductor.org/packages/apComplex
git_branch: RELEASE_3_13
git_last_commit: 9d396a7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/apComplex_2.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/apComplex_2.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/apComplex_2.58.0.tgz
vignettes: vignettes/apComplex/inst/doc/apComplex.pdf
vignetteTitles: apComplex
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/apComplex/inst/doc/apComplex.R
dependsOnMe: ScISI
dependencyCount: 52
Package: apeglm
Version: 1.14.0
Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats,
utils, Rcpp
LinkingTo: Rcpp, RcppEigen, RcppNumerical
Suggests: DESeq2, airway, knitr, rmarkdown, testthat
License: GPL-2
Archs: i386, x64
MD5sum: 28d703ecf247b6ba06aee3d35a0b9ced
NeedsCompilation: yes
Title: Approximate posterior estimation for GLM coefficients
Description: apeglm provides Bayesian shrinkage estimators for effect
sizes for a variety of GLM models, using approximation of the
posterior for individual coefficients.
biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression,
GeneExpression, Bayesian
Author: Anqi Zhu [aut, cre], Joshua Zitovsky [ctb], Joseph Ibrahim
[aut], Michael Love [aut]
Maintainer: Anqi Zhu <anqizhu@live.unc.edu>
VignetteBuilder: knitr, rmarkdown
git_url: https://git.bioconductor.org/packages/apeglm
git_branch: RELEASE_3_13
git_last_commit: c64f333
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/apeglm_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/apeglm_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/apeglm_1.14.0.tgz
vignettes: vignettes/apeglm/inst/doc/apeglm.html
vignetteTitles: Effect size estimation with apeglm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/apeglm/inst/doc/apeglm.R
dependsOnMe: rnaseqGene
importsMe: airpart, debrowser, DiffBind
suggestsMe: bambu, BRGenomics, DESeq2, fishpond, NanoporeRNASeq
dependencyCount: 37
Package: appreci8R
Version: 1.10.0
Imports: shiny, shinyjs, DT, VariantAnnotation, BSgenome,
BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene,
Homo.sapiens, SNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, rsnps, Biostrings,
MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.hs37d5,
MafDb.gnomADex.r2.1.hs37d5, COSMIC.67, rentrez,
PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137, seqinr,
openxlsx, Rsamtools, stringr, utils, stats, GenomicRanges,
S4Vectors, GenomicFeatures, IRanges, GenomicScores,
SummarizedExperiment
Suggests: GO.db, org.Hs.eg.db
License: LGPL-3
MD5sum: 2779c288e20348b97814ddec06371764
NeedsCompilation: no
Title: appreci8R: an R/Bioconductor package for filtering SNVs and
short indels with high sensitivity and high PPV
Description: The appreci8R is an R version of our appreci8-algorithm -
A Pipeline for PREcise variant Calling Integrating 8 tools.
Variant calling results of our standard appreci8-tools (GATK,
Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and
VarDict), as well as up to 5 additional tools is combined,
evaluated and filtered.
biocViews: VariantDetection, GeneticVariability, SNP,
VariantAnnotation, Sequencing,
Author: Sarah Sandmann
Maintainer: Sarah Sandmann <sarah.sandmann@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/appreci8R
git_branch: RELEASE_3_13
git_last_commit: d1399fc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/appreci8R_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/appreci8R_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/appreci8R_1.10.0.tgz
vignettes: vignettes/appreci8R/inst/doc/appreci8R.pdf
vignetteTitles: Using appreci8R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/appreci8R/inst/doc/appreci8R.R
dependencyCount: 159
Package: aroma.light
Version: 3.22.0
Depends: R (>= 2.15.2)
Imports: stats, R.methodsS3 (>= 1.7.1), R.oo (>= 1.23.0), R.utils (>=
2.9.0), matrixStats (>= 0.55.0)
Suggests: princurve (>= 2.1.4)
License: GPL (>= 2)
MD5sum: 5ca5d02161119049876fefbba0f113ff
NeedsCompilation: no
Title: Light-Weight Methods for Normalization and Visualization of
Microarray Data using Only Basic R Data Types
Description: Methods for microarray analysis that take basic data types
such as matrices and lists of vectors. These methods can be
used standalone, be utilized in other packages, or be wrapped
up in higher-level classes.
biocViews: Infrastructure, Microarray, OneChannel, TwoChannel,
MultiChannel, Visualization, Preprocessing
Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb], Aaron
Lun [ctb]
Maintainer: Henrik Bengtsson <henrikb@braju.com>
URL: https://github.com/HenrikBengtsson/aroma.light,
https://www.aroma-project.org
BugReports: https://github.com/HenrikBengtsson/aroma.light/issues
git_url: https://git.bioconductor.org/packages/aroma.light
git_branch: RELEASE_3_13
git_last_commit: e4a668e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/aroma.light_3.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/aroma.light_3.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/aroma.light_3.22.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
importsMe: EDASeq, scone, PSCBS
suggestsMe: TIN, aroma.affymetrix, aroma.cn, aroma.core
dependencyCount: 8
Package: ArrayExpress
Version: 1.52.0
Depends: R (>= 2.9.0), Biobase (>= 2.4.0)
Imports: XML, oligo, limma
Suggests: affy
License: Artistic-2.0
MD5sum: 6d6edd2c2c83c54aaa60274e4c829174
NeedsCompilation: no
Title: Access the ArrayExpress Microarray Database at EBI and build
Bioconductor data structures: ExpressionSet, AffyBatch,
NChannelSet
Description: Access the ArrayExpress Repository at EBI and build
Bioconductor data structures: ExpressionSet, AffyBatch,
NChannelSet
biocViews: Microarray, DataImport, OneChannel, TwoChannel
Author: Audrey Kauffmann, Ibrahim Emam, Michael Schubert
Maintainer: Suhaib Mohammed <suhaib@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/ArrayExpress
git_branch: RELEASE_3_13
git_last_commit: bac4837
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ArrayExpress_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ArrayExpress_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ArrayExpress_1.52.0.tgz
vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf
vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets
into R object
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R
dependsOnMe: DrugVsDisease, maEndToEnd
suggestsMe: Hiiragi2013, bapred
dependencyCount: 56
Package: ArrayExpressHTS
Version: 1.42.0
Depends: sampling, Rsamtools (>= 1.99.0), snow
Imports: Biobase, BiocGenerics, Biostrings, GenomicRanges, Hmisc,
IRanges (>= 2.13.11), R2HTML, RColorBrewer, Rsamtools,
ShortRead, XML, biomaRt, edgeR, grDevices, graphics, methods,
rJava, stats, svMisc, utils, sendmailR, bitops
LinkingTo: Rhtslib (>= 1.15.3)
License: Artistic License 2.0
MD5sum: 67a8ed79c0aa34f6e115f9c81aa6bc1b
NeedsCompilation: yes
Title: ArrayExpress High Throughput Sequencing Processing Pipeline
Description: RNA-Seq processing pipeline for public ArrayExpress
experiments or local datasets
biocViews: ImmunoOncology, RNASeq, Sequencing
Author: Angela Goncalves, Andrew Tikhonov
Maintainer: Angela Goncalves <filimon@ebi.ac.uk>, Andrew Tikhonov
<andrew@ebi.ac.uk>
SystemRequirements: GNU make
git_url: https://git.bioconductor.org/packages/ArrayExpressHTS
git_branch: RELEASE_3_13
git_last_commit: 9a41140
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ArrayExpressHTS_1.42.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/ArrayExpressHTS_1.42.0.tgz
vignettes: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.pdf
vignetteTitles: ArrayExpressHTS: RNA-Seq Pipeline for transcription
profiling experiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.R
dependencyCount: 139
Package: arrayMvout
Version: 1.50.0
Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy
Imports: mdqc, affyContam, lumi
Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata,
hgu133atagcdf
License: Artistic-2.0
MD5sum: dfbae927c8d3594a1d74675ac0eb0a97
NeedsCompilation: no
Title: multivariate outlier detection for expression array QA
Description: This package supports the application of diverse quality
metrics to AffyBatch instances, summarizing these metrics via
PCA, and then performing parametric outlier detection on the
PCs to identify aberrant arrays with a fixed Type I error rate
biocViews: Infrastructure, Microarray, QualityControl
Author: Z. Gao, A. Asare, R. Wang, V. Carey
Maintainer: V. Carey <stvjc@channing.harvard.edu>
git_url: https://git.bioconductor.org/packages/arrayMvout
git_branch: RELEASE_3_13
git_last_commit: c59319e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/arrayMvout_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/arrayMvout_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/arrayMvout_1.50.0.tgz
vignettes: vignettes/arrayMvout/inst/doc/arrayMvout.pdf
vignetteTitles: arrayMvout -- multivariate outlier algorithm for
expression arrays
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/arrayMvout/inst/doc/arrayMvout.R
dependencyCount: 165
Package: arrayQuality
Version: 1.70.0
Depends: R (>= 2.2.0)
Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray,
methods, RColorBrewer, stats, utils
Suggests: mclust, MEEBOdata, HEEBOdata
License: LGPL
MD5sum: 3d2cdf4eeafe03513a2d48607fc0662e
NeedsCompilation: no
Title: Assessing array quality on spotted arrays
Description: Functions for performing print-run and array level quality
assessment.
biocViews: Microarray,TwoChannel,QualityControl,Visualization
Author: Agnes Paquet and Jean Yee Hwa Yang <yeehwa@stat.berkeley.edu>
Maintainer: Agnes Paquet <paquetagnes@yahoo.com>
URL: http://arrays.ucsf.edu/
git_url: https://git.bioconductor.org/packages/arrayQuality
git_branch: RELEASE_3_13
git_last_commit: f319a3a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/arrayQuality_1.70.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/arrayQuality_1.70.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/arrayQuality_1.70.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 12
Package: arrayQualityMetrics
Version: 3.48.0
Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, genefilter,
graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter,
lattice, latticeExtra, limma, methods, RColorBrewer, setRNG,
stats, utils, vsn (>= 3.23.3), XML, svglite
Suggests: ALLMLL, CCl4, BiocStyle, knitr
License: LGPL (>= 2)
MD5sum: 2c393461d95c61c64657afdb57b7ae5f
NeedsCompilation: no
Title: Quality metrics report for microarray data sets
Description: This package generates microarray quality metrics reports
for data in Bioconductor microarray data containers
(ExpressionSet, NChannelSet, AffyBatch). One and two color
array platforms are supported.
biocViews: Microarray, QualityControl, OneChannel, TwoChannel,
ReportWriting
Author: Audrey Kauffmann, Wolfgang Huber
Maintainer: Mike Smith <mike.smith@embl.de>
VignetteBuilder: knitr
BugReports: https://github.com/grimbough/arrayQualityMetrics/issues
git_url: https://git.bioconductor.org/packages/arrayQualityMetrics
git_branch: RELEASE_3_13
git_last_commit: 61bf05b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/arrayQualityMetrics_3.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/arrayQualityMetrics_3.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/arrayQualityMetrics_3.48.0.tgz
vignettes: vignettes/arrayQualityMetrics/inst/doc/aqm.pdf,
vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.pdf
vignetteTitles: Advanced topics: Customizing arrayQualityMetrics
reports and programmatic processing of the output,
Introduction: microarray quality assessment with
arrayQualityMetrics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/arrayQualityMetrics/inst/doc/aqm.R,
vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.R
dependsOnMe: maEndToEnd
dependencyCount: 124
Package: ARRmNormalization
Version: 1.32.0
Depends: R (>= 2.15.1), ARRmData
License: Artistic-2.0
MD5sum: d13e5a6ecaeecccade2b1459d4fd24fe
NeedsCompilation: no
Title: Adaptive Robust Regression normalization for Illumina
methylation data
Description: Perform the Adaptive Robust Regression method (ARRm) for
the normalization of methylation data from the Illumina
Infinium HumanMethylation 450k assay.
biocViews: DNAMethylation, TwoChannel, Preprocessing, Microarray
Author: Jean-Philippe Fortin, Celia M.T. Greenwood, Aurelie Labbe.
Maintainer: Jean-Philippe Fortin <jfortin@jhsph.edu>
git_url: https://git.bioconductor.org/packages/ARRmNormalization
git_branch: RELEASE_3_13
git_last_commit: 242d488
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ARRmNormalization_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ARRmNormalization_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ARRmNormalization_1.32.0.tgz
vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf
vignetteTitles: ARRmNormalization
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.R
dependencyCount: 1
Package: artMS
Version: 1.10.2
Depends: R (>= 4.1.0)
Imports: AnnotationDbi, bit64, circlize, cluster, corrplot, data.table,
dplyr, getopt, ggdendro, ggplot2, gplots, ggrepel, graphics,
grDevices, grid, limma, MSstats, openxlsx, org.Hs.eg.db,
pheatmap, plotly, plyr, RColorBrewer, scales, seqinr, stats,
stringr, tidyr, UpSetR, utils, VennDiagram, yaml
Suggests: BiocStyle, ComplexHeatmap, factoextra, FactoMineR, gProfileR,
knitr, PerformanceAnalytics, org.Mm.eg.db, rmarkdown, testthat
License: GPL (>= 3) + file LICENSE
MD5sum: c69430077516d9d29b17445151f8ac6e
NeedsCompilation: no
Title: Analytical R tools for Mass Spectrometry
Description: artMS provides a set of tools for the analysis of
proteomics label-free datasets. It takes as input the MaxQuant
search result output (evidence.txt file) and performs quality
control, relative quantification using MSstats, downstream
analysis and integration. artMS also provides a set of
functions to re-format and make it compatible with other
analytical tools, including, SAINTq, SAINTexpress, Phosfate,
and PHOTON. Check [http://artms.org](http://artms.org) for
details.
biocViews: Proteomics, DifferentialExpression, BiomedicalInformatics,
SystemsBiology, MassSpectrometry, Annotation, QualityControl,
GeneSetEnrichment, Clustering, Normalization, ImmunoOncology,
MultipleComparison
Author: David Jimenez-Morales [aut, cre]
(<https://orcid.org/0000-0003-4356-6461>), Alexandre Rosa
Campos [aut, ctb] (<https://orcid.org/0000-0003-3988-7764>),
John Von Dollen [aut], Nevan Krogan [aut]
(<https://orcid.org/0000-0003-4902-337X>), Danielle Swaney
[aut, ctb] (<https://orcid.org/0000-0001-6119-6084>)
Maintainer: David Jimenez-Morales <biodavidjm@gmail.com>
URL: http://artms.org
VignetteBuilder: knitr
BugReports: https://github.com/biodavidjm/artMS/issues
git_url: https://git.bioconductor.org/packages/artMS
git_branch: RELEASE_3_13
git_last_commit: 9f507a7
git_last_commit_date: 2021-07-14
Date/Publication: 2021-07-15
source.ver: src/contrib/artMS_1.10.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/artMS_1.10.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/artMS_1.10.2.tgz
vignettes: vignettes/artMS/inst/doc/artMS_vignette.html
vignetteTitles: Learn to use artMS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/artMS/inst/doc/artMS_vignette.R
dependencyCount: 134
Package: ASAFE
Version: 1.18.0
Depends: R (>= 3.2)
Suggests: knitr, testthat
License: Artistic-2.0
MD5sum: b203c6d3f42c5dbcd91bfc45ab7a62ba
NeedsCompilation: no
Title: Ancestry Specific Allele Frequency Estimation
Description: Given admixed individuals' bi-allelic SNP genotypes and
ancestry pairs (where each ancestry can take one of three
values) for multiple SNPs, perform an EM algorithm to deal with
the fact that SNP genotypes are unphased with respect to
ancestry pairs, in order to estimate ancestry-specific allele
frequencies for all SNPs.
biocViews: SNP, GenomeWideAssociation, LinkageDisequilibrium,
BiomedicalInformatics, Genetics, ExperimentalDesign
Author: Qian Zhang <qszhang@uw.edu>
Maintainer: Qian Zhang <qszhang@uw.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ASAFE
git_branch: RELEASE_3_13
git_last_commit: d7d0980
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ASAFE_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ASAFE_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ASAFE_1.18.0.tgz
vignettes: vignettes/ASAFE/inst/doc/ASAFE.pdf
vignetteTitles: ASAFE (Ancestry Specific Allele Frequency Estimation)
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ASAFE/inst/doc/ASAFE.R
dependencyCount: 0
Package: ASEB
Version: 1.36.0
Depends: R (>= 2.8.0), methods
Imports: graphics, methods, utils
License: GPL (>= 3)
Archs: i386, x64
MD5sum: 42ccf68cd0c8075438aef5b5be423803
NeedsCompilation: yes
Title: Predict Acetylated Lysine Sites
Description: ASEB is an R package to predict lysine sites that can be
acetylated by a specific KAT-family.
biocViews: Proteomics
Author: Likun Wang <wanglk@hsc.pku.edu.cn> and Tingting Li
<litt@hsc.pku.edu.cn>.
Maintainer: Likun Wang <wanglk@hsc.pku.edu.cn>
git_url: https://git.bioconductor.org/packages/ASEB
git_branch: RELEASE_3_13
git_last_commit: e3fce2b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ASEB_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ASEB_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ASEB_1.36.0.tgz
vignettes: vignettes/ASEB/inst/doc/ASEB.pdf
vignetteTitles: ASEB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ASEB/inst/doc/ASEB.R
dependencyCount: 3
Package: ASGSCA
Version: 1.26.0
Imports: Matrix, MASS
Suggests: BiocStyle
License: GPL-3
MD5sum: 48b644aa64ab40de612ecbdec148caae
NeedsCompilation: no
Title: Association Studies for multiple SNPs and multiple traits using
Generalized Structured Equation Models
Description: The package provides tools to model and test the
association between multiple genotypes and multiple traits,
taking into account the prior biological knowledge. Genes, and
clinical pathways are incorporated in the model as latent
variables. The method is based on Generalized Structured
Component Analysis (GSCA).
biocViews: StructuralEquationModels
Author: Hela Romdhani, Stepan Grinek , Heungsun Hwang and Aurelie
Labbe.
Maintainer: Hela Romdhani <hela.romdhani@mcgill.ca>
git_url: https://git.bioconductor.org/packages/ASGSCA
git_branch: RELEASE_3_13
git_last_commit: 9ace820
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ASGSCA_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ASGSCA_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ASGSCA_1.26.0.tgz
vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf
vignetteTitles: Association Studies using Generalized Structured
Equation Models.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ASGSCA/inst/doc/ASGSCA.R
suggestsMe: matrixpls
dependencyCount: 9
Package: ASICS
Version: 2.8.0
Depends: R (>= 3.5)
Imports: BiocParallel, ggplot2, glmnet, grDevices, gridExtra, methods,
mvtnorm, PepsNMR, plyr, quadprog, ropls, stats,
SummarizedExperiment, utils, Matrix, zoo
Suggests: knitr, rmarkdown, BiocStyle, testthat, ASICSdata
License: GPL (>= 2)
MD5sum: 90695fcac0424e80257ae3a91d9957e0
NeedsCompilation: no
Title: Automatic Statistical Identification in Complex Spectra
Description: With a set of pure metabolite reference spectra, ASICS
quantifies concentration of metabolites in a complex spectrum.
The identification of metabolites is performed by fitting a
mixture model to the spectra of the library with a sparse
penalty. The method and its statistical properties are
described in Tardivel et al. (2017)
<doi:10.1007/s11306-017-1244-5>.
biocViews: Software, DataImport, Cheminformatics, Metabolomics
Author: Gaëlle Lefort [aut, cre], Rémi Servien [aut], Patrick Tardivel
[aut], Nathalie Vialaneix [aut]
Maintainer: Gaëlle Lefort <gaelle.lefort@inra.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ASICS
git_branch: RELEASE_3_13
git_last_commit: 3169aa8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ASICS_2.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ASICS_2.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ASICS_2.8.0.tgz
vignettes: vignettes/ASICS/inst/doc/ASICS.html,
vignettes/ASICS/inst/doc/ASICSUsersGuide.html
vignetteTitles: ASICS, ASICS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ASICS/inst/doc/ASICS.R,
vignettes/ASICS/inst/doc/ASICSUsersGuide.R
dependencyCount: 87
Package: ASpediaFI
Version: 1.6.0
Depends: R (>= 3.6.0), SummarizedExperiment, ROCR
Imports: BiocParallel, GenomicAlignments, GenomicFeatures,
GenomicRanges, IRanges, IVAS, Rsamtools, biomaRt, limma,
S4Vectors, stats, DRaWR, GenomeInfoDb, Gviz, Matrix, dplyr,
fgsea, reshape2, igraph, graphics, e1071, methods, rtracklayer,
scales, grid, ggplot2, mGSZ, utils
Suggests: knitr
License: GPL-3
MD5sum: ef09992826cc139f4fe9a979c11db3da
NeedsCompilation: no
Title: ASpedia-FI: Functional Interaction Analysis of Alternative
Splicing Events
Description: This package provides functionalities for a systematic and
integrative analysis of alternative splicing events and their
functional interactions.
biocViews: AlternativeSplicing, Annotation, Coverage, GeneExpression,
GeneSetEnrichment, GraphAndNetwork, KEGG, Network,
NetworkInference, Pathways, Reactome, Transcription,
Sequencing, Visualization
Author: Doyeong Yu, Kyubin Lee, Daejin Hyung, Soo Young Cho, Charny
Park
Maintainer: Doyeong Yu <parklab.bi@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/nachoryu/ASpediaFI
git_url: https://git.bioconductor.org/packages/ASpediaFI
git_branch: RELEASE_3_13
git_last_commit: a36105f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ASpediaFI_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ASpediaFI_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ASpediaFI_1.6.0.tgz
vignettes: vignettes/ASpediaFI/inst/doc/ASpediaFI.pdf
vignetteTitles: ASpediaFI.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ASpediaFI/inst/doc/ASpediaFI.R
dependencyCount: 173
Package: ASpli
Version: 2.2.0
Depends: methods, grDevices, stats, utils, parallel, edgeR, limma,
AnnotationDbi
Imports: GenomicRanges, GenomicFeatures, BiocGenerics, IRanges,
GenomicAlignments, Gviz, S4Vectors, Rsamtools, BiocStyle,
igraph, htmltools, data.table, UpSetR, tidyr, DT, MASS, grid,
graphics, pbmcapply
License: GPL
MD5sum: d32084a06718cd58cb5223e5bb64b478
NeedsCompilation: no
Title: Analysis of Alternative Splicing Using RNA-Seq
Description: Integrative pipeline for the analysis of alternative
splicing using RNAseq.
biocViews: ImmunoOncology, GeneExpression, Transcription,
AlternativeSplicing, Coverage, DifferentialExpression,
DifferentialSplicing, TimeCourse, RNASeq, GenomeAnnotation,
Sequencing, Alignment
Author: Estefania Mancini, Andres Rabinovich, Javier Iserte, Marcelo
Yanovsky and Ariel Chernomoretz
Maintainer: Estefania Mancini <emancini@leloir.org.ar>
git_url: https://git.bioconductor.org/packages/ASpli
git_branch: RELEASE_3_13
git_last_commit: 6dddfb0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ASpli_2.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ASpli_2.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ASpli_2.2.0.tgz
vignettes: vignettes/ASpli/inst/doc/ASpli.pdf
vignetteTitles: ASpli
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ASpli/inst/doc/ASpli.R
dependencyCount: 161
Package: AssessORF
Version: 1.10.0
Depends: R (>= 3.5.0), DECIPHER (>= 2.10.0)
Imports: Biostrings, GenomicRanges, IRanges, graphics, grDevices,
methods, stats, utils
Suggests: AssessORFData, BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: e106ea9b614da3fee3f8604fc1c75170
NeedsCompilation: no
Title: Assess Gene Predictions Using Proteomics and Evolutionary
Conservation
Description: In order to assess the quality of a set of predicted genes
for a genome, evidence must first be mapped to that genome.
Next, each gene must be categorized based on how strong the
evidence is for or against that gene. The AssessORF package
provides the functions and class structures necessary for
accomplishing those tasks, using proteomic hits and
evolutionarily conserved start codons as the forms of evidence.
biocViews: ComparativeGenomics, GenePrediction, GenomeAnnotation,
Genetics, Proteomics, QualityControl, Visualization
Author: Deepank Korandla [aut, cre], Erik Wright [aut]
Maintainer: Deepank Korandla <dkorandl@alumni.cmu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AssessORF
git_branch: RELEASE_3_13
git_last_commit: c43e459
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AssessORF_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AssessORF_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AssessORF_1.10.0.tgz
vignettes: vignettes/AssessORF/inst/doc/UsingAssessORF.pdf
vignetteTitles: Using AssessORF
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AssessORF/inst/doc/UsingAssessORF.R
suggestsMe: AssessORFData
dependencyCount: 36
Package: ASSET
Version: 2.10.1
Depends: stats, graphics
Imports: MASS, msm, rmeta
Suggests: RUnit, BiocGenerics, knitr
License: GPL-2 + file LICENSE
MD5sum: 33258b973157ac5b7211c20f40126061
NeedsCompilation: no
Title: An R package for subset-based association analysis of
heterogeneous traits and subtypes
Description: An R package for subset-based analysis of heterogeneous
traits and disease subtypes. The package allows the user to
search through all possible subsets of z-scores to identify the
subset of traits giving the best meta-analyzed z-score.
Further, it returns a p-value adjusting for the
multiple-testing involved in the search. It also allows for
searching for the best combination of disease subtypes
associated with each variant.
biocViews: StatisticalMethod, SNP, GenomeWideAssociation,
MultipleComparison
Author: Samsiddhi Bhattacharjee [aut, cre], Nilanjan Chatterjee [aut],
William Wheeler [aut]
Maintainer: Samsiddhi Bhattacharjee <sb1@nibmg.ac.in>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ASSET
git_branch: RELEASE_3_13
git_last_commit: 7bdf763
git_last_commit_date: 2021-09-10
Date/Publication: 2021-09-12
source.ver: src/contrib/ASSET_2.10.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ASSET_2.10.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ASSET_2.10.1.tgz
vignettes: vignettes/ASSET/inst/doc/vignette.pdf
vignetteTitles: ASSET Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ASSET/inst/doc/vignette.R
dependsOnMe: REBET
dependencyCount: 15
Package: ASSIGN
Version: 1.28.1
Depends: R (>= 3.4)
Imports: gplots, graphics, grDevices, msm, Rlab, stats, sva, utils,
ggplot2, yaml
Suggests: testthat, BiocStyle, lintr, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: c1be993cd611969d94236bad376265a6
NeedsCompilation: no
Title: Adaptive Signature Selection and InteGratioN (ASSIGN)
Description: ASSIGN is a computational tool to evaluate the pathway
deregulation/activation status in individual patient samples.
ASSIGN employs a flexible Bayesian factor analysis approach
that adapts predetermined pathway signatures derived either
from knowledge-based literature or from perturbation
experiments to the cell-/tissue-specific pathway signatures.
The deregulation/activation level of each context-specific
pathway is quantified to a score, which represents the extent
to which a patient sample encompasses the pathway
deregulation/activation signature.
biocViews: Software, GeneExpression, Pathways, Bayesian
Author: Ying Shen, Andrea H. Bild, W. Evan Johnson, and Mumtehena
Rahman
Maintainer: Ying Shen <yshen3@bu.edu>, W. Evan Johnson <wej@bu.edu>,
David Jenkins <dfj@bu.edu>, Mumtehena Rahman
<moom.rahman@utah.edu>
URL: https://compbiomed.github.io/ASSIGN/
VignetteBuilder: knitr
BugReports: https://github.com/compbiomed/ASSIGN/issues
git_url: https://git.bioconductor.org/packages/ASSIGN
git_branch: RELEASE_3_13
git_last_commit: 9e787ec
git_last_commit_date: 2021-06-13
Date/Publication: 2021-06-15
source.ver: src/contrib/ASSIGN_1.28.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ASSIGN_1.28.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ASSIGN_1.28.1.tgz
vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.html
vignetteTitles: Primer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R
importsMe: TBSignatureProfiler
dependencyCount: 98
Package: ATACseqQC
Version: 1.16.0
Depends: R (>= 3.4), BiocGenerics, S4Vectors
Imports: BSgenome, Biostrings, ChIPpeakAnno, IRanges, GenomicRanges,
GenomicAlignments, GenomeInfoDb, GenomicScores, graphics, grid,
limma, Rsamtools (>= 1.31.2), randomForest, rtracklayer, stats,
motifStack, preseqR, utils, KernSmooth, edgeR
Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19,
MotifDb, trackViewer, testthat, rmarkdown
License: GPL (>= 2)
MD5sum: 51b7515f1843b9e16810c3aacd04bc10
NeedsCompilation: no
Title: ATAC-seq Quality Control
Description: ATAC-seq, an assay for Transposase-Accessible Chromatin
using sequencing, is a rapid and sensitive method for chromatin
accessibility analysis. It was developed as an alternative
method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the
other methods, ATAC-seq requires less amount of the biological
samples and time to process. In the process of analyzing
several ATAC-seq dataset produced in our labs, we learned some
of the unique aspects of the quality assessment for ATAC-seq
data.To help users to quickly assess whether their ATAC-seq
experiment is successful, we developed ATACseqQC package
partially following the guideline published in Nature Method
2013 (Greenleaf et al.), including diagnostic plot of fragment
size distribution, proportion of mitochondria reads, nucleosome
positioning pattern, and CTCF or other Transcript Factor
footprints.
biocViews: Sequencing, DNASeq, ATACSeq, GeneRegulation, QualityControl,
Coverage, NucleosomePositioning, ImmunoOncology
Author: Jianhong Ou, Haibo Liu, Feng Yan, Jun Yu, Michelle Kelliher,
Lucio Castilla, Nathan Lawson, Lihua Julie Zhu
Maintainer: Jianhong Ou <jianhong.ou@duke.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ATACseqQC
git_branch: RELEASE_3_13
git_last_commit: e345154
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ATACseqQC_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ATACseqQC_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ATACseqQC_1.16.0.tgz
vignettes: vignettes/ATACseqQC/inst/doc/ATACseqQC.html
vignetteTitles: ATACseqQC Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ATACseqQC/inst/doc/ATACseqQC.R
dependencyCount: 160
Package: atSNP
Version: 1.8.0
Depends: R (>= 3.6)
Imports: BSgenome, BiocFileCache, BiocParallel, Rcpp, data.table,
ggplot2, grDevices, graphics, grid, motifStack, rappdirs,
stats, testthat, utils, lifecycle
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-2
Archs: i386, x64
MD5sum: bb510740c2ed2e685ccb75f399c886a0
NeedsCompilation: yes
Title: Affinity test for identifying regulatory SNPs
Description: atSNP performs affinity tests of motif matches with the
SNP or the reference genomes and SNP-led changes in motif
matches.
biocViews: Software, ChIPSeq, GenomeAnnotation, MotifAnnotation,
Visualization
Author: Chandler Zuo [aut], Sunyoung Shin [aut, cre], Sunduz Keles
[aut]
Maintainer: Sunyoung Shin <sunyoung.shin@utdallas.edu>
URL: https://github.com/sunyoungshin/atSNP
VignetteBuilder: knitr
BugReports: https://github.com/sunyoungshin/atSNP/issues
git_url: https://git.bioconductor.org/packages/atSNP
git_branch: RELEASE_3_13
git_last_commit: 813c1c6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/atSNP_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/atSNP_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/atSNP_1.8.0.tgz
vignettes: vignettes/atSNP/inst/doc/atsnp-vignette.html
vignetteTitles: atsnp-vignette.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/atSNP/inst/doc/atsnp-vignette.R
dependencyCount: 122
Package: attract
Version: 1.44.0
Depends: R (>= 3.4.0), AnnotationDbi
Imports: Biobase, limma, cluster, GOstats, graphics, stats,
reactome.db, KEGGREST, org.Hs.eg.db, utils, methods
Suggests: illuminaHumanv1.db
License: LGPL (>= 2.0)
MD5sum: b72c05eb2a84e52125d421c9d08e4994
NeedsCompilation: no
Title: Methods to Find the Gene Expression Modules that Represent the
Drivers of Kauffman's Attractor Landscape
Description: This package contains the functions to find the gene
expression modules that represent the drivers of Kauffman's
attractor landscape. The modules are the core attractor
pathways that discriminate between different cell types of
groups of interest. Each pathway has a set of synexpression
groups, which show transcriptionally-coordinated changes in
gene expression.
biocViews: ImmunoOncology, KEGG, Reactome, GeneExpression, Pathways,
GeneSetEnrichment, Microarray, RNASeq
Author: Jessica Mar
Maintainer: Samuel Zimmerman <samuel.e.zimmerman@gmail.com>
git_url: https://git.bioconductor.org/packages/attract
git_branch: RELEASE_3_13
git_last_commit: 6497b3f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/attract_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/attract_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/attract_1.44.0.tgz
vignettes: vignettes/attract/inst/doc/attract.pdf
vignetteTitles: Tutorial on How to Use the Functions in the
\texttt{attract} Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/attract/inst/doc/attract.R
dependencyCount: 68
Package: AUCell
Version: 1.14.0
Imports: data.table, graphics, grDevices, GSEABase, methods, mixtools,
R.utils, shiny, stats, SummarizedExperiment, BiocGenerics,
S4Vectors, utils
Suggests: Biobase, BiocStyle, doSNOW, dynamicTreeCut, DT, GEOquery,
knitr, NMF, plyr, R2HTML, rmarkdown, reshape2, plotly, rbokeh,
Rtsne, testthat, zoo
Enhances: doMC, doRNG, doParallel, foreach
License: GPL-3
MD5sum: 4ddecb61938427c577caeb9491a0bab2
NeedsCompilation: no
Title: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq
data (e.g. identify cells with specific gene signatures)
Description: AUCell allows to identify cells with active gene sets
(e.g. signatures, gene modules...) in single-cell RNA-seq data.
AUCell uses the "Area Under the Curve" (AUC) to calculate
whether a critical subset of the input gene set is enriched
within the expressed genes for each cell. The distribution of
AUC scores across all the cells allows exploring the relative
expression of the signature. Since the scoring method is
ranking-based, AUCell is independent of the gene expression
units and the normalization procedure. In addition, since the
cells are evaluated individually, it can easily be applied to
bigger datasets, subsetting the expression matrix if needed.
biocViews: SingleCell, GeneSetEnrichment, Transcriptomics,
Transcription, GeneExpression, WorkflowStep, Normalization
Author: Sara Aibar, Stein Aerts. Laboratory of Computational Biology.
VIB-KU Leuven Center for Brain & Disease Research. Leuven,
Belgium.
Maintainer: Sara Aibar <sara.aibar@kuleuven.vib.be>
URL: http://scenic.aertslab.org
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AUCell
git_branch: RELEASE_3_13
git_last_commit: 8849265
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AUCell_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AUCell_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AUCell_1.14.0.tgz
vignettes: vignettes/AUCell/inst/doc/AUCell.html
vignetteTitles: AUCell: Identifying cells with active gene sets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AUCell/inst/doc/AUCell.R
dependsOnMe: OSCA.basic
importsMe: RcisTarget
dependencyCount: 87
Package: autonomics
Version: 1.0.1
Depends: R (>= 4.0)
Imports: abind, assertive, BiocFileCache, BiocGenerics, colorspace,
data.table, edgeR, ggplot2, ggrepel, graphics, grDevices, grid,
gridExtra, limma, magrittr, matrixStats, methods,
MultiAssayExperiment, parallel, pcaMethods, rappdirs, rlang,
R.utils, readxl, S4Vectors, scales, stats, stringi,
SummarizedExperiment, tidyr, tools, utils
Suggests: affy, AnnotationDbi, BiocManager, diagram, GenomicRanges,
GEOquery, hgu95av2.db, ICSNP, knitr, lme4, lmerTest, MASS,
mixOmics, mpm, nlme, org.Hs.eg.db, org.Mm.eg.db, RCurl,
remotes, rmarkdown, ropls, Rsubread, rtracklayer, seqinr,
statmod, testthat
License: GPL-3
MD5sum: d9721fb4f49ac21990b5f047e4e4c166
NeedsCompilation: no
Title: Generifying and intuifying cross-platform omics analysis
Description: This package offers a generic and intuitive solution for
cross-platform omics data analysis. It has functions for
import, preprocessing, exploration, contrast analysis and
visualization of omics data. It follows a tidy, functional
programming paradigm.
biocViews: DataImport, DimensionReduction, GeneExpression,
MassSpectrometry, Preprocessing, PrincipalComponent, RNASeq,
Software, Transcription
Author: Aditya Bhagwat [aut, cre], Shahina Hayat [aut], Anna Halama
[ctb], Richard Cotton [ctb], Laure Cougnaud [ctb], Rudolf
Engelke [ctb], Hinrich Goehlmann [sad], Karsten Suhre [sad],
Johannes Graumann [aut, sad, rth]
Maintainer: Aditya Bhagwat <aditya.bhagwat@mpi-bn.mpg.de>
URL: https://github.com/bhagwataditya/autonomics
VignetteBuilder: knitr
BugReports: https://bitbucket.org/graumannlabtools/autonomics
git_url: https://git.bioconductor.org/packages/autonomics
git_branch: RELEASE_3_13
git_last_commit: 5df16f7
git_last_commit_date: 2021-05-25
Date/Publication: 2021-06-06
source.ver: src/contrib/autonomics_1.0.1.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/autonomics_1.0.1.tgz
vignettes: vignettes/autonomics/inst/doc/using_autonomics.html
vignetteTitles: using_autonomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/autonomics/inst/doc/using_autonomics.R
dependencyCount: 126
Package: Autotuner
Version: 1.6.0
Depends: R (>= 4.0.0), methods, Biobase, MSnbase (>= 2.14.2)
Imports: RColorBrewer, mzR, assertthat, scales, entropy, cluster,
grDevices, graphics, stats, utils
Suggests: testthat (>= 2.1.0), covr, devtools, knitr, rmarkdown, mtbls2
License: MIT + file LICENSE
MD5sum: c4b42588f6b6fbf1b36ef4a2987528d3
NeedsCompilation: no
Title: Automated parameter selection for untargeted metabolomics data
processing
Description: This package is designed to help faciliate data processing
in untargeted metabolomics. To do this, the algorithm contained
within the package performs statistical inference on raw data
to come up with the best set of parameters to process the raw
data.
biocViews: MassSpectrometry, Metabolomics
Author: Craig McLean
Maintainer: Craig McLean <craigmclean23@gmail.com>
URL: https://github.com/crmclean/Autotuner/
VignetteBuilder: knitr
BugReports: https://github.com/crmclean/Autotuner/issues
git_url: https://git.bioconductor.org/packages/Autotuner
git_branch: RELEASE_3_13
git_last_commit: f373bec
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Autotuner_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Autotuner_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Autotuner_1.6.0.tgz
vignettes: vignettes/Autotuner/inst/doc/Autotuner.html
vignetteTitles: Autotuner
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Autotuner/inst/doc/Autotuner.R
dependencyCount: 80
Package: AWFisher
Version: 1.6.0
Depends: R (>= 3.6)
Imports: edgeR, limma, stats
Suggests: knitr, tightClust
License: GPL-3
Archs: i386, x64
MD5sum: 8d51cec9bd87fa11941da4fe8e040ddd
NeedsCompilation: yes
Title: An R package for fast computing for adaptively weighted fisher's
method
Description: Implementation of the adaptively weighted fisher's method,
including fast p-value computing, variability index, and
meta-pattern.
biocViews: StatisticalMethod, Software
Author: Zhiguang Huo
Maintainer: Zhiguang Huo <zhuo@ufl.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Caleb-Huo/AWFisher/issues
git_url: https://git.bioconductor.org/packages/AWFisher
git_branch: RELEASE_3_13
git_last_commit: d14025a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/AWFisher_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/AWFisher_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/AWFisher_1.6.0.tgz
vignettes: vignettes/AWFisher/inst/doc/AWFisher.html
vignetteTitles: AWFisher
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AWFisher/inst/doc/AWFisher.R
dependencyCount: 11
Package: awst
Version: 1.0.0
Imports: stats, methods, SummarizedExperiment
Suggests: airway, ggplot2, testthat, EDASeq, knitr, BiocStyle,
RefManageR, sessioninfo, rmarkdown
License: MIT + file LICENSE
MD5sum: 2dfc0d465d119ccb32d3ad3d0d635755
NeedsCompilation: no
Title: Asymmetric Within-Sample Transformation
Description: We propose an Asymmetric Within-Sample Transformation
(AWST) to regularize RNA-seq read counts and reduce the effect
of noise on the classification of samples. AWST comprises two
main steps: standardization and smoothing. These steps
transform gene expression data to reduce the noise of the lowly
expressed features, which suffer from background effects and
low signal-to-noise ratio, and the influence of the highly
expressed features, which may be the result of amplification
bias and other experimental artifacts.
biocViews: Normalization, GeneExpression, RNASeq, Software,
Transcriptomics, Sequencing, SingleCell
Author: Davide Risso [aut, cre, cph]
(<https://orcid.org/0000-0001-8508-5012>), Stefano Pagnotta
[aut, cph] (<https://orcid.org/0000-0002-8298-9777>)
Maintainer: Davide Risso <risso.davide@gmail.com>
URL: https://github.com/drisso/awst
VignetteBuilder: knitr
BugReports: https://github.com/drisso/awst/issues
git_url: https://git.bioconductor.org/packages/awst
git_branch: RELEASE_3_13
git_last_commit: e922ee7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/awst_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/awst_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/awst_1.0.0.tgz
vignettes: vignettes/awst/inst/doc/awst_intro.html
vignetteTitles: Introduction to awst
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/awst/inst/doc/awst_intro.R
dependencyCount: 26
Package: BaalChIP
Version: 1.18.0
Depends: R (>= 3.3.1), GenomicRanges, IRanges, Rsamtools,
Imports: GenomicAlignments, GenomeInfoDb, doParallel, parallel, doBy,
reshape2, scales, coda, foreach, ggplot2, methods, utils,
graphics, stats
Suggests: RUnit, BiocGenerics, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 504fdc38a0fab2c9fd35f40b90001207
NeedsCompilation: no
Title: BaalChIP: Bayesian analysis of allele-specific transcription
factor binding in cancer genomes
Description: The package offers functions to process multiple ChIP-seq
BAM files and detect allele-specific events. Computes allele
counts at individual variants (SNPs/SNVs), implements extensive
QC steps to remove problematic variants, and utilizes a
bayesian framework to identify statistically significant
allele- specific events. BaalChIP is able to account for copy
number differences between the two alleles, a known
phenotypical feature of cancer samples.
biocViews: Software, ChIPSeq, Bayesian, Sequencing
Author: Ines de Santiago, Wei Liu, Ke Yuan, Martin O'Reilly, Chandra SR
Chilamakuri, Bruce Ponder, Kerstin Meyer, Florian Markowetz
Maintainer: Ines de Santiago <inesdesantiago@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BaalChIP
git_branch: RELEASE_3_13
git_last_commit: 49e2ae7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BaalChIP_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BaalChIP_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BaalChIP_1.18.0.tgz
vignettes: vignettes/BaalChIP/inst/doc/BaalChIP.html
vignetteTitles: Analyzing ChIP-seq and FAIRE-seq data with the BaalChIP
package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BaalChIP/inst/doc/BaalChIP.R
dependencyCount: 101
Package: BAC
Version: 1.52.0
Depends: R (>= 2.10)
License: Artistic-2.0
Archs: i386, x64
MD5sum: cb59af22c729863afaa510d573221733
NeedsCompilation: yes
Title: Bayesian Analysis of Chip-chip experiment
Description: This package uses a Bayesian hierarchical model to detect
enriched regions from ChIP-chip experiments
biocViews: Microarray, Transcription
Author: Raphael Gottardo
Maintainer: Raphael Gottardo <raph@stat.ubc.ca>
git_url: https://git.bioconductor.org/packages/BAC
git_branch: RELEASE_3_13
git_last_commit: 25895e4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BAC_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BAC_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BAC_1.52.0.tgz
vignettes: vignettes/BAC/inst/doc/BAC.pdf
vignetteTitles: 1. Primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BAC/inst/doc/BAC.R
dependencyCount: 0
Package: bacon
Version: 1.20.0
Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel,
ellipse
Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 3b2c50d55c4e3603c4f65113cac8d120
NeedsCompilation: yes
Title: Controlling bias and inflation in association studies using the
empirical null distribution
Description: Bacon can be used to remove inflation and bias often
observed in epigenome- and transcriptome-wide association
studies. To this end bacon constructs an empirical null
distribution using a Gibbs Sampling algorithm by fitting a
three-component normal mixture on z-scores.
biocViews: ImmunoOncology, StatisticalMethod, Bayesian, Regression,
GenomeWideAssociation, Transcriptomics, RNASeq,
MethylationArray, BatchEffect, MultipleComparison
Author: Maarten van Iterson [aut, cre], Erik van Zwet [ctb]
Maintainer: Maarten van Iterson <mviterson@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/bacon
git_branch: RELEASE_3_13
git_last_commit: 629e321
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bacon_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bacon_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bacon_1.20.0.tgz
vignettes: vignettes/bacon/inst/doc/bacon.html
vignetteTitles: Controlling bias and inflation in association studies
using the empirical null distribution
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bacon/inst/doc/bacon.R
dependencyCount: 47
Package: BADER
Version: 1.30.0
Suggests: pasilla (>= 0.2.10)
License: GPL-2
Archs: i386, x64
MD5sum: 3b921cfd8dce79a30371fe396c628015
NeedsCompilation: yes
Title: Bayesian Analysis of Differential Expression in RNA Sequencing
Data
Description: For RNA sequencing count data, BADER fits a Bayesian
hierarchical model. The algorithm returns the posterior
probability of differential expression for each gene between
two groups A and B. The joint posterior distribution of the
variables in the model can be returned in the form of posterior
samples, which can be used for further down-stream analyses
such as gene set enrichment.
biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression,
Software, SAGE
Author: Andreas Neudecker, Matthias Katzfuss
Maintainer: Andreas Neudecker <a.neudecker@arcor.de>
git_url: https://git.bioconductor.org/packages/BADER
git_branch: RELEASE_3_13
git_last_commit: 461f332
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BADER_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BADER_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BADER_1.30.0.tgz
vignettes: vignettes/BADER/inst/doc/BADER.pdf
vignetteTitles: Analysing RNA-Seq data with the "BADER" package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BADER/inst/doc/BADER.R
dependencyCount: 0
Package: BadRegionFinder
Version: 1.20.0
Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges,
S4Vectors, utils, stats, grDevices, graphics
Suggests: BSgenome.Hsapiens.UCSC.hg19
License: LGPL-3
MD5sum: 03aae3346e05539ca0378bf2c65123c3
NeedsCompilation: no
Title: BadRegionFinder: an R/Bioconductor package for identifying
regions with bad coverage
Description: BadRegionFinder is a package for identifying regions with
a bad, acceptable and good coverage in sequence alignment data
available as bam files. The whole genome may be considered as
well as a set of target regions. Various visual and textual
types of output are available.
biocViews: Coverage, Sequencing, Alignment, WholeGenome, Classification
Author: Sarah Sandmann
Maintainer: Sarah Sandmann <sarah.sandmann@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/BadRegionFinder
git_branch: RELEASE_3_13
git_last_commit: 833821c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BadRegionFinder_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BadRegionFinder_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BadRegionFinder_1.20.0.tgz
vignettes: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.pdf
vignetteTitles: Using BadRegionFinder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.R
dependencyCount: 98
Package: BAGS
Version: 2.32.0
Depends: R (>= 2.10), breastCancerVDX, Biobase
License: Artistic-2.0
Archs: i386, x64
MD5sum: 4fb4a137eef4f0fb3a4bbfcd297acf45
NeedsCompilation: yes
Title: A Bayesian Approach for Geneset Selection
Description: R package providing functions to perform geneset
significance analysis over simple cross-sectional data between
2 and 5 phenotypes of interest.
biocViews: Bayesian
Author: Alejandro Quiroz-Zarate
Maintainer: Alejandro Quiroz-Zarate <aquiroz@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/BAGS
git_branch: RELEASE_3_13
git_last_commit: d94e763
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BAGS_2.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BAGS_2.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BAGS_2.32.0.tgz
vignettes: vignettes/BAGS/inst/doc/BAGS.pdf
vignetteTitles: BAGS: A Bayesian Approach for Geneset Selection.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BAGS/inst/doc/BAGS.R
dependencyCount: 8
Package: ballgown
Version: 2.24.0
Depends: R (>= 3.1.1), methods
Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors
(>= 0.9.39), RColorBrewer, splines, sva, limma, rtracklayer (>=
1.29.25), Biobase (>= 2.25.0), GenomeInfoDb
Suggests: testthat, knitr
License: Artistic-2.0
MD5sum: eb30649e2da81f727f17a48064b4b686
NeedsCompilation: no
Title: Flexible, isoform-level differential expression analysis
Description: Tools for statistical analysis of assembled
transcriptomes, including flexible differential expression
analysis, visualization of transcript structures, and matching
of assembled transcripts to annotation.
biocViews: ImmunoOncology, RNASeq, StatisticalMethod, Preprocessing,
DifferentialExpression
Author: Jack Fu [aut], Alyssa C. Frazee [aut, cre], Leonardo
Collado-Torres [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek
[aut, ths]
Maintainer: Jack Fu <jmfu@jhsph.edu>
VignetteBuilder: knitr
BugReports: https://github.com/alyssafrazee/ballgown/issues
git_url: https://git.bioconductor.org/packages/ballgown
git_branch: RELEASE_3_13
git_last_commit: 34829e1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ballgown_2.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ballgown_2.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ballgown_2.24.0.tgz
vignettes: vignettes/ballgown/inst/doc/ballgown.html
vignetteTitles: Flexible isoform-level differential expression analysis
with Ballgown
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ballgown/inst/doc/ballgown.R
dependsOnMe: VaSP
importsMe: RNASeqR
suggestsMe: polyester, variancePartition
dependencyCount: 82
Package: bambu
Version: 1.2.1
Depends: R(>= 4.0.0), SummarizedExperiment(>= 1.1.6), S4Vectors(>=
0.22.1), IRanges
Imports: BiocGenerics, BiocParallel, data.table, dplyr, GenomeInfoDb,
GenomicAlignments, GenomicFeatures, GenomicRanges, stats,
glmnet, Rsamtools, methods, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Suggests: AnnotationDbi, Biostrings, BiocFileCache, ggplot2,
ComplexHeatmap, circlize, ggbio, gridExtra, knitr, rmarkdown,
testthat, BSgenome.Hsapiens.NCBI.GRCh38,
TxDb.Hsapiens.UCSC.hg38.knownGene, ExperimentHub (>= 1.15.3),
DESeq2, NanoporeRNASeq, BSgenome, apeglm, utils, DEXSeq
Enhances: parallel
License: GPL-3 + file LICENSE
Archs: i386, x64
MD5sum: 4784cb94161b01bc2d8ab4fd6816c6f4
NeedsCompilation: yes
Title: Reference-guided isoform reconstruction and quantification for
long read RNA-Seq data
Description: bambu is a R package for multi-sample transcript discovery
and quantification using long read RNA-Seq data. You can use
bambu after read alignment to obtain expression estimates for
known and novel transcripts and genes. The output from bambu
can directly be used for visualisation and downstream analysis
such as differential gene expression or transcript usage.
biocViews: Alignment, Coverage, DifferentialExpression,
FeatureExtraction, GeneExpression, GenomeAnnotation,
GenomeAssembly, ImmunoOncology, MultipleComparison,
Normalization, RNASeq, Regression, Sequencing, Software,
Transcription, Transcriptomics
Author: Ying Chen [cre, aut], Yuk Kei Wan [aut], Jonathan Goeke [aut]
Maintainer: Ying Chen <chen_ying@gis.a-star.edu.sg>
URL: https://github.com/GoekeLab/bambu
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/bambu
git_branch: RELEASE_3_13
git_last_commit: 1c71eae
git_last_commit_date: 2021-08-30
Date/Publication: 2021-08-31
source.ver: src/contrib/bambu_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bambu_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/bambu_1.2.1.tgz
vignettes: vignettes/bambu/inst/doc/bambu.html
vignetteTitles: bambu
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/bambu/inst/doc/bambu.R
suggestsMe: NanoporeRNASeq
dependencyCount: 105
Package: bamsignals
Version: 1.24.0
Depends: R (>= 3.2.0)
Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges,
GenomicRanges, zlibbioc
LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc
Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown
License: GPL-2
Archs: i386, x64
MD5sum: 585a87fba672a129b71ccc44c1f6e103
NeedsCompilation: yes
Title: Extract read count signals from bam files
Description: This package allows to efficiently obtain count vectors
from indexed bam files. It counts the number of reads in given
genomic ranges and it computes reads profiles and coverage
profiles. It also handles paired-end data.
biocViews: DataImport, Sequencing, Coverage, Alignment
Author: Alessandro Mammana [aut, cre], Johannes Helmuth [aut]
Maintainer: Johannes Helmuth <johannes.helmuth@laborberlin.com>
URL: https://github.com/lamortenera/bamsignals
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/lamortenera/bamsignals/issues
git_url: https://git.bioconductor.org/packages/bamsignals
git_branch: RELEASE_3_13
git_last_commit: 91d1e61
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bamsignals_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bamsignals_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bamsignals_1.24.0.tgz
vignettes: vignettes/bamsignals/inst/doc/bamsignals.html
vignetteTitles: Introduction to the bamsignals package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R
importsMe: AneuFinder, chromstaR, epigraHMM, karyoploteR, normr,
hoardeR
dependencyCount: 19
Package: BANDITS
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: Rcpp, doRNG, MASS, data.table, R.utils, doParallel, parallel,
foreach, methods, stats, graphics, ggplot2, DRIMSeq,
BiocParallel
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, tximport, BiocStyle,
GenomicFeatures, Biostrings
License: GPL (>= 3)
Archs: i386, x64
MD5sum: fac7fb95c2970c9c6ebec78d5197b072
NeedsCompilation: yes
Title: BANDITS: Bayesian ANalysis of DIfferenTial Splicing
Description: BANDITS is a Bayesian hierarchical model for detecting
differential splicing of genes and transcripts, via
differential transcript usage (DTU), between two or more
conditions. The method uses a Bayesian hierarchical framework,
which allows for sample specific proportions in a
Dirichlet-Multinomial model, and samples the allocation of
fragments to the transcripts. Parameters are inferred via
Markov chain Monte Carlo (MCMC) techniques and a DTU test is
performed via a multivariate Wald test on the posterior
densities for the average relative abundance of transcripts.
biocViews: DifferentialSplicing, AlternativeSplicing, Bayesian,
Genetics, RNASeq, Sequencing, DifferentialExpression,
GeneExpression, MultipleComparison, Software, Transcription,
StatisticalMethod, Visualization
Author: Simone Tiberi [aut, cre], Mark D. Robinson [aut].
Maintainer: Simone Tiberi <simone.tiberi@uzh.ch>
URL: https://github.com/SimoneTiberi/BANDITS
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/SimoneTiberi/BANDITS/issues
git_url: https://git.bioconductor.org/packages/BANDITS
git_branch: RELEASE_3_13
git_last_commit: a4ebc87
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BANDITS_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BANDITS_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BANDITS_1.8.0.tgz
vignettes: vignettes/BANDITS/inst/doc/BANDITS.html
vignetteTitles: BANDITS: Bayesian ANalysis of DIfferenTial Splicing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BANDITS/inst/doc/BANDITS.R
dependencyCount: 78
Package: banocc
Version: 1.16.0
Depends: R (>= 3.5.1), rstan (>= 2.17.4)
Imports: coda (>= 0.18.1), mvtnorm, stringr
Suggests: knitr, rmarkdown, methods, testthat
License: MIT + file LICENSE
MD5sum: 7cb4a2fbfd202424c88e31495bf44829
NeedsCompilation: no
Title: Bayesian ANalysis Of Compositional Covariance
Description: BAnOCC is a package designed for compositional data, where
each sample sums to one. It infers the approximate covariance
of the unconstrained data using a Bayesian model coded with
`rstan`. It provides as output the `stanfit` object as well as
posterior median and credible interval estimates for each
correlation element.
biocViews: ImmunoOncology, Metagenomics, Software, Bayesian
Author: Emma Schwager [aut, cre], Curtis Huttenhower [aut]
Maintainer: George Weingart <george.weingart@gmail.com>, Curtis
Huttenhower <chuttenh@hsph.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/banocc
git_branch: RELEASE_3_13
git_last_commit: cf8d0fb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/banocc_1.16.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/banocc_1.16.0.tgz
vignettes: vignettes/banocc/inst/doc/banocc-vignette.html
vignetteTitles: BAnOCC (Bayesian Analysis of Compositional Covariance)
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/banocc/inst/doc/banocc-vignette.R
dependencyCount: 67
Package: barcodetrackR
Version: 1.0.0
Depends: R (>= 4.1)
Imports: cowplot, circlize, dplyr, ggplot2, ggdendro, ggridges,
graphics, grDevices, magrittr, plyr, proxy, RColorBrewer,
rlang, scales, shiny, stats, SummarizedExperiment, S4Vectors,
tibble, tidyr, vegan, viridis, utils
Suggests: BiocStyle, knitr, magick, rmarkdown, testthat
License: file LICENSE
MD5sum: 16fd12da80a1ddc78692f9a0483b0120
NeedsCompilation: no
Title: Functions for Analyzing Cellular Barcoding Data
Description: barcodetrackR is an R package developed for the analysis
and visualization of clonal tracking data. Data required is
samples and tag abundances in matrix form. Usually from
cellular barcoding experiments, integration site retrieval
analyses, or similar technologies.
biocViews: Software, Visualization, Sequencing
Author: Diego Alexander Espinoza [aut, cre], Ryland Mortlock [aut]
Maintainer: Diego Alexander Espinoza
<diego.espinoza@pennmedicine.upenn.edu>
URL: https://github.com/dunbarlabNIH/barcodetrackR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/barcodetrackR
git_branch: RELEASE_3_13
git_last_commit: 875e093
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/barcodetrackR_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/barcodetrackR_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/barcodetrackR_1.0.0.tgz
vignettes:
vignettes/barcodetrackR/inst/doc/Introduction_to_barcodetrackR.html
vignetteTitles: barcodetrackR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/barcodetrackR/inst/doc/Introduction_to_barcodetrackR.R
dependencyCount: 95
Package: basecallQC
Version: 1.16.0
Depends: R (>= 3.4), stats, utils, methods, rmarkdown, knitr,
prettydoc, yaml
Imports: ggplot2, stringr, XML, raster, dplyr, data.table, tidyr,
magrittr, DT, lazyeval, ShortRead
Suggests: testthat, BiocStyle
License: GPL (>= 3)
MD5sum: 70d4f390f493d450d459abfc1c9c485a
NeedsCompilation: no
Title: Working with Illumina Basecalling and Demultiplexing input and
output files
Description: The basecallQC package provides tools to work with
Illumina bcl2Fastq (versions >= 2.1.7) software.Prior to
basecalling and demultiplexing using the bcl2Fastq software,
basecallQC functions allow the user to update Illumina sample
sheets from versions <= 1.8.9 to >= 2.1.7 standards, clean
sample sheets of common problems such as invalid sample names
and IDs, create read and index basemasks and the bcl2Fastq
command. Following the generation of basecalled and
demultiplexed data, the basecallQC packages allows the user to
generate HTML tables, plots and a self contained report of
summary metrics from Illumina XML output files.
biocViews: Sequencing, Infrastructure, DataImport, QualityControl
Author: Thomas Carroll and Marian Dore
Maintainer: Thomas Carroll <tc.infomatics@gmail.com>
SystemRequirements: bcl2Fastq (versions >= 2.1.7)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/basecallQC
git_branch: RELEASE_3_13
git_last_commit: ac2c7cf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/basecallQC_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/basecallQC_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/basecallQC_1.16.0.tgz
vignettes: vignettes/basecallQC/inst/doc/basecallQC.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/basecallQC/inst/doc/basecallQC.R
dependencyCount: 105
Package: BaseSpaceR
Version: 1.36.0
Depends: R (>= 2.15.0), RCurl, RJSONIO
Imports: methods
Suggests: RUnit, IRanges, Rsamtools
License: Apache License 2.0
MD5sum: fa3262b1c81eec6e851a403989a78f6f
NeedsCompilation: no
Title: R SDK for BaseSpace RESTful API
Description: A rich R interface to Illumina's BaseSpace cloud computing
environment, enabling the fast development of data analysis and
visualisation tools.
biocViews: Infrastructure, DataRepresentation, ConnectTools, Software,
DataImport, HighThroughputSequencing, Sequencing, Genetics
Author: Adrian Alexa
Maintainer: Jared O'Connell <joconnell@illumina.com>
git_url: https://git.bioconductor.org/packages/BaseSpaceR
git_branch: RELEASE_3_13
git_last_commit: a99d258
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BaseSpaceR_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BaseSpaceR_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BaseSpaceR_1.36.0.tgz
vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf
vignetteTitles: BaseSpaceR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.R
dependencyCount: 4
Package: Basic4Cseq
Version: 1.28.0
Depends: R (>= 3.4), Biostrings, GenomicAlignments, caTools,
GenomicRanges, grDevices, graphics, stats, utils
Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805
Suggests: BSgenome.Hsapiens.UCSC.hg19
License: LGPL-3
MD5sum: fec643a17dc72d231f2a8699889be61b
NeedsCompilation: no
Title: Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data
Description: Basic4Cseq is an R/Bioconductor package for basic
filtering, analysis and subsequent visualization of 4C-seq
data. Virtual fragment libraries can be created for any
BSGenome package, and filter functions for both reads and
fragments and basic quality controls are included. Fragment
data in the vicinity of the experiment's viewpoint can be
visualized as a coverage plot based on a running median
approach and a multi-scale contact profile.
biocViews: ImmunoOncology, Visualization, QualityControl, Sequencing,
Coverage, Alignment, RNASeq, SequenceMatching, DataImport
Author: Carolin Walter
Maintainer: Carolin Walter <carolin.walter@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/Basic4Cseq
git_branch: RELEASE_3_13
git_last_commit: 0ad4961
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Basic4Cseq_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Basic4Cseq_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Basic4Cseq_1.28.0.tgz
vignettes: vignettes/Basic4Cseq/inst/doc/vignette.pdf
vignetteTitles: Basic4Cseq: an R/Bioconductor package for the analysis
of 4C-seq data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Basic4Cseq/inst/doc/vignette.R
dependencyCount: 48
Package: BASiCS
Version: 2.4.0
Depends: R (>= 4.0), SingleCellExperiment
Imports: Biobase, BiocGenerics, coda, cowplot, ggExtra, ggplot2,
graphics, grDevices, MASS, methods, Rcpp (>= 0.11.3),
S4Vectors, scran, scuttle, stats, stats4, SummarizedExperiment,
viridis, utils, Matrix, matrixStats, assertthat, reshape2,
BiocParallel, hexbin
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle, knitr, rmarkdown, testthat, magick
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 7257aa6221d6be253522ab6d14801722
NeedsCompilation: yes
Title: Bayesian Analysis of Single-Cell Sequencing data
Description: Single-cell mRNA sequencing can uncover novel cell-to-cell
heterogeneity in gene expression levels in seemingly
homogeneous populations of cells. However, these experiments
are prone to high levels of technical noise, creating new
challenges for identifying genes that show genuine
heterogeneous expression within the population of cells under
study. BASiCS (Bayesian Analysis of Single-Cell Sequencing
data) is an integrated Bayesian hierarchical model to perform
statistical analyses of single-cell RNA sequencing datasets in
the context of supervised experiments (where the groups of
cells of interest are known a priori, e.g. experimental
conditions or cell types). BASiCS performs built-in data
normalisation (global scaling) and technical noise
quantification (based on spike-in genes). BASiCS provides an
intuitive detection criterion for highly (or lowly) variable
genes within a single group of cells. Additionally, BASiCS can
compare gene expression patterns between two or more
pre-specified groups of cells. Unlike traditional differential
expression tools, BASiCS quantifies changes in expression that
lie beyond comparisons of means, also allowing the study of
changes in cell-to-cell heterogeneity. The latter can be
quantified via a biological over-dispersion parameter that
measures the excess of variability that is observed with
respect to Poisson sampling noise, after normalisation and
technical noise removal. Due to the strong mean/over-dispersion
confounding that is typically observed for scRNA-seq datasets,
BASiCS also tests for changes in residual over-dispersion,
defined by residual values with respect to a global
mean/over-dispersion trend.
biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software,
GeneExpression, Transcriptomics, SingleCell,
DifferentialExpression, Bayesian, CellBiology, ImmunoOncology
Author: Catalina Vallejos [aut], Nils Eling [aut], Alan O'Callaghan
[aut, cre], Sylvia Richardson [ctb], John Marioni [ctb]
Maintainer: Alan O'Callaghan <alan.ocallaghan@outlook.com>
URL: https://github.com/catavallejos/BASiCS
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/catavallejos/BASiCS/issues
git_url: https://git.bioconductor.org/packages/BASiCS
git_branch: RELEASE_3_13
git_last_commit: 385fa3d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BASiCS_2.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BASiCS_2.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BASiCS_2.4.0.tgz
vignettes: vignettes/BASiCS/inst/doc/BASiCS.html
vignetteTitles: Introduction to BASiCS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BASiCS/inst/doc/BASiCS.R
suggestsMe: splatter
dependencyCount: 122
Package: BasicSTARRseq
Version: 1.20.0
Depends: GenomicRanges,GenomicAlignments
Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats
Suggests: knitr
License: LGPL-3
MD5sum: 04d50f405452d882e4bde2c4b8cf0c24
NeedsCompilation: no
Title: Basic peak calling on STARR-seq data
Description: Basic peak calling on STARR-seq data based on a method
introduced in "Genome-Wide Quantitative Enhancer Activity Maps
Identified by STARR-seq" Arnold et al. Science. 2013 Mar
1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013
Jan 17.
biocViews: PeakDetection, GeneRegulation, FunctionalPrediction,
FunctionalGenomics, Coverage
Author: Annika Buerger
Maintainer: Annika Buerger <annika.buerger@ukmuenster.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BasicSTARRseq
git_branch: RELEASE_3_13
git_last_commit: 6542fef
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BasicSTARRseq_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BasicSTARRseq_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BasicSTARRseq_1.20.0.tgz
vignettes: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.pdf
vignetteTitles: BasicSTARRseq.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.R
dependencyCount: 38
Package: basilisk
Version: 1.4.0
Imports: utils, methods, parallel, reticulate, dir.expiry,
basilisk.utils
Suggests: knitr, rmarkdown, BiocStyle, testthat, callr
License: GPL-3
MD5sum: df6104e0eb1b6411b21bf6b5461c4a14
NeedsCompilation: no
Title: Freezing Python Dependencies Inside Bioconductor Packages
Description: Installs a self-contained conda instance that is managed
by the R/Bioconductor installation machinery. This aims to
provide a consistent Python environment that can be used
reliably by Bioconductor packages. Functions are also provided
to enable smooth interoperability of multiple Python
environments in a single R session.
biocViews: Infrastructure
Author: Aaron Lun [aut, cre, cph], Vince Carey [ctb]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/basilisk
git_branch: RELEASE_3_13
git_last_commit: 11d7d30
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/basilisk_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/basilisk_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/basilisk_1.4.0.tgz
vignettes: vignettes/basilisk/inst/doc/motivation.html
vignetteTitles: Motivation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/basilisk/inst/doc/motivation.R
importsMe: BiocSklearn, cbpManager, dasper, densvis, MACSr, MOFA2,
Rcwl, snifter, velociraptor, zellkonverter
dependencyCount: 21
Package: basilisk.utils
Version: 1.4.0
Imports: utils, methods, tools, dir.expiry
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: GPL-3
MD5sum: e729e3bd23a8048403fd768797b16876
NeedsCompilation: no
Title: Basilisk Installation Utilities
Description: Implements utilities for installation of the basilisk
package, primarily for creation of the underlying Conda
instance. This allows us to avoid re-writing the same R code in
both the configure script (for centrally administered R
installations) and in the lazy installation mechanism (for
distributed package binaries). It is highly unlikely that
developers - or, heaven forbid, end-users! - will need to
interact with this package directly; they should be using the
basilisk package instead.
biocViews: Infrastructure
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/basilisk.utils
git_branch: RELEASE_3_13
git_last_commit: e74f4df
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/basilisk.utils_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/basilisk.utils_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/basilisk.utils_1.4.0.tgz
vignettes: vignettes/basilisk.utils/inst/doc/purpose.html
vignetteTitles: _basilisk_ installation utilities
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/basilisk.utils/inst/doc/purpose.R
importsMe: basilisk
dependencyCount: 5
Package: batchelor
Version: 1.8.1
Depends: SingleCellExperiment
Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats,
methods, utils, igraph, BiocNeighbors, BiocSingular, Matrix,
DelayedArray, DelayedMatrixStats, BiocParallel, scuttle,
ResidualMatrix, ScaledMatrix, beachmat
LinkingTo: Rcpp
Suggests: testthat, BiocStyle, knitr, rmarkdown, scran, scater,
bluster, scRNAseq
License: GPL-3
Archs: i386, x64
MD5sum: 1a186bb9481808c27b90bf728742b345
NeedsCompilation: yes
Title: Single-Cell Batch Correction Methods
Description: Implements a variety of methods for batch correction of
single-cell (RNA sequencing) data. This includes methods based
on detecting mutually nearest neighbors, as well as several
efficient variants of linear regression of the log-expression
values. Functions are also provided to perform global rescaling
to remove differences in depth between batches, and to perform
a principal components analysis that is robust to differences
in the numbers of cells across batches.
biocViews: Sequencing, RNASeq, Software, GeneExpression,
Transcriptomics, SingleCell, BatchEffect, Normalization
Author: Aaron Lun [aut, cre], Laleh Haghverdi [ctb]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/batchelor
git_branch: RELEASE_3_13
git_last_commit: ac1b37a
git_last_commit_date: 2021-08-11
Date/Publication: 2021-08-12
source.ver: src/contrib/batchelor_1.8.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/batchelor_1.8.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/batchelor_1.8.1.tgz
vignettes: vignettes/batchelor/inst/doc/correction.html,
vignettes/batchelor/inst/doc/extension.html
vignetteTitles: 1. Correcting batch effects, 2. Extending methods
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/batchelor/inst/doc/correction.R,
vignettes/batchelor/inst/doc/extension.R
dependsOnMe: OSCA.advanced, OSCA.intro, OSCA.multisample,
OSCA.workflows
importsMe: ChromSCape, mumosa, singleCellTK
suggestsMe: TSCAN, bcTSNE, RaceID
dependencyCount: 49
Package: BatchQC
Version: 1.20.0
Depends: R (>= 3.5.0)
Imports: utils, rmarkdown, knitr, pander, gplots, MCMCpack, shiny, sva,
corpcor, moments, matrixStats, ggvis, heatmaply, reshape2,
limma, grDevices, graphics, stats, methods, Matrix
Suggests: testthat
License: GPL (>= 2)
MD5sum: 02238f995d0bdb6343ac96371ae74e5c
NeedsCompilation: no
Title: Batch Effects Quality Control Software
Description: Sequencing and microarray samples often are collected or
processed in multiple batches or at different times. This often
produces technical biases that can lead to incorrect results in
the downstream analysis. BatchQC is a software tool that
streamlines batch preprocessing and evaluation by providing
interactive diagnostics, visualizations, and statistical
analyses to explore the extent to which batch variation impacts
the data. BatchQC diagnostics help determine whether batch
adjustment needs to be done, and how correction should be
applied before proceeding with a downstream analysis. Moreover,
BatchQC interactively applies multiple common batch effect
approaches to the data, and the user can quickly see the
benefits of each method. BatchQC is developed as a Shiny App.
The output is organized into multiple tabs, and each tab
features an important part of the batch effect analysis and
visualization of the data. The BatchQC interface has the
following analysis groups: Summary, Differential Expression,
Median Correlations, Heatmaps, Circular Dendrogram, PCA
Analysis, Shape, ComBat and SVA.
biocViews: BatchEffect, GraphAndNetwork, Microarray,
PrincipalComponent, Sequencing, Software, Visualization,
QualityControl, RNASeq, Preprocessing, DifferentialExpression,
ImmunoOncology
Author: Solaiappan Manimaran <manimaran_1975@hotmail.com>, W. Evan
Johnson <wej@bu.edu>, Heather Selby <selbyh@bu.edu>, Claire
Ruberman <claireruberman@gmail.com>, Kwame Okrah
<kwame.okrah@gmail.com>, Hector Corrada Bravo
<hcorrada@gmail.com>
Maintainer: Solaiappan Manimaran <manimaran_1975@hotmail.com>
URL: https://github.com/mani2012/BatchQC
SystemRequirements: pandoc (http://pandoc.org/installing.html) for
generating reports from markdown files.
VignetteBuilder: knitr
BugReports: https://github.com/mani2012/BatchQC/issues
git_url: https://git.bioconductor.org/packages/BatchQC
git_branch: RELEASE_3_13
git_last_commit: 696d55c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BatchQC_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BatchQC_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BatchQC_1.20.0.tgz
vignettes: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.pdf,
vignettes/BatchQC/inst/doc/BatchQC_examples.html,
vignettes/BatchQC/inst/doc/BatchQCIntro.html
vignetteTitles: BatchQC_usage_advanced, BatchQC_examples, BatchQCIntro
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.R
dependencyCount: 161
Package: BayesKnockdown
Version: 1.18.0
Depends: R (>= 3.3)
Imports: stats, Biobase
License: GPL-3
MD5sum: f6df8a5ef81ba98d8a0413f93f0beff3
NeedsCompilation: no
Title: BayesKnockdown: Posterior Probabilities for Edges from Knockdown
Data
Description: A simple, fast Bayesian method for computing posterior
probabilities for relationships between a single predictor
variable and multiple potential outcome variables,
incorporating prior probabilities of relationships. In the
context of knockdown experiments, the predictor variable is the
knocked-down gene, while the other genes are potential targets.
Can also be used for differential expression/2-class data.
biocViews: NetworkInference, GeneExpression, GeneTarget, Network,
Bayesian
Author: William Chad Young
Maintainer: William Chad Young <wmchad@uw.edu>
git_url: https://git.bioconductor.org/packages/BayesKnockdown
git_branch: RELEASE_3_13
git_last_commit: d299049
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BayesKnockdown_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BayesKnockdown_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BayesKnockdown_1.18.0.tgz
vignettes: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.pdf
vignetteTitles: BayesKnockdown.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.R
dependencyCount: 7
Package: BayesSpace
Version: 1.2.1
Depends: R (>= 4.0.0), SingleCellExperiment
Imports: Rcpp (>= 1.0.4.6), stats, purrr, scater, scran,
SummarizedExperiment, coda, rhdf5, S4Vectors, Matrix,
assertthat, mclust, RCurl, DirichletReg, xgboost, utils,
ggplot2, scales, BiocFileCache, BiocSingular
LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress
Suggests: testthat, knitr, rmarkdown, igraph, spatialLIBD, dplyr,
viridis, patchwork, RColorBrewer, Seurat
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: f24d780ff4487a78484ae9a6e874dde4
NeedsCompilation: yes
Title: Clustering and Resolution Enhancement of Spatial Transcriptomes
Description: Tools for clustering and enhancing the resolution of
spatial gene expression experiments. BayesSpace clusters a
low-dimensional representation of the gene expression matrix,
incorporating a spatial prior to encourage neighboring spots to
cluster together. The method can enhance the resolution of the
low-dimensional representation into "sub-spots", for which
features such as gene expression or cell type composition can
be imputed.
biocViews: Software, Clustering, Transcriptomics, GeneExpression,
SingleCell, ImmunoOncology, DataImport
Author: Edward Zhao [aut], Matt Stone [aut, cre], Xing Ren [ctb],
Raphael Gottardo [ctb]
Maintainer: Matt Stone <mstone@fredhutch.org>
URL: edward130603.github.io/BayesSpace
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/edward130603/BayesSpace/issues
git_url: https://git.bioconductor.org/packages/BayesSpace
git_branch: RELEASE_3_13
git_last_commit: d1996f9
git_last_commit_date: 2021-09-17
Date/Publication: 2021-09-19
source.ver: src/contrib/BayesSpace_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BayesSpace_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/BayesSpace_1.2.1.tgz
vignettes: vignettes/BayesSpace/inst/doc/BayesSpace.html
vignetteTitles: BayesSpace
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BayesSpace/inst/doc/BayesSpace.R
dependencyCount: 134
Package: bayNorm
Version: 1.10.0
Depends: R (>= 3.5),
Imports: Rcpp (>= 0.12.12), BB, foreach, iterators, doSNOW, Matrix,
parallel, MASS, locfit, fitdistrplus, stats, methods, graphics,
grDevices, SingleCellExperiment, SummarizedExperiment,
BiocParallel, utils
LinkingTo: Rcpp, RcppArmadillo,RcppProgress
Suggests: knitr, rmarkdown, BiocStyle, devtools, testthat
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 7f78214dd4af47da56d10dc570d93b6c
NeedsCompilation: yes
Title: Single-cell RNA sequencing data normalization
Description: bayNorm is used for normalizing single-cell RNA-seq data.
biocViews: ImmunoOncology, Normalization, RNASeq, SingleCell,Sequencing
Author: Wenhao Tang [aut, cre], Fran<U+00E7>ois Bertaux [aut], Philipp
Thomas [aut], Claire Stefanelli [aut], Malika Saint [aut],
Samuel Marguerat [aut], Vahid Shahrezaei [aut]
Maintainer: Wenhao Tang <wt215@ic.ac.uk>
URL: https://github.com/WT215/bayNorm
VignetteBuilder: knitr
BugReports: https://github.com/WT215/bayNorm/issues
git_url: https://git.bioconductor.org/packages/bayNorm
git_branch: RELEASE_3_13
git_last_commit: cbf997b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bayNorm_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bayNorm_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bayNorm_1.10.0.tgz
vignettes: vignettes/bayNorm/inst/doc/bayNorm.html
vignetteTitles: Introduction to bayNorm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bayNorm/inst/doc/bayNorm.R
dependencyCount: 48
Package: baySeq
Version: 2.26.0
Depends: R (>= 2.3.0), methods, GenomicRanges, abind, parallel
Imports: edgeR
Suggests: BiocStyle, BiocGenerics
License: GPL-3
MD5sum: f292776d63393b6a0b68da118dbd36fd
NeedsCompilation: no
Title: Empirical Bayesian analysis of patterns of differential
expression in count data
Description: This package identifies differential expression in
high-throughput 'count' data, such as that derived from
next-generation sequencing machines, calculating estimated
posterior likelihoods of differential expression (or more
complex hypotheses) via empirical Bayesian methods.
biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE
Author: Thomas J. Hardcastle
Maintainer: Thomas J. Hardcastle <tjh48@cam.ac.uk>
git_url: https://git.bioconductor.org/packages/baySeq
git_branch: RELEASE_3_13
git_last_commit: 44ebe60
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/baySeq_2.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/baySeq_2.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/baySeq_2.26.0.tgz
vignettes: vignettes/baySeq/inst/doc/baySeq_generic.pdf,
vignettes/baySeq/inst/doc/baySeq.pdf
vignetteTitles: Advanced baySeq analyses, baySeq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/baySeq/inst/doc/baySeq_generic.R,
vignettes/baySeq/inst/doc/baySeq.R
dependsOnMe: clusterSeq, Rcade, segmentSeq, TCC
importsMe: metaseqR2, riboSeqR, srnadiff
suggestsMe: compcodeR
dependencyCount: 25
Package: BBCAnalyzer
Version: 1.22.0
Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices,
GenomicRanges, IRanges, Biostrings
Suggests: BSgenome.Hsapiens.UCSC.hg19
License: LGPL-3
MD5sum: eec4a3b5c875c8f6f66f3e5d65163864
NeedsCompilation: no
Title: BBCAnalyzer: an R/Bioconductor package for visualizing base
counts
Description: BBCAnalyzer is a package for visualizing the relative or
absolute number of bases, deletions and insertions at defined
positions in sequence alignment data available as bam files in
comparison to the reference bases. Markers for the relative
base frequencies, the mean quality of the detected bases, known
mutations or polymorphisms and variants called in the data may
additionally be included in the plots.
biocViews: Sequencing, Alignment, Coverage, GeneticVariability, SNP
Author: Sarah Sandmann
Maintainer: Sarah Sandmann <sarah.sandmann@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/BBCAnalyzer
git_branch: RELEASE_3_13
git_last_commit: ca0ad63
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BBCAnalyzer_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BBCAnalyzer_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BBCAnalyzer_1.22.0.tgz
vignettes: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.pdf
vignetteTitles: Using BBCAnalyzer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.R
dependencyCount: 98
Package: BCRANK
Version: 1.54.0
Depends: methods
Imports: Biostrings
Suggests: seqLogo
License: GPL-2
Archs: i386, x64
MD5sum: a0c5619c81e19878844ab9678ecc1069
NeedsCompilation: yes
Title: Predicting binding site consensus from ranked DNA sequences
Description: Functions and classes for de novo prediction of
transcription factor binding consensus by heuristic search
biocViews: MotifDiscovery, GeneRegulation
Author: Adam Ameur <Adam.Ameur@genpat.uu.se>
Maintainer: Adam Ameur <Adam.Ameur@genpat.uu.se>
git_url: https://git.bioconductor.org/packages/BCRANK
git_branch: RELEASE_3_13
git_last_commit: 827b00a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BCRANK_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BCRANK_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BCRANK_1.54.0.tgz
vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf
vignetteTitles: BCRANK
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BCRANK/inst/doc/BCRANK.R
dependencyCount: 19
Package: bcSeq
Version: 1.14.0
Depends: R (>= 3.4.0)
Imports: Rcpp (>= 0.12.12), Matrix, Biostrings
LinkingTo: Rcpp, Matrix
Suggests: knitr
License: GPL (>= 2)
Archs: i386, x64
MD5sum: f9d67c85a00a77957153cf4d9fcfea27
NeedsCompilation: yes
Title: Fast Sequence Mapping in High-Throughput shRNA and CRISPR
Screens
Description: This Rcpp-based package implements a highly efficient data
structure and algorithm for performing alignment of short reads
from CRISPR or shRNA screens to reference barcode library.
Sequencing error are considered and matching qualities are
evaluated based on Phred scores. A Bayes' classifier is
employed to predict the originating barcode of a read. The
package supports provision of user-defined probability models
for evaluating matching qualities. The package also supports
multi-threading.
biocViews: ImmunoOncology, Alignment, CRISPR, Sequencing,
SequenceMatching, MultipleSequenceAlignment, Software, ATACSeq
Author: Jiaxing Lin [aut, cre], Jeremy Gresham [aut], Jichun Xie [aut],
Kouros Owzar [aut], Tongrong Wang [ctb], So Young Kim [ctb],
James Alvarez [ctb], Jeffrey S. Damrauer [ctb], Scott Floyd
[ctb], Joshua Granek [ctb], Andrew Allen [ctb], Cliburn Chan
[ctb]
Maintainer: Jiaxing Lin <jiaxing.lin@duke.edu>
URL: https://github.com/jl354/bcSeq
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org
git_url: https://git.bioconductor.org/packages/bcSeq
git_branch: RELEASE_3_13
git_last_commit: d9092ae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bcSeq_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bcSeq_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bcSeq_1.14.0.tgz
vignettes: vignettes/bcSeq/inst/doc/bcSeq.pdf
vignetteTitles: bcSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bcSeq/inst/doc/bcSeq.R
dependencyCount: 23
Package: BDMMAcorrect
Version: 1.10.0
Depends: R (>= 3.5), vegan, ellipse, ggplot2, ape, SummarizedExperiment
Imports: Rcpp (>= 0.12.12), RcppArmadillo, RcppEigen, stats
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
Suggests: knitr, rmarkdown, BiocGenerics
License: GPL (>= 2)
Archs: i386, x64
MD5sum: c50a8a732446be34363512d701905ff9
NeedsCompilation: yes
Title: Meta-analysis for the metagenomic read counts data from
different cohorts
Description: Metagenomic sequencing techniques enable quantitative
analyses of the microbiome. However, combining the microbial
data from these experiments is challenging due to the
variations between experiments. The existing methods for
correcting batch effects do not consider the interactions
between variables—microbial taxa in microbial studies—and the
overdispersion of the microbiome data. Therefore, they are not
applicable to microbiome data. We develop a new method,
Bayesian Dirichlet-multinomial regression meta-analysis
(BDMMA), to simultaneously model the batch effects and detect
the microbial taxa associated with phenotypes. BDMMA
automatically models the dependence among microbial taxa and is
robust to the high dimensionality of the microbiome and their
association sparsity.
biocViews: ImmunoOncology, BatchEffect, Microbiome, Bayesian
Author: ZHENWEI DAI <daizwhao@gmail.com>
Maintainer: ZHENWEI DAI <daizwhao@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BDMMAcorrect
git_branch: RELEASE_3_13
git_last_commit: c22bbaf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BDMMAcorrect_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BDMMAcorrect_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BDMMAcorrect_1.10.0.tgz
vignettes: vignettes/BDMMAcorrect/inst/doc/Vignette.pdf
vignetteTitles: BDMMAcorrect_user_guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BDMMAcorrect/inst/doc/Vignette.R
dependencyCount: 64
Package: beachmat
Version: 2.8.1
Imports: methods, DelayedArray (>= 0.15.14), BiocGenerics, Matrix, Rcpp
LinkingTo: Rcpp
Suggests: testthat, BiocStyle, knitr, rmarkdown, rcmdcheck,
BiocParallel, HDF5Array
License: GPL-3
Archs: i386, x64
MD5sum: ca6ae80e2258e0a8fec6451703703f95
NeedsCompilation: yes
Title: Compiling Bioconductor to Handle Each Matrix Type
Description: Provides a consistent C++ class interface for reading from
and writing data to a variety of commonly used matrix types.
Ordinary matrices and several sparse/dense Matrix classes are
directly supported, third-party S4 classes may be supported by
external linkage, while all other matrices are handled by
DelayedArray block processing.
biocViews: DataRepresentation, DataImport, Infrastructure
Author: Aaron Lun [aut, cre], Hervé Pagès [aut], Mike Smith [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/beachmat
git_branch: RELEASE_3_13
git_last_commit: 5c9ef4d
git_last_commit_date: 2021-08-10
Date/Publication: 2021-08-12
source.ver: src/contrib/beachmat_2.8.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/beachmat_2.8.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/beachmat_2.8.1.tgz
vignettes: vignettes/beachmat/inst/doc/external.html,
vignettes/beachmat/inst/doc/input.html,
vignettes/beachmat/inst/doc/linking.html,
vignettes/beachmat/inst/doc/output.html
vignetteTitles: 4. Supporting arbitrary matrix classes (v2), 2. Reading
data from R matrices in C++ (v2), 1. Developer guide, 3.
Writing data into R matrix objects (v2)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/beachmat/inst/doc/external.R,
vignettes/beachmat/inst/doc/input.R,
vignettes/beachmat/inst/doc/linking.R,
vignettes/beachmat/inst/doc/output.R
importsMe: batchelor, BiocSingular, DropletUtils, mumosa, scater,
scran, scuttle, SingleR
suggestsMe: bsseq, glmGamPoi, mbkmeans, PCAtools, scCB2
linksToMe: BiocSingular, bsseq, DropletUtils, glmGamPoi, mbkmeans,
PCAtools, scran, scuttle, SingleR
dependencyCount: 17
Package: beadarray
Version: 2.42.0
Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8),
hexbin
Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2,
GenomicRanges, IRanges, illuminaio, methods, ggplot2
Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData,
illuminaHumanv3.db, gridExtra, BiocStyle,
TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 67f115ec50e5340bed1d9d3887576702
NeedsCompilation: yes
Title: Quality assessment and low-level analysis for Illumina BeadArray
data
Description: The package is able to read bead-level data (raw TIFFs and
text files) output by BeadScan as well as bead-summary data
from BeadStudio. Methods for quality assessment and low-level
analysis are provided.
biocViews: Microarray, OneChannel, QualityControl, Preprocessing
Author: Mark Dunning, Mike Smith, Jonathan Cairns, Andy Lynch, Matt
Ritchie
Maintainer: Mark Dunning <m.j.dunning@sheffield.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/beadarray
git_branch: RELEASE_3_13
git_last_commit: 028000c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/beadarray_2.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/beadarray_2.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/beadarray_2.42.0.tgz
vignettes: vignettes/beadarray/inst/doc/beadarray.pdf,
vignettes/beadarray/inst/doc/beadlevel.pdf,
vignettes/beadarray/inst/doc/beadsummary.pdf,
vignettes/beadarray/inst/doc/ImageProcessing.pdf
vignetteTitles: beadarray.pdf, beadlevel.pdf, beadsummary.pdf,
ImageProcessing.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/beadarray/inst/doc/beadarray.R,
vignettes/beadarray/inst/doc/beadlevel.R,
vignettes/beadarray/inst/doc/beadsummary.R,
vignettes/beadarray/inst/doc/ImageProcessing.R
dependsOnMe: beadarrayExampleData, beadarrayFilter
importsMe: arrayQualityMetrics, blima, epigenomix, BeadArrayUseCases,
RobLoxBioC
suggestsMe: beadarraySNP, lumi, blimaTestingData, maGUI
dependencyCount: 82
Package: beadarraySNP
Version: 1.58.0
Depends: methods, Biobase (>= 2.14), quantsmooth
Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy
License: GPL-2
MD5sum: 5f20bbe735373465db0e09ca9c690c1f
NeedsCompilation: no
Title: Normalization and reporting of Illumina SNP bead arrays
Description: Importing data from Illumina SNP experiments and
performing copy number calculations and reports.
biocViews: CopyNumberVariation, SNP, GeneticVariability, TwoChannel,
Preprocessing, DataImport
Author: Jan Oosting
Maintainer: Jan Oosting <j.oosting@lumc.nl>
git_url: https://git.bioconductor.org/packages/beadarraySNP
git_branch: RELEASE_3_13
git_last_commit: 6da1ae1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/beadarraySNP_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/beadarraySNP_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/beadarraySNP_1.58.0.tgz
vignettes: vignettes/beadarraySNP/inst/doc/beadarraySNP.pdf
vignetteTitles: beadarraySNP.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/beadarraySNP/inst/doc/beadarraySNP.R
dependencyCount: 19
Package: BeadDataPackR
Version: 1.44.0
Imports: stats, utils
Suggests: BiocStyle, knitr
License: GPL-2
Archs: i386, x64
MD5sum: d8b269dbe2b6277f3c754d78e9457bae
NeedsCompilation: yes
Title: Compression of Illumina BeadArray data
Description: Provides functionality for the compression and
decompression of raw bead-level data from the Illumina
BeadArray platform.
biocViews: Microarray
Author: Mike Smith, Andy Lynch
Maintainer: Mike Smith <grimbough@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BeadDataPackR
git_branch: RELEASE_3_13
git_last_commit: ca45790
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BeadDataPackR_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BeadDataPackR_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BeadDataPackR_1.44.0.tgz
vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf
vignetteTitles: BeadDataPackR.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.R
importsMe: beadarray
dependencyCount: 2
Package: BEARscc
Version: 1.12.0
Depends: R (>= 3.5.0)
Imports: ggplot2, SingleCellExperiment, data.table, stats, utils,
graphics, compiler
Suggests: testthat, cowplot, knitr, rmarkdown, BiocStyle, NMF
License: GPL-3
MD5sum: b61c054462d3181ccf8de5a8b313e41a
NeedsCompilation: no
Title: BEARscc (Bayesian ERCC Assesstment of Robustness of Single Cell
Clusters)
Description: BEARscc is a noise estimation and injection tool that is
designed to assess putative single-cell RNA-seq clusters in the
context of experimental noise estimated by ERCC spike-in
controls.
biocViews: ImmunoOncology, SingleCell, Clustering, Transcriptomics
Author: David T. Severson <david_severson@hms.harvard.edu>
Maintainer: Benjamin Schuster-Boeckler
<benjamin.schuster-boeckler@ludwig.ox.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BEARscc
git_branch: RELEASE_3_13
git_last_commit: 79b6626
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BEARscc_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BEARscc_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BEARscc_1.12.0.tgz
vignettes: vignettes/BEARscc/inst/doc/BEARscc.pdf
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BEARscc/inst/doc/BEARscc.R
dependencyCount: 59
Package: BEAT
Version: 1.30.0
Depends: R (>= 2.13.0)
Imports: GenomicRanges, ShortRead, Biostrings, BSgenome
License: LGPL (>= 3.0)
MD5sum: 49fc8a1fd2ac1a9403a1106051f9fec1
NeedsCompilation: no
Title: BEAT - BS-Seq Epimutation Analysis Toolkit
Description: Model-based analysis of single-cell methylation data
biocViews: ImmunoOncology, Genetics, MethylSeq, Software,
DNAMethylation, Epigenetics
Author: Kemal Akman <akman@mpipz.mpg.de>
Maintainer: Kemal Akman <akman@mpipz.mpg.de>
git_url: https://git.bioconductor.org/packages/BEAT
git_branch: RELEASE_3_13
git_last_commit: 226a8df
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BEAT_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BEAT_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BEAT_1.30.0.tgz
vignettes: vignettes/BEAT/inst/doc/BEAT.pdf
vignetteTitles: Analysing single-cell BS-Seq data with the "BEAT"
package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BEAT/inst/doc/BEAT.R
dependencyCount: 51
Package: BEclear
Version: 2.8.0
Depends: BiocParallel (>= 1.14.2)
Imports: futile.logger, Rdpack, Matrix, data.table (>= 1.11.8), Rcpp,
outliers, abind, stats, graphics, utils, methods
LinkingTo: Rcpp
Suggests: testthat, BiocStyle, knitr, rmarkdown, pander
License: GPL-3
Archs: i386, x64
MD5sum: a7430b0b90b803d0c12fcd1eec991333
NeedsCompilation: yes
Title: Correction of batch effects in DNA methylation data
Description: Provides functions to detect and correct for batch effects
in DNA methylation data. The core function is based on latent
factor models and can also be used to predict missing values in
any other matrix containing real numbers.
biocViews: BatchEffect, DNAMethylation, Software, Preprocessing,
StatisticalMethod
Author: David Rasp [aut, cre]
(<https://orcid.org/0000-0003-0164-2163>), Markus Merl [aut],
Ruslan Akulenko [aut]
Maintainer: David Rasp <david.j.rasp@gmail.com>
URL: https://github.com/uds-helms/BEclear
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/uds-helms/BEclear/issues
git_url: https://git.bioconductor.org/packages/BEclear
git_branch: RELEASE_3_13
git_last_commit: 820b246
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BEclear_2.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BEclear_2.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BEclear_2.8.0.tgz
vignettes: vignettes/BEclear/inst/doc/BEclear.html
vignetteTitles: BEclear tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BEclear/inst/doc/BEclear.R
dependencyCount: 23
Package: BgeeCall
Version: 1.8.0
Depends: R (>= 3.6)
Imports: GenomicFeatures, tximport, Biostrings, rtracklayer, biomaRt,
jsonlite, methods, dplyr, data.table, sjmisc, grDevices,
graphics, stats, utils, rslurm, rhdf5
Suggests: knitr, testthat, rmarkdown, AnnotationHub, httr
License: GPL-3
MD5sum: ebe7acd141b9d860e7f75f9f219de889
NeedsCompilation: no
Title: Automatic RNA-Seq present/absent gene expression calls
generation
Description: BgeeCall allows to generate present/absent gene expression
calls without using an arbitrary cutoff like TPM<1. Calls are
generated based on reference intergenic sequences. These
sequences are generated based on expression of all RNA-Seq
libraries of each species integrated in Bgee
(https://bgee.org).
biocViews: Software, GeneExpression, RNASeq
Author: Julien Wollbrett [aut, cre], Sara Fonseca Costa [aut], Julien
Roux [aut], Marc Robinson Rechavi [ctb], Frederic Bastian [aut]
Maintainer: Julien Wollbrett <julien.wollbrett@unil.ch>
URL: https://github.com/BgeeDB/BgeeCall
SystemRequirements: kallisto
VignetteBuilder: knitr
BugReports: https://github.com/BgeeDB/BgeeCall/issues
git_url: https://git.bioconductor.org/packages/BgeeCall
git_branch: RELEASE_3_13
git_last_commit: 75b35d9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BgeeCall_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BgeeCall_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BgeeCall_1.8.0.tgz
vignettes: vignettes/BgeeCall/inst/doc/bgeecall-manual.html
vignetteTitles: automatic RNA-Seq present/absent gene expression calls
generation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BgeeCall/inst/doc/bgeecall-manual.R
dependencyCount: 106
Package: BgeeDB
Version: 2.18.1
Depends: R (>= 3.6.0), topGO, tidyr
Imports: R.utils, data.table, curl, RCurl, digest, methods, stats,
utils, dplyr, RSQLite, graph, Biobase,
Suggests: knitr, BiocStyle, testthat, rmarkdown, markdown
License: GPL-3 + file LICENSE
MD5sum: 7470f5786c81d5b2d29e07ddf11e3c26
NeedsCompilation: no
Title: Annotation and gene expression data retrieval from Bgee
database. TopAnat, an anatomical entities Enrichment Analysis
tool for UBERON ontology
Description: A package for the annotation and gene expression data
download from Bgee database, and TopAnat analysis: GO-like
enrichment of anatomical terms, mapped to genes by expression
patterns.
biocViews: Software, DataImport, Sequencing, GeneExpression,
Microarray, GO, GeneSetEnrichment
Author: Andrea Komljenovic [aut, cre], Julien Roux [aut, cre]
Maintainer: Julien Wollbrett <julien.wollbrett@unil.ch>, Julien Roux
<julien.roux@unibas.ch>, Andrea Komljenovic
<andreakomljenovic@gmail.com>, Frederic Bastian
<bgee@sib.swiss>
URL: https://github.com/BgeeDB/BgeeDB_R
VignetteBuilder: knitr
BugReports: https://github.com/BgeeDB/BgeeDB_R/issues
git_url: https://git.bioconductor.org/packages/BgeeDB
git_branch: RELEASE_3_13
git_last_commit: 6e3f28d
git_last_commit_date: 2021-06-15
Date/Publication: 2021-06-17
source.ver: src/contrib/BgeeDB_2.18.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BgeeDB_2.18.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/BgeeDB_2.18.1.tgz
vignettes: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.html
vignetteTitles: BgeeDB,, an R package for retrieval of curated
expression datasets and for gene list enrichment tests
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.R
importsMe: psygenet2r, RITAN
dependencyCount: 71
Package: BGmix
Version: 1.52.0
Depends: R (>= 2.3.1), KernSmooth
License: GPL-2
MD5sum: 9a4eaa30f39c26b86cd5f77f245672da
NeedsCompilation: yes
Title: Bayesian models for differential gene expression
Description: Fully Bayesian mixture models for differential gene
expression
biocViews: Microarray, DifferentialExpression, MultipleComparison
Author: Alex Lewin, Natalia Bochkina
Maintainer: Alex Lewin <a.m.lewin@imperial.ac.uk>
git_url: https://git.bioconductor.org/packages/BGmix
git_branch: RELEASE_3_13
git_last_commit: ec212a3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BGmix_1.52.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/BGmix_1.52.0.tgz
vignettes: vignettes/BGmix/inst/doc/BGmix.pdf
vignetteTitles: BGmix Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BGmix/inst/doc/BGmix.R
dependencyCount: 2
Package: bgx
Version: 1.58.0
Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1)
Imports: Rcpp (>= 0.11.0)
LinkingTo: Rcpp
Suggests: affydata, hgu95av2cdf
License: GPL-2
Archs: i386, x64
MD5sum: 5c31fc7229f875092644d1d1c7554ede
NeedsCompilation: yes
Title: Bayesian Gene eXpression
Description: Bayesian integrated analysis of Affymetrix GeneChips
biocViews: Microarray, DifferentialExpression
Author: Ernest Turro, Graeme Ambler, Anne-Mette K Hein
Maintainer: Ernest Turro <et341@cam.ac.uk>
git_url: https://git.bioconductor.org/packages/bgx
git_branch: RELEASE_3_13
git_last_commit: f9fb330
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bgx_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bgx_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bgx_1.58.0.tgz
vignettes: vignettes/bgx/inst/doc/bgx.pdf
vignetteTitles: HowTo BGX
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bgx/inst/doc/bgx.R
dependencyCount: 27
Package: BHC
Version: 1.44.0
License: GPL-3
Archs: i386, x64
MD5sum: 530eae6ebaa22eeb651e1bdaa059d199
NeedsCompilation: yes
Title: Bayesian Hierarchical Clustering
Description: The method performs bottom-up hierarchical clustering,
using a Dirichlet Process (infinite mixture) to model
uncertainty in the data and Bayesian model selection to decide
at each step which clusters to merge. This avoids several
limitations of traditional methods, for example how many
clusters there should be and how to choose a principled
distance metric. This implementation accepts multinomial (i.e.
discrete, with 2+ categories) or time-series data. This version
also includes a randomised algorithm which is more efficient
for larger data sets.
biocViews: Microarray, Clustering
Author: Rich Savage, Emma Cooke, Robert Darkins, Yang Xu
Maintainer: Rich Savage <r.s.savage@warwick.ac.uk>
git_url: https://git.bioconductor.org/packages/BHC
git_branch: RELEASE_3_13
git_last_commit: 8a054fb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BHC_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BHC_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BHC_1.44.0.tgz
vignettes: vignettes/BHC/inst/doc/bhc.pdf
vignetteTitles: Bayesian Hierarchical Clustering
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BHC/inst/doc/bhc.R
dependencyCount: 0
Package: BicARE
Version: 1.50.0
Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase
License: GPL-2
Archs: i386, x64
MD5sum: c7616f36acedf859f15ed54f4347bbc4
NeedsCompilation: yes
Title: Biclustering Analysis and Results Exploration
Description: Biclustering Analysis and Results Exploration
biocViews: Microarray, Transcription, Clustering
Author: Pierre Gestraud
Maintainer: Pierre Gestraud <pierre.gestraud@curie.fr>
URL: http://bioinfo.curie.fr
git_url: https://git.bioconductor.org/packages/BicARE
git_branch: RELEASE_3_13
git_last_commit: 8c27ce5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BicARE_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BicARE_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BicARE_1.50.0.tgz
vignettes: vignettes/BicARE/inst/doc/BicARE.pdf
vignetteTitles: BicARE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BicARE/inst/doc/BicARE.R
dependsOnMe: RcmdrPlugin.BiclustGUI
importsMe: miRSM
dependencyCount: 58
Package: BiFET
Version: 1.12.0
Imports: stats, poibin, GenomicRanges
Suggests: testthat, knitr
License: GPL-3
MD5sum: e69b752c9ef443517d51a1a1cb197d91
NeedsCompilation: no
Title: Bias-free Footprint Enrichment Test
Description: BiFET identifies TFs whose footprints are over-represented
in target regions compared to background regions after
correcting for the bias arising from the imbalance in read
counts and GC contents between the target and background
regions. For a given TF k, BiFET tests the null hypothesis that
the target regions have the same probability of having
footprints for the TF k as the background regions while
correcting for the read count and GC content bias. For this, we
use the number of target regions with footprints for TF k, t_k
as a test statistic and calculate the p-value as the
probability of observing t_k or more target regions with
footprints under the null hypothesis.
biocViews: ImmunoOncology, Genetics, Epigenetics, Transcription,
GeneRegulation, ATACSeq, DNaseSeq, RIPSeq, Software
Author: Ahrim Youn [aut, cre], Eladio Marquez [aut], Nathan Lawlor
[aut], Michael Stitzel [aut], Duygu Ucar [aut]
Maintainer: Ahrim Youn <Ahrim.Youn@jax.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiFET
git_branch: RELEASE_3_13
git_last_commit: 5eb0b62
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BiFET_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BiFET_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BiFET_1.12.0.tgz
vignettes: vignettes/BiFET/inst/doc/BiFET.html
vignetteTitles: "A Guide to using BiFET"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiFET/inst/doc/BiFET.R
dependencyCount: 18
Package: BiGGR
Version: 1.28.0
Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr
Imports: hypergraph, limSolve
License: file LICENSE
MD5sum: deae0796ed11ac9acaae0009b46f4292
NeedsCompilation: no
Title: Constraint based modeling in R using metabolic reconstruction
databases
Description: This package provides an interface to simulate metabolic
reconstruction from the BiGG database(http://bigg.ucsd.edu/)
and other metabolic reconstruction databases. The package
facilitates flux balance analysis (FBA) and the sampling of
feasible flux distributions. Metabolic networks and estimated
fluxes can be visualized with hypergraphs.
biocViews: Systems Biology,Pathway,Network,GraphAndNetwork,
Visualization,Metabolomics
Author: Anand K. Gavai, Hannes Hettling
Maintainer: Anand K. Gavai <anand.gavai@bioinformatics.nl>, Hannes
Hettling <hannes.hettling@naturalis.nl>
URL: http://www.bioconductor.org/
git_url: https://git.bioconductor.org/packages/BiGGR
git_branch: RELEASE_3_13
git_last_commit: 1736ee9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BiGGR_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BiGGR_1.28.0.zip
vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf
vignetteTitles: BiGGR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BiGGR/inst/doc/BiGGR.R
dependencyCount: 26
Package: bigmelon
Version: 1.18.0
Depends: R (>= 3.3), wateRmelon (>= 1.25.0), gdsfmt (>= 1.0.4),
methods, minfi (>= 1.21.0), Biobase, methylumi
Imports: stats, utils, GEOquery, graphics, BiocGenerics
Suggests: BiocGenerics, RUnit, BiocStyle, minfiData, parallel,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b2.hg19, bumphunter
License: GPL-3
MD5sum: a144656a74a4f28cc547fa9f0c8d477e
NeedsCompilation: no
Title: Illumina methylation array analysis for large experiments
Description: Methods for working with Illumina arrays using gdsfmt.
biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing,
QualityControl, MethylationArray, DataImport, CpGIsland
Author: Tyler J. Gorrie-Stone [cre, aut], Ayden Saffari [aut], Karim
Malki [aut], Leonard C. Schalkwyk [aut]
Maintainer: Tyler J. Gorrie-Stone <tyler.gorrie-stone@diamond.ac.uk>
git_url: https://git.bioconductor.org/packages/bigmelon
git_branch: RELEASE_3_13
git_last_commit: 6ca2330
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bigmelon_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bigmelon_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bigmelon_1.18.0.tgz
vignettes: vignettes/bigmelon/inst/doc/bigmelon.pdf
vignetteTitles: The bigmelon Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bigmelon/inst/doc/bigmelon.R
dependencyCount: 168
Package: bigPint
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: DelayedArray (>= 0.12.2), dplyr (>= 0.7.2), GGally (>= 1.3.2),
ggplot2 (>= 2.2.1), graphics (>= 3.5.0), grDevices (>= 3.5.0),
grid (>= 3.5.0), gridExtra (>= 2.3), hexbin (>= 1.27.1), Hmisc
(>= 4.0.3), htmlwidgets (>= 0.9), methods (>= 3.5.2), plotly
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(>= 0.8.7), shiny (>= 1.0.5), shinycssloaders (>= 0.2.0),
shinydashboard (>= 0.6.1), stats (>= 3.5.0), stringr (>=
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utils (>= 3.5.0)
Suggests: BiocGenerics (>= 0.29.1), data.table (>= 1.11.8), EDASeq (>=
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matrixStats (>= 0.53.1), rmarkdown (>= 1.10), roxygen2 (>=
3.0.0), RUnit (>= 0.4.32), tibble (>= 1.4.2),
License: GPL-3
MD5sum: fb78c1da2c94713869c60672cb19bb6d
NeedsCompilation: no
Title: Big multivariate data plotted interactively
Description: Methods for visualizing large multivariate datasets using
static and interactive scatterplot matrices, parallel
coordinate plots, volcano plots, and litre plots. Includes
examples for visualizing RNA-sequencing datasets and
differentially expressed genes.
biocViews: Clustering, DataImport, DifferentialExpression,
GeneExpression, MultipleComparison, Normalization,
Preprocessing, QualityControl, RNASeq, Sequencing, Software,
Transcription, Visualization
Author: Lindsay Rutter [aut, cre], Dianne Cook [aut]
Maintainer: Lindsay Rutter <lindsayannerutter@gmail.com>
URL: https://github.com/lindsayrutter/bigPint
VignetteBuilder: knitr
BugReports: https://github.com/lindsayrutter/bigPint/issues
git_url: https://git.bioconductor.org/packages/bigPint
git_branch: RELEASE_3_13
git_last_commit: d2b72f4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bigPint_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bigPint_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bigPint_1.8.0.tgz
vignettes: vignettes/bigPint/inst/doc/bioconductor.html,
vignettes/bigPint/inst/doc/manuscripts.html,
vignettes/bigPint/inst/doc/summarizedExperiment.html
vignetteTitles: "bigPint Vignette", "Recommended RNA-seq pipeline",
"Data metrics object"
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bigPint/inst/doc/bioconductor.R,
vignettes/bigPint/inst/doc/manuscripts.R,
vignettes/bigPint/inst/doc/summarizedExperiment.R
dependencyCount: 125
Package: bioassayR
Version: 1.30.0
Depends: R (>= 3.5.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods,
Matrix, rjson, BiocGenerics (>= 0.13.8)
Imports: XML, ChemmineR
Suggests: BiocStyle, RCurl, biomaRt, cellHTS2, knitr, knitcitations,
knitrBootstrap, testthat, ggplot2, rmarkdown
License: Artistic-2.0
MD5sum: 91fcdd20096a646e4aa7a1b4ab99804d
NeedsCompilation: no
Title: Cross-target analysis of small molecule bioactivity
Description: bioassayR is a computational tool that enables
simultaneous analysis of thousands of bioassay experiments
performed over a diverse set of compounds and biological
targets. Unique features include support for large-scale
cross-target analyses of both public and custom bioassays,
generation of high throughput screening fingerprints (HTSFPs),
and an optional preloaded database that provides access to a
substantial portion of publicly available bioactivity data.
biocViews: ImmunoOncology, MicrotitrePlateAssay, CellBasedAssays,
Visualization, Infrastructure, DataImport, Bioinformatics,
Proteomics, Metabolomics
Author: Tyler Backman, Ronly Schlenk, Thomas Girke
Maintainer: Daniela Cassol <danicassol@gmail.com>
URL: https://github.com/girke-lab/bioassayR
VignetteBuilder: knitr
BugReports: https://github.com/girke-lab/bioassayR/issues
git_url: https://git.bioconductor.org/packages/bioassayR
git_branch: RELEASE_3_13
git_last_commit: 4c1993a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bioassayR_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bioassayR_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bioassayR_1.30.0.tgz
vignettes: vignettes/bioassayR/inst/doc/bioassayR.html
vignetteTitles: bioassayR Introduction and Examples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R
dependencyCount: 70
Package: Biobase
Version: 2.52.0
Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils
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Suggests: tools, tkWidgets, ALL, RUnit, golubEsets
License: Artistic-2.0
Archs: i386, x64
MD5sum: f90f6de41c79d568600506b71b269961
NeedsCompilation: yes
Title: Biobase: Base functions for Bioconductor
Description: Functions that are needed by many other packages or which
replace R functions.
biocViews: Infrastructure
Author: R. Gentleman, V. Carey, M. Morgan, S. Falcon
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/Biobase
BugReports: https://github.com/Bioconductor/Biobase/issues
git_url: https://git.bioconductor.org/packages/Biobase
git_branch: RELEASE_3_13
git_last_commit: be5163c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Biobase_2.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Biobase_2.52.0.zip
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vignettes: vignettes/Biobase/inst/doc/BiobaseDevelopment.pdf,
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vignetteTitles: Notes for eSet developers, esApply Introduction, An
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Rfiles: vignettes/Biobase/inst/doc/BiobaseDevelopment.R,
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breastCancerVDX, ccTutorial, dorothea, dyebiasexamples,
HMP16SData, HMP2Data, mammaPrintData, mAPKLData, RegParallel,
rheumaticConditionWOLLBOLD, seventyGeneData, yeastExpData,
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HTSCluster, isatabr, mi4p, Modeler, multiclassPairs, NACHO,
optCluster, Patterns, pkgmaker, propr, rknn, Seurat, sigminer,
SourceSet, tinyarray
dependencyCount: 6
Package: biobroom
Version: 1.24.0
Depends: R (>= 3.0.0), broom
Imports: dplyr, tidyr, Biobase
Suggests: limma, DESeq2, airway, ggplot2, plyr, GenomicRanges,
testthat, magrittr, edgeR, qvalue, knitr, data.table, MSnbase,
SummarizedExperiment
License: LGPL
MD5sum: e162b0f55b1370aeb2bd2b42ec88da63
NeedsCompilation: no
Title: Turn Bioconductor objects into tidy data frames
Description: This package contains methods for converting standard
objects constructed by bioinformatics packages, especially
those in Bioconductor, and converting them to tidy data. It
thus serves as a complement to the broom package, and follows
the same the tidy, augment, glance division of tidying methods.
Tidying data makes it easy to recombine, reshape and visualize
bioinformatics analyses.
biocViews: MultipleComparison, DifferentialExpression, Regression,
GeneExpression, Proteomics, DataImport
Author: Andrew J. Bass, David G. Robinson, Steve Lianoglou, Emily
Nelson, John D. Storey, with contributions from Laurent Gatto
Maintainer: John D. Storey <jstorey@princeton.edu> and Andrew J. Bass
<ajbass@princeton.edu>
URL: https://github.com/StoreyLab/biobroom
VignetteBuilder: knitr
BugReports: https://github.com/StoreyLab/biobroom/issues
git_url: https://git.bioconductor.org/packages/biobroom
git_branch: RELEASE_3_13
git_last_commit: 9a686b2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/biobroom_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/biobroom_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/biobroom_1.24.0.tgz
vignettes: vignettes/biobroom/inst/doc/biobroom_vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biobroom/inst/doc/biobroom_vignette.R
importsMe: TPP
dependencyCount: 33
Package: biobtreeR
Version: 1.4.0
Imports: httr, httpuv, stringi,jsonlite,methods,utils
Suggests: BiocStyle, knitr,testthat,rmarkdown,markdown
License: MIT + file LICENSE
MD5sum: bd43d4d10a308494f154dd80f4f4d61b
NeedsCompilation: no
Title: Using biobtree tool from R
Description: The biobtreeR package provides an interface to
[biobtree](https://github.com/tamerh/biobtree) tool which
covers large set of bioinformatics datasets and allows search
and chain mappings functionalities.
biocViews: Annotation
Author: Tamer Gur
Maintainer: Tamer Gur <tgur@ebi.ac.uk>
URL: https://github.com/tamerh/biobtreeR
VignetteBuilder: knitr
BugReports: https://github.com/tamerh/biobtreeR/issues
git_url: https://git.bioconductor.org/packages/biobtreeR
git_branch: RELEASE_3_13
git_last_commit: f2a07b9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/biobtreeR_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/biobtreeR_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/biobtreeR_1.4.0.tgz
vignettes: vignettes/biobtreeR/inst/doc/biobtreeR.html
vignetteTitles: The biobtreeR users guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/biobtreeR/inst/doc/biobtreeR.R
dependencyCount: 19
Package: bioCancer
Version: 1.20.02
Depends: R (>= 3.6.0), radiant.data (>= 0.9.1), cgdsr(>= 1.2.6), XML(>=
3.98)
Imports: DT (>= 0.3), dplyr (>= 0.7.2), shiny (>= 1.0.5), AlgDesign (>=
1.1.7.3), import (>= 1.1.0), methods, AnnotationDbi,
shinythemes, Biobase, geNetClassifier, org.Hs.eg.db,
org.Bt.eg.db, DOSE, clusterProfiler, reactome.db, ReactomePA,
DiagrammeR(<= 1.01), visNetwork, htmlwidgets, plyr, tibble,
GO.db
Suggests: BiocStyle, prettydoc, rmarkdown, knitr, testthat (>= 0.10.0)
License: AGPL-3 | file LICENSE
MD5sum: 7b622f955f654b54264a6051cb90b166
NeedsCompilation: no
Title: Interactive Multi-Omics Cancers Data Visualization and Analysis
Description: bioCancer is a Shiny App to visualize and analyse
interactively Multi-Assays of Cancer Genomic Data.
biocViews: GUI, DataRepresentation, Network, MultipleComparison,
Pathways, Reactome, Visualization,GeneExpression,GeneTarget
Author: Karim Mezhoud [aut, cre]
Maintainer: Karim Mezhoud <kmezhoud@gmail.com>
URL: http://kmezhoud.github.io/bioCancer
VignetteBuilder: knitr
BugReports: https://github.com/kmezhoud/bioCancer/issues
git_url: https://git.bioconductor.org/packages/bioCancer
git_branch: RELEASE_3_13
git_last_commit: 19fc7ba
git_last_commit_date: 2021-06-30
Date/Publication: 2021-07-01
source.ver: src/contrib/bioCancer_1.20.02.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bioCancer_1.20.02.zip
mac.binary.ver: bin/macosx/contrib/4.1/bioCancer_1.20.02.tgz
vignettes: vignettes/bioCancer/inst/doc/bioCancer.html
vignetteTitles: bioCancer: Interactive Multi-OMICS Cancers Data
Visualization and Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/bioCancer/inst/doc/bioCancer.R
dependencyCount: 223
Package: BiocCheck
Version: 1.28.0
Depends: R (>= 3.5.0)
Imports: biocViews (>= 1.33.7), BiocManager, stringdist, graph, httr,
tools, optparse, codetools, methods, utils, knitr
Suggests: RUnit, BiocGenerics, Biobase, RJSONIO, rmarkdown, devtools
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Enhances: codetoolsBioC
License: Artistic-2.0
MD5sum: c8aeba2ae6d33edd8788083b478ba3d6
NeedsCompilation: no
Title: Bioconductor-specific package checks
Description: Executes Bioconductor-specific package checks.
biocViews: Infrastructure
Author: Bioconductor Package Maintainer [aut, cre], Lori Shepherd
[aut], Daniel von Twisk [ctb], Kevin Rue [ctb], Marcel Ramos
[ctb], Leonardo Collado-Torres [ctb], Federico Marini [ctb]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://github.com/Bioconductor/BiocCheck/issues
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiocCheck
git_branch: RELEASE_3_13
git_last_commit: 5fcd8f9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BiocCheck_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BiocCheck_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BiocCheck_1.28.0.tgz
vignettes: vignettes/BiocCheck/inst/doc/BiocCheck.html
vignetteTitles: BiocCheck: Ensuring Bioconductor package guidelines
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocCheck/inst/doc/BiocCheck.R
importsMe: AnnotationHubData
suggestsMe: GEOfastq, packFinder, preciseTAD, scp, SpectralTAD,
HMP16SData, HMP2Data, scpdata
dependencyCount: 39
Package: BiocDockerManager
Version: 1.4.0
Depends: R (>= 4.1)
Imports: httr, whisker, readr, dplyr, utils, methods, memoise
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0)
License: Artistic-2.0
MD5sum: 75557032482fd355b1987e45100f5bf5
NeedsCompilation: no
Title: Access Bioconductor docker images
Description: Package works analogous to BiocManager but for docker
images. Use the BiocDockerManager package to install and manage
docker images provided by the Bioconductor project. A
convenient package to install images, update images and find
which Bioconductor based docker images are available.
biocViews: Software, Infrastructure, ThirdPartyClient
Author: Bioconductor Package Maintainer [cre], Nitesh Turaga [aut]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
SystemRequirements: docker
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BiocDockerManager/issues
git_url: https://git.bioconductor.org/packages/BiocDockerManager
git_branch: RELEASE_3_13
git_last_commit: 16ea460
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BiocDockerManager_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BiocDockerManager_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BiocDockerManager_1.4.0.tgz
vignettes: vignettes/BiocDockerManager/inst/doc/BiocDockerManager.html
vignetteTitles: BiocDockerManager Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocDockerManager/inst/doc/BiocDockerManager.R
dependencyCount: 46
Package: BiocFileCache
Version: 2.0.0
Depends: R (>= 3.4.0), dbplyr (>= 1.0.0)
Imports: methods, stats, utils, dplyr, RSQLite, DBI, rappdirs,
filelock, curl, httr
Suggests: testthat, knitr, BiocStyle, rmarkdown, rtracklayer
License: Artistic-2.0
MD5sum: cf1c1587fc63291045367246c98eba6e
NeedsCompilation: no
Title: Manage Files Across Sessions
Description: This package creates a persistent on-disk cache of files
that the user can add, update, and retrieve. It is useful for
managing resources (such as custom Txdb objects) that are
costly or difficult to create, web resources, and data files
used across sessions.
biocViews: DataImport
Author: Lori Shepherd [aut, cre], Martin Morgan [aut]
Maintainer: Lori Shepherd <lori.shepherd@roswellpark.org>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BiocFileCache/issues
git_url: https://git.bioconductor.org/packages/BiocFileCache
git_branch: RELEASE_3_13
git_last_commit: 280a8f9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BiocFileCache_2.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BiocFileCache_2.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BiocFileCache_2.0.0.tgz
vignettes: vignettes/BiocFileCache/inst/doc/BiocFileCache.html
vignetteTitles: BiocFileCache: Managing File Resources Across Sessions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocFileCache/inst/doc/BiocFileCache.R
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importsMe: AMARETTO, atSNP, autonomics, BayesSpace, BiocPkgTools,
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ontoProc, Organism.dplyr, psichomics, recount3,
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SpatialExperiment, spatialHeatmap, TFutils, tximeta, UMI4Cats,
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SingleCellMultiModal, spatialLIBD, SingscoreAMLMutations
suggestsMe: AnnotationForge, bambu, BiocOncoTK, BiocSet, HiCDCPlus,
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msigdb, scRNAseq, TENxBrainData, TENxPBMCData, chipseqDB,
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dependencyCount: 46
Package: BiocGenerics
Version: 0.38.0
Depends: R (>= 4.0.0), methods, utils, graphics, stats, parallel
Imports: methods, utils, graphics, stats, parallel
Suggests: Biobase, S4Vectors, IRanges, GenomicRanges, DelayedArray,
Biostrings, Rsamtools, AnnotationDbi, affy, affyPLM, DESeq2,
flowClust, MSnbase, annotate, RUnit
License: Artistic-2.0
MD5sum: 71dce70909c177f42bbd613d217304cd
NeedsCompilation: no
Title: S4 generic functions used in Bioconductor
Description: The package defines many S4 generic functions used in
Bioconductor.
biocViews: Infrastructure
Author: The Bioconductor Dev Team
Maintainer: Bioconductor Package Maintainer
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URL: https://bioconductor.org/packages/BiocGenerics
BugReports: https://github.com/Bioconductor/BiocGenerics/issues
git_url: https://git.bioconductor.org/packages/BiocGenerics
git_branch: RELEASE_3_13
git_last_commit: 1db849a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
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win.binary.ver: bin/windows/contrib/4.1/BiocGenerics_0.38.0.zip
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hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
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SNPlocs.Hsapiens.dbSNP149.GRCh38,
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DCLEAR, geno2proteo, MicroSEC, oncoPredict, PACVr, pagoo,
pathwayTMB, RobLoxBioC, Signac, spectralAnalysis,
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dependencyCount: 5
Package: biocGraph
Version: 1.54.0
Depends: Rgraphviz, graph
Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods
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MD5sum: d1632d5aa972bbd6c844a683cde39cd1
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Title: Graph examples and use cases in Bioinformatics
Description: This package provides examples and code that make use of
the different graph related packages produced by Bioconductor.
biocViews: Visualization, GraphAndNetwork
Author: Li Long <li.long@isb-sib.ch>, Robert Gentleman
<rgentlem@fhcrc.org>, Seth Falcon <sethf@fhcrc.org> Florian
Hahne <fhahne@fhcrc.org>
Maintainer: Florian Hahne <florian.hahne@novartis.com>
git_url: https://git.bioconductor.org/packages/biocGraph
git_branch: RELEASE_3_13
git_last_commit: f0f25d4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
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vignettes: vignettes/biocGraph/inst/doc/biocGraph.pdf,
vignettes/biocGraph/inst/doc/layingOutPathways.pdf
vignetteTitles: Examples of plotting graphs Using Rgraphviz, HOWTO
layout pathways
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dependencyCount: 55
Package: BiocIO
Version: 1.2.0
Depends: R (>= 4.0)
Imports: BiocGenerics, S4Vectors, methods, tools
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: e2620b9925f7b088e833cec48beba281
NeedsCompilation: no
Title: Standard Input and Output for Bioconductor Packages
Description: Implements `import()` and `export()` standard generics for
importing and exporting biological data formats. `import()`
supports whole-file as well as chunk-wise iterative import. The
`import()` interface optionally provides a standard mechanism
for 'lazy' access via `filter()` (on row or element-like
components of the file resource), `select()` (on column-like
components of the file resource) and `collect()`. The
`import()` interface optionally provides transparent access to
remote (e.g. via https) as well as local access. Developers can
register a file extension, e.g., `.loom` for dispatch from
character-based URIs to specific `import()` / `export()`
methods based on classes representing file types, e.g.,
`LoomFile()`.
biocViews: Annotation,DataImport
Author: Martin Morgan [aut], Michael Lawrence [aut], Daniel Van Twisk
[aut], Bioconductor Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
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VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BiocIO/issues
git_url: https://git.bioconductor.org/packages/BiocIO
git_branch: RELEASE_3_13
git_last_commit: fef4c1c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
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mac.binary.ver: bin/macosx/contrib/4.1/BiocIO_1.2.0.tgz
vignettes: vignettes/BiocIO/inst/doc/BiocIO.html
vignetteTitles: BiocIO
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hasINSTALL: FALSE
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Rfiles: vignettes/BiocIO/inst/doc/BiocIO.R
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importsMe: BiocSet, GenomicFeatures, rtracklayer
dependencyCount: 9
Package: BiocNeighbors
Version: 1.10.0
Imports: Rcpp, S4Vectors, BiocParallel, stats, methods, Matrix
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Suggests: testthat, BiocStyle, knitr, rmarkdown, FNN, RcppAnnoy,
RcppHNSW
License: GPL-3
Archs: i386, x64
MD5sum: 3c33f083803675ee300ec9c1a8ab04ed
NeedsCompilation: yes
Title: Nearest Neighbor Detection for Bioconductor Packages
Description: Implements exact and approximate methods for nearest
neighbor detection, in a framework that allows them to be
easily switched within Bioconductor packages or workflows.
Exact searches can be performed using the k-means for k-nearest
neighbors algorithm or with vantage point trees. Approximate
searches can be performed using the Annoy or HNSW libraries.
Searching on either Euclidean or Manhattan distances is
supported. Parallelization is achieved for all methods by using
BiocParallel. Functions are also provided to search for all
neighbors within a given distance.
biocViews: Clustering, Classification
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiocNeighbors
git_branch: RELEASE_3_13
git_last_commit: c6779d6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BiocNeighbors_1.10.0.tar.gz
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vignettes/BiocNeighbors/inst/doc/exact.html,
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vignetteTitles: 2. Detecting approximate nearest neighbors, 1.
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within range
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suggestsMe: TrajectoryUtils, TSCAN, SingleRBook
dependencyCount: 21
Package: BiocOncoTK
Version: 1.12.1
Depends: R (>= 3.6.0), methods, utils
Imports: ComplexHeatmap, S4Vectors, bigrquery, shiny, stats, httr,
rjson, dplyr, magrittr, grid, DT, GenomicRanges, IRanges,
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EnsDb.Hsapiens.v75, rmarkdown, rhdf5client
License: Artistic-2.0
MD5sum: 750e34afe2350bb4c947a8527bbba1fc
NeedsCompilation: no
Title: Bioconductor components for general cancer genomics
Description: Provide a central interface to various tools for
genome-scale analysis of cancer studies.
biocViews: CopyNumberVariation, CpGIsland, DNAMethylation,
GeneExpression, GeneticVariability, SNP, Transcription,
ImmunoOncology
Author: Vince Carey
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiocOncoTK
git_branch: RELEASE_3_13
git_last_commit: 5b4a389
git_last_commit_date: 2021-06-28
Date/Publication: 2021-06-29
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vignettes/BiocOncoTK/inst/doc/curatedMSIData.html,
vignettes/BiocOncoTK/inst/doc/maptcga.html
vignetteTitles: BiocOncoTK -- cancer oriented components for
Bioconductor, curatedMSIData overview, "Mapping TCGA tumor
codes to NCIT"
hasREADME: FALSE
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Rfiles: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.R,
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vignettes/BiocOncoTK/inst/doc/maptcga.R
dependencyCount: 207
Package: BioCor
Version: 1.16.0
Depends: R (>= 3.4.0)
Imports: BiocParallel, Matrix, methods, GSEABase
Suggests: reactome.db, org.Hs.eg.db, WGCNA, GOSemSim, testthat, knitr,
rmarkdown, BiocStyle, airway, DESeq2, boot,
targetscan.Hs.eg.db, Hmisc, spelling
License: MIT + file LICENSE
MD5sum: b51e934fb7218761bd21f61ab9178105
NeedsCompilation: no
Title: Functional similarities
Description: Calculates functional similarities based on the pathways
described on KEGG and REACTOME or in gene sets. These
similarities can be calculated for pathways or gene sets,
genes, or clusters and combined with other similarities. They
can be used to improve networks, gene selection, testing
relationships...
biocViews: StatisticalMethod, Clustering, GeneExpression, Network,
Pathways, NetworkEnrichment, SystemsBiology
Author: LluÃs Revilla Sancho [aut, cre]
(<https://orcid.org/0000-0001-9747-2570>), Pau Sancho-Bru [ths]
(<https://orcid.org/0000-0001-5569-9259>), Juan José Salvatella
Lozano [ths] (<https://orcid.org/0000-0001-7613-3908>)
Maintainer: LluÃs Revilla Sancho <lluis.revilla@gmail.com>
URL: https://llrs.github.io/BioCor/
VignetteBuilder: knitr
BugReports: https://github.com/llrs/BioCor/issues
git_url: https://git.bioconductor.org/packages/BioCor
git_branch: RELEASE_3_13
git_last_commit: 8b830d8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BioCor_1.16.0.tar.gz
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vignettes: vignettes/BioCor/inst/doc/BioCor_1_basics.html,
vignettes/BioCor/inst/doc/BioCor_2_advanced.html
vignetteTitles: About BioCor, Advanced usage of BioCor
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dependencyCount: 61
Package: BiocParallel
Version: 1.26.2
Depends: methods
Imports: stats, utils, futile.logger, parallel, snow
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Rsamtools, GenomicAlignments, ShortRead, codetools, RUnit,
BiocStyle, knitr, batchtools, data.table
License: GPL-2 | GPL-3
Archs: i386, x64
MD5sum: 88046f3400849338d9db337984586099
NeedsCompilation: yes
Title: Bioconductor facilities for parallel evaluation
Description: This package provides modified versions and novel
implementation of functions for parallel evaluation, tailored
to use with Bioconductor objects.
biocViews: Infrastructure
Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut],
Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson
[aut], Nitesh Turaga [aut], Aaron Lun [ctb]
Maintainer: Bioconductor Package Maintainer
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URL: https://github.com/Bioconductor/BiocParallel
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BiocParallel/issues
git_url: https://git.bioconductor.org/packages/BiocParallel
git_branch: RELEASE_3_13
git_last_commit: e6f7d36
git_last_commit_date: 2021-08-19
Date/Publication: 2021-08-22
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vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.pdf,
vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.pdf
vignetteTitles: 2. Introduction to BatchtoolsParam, 3. Errors,, Logs
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dependsOnMe: bacon, BEclear, Cardinal, ClassifyR, clusterSeq,
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dependencyCount: 10
Package: BiocPkgTools
Version: 1.10.2
Depends: htmlwidgets
Imports: BiocFileCache, BiocManager, biocViews, tibble, magrittr,
methods, rlang, tidyselect, stringr, rvest, dplyr, xml2, readr,
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Suggests: BiocStyle, knitr, rmarkdown, testthat, tm, SnowballC,
visNetwork, clipr, blastula, kableExtra, DiagrammeR,
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License: MIT + file LICENSE
MD5sum: 96ad2d065ba2042bfea23156ffc6a3af
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Title: Collection of simple tools for learning about Bioc Packages
Description: Bioconductor has a rich ecosystem of metadata around
packages, usage, and build status. This package is a simple
collection of functions to access that metadata from R. The
goal is to expose metadata for data mining and value-added
functionality such as package searching, text mining, and
analytics on packages.
biocViews: Software, Infrastructure
Author: Shian Su [aut, ctb], Lori Shepherd [ctb], Marcel Ramos [ctb],
Felix G.M. Ernst [ctb], Charlotte Soneson [ctb], Martin Morgan
[ctb], Vince Carey [ctb], Sean Davis [aut, cre]
Maintainer: Sean Davis <seandavi@gmail.com>
URL: https://github.com/seandavi/BiocPkgTools
SystemRequirements: mailsend-go
VignetteBuilder: knitr
BugReports: https://github.com/seandavi/BiocPkgTools/issues/new
git_url: https://git.bioconductor.org/packages/BiocPkgTools
git_branch: RELEASE_3_13
git_last_commit: eed4730
git_last_commit_date: 2021-09-16
Date/Publication: 2021-09-19
source.ver: src/contrib/BiocPkgTools_1.10.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BiocPkgTools_1.10.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/BiocPkgTools_1.10.2.tgz
vignettes: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.html
vignetteTitles: Overview of BiocPkgTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.R
dependencyCount: 92
Package: BiocSet
Version: 1.6.1
Depends: R (>= 3.6), dplyr
Imports: methods, tibble, utils, rlang, plyr, S4Vectors, BiocIO,
AnnotationDbi, KEGGREST, ontologyIndex, tidyr
Suggests: GSEABase, airway, org.Hs.eg.db, DESeq2, limma, BiocFileCache,
GO.db, testthat, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 44faf6862e70268f0b3a471c606d9010
NeedsCompilation: no
Title: Representing Different Biological Sets
Description: BiocSet displays different biological sets in a triple
tibble format. These three tibbles are `element`, `set`, and
`elementset`. The user has the abilty to activate one of these
three tibbles to perform common functions from the dplyr
package. Mapping functionality and accessing web references for
elements/sets are also available in BiocSet.
biocViews: GeneExpression, GO, KEGG, Software
Author: Kayla Morrell [aut, cre], Martin Morgan [aut], Kevin
Rue-Albrecht [ctb], LluÃs Revilla Sancho [ctb]
Maintainer: Kayla Morrell <kayla.morrell@roswellpark.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiocSet
git_branch: RELEASE_3_13
git_last_commit: 9572684
git_last_commit_date: 2021-08-05
Date/Publication: 2021-08-08
source.ver: src/contrib/BiocSet_1.6.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BiocSet_1.6.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/BiocSet_1.6.1.tgz
vignettes: vignettes/BiocSet/inst/doc/BiocSet.html
vignetteTitles: BiocSet: Representing Element Sets in the Tidyverse
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocSet/inst/doc/BiocSet.R
dependsOnMe: RegEnrich
suggestsMe: dearseq
dependencyCount: 62
Package: BiocSingular
Version: 1.8.1
Imports: BiocGenerics, S4Vectors, Matrix, methods, utils, DelayedArray,
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LinkingTo: Rcpp, beachmat
Suggests: testthat, BiocStyle, knitr, rmarkdown, ResidualMatrix
License: GPL-3
Archs: i386, x64
MD5sum: e5f008179a2f3941cb13a2aa00210c45
NeedsCompilation: yes
Title: Singular Value Decomposition for Bioconductor Packages
Description: Implements exact and approximate methods for singular
value decomposition and principal components analysis, in a
framework that allows them to be easily switched within
Bioconductor packages or workflows. Where possible,
parallelization is achieved using the BiocParallel framework.
biocViews: Software, DimensionReduction, PrincipalComponent
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/BiocSingular
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git_last_commit_date: 2021-06-08
Date/Publication: 2021-06-08
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Title: interface to python sklearn via Rstudio reticulate
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biocViews: StatisticalMethod, DimensionReduction, Infrastructure
Author: Vince Carey [cre, aut]
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dependencyCount: 25
Package: biocthis
Version: 1.2.0
Imports: BiocManager, fs, glue, rlang, styler, usethis (>= 2.0.1)
Suggests: BiocStyle, covr, devtools, knitr, pkgdown, RefManageR,
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License: Artistic-2.0
MD5sum: 91e17ef553b75239691c002bdf7a6885
NeedsCompilation: no
Title: Automate package and project setup for Bioconductor packages
Description: This package expands the usethis package with the goal of
helping automate the process of creating R packages for
Bioconductor or making them Bioconductor-friendly.
biocViews: Software, ReportWriting
Author: Leonardo Collado-Torres [aut, cre]
(<https://orcid.org/0000-0003-2140-308X>), Marcel Ramos [ctb]
(<https://orcid.org/0000-0002-3242-0582>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/lcolladotor/biocthis
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/biocthis
git_url: https://git.bioconductor.org/packages/biocthis
git_branch: RELEASE_3_13
git_last_commit: bc58cac
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
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vignettes: vignettes/biocthis/inst/doc/biocthis_dev_notes.html,
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importsMe: HubPub
dependencyCount: 54
Package: BiocVersion
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License: Artistic-2.0
MD5sum: 4a15cac357ba19fc81e6508083cdddca
NeedsCompilation: no
Title: Set the appropriate version of Bioconductor packages
Description: This package provides repository information for the
appropriate version of Bioconductor.
biocViews: Infrastructure
Author: Martin Morgan [aut], Marcel Ramos [ctb], Bioconductor Package
Maintainer [ctb, cre]
Maintainer: Bioconductor Package Maintainer
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git_url: https://git.bioconductor.org/packages/BiocVersion
git_branch: master
git_last_commit: 3466413
git_last_commit_date: 2020-10-27
Date/Publication: 2021-03-19
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importsMe: AnnotationHub
suggestsMe: BiocManager
dependencyCount: 0
Package: biocViews
Version: 1.60.0
Depends: R (>= 3.6.0)
Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools,
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Suggests: BiocGenerics, knitr, commonmark
License: Artistic-2.0
MD5sum: 9eff6cb8ccbee3089a1645a1ae47198a
NeedsCompilation: no
Title: Categorized views of R package repositories
Description: Infrastructure to support 'views' used to classify
Bioconductor packages. 'biocViews' are directed acyclic graphs
of terms from a controlled vocabulary. There are three major
classifications, corresponding to 'software', 'annotation', and
'experiment data' packages.
biocViews: Infrastructure
Author: VJ Carey <stvjc@channing.harvard.edu>, BJ Harshfield
<rebjh@channing.harvard.edu>, S Falcon <sfalcon@fhcrc.org> ,
Sonali Arora, Lori Shepherd <lori.shepherd@roswellpark.org>
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: http://bioconductor.org/packages/BiocViews
BugReports: https://github.com/Bioconductor/BiocViews/issues
git_url: https://git.bioconductor.org/packages/biocViews
git_branch: RELEASE_3_13
git_last_commit: b861d8d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
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vignettes: vignettes/biocViews/inst/doc/createReposHtml.pdf,
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hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biocViews/inst/doc/createReposHtml.R,
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dependsOnMe: Risa
importsMe: AnnotationHubData, BiocCheck, BiocPkgTools, monocle,
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suggestsMe: packFinder
dependencyCount: 17
Package: BiocWorkflowTools
Version: 1.18.0
Depends: R (>= 3.4)
Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown,
rstudioapi, stringr, tools, utils, usethis
License: MIT + file LICENSE
MD5sum: 3d55e45e9636b702ef648e68d1e5d8a8
NeedsCompilation: no
Title: Tools to aid the development of Bioconductor Workflow packages
Description: Provides functions to ease the transition between
Rmarkdown and LaTeX documents when authoring a Bioconductor
Workflow.
biocViews: Software, ReportWriting
Author: Mike Smith [aut, cre], Andrzej OleÅ› [aut]
Maintainer: Mike Smith <grimbough@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiocWorkflowTools
git_branch: RELEASE_3_13
git_last_commit: 3894fab
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BiocWorkflowTools_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BiocWorkflowTools_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BiocWorkflowTools_1.18.0.tgz
vignettes:
vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.html
vignetteTitles: Converting Rmarkdown to F1000Research LaTeX Format
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.R
dependsOnMe: RNAseq123
suggestsMe: BiocMetaWorkflow, CAGEWorkflow, recountWorkflow,
SingscoreAMLMutations
dependencyCount: 54
Package: biodb
Version: 1.0.4
Depends: R (>= 4.0)
Imports: R6, methods, chk, lgr, progress, lifecycle, XML, stringr,
plyr, yaml, jsonlite, RCurl, Rcpp, rappdirs, stats, openssl,
RSQLite, withr
LinkingTo: Rcpp, testthat
Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr,
rmarkdown, covr, xml2, git2r
License: AGPL-3
Archs: i386, x64
MD5sum: f08973024771b262763c2b905017dd95
NeedsCompilation: yes
Title: biodb, a library and a development framework for connecting to
chemical and biological databases
Description: The biodb package provides access to standard remote
chemical and biological databases (ChEBI, KEGG, HMDB, ...), as
well as to in-house local database files (CSV, SQLite), with
easy retrieval of entries, access to web services, search of
compounds by mass and/or name, and mass spectra matching for
LCMS and MSMS. Its architecture as a development framework
facilitates the development of new database connectors for
local projects or inside separate published packages.
biocViews: Software, Infrastructure, DataImport, KEGG
Author: Pierrick Roger [aut, cre], Alexis Delabrière [ctb]
Maintainer: Pierrick Roger <pierrick.roger@cea.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/biodb
git_branch: RELEASE_3_13
git_last_commit: 84b7aff
git_last_commit_date: 2021-06-09
Date/Publication: 2021-06-10
source.ver: src/contrib/biodb_1.0.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/biodb_1.0.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/biodb_1.0.4.tgz
vignettes: vignettes/biodb/inst/doc/biodb.html,
vignettes/biodb/inst/doc/details.html,
vignettes/biodb/inst/doc/entries.html,
vignettes/biodb/inst/doc/in_house_compound_db.html,
vignettes/biodb/inst/doc/in_house_mass_db.html,
vignettes/biodb/inst/doc/new_connector.html,
vignettes/biodb/inst/doc/new_entry_field.html
vignetteTitles: Introduction to the biodb package., Details on general
*biodb* usage and principles, Manipulating entry objects,
In-house compound database, In-house LCMS database, Creating a
new connector class for accessing a database., Creating a new
field for entries.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biodb/inst/doc/biodb.R,
vignettes/biodb/inst/doc/details.R,
vignettes/biodb/inst/doc/entries.R,
vignettes/biodb/inst/doc/in_house_compound_db.R,
vignettes/biodb/inst/doc/in_house_mass_db.R,
vignettes/biodb/inst/doc/new_connector.R,
vignettes/biodb/inst/doc/new_entry_field.R
dependencyCount: 63
Package: bioDist
Version: 1.64.0
Depends: R (>= 2.0), methods, Biobase,KernSmooth
Suggests: locfit
License: Artistic-2.0
MD5sum: 4f0d06bf8e817ec68bc2f60f0625ab47
NeedsCompilation: no
Title: Different distance measures
Description: A collection of software tools for calculating distance
measures.
biocViews: Clustering, Classification
Author: B. Ding, R. Gentleman and Vincent Carey
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/bioDist
git_branch: RELEASE_3_13
git_last_commit: acf9621
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bioDist_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bioDist_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bioDist_1.64.0.tgz
vignettes: vignettes/bioDist/inst/doc/bioDist.pdf
vignetteTitles: bioDist Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bioDist/inst/doc/bioDist.R
importsMe: CHETAH, PhyloProfile
dependencyCount: 8
Package: biomaRt
Version: 2.48.3
Depends: methods
Imports: utils, XML, AnnotationDbi, progress, stringr, httr, digest,
BiocFileCache, rappdirs, xml2
Suggests: BiocStyle, knitr, rmarkdown, testthat, mockery
License: Artistic-2.0
MD5sum: e1b8dd1277db4a59c93dd744dab43053
NeedsCompilation: no
Title: Interface to BioMart databases (i.e. Ensembl)
Description: In recent years a wealth of biological data has become
available in public data repositories. Easy access to these
valuable data resources and firm integration with data analysis
is needed for comprehensive bioinformatics data analysis.
biomaRt provides an interface to a growing collection of
databases implementing the BioMart software suite
(<http://www.biomart.org>). The package enables retrieval of
large amounts of data in a uniform way without the need to know
the underlying database schemas or write complex SQL queries.
The most prominent examples of BioMart databases are maintain
by Ensembl, which provides biomaRt users direct access to a
diverse set of data and enables a wide range of powerful online
queries from gene annotation to database mining.
biocViews: Annotation
Author: Steffen Durinck [aut], Wolfgang Huber [aut], Sean Davis [ctb],
Francois Pepin [ctb], Vince S Buffalo [ctb], Mike Smith [ctb,
cre] (<https://orcid.org/0000-0002-7800-3848>)
Maintainer: Mike Smith <grimbough@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/biomaRt
git_branch: RELEASE_3_13
git_last_commit: 66a592e
git_last_commit_date: 2021-08-12
Date/Publication: 2021-08-15
source.ver: src/contrib/biomaRt_2.48.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/biomaRt_2.48.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/biomaRt_2.48.3.tgz
vignettes: vignettes/biomaRt/inst/doc/accessing_ensembl.html,
vignettes/biomaRt/inst/doc/accessing_other_marts.html
vignetteTitles: Accessing Ensembl annotation with biomaRt, Using a
BioMart other than Ensembl
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biomaRt/inst/doc/accessing_ensembl.R,
vignettes/biomaRt/inst/doc/accessing_other_marts.R
dependsOnMe: BrainSABER, chromPlot, coMET, customProDB, DrugVsDisease,
genefu, GenomicOZone, MineICA, NetSAM, PPInfer, PSICQUIC,
RepViz, VegaMC, annotation
importsMe: ArrayExpressHTS, ASpediaFI, BadRegionFinder, BgeeCall,
branchpointer, BUSpaRse, ChIPpeakAnno, CHRONOS, conclus,
cosmosR, cTRAP, dagLogo, DEXSeq, diffloop, DominoEffect,
easyRNASeq, EDASeq, ELMER, EWCE, FRASER, GDCRNATools,
GeneAccord, GenomicFeatures, GenVisR, gespeR, glmSparseNet,
GOexpress, goSTAG, gpart, Gviz, InterCellar, isobar, mCSEA,
MEDIPS, MetaboSignal, metaseqR2, methyAnalysis, MGFR,
OncoScore, oposSOM, pcaExplorer, phenoTest,
PrecisionTrialDrawer, pRoloc, ProteoMM, psygenet2r, pwOmics,
R453Plus1Toolbox, ramwas, recoup, rgsepd, RIPAT, scPipe,
seq2pathway, SeqGSEA, sitadela, SPLINTER, SWATH2stats,
TCGAbiolinks, TFEA.ChIP, TimiRGeN, transcriptogramer, trena,
ViSEAGO, XCIR, yarn, ExpHunterSuite, TCGAWorkflow, biomartr,
BioVenn, convertid, DiNAMIC.Duo, GOxploreR, intePareto,
kangar00, liayson, snplist, utr.annotation
suggestsMe: AnnotationForge, bioassayR, celda, cellTree, chromstaR,
ClusterJudge, CNVgears, ctgGEM, fedup, FELLA, h5vc,
MAGeCKFlute, martini, massiR, MethReg, MineICA, miQC, MiRaGE,
MutationalPatterns, netSmooth, oligo, OrganismDbi, piano,
Pigengene, progeny, PubScore, R3CPET, Rcade, RnBeads, rTRM,
scater, ShortRead, SIM, sincell, SummarizedBenchmark,
systemPipeR, trackViewer, wiggleplotr, zinbwave,
BloodCancerMultiOmics2017, ccTutorial, leeBamViews,
RegParallel, RforProteomics, BED, BioInsight, cinaR, DGEobj,
DGEobj.utils, loose.rock, Patterns, R.SamBada, scDiffCom,
SNPassoc
dependencyCount: 71
Package: biomformat
Version: 1.20.0
Depends: R (>= 3.2), methods
Imports: plyr (>= 1.8), jsonlite (>= 0.9.16), Matrix (>= 1.2), rhdf5
Suggests: testthat (>= 0.10), knitr (>= 1.10), BiocStyle (>= 1.6),
rmarkdown (>= 0.7)
License: GPL-2
MD5sum: 571c3ec839e5519bb8a2f0d63ed92010
NeedsCompilation: no
Title: An interface package for the BIOM file format
Description: This is an R package for interfacing with the BIOM format.
This package includes basic tools for reading biom-format
files, accessing and subsetting data tables from a biom object
(which is more complex than a single table), as well as limited
support for writing a biom-object back to a biom-format file.
The design of this API is intended to match the python API and
other tools included with the biom-format project, but with a
decidedly "R flavor" that should be familiar to R users. This
includes S4 classes and methods, as well as extensions of
common core functions/methods.
biocViews: ImmunoOncology, DataImport, Metagenomics, Microbiome
Author: Paul J. McMurdie <mcmurdie@stanford.edu> and Joseph N Paulson
<jpaulson@jimmy.harvard.edu>
Maintainer: Paul J. McMurdie <mcmurdie@stanford.edu>
URL: https://github.com/joey711/biomformat/, http://biom-format.org/
VignetteBuilder: knitr
BugReports: https://github.com/joey711/biomformat/issues
git_url: https://git.bioconductor.org/packages/biomformat
git_branch: RELEASE_3_13
git_last_commit: 986d2aa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/biomformat_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/biomformat_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/biomformat_1.20.0.tgz
vignettes: vignettes/biomformat/inst/doc/biomformat.html
vignetteTitles: The biomformat package Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biomformat/inst/doc/biomformat.R
importsMe: animalcules, microbiomeExplorer, phyloseq
suggestsMe: metagenomeSeq, mia, MicrobiotaProcess, metacoder, PLNmodels
dependencyCount: 14
Package: BioMM
Version: 1.8.0
Depends: R (>= 3.6)
Imports: stats, utils, grDevices, lattice, BiocParallel, glmnet, rms,
precrec, nsprcomp, ranger, e1071, ggplot2, vioplot, CMplot,
imager, topGO, xlsx
Suggests: BiocStyle, knitr, RUnit, BiocGenerics
License: GPL-3
MD5sum: 0d0d957d7bb3d3c7695a6917202edc56
NeedsCompilation: no
Title: BioMM: Biological-informed Multi-stage Machine learning
framework for phenotype prediction using omics data
Description: The identification of reproducible biological patterns
from high-dimensional omics data is a key factor in
understanding the biology of complex disease or traits.
Incorporating prior biological knowledge into machine learning
is an important step in advancing such research. We have
proposed a biologically informed multi-stage machine learing
framework termed BioMM specifically for phenotype prediction
based on omics-scale data where we can evaluate different
machine learning models with prior biological meta information.
biocViews: Genetics, Classification, Regression, Pathways, GO, Software
Author: Junfang Chen and Emanuel Schwarz
Maintainer: Junfang Chen <junfang.chen33@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BioMM
git_branch: RELEASE_3_13
git_last_commit: 765ffb7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BioMM_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BioMM_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BioMM_1.8.0.tgz
vignettes: vignettes/BioMM/inst/doc/BioMMtutorial.html
vignetteTitles: BioMMtutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BioMM/inst/doc/BioMMtutorial.R
dependencyCount: 147
Package: BioMVCClass
Version: 1.60.0
Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz
License: LGPL
MD5sum: 762b576b94002464c02379783982ee34
NeedsCompilation: no
Title: Model-View-Controller (MVC) Classes That Use Biobase
Description: Creates classes used in model-view-controller (MVC) design
biocViews: Visualization, Infrastructure, GraphAndNetwork
Author: Elizabeth Whalen
Maintainer: Elizabeth Whalen <ewhalen@hsph.harvard.edu>
git_url: https://git.bioconductor.org/packages/BioMVCClass
git_branch: RELEASE_3_13
git_last_commit: b626fb8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BioMVCClass_1.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BioMVCClass_1.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BioMVCClass_1.60.0.tgz
vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf
vignetteTitles: BioMVCClass
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 13
Package: biomvRCNS
Version: 1.32.0
Depends: IRanges, GenomicRanges, Gviz
Imports: methods, mvtnorm
Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut,
Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL (>= 2)
Archs: i386, x64
MD5sum: b7240d55047faf5c5f54b1cf91a2848a
NeedsCompilation: yes
Title: Copy Number study and Segmentation for multivariate biological
data
Description: In this package, a Hidden Semi Markov Model (HSMM) and one
homogeneous segmentation model are designed and implemented for
segmentation genomic data, with the aim of assisting in
transcripts detection using high throughput technology like
RNA-seq or tiling array, and copy number analysis using aCGH or
sequencing.
biocViews: aCGH, CopyNumberVariation, Microarray, Sequencing,
Visualization, Genetics
Author: Yang Du
Maintainer: Yang Du <tooyoung@gmail.com>
git_url: https://git.bioconductor.org/packages/biomvRCNS
git_branch: RELEASE_3_13
git_last_commit: c4c6948
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/biomvRCNS_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/biomvRCNS_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/biomvRCNS_1.32.0.tgz
vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf
vignetteTitles: biomvRCNS package introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biomvRCNS/inst/doc/biomvRCNS.R
dependencyCount: 143
Package: BioNERO
Version: 1.0.4
Depends: R (>= 4.1)
Imports: WGCNA, dynamicTreeCut, matrixStats, DESeq2, sva, RColorBrewer,
ComplexHeatmap, ggplot2, reshape2, igraph, ggnetwork,
intergraph, networkD3, ggnewscale, ggpubr, NetRep, stats,
grDevices, graphics, utils, methods, BiocParallel, minet,
GENIE3, SummarizedExperiment
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle, covr
License: GPL-3
MD5sum: c94eae1c36051e89beceafd39bfdf3bc
NeedsCompilation: no
Title: Biological Network Reconstruction Omnibus
Description: BioNERO aims to integrate all aspects of biological
network inference in a single package, including data
preprocessing, exploratory analyses, network inference, and
analyses for biological interpretations. BioNERO can be used to
infer gene coexpression networks (GCNs) and gene regulatory
networks (GRNs) from gene expression data. Additionally, it can
be used to explore topological properties of protein-protein
interaction (PPI) networks. GCN inference relies on the popular
WGCNA algorithm. GRN inference is based on the "wisdom of the
crowds" principle, which consists in inferring GRNs with
multiple algorithms (here, CLR, GENIE3 and ARACNE) and
calculating the average rank for each interaction pair. As all
steps of network analyses are included in this package, BioNERO
makes users avoid having to learn the syntaxes of several
packages and how to communicate between them. Finally, users
can also identify consensus modules across independent
expression sets and calculate intra and interspecies module
preservation statistics between different networks.
biocViews: Software, GeneExpression, GeneRegulation, SystemsBiology,
GraphAndNetwork, Preprocessing, Network
Author: Fabricio Almeida-Silva [cre, aut]
(<https://orcid.org/0000-0002-5314-2964>), Thiago Venancio
[aut] (<https://orcid.org/0000-0002-2215-8082>)
Maintainer: Fabricio Almeida-Silva <fabricio_almeidasilva@hotmail.com>
URL: https://github.com/almeidasilvaf/BioNERO
VignetteBuilder: knitr
BugReports: https://github.com/almeidasilvaf/BioNERO/issues
git_url: https://git.bioconductor.org/packages/BioNERO
git_branch: RELEASE_3_13
git_last_commit: 31c35e0
git_last_commit_date: 2021-08-26
Date/Publication: 2021-08-29
source.ver: src/contrib/BioNERO_1.0.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BioNERO_1.0.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/BioNERO_1.0.4.tgz
vignettes: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.html,
vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.html,
vignettes/BioNERO/inst/doc/vignette_03_network_comparison.html
vignetteTitles: Gene coexpression network inference, Gene regulatory
network inference with BioNERO, Network comparison: consensus
modules and module preservation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.R,
vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.R,
vignettes/BioNERO/inst/doc/vignette_03_network_comparison.R
dependencyCount: 202
Package: BioNet
Version: 1.52.0
Depends: R (>= 2.10.0), graph, RBGL
Imports: igraph (>= 1.0.1), AnnotationDbi, Biobase
Suggests: rgl, impute, DLBCL, genefilter, xtable, ALL, limma,
hgu95av2.db, XML
License: GPL (>= 2)
MD5sum: e2fdccded3c80cd3729b4725bec71828
NeedsCompilation: no
Title: Routines for the functional analysis of biological networks
Description: This package provides functions for the integrated
analysis of protein-protein interaction networks and the
detection of functional modules. Different datasets can be
integrated into the network by assigning p-values of
statistical tests to the nodes of the network. E.g. p-values
obtained from the differential expression of the genes from an
Affymetrix array are assigned to the nodes of the network. By
fitting a beta-uniform mixture model and calculating scores
from the p-values, overall scores of network regions can be
calculated and an integer linear programming algorithm
identifies the maximum scoring subnetwork.
biocViews: Microarray, DataImport, GraphAndNetwork, Network,
NetworkEnrichment, GeneExpression, DifferentialExpression
Author: Marcus Dittrich and Daniela Beisser
Maintainer: Marcus Dittrich
<marcus.dittrich@biozentrum.uni-wuerzburg.de>
URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/
git_url: https://git.bioconductor.org/packages/BioNet
git_branch: RELEASE_3_13
git_last_commit: 5045304
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BioNet_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BioNet_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BioNet_1.52.0.tgz
vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf
vignetteTitles: BioNet Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BioNet/inst/doc/Tutorial.R
importsMe: SMITE
suggestsMe: SANTA
dependencyCount: 54
Package: BioNetStat
Version: 1.12.0
Depends: R (>= 4.0), shiny, igraph, shinyBS, pathview, DT
Imports: BiocParallel, RJSONIO, whisker, yaml, pheatmap, ggplot2, plyr,
utils, stats, RColorBrewer, Hmisc, psych, knitr, rmarkdown,
markdown
License: GPL (>= 3)
MD5sum: 87e7808be0fce32784c99f576350446e
NeedsCompilation: no
Title: Biological Network Analysis
Description: A package to perform differential network analysis,
differential node analysis (differential coexpression
analysis), network and metabolic pathways view.
biocViews: Network, NetworkInference, Pathways, GraphAndNetwork,
Sequencing, Microarray, Metabolomics, Proteomics,
GeneExpression, RNASeq, SystemsBiology, DifferentialExpression,
GeneSetEnrichment, ImmunoOncology
Author: VinÃcius Jardim, Suzana Santos, André Fujita, and Marcos
Buckeridge
Maintainer: Vinicius Jardim <viniciusjc@gmail.com>
URL: http://github.com/jardimViniciusC/BioNetStat
VignetteBuilder: knitr, rmarkdown
BugReports: http://github.com/jardimViniciusC/BioNetStat/issues
git_url: https://git.bioconductor.org/packages/BioNetStat
git_branch: RELEASE_3_13
git_last_commit: 1bd4d5e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BioNetStat_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BioNetStat_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BioNetStat_1.12.0.tgz
vignettes:
vignettes/BioNetStat/inst/doc/BNS_tutorial_by_command_line_pt.html,
vignettes/BioNetStat/inst/doc/BNS_tutorial_by_command_line_us.html,
vignettes/BioNetStat/inst/doc/vignette.html
vignetteTitles: 3. Tutorial para o console do R, 2. R console tutorial,
1. Interface tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 139
Package: BioQC
Version: 1.20.0
Depends: R (>= 3.5.0), Biobase
Imports: edgeR, Rcpp, methods, stats, utils
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, lattice, latticeExtra,
rbenchmark, gplots, gridExtra, org.Hs.eg.db, hgu133plus2.db,
ggplot2, reshape2, plyr, ineq, covr, limma, RColorBrewer
License: GPL (>=3) + file LICENSE
Archs: i386, x64
MD5sum: 946a03e11707bf2bcf4351a3390b09f9
NeedsCompilation: yes
Title: Detect tissue heterogeneity in expression profiles with gene
sets
Description: BioQC performs quality control of high-throughput
expression data based on tissue gene signatures. It can detect
tissue heterogeneity in gene expression data. The core
algorithm is a Wilcoxon-Mann-Whitney test that is optimised for
high performance.
biocViews: GeneExpression,QualityControl,StatisticalMethod,
GeneSetEnrichment
Author: Jitao David Zhang [cre, aut], Laura Badi [aut], Gregor Sturm
[aut], Roland Ambs [aut], Iakov Davydov [aut]
Maintainer: Jitao David Zhang <jitao_david.zhang@roche.com>
URL: https://accio.github.io/BioQC
VignetteBuilder: knitr
BugReports: https://accio.github.io/BioQC/issues
git_url: https://git.bioconductor.org/packages/BioQC
git_branch: RELEASE_3_13
git_last_commit: 1f94f0f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BioQC_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BioQC_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BioQC_1.20.0.tgz
vignettes: vignettes/BioQC/inst/doc/bioqc-efficiency.html,
vignettes/BioQC/inst/doc/bioqc-introduction.html,
vignettes/BioQC/inst/doc/bioqc-signedGenesets.html,
vignettes/BioQC/inst/doc/bioqc-simulation.html,
vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.html,
vignettes/BioQC/inst/doc/BioQC.html
vignetteTitles: BioQC Algorithm: Speeding up the Wilcoxon-Mann-Whitney
Test, BioQC: Detect tissue heterogeneity in gene expression
data, Using BioQC with signed genesets, BioQC-benchmark:
Testing Efficiency,, Sensitivity and Specificity of BioQC on
simulated and real-world data, Comparing the
Wilcoxon-Mann-Whitney to alternative statistical tests,
BioQC-kidney: The kidney expression example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BioQC/inst/doc/bioqc-efficiency.R,
vignettes/BioQC/inst/doc/bioqc-introduction.R,
vignettes/BioQC/inst/doc/bioqc-signedGenesets.R,
vignettes/BioQC/inst/doc/bioqc-simulation.R,
vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.R,
vignettes/BioQC/inst/doc/BioQC.R
dependencyCount: 14
Package: biosigner
Version: 1.20.0
Depends: Biobase, ropls
Imports: methods, e1071, MultiDataSet, randomForest
Suggests: BioMark, BiocGenerics, BiocStyle, golubEsets, hu6800.db,
knitr, omicade4, rmarkdown, testthat
License: CeCILL
MD5sum: 7aabe07f9ac432b7173f3c3444296270
NeedsCompilation: no
Title: Signature discovery from omics data
Description: Feature selection is critical in omics data analysis to
extract restricted and meaningful molecular signatures from
complex and high-dimension data, and to build robust
classifiers. This package implements a new method to assess the
relevance of the variables for the prediction performances of
the classifier. The approach can be run in parallel with the
PLS-DA, Random Forest, and SVM binary classifiers. The
signatures and the corresponding 'restricted' models are
returned, enabling future predictions on new datasets. A Galaxy
implementation of the package is available within the
Workflow4metabolomics.org online infrastructure for
computational metabolomics.
biocViews: Classification, FeatureExtraction, Transcriptomics,
Proteomics, Metabolomics, Lipidomics
Author: Philippe Rinaudo <phd.rinaudo@gmail.com>, Etienne Thevenot
<etienne.thevenot@cea.fr>
Maintainer: Philippe Rinaudo <phd.rinaudo@gmail.com>, Etienne Thevenot
<etienne.thevenot@cea.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/biosigner
git_branch: RELEASE_3_13
git_last_commit: 2186320
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/biosigner_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/biosigner_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/biosigner_1.20.0.tgz
vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R
importsMe: multiSight
dependencyCount: 67
Package: Biostrings
Version: 2.60.2
Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>=
0.27.12), IRanges (>= 2.23.9), XVector (>= 0.29.2),
GenomeInfoDb
Imports: methods, utils, grDevices, graphics, stats, crayon,
LinkingTo: S4Vectors, IRanges, XVector
Suggests: BSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>=
1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11),
BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe,
hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy
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Enhances: Rmpi
License: Artistic-2.0
Archs: i386, x64
MD5sum: 7fe01dff7e42438da469b9bfac1a5824
NeedsCompilation: yes
Title: Efficient manipulation of biological strings
Description: Memory efficient string containers, string matching
algorithms, and other utilities, for fast manipulation of large
biological sequences or sets of sequences.
biocViews: SequenceMatching, Alignment, Sequencing, Genetics,
DataImport, DataRepresentation, Infrastructure
Author: H. Pagès, P. Aboyoun, R. Gentleman, and S. DebRoy
Maintainer: H. Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/Biostrings
BugReports: https://github.com/Bioconductor/Biostrings/issues
git_url: https://git.bioconductor.org/packages/Biostrings
git_branch: RELEASE_3_13
git_last_commit: 2024073
git_last_commit_date: 2021-08-04
Date/Publication: 2021-08-05
source.ver: src/contrib/Biostrings_2.60.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Biostrings_2.60.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/Biostrings_2.60.2.tgz
vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf,
vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf,
vignettes/Biostrings/inst/doc/matchprobes.pdf,
vignettes/Biostrings/inst/doc/MultipleAlignments.pdf,
vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf
vignetteTitles: A short presentation of the basic classes defined in
Biostrings 2, Biostrings Quick Overview, Handling probe
sequence information, Multiple Alignments, Pairwise Sequence
Alignments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R,
vignettes/Biostrings/inst/doc/matchprobes.R,
vignettes/Biostrings/inst/doc/MultipleAlignments.R,
vignettes/Biostrings/inst/doc/PairwiseAlignments.R
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pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b,
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pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1,
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pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba,
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pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a,
pd.plasmodium.anopheles, pd.poplar, pd.porcine,
pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st,
pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1,
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pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st,
pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c,
pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice,
pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0,
pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean,
pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato,
pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2,
pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98,
pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, harbChIP,
JASPAR2014, NestLink, generegulation, sequencing,
CleanBSequences, FcircSEC, ionflows, pagoo, SimRAD, STRMPS,
SubVis
importsMe: AffyCompatible, AllelicImbalance, alpine, AneuFinder,
AnnotationHubData, appreci8R, ArrayExpressHTS, AssessORF,
ATACseqQC, BBCAnalyzer, BCRANK, bcSeq, BEAT, BgeeCall,
biovizBase, brainflowprobes, branchpointer, BSgenome, bsseq,
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ChIPsim, chromVAR, circRNAprofiler, cleanUpdTSeq, CNEr,
CNVfilteR, compEpiTools, consensusDE, coRdon, CrispRVariants,
customProDB, dada2, dagLogo, DAMEfinder, decompTumor2Sig,
diffHic, DNAshapeR, DominoEffect, easyRNASeq, EDASeq,
ensembldb, ensemblVEP, EpiTxDb, esATAC, eudysbiome,
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gcrma, genbankr, GeneRegionScan, GenoGAM, genomation,
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h5vc, heatmaps, HiLDA, HiTC, HTSeqGenie, icetea, idpr, IMMAN,
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IsoformSwitchAnalyzeR, KEGGREST, LowMACA, LymphoSeq, MACPET,
MADSEQ, MatrixRider, MDTS, MEDIPS, MEDME, memes, MesKit,
metaseqR2, methimpute, methylPipe, methylscaper, mia,
MicrobiotaProcess, microRNA, MMDiff2, motifbreakR,
motifcounter, motifmatchr, motifStack, MSnID, MSstatsPTM,
multicrispr, MungeSumstats, musicatk, MutationalPatterns,
NanoStringNCTools, ngsReports, nucleR, oligoClasses, OmaDB,
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primirTSS, proBAMr, procoil, ProteomicsAnnotationHubData,
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RCAS, Rcpi, recoup, regioneR, regutools, REMP, Repitools,
rfaRm, rGADEM, ribosomeProfilingQC, RNAmodR, RNASeqR, Rqc,
rtracklayer, sarks, scmeth, SCOPE, scoreInvHap, scRepertoire,
scruff, SeqArray, seqcombo, seqPattern, SGSeq, signeR,
SigsPack, SingleMoleculeFootprinting, sitadela, SNPhood, soGGi,
SomaticSignatures, SparseSignatures, SPLINTER, sscu,
StructuralVariantAnnotation, supersigs, SynExtend, SynMut,
TAPseq, TarSeqQC, TFBSTools, transite, trena, tRNA,
tRNAdbImport, tRNAscanImport, TVTB, tximeta, Ularcirc,
UMI4Cats, universalmotif, VariantAnnotation, VariantExperiment,
VariantFiltering, VariantTools, wavClusteR, XNAString, YAPSA,
EuPathDB, FDb.InfiniumMethylation.hg18,
FDb.InfiniumMethylation.hg19,
pd.081229.hg18.promoter.medip.hx1,
pd.2006.07.18.hg18.refseq.promoter,
pd.2006.07.18.mm8.refseq.promoter,
pd.2006.10.31.rn34.refseq.promoter, pd.charm.hg18.example,
pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.mirna.3.1,
microbiomeDataSets, pd.atdschip.tiling, PhyloProfileData,
ActiveDriverWGS, alakazam, BALCONY, BASiNET, biomartr, BioMedR,
crispRdesignR, CSESA, deepredeff, dowser, EncDNA, ensembleTax,
ExomeDepth, genBaRcode, hoardeR, ICAMS, immuneSIM, kibior,
metaCluster, microbial, MicroSEC, PACVr, PredCRG, ptm, RAPIDR,
seqmagick, simMP, SMITIDstruct, utr.annotation, vhcub
suggestsMe: annotate, AnnotationForge, AnnotationHub, bambu, BANDITS,
BiocGenerics, BRGenomics, CSAR, eisaR, exomeCopy, GenomicFiles,
GenomicRanges, GWASTools, maftools, methrix, methylumi, MiRaGE,
nuCpos, RNAmodR.AlkAnilineSeq, rpx, rSWeeP, rTRM, splatter,
systemPipeTools, treeio, XVector,
SNPlocs.Hsapiens.dbSNP.20101109,
SNPlocs.Hsapiens.dbSNP.20120608,
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SNPlocs.Hsapiens.dbSNP142.GRCh37,
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SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP151.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BeadArrayUseCases,
AhoCorasickTrie, apcluster, bbl, bio3d, DDPNA, file2meco,
gkmSVM, maGUI, minSNPs, msaR, NameNeedle, phangorn, polyRAD,
protr, rDNAse, sigminer, Signac, tidysq
linksToMe: DECIPHER, kebabs, MatrixRider, Rsamtools, ShortRead,
triplex, VariantAnnotation, VariantFiltering
dependencyCount: 18
Package: BioTIP
Version: 1.6.0
Depends: R (>= 3.6)
Imports: igraph, cluster, psych, stringr, GenomicRanges, Hmisc, MASS
Suggests: knitr, markdown, base, rmarkdown, ggplot2
License: GPL-2
MD5sum: 4e8af952bf601f1b187eff5dd0d11b3b
NeedsCompilation: no
Title: BioTIP: An R package for characterization of Biological
Tipping-Point
Description: Adopting tipping-point theory to transcriptome profiles to
unravel disease regulatory trajectory.
biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Software
Author: Zhezhen Wang, Andrew Goldstein, Yuxi Sun, Biniam Feleke, Qier
An, Antonio Feliciano, Xinan Yang
Maintainer: Yuxi (Jennifer) Sun <ysun11@uchicago.edu>, Zhezhen Wang
<zhezhen@uchicago.edu>, and X Holly Yang <xyang2@uchicago.edu>
URL: https://github.com/xyang2uchicago/BioTIP
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BioTIP
git_branch: RELEASE_3_13
git_last_commit: 42fb23f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BioTIP_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BioTIP_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BioTIP_1.6.0.tgz
vignettes: vignettes/BioTIP/inst/doc/BioTIP.html
vignetteTitles: BioTIP- an R package for characterization of Biological
Tipping-Point
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BioTIP/inst/doc/BioTIP.R
dependencyCount: 84
Package: biotmle
Version: 1.16.0
Depends: R (>= 3.4)
Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, superheat,
assertthat, future, doFuture, drtmle (>= 1.0.4), S4Vectors,
BiocGenerics, BiocParallel, SummarizedExperiment, limma
Suggests: testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger,
SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1)
License: file LICENSE
MD5sum: fde0cc24d59a2ed6288edf0abdcdd52b
NeedsCompilation: no
Title: Targeted Learning with Moderated Statistics for Biomarker
Discovery
Description: Tools for differential expression biomarker discovery
based on microarray and next-generation sequencing data that
leverage efficient semiparametric estimators of the average
treatment effect for variable importance analysis. Estimation
and inference of the (marginal) average treatment effects of
potential biomarkers are computed by targeted minimum
loss-based estimation, with joint, stable inference constructed
across all biomarkers using a generalization of moderated
statistics for use with the estimated efficient influence
function. The procedure accommodates the use of ensemble
machine learning for the estimation of nuisance functions.
biocViews: Regression, GeneExpression, DifferentialExpression,
Sequencing, Microarray, RNASeq, ImmunoOncology
Author: Nima Hejazi [aut, cre, cph]
(<https://orcid.org/0000-0002-7127-2789>), Alan Hubbard [aut,
ths] (<https://orcid.org/0000-0002-3769-0127>), Mark van der
Laan [aut, ths] (<https://orcid.org/0000-0003-1432-5511>),
Weixin Cai [ctb] (<https://orcid.org/0000-0003-2680-3066>)
Maintainer: Nima Hejazi <nh@nimahejazi.org>
URL: https://code.nimahejazi.org/biotmle
VignetteBuilder: knitr
BugReports: https://github.com/nhejazi/biotmle/issues
git_url: https://git.bioconductor.org/packages/biotmle
git_branch: RELEASE_3_13
git_last_commit: 64f9cfb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/biotmle_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/biotmle_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/biotmle_1.16.0.tgz
vignettes: vignettes/biotmle/inst/doc/exposureBiomarkers.html
vignetteTitles: Identifying Biomarkers from an Exposure Variable
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/biotmle/inst/doc/exposureBiomarkers.R
dependencyCount: 103
Package: biovizBase
Version: 1.40.0
Depends: R (>= 3.5.0), methods
Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat,
BiocGenerics, S4Vectors (>= 0.23.19), IRanges (>= 1.99.28),
GenomeInfoDb (>= 1.5.14), GenomicRanges (>= 1.23.21),
SummarizedExperiment, Biostrings (>= 2.33.11), Rsamtools (>=
1.17.28), GenomicAlignments (>= 1.1.16), GenomicFeatures (>=
1.21.19), AnnotationDbi, VariantAnnotation (>= 1.11.4),
ensembldb (>= 1.99.13), AnnotationFilter (>= 0.99.8), rlang
Suggests: BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer,
EnsDb.Hsapiens.v75, RUnit
License: Artistic-2.0
Archs: i386, x64
MD5sum: 83ad0eb484435c661f92f3fc661ed4f5
NeedsCompilation: yes
Title: Basic graphic utilities for visualization of genomic data.
Description: The biovizBase package is designed to provide a set of
utilities, color schemes and conventions for genomic data. It
serves as the base for various high-level packages for
biological data visualization. This saves development effort
and encourages consistency.
biocViews: Infrastructure, Visualization, Preprocessing
Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne
Cook [aut, ths], Johannes Rainer [ctb]
Maintainer: Michael Lawrence <michafla@gene.com>
git_url: https://git.bioconductor.org/packages/biovizBase
git_branch: RELEASE_3_13
git_last_commit: e741735
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/biovizBase_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/biovizBase_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/biovizBase_1.40.0.tgz
vignettes: vignettes/biovizBase/inst/doc/intro.pdf
vignetteTitles: An Introduction to biovizBase
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biovizBase/inst/doc/intro.R
dependsOnMe: CAFE, qrqc
importsMe: BubbleTree, ChIPexoQual, ggbio, Gviz, karyoploteR, Pviz,
qrqc, Rqc
suggestsMe: CINdex, derfinderPlot, NanoStringNCTools, R3CPET,
regionReport, StructuralVariantAnnotation, Signac
dependencyCount: 140
Package: BiRewire
Version: 3.24.0
Depends: igraph, slam, tsne, Matrix
Suggests: RUnit, BiocGenerics
License: GPL-3
Archs: i386, x64
MD5sum: 6ade1bd63fcd516a946b71888bfec2f9
NeedsCompilation: yes
Title: High-performing routines for the randomization of a bipartite
graph (or a binary event matrix), undirected and directed
signed graph preserving degree distribution (or marginal
totals)
Description: Fast functions for bipartite network rewiring through N
consecutive switching steps (See References) and for the
computation of the minimal number of switching steps to be
performed in order to maximise the dissimilarity with respect
to the original network. Includes functions for the analysis of
the introduced randomness across the switching steps and
several other routines to analyse the resulting networks and
their natural projections. Extension to undirected networks and
directed signed networks is also provided. Starting from
version 1.9.7 a more precise bound (especially for small
network) has been implemented. Starting from version 2.2.0 the
analysis routine is more complete and a visual montioring of
the underlying Markov Chain has been implemented. Starting from
3.6.0 the library can handle also matrices with NA (not for the
directed signed graphs).
biocViews: Network
Author: Andrea Gobbi [aut], Francesco Iorio [aut], Giuseppe Jurman
[cbt], Davide Albanese [cbt], Julio Saez-Rodriguez [cbt].
Maintainer: Andrea Gobbi <gobbi.andrea@mail.com>
URL: http://www.ebi.ac.uk/~iorio/BiRewire
git_url: https://git.bioconductor.org/packages/BiRewire
git_branch: RELEASE_3_13
git_last_commit: 6a27de6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BiRewire_3.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BiRewire_3.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BiRewire_3.24.0.tgz
vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf
vignetteTitles: BiRewire
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiRewire/inst/doc/BiRewire.R
importsMe: NetSci
dependencyCount: 13
Package: biscuiteer
Version: 1.6.0
Depends: R (>= 3.6), biscuiteerData, bsseq
Imports: readr, qualV, Matrix, impute, HDF5Array, S4Vectors, Rsamtools,
data.table, Biobase, GenomicRanges, BiocGenerics,
VariantAnnotation, DelayedMatrixStats, SummarizedExperiment,
GenomeInfoDb, Mus.musculus, Homo.sapiens, matrixStats,
rtracklayer, QDNAseq, dmrseq, methods, utils, R.utils, gtools,
BiocParallel
Suggests: DSS, covr, knitr, rlang, scmeth, pkgdown, roxygen2, testthat,
QDNAseq.hg19, QDNAseq.mm10
License: GPL-3
MD5sum: 2d9427247e55a8a4733b6e28e576629b
NeedsCompilation: no
Title: Convenience Functions for Biscuit
Description: A test harness for bsseq loading of Biscuit output,
summarization of WGBS data over defined regions and in mappable
samples, with or without imputation, dropping of mostly-NA
rows, age estimates, etc.
biocViews: DataImport, MethylSeq, DNAMethylation
Author: Tim Triche, Jr. [aut, cre], Wanding Zhou [aut], Ben Johnson
[aut], Jacob Morrison [aut], Lyong Heo [aut]
Maintainer: "Jacob Morrison" <jacob.morrison@vai.org>
URL: https://github.com/trichelab/biscuiteer
VignetteBuilder: knitr
BugReports: https://github.com/trichelab/biscuiteer/issues
git_url: https://git.bioconductor.org/packages/biscuiteer
git_branch: RELEASE_3_13
git_last_commit: 6d86b7b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/biscuiteer_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/biscuiteer_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/biscuiteer_1.6.0.tgz
vignettes: vignettes/biscuiteer/inst/doc/biscuiteer.html
vignetteTitles: Biscuiteer User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biscuiteer/inst/doc/biscuiteer.R
dependencyCount: 193
Package: BiSeq
Version: 1.32.0
Depends: R (>= 2.15.2), methods, S4Vectors, IRanges (>= 1.17.24),
GenomicRanges, SummarizedExperiment (>= 0.2.0), Formula
Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges,
GenomeInfoDb, GenomicRanges, SummarizedExperiment, rtracklayer,
parallel, betareg, lokern, Formula, globaltest
License: LGPL-3
MD5sum: 860251482e8b71a756166beabb36066a
NeedsCompilation: no
Title: Processing and analyzing bisulfite sequencing data
Description: The BiSeq package provides useful classes and functions to
handle and analyze targeted bisulfite sequencing (BS) data such
as reduced-representation bisulfite sequencing (RRBS) data. In
particular, it implements an algorithm to detect differentially
methylated regions (DMRs). The package takes already aligned BS
data from one or multiple samples.
biocViews: Genetics, Sequencing, MethylSeq, DNAMethylation
Author: Katja Hebestreit, Hans-Ulrich Klein
Maintainer: Katja Hebestreit <katja.hebestreit@gmail.com>
git_url: https://git.bioconductor.org/packages/BiSeq
git_branch: RELEASE_3_13
git_last_commit: d5b9175
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BiSeq_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BiSeq_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BiSeq_1.32.0.tgz
vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf
vignetteTitles: An Introduction to BiSeq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiSeq/inst/doc/BiSeq.R
dependsOnMe: RRBSdata
dependencyCount: 85
Package: BitSeq
Version: 1.36.0
Depends: Rsamtools (>= 1.99.3)
Imports: S4Vectors, IRanges, methods, utils
LinkingTo: Rhtslib (>= 1.15.5)
Suggests: BiocStyle
License: Artistic-2.0 + file LICENSE
Archs: i386, x64
MD5sum: 064f5a2290153aab6119a09fa150fe76
NeedsCompilation: yes
Title: Transcript expression inference and differential expression
analysis for RNA-seq data
Description: The BitSeq package is targeted for transcript expression
analysis and differential expression analysis of RNA-seq data
in two stage process. In the first stage it uses Bayesian
inference methodology to infer expression of individual
transcripts from individual RNA-seq experiments. The second
stage of BitSeq embraces the differential expression analysis
of transcript expression. Providing expression estimates from
replicates of multiple conditions, Log-Normal model of the
estimates is used for inferring the condition mean transcript
expression and ranking the transcripts based on the likelihood
of differential expression.
biocViews: ImmunoOncology, GeneExpression, DifferentialExpression,
Sequencing, RNASeq, Bayesian, AlternativeSplicing,
DifferentialSplicing, Transcription
Author: Peter Glaus, Antti Honkela and Magnus Rattray
Maintainer: Antti Honkela <antti.honkela@helsinki.fi>, Panagiotis
Papastamoulis <papastamoulis@aueb.gr>
SystemRequirements: GNU make
git_url: https://git.bioconductor.org/packages/BitSeq
git_branch: RELEASE_3_13
git_last_commit: 9a22089
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BitSeq_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BitSeq_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BitSeq_1.36.0.tgz
vignettes: vignettes/BitSeq/inst/doc/BitSeq.pdf
vignetteTitles: BitSeq User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BitSeq/inst/doc/BitSeq.R
dependencyCount: 29
Package: blacksheepr
Version: 1.6.0
Depends: R (>= 3.6)
Imports: grid, stats, grDevices, utils, circlize, viridis,
RColorBrewer, ComplexHeatmap, SummarizedExperiment, pasilla
Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, curl
License: MIT + file LICENSE
MD5sum: 2924fa0deced5f622dd30136e0e39bd1
NeedsCompilation: no
Title: Outlier Analysis for pairwise differential comparison
Description: Blacksheep is a tool designed for outlier analysis in the
context of pairwise comparisons in an effort to find
distinguishing characteristics from two groups. This tool was
designed to be applied for biological applications such as
phosphoproteomics or transcriptomics, but it can be used for
any data that can be represented by a 2D table, and has two sub
populations within the table to compare.
biocViews: Sequencing, RNASeq, GeneExpression, Transcription,
DifferentialExpression, Transcriptomics
Author: MacIntosh Cornwell [aut], RugglesLab [cre]
Maintainer: RugglesLab <ruggleslab@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/ruggleslab/blacksheepr/issues
git_url: https://git.bioconductor.org/packages/blacksheepr
git_branch: RELEASE_3_13
git_last_commit: 665a6ef
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/blacksheepr_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/blacksheepr_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/blacksheepr_1.6.0.tgz
vignettes: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.html
vignetteTitles: Outlier Analysis using blacksheepr - Phosphoprotein
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.R
dependencyCount: 73
Package: blima
Version: 1.26.0
Depends: R(>= 3.3)
Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), Rcpp (>= 0.12.8),
BiocGenerics, grDevices, stats, graphics
LinkingTo: Rcpp
Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db,
lumi, knitr
License: GPL-3
Archs: i386, x64
MD5sum: cbd6964c97503a615dc5c40bfdbf207d
NeedsCompilation: yes
Title: Tools for the preprocessing and analysis of the Illumina
microarrays on the detector (bead) level
Description: Package blima includes several algorithms for the
preprocessing of Illumina microarray data. It focuses to the
bead level analysis and provides novel approach to the quantile
normalization of the vectors of unequal lengths. It provides
variety of the methods for background correction including
background subtraction, RMA like convolution and background
outlier removal. It also implements variance stabilizing
transformation on the bead level. There are also implemented
methods for data summarization. It also provides the methods
for performing T-tests on the detector (bead) level and on the
probe level for differential expression testing.
biocViews: Microarray, Preprocessing, Normalization,
DifferentialExpression, GeneRegulation, GeneExpression
Author: Vojtěch Kulvait
Maintainer: Vojtěch Kulvait <kulvait@gmail.com>
URL: https://bitbucket.org/kulvait/blima
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/blima
git_branch: RELEASE_3_13
git_last_commit: f56780e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/blima_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/blima_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/blima_1.26.0.tgz
vignettes: vignettes/blima/inst/doc/blima.pdf
vignetteTitles: blima.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/blima/inst/doc/blima.R
suggestsMe: blimaTestingData
dependencyCount: 83
Package: BLMA
Version: 1.16.0
Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel,
Biobase, metafor, methods
Suggests: RUnit, BiocGenerics
License: GPL (>=2)
MD5sum: 758c53e5198d2f77d77318ebcc4be708
NeedsCompilation: no
Title: BLMA: A package for bi-level meta-analysis
Description: Suit of tools for bi-level meta-analysis. The package can
be used in a wide range of applications, including general
hypothesis testings, differential expression analysis,
functional analysis, and pathway analysis.
biocViews: GeneSetEnrichment, Pathways, DifferentialExpression,
Microarray
Author: Tin Nguyen <tinn@unr.edu>, Hung Nguyen <hungnp@nevada.unr.edu>,
and Sorin Draghici <sorin@wayne.edu>
Maintainer: Hung Nguyen <hungnp@nevada.unr.edu>
git_url: https://git.bioconductor.org/packages/BLMA
git_branch: RELEASE_3_13
git_last_commit: 9b4e0e3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BLMA_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BLMA_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BLMA_1.16.0.tgz
vignettes: vignettes/BLMA/inst/doc/BLMA.pdf
vignetteTitles: BLMA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BLMA/inst/doc/BLMA.R
dependencyCount: 72
Package: BloodGen3Module
Version: 1.0.0
Depends: R (>= 4.1)
Imports: SummarizedExperiment, ExperimentHub, methods, grid, graphics,
stats, grDevices, circlize, testthat, ComplexHeatmap(>=
1.99.8), ggplot2, matrixStats, gtools, reshape2,
preprocessCore, randomcoloR, V8, limma
Suggests: RUnit, devtools, BiocGenerics, knitr, rmarkdown
License: GPL-2
MD5sum: 659d692fe121a5ec79e5ae4b70b6176e
NeedsCompilation: no
Title: This R package for performing module repertoire analyses and
generating fingerprint representations
Description: The BloodGen3Module package provides functions for R user
performing module repertoire analyses and generating
fingerprint representations. Functions can perform group
comparison or individual sample analysis and visualization by
fingerprint grid plot or fingerprint heatmap. Module repertoire
analyses typically involve determining the percentage of the
constitutive genes for each module that are significantly
increased or decreased. As we describe in
details;https://www.biorxiv.org/content/10.1101/525709v2 and
https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of
module repertoire analyses can be represented in a fingerprint
format, where red and blue spots indicate increases or
decreases in module activity. These spots are subsequently
represented either on a grid, with each position being assigned
to a given module, or in a heatmap where the samples are
arranged in columns and the modules in rows.
biocViews: Software, Visualization, GeneExpression
Author: Darawan Rinchai [aut, cre]
(<https://orcid.org/0000-0001-8851-7730>)
Maintainer: Darawan Rinchai <drinchai@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BloodGen3Module
git_branch: RELEASE_3_13
git_last_commit: 2f62a72
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BloodGen3Module_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BloodGen3Module_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BloodGen3Module_1.0.0.tgz
vignettes: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.html
vignetteTitles: BloodGen3Module: Modular Repertoire Analysis and
Visualization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.R
dependencyCount: 147
Package: bluster
Version: 1.2.1
Imports: stats, methods, utils, cluster, Matrix, Rcpp, igraph,
S4Vectors, BiocParallel, BiocNeighbors
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat, BiocStyle, dynamicTreeCut,
scRNAseq, scuttle, scater, scran, pheatmap, viridis, mbkmeans,
kohonen, apcluster
License: GPL-3
Archs: i386, x64
MD5sum: 9704c99eda7a3abd6d647b4df013eda9
NeedsCompilation: yes
Title: Clustering Algorithms for Bioconductor
Description: Wraps common clustering algorithms in an easily extended
S4 framework. Backends are implemented for hierarchical,
k-means and graph-based clustering. Several utilities are also
provided to compare and evaluate clustering results.
biocViews: ImmunoOncology, Software, GeneExpression, Transcriptomics,
SingleCell, Clustering
Author: Aaron Lun [aut, cre], Stephanie Hicks [ctb]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/bluster
git_branch: RELEASE_3_13
git_last_commit: 5657043
git_last_commit_date: 2021-05-26
Date/Publication: 2021-05-27
source.ver: src/contrib/bluster_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bluster_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/bluster_1.2.1.tgz
vignettes: vignettes/bluster/inst/doc/clusterRows.html,
vignettes/bluster/inst/doc/diagnostics.html
vignetteTitles: 1. Clustering algorithms, 2. Clustering diagnostics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bluster/inst/doc/clusterRows.R,
vignettes/bluster/inst/doc/diagnostics.R
dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample,
OSCA.workflows
importsMe: scDblFinder, scran
suggestsMe: batchelor, dittoSeq, mbkmeans, mumosa, SingleRBook
dependencyCount: 26
Package: bnbc
Version: 1.14.0
Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment,
GenomicRanges
Imports: Rcpp (>= 0.12.12), IRanges, rhdf5, data.table, GenomeInfoDb,
S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage,
utils
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, RUnit
License: Artistic-2.0
Archs: i386, x64
MD5sum: da92967d20be9183289894f808c13257
NeedsCompilation: yes
Title: Bandwise normalization and batch correction of Hi-C data
Description: Tools to normalize (several) Hi-C data from replicates.
biocViews: HiC, Preprocessing, Normalization, Software
Author: Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut]
Maintainer: Kipper Fletez-Brant <cafletezbrant@gmail.com>
URL: https://github.com/hansenlab/bnbc
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/bnbc/issues
git_url: https://git.bioconductor.org/packages/bnbc
git_branch: RELEASE_3_13
git_last_commit: 210c4d2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bnbc_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bnbc_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bnbc_1.14.0.tgz
vignettes: vignettes/bnbc/inst/doc/bnbc.html
vignetteTitles: bnbc User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bnbc/inst/doc/bnbc.R
dependencyCount: 88
Package: bnem
Version: 1.0.0
Depends: R (>= 4.1)
Imports: CellNOptR, matrixStats, snowfall, Rgraphviz, cluster,
flexclust, stats, RColorBrewer, epiNEM, mnem, Biobase, methods,
utils, graphics, graph, affy, binom, limma, sva, vsn, rmarkdown
Suggests: knitr, BiocGenerics
License: GPL-3
MD5sum: 799b749b59a30d2175b816f10ee1c50f
NeedsCompilation: no
Title: Training of logical models from indirect measurements of
perturbation experiments
Description: bnem combines the use of indirect measurements of Nested
Effects Models (package mnem) with the Boolean networks of
CellNOptR. Perturbation experiments of signalling nodes in
cells are analysed for their effect on the global gene
expression profile. Those profiles give evidence for the
Boolean regulation of down-stream nodes in the network, e.g.,
whether two parents activate their child independently
(OR-gate) or jointly (AND-gate).
biocViews: Pathways, SystemsBiology, NetworkInference, Network,
GeneExpression, GeneRegulation, Preprocessing
Author: Martin Pirkl [aut, cre]
Maintainer: Martin Pirkl <martinpirkl@yahoo.de>
URL: https://github.com/MartinFXP/bnem/
VignetteBuilder: knitr
BugReports: https://github.com/MartinFXP/bnem/issues
git_url: https://git.bioconductor.org/packages/bnem
git_branch: RELEASE_3_13
git_last_commit: 3109efd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bnem_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bnem_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bnem_1.0.0.tgz
vignettes: vignettes/bnem/inst/doc/bnem.html
vignetteTitles: bnem.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bnem/inst/doc/bnem.R
dependencyCount: 167
Package: BPRMeth
Version: 1.18.0
Depends: R (>= 3.5.0), GenomicRanges
Imports: assertthat, methods, MASS, doParallel, parallel, e1071, earth,
foreach, randomForest, stats, IRanges, S4Vectors, data.table,
graphics, truncnorm, mvtnorm, Rcpp (>= 0.12.14), matrixcalc,
magrittr, kernlab, ggplot2, cowplot, BiocStyle
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown
License: GPL-3 | file LICENSE
Archs: i386, x64
MD5sum: 7e75c0c13f889e7cee2b04ff7aa82aa8
NeedsCompilation: yes
Title: Model higher-order methylation profiles
Description: The BPRMeth package is a probabilistic method to quantify
explicit features of methylation profiles, in a way that would
make it easier to formally use such profiles in downstream
modelling efforts, such as predicting gene expression levels or
clustering genomic regions or cells according to their
methylation profiles.
biocViews: ImmunoOncology, DNAMethylation, GeneExpression,
GeneRegulation, Epigenetics, Genetics, Clustering,
FeatureExtraction, Regression, RNASeq, Bayesian, KEGG,
Sequencing, Coverage, SingleCell
Author: Chantriolnt-Andreas Kapourani [aut, cre]
Maintainer: Chantriolnt-Andreas Kapourani
<kapouranis.andreas@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BPRMeth
git_branch: RELEASE_3_13
git_last_commit: 2d62182
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BPRMeth_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BPRMeth_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BPRMeth_1.18.0.tgz
vignettes: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.html
vignetteTitles: BPRMeth: Model higher-order methylation profiles
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.R
dependsOnMe: Melissa
dependencyCount: 90
Package: BRAIN
Version: 1.38.0
Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice
License: GPL-2
MD5sum: 363e4448d977951768c1352cb9c52cef
NeedsCompilation: no
Title: Baffling Recursive Algorithm for Isotope distributioN
calculations
Description: Package for calculating aggregated isotopic distribution
and exact center-masses for chemical substances (in this
version composed of C, H, N, O and S). This is an
implementation of the BRAIN algorithm described in the paper by
J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics
Author: Piotr Dittwald, with contributions of Dirk Valkenborg and
Jurgen Claesen
Maintainer: Piotr Dittwald <piotr.dittwald@mimuw.edu.pl>
git_url: https://git.bioconductor.org/packages/BRAIN
git_branch: RELEASE_3_13
git_last_commit: eea0fc0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BRAIN_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BRAIN_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BRAIN_1.38.0.tgz
vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf
vignetteTitles: BRAIN Usage
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BRAIN/inst/doc/BRAIN-vignette.R
suggestsMe: cleaver, RforProteomics
dependencyCount: 23
Package: brainflowprobes
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: Biostrings (>= 2.52.0), BSgenome.Hsapiens.UCSC.hg19 (>=
1.4.0), bumphunter (>= 1.26.0), cowplot (>= 1.0.0), derfinder
(>= 1.18.1), derfinderPlot (>= 1.18.1), GenomicRanges (>=
1.36.0), ggplot2 (>= 3.1.1), RColorBrewer (>= 1.1), utils,
grDevices, GenomicState (>= 0.99.7)
Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo,
testthat (>= 2.1.0), covr
License: Artistic-2.0
MD5sum: 2593847a9e05c50efd0bb9eff97c586d
NeedsCompilation: no
Title: Plots and annotation for choosing BrainFlow target probe
sequence
Description: Use these functions to characterize genomic regions for
BrainFlow target probe design.
biocViews: Coverage, Visualization, ExperimentalDesign,
Transcriptomics, FlowCytometry, GeneTarget
Author: Amanda Price [aut, cre]
(<https://orcid.org/0000-0001-7352-3732>), Leonardo
Collado-Torres [ctb] (<https://orcid.org/0000-0003-2140-308X>)
Maintainer: Amanda Price <amanda.joy.price@gmail.com>
URL: https://github.com/LieberInstitute/brainflowprobes
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/brainflowprobes
git_url: https://git.bioconductor.org/packages/brainflowprobes
git_branch: RELEASE_3_13
git_last_commit: 3bae675
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/brainflowprobes_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/brainflowprobes_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/brainflowprobes_1.6.0.tgz
vignettes:
vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.html
vignetteTitles: brainflowprobes users guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.R
dependencyCount: 187
Package: BrainSABER
Version: 1.2.0
Depends: R (>= 4.0), biomaRt, SummarizedExperiment
Imports: data.table, lsa, methods, S4Vectors, utils, BiocFileCache
Suggests: BiocStyle, ComplexHeatmap, fastcluster, heatmaply, knitr,
plotly
License: Artistic-2.0
MD5sum: c6dfd33ab7845bec573782837f3b042d
NeedsCompilation: no
Title: Brain Span Atlas in Biobase Expressionset R toolset
Description: The Allen Institute for Brain Science provides an RNA
sequencing (RNA-Seq) data resource for studying transcriptional
mechanisms involved in human brain development known as
BrainSpan. BrainSABER is an R package that facilitates
comparison of user data with the various developmental stages
and brain structures found in the BrainSpan atlas by generating
dynamic similarity heatmaps for the two data sets. It also
provides a self-validating container for user data.
biocViews: GeneExpression, Visualization, Sequencing
Author: Carrie Minette and Evgeni Radichev
Maintainer: USD Biomedical Engineering <bicbioeng@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BrainSABER
git_branch: RELEASE_3_13
git_last_commit: 0c1b22d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BrainSABER_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BrainSABER_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BrainSABER_1.2.0.tgz
vignettes:
vignettes/BrainSABER/inst/doc/Installing_and_Using_BrainSABER.html
vignetteTitles: BrainSABER
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BrainSABER/inst/doc/Installing_and_Using_BrainSABER.R
dependencyCount: 83
Package: branchpointer
Version: 1.18.0
Depends: caret, R(>= 3.4)
Imports: plyr, kernlab, gbm, stringr, cowplot, ggplot2, biomaRt,
Biostrings, parallel, utils, stats,
BSgenome.Hsapiens.UCSC.hg38, rtracklayer, GenomicRanges,
GenomeInfoDb, IRanges, S4Vectors, data.table
Suggests: knitr, BiocStyle
License: BSD_3_clause + file LICENSE
MD5sum: 523c92c1f991aca3e1381dacca600018
NeedsCompilation: no
Title: Prediction of intronic splicing branchpoints
Description: Predicts branchpoint probability for sites in intronic
branchpoint windows. Queries can be supplied as intronic
regions; or to evaluate the effects of mutations, SNPs.
biocViews: Software, GenomeAnnotation, GenomicVariation,
MotifAnnotation
Author: Beth Signal
Maintainer: Beth Signal <b.signal@garvan.org.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/branchpointer
git_branch: RELEASE_3_13
git_last_commit: 0e0e947
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/branchpointer_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/branchpointer_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/branchpointer_1.18.0.tgz
vignettes: vignettes/branchpointer/inst/doc/branchpointer.pdf
vignetteTitles: Using Branchpointer for annotation of intronic human
splicing branchpoints
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/branchpointer/inst/doc/branchpointer.R
dependencyCount: 147
Package: breakpointR
Version: 1.10.0
Depends: R (>= 3.5), GenomicRanges, cowplot, breakpointRdata
Imports: methods, utils, grDevices, stats, S4Vectors, GenomeInfoDb (>=
1.12.3), IRanges, Rsamtools, GenomicAlignments, ggplot2,
BiocGenerics, gtools, doParallel, foreach
Suggests: knitr, BiocStyle, testthat
License: file LICENSE
MD5sum: 6b5ba34d042d0139adcc8a59576b75b7
NeedsCompilation: no
Title: Find breakpoints in Strand-seq data
Description: This package implements functions for finding breakpoints,
plotting and export of Strand-seq data.
biocViews: Software, Sequencing, DNASeq, SingleCell, Coverage
Author: David Porubsky, Ashley Sanders, Aaron Taudt
Maintainer: David Porubsky <david.porubsky@gmail.com>
URL: https://github.com/daewoooo/BreakPointR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/breakpointR
git_branch: RELEASE_3_13
git_last_commit: c1d2077
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/breakpointR_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/breakpointR_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/breakpointR_1.10.0.tgz
vignettes: vignettes/breakpointR/inst/doc/breakpointR.pdf
vignetteTitles: How to use breakpointR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/breakpointR/inst/doc/breakpointR.R
dependencyCount: 74
Package: brendaDb
Version: 1.6.0
Imports: dplyr, Rcpp, tibble, stringr, magrittr, purrr, BiocParallel,
crayon, utils, tidyr, curl, xml2, grDevices, rlang,
BiocFileCache, rappdirs
LinkingTo: Rcpp
Suggests: testthat, BiocStyle, knitr, rmarkdown, devtools
License: MIT + file LICENSE
MD5sum: 681ff31d687d330e1ef168ce234e09a5
NeedsCompilation: yes
Title: The BRENDA Enzyme Database
Description: R interface for importing and analyzing enzyme information
from the BRENDA database.
biocViews: ThirdPartyClient, Annotation, DataImport
Author: Yi Zhou [aut, cre] (<https://orcid.org/0000-0003-0969-3993>)
Maintainer: Yi Zhou <yi.zhou@uga.edu>
URL: https://github.com/y1zhou/brendaDb
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/y1zhou/brendaDb/issues
git_url: https://git.bioconductor.org/packages/brendaDb
git_branch: RELEASE_3_13
git_last_commit: afc5e0a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/brendaDb_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/brendaDb_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/brendaDb_1.6.0.tgz
vignettes: vignettes/brendaDb/inst/doc/brendaDb.html
vignetteTitles: brendaDb
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/brendaDb/inst/doc/brendaDb.R
dependencyCount: 60
Package: BRGenomics
Version: 1.4.0
Depends: R (>= 4.0), rtracklayer, GenomeInfoDb, S4Vectors
Imports: GenomicRanges, parallel, IRanges, stats, Rsamtools,
GenomicAlignments, DESeq2, SummarizedExperiment, utils, methods
Suggests: BiocStyle, knitr, rmarkdown, testthat, apeglm, remotes,
ggplot2, reshape2, Biostrings
License: Artistic-2.0
Archs: i386, x64
MD5sum: 90909e392a5ced0c93fcaed7b527b3e3
NeedsCompilation: no
Title: Tools for the Efficient Analysis of High-Resolution Genomics
Data
Description: This package provides useful and efficient utilites for
the analysis of high-resolution genomic data using standard
Bioconductor methods and classes. BRGenomics is feature-rich
and simplifies a number of post-alignment processing steps and
data handling. Emphasis is on efficient analysis of multiple
datasets, with support for normalization and blacklisting.
Included are functions for: spike-in normalizing data;
generating basepair-resolution readcounts and coverage data
(e.g. for heatmaps); importing and processing bam files (e.g.
for conversion to bigWig files); generating
metaplots/metaprofiles (bootstrapped mean profiles) with
confidence intervals; conveniently calling DESeq2 without using
sample-blind estimates of genewise dispersion; among other
features.
biocViews: Software, DataImport, Sequencing, Coverage, RNASeq, ATACSeq,
ChIPSeq, Transcription, GeneRegulation, GeneExpression,
Normalization
Author: Mike DeBerardine [aut, cre]
Maintainer: Mike DeBerardine <mike.deberardine@gmail.com>
URL: https://mdeber.github.io
VignetteBuilder: knitr
BugReports: https://github.com/mdeber/BRGenomics/issues
git_url: https://git.bioconductor.org/packages/BRGenomics
git_branch: RELEASE_3_13
git_last_commit: b006074
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BRGenomics_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BRGenomics_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BRGenomics_1.4.0.tgz
vignettes:
vignettes/BRGenomics/inst/doc/AnalyzingMultipleDatasets.html,
vignettes/BRGenomics/inst/doc/DESeq2WithGlobalPerturbations.html,
vignettes/BRGenomics/inst/doc/GettingStarted.html,
vignettes/BRGenomics/inst/doc/ImportingModifyingAnnotations.html,
vignettes/BRGenomics/inst/doc/ImportingProcessingData.html,
vignettes/BRGenomics/inst/doc/Overview.html,
vignettes/BRGenomics/inst/doc/ProfilePlotsAndBootstrapping.html,
vignettes/BRGenomics/inst/doc/SequenceExtraction.html,
vignettes/BRGenomics/inst/doc/SignalCounting.html,
vignettes/BRGenomics/inst/doc/SpikeInNormalization.html
vignetteTitles: Analyzing Multiple Datasets, DESeq2 with Global
Perturbations, Getting Started, Importing and Modifying
Annotations, Importing and Processing Data, Overview, Profile
Plots and Bootstrapping, Sequence Extraction, Signal Counting,
Spike-in Normalization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BRGenomics/inst/doc/AnalyzingMultipleDatasets.R,
vignettes/BRGenomics/inst/doc/DESeq2WithGlobalPerturbations.R,
vignettes/BRGenomics/inst/doc/GettingStarted.R,
vignettes/BRGenomics/inst/doc/ImportingModifyingAnnotations.R,
vignettes/BRGenomics/inst/doc/ImportingProcessingData.R,
vignettes/BRGenomics/inst/doc/ProfilePlotsAndBootstrapping.R,
vignettes/BRGenomics/inst/doc/SequenceExtraction.R,
vignettes/BRGenomics/inst/doc/SignalCounting.R,
vignettes/BRGenomics/inst/doc/SpikeInNormalization.R
dependencyCount: 101
Package: bridge
Version: 1.56.0
Depends: R (>= 1.9.0), rama
License: GPL (>= 2)
MD5sum: 455738fee88083a39b6881c1e6191c6b
NeedsCompilation: yes
Title: Bayesian Robust Inference for Differential Gene Expression
Description: Test for differentially expressed genes with microarray
data. This package can be used with both cDNA microarrays or
Affymetrix chip. The packge fits a robust Bayesian hierarchical
model for testing for differential expression. Outliers are
modeled explicitly using a $t$-distribution. The model includes
an exchangeable prior for the variances which allow different
variances for the genes but still shrink extreme empirical
variances. Our model can be used for testing for differentially
expressed genes among multiple samples, and can distinguish
between the different possible patterns of differential
expression when there are three or more samples. Parameter
estimation is carried out using a novel version of Markov Chain
Monte Carlo that is appropriate when the model puts mass on
subspaces of the full parameter space.
biocViews: Microarray,OneChannel,TwoChannel,DifferentialExpression
Author: Raphael Gottardo
Maintainer: Raphael Gottardo <raph@stat.ubc.ca>
git_url: https://git.bioconductor.org/packages/bridge
git_branch: RELEASE_3_13
git_last_commit: f04b4aa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bridge_1.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bridge_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bridge_1.56.0.tgz
vignettes: vignettes/bridge/inst/doc/bridge.pdf
vignetteTitles: bridge Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bridge/inst/doc/bridge.R
dependencyCount: 1
Package: BridgeDbR
Version: 2.2.1
Depends: R (>= 3.3.0), rJava
Imports: curl
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: AGPL-3
Archs: i386, x64
MD5sum: 4bcfb4daf5033d9c0224f38445e73c6e
NeedsCompilation: no
Title: Code for using BridgeDb identifier mapping framework from within
R
Description: Use BridgeDb functions and load identifier mapping
databases in R. It uses GitHub, Zenodo, and Figshare if you use
this package to download identifier mappings files.
biocViews: Software, Annotation, Metabolomics, Cheminformatics
Author: Christ Leemans <christleemans@gmail.com>, Egon Willighagen
<egon.willighagen@gmail.com>, Anwesha Bohler
<anweshabohler@gmail.com>, Lars Eijssen
<l.eijssen@maastrichtuniversity.nl>
Maintainer: Egon Willighagen <egon.willighagen@gmail.com>
URL: https://github.com/bridgedb/BridgeDbR
VignetteBuilder: knitr
BugReports: https://github.com/bridgedb/BridgeDbR/issues
git_url: https://git.bioconductor.org/packages/BridgeDbR
git_branch: RELEASE_3_13
git_last_commit: c49fd62
git_last_commit_date: 2021-04-26
Date/Publication: 2021-05-25
source.ver: src/contrib/BridgeDbR_2.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BridgeDbR_2.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/BridgeDbR_2.2.1.tgz
vignettes: vignettes/BridgeDbR/inst/doc/tutorial.html
vignetteTitles: Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R
dependencyCount: 3
Package: BrowserViz
Version: 2.14.1
Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0)
Imports: methods, BiocGenerics
Suggests: RUnit, BiocStyle, knitr, rmarkdown
License: GPL-2
MD5sum: 72c590ceea1534446a59a3b33b9395e0
NeedsCompilation: no
Title: BrowserViz: interactive R/browser graphics using websockets and
JSON
Description: Interactvive graphics in a web browser from R, using
websockets and JSON.
biocViews: Visualization, ThirdPartyClient
Author: Paul Shannon
Maintainer: Paul Shannon <pshannon@systemsbiology.org>
URL: https://paul-shannon.github.io/BrowserViz/
VignetteBuilder: knitr
BugReports: https://github.com/paul-shannon/BrowserViz/issues
git_url: https://git.bioconductor.org/packages/BrowserViz
git_branch: RELEASE_3_13
git_last_commit: 52e8d96
git_last_commit_date: 2021-09-14
Date/Publication: 2021-09-16
source.ver: src/contrib/BrowserViz_2.14.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BrowserViz_2.14.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/BrowserViz_2.14.1.tgz
vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.html
vignetteTitles: "BrowserViz: support programmatic access to javascript
apps running in your web browser"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R
dependsOnMe: igvR, RCyjs
dependencyCount: 14
Package: BSgenome
Version: 1.60.0
Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>=
0.17.28), IRanges (>= 2.13.16), GenomeInfoDb (>= 1.25.6),
GenomicRanges (>= 1.31.10), Biostrings (>= 2.47.6), rtracklayer
(>= 1.39.7)
Imports: methods, utils, stats, matrixStats, BiocGenerics, S4Vectors,
IRanges, XVector (>= 0.29.3), GenomeInfoDb, GenomicRanges,
Biostrings, Rsamtools, rtracklayer
Suggests: BiocManager, Biobase, BSgenome.Celegans.UCSC.ce2,
BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Hsapiens.UCSC.hg38.masked,
BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5,
BSgenome.Scerevisiae.UCSC.sacCer1,
BSgenome.Hsapiens.NCBI.GRCh38,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, hgu95av2probe, RUnit
License: Artistic-2.0
MD5sum: 1242d18c41a3d3d709938179e561ba85
NeedsCompilation: no
Title: Software infrastructure for efficient representation of full
genomes and their SNPs
Description: Infrastructure shared by all the Biostrings-based genome
data packages.
biocViews: Genetics, Infrastructure, DataRepresentation,
SequenceMatching, Annotation, SNP
Author: Hervé Pagès
Maintainer: H. Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/BSgenome
BugReports: https://github.com/Bioconductor/BSgenome/issues
git_url: https://git.bioconductor.org/packages/BSgenome
git_branch: RELEASE_3_13
git_last_commit: 6643064
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BSgenome_1.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BSgenome_1.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BSgenome_1.60.0.tgz
vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf,
vignettes/BSgenome/inst/doc/GenomeSearching.pdf
vignetteTitles: How to forge a BSgenome data package, Efficient genome
searching with Biostrings and the BSgenome data packages
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BSgenome/inst/doc/BSgenomeForge.R,
vignettes/BSgenome/inst/doc/GenomeSearching.R
dependsOnMe: ChIPanalyser, GOTHiC, HelloRanges, MEDIPS, periodicDNA,
REDseq, rGADEM, VarCon, BSgenome.Alyrata.JGI.v1,
BSgenome.Amellifera.BeeBase.assembly4,
BSgenome.Amellifera.NCBI.AmelHAv3.1,
BSgenome.Amellifera.UCSC.apiMel2,
BSgenome.Amellifera.UCSC.apiMel2.masked,
BSgenome.Aofficinalis.NCBI.V1,
BSgenome.Athaliana.TAIR.04232008,
BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3,
BSgenome.Btaurus.UCSC.bosTau3.masked,
BSgenome.Btaurus.UCSC.bosTau4,
BSgenome.Btaurus.UCSC.bosTau4.masked,
BSgenome.Btaurus.UCSC.bosTau6,
BSgenome.Btaurus.UCSC.bosTau6.masked,
BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9,
BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10,
BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2,
BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2,
BSgenome.Cfamiliaris.UCSC.canFam2.masked,
BSgenome.Cfamiliaris.UCSC.canFam3,
BSgenome.Cfamiliaris.UCSC.canFam3.masked,
BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Creinhardtii.JGI.v5.6,
BSgenome.Dmelanogaster.UCSC.dm2,
BSgenome.Dmelanogaster.UCSC.dm2.masked,
BSgenome.Dmelanogaster.UCSC.dm3,
BSgenome.Dmelanogaster.UCSC.dm3.masked,
BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10,
BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5,
BSgenome.Drerio.UCSC.danRer5.masked,
BSgenome.Drerio.UCSC.danRer6,
BSgenome.Drerio.UCSC.danRer6.masked,
BSgenome.Drerio.UCSC.danRer7,
BSgenome.Drerio.UCSC.danRer7.masked,
BSgenome.Dvirilis.Ensembl.dvircaf1,
BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1,
BSgenome.Gaculeatus.UCSC.gasAcu1.masked,
BSgenome.Ggallus.UCSC.galGal3,
BSgenome.Ggallus.UCSC.galGal3.masked,
BSgenome.Ggallus.UCSC.galGal4,
BSgenome.Ggallus.UCSC.galGal4.masked,
BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6,
BSgenome.Hsapiens.1000genomes.hs37d5,
BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.UCSC.hg17,
BSgenome.Hsapiens.UCSC.hg17.masked,
BSgenome.Hsapiens.UCSC.hg18,
BSgenome.Hsapiens.UCSC.hg18.masked,
BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg19.masked,
BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Hsapiens.UCSC.hg38.dbSNP151.major,
BSgenome.Hsapiens.UCSC.hg38.dbSNP151.minor,
BSgenome.Hsapiens.UCSC.hg38.masked,
BSgenome.Mdomestica.UCSC.monDom5,
BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfuro.UCSC.musFur1,
BSgenome.Mmulatta.UCSC.rheMac10,
BSgenome.Mmulatta.UCSC.rheMac2,
BSgenome.Mmulatta.UCSC.rheMac2.masked,
BSgenome.Mmulatta.UCSC.rheMac3,
BSgenome.Mmulatta.UCSC.rheMac3.masked,
BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10,
BSgenome.Mmusculus.UCSC.mm10.masked,
BSgenome.Mmusculus.UCSC.mm8,
BSgenome.Mmusculus.UCSC.mm8.masked,
BSgenome.Mmusculus.UCSC.mm9,
BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7,
BSgenome.Ppaniscus.UCSC.panPan1,
BSgenome.Ppaniscus.UCSC.panPan2,
BSgenome.Ptroglodytes.UCSC.panTro2,
BSgenome.Ptroglodytes.UCSC.panTro2.masked,
BSgenome.Ptroglodytes.UCSC.panTro3,
BSgenome.Ptroglodytes.UCSC.panTro3.masked,
BSgenome.Ptroglodytes.UCSC.panTro5,
BSgenome.Ptroglodytes.UCSC.panTro6,
BSgenome.Rnorvegicus.UCSC.rn4,
BSgenome.Rnorvegicus.UCSC.rn4.masked,
BSgenome.Rnorvegicus.UCSC.rn5,
BSgenome.Rnorvegicus.UCSC.rn5.masked,
BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7,
BSgenome.Scerevisiae.UCSC.sacCer1,
BSgenome.Scerevisiae.UCSC.sacCer2,
BSgenome.Scerevisiae.UCSC.sacCer3,
BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3,
BSgenome.Sscrofa.UCSC.susScr3.masked,
BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1,
BSgenome.Tguttata.UCSC.taeGut1.masked,
BSgenome.Tguttata.UCSC.taeGut2,
BSgenome.Vvinifera.URGI.IGGP12Xv0,
BSgenome.Vvinifera.URGI.IGGP12Xv2,
BSgenome.Vvinifera.URGI.IGGP8X,
SNPlocs.Hsapiens.dbSNP.20120608,
SNPlocs.Hsapiens.dbSNP141.GRCh38,
SNPlocs.Hsapiens.dbSNP142.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP151.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP141.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, leeBamViews, annotation
importsMe: AllelicImbalance, appreci8R, ATACseqQC, atSNP, BEAT, bsseq,
BUSpaRse, CAGEr, chromVAR, cleanUpdTSeq, CRISPRseek,
crisprseekplus, diffHic, dpeak, enrichTF, esATAC, EventPointer,
FRASER, gcapc, genomation, GenVisR, ggbio, gmapR, GreyListChIP,
GUIDEseq, Gviz, hiAnnotator, InPAS, IsoformSwitchAnalyzeR,
MADSEQ, methrix, MethylSeekR, MMDiff2, motifbreakR,
motifmatchr, msgbsR, multicrispr, MungeSumstats, musicatk,
MutationalPatterns, ORFik, PING, pipeFrame, podkat, qsea,
QuasR, R453Plus1Toolbox, RareVariantVis, RCAS, regioneR, REMP,
Repitools, ribosomeProfilingQC, RNAmodR, scmeth, SCOPE,
SigsPack, SingleMoleculeFootprinting, SparseSignatures, TAPseq,
TFBSTools, trena, tRNAscanImport, Ularcirc, UMI4Cats,
VariantAnnotation, VariantFiltering, VariantTools, XNAString,
BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4,
BSgenome.Amellifera.NCBI.AmelHAv3.1,
BSgenome.Amellifera.UCSC.apiMel2,
BSgenome.Amellifera.UCSC.apiMel2.masked,
BSgenome.Aofficinalis.NCBI.V1,
BSgenome.Athaliana.TAIR.04232008,
BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3,
BSgenome.Btaurus.UCSC.bosTau3.masked,
BSgenome.Btaurus.UCSC.bosTau4,
BSgenome.Btaurus.UCSC.bosTau4.masked,
BSgenome.Btaurus.UCSC.bosTau6,
BSgenome.Btaurus.UCSC.bosTau6.masked,
BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9,
BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10,
BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2,
BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2,
BSgenome.Cfamiliaris.UCSC.canFam2.masked,
BSgenome.Cfamiliaris.UCSC.canFam3,
BSgenome.Cfamiliaris.UCSC.canFam3.masked,
BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Creinhardtii.JGI.v5.6,
BSgenome.Dmelanogaster.UCSC.dm2,
BSgenome.Dmelanogaster.UCSC.dm2.masked,
BSgenome.Dmelanogaster.UCSC.dm3,
BSgenome.Dmelanogaster.UCSC.dm3.masked,
BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10,
BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5,
BSgenome.Drerio.UCSC.danRer5.masked,
BSgenome.Drerio.UCSC.danRer6,
BSgenome.Drerio.UCSC.danRer6.masked,
BSgenome.Drerio.UCSC.danRer7,
BSgenome.Drerio.UCSC.danRer7.masked,
BSgenome.Dvirilis.Ensembl.dvircaf1,
BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1,
BSgenome.Gaculeatus.UCSC.gasAcu1.masked,
BSgenome.Ggallus.UCSC.galGal3,
BSgenome.Ggallus.UCSC.galGal3.masked,
BSgenome.Ggallus.UCSC.galGal4,
BSgenome.Ggallus.UCSC.galGal4.masked,
BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6,
BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.UCSC.hg17,
BSgenome.Hsapiens.UCSC.hg17.masked,
BSgenome.Hsapiens.UCSC.hg18,
BSgenome.Hsapiens.UCSC.hg18.masked,
BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg19.masked,
BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Hsapiens.UCSC.hg38.masked,
BSgenome.Mdomestica.UCSC.monDom5,
BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfuro.UCSC.musFur1,
BSgenome.Mmulatta.UCSC.rheMac10,
BSgenome.Mmulatta.UCSC.rheMac2,
BSgenome.Mmulatta.UCSC.rheMac2.masked,
BSgenome.Mmulatta.UCSC.rheMac3,
BSgenome.Mmulatta.UCSC.rheMac3.masked,
BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10,
BSgenome.Mmusculus.UCSC.mm10.masked,
BSgenome.Mmusculus.UCSC.mm8,
BSgenome.Mmusculus.UCSC.mm8.masked,
BSgenome.Mmusculus.UCSC.mm9,
BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7,
BSgenome.Ppaniscus.UCSC.panPan1,
BSgenome.Ppaniscus.UCSC.panPan2,
BSgenome.Ptroglodytes.UCSC.panTro2,
BSgenome.Ptroglodytes.UCSC.panTro2.masked,
BSgenome.Ptroglodytes.UCSC.panTro3,
BSgenome.Ptroglodytes.UCSC.panTro3.masked,
BSgenome.Ptroglodytes.UCSC.panTro5,
BSgenome.Ptroglodytes.UCSC.panTro6,
BSgenome.Rnorvegicus.UCSC.rn4,
BSgenome.Rnorvegicus.UCSC.rn4.masked,
BSgenome.Rnorvegicus.UCSC.rn5,
BSgenome.Rnorvegicus.UCSC.rn5.masked,
BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7,
BSgenome.Scerevisiae.UCSC.sacCer1,
BSgenome.Scerevisiae.UCSC.sacCer2,
BSgenome.Scerevisiae.UCSC.sacCer3,
BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3,
BSgenome.Sscrofa.UCSC.susScr3.masked,
BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1,
BSgenome.Tguttata.UCSC.taeGut1.masked,
BSgenome.Tguttata.UCSC.taeGut2,
BSgenome.Vvinifera.URGI.IGGP12Xv0,
BSgenome.Vvinifera.URGI.IGGP12Xv2,
BSgenome.Vvinifera.URGI.IGGP8X, fitCons.UCSC.hg19,
MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5,
MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5,
MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5,
MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5,
MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5,
MafDb.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38,
MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19,
MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38,
MafH5.gnomAD.v3.1.1.GRCh38, phastCons100way.UCSC.hg19,
phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38,
SNPlocs.Hsapiens.dbSNP.20120608,
SNPlocs.Hsapiens.dbSNP141.GRCh38,
SNPlocs.Hsapiens.dbSNP142.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP151.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP141.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, GenomicDistributionsData,
ActiveDriverWGS, deconstructSigs, ICAMS, simMP
suggestsMe: bambu, Biostrings, biovizBase, ChIPpeakAnno, chipseq,
easyRNASeq, eisaR, GeneRegionScan, GenomeInfoDb,
GenomicAlignments, GenomicFeatures, GenomicRanges, maftools,
metaseqR2, MiRaGE, PWMEnrich, QDNAseq, recoup, rtracklayer,
sitadela, SNPlocs.Hsapiens.dbSNP.20101109, gkmSVM, sigminer,
Signac
dependencyCount: 44
Package: bsseq
Version: 1.28.0
Depends: R (>= 4.0), methods, BiocGenerics, GenomicRanges (>= 1.41.5),
SummarizedExperiment (>= 1.19.5)
Imports: IRanges (>= 2.23.9), GenomeInfoDb, scales, stats, graphics,
Biobase, locfit, gtools, data.table (>= 1.11.8), S4Vectors (>=
0.27.12), R.utils (>= 2.0.0), DelayedMatrixStats (>= 1.5.2),
permute, limma, DelayedArray (>= 0.15.16), Rcpp, BiocParallel,
BSgenome, Biostrings, utils, HDF5Array (>= 1.19.11), rhdf5
LinkingTo: Rcpp, beachmat
Suggests: testthat, bsseqData, BiocStyle, rmarkdown, knitr, Matrix,
doParallel, rtracklayer, BSgenome.Hsapiens.UCSC.hg38, beachmat
(>= 1.5.2), BatchJobs
License: Artistic-2.0
MD5sum: 29efb76e1e573f8748f4bd357a1308cd
NeedsCompilation: yes
Title: Analyze, manage and store bisulfite sequencing data
Description: A collection of tools for analyzing and visualizing
bisulfite sequencing data.
biocViews: DNAMethylation
Author: Kasper Daniel Hansen [aut, cre], Peter Hickey [aut]
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
URL: https://github.com/kasperdanielhansen/bsseq
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/kasperdanielhansen/bsseq/issues
git_url: https://git.bioconductor.org/packages/bsseq
git_branch: RELEASE_3_13
git_last_commit: 57084fa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bsseq_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bsseq_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bsseq_1.28.0.tgz
vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.html,
vignettes/bsseq/inst/doc/bsseq.html
vignetteTitles: Analyzing WGBS data with bsseq, bsseq User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bsseq/inst/doc/bsseq_analysis.R,
vignettes/bsseq/inst/doc/bsseq.R
dependsOnMe: biscuiteer, dmrseq, DSS, bsseqData
importsMe: DMRcate, MethCP, methylCC, methylSig, MIRA, NanoMethViz,
scmeth, tcgaWGBSData.hg19
suggestsMe: methrix, tissueTreg
dependencyCount: 72
Package: BubbleTree
Version: 2.22.0
Depends: R (>= 3.5), IRanges, GenomicRanges, plyr, dplyr, magrittr
Imports: BiocGenerics (>= 0.31.6), BiocStyle, Biobase, ggplot2,
WriteXLS, gtools, RColorBrewer, limma, grid, gtable, gridExtra,
biovizBase, e1071, methods, grDevices, stats, utils
Suggests: knitr, rmarkdown
License: LGPL (>= 3)
Archs: i386, x64
MD5sum: 6ef3a6161050c9136bf40f3e29f8ae66
NeedsCompilation: no
Title: BubbleTree: an intuitive visualization to elucidate tumoral
aneuploidy and clonality in somatic mosaicism using next
generation sequencing data
Description: CNV analysis in groups of tumor samples.
biocViews: CopyNumberVariation, Software, Sequencing, Coverage
Author: Wei Zhu <zhuw@medimmune.com>, Michael Kuziora
<kuzioram@medimmune.com>, Todd Creasy <creasyt@medimmune.com>,
Brandon Higgs <higgsb@medimmune.com>
Maintainer: Todd Creasy <creasyt@medimmune.com>, Wei Zhu
<weizhu365@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BubbleTree
git_branch: RELEASE_3_13
git_last_commit: d94bc48
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BubbleTree_2.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BubbleTree_2.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BubbleTree_2.22.0.tgz
vignettes: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html
vignetteTitles: BubbleTree Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.R
dependencyCount: 154
Package: BufferedMatrix
Version: 1.56.0
Depends: R (>= 2.6.0), methods
License: LGPL (>= 2)
MD5sum: a135b71cb4866118fdc79effbfb94d8b
NeedsCompilation: yes
Title: A matrix data storage object held in temporary files
Description: A tabular style data object where most data is stored
outside main memory. A buffer is used to speed up access to
data.
biocViews: Infrastructure
Author: Ben Bolstad <bmb@bmbolstad.com>
Maintainer: Ben Bolstad <bmb@bmbolstad.com>
URL: https://github.com/bmbolstad/BufferedMatrix
git_url: https://git.bioconductor.org/packages/BufferedMatrix
git_branch: RELEASE_3_13
git_last_commit: 64ce6a6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BufferedMatrix_1.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BufferedMatrix_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BufferedMatrix_1.56.0.tgz
vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf
vignetteTitles: BufferedMatrix: Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.R
dependsOnMe: BufferedMatrixMethods
linksToMe: BufferedMatrixMethods
dependencyCount: 1
Package: BufferedMatrixMethods
Version: 1.56.0
Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods
LinkingTo: BufferedMatrix
Suggests: affyio, affy
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 86cb4bffae827ad2c6753c978acecf0f
NeedsCompilation: yes
Title: Microarray Data related methods that utlize BufferedMatrix
objects
Description: Microarray analysis methods that use BufferedMatrix
objects
biocViews: Infrastructure
Author: Ben Bolstad <bmb@bmbolstad.com>
Maintainer: Ben Bolstad <bmb@bmbolstad.com>
URL: https://github.bom/bmbolstad/BufferedMatrixMethods
git_url: https://git.bioconductor.org/packages/BufferedMatrixMethods
git_branch: RELEASE_3_13
git_last_commit: e312294
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BufferedMatrixMethods_1.56.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/BufferedMatrixMethods_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BufferedMatrixMethods_1.56.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 2
Package: BUMHMM
Version: 1.16.0
Depends: R (>= 3.4)
Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment,
Biostrings, IRanges
Suggests: testthat, knitr, BiocStyle
License: GPL-3
Archs: i386, x64
MD5sum: ff6209c8680402aa8564ec0449ee2995
NeedsCompilation: no
Title: Computational pipeline for computing probability of modification
from structure probing experiment data
Description: This is a probabilistic modelling pipeline for computing
per- nucleotide posterior probabilities of modification from
the data collected in structure probing experiments. The model
supports multiple experimental replicates and empirically
corrects coverage- and sequence-dependent biases. The model
utilises the measure of a "drop-off rate" for each nucleotide,
which is compared between replicates through a log-ratio (LDR).
The LDRs between control replicates define a null distribution
of variability in drop-off rate observed by chance and LDRs
between treatment and control replicates gets compared to this
distribution. Resulting empirical p-values (probability of
being "drawn" from the null distribution) are used as
observations in a Hidden Markov Model with a Beta-Uniform
Mixture model used as an emission model. The resulting
posterior probabilities indicate the probability of a
nucleotide of having being modified in a structure probing
experiment.
biocViews: ImmunoOncology, GeneticVariability, Transcription,
GeneExpression, GeneRegulation, Coverage, Genetics,
StructuralPrediction, Transcriptomics, Bayesian,
Classification, FeatureExtraction, HiddenMarkovModel,
Regression, RNASeq, Sequencing
Author: Alina Selega (alina.selega@gmail.com), Sander Granneman, Guido
Sanguinetti
Maintainer: Alina Selega <alina.selega@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BUMHMM
git_branch: RELEASE_3_13
git_last_commit: 96ebaad
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BUMHMM_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BUMHMM_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BUMHMM_1.16.0.tgz
vignettes: vignettes/BUMHMM/inst/doc/BUMHMM.pdf
vignetteTitles: An Introduction to the BUMHMM pipeline
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BUMHMM/inst/doc/BUMHMM.R
dependencyCount: 99
Package: bumphunter
Version: 1.34.0
Depends: R (>= 3.5), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23),
GenomeInfoDb, GenomicRanges, foreach, iterators, methods,
parallel, locfit
Imports: matrixStats, limma, doRNG, BiocGenerics, utils,
GenomicFeatures, AnnotationDbi, stats
Suggests: testthat, RUnit, doParallel, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: Artistic-2.0
MD5sum: 50b1b1a560f8a69b5cd3afd14a8466c9
NeedsCompilation: no
Title: Bump Hunter
Description: Tools for finding bumps in genomic data
biocViews: DNAMethylation, Epigenetics, Infrastructure,
MultipleComparison, ImmunoOncology
Author: Rafael A. Irizarry [cre, aut], Martin Aryee [aut], Kasper
Daniel Hansen [aut], Hector Corrada Bravo [aut], Shan Andrews
[ctb], Andrew E. Jaffe [ctb], Harris Jaffee [ctb], Leonardo
Collado-Torres [ctb]
Maintainer: Rafael A. Irizarry <rafa@jimmy.harvard.edu>
URL: https://github.com/rafalab/bumphunter
git_url: https://git.bioconductor.org/packages/bumphunter
git_branch: RELEASE_3_13
git_last_commit: 905ec98
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/bumphunter_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/bumphunter_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/bumphunter_1.34.0.tgz
vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf
vignetteTitles: The bumphunter user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bumphunter/inst/doc/bumphunter.R
dependsOnMe: minfi
importsMe: brainflowprobes, DAMEfinder, derfinder, dmrseq, epivizr,
methylCC, rnaEditr, GenomicState, recountWorkflow
suggestsMe: bigmelon, derfinderPlot, epivizrData, regionReport
dependencyCount: 103
Package: BumpyMatrix
Version: 1.0.1
Imports: utils, methods, Matrix, S4Vectors, IRanges
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: 1eb71c420fb9ac68e0ea45c38518dc39
NeedsCompilation: no
Title: Bumpy Matrix of Non-Scalar Objects
Description: Implements the BumpyMatrix class and several subclasses
for holding non-scalar objects in each entry of the matrix.
This is akin to a ragged array but the raggedness is in the
third dimension, much like a bumpy surface - hence the name. Of
particular interest is the BumpyDataFrameMatrix, where each
entry is a Bioconductor data frame. This allows us to naturally
represent multivariate data in a format that is compatible with
two-dimensional containers like the SummarizedExperiment and
MultiAssayExperiment objects.
biocViews: Software, Infrastructure, DataRepresentation
Author: Aaron Lun [aut, cre], Genentech, Inc. [cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://bioconductor.org/packages/BumpyMatrix
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/BumpyMatrix/issues
git_url: https://git.bioconductor.org/packages/BumpyMatrix
git_branch: RELEASE_3_13
git_last_commit: 24a8f24
git_last_commit_date: 2021-07-03
Date/Publication: 2021-07-04
source.ver: src/contrib/BumpyMatrix_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BumpyMatrix_1.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/BumpyMatrix_1.0.1.tgz
vignettes: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.html
vignetteTitles: The BumpyMatrix class
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.R
importsMe: MouseGastrulationData
suggestsMe: SpatialExperiment
dependencyCount: 13
Package: BUS
Version: 1.48.0
Depends: R (>= 2.3.0), minet
Imports: stats, infotheo
License: GPL-3
MD5sum: fcf872999125cf981e9112953fc1d1a0
NeedsCompilation: yes
Title: Gene network reconstruction
Description: This package can be used to compute associations among
genes (gene-networks) or between genes and some external traits
(i.e. clinical).
biocViews: Preprocessing
Author: Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and
Christine Nardini
Maintainer: Yuanhua Liu <liuyuanhua@picb.ac.cn>
git_url: https://git.bioconductor.org/packages/BUS
git_branch: RELEASE_3_13
git_last_commit: 4069c62
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BUS_1.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BUS_1.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BUS_1.48.0.tgz
vignettes: vignettes/BUS/inst/doc/bus.pdf
vignetteTitles: bus.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BUS/inst/doc/bus.R
dependencyCount: 3
Package: BUScorrect
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: gplots, methods, grDevices, stats, SummarizedExperiment
Suggests: BiocStyle, knitr, RUnit, BiocGenerics
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 769fd235046b9b7840ab1ce165099982
NeedsCompilation: yes
Title: Batch Effects Correction with Unknown Subtypes
Description: High-throughput experimental data are accumulating
exponentially in public databases. However, mining valid
scientific discoveries from these abundant resources is
hampered by technical artifacts and inherent biological
heterogeneity. The former are usually termed "batch effects,"
and the latter is often modelled by "subtypes." The R package
BUScorrect fits a Bayesian hierarchical model, the
Batch-effects-correction-with-Unknown-Subtypes model (BUS), to
correct batch effects in the presence of unknown subtypes. BUS
is capable of (a) correcting batch effects explicitly, (b)
grouping samples that share similar characteristics into
subtypes, (c) identifying features that distinguish subtypes,
and (d) enjoying a linear-order computation complexity.
biocViews: GeneExpression, StatisticalMethod, Bayesian, Clustering,
FeatureExtraction, BatchEffect
Author: Xiangyu Luo <xyluo1991@gmail.com>, Yingying Wei
<yweicuhk@gmail.com>
Maintainer: Xiangyu Luo <xyluo1991@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BUScorrect
git_branch: RELEASE_3_13
git_last_commit: 023b2d8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/BUScorrect_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BUScorrect_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BUScorrect_1.10.0.tgz
vignettes: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.pdf
vignetteTitles: BUScorrect_user_guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.R
dependencyCount: 30
Package: BUSpaRse
Version: 1.6.1
Depends: R (>= 3.6)
Imports: AnnotationDbi, AnnotationFilter, biomaRt, BiocGenerics,
Biostrings, BSgenome, dplyr, ensembldb, GenomeInfoDb,
GenomicFeatures, GenomicRanges, ggplot2, IRanges, magrittr,
Matrix, methods, plyranges, Rcpp, S4Vectors, stats, stringr,
tibble, tidyr, utils, zeallot
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, BH
Suggests: knitr, rmarkdown, testthat, BiocStyle, TENxBUSData,
TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38,
EnsDb.Hsapiens.v86
License: BSD_2_clause + file LICENSE
Archs: i386, x64
MD5sum: a82009b4f7d9aa56416fb5017e081019
NeedsCompilation: yes
Title: kallisto | bustools R utilities
Description: The kallisto | bustools pipeline is a fast and modular set
of tools to convert single cell RNA-seq reads in fastq files
into gene count or transcript compatibility counts (TCC)
matrices for downstream analysis. Central to this pipeline is
the barcode, UMI, and set (BUS) file format. This package
serves the following purposes: First, this package allows users
to manipulate BUS format files as data frames in R and then
convert them into gene count or TCC matrices. Furthermore,
since R and Rcpp code is easier to handle than pure C++ code,
users are encouraged to tweak the source code of this package
to experiment with new uses of BUS format and different ways to
convert the BUS file into gene count matrix. Second, this
package can conveniently generate files required to generate
gene count matrices for spliced and unspliced transcripts for
RNA velocity. Here biotypes can be filtered and scaffolds and
haplotypes can be removed, and the filtered transcriptome can
be extracted and written to disk. Third, this package
implements utility functions to get transcripts and associated
genes required to convert BUS files to gene count matrices, to
write the transcript to gene information in the format required
by bustools, and to read output of bustools into R as sparses
matrices.
biocViews: SingleCell, RNASeq, WorkflowStep
Author: Lambda Moses [aut, cre]
(<https://orcid.org/0000-0002-7092-9427>), Lior Pachter [aut,
ths] (<https://orcid.org/0000-0002-9164-6231>)
Maintainer: Lambda Moses <dlu2@caltech.edu>
URL: https://github.com/BUStools/BUSpaRse
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/BUStools/BUSpaRse/issues
git_url: https://git.bioconductor.org/packages/BUSpaRse
git_branch: RELEASE_3_13
git_last_commit: 10db5d4
git_last_commit_date: 2021-06-07
Date/Publication: 2021-06-08
source.ver: src/contrib/BUSpaRse_1.6.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/BUSpaRse_1.6.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/BUSpaRse_1.6.1.tgz
vignettes: vignettes/BUSpaRse/inst/doc/sparse-matrix.html,
vignettes/BUSpaRse/inst/doc/tr2g.html
vignetteTitles: Converting BUS format into sparse matrix, Transcript to
gene
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BUSpaRse/inst/doc/sparse-matrix.R,
vignettes/BUSpaRse/inst/doc/tr2g.R
dependencyCount: 121
Package: CAEN
Version: 1.0.0
Depends: R (>= 4.1)
Imports: stats,PoiClaClu,SummarizedExperiment,methods
Suggests: knitr,rmarkdown
License: GPL-2
Archs: i386, x64
MD5sum: 7edb7daa0585d3aea9201e8c6dc4a349
NeedsCompilation: no
Title: Category encoding method for selecting feature genes for the
classification of single-cell RNA-seq
Description: With the development of high-throughput techniques, more
and more gene expression analysis tend to replace
hybridization-based microarrays with the revolutionary
technology.The novel method encodes the category again by
employing the rank of samples for each gene in each class. We
then consider the correlation coefficient of gene and class
with rank of sample and new rank of category. The highest
correlation coefficient genes are considered as the feature
genes which are most effective to classify the samples.
biocViews: DifferentialExpression, Sequencing, Classification, RNASeq,
ATACSeq, SingleCell, GeneExpression, RIPSeq
Author: Zhou Yan [aut, cre]
Maintainer: Zhou Yan <2160090406@email.szu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CAEN
git_branch: RELEASE_3_13
git_last_commit: da2a7b2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CAEN_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CAEN_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CAEN_1.0.0.tgz
vignettes: vignettes/CAEN/inst/doc/CAEN.html
vignetteTitles: CAEN Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CAEN/inst/doc/CAEN.R
dependencyCount: 27
Package: CAFE
Version: 1.28.0
Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio
Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase
Suggests: RUnit, BiocGenerics, BiocStyle
License: GPL-3
MD5sum: 4bb462d696b914d4fe34d26c6de8a6c0
NeedsCompilation: no
Title: Chromosmal Aberrations Finder in Expression data
Description: Detection and visualizations of gross chromosomal
aberrations using Affymetrix expression microarrays as input
biocViews: GeneExpression, Microarray, OneChannel, GeneSetEnrichment
Author: Sander Bollen
Maintainer: Sander Bollen <sander.h.bollen@gmail.com>
git_url: https://git.bioconductor.org/packages/CAFE
git_branch: RELEASE_3_13
git_last_commit: 29db053
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CAFE_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CAFE_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CAFE_1.28.0.tgz
vignettes: vignettes/CAFE/inst/doc/CAFE-manual.pdf
vignetteTitles: Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CAFE/inst/doc/CAFE-manual.R
dependencyCount: 159
Package: CAGEfightR
Version: 1.12.0
Depends: R (>= 3.5), GenomicRanges (>= 1.30.1), rtracklayer (>=
1.38.2), SummarizedExperiment (>= 1.8.1)
Imports: pryr(>= 0.1.3), assertthat(>= 0.2.0), methods(>= 3.6.3),
Matrix(>= 1.2-12), BiocGenerics(>= 0.24.0), S4Vectors(>=
0.16.0), IRanges(>= 2.12.0), GenomeInfoDb(>= 1.14.0),
GenomicFeatures(>= 1.29.11), GenomicAlignments(>= 1.22.1),
BiocParallel(>= 1.12.0), GenomicFiles(>= 1.14.0), Gviz(>=
1.22.2), InteractionSet(>= 1.9.4), GenomicInteractions(>=
1.15.1)
Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db,
TxDb.Mmusculus.UCSC.mm9.knownGene
License: GPL-3 + file LICENSE
MD5sum: ff430bb22c862b879df3e6dc267cdb40
NeedsCompilation: no
Title: Analysis of Cap Analysis of Gene Expression (CAGE) data using
Bioconductor
Description: CAGE is a widely used high throughput assay for measuring
transcription start site (TSS) activity. CAGEfightR is an
R/Bioconductor package for performing a wide range of common
data analysis tasks for CAGE and 5'-end data in general. Core
functionality includes: import of CAGE TSSs (CTSSs), tag (or
unidirectional) clustering for TSS identification,
bidirectional clustering for enhancer identification,
annotation with transcript and gene models, correlation of TSS
and enhancer expression, calculation of TSS shapes,
quantification of CAGE expression as expression matrices and
genome brower visualization.
biocViews: Software, Transcription, Coverage, GeneExpression,
GeneRegulation, PeakDetection, DataImport, DataRepresentation,
Transcriptomics, Sequencing, Annotation, GenomeBrowsers,
Normalization, Preprocessing, Visualization
Author: Malte Thodberg
Maintainer: Malte Thodberg <maltethodberg@gmail.com>
URL: https://github.com/MalteThodberg/CAGEfightR
VignetteBuilder: knitr
BugReports: https://github.com/MalteThodberg/CAGEfightR/issues
git_url: https://git.bioconductor.org/packages/CAGEfightR
git_branch: RELEASE_3_13
git_last_commit: f776ea4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CAGEfightR_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CAGEfightR_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CAGEfightR_1.12.0.tgz
vignettes:
vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.html
vignetteTitles: Introduction to CAGEfightR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.R
dependsOnMe: CAGEWorkflow
suggestsMe: nanotubes
dependencyCount: 149
Package: CAGEr
Version: 1.34.0
Depends: methods, MultiAssayExperiment, R (>= 3.5.0)
Imports: beanplot, BiocGenerics, BiocParallel, BSgenome, data.table,
DelayedArray, formula.tools, GenomeInfoDb, GenomicAlignments,
GenomicRanges (>= 1.37.16), ggplot2 (>= 2.2.0), gtools, IRanges
(>= 2.18.0), KernSmooth, memoise, plyr, Rsamtools, reshape,
rtracklayer, S4Vectors (>= 0.27.5), som, stringdist, stringi,
SummarizedExperiment, utils, vegan, VGAM
Suggests: BSgenome.Drerio.UCSC.danRer7, DESeq2, FANTOM3and4CAGE,
BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 2f1b075e0872267fc821a90189a3f5f0
NeedsCompilation: no
Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing
data for precise mapping of transcription start sites and
promoterome mining
Description: Preprocessing of CAGE sequencing data, identification and
normalization of transcription start sites and downstream
analysis of transcription start sites clusters (promoters).
biocViews: Preprocessing, Sequencing, Normalization,
FunctionalGenomics, Transcription, GeneExpression, Clustering,
Visualization
Author: Vanja Haberle [aut], Charles Plessy [cre], Damir Baranasic
[ctb], Sarvesh Nikumbh [ctb]
Maintainer: Charles Plessy <charles.plessy@oist.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CAGEr
git_branch: RELEASE_3_13
git_last_commit: 8e7ccda
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CAGEr_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CAGEr_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CAGEr_1.34.0.tgz
vignettes: vignettes/CAGEr/inst/doc/CAGE_Resources.html,
vignettes/CAGEr/inst/doc/CAGEexp.html
vignetteTitles: Use of CAGE resources with CAGEr, CAGEr: an R package
for CAGE data analysis and promoterome mining
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CAGEr/inst/doc/CAGE_Resources.R,
vignettes/CAGEr/inst/doc/CAGEexp.R
suggestsMe: seqPattern
dependencyCount: 100
Package: calm
Version: 1.6.0
Imports: mgcv, stats, graphics
Suggests: knitr, rmarkdown
License: GPL (>=2)
MD5sum: b076f5c4ecb6b8f768816776c22bb254
NeedsCompilation: no
Title: Covariate Assisted Large-scale Multiple testing
Description: Statistical methods for multiple testing with covariate
information. Traditional multiple testing methods only consider
a list of test statistics, such as p-values. Our methods
incorporate the auxiliary information, such as the lengths of
gene coding regions or the minor allele frequencies of SNPs, to
improve power.
biocViews: Bayesian, DifferentialExpression, GeneExpression,
Regression, Microarray, Sequencing, RNASeq, MultipleComparison,
Genetics, ImmunoOncology, Metabolomics, Proteomics,
Transcriptomics
Author: Kun Liang [aut, cre]
Maintainer: Kun Liang <kun.liang@uwaterloo.ca>
VignetteBuilder: knitr
BugReports: https://github.com/k22liang/calm/issues
git_url: https://git.bioconductor.org/packages/calm
git_branch: RELEASE_3_13
git_last_commit: e837e6d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/calm_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/calm_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/calm_1.6.0.tgz
vignettes: vignettes/calm/inst/doc/calm_intro.html
vignetteTitles: Userguide for calm package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/calm/inst/doc/calm_intro.R
dependencyCount: 11
Package: CAMERA
Version: 1.48.0
Depends: R (>= 2.1.0), methods, Biobase, xcms (>= 1.13.5)
Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils,
Hmisc, igraph
Suggests: faahKO, RUnit, BiocGenerics
Enhances: Rmpi, snow
License: GPL (>= 2)
MD5sum: 7b6e0ef39f4c9312bd0ebf80a2d5adef
NeedsCompilation: yes
Title: Collection of annotation related methods for mass spectrometry
data
Description: Annotation of peaklists generated by xcms, rule based
annotation of isotopes and adducts, isotope validation, EIC
correlation based tagging of unknown adducts and fragments
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics
Author: Carsten Kuhl, Ralf Tautenhahn, Hendrik Treutler, Steffen
Neumann {ckuhl|htreutle|sneumann}@ipb-halle.de,
rtautenh@scripps.edu
Maintainer: Steffen Neumann <sneumann@ipb-halle.de>
URL: http://msbi.ipb-halle.de/msbi/CAMERA/
BugReports: https://github.com/sneumann/CAMERA/issues/new
git_url: https://git.bioconductor.org/packages/CAMERA
git_branch: RELEASE_3_13
git_last_commit: 5ad3135
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CAMERA_1.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CAMERA_1.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CAMERA_1.48.0.tgz
vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf,
vignettes/CAMERA/inst/doc/compoundQuantilesVignette.pdf,
vignettes/CAMERA/inst/doc/IsotopeDetectionVignette.pdf
vignetteTitles: Molecule Identification with CAMERA, Atom count
expectations with compoundQuantiles, Isotope pattern validation
with CAMERA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R
dependsOnMe: flagme, IPO, LOBSTAHS, MAIT, metaMS, PtH2O2lipids
suggestsMe: cliqueMS, msPurity, RMassBank, mtbls2
dependencyCount: 125
Package: canceR
Version: 1.26.0
Depends: R (>= 3.4), tcltk, tcltk2, cgdsr
Imports: GSEABase, tkrplot, geNetClassifier, RUnit, Formula, rpart,
survival, Biobase, phenoTest, circlize, plyr, graphics, stats,
utils, grDevices
Suggests: testthat (>= 0.10.0), R.rsp
License: GPL-2
Archs: i386, x64
MD5sum: 3c72ad1b32616cce68a2374ed310db4d
NeedsCompilation: no
Title: A Graphical User Interface for accessing and modeling the Cancer
Genomics Data of MSKCC
Description: The package is user friendly interface based on the cgdsr
and other modeling packages to explore, compare, and analyse
all available Cancer Data (Clinical data, Gene Mutation, Gene
Methylation, Gene Expression, Protein Phosphorylation, Copy
Number Alteration) hosted by the Computational Biology Center
at Memorial-Sloan-Kettering Cancer Center (MSKCC).
biocViews: GUI, GeneExpression, Software
Author: Karim Mezhoud. Nuclear Safety & Security Department. Nuclear
Science Center of Tunisia.
Maintainer: Karim Mezhoud <kmezhoud@gmail.com>
VignetteBuilder: R.rsp
git_url: https://git.bioconductor.org/packages/canceR
git_branch: RELEASE_3_13
git_last_commit: 317c568
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/canceR_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/canceR_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/canceR_1.26.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 158
Package: cancerclass
Version: 1.36.0
Depends: R (>= 2.14.0), Biobase, binom, methods, stats
Suggests: cancerdata
License: GPL 3
MD5sum: 8359a248c09c9c94e4c33824ddf1fa57
NeedsCompilation: yes
Title: Development and validation of diagnostic tests from
high-dimensional molecular data
Description: The classification protocol starts with a feature
selection step and continues with nearest-centroid
classification. The accurarcy of the predictor can be evaluated
using training and test set validation, leave-one-out
cross-validation or in a multiple random validation protocol.
Methods for calculation and visualization of continuous
prediction scores allow to balance sensitivity and specificity
and define a cutoff value according to clinical requirements.
biocViews: Cancer, Microarray, Classification, Visualization
Author: Jan Budczies, Daniel Kosztyla
Maintainer: Daniel Kosztyla <danielkossi@hotmail.com>
git_url: https://git.bioconductor.org/packages/cancerclass
git_branch: RELEASE_3_13
git_last_commit: d4617dd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cancerclass_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cancerclass_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cancerclass_1.36.0.tgz
vignettes: vignettes/cancerclass/inst/doc/vignette_cancerclass.pdf
vignetteTitles: Cancerclass: An R package for development and
validation of diagnostic tests from high-dimensional molecular
data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cancerclass/inst/doc/vignette_cancerclass.R
dependencyCount: 8
Package: CancerInSilico
Version: 2.12.0
Depends: R (>= 3.4), Rcpp
Imports: methods, utils, graphics, stats
LinkingTo: Rcpp, BH
Suggests: testthat, knitr, rmarkdown, BiocStyle, Rtsne, viridis, rgl,
gplots
License: GPL-2
Archs: i386, x64
MD5sum: 7d3c55254bc70d782faf5f0003b14052
NeedsCompilation: yes
Title: An R interface for computational modeling of tumor progression
Description: The CancerInSilico package provides an R interface for
running mathematical models of tumor progresson and generating
gene expression data from the results. This package has the
underlying models implemented in C++ and the output and
analysis features implemented in R.
biocViews: ImmunoOncology, MathematicalBiology, SystemsBiology,
CellBiology, BiomedicalInformatics, GeneExpression, RNASeq,
SingleCell
Author: Thomas D. Sherman, Raymond Cheng, Elana J. Fertig
Maintainer: Thomas D. Sherman <tomsherman159@gmail.com>, Elana J.
Fertig <ejfertig@jhmi.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CancerInSilico
git_branch: RELEASE_3_13
git_last_commit: 1336eb2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CancerInSilico_2.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CancerInSilico_2.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CancerInSilico_2.12.0.tgz
vignettes: vignettes/CancerInSilico/inst/doc/CancerInSilico.html
vignetteTitles: The CancerInSilico Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CancerInSilico/inst/doc/CancerInSilico.R
dependencyCount: 6
Package: CancerSubtypes
Version: 1.18.0
Depends: R (>= 4.0), sigclust, NMF
Imports: iCluster, cluster, impute, limma, ConsensusClusterPlus,
grDevices, survival
Suggests: BiocGenerics, knitr, RTCGA.mRNA, rmarkdown
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 0b28da8a1ba2739fb9f2e7b909336877
NeedsCompilation: no
Title: Cancer subtypes identification, validation and visualization
based on multiple genomic data sets
Description: CancerSubtypes integrates the current common computational
biology methods for cancer subtypes identification and provides
a standardized framework for cancer subtype analysis based
multi-omics data, such as gene expression, miRNA expression,
DNA methylation and others.
biocViews: Clustering, Software, Visualization, GeneExpression
Author: Taosheng Xu [aut, cre]
Maintainer: Taosheng Xu <taosheng.x@gmail.com>
URL: https://github.com/taoshengxu/CancerSubtypes
VignetteBuilder: knitr
BugReports: https://github.com/taoshengxu/CancerSubtypes/issues
git_url: https://git.bioconductor.org/packages/CancerSubtypes
git_branch: RELEASE_3_13
git_last_commit: 66e771e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CancerSubtypes_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CancerSubtypes_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CancerSubtypes_1.18.0.tgz
vignettes:
vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.html
vignetteTitles: CancerSubtypes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.R
dependencyCount: 73
Package: CAnD
Version: 1.24.0
Imports: methods, ggplot2, reshape
Suggests: RUnit, BiocGenerics, BiocStyle
License: Artistic-2.0
MD5sum: 039660ce0f855831098ccd6a52d9ddb1
NeedsCompilation: no
Title: Perform Chromosomal Ancestry Differences (CAnD) Analyses
Description: Functions to perform the CAnD test on a set of ancestry
proportions. For a particular ancestral subpopulation, a user
will supply the estimated ancestry proportion for each sample,
and each chromosome or chromosomal segment of interest. A
p-value for each chromosome as well as an overall CAnD p-value
will be returned for each test. Plotting functions are also
available.
biocViews: Genetics, StatisticalMethod, GeneticVariability, SNP
Author: Caitlin McHugh, Timothy Thornton
Maintainer: Caitlin McHugh <mchughc@uw.edu>
git_url: https://git.bioconductor.org/packages/CAnD
git_branch: RELEASE_3_13
git_last_commit: c6d5434
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CAnD_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CAnD_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CAnD_1.24.0.tgz
vignettes: vignettes/CAnD/inst/doc/CAnD.pdf
vignetteTitles: Detecting heterogenity in population structure across
chromosomes with the "CAnD" package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CAnD/inst/doc/CAnD.R
dependencyCount: 41
Package: caOmicsV
Version: 1.22.0
Depends: R (>= 3.2), igraph (>= 0.7.1), bc3net (>= 1.0.2)
License: GPL (>=2.0)
MD5sum: 2b9795e0e0679e633fa36635013625e7
NeedsCompilation: no
Title: Visualization of multi-dimentional cancer genomics data
Description: caOmicsV package provides methods to visualize
multi-dimentional cancer genomics data including of patient
information, gene expressions, DNA methylations, DNA copy
number variations, and SNP/mutations in matrix layout or
network layout.
biocViews: ImmunoOncology, Visualization, Network, RNASeq
Author: Henry Zhang
Maintainer: Henry Zhang <hzhang@mail.nih.gov>
git_url: https://git.bioconductor.org/packages/caOmicsV
git_branch: RELEASE_3_13
git_last_commit: a570ed1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/caOmicsV_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/caOmicsV_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/caOmicsV_1.22.0.tgz
vignettes: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.pdf
vignetteTitles: Intrudoction_to_caOmicsV
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.R
dependencyCount: 14
Package: Cardinal
Version: 2.10.0
Depends: BiocGenerics, BiocParallel, EBImage, graphics, methods,
S4Vectors (>= 0.27.3), stats, ProtGenerics
Imports: Biobase, dplyr, irlba, lattice, Matrix, matter, magrittr,
mclust, nlme, parallel, signal, sp, stats4, utils, viridisLite
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 64fd3b7762ffbf42555e744772e4e480
NeedsCompilation: yes
Title: A mass spectrometry imaging toolbox for statistical analysis
Description: Implements statistical & computational tools for analyzing
mass spectrometry imaging datasets, including methods for
efficient pre-processing, spatial segmentation, and
classification.
biocViews: Software, Infrastructure, Proteomics, Lipidomics,
MassSpectrometry, ImagingMassSpectrometry, ImmunoOncology,
Normalization, Clustering, Classification, Regression
Author: Kylie A. Bemis <k.bemis@northeastern.edu>
Maintainer: Kylie A. Bemis <k.bemis@northeastern.edu>
URL: http://www.cardinalmsi.org
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Cardinal
git_branch: RELEASE_3_13
git_last_commit: 174f14c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Cardinal_2.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Cardinal_2.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Cardinal_2.10.0.tgz
vignettes: vignettes/Cardinal/inst/doc/Cardinal-2-guide.html,
vignettes/Cardinal/inst/doc/Cardinal-2-stats.html
vignetteTitles: 1. Cardinal 2: User guide for mass spectrometry imaging
analysis, 2. Cardinal 2: Statistical methods for mass
spectrometry imaging
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Cardinal/inst/doc/Cardinal-2-guide.R,
vignettes/Cardinal/inst/doc/Cardinal-2-stats.R
dependsOnMe: CardinalWorkflows
dependencyCount: 65
Package: CARNIVAL
Version: 2.2.0
Depends: R (>= 4.0)
Imports: readr, stringr, lpSolve, igraph, dplyr, rjson, rmarkdown,
methods
Suggests: knitr, testthat (>= 2.1.0)
License: GPL-3
Archs: i386, x64
MD5sum: f46bc9fee4e23e78b6e97c863cdca7b8
NeedsCompilation: no
Title: A CAusal Reasoning tool for Network Identification (from gene
expression data) using Integer VALue programming
Description: An upgraded causal reasoning tool from Melas et al in R
with updated assignments of TFs' weights from PROGENy scores.
Optimization parameters can be freely adjusted and multiple
solutions can be obtained and aggregated.
biocViews: Transcriptomics, GeneExpression, Network
Author: Enio Gjerga [aut] (<https://orcid.org/0000-0002-3060-5786>),
Panuwat Trairatphisan [aut], Anika Liu [ctb], Alberto
Valdeolivas [ctb], Nikolas Peschke [ctb], Aurelien Dugourd
[ctb], Olga Ivanova [cre]
Maintainer: Olga Ivanova <olga.ivanova@bioquant.uni-heidelberg.de>
URL: https://github.com/saezlab/CARNIVAL
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/CARNIVAL/issues
git_url: https://git.bioconductor.org/packages/CARNIVAL
git_branch: RELEASE_3_13
git_last_commit: 06a5d06
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CARNIVAL_2.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CARNIVAL_2.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CARNIVAL_2.2.0.tgz
vignettes: vignettes/CARNIVAL/inst/doc/CARNIVAL.html
vignetteTitles: narray Usage Examples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CARNIVAL/inst/doc/CARNIVAL.R
importsMe: cosmosR
dependencyCount: 56
Package: casper
Version: 2.26.0
Depends: R (>= 3.6.0), Biobase, IRanges, methods, GenomicRanges
Imports: BiocGenerics (>= 0.31.6), coda, EBarrays, gaga, gtools,
GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools,
rtracklayer, S4Vectors (>= 0.9.25), sqldf, survival, VGAM
Enhances: parallel
License: GPL (>=2)
MD5sum: 2f4e512c301672e60ab3d0781a69305f
NeedsCompilation: yes
Title: Characterization of Alternative Splicing based on Paired-End
Reads
Description: Infer alternative splicing from paired-end RNA-seq data.
The model is based on counting paths across exons, rather than
pairwise exon connections, and estimates the fragment size and
start distributions non-parametrically, which improves
estimation precision.
biocViews: ImmunoOncology, GeneExpression, DifferentialExpression,
Transcription, RNASeq, Sequencing
Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda
Stobbe, Victor Pena
Maintainer: David Rossell <rosselldavid@gmail.com>
git_url: https://git.bioconductor.org/packages/casper
git_branch: RELEASE_3_13
git_last_commit: 3de669e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/casper_2.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/casper_2.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/casper_2.26.0.tgz
vignettes: vignettes/casper/inst/doc/casper.pdf,
vignettes/casper/inst/doc/DesignRNASeq.pdf
vignetteTitles: Manual for the casper library, DesignRNASeq.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/casper/inst/doc/casper.R
dependencyCount: 111
Package: CATALYST
Version: 1.16.2
Depends: R (>= 4.0), SingleCellExperiment
Imports: circlize, ComplexHeatmap, ConsensusClusterPlus, cowplot,
data.table, dplyr, drc, flowCore, FlowSOM, ggplot2, ggrepel,
ggridges, graphics, grDevices, grid, gridExtra, magrittr,
Matrix, matrixStats, methods, nnls, purrr, RColorBrewer,
reshape2, Rtsne, SummarizedExperiment, S4Vectors, scales,
scater, stats
Suggests: BiocStyle, diffcyt, flowWorkspace, ggcyto, knitr, openCyto,
rmarkdown, testthat, uwot
License: GPL (>=2)
Archs: i386, x64
MD5sum: 26f23531e16e1f4658fdb8050888969a
NeedsCompilation: no
Title: Cytometry dATa anALYSis Tools
Description: Mass cytometry (CyTOF) uses heavy metal isotopes rather
than fluorescent tags as reporters to label antibodies, thereby
substantially decreasing spectral overlap and allowing for
examination of over 50 parameters at the single cell level.
While spectral overlap is significantly less pronounced in
CyTOF than flow cytometry, spillover due to detection
sensitivity, isotopic impurities, and oxide formation can
impede data interpretability. We designed CATALYST (Cytometry
dATa anALYSis Tools) to provide a pipeline for preprocessing of
cytometry data, including i) normalization using bead
standards, ii) single-cell deconvolution, and iii) bead-based
compensation.
biocViews: Clustering, DifferentialExpression, ExperimentalDesign,
FlowCytometry, ImmunoOncology, MassSpectrometry, Normalization,
Preprocessing, SingleCell, Software, StatisticalMethod,
Visualization
Author: Helena L. Crowell [aut, cre], Vito R.T. Zanotelli [aut],
Stéphane Chevrier [aut, dtc], Mark D. Robinson [aut, fnd],
Bernd Bodenmiller [fnd]
Maintainer: Helena L. Crowell <helena.crowell@uzh.ch>
URL: https://github.com/HelenaLC/CATALYST
VignetteBuilder: knitr
BugReports: https://github.com/HelenaLC/CATALYST/issues
git_url: https://git.bioconductor.org/packages/CATALYST
git_branch: RELEASE_3_13
git_last_commit: 5e3b4f4
git_last_commit_date: 2021-07-13
Date/Publication: 2021-07-13
source.ver: src/contrib/CATALYST_1.16.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CATALYST_1.16.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/CATALYST_1.16.2.tgz
vignettes: vignettes/CATALYST/inst/doc/differential.html,
vignettes/CATALYST/inst/doc/preprocessing.html
vignetteTitles: "2. Differential discovery", "1. Preprocessing"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CATALYST/inst/doc/differential.R,
vignettes/CATALYST/inst/doc/preprocessing.R
dependsOnMe: cytofWorkflow
suggestsMe: diffcyt, treekoR
dependencyCount: 242
Package: Category
Version: 2.58.0
Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix
Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, DBI
Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6),
hgu95av2.db, KEGGREST, karyoploteR, geneplotter, limma,
lattice, RUnit, org.Sc.sgd.db, GOstats, GO.db
License: Artistic-2.0
MD5sum: 72b174edd876b795bb77cd3a9e4846df
NeedsCompilation: no
Title: Category Analysis
Description: A collection of tools for performing category (gene set
enrichment) analysis.
biocViews: Annotation, GO, Pathways, GeneSetEnrichment
Author: Robert Gentleman [aut], Seth Falcon [ctb], Deepayan Sarkar
[ctb], Robert Castelo [ctb], Bioconductor Package Maintainer
[cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/Category
git_branch: RELEASE_3_13
git_last_commit: 5a966e0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Category_2.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Category_2.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Category_2.58.0.tgz
vignettes: vignettes/Category/inst/doc/Category.pdf,
vignettes/Category/inst/doc/ChromBand.pdf
vignetteTitles: Using Categories to Analyze Microarray Data, Using
Chromosome Bands as Categories
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Category/inst/doc/Category.R,
vignettes/Category/inst/doc/ChromBand.R
dependsOnMe: GOstats
importsMe: categoryCompare, cellHTS2, GmicR, interactiveDisplay, meshr,
miRLAB, phenoTest, ppiStats, scTensor
suggestsMe: qpgraph, RnBeads, maGUI
dependencyCount: 59
Package: categoryCompare
Version: 1.36.0
Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8),
Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1),
GOstats, annotate, colorspace, graph, RCy3 (>= 1.99.29),
methods, grDevices, utils
Suggests: knitr, GO.db, KEGGREST, estrogen, org.Hs.eg.db, hgu95av2.db,
limma, affy, genefilter
License: GPL-2
MD5sum: 8edbc2df0fd32ca7a37092c09590a373
NeedsCompilation: no
Title: Meta-analysis of high-throughput experiments using feature
annotations
Description: Calculates significant annotations (categories) in each of
two (or more) feature (i.e. gene) lists, determines the overlap
between the annotations, and returns graphical and tabular data
about the significant annotations and which combinations of
feature lists the annotations were found to be significant.
Interactive exploration is facilitated through the use of
RCytoscape (heavily suggested).
biocViews: Annotation, GO, MultipleComparison, Pathways, GeneExpression
Author: Robert M. Flight <rflight79@gmail.com>
Maintainer: Robert M. Flight <rflight79@gmail.com>
URL: https://github.com/rmflight/categoryCompare
SystemRequirements: Cytoscape (>= 3.6.1) (if used for visualization of
results, heavily suggested)
VignetteBuilder: knitr
BugReports: https://github.com/rmflight/categoryCompare/issues
git_url: https://git.bioconductor.org/packages/categoryCompare
git_branch: RELEASE_3_13
git_last_commit: 325b381
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/categoryCompare_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/categoryCompare_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/categoryCompare_1.36.0.tgz
vignettes:
vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html
vignetteTitles: categoryCompare: High-throughput data meta-analysis
using gene annotations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R
dependencyCount: 98
Package: CausalR
Version: 1.24.0
Depends: R (>= 3.2.0)
Imports: igraph
Suggests: knitr, RUnit, BiocGenerics
License: GPL (>= 2)
MD5sum: 361104a2c5c80477a48f793a05f0a9d1
NeedsCompilation: no
Title: Causal network analysis methods
Description: Causal network analysis methods for regulator prediction
and network reconstruction from genome scale data.
biocViews: ImmunoOncology, SystemsBiology, Network, GraphAndNetwork,
Network Inference, Transcriptomics, Proteomics,
DifferentialExpression, RNASeq, Microarray
Author: Glyn Bradley, Steven Barrett, Chirag Mistry, Mark Pipe, David
Wille, David Riley, Bhushan Bonde, Peter Woollard
Maintainer: Glyn Bradley <glyn.x.bradley@gsk.com>, Steven Barrett
<steven.j.barrett@gsk.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CausalR
git_branch: RELEASE_3_13
git_last_commit: 4a392e7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CausalR_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CausalR_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CausalR_1.24.0.tgz
vignettes: vignettes/CausalR/inst/doc/CausalR.pdf
vignetteTitles: CausalR.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CausalR/inst/doc/CausalR.R
dependencyCount: 11
Package: cbaf
Version: 1.14.0
Depends: R (>= 3.5.0)
Imports: BiocFileCache, RColorBrewer, cgdsr, genefilter, gplots,
grDevices, stats, utils, openxlsx
Suggests: knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 7aa310737722b7568f4652bd08f554a6
NeedsCompilation: no
Title: Automated functions for comparing various omic data from
cbioportal.org
Description: This package contains functions that allow analysing and
comparing omic data across various cancers/cancer subgroups
easily. So far, it is compatible with RNA-seq, microRNA-seq,
microarray and methylation datasets that are stored on
cbioportal.org.
biocViews: Software, AssayDomain, DNAMethylation, GeneExpression,
Transcription, ResearchField, BiomedicalInformatics,
ComparativeGenomics, Epigenetics, Genetics, Transcriptomics
Author: Arman Shahrisa [aut, cre, cph], Maryam Tahmasebi Birgani [aut]
Maintainer: Arman Shahrisa <shahrisa.arman@hotmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cbaf
git_branch: RELEASE_3_13
git_last_commit: 3fcea28
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cbaf_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cbaf_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cbaf_1.14.0.tgz
vignettes: vignettes/cbaf/inst/doc/cbaf.html
vignetteTitles: cbaf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cbaf/inst/doc/cbaf.R
dependencyCount: 83
Package: cBioPortalData
Version: 2.4.10
Depends: R (>= 4.0.0), AnVIL, MultiAssayExperiment
Imports: BiocFileCache (>= 1.5.3), digest, dplyr, GenomeInfoDb,
GenomicRanges, httr, IRanges, methods, readr, RaggedExperiment,
RTCGAToolbox (>= 2.19.7), S4Vectors, SummarizedExperiment,
stats, tibble, tidyr, TCGAutils (>= 1.9.4), utils
Suggests: BiocStyle, knitr, survival, survminer, rmarkdown, testthat
License: AGPL-3
MD5sum: ea83d967082a408e7ca9fe1111f4486d
NeedsCompilation: no
Title: Exposes and makes available data from the cBioPortal web
resources
Description: The cBioPortalData package takes compressed resources from
repositories such as cBioPortal and assembles a
MultiAssayExperiment object with Bioconductor classes.
biocViews: Software, Infrastructure, ThirdPartyClient
Author: Levi Waldron [aut], Marcel Ramos [aut, cre]
Maintainer: Marcel Ramos <marcel.ramos@roswellpark.org>
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/cBioPortalData/issues
git_url: https://git.bioconductor.org/packages/cBioPortalData
git_branch: RELEASE_3_13
git_last_commit: e5d1319
git_last_commit_date: 2021-10-04
Date/Publication: 2021-10-07
source.ver: src/contrib/cBioPortalData_2.4.10.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cBioPortalData_2.4.10.zip
mac.binary.ver: bin/macosx/contrib/4.1/cBioPortalData_2.4.10.tgz
vignettes: vignettes/cBioPortalData/inst/doc/cBioPortalData.html,
vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.html,
vignettes/cBioPortalData/inst/doc/cBioPortalRClient.html
vignetteTitles: cBioPortal User Guide, cBioPortal Data Build Errors,
cBioPortal Quick-start Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cBioPortalData/inst/doc/cBioPortalData.R,
vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.R,
vignettes/cBioPortalData/inst/doc/cBioPortalRClient.R
dependencyCount: 118
Package: cbpManager
Version: 1.0.2
Depends: shiny, shinydashboard
Imports: utils, DT, htmltools, vroom, plyr, dplyr, magrittr, jsonlite,
rapportools, basilisk, reticulate, shinyBS, shinycssloaders,
rintrojs
Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0)
License: AGPL-3 + file LICENSE
MD5sum: f914e83e6062b39fb5ff89f628778c47
NeedsCompilation: no
Title: Generate, manage, and edit data and metadata files suitable for
the import in cBioPortal for Cancer Genomics
Description: This R package provides an R Shiny application that
enables the user to generate, manage, and edit data and
metadata files suitable for the import in cBioPortal for Cancer
Genomics. Create cancer studies and edit its metadata. Upload
mutation data of a patient that will be concatenated to the
data_mutation_extended.txt file of the study. Create and edit
clinical patient data, sample data, and timeline data. Create
custom timeline tracks for patients.
biocViews: ImmunoOncology, DataImport, DataRepresentation, GUI,
ThirdPartyClient, Preprocessing, Visualization
Author: Arsenij Ustjanzew [aut, cre, cph]
(<https://orcid.org/0000-0002-1014-4521>), Federico Marini
[aut] (<https://orcid.org/0000-0003-3252-7758>)
Maintainer: Arsenij Ustjanzew <arsenij.ustjanzew@gmail.com>
URL: https://arsenij-ust.github.io/cbpManager/index.html
VignetteBuilder: knitr
BugReports: https://github.com/arsenij-ust/cbpManager/issues
git_url: https://git.bioconductor.org/packages/cbpManager
git_branch: RELEASE_3_13
git_last_commit: 9598ce4
git_last_commit_date: 2021-08-04
Date/Publication: 2021-08-05
source.ver: src/contrib/cbpManager_1.0.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cbpManager_1.0.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/cbpManager_1.0.2.tgz
vignettes: vignettes/cbpManager/inst/doc/intro.html
vignetteTitles: intro.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cbpManager/inst/doc/intro.R
dependencyCount: 79
Package: ccfindR
Version: 1.12.0
Depends: R (>= 3.6.0)
Imports: stats, S4Vectors, utils, methods, Matrix,
SummarizedExperiment, SingleCellExperiment, Rtsne, graphics,
grDevices, gtools, RColorBrewer, ape, Rmpi, irlba, Rcpp, Rdpack
(>= 0.7)
LinkingTo: Rcpp, RcppEigen
Suggests: BiocStyle, knitr, rmarkdown
License: GPL (>= 2)
MD5sum: 630e8984c10f23498c6a4d207a708678
NeedsCompilation: yes
Title: Cancer Clone Finder
Description: A collection of tools for cancer genomic data clustering
analyses, including those for single cell RNA-seq. Cell
clustering and feature gene selection analysis employ Bayesian
(and maximum likelihood) non-negative matrix factorization
(NMF) algorithm. Input data set consists of RNA count matrix,
gene, and cell bar code annotations. Analysis outputs are
factor matrices for multiple ranks and marginal likelihood
values for each rank. The package includes utilities for
downstream analyses, including meta-gene identification,
visualization, and construction of rank-based trees for
clusters.
biocViews: Transcriptomics, SingleCell, ImmunoOncology, Bayesian,
Clustering
Author: Jun Woo [aut, cre], Jinhua Wang [aut]
Maintainer: Jun Woo <jwoo@umn.edu>
URL: http://dx.doi.org/10.26508/lsa.201900443
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ccfindR
git_branch: RELEASE_3_13
git_last_commit: d3220ba
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ccfindR_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ccfindR_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ccfindR_1.12.0.tgz
vignettes: vignettes/ccfindR/inst/doc/ccfindR.html
vignetteTitles: ccfindR: single-cell RNA-seq analysis using Bayesian
non-negative matrix factorization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ccfindR/inst/doc/ccfindR.R
suggestsMe: MutationalPatterns
dependencyCount: 38
Package: ccmap
Version: 1.18.0
Imports: AnnotationDbi (>= 1.36.2), BiocManager (>= 1.30.4), ccdata (>=
1.1.2), doParallel (>= 1.0.10), data.table (>= 1.10.4), foreach
(>= 1.4.3), parallel (>= 3.3.3), xgboost (>= 0.6.4), lsa (>=
0.73.1)
Suggests: crossmeta, knitr, rmarkdown, testthat, lydata
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 1ab5bd227f78704f2b272b1362c04652
NeedsCompilation: no
Title: Combination Connectivity Mapping
Description: Finds drugs and drug combinations that are predicted to
reverse or mimic gene expression signatures. These drugs might
reverse diseases or mimic healthy lifestyles.
biocViews: GeneExpression, Transcription, Microarray,
DifferentialExpression
Author: Alex Pickering
Maintainer: Alex Pickering <alexvpickering@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ccmap
git_branch: RELEASE_3_13
git_last_commit: 7f8150c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-20
source.ver: src/contrib/ccmap_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ccmap_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ccmap_1.18.0.tgz
vignettes: vignettes/ccmap/inst/doc/ccmap-vignette.html
vignetteTitles: ccmap vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ccmap/inst/doc/ccmap-vignette.R
dependencyCount: 60
Package: CCPROMISE
Version: 1.18.0
Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase,
utils
License: GPL (>= 2)
MD5sum: ae77835ff40d3572a4c76adc9e49597b
NeedsCompilation: no
Title: PROMISE analysis with Canonical Correlation for Two Forms of
High Dimensional Genetic Data
Description: Perform Canonical correlation between two forms of high
demensional genetic data, and associate the first compoent of
each form of data with a specific biologically interesting
pattern of associations with multiple endpoints. A probe level
analysis is also implemented.
biocViews: Microarray, GeneExpression
Author: Xueyuan Cao <xueyuan.cao@stjude.org> and Stanley.pounds
<stanley.pounds@stjude.org>
Maintainer: Xueyuan Cao <xueyuan.cao@stjude.org>
git_url: https://git.bioconductor.org/packages/CCPROMISE
git_branch: RELEASE_3_13
git_last_commit: 69c49fa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CCPROMISE_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CCPROMISE_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CCPROMISE_1.18.0.tgz
vignettes: vignettes/CCPROMISE/inst/doc/CCPROMISE.pdf
vignetteTitles: An introduction to CCPROMISE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CCPROMISE/inst/doc/CCPROMISE.R
dependencyCount: 53
Package: ccrepe
Version: 1.28.0
Imports: infotheo (>= 1.1)
Suggests: knitr, BiocStyle, BiocGenerics, testthat
License: MIT + file LICENSE
MD5sum: 74136774f8a95c9278fef03507c324c9
NeedsCompilation: no
Title: ccrepe_and_nc.score
Description: The CCREPE (Compositionality Corrected by REnormalizaion
and PErmutation) package is designed to assess the significance
of general similarity measures in compositional datasets. In
microbial abundance data, for example, the total abundances of
all microbes sum to one; CCREPE is designed to take this
constraint into account when assigning p-values to similarity
measures between the microbes. The package has two functions:
ccrepe: Calculates similarity measures, p-values and q-values
for relative abundances of bugs in one or two body sites using
bootstrap and permutation matrices of the data. nc.score:
Calculates species-level co-variation and co-exclusion patterns
based on an extension of the checkerboard score to ordinal
data.
biocViews: ImmunoOncology, Statistics, Metagenomics, Bioinformatics,
Software
Author: Emma Schwager <emh146@mail.harvard.edu>,Craig
Bielski<craig.bielski@gmail.com>, George
Weingart<george.weingart@gmail.com>
Maintainer: Emma Schwager <emma.schwager@gmail.com>,Craig
Bielski<craig.bielski@gmail.com>, George
Weingart<george.weingart@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ccrepe
git_branch: RELEASE_3_13
git_last_commit: fd49cc5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ccrepe_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ccrepe_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ccrepe_1.28.0.tgz
vignettes: vignettes/ccrepe/inst/doc/ccrepe.pdf
vignetteTitles: ccrepe
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ccrepe/inst/doc/ccrepe.R
dependencyCount: 1
Package: celaref
Version: 1.10.0
Depends: R (>= 3.5.0), SummarizedExperiment
Imports: MAST, ggplot2, Matrix, dplyr, magrittr, stats, utils, rlang,
BiocGenerics, S4Vectors, readr, tibble, DelayedArray
Suggests: limma, parallel, knitr, rmarkdown, ExperimentHub, testthat
License: GPL-3
MD5sum: ac3e928a5ad206ecc2c2a4b97fc9e9b4
NeedsCompilation: no
Title: Single-cell RNAseq cell cluster labelling by reference
Description: After the clustering step of a single-cell RNAseq
experiment, this package aims to suggest labels/cell types for
the clusters, on the basis of similarity to a reference
dataset. It requires a table of read counts per cell per gene,
and a list of the cells belonging to each of the clusters, (for
both test and reference data).
biocViews: SingleCell
Author: Sarah Williams [aut, cre]
Maintainer: Sarah Williams <sarah.williams1@monash.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/celaref
git_branch: RELEASE_3_13
git_last_commit: 633ad23
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/celaref_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/celaref_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/celaref_1.10.0.tgz
vignettes: vignettes/celaref/inst/doc/celaref_doco.html
vignetteTitles: Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/celaref/inst/doc/celaref_doco.R
dependencyCount: 79
Package: celda
Version: 1.8.1
Depends: R (>= 4.0)
Imports: plyr, foreach, ggplot2, RColorBrewer, grid, scales, gtable,
grDevices, graphics, matrixStats, doParallel, digest, methods,
reshape2, S4Vectors, data.table, Rcpp, RcppEigen, uwot,
enrichR, SummarizedExperiment, MCMCprecision, ggrepel, Rtsne,
withr, scater (>= 1.14.4), scran, SingleCellExperiment, dbscan,
DelayedArray, stringr, Matrix, ComplexHeatmap,
multipanelfigure, circlize
LinkingTo: Rcpp, RcppEigen
Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr,
BiocManager, BiocStyle, M3DExampleData, TENxPBMCData,
singleCellTK
License: MIT + file LICENSE
MD5sum: 3f5bf2a70d361085d3d5d13c3099a2db
NeedsCompilation: yes
Title: CEllular Latent Dirichlet Allocation
Description: Celda is a suite of Bayesian hierarchical models for
clustering single-cell RNA-sequencing (scRNA-seq) data. It is
able to perform "bi-clustering" and simultaneously cluster
genes into gene modules and cells into cell subpopulations. It
also contains DecontX, a novel Bayesian method to
computationally estimate and remove RNA contamination in
individual cells without empty droplet information. A variety
of scRNA-seq data visualization functions is also included.
biocViews: SingleCell, GeneExpression, Clustering, Sequencing, Bayesian
Author: Joshua Campbell [aut, cre], Sean Corbett [aut], Yusuke Koga
[aut], Shiyi Yang [aut], Eric Reed [aut], Zhe Wang [aut]
Maintainer: Joshua Campbell <camp@bu.edu>
VignetteBuilder: knitr
BugReports: https://github.com/campbio/celda/issues
git_url: https://git.bioconductor.org/packages/celda
git_branch: RELEASE_3_13
git_last_commit: f4cf6f05
git_last_commit_date: 2021-05-27
Date/Publication: 2021-05-30
source.ver: src/contrib/celda_1.8.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/celda_1.8.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/celda_1.8.1.tgz
vignettes: vignettes/celda/inst/doc/celda.pdf,
vignettes/celda/inst/doc/decontX.pdf
vignetteTitles: Analysis of single-cell genomic data with celda,
Estimate and remove cross-contamination from ambient RNA in
single-cell data with DecontX
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/celda/inst/doc/celda.R,
vignettes/celda/inst/doc/decontX.R
importsMe: singleCellTK
dependencyCount: 138
Package: CellaRepertorium
Version: 1.2.0
Depends: R (>= 4.0)
Imports: dplyr, tibble, stringr, Biostrings, Rcpp, reshape2, methods,
rlang (>= 0.3), purrr, Matrix, S4Vectors, BiocGenerics, tidyr,
forcats, progress, stats, utils
LinkingTo: Rcpp
Suggests: testthat, readr, knitr, rmarkdown, ggplot2, BiocStyle,
ggdendro, broom, lme4, RColorBrewer, SingleCellExperiment,
scater, broom.mixed, cowplot
License: GPL-3
Archs: i386, x64
MD5sum: 38e2c328a5909121a1051692686d19cd
NeedsCompilation: yes
Title: Data structures, clustering and testing for single cell immune
receptor repertoires (scRNAseq RepSeq/AIRR-seq)
Description: Methods to cluster and analyze high-throughput single cell
immune cell repertoires, especially from the 10X Genomics VDJ
solution. Contains an R interface to CD-HIT (Li and Godzik
2006). Methods to visualize and analyze paired heavy-light
chain data. Tests for specific expansion, as well as omnibus
oligoclonality under hypergeometric models.
biocViews: RNASeq, Transcriptomics, SingleCell, TargetedResequencing,
Technology, ImmunoOncology, Clustering
Author: Andrew McDavid [aut, cre], Yu Gu [aut], Erik VonKaenel [aut],
Thomas Lin Pedersen [ctb]
Maintainer: Andrew McDavid <Andrew_McDavid@urmc.rochester.edu>
URL: https://github.com/amcdavid/CellaRepertorium
VignetteBuilder: knitr
BugReports: https://github.com/amcdavid/CellaRepertorium/issues
git_url: https://git.bioconductor.org/packages/CellaRepertorium
git_branch: RELEASE_3_13
git_last_commit: 86b7f10
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CellaRepertorium_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CellaRepertorium_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CellaRepertorium_1.2.0.tgz
vignettes: vignettes/CellaRepertorium/inst/doc/cdr3_clustering.html,
vignettes/CellaRepertorium/inst/doc/cr-overview.html,
vignettes/CellaRepertorium/inst/doc/mouse_tcell_qc.html,
vignettes/CellaRepertorium/inst/doc/repertoire_and_expression.html
vignetteTitles: Clustering and differential usage of repertoire CDR3
sequences, An Introduction to CellaRepertorium, Quality control
and Exploration of UMI-based repertoire data, Combining
Repertoire with Expression with SingleCellExperiment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellaRepertorium/inst/doc/cdr3_clustering.R,
vignettes/CellaRepertorium/inst/doc/cr-overview.R,
vignettes/CellaRepertorium/inst/doc/mouse_tcell_qc.R,
vignettes/CellaRepertorium/inst/doc/repertoire_and_expression.R
dependencyCount: 50
Package: cellbaseR
Version: 1.16.0
Depends: R(>= 3.4)
Imports: methods, jsonlite, httr, data.table, pbapply, tidyr, R.utils,
Rsamtools, BiocParallel, foreach, utils, parallel, doParallel
Suggests: BiocStyle, knitr, rmarkdown, Gviz, VariantAnnotation
License: Apache License (== 2.0)
Archs: i386, x64
MD5sum: 4fef813c71599e936d0f8d532b85daf6
NeedsCompilation: no
Title: Querying annotation data from the high performance Cellbase web
Description: This R package makes use of the exhaustive RESTful Web
service API that has been implemented for the Cellabase
database. It enable researchers to query and obtain a wealth of
biological information from a single database saving a lot of
time. Another benefit is that researchers can easily make
queries about different biological topics and link all this
information together as all information is integrated.
biocViews: Annotation, VariantAnnotation
Author: Mohammed OE Abdallah
Maintainer: Mohammed OE Abdallah <melsiddieg@gmail.com>
URL: https://github.com/melsiddieg/cellbaseR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cellbaseR
git_branch: RELEASE_3_13
git_last_commit: 3bb8377
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cellbaseR_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cellbaseR_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cellbaseR_1.16.0.tgz
vignettes: vignettes/cellbaseR/inst/doc/cellbaseR.html
vignetteTitles: "Simplifying Genomic Annotations in R"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cellbaseR/inst/doc/cellbaseR.R
dependencyCount: 64
Package: CellBench
Version: 1.8.0
Depends: R (>= 3.6), SingleCellExperiment, magrittr, methods, stats,
tibble, utils
Imports: BiocGenerics, BiocFileCache, BiocParallel, dplyr, rlang, glue,
memoise, purrr (>= 0.3.0), rappdirs, tidyr, tidyselect,
lubridate
Suggests: BiocStyle, covr, knitr, rmarkdown, testthat, limma, ggplot2
License: GPL-3
MD5sum: 1e84294773bede7b35af0d6279ba0fe7
NeedsCompilation: no
Title: Construct Benchmarks for Single Cell Analysis Methods
Description: This package contains infrastructure for benchmarking
analysis methods and access to single cell mixture benchmarking
data. It provides a framework for organising analysis methods
and testing combinations of methods in a pipeline without
explicitly laying out each combination. It also provides
utilities for sampling and filtering SingleCellExperiment
objects, constructing lists of functions with varying
parameters, and multithreaded evaluation of analysis methods.
biocViews: Software, Infrastructure
Author: Shian Su [cre, aut], Saskia Freytag [aut], Luyi Tian [aut],
Xueyi Dong [aut], Matthew Ritchie [aut], Peter Hickey [ctb],
Stuart Lee [ctb]
Maintainer: Shian Su <su.s@wehi.edu.au>
URL: https://github.com/shians/cellbench
VignetteBuilder: knitr
BugReports: https://github.com/Shians/CellBench/issues
git_url: https://git.bioconductor.org/packages/CellBench
git_branch: RELEASE_3_13
git_last_commit: aa35afe
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CellBench_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CellBench_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CellBench_1.8.0.tgz
vignettes: vignettes/CellBench/inst/doc/DataManipulation.pdf,
vignettes/CellBench/inst/doc/TidyversePatterns.pdf,
vignettes/CellBench/inst/doc/CellBenchCaseStudy.html,
vignettes/CellBench/inst/doc/Introduction.html,
vignettes/CellBench/inst/doc/Timing.html,
vignettes/CellBench/inst/doc/WritingWrappers.html
vignetteTitles: Data Manipulation, Tidyverse Patterns,
CellBenchCaseStudy.html, Introduction, Timing, Writing Wrappers
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellBench/inst/doc/DataManipulation.R,
vignettes/CellBench/inst/doc/Introduction.R,
vignettes/CellBench/inst/doc/TidyversePatterns.R,
vignettes/CellBench/inst/doc/Timing.R,
vignettes/CellBench/inst/doc/WritingWrappers.R
suggestsMe: corral
dependencyCount: 78
Package: cellHTS2
Version: 2.56.0
Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter,
splots, vsn, hwriter, locfit, grid
Imports: GSEABase, Category, stats4, BiocGenerics
Suggests: ggplot2
License: Artistic-2.0
MD5sum: a6b27848f0d4b869b9fb9b4e02d86762
NeedsCompilation: no
Title: Analysis of cell-based screens - revised version of cellHTS
Description: This package provides tools for the analysis of
high-throughput assays that were performed in microtitre plate
formats (including but not limited to 384-well plates). The
functionality includes data import and management,
normalisation, quality assessment, replicate summarisation and
statistical scoring. A webpage that provides a detailed
graphical overview over the data and analysis results is
produced. In our work, we have applied the package to RNAi
screens on fly and human cells, and for screens of yeast
libraries. See ?cellHTS2 for a brief introduction.
biocViews: ImmunoOncology, CellBasedAssays, Preprocessing,
Visualization
Author: Ligia Bras, Wolfgang Huber <whuber@embl.de>, Michael Boutros
<m.boutros@dkfz.de>, Gregoire Pau <gpau@embl.de>, Florian Hahne
<florian.hahne@novartis.com>
Maintainer: Joseph Barry <joseph.barry@embl.de>
URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber
git_url: https://git.bioconductor.org/packages/cellHTS2
git_branch: RELEASE_3_13
git_last_commit: 72c1d14
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cellHTS2_2.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cellHTS2_2.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cellHTS2_2.56.0.tgz
vignettes: vignettes/cellHTS2/inst/doc/cellhts2.pdf,
vignettes/cellHTS2/inst/doc/cellhts2Complete.pdf,
vignettes/cellHTS2/inst/doc/twoChannels.pdf,
vignettes/cellHTS2/inst/doc/twoWay.pdf
vignetteTitles: Main vignette: End-to-end analysis of cell-based
screens, Main vignette (complete version): End-to-end analysis
of cell-based screens, Supplement: multi-channel assays,
Supplement: enhancer-suppressor screens
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cellHTS2/inst/doc/cellhts2.R,
vignettes/cellHTS2/inst/doc/cellhts2Complete.R,
vignettes/cellHTS2/inst/doc/twoChannels.R,
vignettes/cellHTS2/inst/doc/twoWay.R
dependsOnMe: imageHTS, staRank
importsMe: gespeR, RNAinteract
suggestsMe: bioassayR
dependencyCount: 91
Package: CelliD
Version: 1.0.0
Depends: R (>= 4.1), Seurat (>= 4.0.1), SingleCellExperiment
Imports: Rcpp, RcppArmadillo, stats, utils, Matrix, tictoc, scater,
stringr, irlba, data.table, glue, pbapply, umap, Rtsne,
reticulate, fastmatch, matrixStats, ggplot2, BiocParallel,
SummarizedExperiment, fgsea
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, BiocStyle, testthat, tidyverse, ggpubr,
destiny, ggrepel
License: GPL-3 + file LICENSE
MD5sum: 2e85faa191b38954cbe24470643780e2
NeedsCompilation: yes
Title: Unbiased Extraction of Single Cell gene signatures using
Multiple Correspondence Analysis
Description: CelliD is a clustering-free multivariate statistical
method for the robust extraction of per-cell gene signatures
from single-cell RNA-seq. CelliD allows unbiased cell identity
recognition across different donors, tissues-of-origin, model
organisms and single-cell omics protocols. The package can also
be used to explore functional pathways enrichment in single
cell data.
biocViews: RNASeq, SingleCell, DimensionReduction, Clustering,
GeneSetEnrichment, GeneExpression, ATACSeq
Author: Akira Cortal [aut, cre], Antonio Rausell [aut, ctb]
Maintainer: Akira Cortal <akira.cortal@institutimagine.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CelliD
git_branch: RELEASE_3_13
git_last_commit: 4ab073e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CelliD_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CelliD_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CelliD_1.0.0.tgz
vignettes: vignettes/CelliD/inst/doc/BioconductorVignette.html
vignetteTitles: CelliD Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CelliD/inst/doc/BioconductorVignette.R
dependencyCount: 180
Package: cellity
Version: 1.20.0
Depends: R (>= 3.3)
Imports: AnnotationDbi, e1071, ggplot2, graphics, grDevices, grid,
mvoutlier, org.Hs.eg.db, org.Mm.eg.db, robustbase, stats,
topGO, utils
Suggests: BiocStyle, caret, knitr, testthat, rmarkdown
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 188cdc4549d56ebb0f31aeb887f1a002
NeedsCompilation: no
Title: Quality Control for Single-Cell RNA-seq Data
Description: A support vector machine approach to identifying and
filtering low quality cells from single-cell RNA-seq datasets.
biocViews: ImmunoOncology, RNASeq, QualityControl, Preprocessing,
Normalization, Visualization, DimensionReduction,
Transcriptomics, GeneExpression, Sequencing, Software,
SupportVectorMachine
Author: Tomislav Illicic, Davis McCarthy
Maintainer: Tomislav Ilicic <ti243@cam.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cellity
git_branch: RELEASE_3_13
git_last_commit: a34d727
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cellity_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cellity_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cellity_1.20.0.tgz
vignettes: vignettes/cellity/inst/doc/cellity_vignette.html
vignetteTitles: An introduction to the cellity package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R
dependencyCount: 86
Package: CellMapper
Version: 1.18.0
Depends: S4Vectors, methods
Imports: stats, utils
Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle,
ExperimentHub
License: Artistic-2.0
MD5sum: 2841613fc2967ee425434f5aa551b7d7
NeedsCompilation: no
Title: Predict genes expressed selectively in specific cell types
Description: Infers cell type-specific expression based on
co-expression similarity with known cell type marker genes. Can
make accurate predictions using publicly available expression
data, even when a cell type has not been isolated before.
biocViews: Microarray, Software, GeneExpression
Author: Brad Nelms
Maintainer: Brad Nelms <bnelms.research@gmail.com>
git_url: https://git.bioconductor.org/packages/CellMapper
git_branch: RELEASE_3_13
git_last_commit: 7c1a001
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CellMapper_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CellMapper_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CellMapper_1.18.0.tgz
vignettes: vignettes/CellMapper/inst/doc/CellMapper.pdf
vignetteTitles: CellMapper Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellMapper/inst/doc/CellMapper.R
dependsOnMe: CellMapperData
dependencyCount: 8
Package: cellmigRation
Version: 1.0.0
Depends: R (>= 4.1), methods, foreach
Imports: tiff, graphics, stats, utils, reshape2, parallel, doParallel,
grDevices, matrixStats, FME, SpatialTools, sp, vioplot,
FactoMineR, Hmisc
Suggests: knitr, rmarkdown, dplyr, ggplot2, RUnit, BiocGenerics,
BiocManager, kableExtra, rgl
License: GPL-2
MD5sum: c5628538bcb14aff35c7ef5a674b28bb
NeedsCompilation: no
Title: Track Cells, Analyze Cell Trajectories and Compute Migration
Statistics
Description: Import TIFF images of fluorescently labeled cells, and
track cell movements over time. Parallelization is supported
for image processing and for fast computation of cell
trajectories. In-depth analysis of cell trajectories is enabled
by 15 trajectory analysis functions.
biocViews: CellBiology, DataRepresentation, DataImport
Author: Salim Ghannoum [aut, cph], Damiano Fantini [aut, cph], Waldir
Leoncio [cre, aut], Øystein Sørensen [aut]
Maintainer: Waldir Leoncio <w.l.netto@medisin.uio.no>
URL: https://github.com/ocbe-uio/cellmigRation/
VignetteBuilder: knitr
BugReports: https://github.com/ocbe-uio/cellmigRation/issues
git_url: https://git.bioconductor.org/packages/cellmigRation
git_branch: RELEASE_3_13
git_last_commit: 5029a2f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cellmigRation_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cellmigRation_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cellmigRation_1.0.0.tgz
vignettes: vignettes/cellmigRation/inst/doc/cellmigRation.html
vignetteTitles: cellmigRation
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cellmigRation/inst/doc/cellmigRation.R
dependencyCount: 143
Package: CellMixS
Version: 1.8.0
Depends: kSamples, R (>= 4.0)
Imports: BiocNeighbors, ggplot2, scater, viridis, cowplot,
SummarizedExperiment, SingleCellExperiment, tidyr, magrittr,
dplyr, ggridges, stats, purrr, methods, BiocParallel,
BiocGenerics
Suggests: BiocStyle, knitr, rmarkdown, testthat, limma, Rtsne
License: GPL (>=2)
MD5sum: 1506c04bbef699a99731f7423eaf68d6
NeedsCompilation: no
Title: Evaluate Cellspecific Mixing
Description: CellMixS provides metrics and functions to evaluate batch
effects, data integration and batch effect correction in single
cell trancriptome data with single cell resolution. Results can
be visualized and summarised on different levels, e.g. on cell,
celltype or dataset level.
biocViews: SingleCell, Transcriptomics, GeneExpression, BatchEffect
Author: Almut Lütge [aut, cre]
Maintainer: Almut Lütge <almut.luetge@uzh.ch>
URL: https://github.com/almutlue/CellMixS
VignetteBuilder: knitr
BugReports: https://github.com/almutlue/CellMixS/issues
git_url: https://git.bioconductor.org/packages/CellMixS
git_branch: RELEASE_3_13
git_last_commit: bed2b5c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CellMixS_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CellMixS_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CellMixS_1.8.0.tgz
vignettes: vignettes/CellMixS/inst/doc/CellMixS.html
vignetteTitles: Explore data integration and batch effects
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellMixS/inst/doc/CellMixS.R
dependencyCount: 93
Package: CellNOptR
Version: 1.38.0
Depends: R (>= 3.5.0), RBGL, graph, methods, hash, RCurl, Rgraphviz,
XML, ggplot2
Imports: igraph, stringi, stringr,
Suggests: data.table, dplyr, tidyr, readr, RUnit, BiocGenerics,
Enhances: doParallel
License: GPL-3
MD5sum: db5fbcc68292fc2b843a49e944eb1b3d
NeedsCompilation: yes
Title: Training of boolean logic models of signalling networks using
prior knowledge networks and perturbation data
Description: This package does optimisation of boolean logic networks
of signalling pathways based on a previous knowledge network
and a set of data upon perturbation of the nodes in the
network.
biocViews: CellBasedAssays, CellBiology, Proteomics, Pathways, Network,
TimeCourse, ImmunoOncology
Author: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve,
E.Gjerga, A.Gabor
Maintainer: A.Gabor <attila.gabor@uni-heidelberg.de>
SystemRequirements: Graphviz version >= 2.2
git_url: https://git.bioconductor.org/packages/CellNOptR
git_branch: RELEASE_3_13
git_last_commit: 8e211e0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CellNOptR_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CellNOptR_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CellNOptR_1.38.0.tgz
vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.pdf
vignetteTitles: Main vignette:Playing with networks using CellNOptR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R
dependsOnMe: CNORdt, CNORfeeder, CNORfuzzy, CNORode
importsMe: bnem
suggestsMe: MEIGOR
dependencyCount: 53
Package: cellscape
Version: 1.16.0
Depends: R (>= 3.3)
Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), reshape2 (>=
1.4.1), stringr (>= 1.0.0), plyr (>= 1.8.3), dplyr (>= 0.4.3),
gtools (>= 3.5.0)
Suggests: knitr, rmarkdown
License: GPL-3
Archs: i386, x64
MD5sum: d45f1df5b32d00d087fae33d0701a723
NeedsCompilation: no
Title: Explores single cell copy number profiles in the context of a
single cell tree
Description: CellScape facilitates interactive browsing of single cell
clonal evolution datasets. The tool requires two main inputs:
(i) the genomic content of each single cell in the form of
either copy number segments or targeted mutation values, and
(ii) a single cell phylogeny. Phylogenetic formats can vary
from dendrogram-like phylogenies with leaf nodes to
evolutionary model-derived phylogenies with observed or latent
internal nodes. The CellScape phylogeny is flexibly input as a
table of source-target edges to support arbitrary
representations, where each node may or may not have associated
genomic data. The output of CellScape is an interactive
interface displaying a single cell phylogeny and a
cell-by-locus genomic heatmap representing the mutation status
in each cell for each locus.
biocViews: Visualization
Author: Maia Smith [aut, cre]
Maintainer: Maia Smith <maiaannesmith@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cellscape
git_branch: RELEASE_3_13
git_last_commit: 33e4726
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cellscape_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cellscape_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cellscape_1.16.0.tgz
vignettes: vignettes/cellscape/inst/doc/cellscape_vignette.html
vignetteTitles: CellScape vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cellscape/inst/doc/cellscape_vignette.R
dependencyCount: 36
Package: CellScore
Version: 1.12.0
Depends: R (>= 3.5.0)
Imports: Biobase (>= 2.39.1), graphics (>= 3.5.0), grDevices (>=
3.5.0), gplots (>= 3.0.1), lsa (>= 0.73.1), methods (>= 3.5.0),
RColorBrewer(>= 1.1-2), squash (>= 1.0.8), stats (>= 3.5.0),
utils(>= 3.5.0)
Suggests: hgu133plus2CellScore, knitr
License: GPL-3
MD5sum: 0c14a55539626416aa51e3ef1b0d6f3b
NeedsCompilation: no
Title: Tool for Evaluation of Cell Identity from Transcription Profiles
Description: The CellScore package contains functions to evaluate the
cell identity of a test sample, given a cell transition defined
with a starting (donor) cell type and a desired target cell
type. The evaluation is based upon a scoring system, which uses
a set of standard samples of known cell types, as the reference
set. The functions have been carried out on a large set of
microarray data from one platform (Affymetrix Human Genome U133
Plus 2.0). In principle, the method could be applied to any
expression dataset, provided that there are a sufficient number
of standard samples and that the data are normalized.
biocViews: GeneExpression, Transcription, Microarray,
MultipleComparison, ReportWriting, DataImport, Visualization
Author: Nancy Mah, Katerina Taskova
Maintainer: Nancy Mah <nancy.l.mah@googlemail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CellScore
git_branch: RELEASE_3_13
git_last_commit: 10431e2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CellScore_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CellScore_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CellScore_1.12.0.tgz
vignettes: vignettes/CellScore/inst/doc/CellScoreVignette.pdf
vignetteTitles: R packages: CellScore
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellScore/inst/doc/CellScoreVignette.R
dependencyCount: 17
Package: CellTrails
Version: 1.10.0
Depends: R (>= 3.5), SingleCellExperiment
Imports: BiocGenerics, Biobase, cba, dendextend, dtw, EnvStats,
ggplot2, ggrepel, grDevices, igraph, maptree, methods, mgcv,
reshape2, Rtsne, stats, splines, SummarizedExperiment, utils
Suggests: AnnotationDbi, destiny, RUnit, scater, scran, knitr,
org.Mm.eg.db, rmarkdown
License: Artistic-2.0
MD5sum: 215ba17d0a2e56c876459aaf5829049d
NeedsCompilation: no
Title: Reconstruction, visualization and analysis of branching
trajectories
Description: CellTrails is an unsupervised algorithm for the de novo
chronological ordering, visualization and analysis of
single-cell expression data. CellTrails makes use of a
geometrically motivated concept of lower-dimensional manifold
learning, which exhibits a multitude of virtues that counteract
intrinsic noise of single cell data caused by drop-outs,
technical variance, and redundancy of predictive variables.
CellTrails enables the reconstruction of branching trajectories
and provides an intuitive graphical representation of
expression patterns along all branches simultaneously. It
allows the user to define and infer the expression dynamics of
individual and multiple pathways towards distinct phenotypes.
biocViews: ImmunoOncology, Clustering, DataRepresentation,
DifferentialExpression, DimensionReduction, GeneExpression,
Sequencing, SingleCell, Software, TimeCourse
Author: Daniel Ellwanger [aut, cre, cph]
Maintainer: Daniel Ellwanger <dc.ellwanger.dev@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CellTrails
git_branch: RELEASE_3_13
git_last_commit: 2aa606f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CellTrails_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CellTrails_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CellTrails_1.10.0.tgz
vignettes: vignettes/CellTrails/inst/doc/vignette.pdf
vignetteTitles: CellTrails: Reconstruction,, visualization,, and
analysis of branching trajectories from single-cell expression
data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellTrails/inst/doc/vignette.R
dependencyCount: 77
Package: cellTree
Version: 1.22.0
Depends: R (>= 3.3), topGO
Imports: topicmodels, slam, maptpx, igraph, xtable, gplots
Suggests: BiocStyle, knitr, HSMMSingleCell, biomaRt, org.Hs.eg.db,
Biobase, tools
License: Artistic-2.0
MD5sum: ed5ee3d7719c42b05416c74bb62b6141
NeedsCompilation: no
Title: Inference and visualisation of Single-Cell RNA-seq data as a
hierarchical tree structure
Description: This packages computes a Latent Dirichlet Allocation (LDA)
model of single-cell RNA-seq data and builds a compact tree
modelling the relationship between individual cells over time
or space.
biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering,
GraphAndNetwork, Visualization, GeneExpression,
GeneSetEnrichment, BiomedicalInformatics, CellBiology,
FunctionalGenomics, SystemsBiology, GO, TimeCourse, Microarray
Author: David duVerle [aut, cre], Koji Tsuda [aut]
Maintainer: David duVerle <dave@cb.k.u-tokyo.ac.jp>
URL: http://tsudalab.org
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cellTree
git_branch: RELEASE_3_13
git_last_commit: 65701cf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cellTree_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cellTree_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cellTree_1.22.0.tgz
vignettes: vignettes/cellTree/inst/doc/cellTree-vignette.pdf
vignetteTitles: Inference and visualisation of Single-Cell RNA-seq Data
data as a hierarchical tree structure
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cellTree/inst/doc/cellTree-vignette.R
dependencyCount: 69
Package: CEMiTool
Version: 1.16.0
Depends: R (>= 4.0)
Imports: methods, scales, dplyr, data.table (>= 1.9.4), WGCNA, grid,
ggplot2, ggpmisc, ggthemes, ggrepel, sna, clusterProfiler,
fgsea, stringr, knitr, rmarkdown, igraph, DT, htmltools,
pracma, intergraph, grDevices, utils, network, matrixStats,
ggdendro, gridExtra, gtable, fastcluster
Suggests: testthat, BiocManager
License: GPL-3
MD5sum: cde0a4dab48f5abc51804a9dc1171e68
NeedsCompilation: no
Title: Co-expression Modules identification Tool
Description: The CEMiTool package unifies the discovery and the
analysis of coexpression gene modules in a fully automatic
manner, while providing a user-friendly html report with high
quality graphs. Our tool evaluates if modules contain genes
that are over-represented by specific pathways or that are
altered in a specific sample group. Additionally, CEMiTool is
able to integrate transcriptomic data with interactome
information, identifying the potential hubs on each network.
biocViews: GeneExpression, Transcriptomics, GraphAndNetwork,
mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways,
ImmunoOncology
Author: Pedro Russo [aut], Gustavo Ferreira [aut], Matheus Bürger
[aut], Lucas Cardozo [aut], Diogenes Lima [aut], Thiago Hirata
[aut], Melissa Lever [aut], Helder Nakaya [aut, cre]
Maintainer: Helder Nakaya <hnakaya@usp.br>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CEMiTool
git_branch: RELEASE_3_13
git_last_commit: 6cf15bd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CEMiTool_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CEMiTool_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CEMiTool_1.16.0.tgz
vignettes: vignettes/CEMiTool/inst/doc/CEMiTool.html
vignetteTitles: CEMiTool: Co-expression Modules Identification Tool
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CEMiTool/inst/doc/CEMiTool.R
dependencyCount: 184
Package: censcyt
Version: 1.0.0
Depends: R (>= 4.0), diffcyt
Imports: BiocParallel, broom.mixed, dirmult, dplyr, edgeR,
fitdistrplus, lme4, magrittr, MASS, methods, mice, multcomp,
purrr, rlang, S4Vectors, stats, stringr, SummarizedExperiment,
survival, tibble, tidyr, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2
License: MIT + file LICENSE
MD5sum: 6b30a82fe6da4ea4689fd31ce2bfe7ed
NeedsCompilation: no
Title: Differential abundance analysis with a right censored covariate
in high-dimensional cytometry
Description: Methods for differential abundance analysis in
high-dimensional cytometry data when a covariate is subject to
right censoring (e.g. survival time) based on multiple
imputation and generalized linear mixed models.
biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell,
CellBasedAssays, CellBiology, Clustering, FeatureExtraction,
Software, Survival
Author: Reto Gerber [aut, cre]
(<https://orcid.org/0000-0001-5414-8906>)
Maintainer: Reto Gerber <gerberreto@pm.me>
URL: https://github.com/retogerber/censcyt
VignetteBuilder: knitr
BugReports: https://github.com/retogerber/censcyt/issues
git_url: https://git.bioconductor.org/packages/censcyt
git_branch: RELEASE_3_13
git_last_commit: 916b30f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/censcyt_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/censcyt_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/censcyt_1.0.0.tgz
vignettes: vignettes/censcyt/inst/doc/censored_covariate.html
vignetteTitles: Censored covariate
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/censcyt/inst/doc/censored_covariate.R
dependencyCount: 230
Package: ceRNAnetsim
Version: 1.4.0
Depends: R (>= 4.0.0), dplyr, tidygraph
Imports: furrr, rlang, tibble, ggplot2, ggraph, igraph, purrr, tidyr,
future, stats
Suggests: knitr, png, rmarkdown, testthat, covr
License: GPL (>= 3.0)
MD5sum: ff59fd5d0e0749d6fc9320504b4bd176
NeedsCompilation: no
Title: Regulation Simulator of Interaction between miRNA and Competing
RNAs (ceRNA)
Description: This package simulates regulations of ceRNA (Competing
Endogenous) expression levels after a expression level change
in one or more miRNA/mRNAs. The methodolgy adopted by the
package has potential to incorparate any ceRNA (circRNA,
lincRNA, etc.) into miRNA:target interaction network. The
package basically distributes miRNA expression over available
ceRNAs where each ceRNA attracks miRNAs proportional to its
amount. But, the package can utilize multiple parameters that
modify miRNA effect on its target (seed type, binding energy,
binding location, etc.). The functions handle the given
dataset as graph object and the processes progress via edge and
node variables.
biocViews: NetworkInference, SystemsBiology, Network, GraphAndNetwork,
Transcriptomics
Author: Selcen Ari Yuka [aut, cre]
(<https://orcid.org/0000-0002-0028-2453>), Alper Yilmaz [aut]
(<https://orcid.org/0000-0002-8827-4887>)
Maintainer: Selcen Ari Yuka <selcenarii@gmail.com>
URL: https://github.com/selcenari/ceRNAnetsim
VignetteBuilder: knitr
BugReports: https://github.com/selcenari/ceRNAnetsim/issues
git_url: https://git.bioconductor.org/packages/ceRNAnetsim
git_branch: RELEASE_3_13
git_last_commit: fd4f5da
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ceRNAnetsim_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ceRNAnetsim_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ceRNAnetsim_1.4.0.tgz
vignettes: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.html,
vignettes/ceRNAnetsim/inst/doc/basic_usage.html,
vignettes/ceRNAnetsim/inst/doc/convenient_iteration.html,
vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.html
vignetteTitles: auxiliary_commands, basic_usage, A Suggestion: How to
Find the Appropriate Iteration for Simulation, An TCGA dataset
application
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.R,
vignettes/ceRNAnetsim/inst/doc/basic_usage.R,
vignettes/ceRNAnetsim/inst/doc/convenient_iteration.R,
vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.R
dependencyCount: 65
Package: CeTF
Version: 1.4.5
Depends: R (>= 4.0), methods
Imports: circlize, ComplexHeatmap, clusterProfiler, DESeq2, dplyr,
GenomicTools, GenomicTools.fileHandler, GGally, ggnetwork,
ggplot2, ggpubr, ggrepel, graphics, grid, igraph, Matrix,
methods, network, Rcpp, RCy3, stats, SummarizedExperiment,
S4Vectors, utils, WebGestaltR
LinkingTo: Rcpp, RcppArmadillo
Suggests: airway, kableExtra, knitr, org.Hs.eg.db, rmarkdown, testthat
License: GPL-3
MD5sum: b3351fe2f84e0ccfdea248850c1673f3
NeedsCompilation: yes
Title: Coexpression for Transcription Factors using Regulatory Impact
Factors and Partial Correlation and Information Theory analysis
Description: This package provides the necessary functions for
performing the Partial Correlation coefficient with Information
Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact
Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT
algorithm identifies meaningful correlations to define edges in
a weighted network and can be applied to any correlation-based
network including but not limited to gene co-expression
networks, while the RIF algorithm identify critical
Transcription Factors (TF) from gene expression data. These two
algorithms when combined provide a very relevant layer of
information for gene expression studies (Microarray, RNA-seq
and single-cell RNA-seq data).
biocViews: Sequencing, RNASeq, Microarray, GeneExpression,
Transcription, Normalization, DifferentialExpression,
SingleCell, Network, Regression, ChIPSeq, ImmunoOncology,
Coverage
Author: Carlos Alberto Oliveira de Biagi Junior [aut, cre], Ricardo
Perecin Nociti [aut], Breno Osvaldo Funicheli [aut], João Paulo
Bianchi Ximenez [ctb], PatrÃcia de Cássia Ruy [ctb], Marcelo
Gomes de Paula [ctb], Rafael dos Santos Bezerra [ctb], Wilson
Araújo da Silva Junior [aut, ths]
Maintainer: Carlos Alberto Oliveira de Biagi Junior
<cbiagijr@gmail.com>
SystemRequirements: libcurl4-openssl-dev, libxml2-dev, libssl-dev,
gfortran, build-essential, libz-dev, zlib1g-dev
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CeTF
git_branch: RELEASE_3_13
git_last_commit: bd93884
git_last_commit_date: 2021-09-09
Date/Publication: 2021-09-12
source.ver: src/contrib/CeTF_1.4.5.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CeTF_1.4.5.zip
mac.binary.ver: bin/macosx/contrib/4.1/CeTF_1.4.5.tgz
vignettes: vignettes/CeTF/inst/doc/CeTF.html
vignetteTitles: Analyzing Regulatory Impact Factors and Partial
Correlation and Information Theory
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CeTF/inst/doc/CeTF.R
dependencyCount: 234
Package: CFAssay
Version: 1.26.0
Depends: R (>= 2.10.0)
License: LGPL
Archs: i386, x64
MD5sum: 1807baffb9c928f60c42d9ce917a92b8
NeedsCompilation: no
Title: Statistical analysis for the Colony Formation Assay
Description: The package provides functions for calculation of
linear-quadratic cell survival curves and for ANOVA of
experimental 2-way designs along with the colony formation
assay.
biocViews: CellBasedAssays, CellBiology, ImmunoOncology, Regression,
Survival
Author: Herbert Braselmann
Maintainer: Herbert Braselmann <hbraselmann@online.de>
git_url: https://git.bioconductor.org/packages/CFAssay
git_branch: RELEASE_3_13
git_last_commit: 0683af9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CFAssay_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CFAssay_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CFAssay_1.26.0.tgz
vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf
vignetteTitles: CFAssay
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CFAssay/inst/doc/cfassay.R
dependencyCount: 0
Package: CGEN
Version: 3.28.0
Depends: R (>= 4.0), survival, mvtnorm
Imports: stats, graphics, utils, grDevices
Suggests: cluster
License: GPL-2 + file LICENSE
MD5sum: 15e6c64f5d0e9662c977e51f57cc9d8a
NeedsCompilation: yes
Title: An R package for analysis of case-control studies in genetic
epidemiology
Description: This is a package for analysis of case-control data in
genetic epidemiology. It provides a set of statistical methods
for evaluating gene-environment (or gene-genes) interactions
under multiplicative and additive risk models, with or without
assuming gene-environment (or gene-gene) independence in the
underlying population.
biocViews: SNP, MultipleComparison, Clustering
Author: Samsiddhi Bhattacharjee [aut], Nilanjan Chatterjee [aut],
Summer Han [aut], Minsun Song [aut], William Wheeler [aut],
Matthieu de Rochemonteix [aut], Nilotpal Sanyal [aut, cre]
Maintainer: Nilotpal Sanyal <nsanyal@stanford.edu>
git_url: https://git.bioconductor.org/packages/CGEN
git_branch: RELEASE_3_13
git_last_commit: ded8343
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CGEN_3.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CGEN_3.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CGEN_3.28.0.tgz
vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf,
vignettes/CGEN/inst/doc/vignette.pdf
vignetteTitles: CGEN Scan Vignette, CGEN Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CGEN/inst/doc/vignette_GxE.R,
vignettes/CGEN/inst/doc/vignette.R
dependencyCount: 11
Package: CGHbase
Version: 1.52.0
Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray
License: GPL
Archs: i386, x64
MD5sum: 0306ece704bce0faf72d89b0bf486a85
NeedsCompilation: no
Title: CGHbase: Base functions and classes for arrayCGH data analysis.
Description: Contains functions and classes that are needed by arrayCGH
packages.
biocViews: Infrastructure, Microarray, CopyNumberVariation
Author: Sjoerd Vosse, Mark van de Wiel
Maintainer: Mark van de Wiel <mark.vdwiel@vumc.nl>
URL: https://github.com/tgac-vumc/CGHbase
BugReports: https://github.com/tgac-vumc/CGHbase/issues
git_url: https://git.bioconductor.org/packages/CGHbase
git_branch: RELEASE_3_13
git_last_commit: 25d9f08
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CGHbase_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CGHbase_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CGHbase_1.52.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak
importsMe: CGHnormaliter, QDNAseq, ragt2ridges
dependencyCount: 10
Package: CGHcall
Version: 2.54.0
Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods,
Biobase, CGHbase (>= 1.15.1), snowfall
License: GPL (http://www.gnu.org/copyleft/gpl.html)
MD5sum: b555ed0abe09720ef05f2f0be60ec9f6
NeedsCompilation: no
Title: Calling aberrations for array CGH tumor profiles.
Description: Calls aberrations for array CGH data using a six state
mixture model as well as several biological concepts that are
ignored by existing algorithms. Visualization of profiles is
also provided.
biocViews: Microarray,Preprocessing,Visualization
Author: Mark van de Wiel, Sjoerd Vosse
Maintainer: Mark van de Wiel <mark.vdwiel@vumc.nl>
git_url: https://git.bioconductor.org/packages/CGHcall
git_branch: RELEASE_3_13
git_last_commit: b30726c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CGHcall_2.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CGHcall_2.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CGHcall_2.54.0.tgz
vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf
vignetteTitles: CGHcall
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CGHcall/inst/doc/CGHcall.R
dependsOnMe: CGHnormaliter, GeneBreak
importsMe: CGHnormaliter, QDNAseq
dependencyCount: 15
Package: cghMCR
Version: 1.50.0
Depends: methods, DNAcopy, CNTools, limma
Imports: BiocGenerics (>= 0.1.6), stats4
License: LGPL
MD5sum: 2e76e3884f719c5077a174e7e3770c09
NeedsCompilation: no
Title: Find chromosome regions showing common gains/losses
Description: This package provides functions to identify genomic
regions of interests based on segmented copy number data from
multiple samples.
biocViews: Microarray, CopyNumberVariation
Author: J. Zhang and B. Feng
Maintainer: J. Zhang <jzhang@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/cghMCR
git_branch: RELEASE_3_13
git_last_commit: ee62229
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cghMCR_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cghMCR_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cghMCR_1.50.0.tgz
vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf
vignetteTitles: cghMCR findMCR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cghMCR/inst/doc/findMCR.R
dependencyCount: 58
Package: CGHnormaliter
Version: 1.46.0
Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0)
Imports: Biobase, CGHbase, CGHcall, methods, stats, utils
License: GPL (>= 3)
MD5sum: 4576d2e4c548bb2576ad6dc8a5e1bc57
NeedsCompilation: no
Title: Normalization of array CGH data with imbalanced aberrations.
Description: Normalization and centralization of array comparative
genomic hybridization (aCGH) data. The algorithm uses an
iterative procedure that effectively eliminates the influence
of imbalanced copy numbers. This leads to a more reliable
assessment of copy number alterations (CNAs).
biocViews: Microarray, Preprocessing
Author: Bart P.P. van Houte, Thomas W. Binsl, Hannes Hettling
Maintainer: Bart P.P. van Houte <bvhoute@few.vu.nl>
git_url: https://git.bioconductor.org/packages/CGHnormaliter
git_branch: RELEASE_3_13
git_last_commit: d0de582
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CGHnormaliter_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CGHnormaliter_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CGHnormaliter_1.46.0.tgz
vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf
vignetteTitles: CGHnormaliter
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.R
dependencyCount: 16
Package: CGHregions
Version: 1.50.0
Depends: R (>= 2.0.0), methods, Biobase, CGHbase
License: GPL (http://www.gnu.org/copyleft/gpl.html)
MD5sum: 42170cc24f13419623da18b1d33da778
NeedsCompilation: no
Title: Dimension Reduction for Array CGH Data with Minimal Information
Loss.
Description: Dimension Reduction for Array CGH Data with Minimal
Information Loss
biocViews: Microarray, CopyNumberVariation, Visualization
Author: Sjoerd Vosse & Mark van de Wiel
Maintainer: Sjoerd Vosse <info@vossewebdevelopment.nl>
git_url: https://git.bioconductor.org/packages/CGHregions
git_branch: RELEASE_3_13
git_last_commit: 123fe62
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CGHregions_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CGHregions_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CGHregions_1.50.0.tgz
vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf
vignetteTitles: CGHcall
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CGHregions/inst/doc/CGHregions.R
suggestsMe: ADaCGH2
dependencyCount: 11
Package: ChAMP
Version: 2.22.0
Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0),DMRcate,
Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest,
DT, RPMM
Imports:
prettydoc,Hmisc,globaltest,sva,illuminaio,rmarkdown,IlluminaHumanMethylation450kmanifest,IlluminaHumanMethylationEPICanno.ilm10b4.hg19,
limma, DNAcopy, preprocessCore,impute, marray, wateRmelon,
plyr,goseq,missMethyl,kpmt,ggplot2,
GenomicRanges,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly
(>= 4.5.6),RColorBrewer,dendextend, matrixStats,combinat
Suggests: knitr,rmarkdown
License: GPL-3
MD5sum: b1beeb45f508b90ced3d60c11f6d900a
NeedsCompilation: no
Title: Chip Analysis Methylation Pipeline for Illumina
HumanMethylation450 and EPIC
Description: The package includes quality control metrics, a selection
of normalization methods and novel methods to identify
differentially methylated regions and to highlight copy number
alterations.
biocViews: Microarray, MethylationArray, Normalization, TwoChannel,
CopyNumber, DNAMethylation
Author: Yuan Tian [cre,aut], Tiffany Morris [ctb], Lee Stirling [ctb],
Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur
Chakravarthy [ctb]
Maintainer: Yuan Tian <champ450k@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ChAMP
git_branch: RELEASE_3_13
git_last_commit: 2350b07
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChAMP_2.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChAMP_2.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChAMP_2.22.0.tgz
vignettes: vignettes/ChAMP/inst/doc/ChAMP.html
vignetteTitles: ChAMP: The Chip Analysis Methylation Pipeline
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R
suggestsMe: GeoTcgaData
dependencyCount: 253
Package: ChemmineOB
Version: 1.30.0
Depends: R (>= 2.15.1), methods
Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0)
LinkingTo: BH, Rcpp
Suggests: ChemmineR, BiocStyle, knitr, knitrBootstrap, BiocManager,
rmarkdown
Enhances: ChemmineR (>= 2.13.0)
License: file LICENSE
MD5sum: ea2abea0c081f05f9ebecfdd0bb58b6a
NeedsCompilation: yes
Title: R interface to a subset of OpenBabel functionalities
Description: ChemmineOB provides an R interface to a subset of
cheminformatics functionalities implemented by the OpelBabel
C++ project. OpenBabel is an open source cheminformatics
toolbox that includes utilities for structure format
interconversions, descriptor calculations, compound similarity
searching and more. ChemineOB aims to make a subset of these
utilities available from within R. For non-developers,
ChemineOB is primarily intended to be used from ChemmineR as an
add-on package rather than used directly.
biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics,
Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays,
Visualization, Infrastructure, DataImport, Clustering,
Proteomics, Metabolomics
Author: Kevin Horan, Thomas Girke
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://github.com/girke-lab/ChemmineOB
SystemRequirements: OpenBabel (>= 3.0.0) with headers
(http://openbabel.org). Eigen3 with headers.
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ChemmineOB
git_branch: RELEASE_3_13
git_last_commit: 792d07a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChemmineOB_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChemmineOB_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChemmineOB_1.30.0.tgz
vignettes: vignettes/ChemmineOB/inst/doc/ChemmineOB.html
vignetteTitles: ChemmineOB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: TRUE
Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R
dependencyCount: 9
Package: ChemmineR
Version: 3.44.0
Depends: R (>= 2.10.0), methods
Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp
(>= 0.11.0), ggplot2,grid,gridExtra, png,base64enc,DT,rsvg
LinkingTo: Rcpp, BH
Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL,
BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineDrugs,
png,rmarkdown, BiocManager
Enhances: ChemmineOB
License: Artistic-2.0
Archs: i386, x64
MD5sum: ba81826e7515f382c4c4bc72c5925130
NeedsCompilation: yes
Title: Cheminformatics Toolkit for R
Description: ChemmineR is a cheminformatics package for analyzing
drug-like small molecule data in R. Its latest version contains
functions for efficient processing of large numbers of
molecules, physicochemical/structural property predictions,
structural similarity searching, classification and clustering
of compound libraries with a wide spectrum of algorithms. In
addition, it offers visualization functions for compound
clustering results and chemical structures.
biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics,
Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays,
Visualization, Infrastructure, DataImport, Clustering,
Proteomics,Metabolomics
Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://github.com/girke-lab/ChemmineR
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ChemmineR
git_branch: RELEASE_3_13
git_last_commit: 6a834ab
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChemmineR_3.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChemmineR_3.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChemmineR_3.44.0.tgz
vignettes: vignettes/ChemmineR/inst/doc/ChemmineR.html
vignetteTitles: ChemmineR
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R
dependsOnMe: eiR, fmcsR, ChemmineDrugs
importsMe: bioassayR, customCMPdb, eiR, fmcsR, MetID, Rcpi, BioMedR,
MetaDBparse, uCAREChemSuiteCLI
suggestsMe: ChemmineOB, xnet
dependencyCount: 61
Package: CHETAH
Version: 1.8.0
Depends: R (>= 3.6), ggplot2, SingleCellExperiment
Imports: gplots, shiny, plotly, pheatmap, bioDist, dendextend, cowplot,
corrplot, grDevices, stats, graphics, reshape2, S4Vectors,
SummarizedExperiment
Suggests: knitr, rmarkdown, Matrix, testthat, vdiffr
License: file LICENSE
Archs: i386, x64
MD5sum: 9e65b54e247ca639bc223b1b03901624
NeedsCompilation: no
Title: Fast and accurate scRNA-seq cell type identification
Description: CHETAH (CHaracterization of cEll Types Aided by
Hierarchical classification) is an accurate, selective and fast
scRNA-seq classifier. Classification is guided by a reference
dataset, preferentially also a scRNA-seq dataset. By
hierarchical clustering of the reference data, CHETAH creates a
classification tree that enables a step-wise, top-to-bottom
classification. Using a novel stopping rule, CHETAH classifies
the input cells to the cell types of the references and to
"intermediate types": more general classifications that ended
in an intermediate node of the tree.
biocViews: Classification, RNASeq, SingleCell, Clustering
Author: Jurrian de Kanter [aut, cre], Philip Lijnzaad [aut]
Maintainer: Jurrian de Kanter <jurriandekanter@gmail.com>
URL: https://github.com/jdekanter/CHETAH
VignetteBuilder: knitr
BugReports: https://github.com/jdekanter/CHETAH
git_url: https://git.bioconductor.org/packages/CHETAH
git_branch: RELEASE_3_13
git_last_commit: 1eacdb8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CHETAH_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CHETAH_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CHETAH_1.8.0.tgz
vignettes: vignettes/CHETAH/inst/doc/CHETAH_introduction.html
vignetteTitles: Introduction to the CHETAH package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CHETAH/inst/doc/CHETAH_introduction.R
dependencyCount: 110
Package: ChIC
Version: 1.12.0
Depends: spp, R (>= 3.6)
Imports: ChIC.data (>= 1.11.1), caTools, methods, GenomicRanges,
IRanges, parallel, progress, randomForest, caret, grDevices,
stats, utils, graphics, S4Vectors, BiocGenerics,
genomeIntervals, Rsamtools
License: GPL-2
MD5sum: 92265bc933fea1af0f407cb373a5c4ea
NeedsCompilation: no
Title: Quality Control Pipeline for ChIP-Seq Data
Description: Quality control (QC) pipeline for ChIP-seq data using a
comprehensive set of QC metrics, including previously proposed
metrics as well as novel ones, based on local characteristics
of the enrichment profile. The package provides functions to
calculate a set of QC metrics, a compendium with reference
values and machine learning models to score sample quality.
biocViews: ChIPSeq, QualityControl
Author: Carmen Maria Livi
Maintainer: Carmen Maria Livi <carmen.livi@external.ifom.eu>
git_url: https://git.bioconductor.org/packages/ChIC
git_branch: RELEASE_3_13
git_last_commit: 11f21c4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChIC_1.12.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/ChIC_1.12.0.tgz
vignettes: vignettes/ChIC/inst/doc/ChIC-Vignette.pdf
vignetteTitles: ChIC
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIC/inst/doc/ChIC-Vignette.R
dependencyCount: 110
Package: Chicago
Version: 1.20.0
Depends: R (>= 3.3.1), data.table
Imports: matrixStats, MASS, Hmisc, Delaporte, methods, grDevices,
graphics, stats, utils
Suggests: argparser, BiocStyle, knitr, rmarkdown, PCHiCdata, testthat,
Rsamtools, GenomicInteractions, GenomicRanges, IRanges,
AnnotationHub
License: Artistic-2.0
MD5sum: 28fff879ee82bf7e9f014e07bde6264b
NeedsCompilation: no
Title: CHiCAGO: Capture Hi-C Analysis of Genomic Organization
Description: A pipeline for analysing Capture Hi-C data.
biocViews: Epigenetics, HiC, Sequencing, Software
Author: Jonathan Cairns, Paula Freire Pritchett, Steven Wingett,
Mikhail Spivakov
Maintainer: Mikhail Spivakov <mikhail.spivakov@lms.mrc.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Chicago
git_branch: RELEASE_3_13
git_last_commit: 116655f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Chicago_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Chicago_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Chicago_1.20.0.tgz
vignettes: vignettes/Chicago/inst/doc/Chicago.html
vignetteTitles: CHiCAGO Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Chicago/inst/doc/Chicago.R
dependsOnMe: PCHiCdata
dependencyCount: 70
Package: chimeraviz
Version: 1.18.0
Depends: Biostrings, GenomicRanges, IRanges, Gviz, S4Vectors,
ensembldb, AnnotationFilter, data.table
Imports: methods, grid, Rsamtools, GenomeInfoDb, GenomicAlignments,
RColorBrewer, graphics, AnnotationDbi, RCircos, org.Hs.eg.db,
org.Mm.eg.db, rmarkdown, graph, Rgraphviz, DT, plyr, dplyr,
BiocStyle, checkmate, gtools, magick
Suggests: testthat, roxygen2, devtools, knitr, lintr
License: Artistic-2.0
MD5sum: d8b1b19903fc4af4b3b286fba7970b06
NeedsCompilation: no
Title: Visualization tools for gene fusions
Description: chimeraviz manages data from fusion gene finders and
provides useful visualization tools.
biocViews: Infrastructure, Alignment
Author: Stian LÃ¥gstad [aut, cre], Sen Zhao [ctb], Andreas M. Hoff
[ctb], Bjarne Johannessen [ctb], Ole Christian Lingjærde [ctb],
Rolf Skotheim [ctb]
Maintainer: Stian LÃ¥gstad <stianlagstad@gmail.com>
URL: https://github.com/stianlagstad/chimeraviz
SystemRequirements: bowtie, samtools, and egrep are required for some
functionalities
VignetteBuilder: knitr
BugReports: https://github.com/stianlagstad/chimeraviz/issues
git_url: https://git.bioconductor.org/packages/chimeraviz
git_branch: RELEASE_3_13
git_last_commit: bc2b277
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/chimeraviz_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/chimeraviz_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/chimeraviz_1.18.0.tgz
vignettes: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.html
vignetteTitles: chimeraviz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R
dependencyCount: 160
Package: ChIPanalyser
Version: 1.14.0
Depends: R (>= 3.5.0),GenomicRanges, Biostrings, BSgenome, RcppRoll,
parallel
Imports: methods, IRanges,
S4Vectors,grDevices,graphics,stats,utils,rtracklayer,ROCR,
BiocManager,GenomeInfoDb
Suggests: BSgenome.Dmelanogaster.UCSC.dm3,knitr, RUnit, BiocGenerics
License: GPL-3
MD5sum: 66cfb57702c44322091eb950403d8e83
NeedsCompilation: no
Title: ChIPanalyser: Predicting Transcription Factor Binding Sites
Description: Based on a statistical thermodynamic framework,
ChIPanalyser tries to produce ChIP-seq like profile. The model
relies on four consideration: TF binding sites can be scored
using a Position weight Matrix, DNA accessibility plays a role
in Transcription Factor binding, binding profiles are dependant
on the number of transcription factors bound to DNA and finally
binding energy (another way of describing PWM's) or binding
specificity should be modulated (hence the introduction of a
binding specificity modulator). The end result of ChIPanalyser
is to produce profiles simulating real ChIP-seq profile and
provide accuracy measurements of these predicted profiles after
being compared to real ChIP-seq data. The ultimate goal is to
produce ChIP-seq like profiles predicting ChIP-seq like profile
to circumvent the need to produce costly ChIP-seq experiments.
biocViews: Software, BiologicalQuestion, WorkflowStep, Transcription,
Sequencing, ChipOnChip, Coverage, Alignment, ChIPSeq,
SequenceMatching, DataImport ,PeakDetection
Author: Patrick C.N.Martin & Nicolae Radu Zabet
Maintainer: Patrick C.N. Martin <pm16057@essex.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ChIPanalyser
git_branch: RELEASE_3_13
git_last_commit: 68caeca
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChIPanalyser_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChIPanalyser_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChIPanalyser_1.14.0.tgz
vignettes: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.pdf
vignetteTitles: ChIPanalyser User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.R
dependencyCount: 53
Package: ChIPComp
Version: 1.22.0
Depends: R (>=
3.2.0),GenomicRanges,IRanges,rtracklayer,GenomeInfoDb,S4Vectors
Imports: Rsamtools,limma,BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm9,BiocGenerics
Suggests: BiocStyle,RUnit
License: GPL
MD5sum: 3da2d99cb5120a8b65a4c0e301f016d0
NeedsCompilation: yes
Title: Quantitative comparison of multiple ChIP-seq datasets
Description: ChIPComp detects differentially bound sharp binding sites
across multiple conditions considering matching control.
biocViews: ChIPSeq, Sequencing, Transcription, Genetics,Coverage,
MultipleComparison, DataImport
Author: Hao Wu, Li Chen, Zhaohui S.Qin, Chi Wang
Maintainer: Li Chen <li.chen@emory.edu>
git_url: https://git.bioconductor.org/packages/ChIPComp
git_branch: RELEASE_3_13
git_last_commit: 2b6dc05
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChIPComp_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChIPComp_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChIPComp_1.22.0.tgz
vignettes: vignettes/ChIPComp/inst/doc/ChIPComp.pdf
vignetteTitles: ChIPComp
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPComp/inst/doc/ChIPComp.R
dependencyCount: 48
Package: chipenrich
Version: 2.16.0
Depends: R (>= 3.4.0)
Imports: AnnotationDbi, BiocGenerics, chipenrich.data, GenomeInfoDb,
GenomicRanges, grDevices, grid, IRanges, lattice, latticeExtra,
MASS, methods, mgcv, org.Dm.eg.db, org.Dr.eg.db, org.Hs.eg.db,
org.Mm.eg.db, org.Rn.eg.db, parallel, plyr, rms, rtracklayer,
S4Vectors (>= 0.23.10), stats, stringr, utils
Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat
License: GPL-3
Archs: i386, x64
MD5sum: c2740e93fde0d24058e0bb6f47d9286e
NeedsCompilation: no
Title: Gene Set Enrichment For ChIP-seq Peak Data
Description: ChIP-Enrich and Poly-Enrich perform gene set enrichment
testing using peaks called from a ChIP-seq experiment. The
method empirically corrects for confounding factors such as the
length of genes, and the mappability of the sequence
surrounding genes.
biocViews: ImmunoOncology, ChIPSeq, Epigenetics, FunctionalGenomics,
GeneSetEnrichment, HistoneModification, Regression
Author: Ryan P. Welch [aut, cph], Chee Lee [aut], Raymond G. Cavalcante
[aut], Kai Wang [cre], Chris Lee [aut], Laura J. Scott [ths],
Maureen A. Sartor [ths]
Maintainer: Raymond G. Cavalcante <rcavalca@umich.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/chipenrich
git_branch: RELEASE_3_13
git_last_commit: 4653bc8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/chipenrich_2.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/chipenrich_2.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/chipenrich_2.16.0.tgz
vignettes: vignettes/chipenrich/inst/doc/chipenrich-vignette.html
vignetteTitles: chipenrich_vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chipenrich/inst/doc/chipenrich-vignette.R
dependencyCount: 148
Package: ChIPexoQual
Version: 1.16.0
Depends: R (>= 3.4.0), GenomicAlignments (>= 1.0.1)
Imports: methods, utils, GenomeInfoDb, stats, BiocParallel,
GenomicRanges (>= 1.14.4), ggplot2 (>= 1.0), data.table (>=
1.9.6), Rsamtools (>= 1.16.1), IRanges (>= 1.6), S4Vectors (>=
0.8), biovizBase (>= 1.18), broom (>= 0.4), RColorBrewer (>=
1.1), dplyr (>= 0.5), scales (>= 0.4.0), viridis (>= 0.3),
hexbin (>= 1.27), rmarkdown
Suggests: ChIPexoQualExample (>= 0.99.1), knitr (>= 1.10), BiocStyle,
gridExtra (>= 2.2), testthat
License: GPL (>=2)
MD5sum: 1ee31cef8492b8cdfc87b8b2d0fcc946
NeedsCompilation: no
Title: ChIPexoQual
Description: Package with a quality control pipeline for ChIP-exo/nexus
data.
biocViews: ChIPSeq, Sequencing, Transcription, Visualization,
QualityControl, Coverage, Alignment
Author: Rene Welch, Dongjun Chung, Sunduz Keles
Maintainer: Rene Welch <welch@stat.wisc.edu>
URL: https:github.com/keleslab/ChIPexoQual
VignetteBuilder: knitr
BugReports: https://github.com/welch16/ChIPexoQual/issues
git_url: https://git.bioconductor.org/packages/ChIPexoQual
git_branch: RELEASE_3_13
git_last_commit: ed02199
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChIPexoQual_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChIPexoQual_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChIPexoQual_1.16.0.tgz
vignettes: vignettes/ChIPexoQual/inst/doc/vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPexoQual/inst/doc/vignette.R
dependencyCount: 148
Package: ChIPpeakAnno
Version: 3.26.4
Depends: R (>= 3.5), methods, IRanges (>= 2.13.12), GenomicRanges (>=
1.31.8), S4Vectors (>= 0.17.25)
Imports: AnnotationDbi, BiocGenerics (>= 0.1.0), Biostrings (>=
2.47.6), DBI, dplyr, ensembldb, GenomeInfoDb,
GenomicAlignments, GenomicFeatures, RBGL, Rsamtools,
SummarizedExperiment, VennDiagram, biomaRt, ggplot2, grDevices,
graph, graphics, grid, InteractionSet, KEGGREST, matrixStats,
multtest, regioneR, rtracklayer, stats, utils
Suggests: AnnotationHub, BSgenome, limma, reactome.db, BiocManager,
BiocStyle, BSgenome.Ecoli.NCBI.20080805,
BSgenome.Hsapiens.UCSC.hg19, org.Ce.eg.db, org.Hs.eg.db,
BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7,
BSgenome.Hsapiens.UCSC.hg38, DelayedArray, idr, seqinr,
EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene, GO.db, gplots, UpSetR,
knitr, rmarkdown, testthat, trackViewer, motifStack,
OrganismDbi
License: GPL (>= 2)
MD5sum: b4dfeb99e9849b9b047becfc72135c02
NeedsCompilation: no
Title: Batch annotation of the peaks identified from either ChIP-seq,
ChIP-chip experiments or any experiments resulted in large
number of chromosome ranges
Description: The package includes functions to retrieve the sequences
around the peak, obtain enriched Gene Ontology (GO) terms, find
the nearest gene, exon, miRNA or custom features such as most
conserved elements and other transcription factor binding sites
supplied by users. Starting 2.0.5, new functions have been
added for finding the peaks with bi-directional promoters with
summary statistics (peaksNearBDP), for summarizing the
occurrence of motifs in peaks (summarizePatternInPeaks) and for
adding other IDs to annotated peaks or enrichedGO (addGeneIDs).
This package leverages the biomaRt, IRanges, Biostrings,
BSgenome, GO.db, multtest and stat packages.
biocViews: Annotation, ChIPSeq, ChIPchip
Author: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Kai Hu, Haibo Liu, Hervé
Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin,
David Lapointe and Michael Green
Maintainer: Jianhong Ou <ou.jianhong@gmail.com>, Lihua Julie Zhu
<julie.zhu@umassmed.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ChIPpeakAnno
git_branch: RELEASE_3_13
git_last_commit: 5104b7d
git_last_commit_date: 2021-09-09
Date/Publication: 2021-09-12
source.ver: src/contrib/ChIPpeakAnno_3.26.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChIPpeakAnno_3.26.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChIPpeakAnno_3.26.4.tgz
vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html,
vignettes/ChIPpeakAnno/inst/doc/FAQs.html,
vignettes/ChIPpeakAnno/inst/doc/pipeline.html,
vignettes/ChIPpeakAnno/inst/doc/quickStart.html
vignetteTitles: ChIPpeakAnno Vignette, ChIPpeakAnno FAQs, ChIPpeakAnno
Annotation Pipeline, ChIPpeakAnno Quick Start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R,
vignettes/ChIPpeakAnno/inst/doc/FAQs.R,
vignettes/ChIPpeakAnno/inst/doc/pipeline.R,
vignettes/ChIPpeakAnno/inst/doc/quickStart.R
dependsOnMe: REDseq, csawBook
importsMe: ATACseqQC, DEScan2, GUIDEseq
suggestsMe: R3CPET, seqsetvis, chipseqDB
dependencyCount: 122
Package: ChIPQC
Version: 1.28.0
Depends: R (>= 3.0.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19)
Imports: BiocGenerics (>= 0.11.3), S4Vectors (>= 0.1.0), IRanges (>=
1.99.17), Rsamtools (>= 1.17.28), GenomicAlignments (>=
1.1.16), chipseq (>= 1.12.0), gtools, BiocParallel, methods,
reshape2, Nozzle.R1, Biobase, grDevices, stats, utils,
GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,
TxDb.Rnorvegicus.UCSC.rn4.ensGene,
TxDb.Celegans.UCSC.ce6.ensGene,
TxDb.Dmelanogaster.UCSC.dm3.ensGene
Suggests: BiocStyle
License: GPL (>= 3)
MD5sum: 902a4c4eea30b12d4147462ae0efc02c
NeedsCompilation: no
Title: Quality metrics for ChIPseq data
Description: Quality metrics for ChIPseq data.
biocViews: Sequencing, ChIPSeq, QualityControl, ReportWriting
Author: Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark
Maintainer: Tom Carroll <tc.infomatics@gmail.com>, Rory Stark
<rory.stark@cruk.cam.ac.uk>
git_url: https://git.bioconductor.org/packages/ChIPQC
git_branch: RELEASE_3_13
git_last_commit: 047a9a4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChIPQC_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChIPQC_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChIPQC_1.28.0.tgz
vignettes: vignettes/ChIPQC/inst/doc/ChIPQC.pdf,
vignettes/ChIPQC/inst/doc/ChIPQCSampleReport.pdf
vignetteTitles: Assessing ChIP-seq sample quality with ChIPQC,
ChIPQCSampleReport.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPQC/inst/doc/ChIPQC.R
dependencyCount: 195
Package: ChIPseeker
Version: 1.28.3
Depends: R (>= 3.5.0)
Imports: AnnotationDbi, BiocGenerics, boot, enrichplot, IRanges,
GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots,
graphics, grDevices, gtools, methods, plotrix, dplyr, parallel,
magrittr, RColorBrewer, rtracklayer, S4Vectors, stats,
TxDb.Hsapiens.UCSC.hg19.knownGene, utils
Suggests: clusterProfiler, ggimage, ggplotify, ggupset, ReactomePA,
org.Hs.eg.db, knitr, rmarkdown, testthat, tibble
License: Artistic-2.0
MD5sum: 18ff9e8b1a10956aac8584306dbe8cf2
NeedsCompilation: no
Title: ChIPseeker for ChIP peak Annotation, Comparison, and
Visualization
Description: This package implements functions to retrieve the nearest
genes around the peak, annotate genomic region of the peak,
statstical methods for estimate the significance of overlap
among ChIP peak data sets, and incorporate GEO database for
user to compare the own dataset with those deposited in
database. The comparison can be used to infer cooperative
regulation and thus can be used to generate hypotheses. Several
visualization functions are implemented to summarize the
coverage of the peak experiment, average profile and heatmap of
peaks binding to TSS regions, genomic annotation, distance to
TSS, and overlap of peaks or genes.
biocViews: Annotation, ChIPSeq, Software, Visualization,
MultipleComparison
Author: Guangchuang Yu [aut, cre]
(<https://orcid.org/0000-0002-6485-8781>), Yun Yan [ctb], Hervé
Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb],
Zhougeng Xu [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://guangchuangyu.github.io/software/ChIPseeker
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/ChIPseeker/issues
git_url: https://git.bioconductor.org/packages/ChIPseeker
git_branch: RELEASE_3_13
git_last_commit: 2c3e718
git_last_commit_date: 2021-05-21
Date/Publication: 2021-05-21
source.ver: src/contrib/ChIPseeker_1.28.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChIPseeker_1.28.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChIPseeker_1.28.3.tgz
vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.html
vignetteTitles: ChIPseeker: an R package for ChIP peak Annotation,,
Comparison and Visualization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R
importsMe: ALPS, esATAC, TCGAWorkflow, cinaR
suggestsMe: curatedAdipoChIP
dependencyCount: 156
Package: chipseq
Version: 1.42.0
Depends: R (>= 2.10), methods, BiocGenerics (>= 0.1.0), S4Vectors (>=
0.17.25), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8),
ShortRead
Imports: methods, stats, lattice, BiocGenerics, IRanges, GenomicRanges,
ShortRead
Suggests: BSgenome, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene
License: Artistic-2.0
MD5sum: d43c8958c7a40bd29f4b240dac4b9484
NeedsCompilation: yes
Title: chipseq: A package for analyzing chipseq data
Description: Tools for helping process short read data for chipseq
experiments
biocViews: ChIPSeq, Sequencing, Coverage, QualityControl, DataImport
Author: Deepayan Sarkar, Robert Gentleman, Michael Lawrence, Zizhen Yao
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/chipseq
git_branch: RELEASE_3_13
git_last_commit: 735c9dd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/chipseq_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/chipseq_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/chipseq_1.42.0.tgz
vignettes: vignettes/chipseq/inst/doc/Workflow.pdf
vignetteTitles: A Sample ChIP-Seq analysis workflow
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chipseq/inst/doc/Workflow.R
importsMe: ChIPQC, CopywriteR, HTSeqGenie, soGGi, transcriptR
suggestsMe: GenoGAM
dependencyCount: 44
Package: ChIPseqR
Version: 1.46.0
Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors (>= 0.9.25)
Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.5.14),
graphics, grDevices, HilbertVis, ShortRead, stats, timsac,
utils
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 82429a53bd78744bc963f8b44068da79
NeedsCompilation: yes
Title: Identifying Protein Binding Sites in High-Throughput Sequencing
Data
Description: ChIPseqR identifies protein binding sites from ChIP-seq
and nucleosome positioning experiments. The model used to
describe binding events was developed to locate nucleosomes but
should flexible enough to handle other types of experiments as
well.
biocViews: ChIPSeq, Infrastructure
Author: Peter Humburg
Maintainer: Peter Humburg <peter.humburg@gmail.com>
git_url: https://git.bioconductor.org/packages/ChIPseqR
git_branch: RELEASE_3_13
git_last_commit: d242683
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChIPseqR_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChIPseqR_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChIPseqR_1.46.0.tgz
vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf
vignetteTitles: Introduction to ChIPseqR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPseqR/inst/doc/Introduction.R
dependencyCount: 53
Package: ChIPsim
Version: 1.46.0
Depends: Biostrings (>= 2.29.2)
Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods,
stats, utils
Suggests: actuar, zoo
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 397e67464730cb61853794f52aa5645a
NeedsCompilation: no
Title: Simulation of ChIP-seq experiments
Description: A general framework for the simulation of ChIP-seq data.
Although currently focused on nucleosome positioning the
package is designed to support different types of experiments.
biocViews: Infrastructure, ChIPSeq
Author: Peter Humburg
Maintainer: Peter Humburg <Peter.Humburg@gmail.com>
git_url: https://git.bioconductor.org/packages/ChIPsim
git_branch: RELEASE_3_13
git_last_commit: 410a1f4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChIPsim_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChIPsim_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChIPsim_1.46.0.tgz
vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf
vignetteTitles: Simulating ChIP-seq experiments
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPsim/inst/doc/ChIPsimIntro.R
dependencyCount: 44
Package: ChIPXpress
Version: 1.36.0
Depends: R (>= 2.10), ChIPXpressData
Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics
Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit,
BiocGenerics
License: GPL(>=2)
MD5sum: 9807da3d4fc190a70c26b750991c3144
NeedsCompilation: no
Title: ChIPXpress: enhanced transcription factor target gene
identification from ChIP-seq and ChIP-chip data using publicly
available gene expression profiles
Description: ChIPXpress takes as input predicted TF bound genes from
ChIPx data and uses a corresponding database of gene expression
profiles downloaded from NCBI GEO to rank the TF bound targets
in order of which gene is most likely to be functional TF
target.
biocViews: ChIPchip, ChIPSeq
Author: George Wu
Maintainer: George Wu <georgetwu@gmail.com>
git_url: https://git.bioconductor.org/packages/ChIPXpress
git_branch: RELEASE_3_13
git_last_commit: 0bc3ec6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChIPXpress_1.36.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/ChIPXpress_1.36.0.tgz
vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf
vignetteTitles: ChIPXpress
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPXpress/inst/doc/ChIPXpress.R
dependencyCount: 95
Package: chopsticks
Version: 1.58.0
Imports: graphics, stats, utils, methods, survival
Suggests: hexbin
License: GPL-3
MD5sum: e87fe726ffe3e0a240dab8600097849e
NeedsCompilation: yes
Title: The 'snp.matrix' and 'X.snp.matrix' Classes
Description: Implements classes and methods for large-scale SNP
association studies
biocViews: Microarray, SNPsAndGeneticVariability, SNP,
GeneticVariability
Author: Hin-Tak Leung <htl10@users.sourceforge.net>
Maintainer: Hin-Tak Leung <htl10@users.sourceforge.net>
URL: http://outmodedbonsai.sourceforge.net/
git_url: https://git.bioconductor.org/packages/chopsticks
git_branch: RELEASE_3_13
git_last_commit: 67665da
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/chopsticks_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/chopsticks_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/chopsticks_1.58.0.tgz
vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf
vignetteTitles: snpMatrix
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chopsticks/inst/doc/chopsticks-vignette.R
importsMe: CrypticIBDcheck, rJPSGCS
dependencyCount: 10
Package: chromDraw
Version: 2.22.0
Depends: R (>= 3.0.0)
Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46)
LinkingTo: Rcpp
License: GPL-3
MD5sum: c6c80170f476b35596ad1477b771161f
NeedsCompilation: yes
Title: chromDraw is a R package for drawing the schemes of karyotypes
in the linear and circular fashion.
Description: ChromDraw is a R package for drawing the schemes of
karyotype(s) in the linear and circular fashion. It is possible
to visualized cytogenetic marsk on the chromosomes. This tool
has own input data format. Input data can be imported from the
GenomicRanges data structure. This package can visualized the
data in the BED file format. Here is requirement on to the
first nine fields of the BED format. Output files format are
*.eps and *.svg.
biocViews: Software
Author: Jan Janecka, Ing., Mgr. CEITEC Masaryk University
Maintainer: Jan Janecka <jan.janecka@ceitec.muni.cz>
URL: www.plantcytogenomics.org/chromDraw
SystemRequirements: Rtools (>= 3.1)
git_url: https://git.bioconductor.org/packages/chromDraw
git_branch: RELEASE_3_13
git_last_commit: f67574f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/chromDraw_2.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/chromDraw_2.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/chromDraw_2.22.0.tgz
vignettes: vignettes/chromDraw/inst/doc/chromDraw.pdf
vignetteTitles: chromDraw
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chromDraw/inst/doc/chromDraw.R
dependencyCount: 18
Package: ChromHeatMap
Version: 1.46.0
Depends: R (>= 2.9.0), BiocGenerics (>= 0.3.2), annotate (>= 1.20.0),
AnnotationDbi (>= 1.4.0)
Imports: Biobase (>= 2.17.8), graphics, grDevices, methods, stats,
IRanges, rtracklayer, GenomicRanges
Suggests: ALL, hgu95av2.db
License: Artistic-2.0
Archs: i386, x64
MD5sum: e70ee846da8701f09ad80b5030b8447a
NeedsCompilation: no
Title: Heat map plotting by genome coordinate
Description: The ChromHeatMap package can be used to plot genome-wide
data (e.g. expression, CGH, SNP) along each strand of a given
chromosome as a heat map. The generated heat map can be used to
interactively identify probes and genes of interest.
biocViews: Visualization
Author: Tim F. Rayner
Maintainer: Tim F. Rayner <tfrayner@gmail.com>
git_url: https://git.bioconductor.org/packages/ChromHeatMap
git_branch: RELEASE_3_13
git_last_commit: 8f112f2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ChromHeatMap_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChromHeatMap_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChromHeatMap_1.46.0.tgz
vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf
vignetteTitles: Plotting expression data with ChromHeatMap
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.R
dependencyCount: 72
Package: chromPlot
Version: 1.20.0
Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt,
GenomicRanges, R (>= 3.1.0)
Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL (>= 2)
Archs: i386, x64
MD5sum: cb49c1199083e16f91fcc9d5ba525349
NeedsCompilation: no
Title: Global visualization tool of genomic data
Description: Package designed to visualize genomic data along the
chromosomes, where the vertical chromosomes are sorted by
number, with sex chromosomes at the end.
biocViews: DataRepresentation, FunctionalGenomics, Genetics,
Sequencing, Annotation, Visualization
Author: Ricardo A. Verdugo and Karen Y. Orostica
Maintainer: Karen Y. Orostica <korostica09@gmail.com>
git_url: https://git.bioconductor.org/packages/chromPlot
git_branch: RELEASE_3_13
git_last_commit: f4eb7af
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/chromPlot_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/chromPlot_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/chromPlot_1.20.0.tgz
vignettes: vignettes/chromPlot/inst/doc/chromPlot.pdf
vignetteTitles: General Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chromPlot/inst/doc/chromPlot.R
dependencyCount: 75
Package: ChromSCape
Version: 1.2.62
Depends: R (>= 4.1)
Imports: shiny, colourpicker, shinyjs, rtracklayer, shinyFiles,
shinyhelper, shinyWidgets, shinydashboardPlus, shinycssloaders,
Matrix, plotly, shinydashboard, colorRamps, kableExtra,
viridis, batchelor, BiocParallel, parallel, Rsamtools, ggplot2,
qualV, stringdist, fs, qs, DT, scran, scater,
ConsensusClusterPlus, Rtsne, dplyr, tidyr, GenomicRanges,
IRanges, irlba, rlist, umap, tibble, methods, jsonlite, edgeR,
stats, graphics, grDevices, utils, S4Vectors,
SingleCellExperiment, SummarizedExperiment, msigdbr, Sushi,
forcats, Rcpp, coop, matrixTests, DelayedArray
LinkingTo: Rcpp
Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, Signac,
future
License: GPL-3
MD5sum: ff2ee0ea1f13a255fcc3d9827e88cb4b
NeedsCompilation: yes
Title: Analysis of single-cell epigenomics datasets with a Shiny App
Description: ChromSCape - Chromatin landscape profiling for Single
Cells - is a ready-to-launch user-friendly Shiny Application
for the analysis of single-cell epigenomics datasets
(scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to
differential analysis & gene set enrichment analysis. It is
highly interactive, enables users to save their analysis and
covers a wide range of analytical steps: QC, preprocessing,
filtering, batch correction, dimensionality reduction,
vizualisation, clustering, differential analysis and gene set
analysis.
biocViews: Software, SingleCell, ChIPSeq, ATACSeq, MethylSeq,
Classification, Clustering, Epigenetics, PrincipalComponent,
SingleCell, ATACSeq, ChIPSeq, Annotation, BatchEffect,
MultipleComparison, Normalization, Pathways, Preprocessing,
QualityControl, ReportWriting, Visualization,
GeneSetEnrichment, DifferentialPeakCalling
Author: Pacome Prompsy [aut, cre]
(<https://orcid.org/0000-0003-4375-7583>), Celine Vallot [aut]
(<https://orcid.org/0000-0003-1601-2359>)
Maintainer: Pacome Prompsy <pacome.prompsy@curie.fr>
URL: https://github.com/vallotlab/ChromSCape
VignetteBuilder: knitr
BugReports: https://github.com/vallotlab/ChromSCape/issues
git_url: https://git.bioconductor.org/packages/ChromSCape
git_branch: RELEASE_3_13
git_last_commit: 752d173
git_last_commit_date: 2021-09-14
Date/Publication: 2021-09-14
source.ver: src/contrib/ChromSCape_1.2.62.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ChromSCape_1.2.62.zip
mac.binary.ver: bin/macosx/contrib/4.1/ChromSCape_1.2.62.tgz
vignettes: vignettes/ChromSCape/inst/doc/vignette.html
vignetteTitles: ChromSCape
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChromSCape/inst/doc/vignette.R
dependencyCount: 214
Package: chromstaR
Version: 1.18.0
Depends: R (>= 3.3), GenomicRanges, ggplot2, chromstaRData
Imports: methods, utils, grDevices, graphics, stats, foreach,
doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb,
IRanges, reshape2, Rsamtools, GenomicAlignments, bamsignals,
mvtnorm
Suggests: knitr, BiocStyle, testthat, biomaRt
License: Artistic-2.0
MD5sum: 6fbb3dba2abcf6d4e13157eef75dc6e9
NeedsCompilation: yes
Title: Combinatorial and Differential Chromatin State Analysis for
ChIP-Seq Data
Description: This package implements functions for combinatorial and
differential analysis of ChIP-seq data. It includes uni- and
multivariate peak-calling, export to genome browser viewable
files, and functions for enrichment analyses.
biocViews: ImmunoOncology, Software, DifferentialPeakCalling,
HiddenMarkovModel, ChIPSeq, HistoneModification,
MultipleComparison, Sequencing, PeakDetection, ATACSeq
Author: Aaron Taudt, Maria Colome Tatche, Matthias Heinig, Minh Anh
Nguyen
Maintainer: Aaron Taudt <aaron.taudt@gmail.com>
URL: https://github.com/ataudt/chromstaR
VignetteBuilder: knitr
BugReports: https://github.com/ataudt/chromstaR/issues
git_url: https://git.bioconductor.org/packages/chromstaR
git_branch: RELEASE_3_13
git_last_commit: 8f44dd4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/chromstaR_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/chromstaR_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/chromstaR_1.18.0.tgz
vignettes: vignettes/chromstaR/inst/doc/chromstaR.pdf
vignetteTitles: The chromstaR user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chromstaR/inst/doc/chromstaR.R
dependencyCount: 79
Package: chromswitch
Version: 1.14.0
Depends: R (>= 3.5.0), GenomicRanges (>= 1.26.4)
Imports: cluster (>= 2.0.6), Biobase (>= 2.36.2), BiocParallel (>=
1.8.2), dplyr (>= 0.5.0), gplots(>= 3.0.1), graphics,
grDevices, IRanges (>= 2.4.8), lazyeval (>= 0.2.0), matrixStats
(>= 0.52), magrittr (>= 1.5), methods, NMF (>= 0.20.6),
rtracklayer (>= 1.36.4), S4Vectors (>= 0.23.19), stats, tidyr
(>= 0.6.3)
Suggests: BiocStyle, DescTools (>= 0.99.19), devtools (>= 1.13.3),
GenomeInfoDb (>= 1.16.0), knitr, rmarkdown, mclust (>= 5.3),
testthat
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 36c3eaee85a16400486a338bd1bccc8b
NeedsCompilation: no
Title: An R package to detect chromatin state switches from epigenomic
data
Description: Chromswitch implements a flexible method to detect
chromatin state switches between samples in two biological
conditions in a specific genomic region of interest given peaks
or chromatin state calls from ChIP-seq data.
biocViews: ImmunoOncology, MultipleComparison, Transcription,
GeneExpression, DifferentialPeakCalling, HistoneModification,
Epigenetics, FunctionalGenomics, Clustering
Author: Selin Jessa [aut, cre], Claudia L. Kleinman [aut]
Maintainer: Selin Jessa <selinjessa@gmail.com>
URL: https://github.com/sjessa/chromswitch
VignetteBuilder: knitr
BugReports: https://github.com/sjessa/chromswitch/issues
git_url: https://git.bioconductor.org/packages/chromswitch
git_branch: RELEASE_3_13
git_last_commit: e364a04
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/chromswitch_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/chromswitch_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/chromswitch_1.14.0.tgz
vignettes: vignettes/chromswitch/inst/doc/chromswitch_intro.html
vignetteTitles: An introduction to `chromswitch` for detecting
chromatin state switches
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/chromswitch/inst/doc/chromswitch_intro.R
dependencyCount: 102
Package: chromVAR
Version: 1.14.0
Depends: R (>= 3.4)
Imports: IRanges, GenomeInfoDb, GenomicRanges, ggplot2, nabor,
BiocParallel, BiocGenerics, Biostrings, TFBSTools, Rsamtools,
S4Vectors, methods, Rcpp, grid, plotly, shiny, miniUI, stats,
utils, graphics, DT, Rtsne, Matrix, SummarizedExperiment,
RColorBrewer, BSgenome
LinkingTo: Rcpp, RcppArmadillo
Suggests: JASPAR2016, BSgenome.Hsapiens.UCSC.hg19, readr, testthat,
knitr, rmarkdown, pheatmap, motifmatchr
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: df45ce98b934b87ce7f2e6fd22a7e781
NeedsCompilation: yes
Title: Chromatin Variation Across Regions
Description: Determine variation in chromatin accessibility across sets
of annotations or peaks. Designed primarily for single-cell or
sparse chromatin accessibility data, e.g. from scATAC-seq or
sparse bulk ATAC or DNAse-seq experiments.
biocViews: SingleCell, Sequencing, GeneRegulation, ImmunoOncology
Author: Alicia Schep [aut, cre], Jason Buenrostro [ctb], Caleb Lareau
[ctb], William Greenleaf [ths], Stanford University [cph]
Maintainer: Alicia Schep <aschep@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/chromVAR
git_branch: RELEASE_3_13
git_last_commit: 2dc5547
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/chromVAR_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/chromVAR_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/chromVAR_1.14.0.tgz
vignettes: vignettes/chromVAR/inst/doc/Introduction.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/chromVAR/inst/doc/Introduction.R
suggestsMe: Signac
dependencyCount: 151
Package: CHRONOS
Version: 1.20.0
Depends: R (>= 3.5)
Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx,
igraph, circlize, graph, stats, utils, grDevices, graphics,
methods, biomaRt, rJava
Suggests: RUnit, BiocGenerics, knitr
License: GPL-2
MD5sum: 62c92eced247aa9d363dc075e6d1c948
NeedsCompilation: no
Title: CHRONOS: A time-varying method for microRNA-mediated sub-pathway
enrichment analysis
Description: A package used for efficient unraveling of the inherent
dynamic properties of pathways. MicroRNA-mediated subpathway
topologies are extracted and evaluated by exploiting the
temporal transition and the fold change activity of the linked
genes/microRNAs.
biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG
Author: Aristidis G. Vrahatis, Konstantina Dimitrakopoulou, Panos
Balomenos
Maintainer: Panos Balomenos <balomenos@upatras.gr>
SystemRequirements: Java version >= 1.7, Pandoc
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CHRONOS
git_branch: RELEASE_3_13
git_last_commit: 696cc6f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CHRONOS_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CHRONOS_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CHRONOS_1.20.0.tgz
vignettes: vignettes/CHRONOS/inst/doc/CHRONOS.pdf
vignetteTitles: CHRONOS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CHRONOS/inst/doc/CHRONOS.R
dependencyCount: 90
Package: cicero
Version: 1.10.1
Depends: R (>= 3.5.0), monocle, Gviz (>= 1.22.3)
Imports: assertthat (>= 0.2.0), Biobase (>= 2.37.2), BiocGenerics (>=
0.23.0), data.table (>= 1.10.4), dplyr (>= 0.7.4), FNN (>=
1.1), GenomicRanges (>= 1.30.3), ggplot2 (>= 2.2.1), glasso (>=
1.8), grDevices, igraph (>= 1.1.0), IRanges (>= 2.10.5), Matrix
(>= 1.2-12), methods, parallel, plyr (>= 1.8.4), reshape2 (>=
1.4.3), S4Vectors (>= 0.14.7), stats, stringi, stringr (>=
1.2.0), tibble (>= 1.4.2), tidyr, VGAM (>= 1.0-5), utils
Suggests: AnnotationDbi (>= 1.38.2), knitr, rmarkdown, rtracklayer (>=
1.36.6), testthat, vdiffr (>= 0.2.3), covr
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: bc16816bcf46e329679bea4d937e2374
NeedsCompilation: no
Title: Precict cis-co-accessibility from single-cell chromatin
accessibility data
Description: Cicero computes putative cis-regulatory maps from
single-cell chromatin accessibility data. It also extends
monocle 2 for use in chromatin accessibility data.
biocViews: Sequencing, Clustering, CellBasedAssays, ImmunoOncology,
GeneRegulation, GeneTarget, Epigenetics, ATACSeq, SingleCell
Author: Hannah Pliner [aut, cre], Cole Trapnell [aut]
Maintainer: Hannah Pliner <hpliner@uw.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cicero
git_branch: RELEASE_3_13
git_last_commit: 1d348d0
git_last_commit_date: 2021-08-26
Date/Publication: 2021-08-29
source.ver: src/contrib/cicero_1.10.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cicero_1.10.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/cicero_1.10.1.tgz
vignettes: vignettes/cicero/inst/doc/website.html
vignetteTitles: Vignette from Cicero Website
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cicero/inst/doc/website.R
dependencyCount: 173
Package: CIMICE
Version: 1.0.0
Imports: dplyr, ggplot2, glue, tidyr, igraph, networkD3, visNetwork,
ggcorrplot, purrr, ggraph, stats, utils, relations, maftools,
assertthat, Matrix
Suggests: BiocStyle, knitr, rmarkdown, testthat, webshot
License: Artistic-2.0
MD5sum: 5bc773b1f19d85101812c2cc8d614f98
NeedsCompilation: no
Title: CIMICE-R: (Markov) Chain Method to Inferr Cancer Evolution
Description: CIMICE is a tool in the field of tumor phylogenetics and
its goal is to build a Markov Chain (called Cancer Progression
Markov Chain, CPMC) in order to model tumor subtypes evolution.
The input of CIMICE is a Mutational Matrix, so a boolean matrix
representing altered genes in a collection of samples. These
samples are assumed to be obtained with single-cell DNA
analysis techniques and the tool is specifically written to use
the peculiarities of this data for the CMPC construction.
biocViews: Software, BiologicalQuestion, NetworkInference,
ResearchField, Phylogenetics, StatisticalMethod,
GraphAndNetwork, Technology, SingleCell
Author: Nicolò Rossi [aut, cre] (Lab. of Computational Biology and
Bioinformatics, Department of Mathematics, Computer Science and
Physics, University of Udine,
<https://orcid.org/0000-0002-6353-7396>)
Maintainer: Nicolò Rossi <olocin.issor@gmail.com>
URL: https://github.com/redsnic/CIMICE
VignetteBuilder: knitr
BugReports: https://github.com/redsnic/CIMICE/issues
git_url: https://git.bioconductor.org/packages/CIMICE
git_branch: RELEASE_3_13
git_last_commit: 4e2d4f7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CIMICE_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CIMICE_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CIMICE_1.0.0.tgz
vignettes: vignettes/CIMICE/inst/doc/CIMICE_SHORT.html,
vignettes/CIMICE/inst/doc/CIMICER.html
vignetteTitles: Quick guide, CIMICE-R: (Markov) Chain Method to Infer
Cancer Evolution
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CIMICE/inst/doc/CIMICE_SHORT.R,
vignettes/CIMICE/inst/doc/CIMICER.R
dependencyCount: 81
Package: CINdex
Version: 1.20.0
Depends: R (>= 3.3), GenomicRanges
Imports: bitops,gplots,grDevices,som,
dplyr,gridExtra,png,stringr,S4Vectors, IRanges,
GenomeInfoDb,graphics, stats, utils
Suggests: knitr, testthat, ReactomePA, RUnit, BiocGenerics,
AnnotationHub, rtracklayer, pd.genomewidesnp.6, org.Hs.eg.db,
biovizBase, TxDb.Hsapiens.UCSC.hg18.knownGene, methods,
Biostrings,Homo.sapiens, R.utils
License: GPL (>= 2)
MD5sum: 6a8f42f5e2e2ef1778315ec60ca0094c
NeedsCompilation: no
Title: Chromosome Instability Index
Description: The CINdex package addresses important area of
high-throughput genomic analysis. It allows the automated
processing and analysis of the experimental DNA copy number
data generated by Affymetrix SNP 6.0 arrays or similar high
throughput technologies. It calculates the chromosome
instability (CIN) index that allows to quantitatively
characterize genome-wide DNA copy number alterations as a
measure of chromosomal instability. This package calculates not
only overall genomic instability, but also instability in terms
of copy number gains and losses separately at the chromosome
and cytoband level.
biocViews: Software, CopyNumberVariation, GenomicVariation, aCGH,
Microarray, Genetics, Sequencing
Author: Lei Song [aut] (Innovation Center for Biomedical Informatics,
Georgetown University Medical Center), Krithika Bhuvaneshwar
[aut] (Innovation Center for Biomedical Informatics, Georgetown
University Medical Center), Yue Wang [aut, ths] (Virginia
Polytechnic Institute and State University), Yuanjian Feng
[aut] (Virginia Polytechnic Institute and State University),
Ie-Ming Shih [aut] (Johns Hopkins University School of
Medicine), Subha Madhavan [aut] (Innovation Center for
Biomedical Informatics, Georgetown University Medical Center),
Yuriy Gusev [aut, cre] (Innovation Center for Biomedical
Informatics, Georgetown University Medical Center)
Maintainer: Yuriy Gusev <yg63@georgetown.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CINdex
git_branch: RELEASE_3_13
git_last_commit: 9a99af4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CINdex_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CINdex_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CINdex_1.20.0.tgz
vignettes: vignettes/CINdex/inst/doc/CINdex.pdf,
vignettes/CINdex/inst/doc/HowToDownloadCytobandInfo.pdf,
vignettes/CINdex/inst/doc/PrepareInputData.pdf
vignetteTitles: CINdex Tutorial, How to obtain Cytoband and Stain
Information, Prepare input data for CINdex
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CINdex/inst/doc/CINdex.R,
vignettes/CINdex/inst/doc/HowToDownloadCytobandInfo.R,
vignettes/CINdex/inst/doc/PrepareInputData.R
dependencyCount: 47
Package: circRNAprofiler
Version: 1.6.0
Depends: R(>= 4.1.0)
Imports: dplyr, magrittr, readr, rtracklayer, stringr, stringi, DESeq2,
edgeR, GenomicRanges, IRanges, seqinr, R.utils, reshape2,
ggplot2, utils, rlang, S4Vectors, stats, GenomeInfoDb,
universalmotif, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19,
Biostrings, gwascat, BSgenome,
Suggests: testthat, knitr, roxygen2, rmarkdown, devtools, gridExtra,
ggpubr, VennDiagram, BSgenome.Mmusculus.UCSC.mm9,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10,
BiocManager,
License: GPL-3
MD5sum: 76bd9a2ec44b96accfe2328e4b95fb0e
NeedsCompilation: no
Title: circRNAprofiler: An R-Based Computational Framework for the
Downstream Analysis of Circular RNAs
Description: R-based computational framework for a comprehensive in
silico analysis of circRNAs. This computational framework
allows to combine and analyze circRNAs previously detected by
multiple publicly available annotation-based circRNA detection
tools. It covers different aspects of circRNAs analysis from
differential expression analysis, evolutionary conservation,
biogenesis to functional analysis.
biocViews: Annotation, StructuralPrediction, FunctionalPrediction,
GenePrediction, GenomeAssembly, DifferentialExpression
Author: Simona Aufiero
Maintainer: Simona Aufiero <simo.aufiero@gmail.com>
URL: https://github.com/Aufiero/circRNAprofiler
VignetteBuilder: knitr
BugReports: https://github.com/Aufiero/circRNAprofiler/issues
git_url: https://git.bioconductor.org/packages/circRNAprofiler
git_branch: RELEASE_3_13
git_last_commit: 405f392
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/circRNAprofiler_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/circRNAprofiler_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/circRNAprofiler_1.6.0.tgz
vignettes: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.html
vignetteTitles: circRNAprofiler
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.R
dependencyCount: 163
Package: cisPath
Version: 1.32.0
Depends: R (>= 2.10.0)
Imports: methods, utils
License: GPL (>= 3)
MD5sum: 87f1e9b2109d9e71f0c203ea3d4f237d
NeedsCompilation: yes
Title: Visualization and management of the protein-protein interaction
networks.
Description: cisPath is an R package that uses web browsers to
visualize and manage protein-protein interaction networks.
biocViews: Proteomics
Author: Likun Wang <wanglk@hsc.pku.edu.cn>
Maintainer: Likun Wang <wanglk@hsc.pku.edu.cn>
git_url: https://git.bioconductor.org/packages/cisPath
git_branch: RELEASE_3_13
git_last_commit: 32610c8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cisPath_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cisPath_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cisPath_1.32.0.tgz
vignettes: vignettes/cisPath/inst/doc/cisPath.pdf
vignetteTitles: cisPath
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cisPath/inst/doc/cisPath.R
dependencyCount: 2
Package: CiteFuse
Version: 1.4.0
Depends: R (>= 4.0)
Imports: SingleCellExperiment (>= 1.8.0), SummarizedExperiment (>=
1.16.0), Matrix, mixtools, cowplot, ggplot2, gridExtra, grid,
dbscan, propr, uwot, Rtsne, S4Vectors (>= 0.24.0), igraph,
scales, scran (>= 1.14.6), graphics, methods, stats, utils,
reshape2, ggridges, randomForest, pheatmap, ggraph, grDevices,
rhdf5, rlang
Suggests: knitr, rmarkdown, DT, mclust, scater, ExPosition, BiocStyle,
pkgdown
License: GPL-3
MD5sum: c5ffb33ecdd7c3ab8ff6d295f47f37db
NeedsCompilation: no
Title: CiteFuse: multi-modal analysis of CITE-seq data
Description: CiteFuse pacakage implements a suite of methods and tools
for CITE-seq data from pre-processing to integrative analytics,
including doublet detection, network-based modality
integration, cell type clustering, differential RNA and protein
expression analysis, ADT evaluation, ligand-receptor
interaction analysis, and interactive web-based visualisation
of the analyses.
biocViews: SingleCell, GeneExpression
Author: Yingxin Lin [aut, cre], Hani Kim [aut]
Maintainer: Yingxin Lin <yingxin.lin@sydney.edu.au>
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/CiteFuse/issues
git_url: https://git.bioconductor.org/packages/CiteFuse
git_branch: RELEASE_3_13
git_last_commit: aefd68a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CiteFuse_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CiteFuse_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CiteFuse_1.4.0.tgz
vignettes: vignettes/CiteFuse/inst/doc/CiteFuse.html
vignetteTitles: CiteFuse
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CiteFuse/inst/doc/CiteFuse.R
dependencyCount: 127
Package: ClassifyR
Version: 2.12.0
Depends: R (>= 3.5.0), methods, S4Vectors (>= 0.18.0),
MultiAssayExperiment (>= 1.6.0), BiocParallel
Imports: locfit, grid, utils, plyr
Suggests: limma, genefilter, edgeR, car, Rmixmod, ggplot2 (>= 3.0.0),
gridExtra (>= 2.0.0), cowplot, BiocStyle, pamr, PoiClaClu,
parathyroidSE, knitr, htmltools, gtable, scales, e1071,
rmarkdown, IRanges, randomForest, robustbase, glmnet, class
License: GPL-3
Archs: i386, x64
MD5sum: a0afa3d3eb016f17bff07e61a277288d
NeedsCompilation: no
Title: A framework for cross-validated classification problems, with
applications to differential variability and differential
distribution testing
Description: The software formalises a framework for classification in
R. There are four stages; Data transformation, feature
selection, classifier training, and prediction. The
requirements of variable types and names are fixed, but
specialised variables for functions can also be provided. The
classification framework is wrapped in a driver loop, that
reproducibly carries out a number of cross-validation schemes.
Functions for differential expression, differential
variability, and differential distribution are included.
Additional functions may be developed by the user, by creating
an interface to the framework.
biocViews: Classification, Survival
Author: Dario Strbenac, John Ormerod, Graham Mann, Jean Yang
Maintainer: Dario Strbenac <dario.strbenac@sydney.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ClassifyR
git_branch: RELEASE_3_13
git_last_commit: ede130d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ClassifyR_2.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ClassifyR_2.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ClassifyR_2.12.0.tgz
vignettes: vignettes/ClassifyR/inst/doc/ClassifyR.html,
vignettes/ClassifyR/inst/doc/wrapper.html
vignetteTitles: An Introduction to the ClassifyR Package, Example:
Creating a Wrapper Function for the k-NN Classifier
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R,
vignettes/ClassifyR/inst/doc/wrapper.R
dependencyCount: 57
Package: cleanUpdTSeq
Version: 1.30.0
Depends: R (>= 3.5.0), BSgenome.Drerio.UCSC.danRer7, methods
Imports: BSgenome, GenomicRanges, seqinr, e1071, Biostrings,
GenomeInfoDb, IRanges, utils, stringr, stats
Suggests: BiocStyle, rmarkdown, knitr, RUnit, BiocGenerics (>= 0.1.0)
License: GPL-2
MD5sum: 2f5ad25f76aeed10fb5a71a796fc3d9c
NeedsCompilation: no
Title: cleanUpdTSeq cleans up artifacts from polyadenylation sites from
oligo(dT)-mediated 3' end RNA sequending data
Description: This package implements a Naive Bayes classifier for
accurately differentiating true polyadenylation sites (pA
sites) from oligo(dT)-mediated 3' end sequencing such as
PAS-Seq, PolyA-Seq and RNA-Seq by filtering out false
polyadenylation sites, mainly due to oligo(dT)-mediated
internal priming during reverse transcription. The classifer is
highly accurate and outperforms other heuristic methods.
biocViews: Sequencing, 3' end sequencing, polyadenylation site,
internal priming
Author: Sarah Sheppard, Haibo Liu, Jianhong Ou, Nathan Lawson, Lihua
Julie Zhu
Maintainer: Jianhong Ou <Jianhong.Ou@duke.edu>; Lihua Julie Zhu
<Julie.Zhu@umassmed.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cleanUpdTSeq
git_branch: RELEASE_3_13
git_last_commit: 45e4e8b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cleanUpdTSeq_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cleanUpdTSeq_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cleanUpdTSeq_1.30.0.tgz
vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html
vignetteTitles: cleanUpdTSeq Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R
importsMe: InPAS
dependencyCount: 59
Package: cleaver
Version: 1.30.0
Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8)
Imports: S4Vectors, IRanges
Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), rmarkdown,
BRAIN, UniProt.ws (>= 2.1.4)
License: GPL (>= 3)
MD5sum: da5b1e77658d4224f1d1cef2a2d6814d
NeedsCompilation: no
Title: Cleavage of Polypeptide Sequences
Description: In-silico cleavage of polypeptide sequences. The cleavage
rules are taken from:
http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html
biocViews: Proteomics
Author: Sebastian Gibb [aut, cre]
(<https://orcid.org/0000-0001-7406-4443>)
Maintainer: Sebastian Gibb <mail@sebastiangibb.de>
URL: https://github.com/sgibb/cleaver/
VignetteBuilder: knitr
BugReports: https://github.com/sgibb/cleaver/issues/
git_url: https://git.bioconductor.org/packages/cleaver
git_branch: RELEASE_3_13
git_last_commit: e528be9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-20
source.ver: src/contrib/cleaver_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cleaver_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cleaver_1.30.0.tgz
vignettes: vignettes/cleaver/inst/doc/cleaver.html
vignetteTitles: In-silico cleavage of polypeptides
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cleaver/inst/doc/cleaver.R
suggestsMe: RforProteomics
dependencyCount: 19
Package: clippda
Version: 1.42.0
Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d,
graphics, grDevices, stats, utils, Biobase, tools, methods
License: GPL (>=2)
MD5sum: 7883fd848f54c3a8663e493392acd1b1
NeedsCompilation: no
Title: A package for the clinical proteomic profiling data analysis
Description: Methods for the nalysis of data from clinical proteomic
profiling studies. The focus is on the studies of human
subjects, which are often observational case-control by design
and have technical replicates. A method for sample size
determination for planning these studies is proposed. It
incorporates routines for adjusting for the expected
heterogeneities and imbalances in the data and the
within-sample replicate correlations.
biocViews: Proteomics, OneChannel, Preprocessing,
DifferentialExpression, MultipleComparison
Author: Stephen Nyangoma
Maintainer: Stephen Nyangoma <s.o.nyangoma@bham.ac.uk>
URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml
git_url: https://git.bioconductor.org/packages/clippda
git_branch: RELEASE_3_13
git_last_commit: 4c5fe1e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/clippda_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/clippda_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/clippda_1.42.0.tgz
vignettes: vignettes/clippda/inst/doc/clippda.pdf
vignetteTitles: Sample Size Calculation
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clippda/inst/doc/clippda.R
dependencyCount: 34
Package: clipper
Version: 1.32.0
Depends: R (>= 2.15.0), Matrix, graph
Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph,
KEGGgraph, corpcor, RBGL
Suggests: RUnit, BiocGenerics, graphite, ALL, hgu95av2.db, MASS,
BiocStyle
Enhances: RCy3
License: AGPL-3
MD5sum: 9e2c49f1a05dc4ff84a6e89906e35555
NeedsCompilation: no
Title: Gene Set Analysis Exploiting Pathway Topology
Description: Implements topological gene set analysis using a two-step
empirical approach. It exploits graph decomposition theory to
create a junction tree and reconstruct the most relevant signal
path. In the first step clipper selects significant pathways
according to statistical tests on the means and the
concentration matrices of the graphs derived from pathway
topologies. Then, it "clips" the whole pathway identifying the
signal paths having the greatest association with a specific
phenotype.
Author: Paolo Martini <paolo.cavei@gmail.com>, Gabriele Sales
<gabriele.sales@unipd.it>, Chiara Romualdi
<chiara.romualdi@unipd.it>
Maintainer: Paolo Martini <paolo.cavei@gmail.com>
git_url: https://git.bioconductor.org/packages/clipper
git_branch: RELEASE_3_13
git_last_commit: 2faaf4e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/clipper_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/clipper_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/clipper_1.32.0.tgz
vignettes: vignettes/clipper/inst/doc/clipper.pdf
vignetteTitles: clipper
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clipper/inst/doc/clipper.R
suggestsMe: graphite
dependencyCount: 110
Package: cliqueMS
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: Rcpp (>= 0.12.15), xcms(>= 3.0.0), MSnbase, igraph, qlcMatrix,
matrixStats, methods
LinkingTo: Rcpp, BH, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, CAMERA
License: GPL (>= 2)
MD5sum: 29a33ea4e0765b7f3fc2a5d1fa5160f0
NeedsCompilation: yes
Title: Annotation of Isotopes, Adducts and Fragmentation Adducts for
in-Source LC/MS Metabolomics Data
Description: Annotates data from liquid chromatography coupled to mass
spectrometry (LC/MS) metabolomics experiments. Based on a
network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O.
Yanes, R.Guimerà and M. Sales-Pardo, Bioinformatics, 35(20),
2019), 'CliqueMS' builds a weighted similarity network where
nodes are features and edges are weighted according to the
similarity of this features. Then it searches for the most
plausible division of the similarity network into cliques
(fully connected components). Finally it annotates metabolites
within each clique, obtaining for each annotated metabolite the
neutral mass and their features, corresponding to isotopes,
ionization adducts and fragmentation adducts of that
metabolite.
biocViews: Metabolomics, MassSpectrometry, Network, NetworkInference
Author: Oriol Senan Campos [aut, cre], Antoni Aguilar-Mogas [aut],
Jordi Capellades [aut], Miriam Navarro [aut], Oscar Yanes
[aut], Roger Guimera [aut], Marta Sales-Pardo [aut]
Maintainer: Oriol Senan Campos <oriol.senan@praenoscere.com>
URL: http://cliquems.seeslab.net
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/osenan/cliqueMS/issues
git_url: https://git.bioconductor.org/packages/cliqueMS
git_branch: RELEASE_3_13
git_last_commit: d5ddd2d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cliqueMS_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cliqueMS_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cliqueMS_1.6.0.tgz
vignettes: vignettes/cliqueMS/inst/doc/annotate_features.html
vignetteTitles: Annotating LC/MS data with cliqueMS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cliqueMS/inst/doc/annotate_features.R
dependencyCount: 100
Package: Clomial
Version: 1.28.0
Depends: R (>= 2.10), matrixStats
Imports: methods, permute
License: GPL (>= 2)
MD5sum: 86f6c393a86564d49b946bfab0e53f22
NeedsCompilation: no
Title: Infers clonal composition of a tumor
Description: Clomial fits binomial distributions to counts obtained
from Next Gen Sequencing data of multiple samples of the same
tumor. The trained parameters can be interpreted to infer the
clonal structure of the tumor.
biocViews: Genetics, GeneticVariability, Sequencing, Clustering,
MultipleComparison, Bayesian, DNASeq, ExomeSeq,
TargetedResequencing, ImmunoOncology
Author: Habil Zare and Alex Hu
Maintainer: Habil Zare <zare@u.washington.edu>
git_url: https://git.bioconductor.org/packages/Clomial
git_branch: RELEASE_3_13
git_last_commit: 40f27cb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Clomial_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Clomial_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Clomial_1.28.0.tgz
vignettes:
vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.pdf
vignetteTitles: A likelihood maximization approach to infer the clonal
structure of a cancer using multiple tumor samples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.R
dependencyCount: 4
Package: Clonality
Version: 1.40.0
Depends: R (>= 2.12.2), DNAcopy
Imports: grDevices, graphics, stats, utils
Suggests: gdata
License: GPL-3
Archs: i386, x64
MD5sum: 65bd99eb085c3a9a3e824a3907fbe654
NeedsCompilation: no
Title: Clonality testing
Description: Statistical tests for clonality versus independence of
tumors from the same patient based on their LOH or genomewide
copy number profiles
biocViews: CopyNumber, Classification, aCGH, Mutations, Diagnosis,
metastasis
Author: Irina Ostrovnaya
Maintainer: Irina Ostrovnaya <ostrovni@mskcc.org>
git_url: https://git.bioconductor.org/packages/Clonality
git_branch: RELEASE_3_13
git_last_commit: a298581
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Clonality_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Clonality_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Clonality_1.40.0.tgz
vignettes: vignettes/Clonality/inst/doc/Clonality.pdf
vignetteTitles: Clonality
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Clonality/inst/doc/Clonality.R
dependencyCount: 5
Package: clonotypeR
Version: 1.30.0
Imports: methods
Suggests: BiocGenerics, edgeR, knitr, pvclust, RUnit, vegan
License: file LICENSE
MD5sum: 31660de5f5d40bec13a644d3f179df2f
NeedsCompilation: no
Title: High throughput analysis of T cell antigen receptor sequences
Description: High throughput analysis of T cell antigen receptor
sequences The genes encoding T cell receptors are created by
somatic recombination, generating an immense combination of V,
(D) and J segments. Additional processes during the
recombination create extra sequence diversity between the V an
J segments. Collectively, this hyper-variable region is called
the CDR3 loop. The purpose of this package is to process and
quantitatively analyse millions of V-CDR3-J combination, called
clonotypes, from multiple sequence libraries.
biocViews: Sequencing
Author: Charles Plessy <plessy@riken.jp>
Maintainer: Charles Plessy <plessy@riken.jp>
URL: http://clonotyper.branchable.com/
VignetteBuilder: knitr
BugReports: http://clonotyper.branchable.com/Bugs/
git_url: https://git.bioconductor.org/packages/clonotypeR
git_branch: RELEASE_3_13
git_last_commit: c404ad8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/clonotypeR_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/clonotypeR_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/clonotypeR_1.30.0.tgz
vignettes: vignettes/clonotypeR/inst/doc/clonotypeR.html
vignetteTitles: clonotypeR User's Guide
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/clonotypeR/inst/doc/clonotypeR.R
dependencyCount: 1
Package: clst
Version: 1.40.0
Depends: R (>= 2.10)
Imports: ROC, lattice
Suggests: RUnit
License: GPL-3
MD5sum: f62e88cfc56a9e8766949cb411929299
NeedsCompilation: no
Title: Classification by local similarity threshold
Description: Package for modified nearest-neighbor classification based
on calculation of a similarity threshold distinguishing
within-group from between-group comparisons.
biocViews: Classification
Author: Noah Hoffman
Maintainer: Noah Hoffman <ngh2@uw.edu>
git_url: https://git.bioconductor.org/packages/clst
git_branch: RELEASE_3_13
git_last_commit: 1ec5f0e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/clst_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/clst_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/clst_1.40.0.tgz
vignettes: vignettes/clst/inst/doc/clstDemo.pdf
vignetteTitles: clst
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clst/inst/doc/clstDemo.R
dependsOnMe: clstutils
dependencyCount: 18
Package: clstutils
Version: 1.40.1
Depends: R (>= 2.10), clst, rjson, ape
Imports: lattice, RSQLite
Suggests: RUnit
License: GPL-3
MD5sum: f060aa364fcd19f8b9cbbdba37a7d6b1
NeedsCompilation: no
Title: Tools for performing taxonomic assignment
Description: Tools for performing taxonomic assignment based on
phylogeny using pplacer and clst.
biocViews: Sequencing, Classification, Visualization, QualityControl
Author: Noah Hoffman
Maintainer: Noah Hoffman <ngh2@uw.edu>
git_url: https://git.bioconductor.org/packages/clstutils
git_branch: RELEASE_3_13
git_last_commit: 97804e0
git_last_commit_date: 2021-10-07
Date/Publication: 2021-10-10
source.ver: src/contrib/clstutils_1.40.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/clstutils_1.40.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/clstutils_1.40.1.tgz
vignettes: vignettes/clstutils/inst/doc/pplacerDemo.pdf,
vignettes/clstutils/inst/doc/refSet.pdf
vignetteTitles: clst, clstutils
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clstutils/inst/doc/pplacerDemo.R,
vignettes/clstutils/inst/doc/refSet.R
dependencyCount: 37
Package: CluMSID
Version: 1.8.0
Depends: R (>= 3.6)
Imports: mzR, S4Vectors, dbscan, RColorBrewer, ape, network, GGally,
ggplot2, plotly, methods, utils, stats, sna, grDevices,
graphics, Biobase, gplots, MSnbase
Suggests: knitr, rmarkdown, testthat, dplyr, readr, stringr, magrittr,
CluMSIDdata, metaMS, metaMSdata, xcms
License: MIT + file LICENSE
MD5sum: f007dc9464cc0024facf72a83255fdb2
NeedsCompilation: no
Title: Clustering of MS2 Spectra for Metabolite Identification
Description: CluMSID is a tool that aids the identification of features
in untargeted LC-MS/MS analysis by the use of MS2 spectra
similarity and unsupervised statistical methods. It offers
functions for a complete and customisable workflow from raw
data to visualisations and is interfaceable with the xmcs
family of preprocessing packages.
biocViews: Metabolomics, Preprocessing, Clustering
Author: Tobias Depke [aut, cre], Raimo Franke [ctb], Mark Broenstrup
[ths]
Maintainer: Tobias Depke <tobias.depke@helmholtz-hzi.de>
URL: https://github.com/tdepke/CluMSID
VignetteBuilder: knitr
BugReports: https://github.com/tdepke/CluMSID/issues
git_url: https://git.bioconductor.org/packages/CluMSID
git_branch: RELEASE_3_13
git_last_commit: d3ba495
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CluMSID_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CluMSID_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CluMSID_1.8.0.tgz
vignettes: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.html,
vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.html,
vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.html,
vignettes/CluMSID/inst/doc/CluMSID_MTBLS.html,
vignettes/CluMSID/inst/doc/CluMSID_tutorial.html
vignetteTitles: CluMSID DI-MS/MS Tutorial, CluMSID GC-EI-MS Tutorial,
CluMSID LowRes Tutorial, CluMSID MTBLS Tutorial, CluMSID
Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.R,
vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.R,
vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.R,
vignettes/CluMSID/inst/doc/CluMSID_MTBLS.R,
vignettes/CluMSID/inst/doc/CluMSID_tutorial.R
dependencyCount: 119
Package: clustComp
Version: 1.20.0
Depends: R (>= 3.3)
Imports: sm, stats, graphics, grDevices
Suggests: Biobase, colonCA, RUnit, BiocGenerics
License: GPL (>= 2)
MD5sum: aaeed7cb033ee7d2495ceba024b3e726
NeedsCompilation: no
Title: Clustering Comparison Package
Description: clustComp is a package that implements several techniques
for the comparison and visualisation of relationships between
different clustering results, either flat versus flat or
hierarchical versus flat. These relationships among clusters
are displayed using a weighted bi-graph, in which the nodes
represent the clusters and the edges connect pairs of nodes
with non-empty intersection; the weight of each edge is the
number of elements in that intersection and is displayed
through the edge thickness. The best layout of the bi-graph is
provided by the barycentre algorithm, which minimises the
weighted number of crossings. In the case of comparing a
hierarchical and a non-hierarchical clustering, the dendrogram
is pruned at different heights, selected by exploring the tree
by depth-first search, starting at the root. Branches are
decided to be split according to the value of a scoring
function, that can be based either on the aesthetics of the
bi-graph or on the mutual information between the hierarchical
and the flat clusterings. A mapping between groups of clusters
from each side is constructed with a greedy algorithm, and can
be additionally visualised.
biocViews: GeneExpression, Clustering, Visualization
Author: Aurora Torrente and Alvis Brazma.
Maintainer: Aurora Torrente <aurora@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/clustComp
git_branch: RELEASE_3_13
git_last_commit: 9d5ce5d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/clustComp_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/clustComp_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/clustComp_1.20.0.tgz
vignettes: vignettes/clustComp/inst/doc/clustComp.pdf
vignetteTitles: The clustComp Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clustComp/inst/doc/clustComp.R
dependencyCount: 4
Package: clusterExperiment
Version: 2.12.0
Depends: R (>= 3.6.0), SingleCellExperiment, SummarizedExperiment (>=
1.15.4), BiocGenerics
Imports: methods, NMF, RColorBrewer, ape (>= 5.0), cluster, stats,
limma, howmany, locfdr, matrixStats, graphics, parallel,
BiocSingular, kernlab, stringr, S4Vectors, grDevices,
DelayedArray (>= 0.7.48), HDF5Array (>= 1.7.10), Matrix, Rcpp,
edgeR, scales, zinbwave, phylobase, pracma, mbkmeans
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, testthat, MAST, Rtsne, scran, igraph
License: Artistic-2.0
MD5sum: 3e92c9260ea9bddfe5c3fbc55b77e713
NeedsCompilation: yes
Title: Compare Clusterings for Single-Cell Sequencing
Description: Provides functionality for running and comparing many
different clusterings of single-cell sequencing data or other
large mRNA Expression data sets.
biocViews: Clustering, RNASeq, Sequencing, Software, SingleCell
Author: Elizabeth Purdom [aut, cre, cph], Davide Risso [aut]
Maintainer: Elizabeth Purdom <epurdom@stat.berkeley.edu>
VignetteBuilder: knitr
BugReports: https://github.com/epurdom/clusterExperiment/issues
git_url: https://git.bioconductor.org/packages/clusterExperiment
git_branch: RELEASE_3_13
git_last_commit: 3dc45a6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/clusterExperiment_2.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/clusterExperiment_2.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/clusterExperiment_2.11.3.tgz
vignettes:
vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.html,
vignettes/clusterExperiment/inst/doc/largeDataSets.html
vignetteTitles: clusterExperiment Vignette, Working with Large Datasets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.R,
vignettes/clusterExperiment/inst/doc/largeDataSets.R
dependsOnMe: netSmooth
suggestsMe: slingshot, tradeSeq
dependencyCount: 151
Package: ClusterJudge
Version: 1.14.0
Depends: R (>= 3.6), stats, utils, graphics, infotheo, lattice,
latticeExtra, httr, jsonlite
Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt
License: Artistic-2.0
MD5sum: 6a6549fde560a2744724a2ca32e25b3e
NeedsCompilation: no
Title: Judging Quality of Clustering Methods using Mutual Information
Description: ClusterJudge implements the functions, examples and other
software published as an algorithm by Gibbons, FD and Roth FP.
The article is called "Judging the Quality of Gene
Expression-Based Clustering Methods Using Gene Annotation" and
it appeared in Genome Research, vol. 12, pp1574-1581 (2002).
See package?ClusterJudge for an overview.
biocViews: Software, StatisticalMethod, Clustering, GeneExpression, GO
Author: Adrian Pasculescu
Maintainer: Adrian Pasculescu <a.pasculescu@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ClusterJudge
git_branch: RELEASE_3_13
git_last_commit: 450a0c8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ClusterJudge_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ClusterJudge_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ClusterJudge_1.14.0.tgz
vignettes: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.R
dependencyCount: 21
Package: clusterProfiler
Version: 4.0.5
Depends: R (>= 3.5.0)
Imports: AnnotationDbi, downloader, DOSE (>= 3.13.1), dplyr, enrichplot
(>= 1.9.3), GO.db, GOSemSim, magrittr, methods, plyr, qvalue,
rlang, stats, tidyr, utils, yulab.utils
Suggests: AnnotationHub, knitr, rmarkdown, org.Hs.eg.db, prettydoc,
ReactomePA, testthat
License: Artistic-2.0
Archs: i386, x64
MD5sum: 315e7070d6a5ec7976e076e90afe1b05
NeedsCompilation: no
Title: A universal enrichment tool for interpreting omics data
Description: This package supports functional characteristics of both
coding and non-coding genomics data for thousands of species
with up-to-date gene annotation. It provides a univeral
interface for gene functional annotation from a variety of
sources and thus can be applied in diverse scenarios. It
provides a tidy interface to access, manipulate, and visualize
enrichment results to help users achieve efficient data
interpretation. Datasets obtained from multiple treatments and
time points can be analyzed and compared in a single run,
easily revealing functional consensus and differences among
distinct conditions.
biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG,
MultipleComparison, Pathways, Reactome, Visualization
Author: Guangchuang Yu [aut, cre, cph]
(<https://orcid.org/0000-0002-6485-8781>), Li-Gen Wang [ctb],
Erqiang Hu [ctb], Meijun Chen [ctb], Giovanni Dall'Olio [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/biomedical-knowledge-mining-book/
VignetteBuilder: knitr
BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues
git_url: https://git.bioconductor.org/packages/clusterProfiler
git_branch: RELEASE_3_13
git_last_commit: 1b76287
git_last_commit_date: 2021-08-20
Date/Publication: 2021-08-22
source.ver: src/contrib/clusterProfiler_4.0.5.tar.gz
win.binary.ver: bin/windows/contrib/4.1/clusterProfiler_4.0.5.zip
mac.binary.ver: bin/macosx/contrib/4.1/clusterProfiler_4.0.5.tgz
vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.html
vignetteTitles: Statistical analysis and visualization of functional
profiles for genes and gene clusters
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R
dependsOnMe: maEndToEnd
importsMe: bioCancer, CEMiTool, CeTF, conclus, DAPAR, debrowser, eegc,
enrichTF, esATAC, famat, fcoex, GDCRNATools, IRISFGM,
MAGeCKFlute, methylGSA, miRspongeR, MoonlightR, multiSight,
netboxr, PFP, RNASeqR, signatureSearch, TCGAbiolinksGUI,
TimiRGeN, ExpHunterSuite, recountWorkflow, TCGAWorkflow,
genekitr, immcp, pathwayTMB, RVA, SEAA, tinyarray
suggestsMe: ChIPseeker, cola, DOSE, enrichplot, epihet, GeneTonic,
GenomicSuperSignature, GOSemSim, GSEAmining, MesKit, paxtoolsr,
ReactomePA, rrvgo, scGPS, simplifyEnrichment, TCGAbiolinks,
tidybulk, org.Mxanthus.db, cRegulome, GeoTcgaData
dependencyCount: 125
Package: clusterSeq
Version: 1.16.0
Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats,
utils
Imports: BiocGenerics
Suggests: BiocStyle
License: GPL-3
MD5sum: 21b6c5f665615fa5b03ee774660eba2c
NeedsCompilation: no
Title: Clustering of high-throughput sequencing data by identifying
co-expression patterns
Description: Identification of clusters of co-expressed genes based on
their expression across multiple (replicated) biological
samples.
biocViews: Sequencing, DifferentialExpression, MultipleComparison,
Clustering, GeneExpression
Author: Thomas J. Hardcastle & Irene Papatheodorou
Maintainer: Thomas J. Hardcastle <tjh48@cam.ac.uk>
git_url: https://git.bioconductor.org/packages/clusterSeq
git_branch: RELEASE_3_13
git_last_commit: 1b3a71b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/clusterSeq_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/clusterSeq_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/clusterSeq_1.16.0.tgz
vignettes: vignettes/clusterSeq/inst/doc/clusterSeq.pdf
vignetteTitles: Advanced baySeq analyses
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clusterSeq/inst/doc/clusterSeq.R
dependencyCount: 33
Package: ClusterSignificance
Version: 1.20.0
Depends: R (>= 3.3.0)
Imports: methods, pracma, princurve (>= 2.0.5), scatterplot3d,
RColorBrewer, grDevices, graphics, utils, stats
Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics,
covr
License: GPL-3
MD5sum: 944841eefb2b4f63a531d18c168870f8
NeedsCompilation: no
Title: The ClusterSignificance package provides tools to assess if
class clusters in dimensionality reduced data representations
have a separation different from permuted data
Description: The ClusterSignificance package provides tools to assess
if class clusters in dimensionality reduced data
representations have a separation different from permuted data.
The term class clusters here refers to, clusters of points
representing known classes in the data. This is particularly
useful to determine if a subset of the variables, e.g. genes in
a specific pathway, alone can separate samples into these
established classes. ClusterSignificance accomplishes this by,
projecting all points onto a one dimensional line. Cluster
separations are then scored and the probability of the seen
separation being due to chance is evaluated using a permutation
method.
biocViews: Clustering, Classification, PrincipalComponent,
StatisticalMethod
Author: Jason T. Serviss [aut, cre], Jesper R. Gadin [aut]
Maintainer: Jason T Serviss <jason.serviss@ki.se>
URL: https://github.com/jasonserviss/ClusterSignificance/
VignetteBuilder: knitr
BugReports: https://github.com/jasonserviss/ClusterSignificance/issues
git_url: https://git.bioconductor.org/packages/ClusterSignificance
git_branch: RELEASE_3_13
git_last_commit: 493db1d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ClusterSignificance_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ClusterSignificance_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ClusterSignificance_1.20.0.tgz
vignettes:
vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html
vignetteTitles: ClusterSignificance Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R
dependencyCount: 10
Package: clusterStab
Version: 1.64.0
Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods
Suggests: fibroEset, genefilter
License: Artistic-2.0
MD5sum: 4e76b49f6b61ac18bdad2d19d8242679
NeedsCompilation: no
Title: Compute cluster stability scores for microarray data
Description: This package can be used to estimate the number of
clusters in a set of microarray data, as well as test the
stability of these clusters.
biocViews: Clustering
Author: James W. MacDonald, Debashis Ghosh, Mark Smolkin
Maintainer: James W. MacDonald <jmacdon@u.washington.edu>
git_url: https://git.bioconductor.org/packages/clusterStab
git_branch: RELEASE_3_13
git_last_commit: cb54c0e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/clusterStab_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/clusterStab_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/clusterStab_1.64.0.tgz
vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf
vignetteTitles: clusterStab Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clusterStab/inst/doc/clusterStab.R
dependencyCount: 7
Package: clustifyr
Version: 1.4.0
Depends: R (>= 4.0)
Imports: cowplot, dplyr, entropy, fgsea, ggplot2, Matrix, readr, rlang,
scales, stringr, tibble, tidyr, stats, methods,
SingleCellExperiment, SummarizedExperiment, matrixStats,
S4Vectors, proxy, httr, utils
Suggests: ComplexHeatmap, covr, knitr, rmarkdown, testthat, ggrepel,
BiocStyle, BiocManager, remotes, shiny, gprofiler2, purrr
License: MIT + file LICENSE
MD5sum: 3998f3ce3b920f9e64cf35f84406f0d6
NeedsCompilation: no
Title: Classifier for Single-cell RNA-seq Using Cell Clusters
Description: Package designed to aid in classifying cells from
single-cell RNA sequencing data using external reference data
(e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A
variety of correlation based methods and gene list enrichment
methods are provided to assist cell type assignment.
biocViews: SingleCell, Annotation, Sequencing, Microarray,
GeneExpression
Author: Rui Fu [aut, cre], Kent Riemondy [aut], Austin Gillen [ctb],
Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb],
Michelle Daya [ctb], Sidhant Puntambekar [ctb]
Maintainer: Rui Fu <raysinensis@gmail.com>
URL: http://github.com/rnabioco/clustifyr#readme,
https://rnabioco.github.io/clustifyr/
VignetteBuilder: knitr
BugReports: https://github.com/rnabioco/clustifyr/issues
git_url: https://git.bioconductor.org/packages/clustifyr
git_branch: RELEASE_3_13
git_last_commit: 7a53859
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/clustifyr_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/clustifyr_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/clustifyr_1.4.0.tgz
vignettes: vignettes/clustifyr/inst/doc/clustifyR.html,
vignettes/clustifyr/inst/doc/geo-annotations.html
vignetteTitles: Introduction to clustifyr, geo-annotations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/clustifyr/inst/doc/clustifyR.R,
vignettes/clustifyr/inst/doc/geo-annotations.R
suggestsMe: clustifyrdatahub
dependencyCount: 96
Package: CMA
Version: 1.50.0
Depends: R (>= 2.10), methods, stats, Biobase
Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics,
gbm, mgcv, corpcor, limma, st, mvtnorm
License: GPL (>= 2)
MD5sum: 7621773a3ef69d483f1ac169eff9fae0
NeedsCompilation: no
Title: Synthesis of microarray-based classification
Description: This package provides a comprehensive collection of
various microarray-based classification algorithms both from
Machine Learning and Statistics. Variable Selection,
Hyperparameter tuning, Evaluation and Comparison can be
performed combined or stepwise in a user-friendly environment.
biocViews: Classification, DecisionTree
Author: Martin Slawski <ms@cs.uni-sb.de>, Anne-Laure Boulesteix
<boulesteix@ibe.med.uni-muenchen.de>, Christoph Bernau
<bernau@ibe.med.uni-muenchen.de>.
Maintainer: Roman Hornung <hornung@ibe.med.uni-muenchen.de>
git_url: https://git.bioconductor.org/packages/CMA
git_branch: RELEASE_3_13
git_last_commit: 2f87021
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CMA_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CMA_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CMA_1.50.0.tgz
vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf
vignetteTitles: CMA_vignette.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CMA/inst/doc/CMA_vignette.R
dependencyCount: 7
Package: cmapR
Version: 1.4.0
Depends: R (>= 4.0)
Imports: methods, rhdf5, data.table, flowCore, SummarizedExperiment,
matrixStats
Suggests: knitr, testthat, BiocStyle
License: file LICENSE
MD5sum: 2c27b7a0d154fe0fcb56c35e106f3d5e
NeedsCompilation: no
Title: CMap Tools in R
Description: The Connectivity Map (CMap) is a massive resource of
perturbational gene expression profiles built by researchers at
the Broad Institute and funded by the NIH Library of Integrated
Network-Based Cellular Signatures (LINCS) program. Please visit
https://clue.io for more information. The cmapR package
implements methods to parse, manipulate, and write common CMap
data objects, such as annotated matrices and collections of
gene sets.
biocViews: DataImport, DataRepresentation, GeneExpression
Author: Ted Natoli [aut, cre] (<https://orcid.org/0000-0002-0953-0206>)
Maintainer: Ted Natoli <ted.e.natoli@gmail.com>
URL: https://github.com/cmap/cmapR
VignetteBuilder: knitr
BugReports: https://github.com/cmap/cmapR/issues
git_url: https://git.bioconductor.org/packages/cmapR
git_branch: RELEASE_3_13
git_last_commit: e2fd1ea
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cmapR_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cmapR_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cmapR_1.4.0.tgz
vignettes: vignettes/cmapR/inst/doc/tutorial.html
vignetteTitles: cmapR Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cmapR/inst/doc/tutorial.R
dependencyCount: 37
Package: cn.farms
Version: 1.40.0
Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow
Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice
Suggests: pd.mapping250k.sty, pd.mapping250k.nsp, pd.genomewidesnp.5,
pd.genomewidesnp.6
License: LGPL (>= 2.0)
MD5sum: 6748fba8f92f20900a1b4f12bea6127a
NeedsCompilation: yes
Title: cn.FARMS - factor analysis for copy number estimation
Description: This package implements the cn.FARMS algorithm for copy
number variation (CNV) analysis. cn.FARMS allows to analyze the
most common Affymetrix (250K-SNP6.0) array types, supports
high-performance computing using snow and ff.
biocViews: Microarray, CopyNumberVariation
Author: Andreas Mitterecker, Djork-Arne Clevert
Maintainer: Andreas Mitterecker <mitterecker@ml.jku.at>
URL: http://www.bioinf.jku.at/software/cnfarms/cnfarms.html
git_url: https://git.bioconductor.org/packages/cn.farms
git_branch: RELEASE_3_13
git_last_commit: 8b80539
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cn.farms_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cn.farms_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cn.farms_1.40.0.tgz
vignettes: vignettes/cn.farms/inst/doc/cn.farms.pdf
vignetteTitles: cn.farms: Manual for the R package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cn.farms/inst/doc/cn.farms.R
dependencyCount: 56
Package: cn.mops
Version: 1.38.0
Depends: R (>= 2.12), methods, utils, stats, graphics, parallel,
GenomicRanges
Imports: BiocGenerics, Biobase, IRanges, Rsamtools, GenomeInfoDb,
S4Vectors, exomeCopy
Suggests: DNAcopy
License: LGPL (>= 2.0)
MD5sum: 12879c5c449d30a1370867065e0947a3
NeedsCompilation: yes
Title: cn.mops - Mixture of Poissons for CNV detection in NGS data
Description: cn.mops (Copy Number estimation by a Mixture Of PoissonS)
is a data processing pipeline for copy number variations and
aberrations (CNVs and CNAs) from next generation sequencing
(NGS) data. The package supplies functions to convert BAM files
into read count matrices or genomic ranges objects, which are
the input objects for cn.mops. cn.mops models the depths of
coverage across samples at each genomic position. Therefore, it
does not suffer from read count biases along chromosomes. Using
a Bayesian approach, cn.mops decomposes read variations across
samples into integer copy numbers and noise by its mixture
components and Poisson distributions, respectively. cn.mops
guarantees a low FDR because wrong detections are indicated by
high noise and filtered out. cn.mops is very fast and written
in C++.
biocViews: Sequencing, CopyNumberVariation, Homo_sapiens, CellBiology,
HapMap, Genetics
Author: Guenter Klambauer
Maintainer: Gundula Povysil <povysil@bioinf.jku.at>
URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html
git_url: https://git.bioconductor.org/packages/cn.mops
git_branch: RELEASE_3_13
git_last_commit: c1ccf44
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cn.mops_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cn.mops_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cn.mops_1.38.0.tgz
vignettes: vignettes/cn.mops/inst/doc/cn.mops.pdf
vignetteTitles: cn.mops: Manual for the R package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cn.mops/inst/doc/cn.mops.R
dependsOnMe: panelcn.mops
importsMe: CopyNumberPlots
suggestsMe: CNVgears
dependencyCount: 31
Package: CNAnorm
Version: 1.38.0
Depends: R (>= 2.10.1), methods
Imports: DNAcopy
License: GPL-2
Archs: i386, x64
MD5sum: d9b914b60e166e6f988e54c6ab843a1c
NeedsCompilation: yes
Title: A normalization method for Copy Number Aberration in cancer
samples
Description: Performs ratio, GC content correction and normalization of
data obtained using low coverage (one read every 100-10,000 bp)
high troughput sequencing. It performs a "discrete"
normalization looking for the ploidy of the genome. It will
also provide tumour content if at least two ploidy states can
be found.
biocViews: CopyNumberVariation, Sequencing, Coverage, Normalization,
WholeGenome, DNASeq, GenomicVariation
Author: Stefano Berri <sberri@illumina.com>, Henry M. Wood
<H.M.Wood@leeds.ac.uk>, Arief Gusnanto <a.gusnanto@leeds.ac.uk>
Maintainer: Stefano Berri <sberri@illumina.com>
URL: http://www.r-project.org,
git_url: https://git.bioconductor.org/packages/CNAnorm
git_branch: RELEASE_3_13
git_last_commit: fd0b875
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNAnorm_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNAnorm_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNAnorm_1.38.0.tgz
vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf
vignetteTitles: CNAnorm.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNAnorm/inst/doc/CNAnorm.R
dependencyCount: 2
Package: CNEr
Version: 1.28.0
Depends: R (>= 3.4)
Imports: Biostrings (>= 2.33.4), DBI (>= 0.7), RSQLite (>= 0.11.4),
GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.23.16),
rtracklayer (>= 1.25.5), XVector (>= 0.5.4), GenomicAlignments
(>= 1.1.9), methods, S4Vectors (>= 0.13.13), IRanges (>=
2.5.27), readr (>= 0.2.2), BiocGenerics, tools, parallel,
reshape2 (>= 1.4.1), ggplot2 (>= 2.1.0), poweRlaw (>= 0.60.3),
annotate (>= 1.50.0), GO.db (>= 3.3.0), R.utils (>= 2.3.0),
KEGGREST (>= 1.14.0)
LinkingTo: S4Vectors, IRanges, XVector
Suggests: Gviz (>= 1.7.4), BiocStyle, knitr, rmarkdown, testthat,
BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38,
TxDb.Drerio.UCSC.danRer10.refGene, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Ggallus.UCSC.galGal3
License: GPL-2 | file LICENSE
License_restricts_use: yes
Archs: i386, x64
MD5sum: c138e5e209aef008460b8b6b2a455c15
NeedsCompilation: yes
Title: CNE Detection and Visualization
Description: Large-scale identification and advanced visualization of
sets of conserved noncoding elements.
biocViews: GeneRegulation, Visualization, DataImport
Author: Ge Tan <ge_tan@live.com>
Maintainer: Ge Tan <ge_tan@live.com>
URL: https://github.com/ge11232002/CNEr
VignetteBuilder: knitr
BugReports: https://github.com/ge11232002/CNEr/issues
git_url: https://git.bioconductor.org/packages/CNEr
git_branch: RELEASE_3_13
git_last_commit: fdeac98
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNEr_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNEr_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNEr_1.28.0.tgz
vignettes: vignettes/CNEr/inst/doc/CNEr.html,
vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html
vignetteTitles: CNE identification and visualisation, Pairwise whole
genome alignment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CNEr/inst/doc/CNEr.R,
vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R
importsMe: TFBSTools
dependencyCount: 115
Package: CNORdt
Version: 1.34.0
Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind
License: GPL-2
Archs: i386, x64
MD5sum: 01cfa176cabcf082c63023ce366a31d3
NeedsCompilation: yes
Title: Add-on to CellNOptR: Discretized time treatments
Description: This add-on to the package CellNOptR handles time-course
data, as opposed to steady state data in CellNOptR. It scales
the simulation step to allow comparison and model fitting for
time-course data. Future versions will optimize delays and
strengths for each edge.
biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics,
TimeCourse
Author: A. MacNamara
Maintainer: A. MacNamara <aidan.macnamara@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/CNORdt
git_branch: RELEASE_3_13
git_last_commit: 2fc1fa3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNORdt_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNORdt_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNORdt_1.34.0.tgz
vignettes: vignettes/CNORdt/inst/doc/CNORdt-vignette.pdf
vignetteTitles: Using multiple time points to train logic models to
data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNORdt/inst/doc/CNORdt-vignette-example.R,
vignettes/CNORdt/inst/doc/CNORdt-vignette.R
dependencyCount: 55
Package: CNORfeeder
Version: 1.32.0
Depends: R (>= 3.6.0), CellNOptR (>= 1.4.0), graph
Suggests: minet, catnet, Rgraphviz, RUnit, BiocGenerics, igraph
Enhances: MEIGOR
License: GPL-3
Archs: i386, x64
MD5sum: e8cb201a9fb64051e683e88f4d0b5d51
NeedsCompilation: no
Title: Integration of CellNOptR to add missing links
Description: This package integrates literature-constrained and
data-driven methods to infer signalling networks from
perturbation experiments. It permits to extends a given network
with links derived from the data via various inference methods
and uses information on physical interactions of proteins to
guide and validate the integration of links.
biocViews: CellBasedAssays, CellBiology, Proteomics, NetworkInference
Author: F.Eduati, E. Gjerga
Maintainer: E.Gjerga <enio.gjerga@gmail.com>
git_url: https://git.bioconductor.org/packages/CNORfeeder
git_branch: RELEASE_3_13
git_last_commit: 297743c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNORfeeder_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNORfeeder_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNORfeeder_1.32.0.tgz
vignettes: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.pdf
vignetteTitles: Main vignette:Playing with networks using CNORfeeder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.R
dependencyCount: 54
Package: CNORfuzzy
Version: 1.34.0
Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5)
Suggests: xtable, Rgraphviz, RUnit, BiocGenerics
License: GPL-2
Archs: i386, x64
MD5sum: b2ea09d6f691a1b47b0808ddef474464
NeedsCompilation: yes
Title: Addon to CellNOptR: Fuzzy Logic
Description: This package is an extension to CellNOptR. It contains
additional functionality needed to simulate and train a prior
knowledge network to experimental data using constrained fuzzy
logic (cFL, rather than Boolean logic as is the case in
CellNOptR). Additionally, this package will contain functions
to use for the compilation of multiple optimization results
(either Boolean or cFL).
biocViews: Network
Author: M. Morris, T. Cokelaer
Maintainer: T. Cokelaer <cokelaer@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/CNORfuzzy
git_branch: RELEASE_3_13
git_last_commit: 0e3dc04
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNORfuzzy_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNORfuzzy_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNORfuzzy_1.34.0.tgz
vignettes: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.pdf
vignetteTitles: Main vignette:Playing with networks using CNORfuzzyl
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.R
dependencyCount: 55
Package: CNORode
Version: 1.34.0
Depends: CellNOptR (>= 1.5.14), genalg
Enhances: MEIGOR
License: GPL-2
MD5sum: c366e5189e46355caaf90e48e620ea05
NeedsCompilation: yes
Title: ODE add-on to CellNOptR
Description: ODE add-on to CellNOptR
biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics,
Bioinformatics, TimeCourse
Author: David Henriques, Thomas Cokelaer, Attila Gabor, Federica
Eduati, Enio Gjerga
Maintainer: Enio Gjerga <enio.gjerga@gmail.com>
git_url: https://git.bioconductor.org/packages/CNORode
git_branch: RELEASE_3_13
git_last_commit: 06fde26
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNORode_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNORode_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNORode_1.34.0.tgz
vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.pdf
vignetteTitles: Main vignette:Playing with networks using CNORode
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R
dependsOnMe: MEIGOR
dependencyCount: 55
Package: CNTools
Version: 1.48.0
Depends: R (>= 2.10), methods, tools, stats, genefilter
License: LGPL
Archs: i386, x64
MD5sum: 7c020939e4c5dbf57f54feb7f67a6209
NeedsCompilation: yes
Title: Convert segment data into a region by sample matrix to allow for
other high level computational analyses.
Description: This package provides tools to convert the output of
segmentation analysis using DNAcopy to a matrix structure with
overlapping segments as rows and samples as columns so that
other computational analyses can be applied to segmented data
biocViews: Microarray, CopyNumberVariation
Author: Jianhua Zhang
Maintainer: J. Zhang <jzhang@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/CNTools
git_branch: RELEASE_3_13
git_last_commit: f850d02
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNTools_1.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNTools_1.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNTools_1.48.0.tgz
vignettes: vignettes/CNTools/inst/doc/HowTo.pdf
vignetteTitles: NCTools HowTo
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNTools/inst/doc/HowTo.R
dependsOnMe: cghMCR
dependencyCount: 55
Package: CNVfilteR
Version: 1.6.2
Depends: R (>= 4.0)
Imports: IRanges, GenomicRanges, SummarizedExperiment, pracma,
regioneR, assertthat, karyoploteR, CopyNumberPlots, graphics,
utils, VariantAnnotation, Rsamtools, GenomeInfoDb, Biostrings,
methods
Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg19.masked, rmarkdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: 8ed8c7a945ccf8e35a71cb9c2d404786
NeedsCompilation: no
Title: Identifies false positives of CNV calling tools by using SNV
calls
Description: CNVfilteR identifies those CNVs that can be discarded by
using the single nucleotide variant (SNV) calls that are
usually obtained in common NGS pipelines.
biocViews: CopyNumberVariation, Sequencing, DNASeq, Visualization,
DataImport
Author: Jose Marcos Moreno-Cabrera [aut, cre]
(<https://orcid.org/0000-0001-8570-0345>), Bernat Gel [aut]
Maintainer: Jose Marcos Moreno-Cabrera <jpuntomarcos@gmail.com>
URL: https://github.com/jpuntomarcos/CNVfilteR
VignetteBuilder: knitr
BugReports: https://github.com/jpuntomarcos/CNVfilteR/issues
git_url: https://git.bioconductor.org/packages/CNVfilteR
git_branch: RELEASE_3_13
git_last_commit: a422dc0
git_last_commit_date: 2021-09-08
Date/Publication: 2021-09-09
source.ver: src/contrib/CNVfilteR_1.6.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNVfilteR_1.6.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNVfilteR_1.6.2.tgz
vignettes: vignettes/CNVfilteR/inst/doc/CNVfilteR.html
vignetteTitles: CNVfilteR vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNVfilteR/inst/doc/CNVfilteR.R
dependencyCount: 152
Package: CNVgears
Version: 1.0.0
Depends: R (>= 4.1), data.table
Imports: ggplot2
Suggests: VariantAnnotation, DelayedArray, knitr, biomaRt, evobiR,
rmarkdown, devtools, cowplot, usethis, scales, testthat,
GenomicRanges, cn.mops, R.utils
License: GPL-3
Archs: i386, x64
MD5sum: 7ccde09242953bcf91546bf32263d9c3
NeedsCompilation: no
Title: A Framework of Functions to Combine, Analize and Interpret CNVs
Calling Results
Description: This package contains a set of functions to perform
several type of processing and analysis on CNVs calling
pipelines/algorithms results in an integrated manner and
regardless of the raw data type (SNPs array or NGS). It
provides functions to combine multiple CNV calling results into
a single object, filter them, compute CNVRs (CNV Regions) and
inheritance patterns, detect genic load, and more. The package
is best suited for studies in human family-based cohorts.
biocViews: Software, WorkflowStep, Preprocessing
Author: Simone Montalbano [cre, aut]
Maintainer: Simone Montalbano <simone.montalbano@protonmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/SinomeM/CNVgears/issues
git_url: https://git.bioconductor.org/packages/CNVgears
git_branch: RELEASE_3_13
git_last_commit: 3167245
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNVgears_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNVgears_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNVgears_1.0.0.tgz
vignettes: vignettes/CNVgears/inst/doc/CNVgears.html
vignetteTitles: CNVgears package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNVgears/inst/doc/CNVgears.R
dependencyCount: 39
Package: cnvGSA
Version: 1.36.0
Depends: brglm, doParallel, foreach, GenomicRanges, methods,
splitstackshape
Suggests: cnvGSAdata, org.Hs.eg.db
License: LGPL
MD5sum: 85334af07a287e97bd48a3d18494e17a
NeedsCompilation: no
Title: Gene Set Analysis of (Rare) Copy Number Variants
Description: This package is intended to facilitate gene-set
association with rare CNVs in case-control studies.
biocViews: MultipleComparison
Author: Daniele Merico <daniele.merico@gmail.com>, Robert Ziman
<rziman@gmail.com>; packaged by Joseph Lugo
<joseph.r.lugo@gmail.com>
Maintainer: Joseph Lugo <joseph.r.lugo@gmail.com>
git_url: https://git.bioconductor.org/packages/cnvGSA
git_branch: RELEASE_3_13
git_last_commit: acbd086
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cnvGSA_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cnvGSA_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cnvGSA_1.36.0.tgz
vignettes: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf,
vignettes/cnvGSA/inst/doc/cnvGSAUsersGuide.pdf
vignetteTitles: cnvGSA - Gene-Set Analysis of Rare Copy Number
Variants, cnvGSAUsersGuide.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: cnvGSAdata
dependencyCount: 25
Package: CNViz
Version: 1.0.0
Depends: R (>= 4.0), shiny (>= 1.5.0)
Imports: dplyr, stats, utils, grDevices, plotly, karyoploteR,
CopyNumberPlots, GenomicRanges, magrittr, DT, scales, graphics
Suggests: rmarkdown, knitr
License: Artistic-2.0
MD5sum: 13e2007228b12d0646b22afce8799e9e
NeedsCompilation: no
Title: Copy Number Visualization
Description: CNViz takes probe, gene, and segment-level log2 copy
number ratios and launches a Shiny app to visualize your
sample's copy number profile. You can also integrate loss of
heterozygosity (LOH) and single nucleotide variant (SNV) data.
biocViews: Visualization, CopyNumberVariation, Sequencing, DNASeq
Author: Rebecca Greenblatt [aut, cre]
Maintainer: Rebecca Greenblatt <rebecca.greenblatt@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CNViz
git_branch: RELEASE_3_13
git_last_commit: a206cc8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNViz_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNViz_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNViz_1.0.0.tgz
vignettes: vignettes/CNViz/inst/doc/CNViz.html
vignetteTitles: CNViz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNViz/inst/doc/CNViz.R
dependencyCount: 168
Package: CNVPanelizer
Version: 1.24.0
Depends: R (>= 3.2.0), GenomicRanges
Imports: BiocGenerics, S4Vectors, grDevices, stats, utils, NOISeq,
IRanges, Rsamtools, exomeCopy, foreach, ggplot2, plyr,
GenomeInfoDb, gplots, reshape2, stringr, testthat, graphics,
methods, shiny, shinyFiles, shinyjs, grid, openxlsx
Suggests: knitr, RUnit
License: GPL-3
MD5sum: 136fe903cd18e037d6737c5c2a7bc493
NeedsCompilation: no
Title: Reliable CNV detection in targeted sequencing applications
Description: A method that allows for the use of a collection of
non-matched normal tissue samples. Our approach uses a
non-parametric bootstrap subsampling of the available reference
samples to estimate the distribution of read counts from
targeted sequencing. As inspired by random forest, this is
combined with a procedure that subsamples the amplicons
associated with each of the targeted genes. The obtained
information allows us to reliably classify the copy number
aberrations on the gene level.
biocViews: Classification, Sequencing, Normalization,
CopyNumberVariation, Coverage
Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht
Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb],
Benedikt Brors [ths], Wilko Weichert [ths]
Maintainer: Thomas Wolf <thomas_wolf71@gmx.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CNVPanelizer
git_branch: RELEASE_3_13
git_last_commit: b0dc959
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNVPanelizer_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNVPanelizer_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNVPanelizer_1.24.0.tgz
vignettes: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.pdf
vignetteTitles: CNVPanelizer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.R
dependencyCount: 113
Package: CNVRanger
Version: 1.8.0
Depends: GenomicRanges, RaggedExperiment
Imports: BiocGenerics, BiocParallel, GDSArray, GenomeInfoDb, IRanges,
S4Vectors, SNPRelate, SummarizedExperiment, data.table, edgeR,
gdsfmt, grDevices, lattice, limma, methods, plyr, qqman,
rappdirs, reshape2, stats, utils
Suggests: AnnotationHub, BSgenome.Btaurus.UCSC.bosTau6.masked,
BiocStyle, ComplexHeatmap, Gviz, MultiAssayExperiment,
TCGAutils, curatedTCGAData, ensembldb, grid, knitr, regioneR,
rmarkdown
License: Artistic-2.0
MD5sum: 9c95258b63b02d7fff162cdd96bcfacb
NeedsCompilation: no
Title: Summarization and expression/phenotype association of CNV ranges
Description: The CNVRanger package implements a comprehensive tool
suite for CNV analysis. This includes functionality for
summarizing individual CNV calls across a population, assessing
overlap with functional genomic regions, and association
analysis with gene expression and quantitative phenotypes.
biocViews: CopyNumberVariation, DifferentialExpression, GeneExpression,
GenomeWideAssociation, GenomicVariation, Microarray, RNASeq,
SNP
Author: Ludwig Geistlinger [aut, cre], Vinicius Henrique da Silva
[aut], Marcel Ramos [ctb], Levi Waldron [ctb]
Maintainer: Ludwig Geistlinger <ludwig_geistlinger@hms.harvard.edu>
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/CNVRanger/issues
git_url: https://git.bioconductor.org/packages/CNVRanger
git_branch: RELEASE_3_13
git_last_commit: dfb9864
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNVRanger_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNVRanger_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNVRanger_1.8.0.tgz
vignettes: vignettes/CNVRanger/inst/doc/CNVRanger.html
vignetteTitles: Summarization and quantitative trait analysis of CNV
ranges
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNVRanger/inst/doc/CNVRanger.R
dependencyCount: 55
Package: CNVrd2
Version: 1.30.0
Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags,
ggplot2, gridExtra
Imports: DNAcopy, IRanges, Rsamtools
Suggests: knitr
License: GPL-2
MD5sum: 4a235a36024bf75e372ae1600a172830
NeedsCompilation: no
Title: CNVrd2: a read depth-based method to detect and genotype complex
common copy number variants from next generation sequencing
data.
Description: CNVrd2 uses next-generation sequencing data to measure
human gene copy number for multiple samples, indentify SNPs
tagging copy number variants and detect copy number polymorphic
genomic regions.
biocViews: CopyNumberVariation, SNP, Sequencing, Software, Coverage,
LinkageDisequilibrium, Clustering.
Author: Hoang Tan Nguyen, Tony R Merriman and Mik Black
Maintainer: Hoang Tan Nguyen <hoangtannguyenvn@gmail.com>
URL: https://github.com/hoangtn/CNVrd2
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CNVrd2
git_branch: RELEASE_3_13
git_last_commit: 0a3ceac
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CNVrd2_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CNVrd2_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CNVrd2_1.30.0.tgz
vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf
vignetteTitles: A Markdown Vignette with knitr
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNVrd2/inst/doc/CNVrd2.R
dependencyCount: 116
Package: CoCiteStats
Version: 1.64.0
Depends: R (>= 2.0), org.Hs.eg.db
Imports: AnnotationDbi
License: CPL
MD5sum: 5b18f3875fc6f01faf5c9949232af7de
NeedsCompilation: no
Title: Different test statistics based on co-citation.
Description: A collection of software tools for dealing with
co-citation data.
biocViews: Software
Author: B. Ding and R. Gentleman
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/CoCiteStats
git_branch: RELEASE_3_13
git_last_commit: 9a21bd3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CoCiteStats_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CoCiteStats_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CoCiteStats_1.64.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 47
Package: COCOA
Version: 2.6.0
Depends: R (>= 3.5), GenomicRanges
Imports: BiocGenerics, S4Vectors, IRanges, data.table, ggplot2,
Biobase, stats, methods, ComplexHeatmap, MIRA, tidyr, grid,
grDevices, simpleCache, fitdistrplus
Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown,
AnnotationHub, LOLA
License: GPL-3
MD5sum: d59cff4960d995207c860512cf89d6c3
NeedsCompilation: no
Title: Coordinate Covariation Analysis
Description: COCOA is a method for understanding epigenetic variation
among samples. COCOA can be used with epigenetic data that
includes genomic coordinates and an epigenetic signal, such as
DNA methylation and chromatin accessibility data. To describe
the method on a high level, COCOA quantifies inter-sample
variation with either a supervised or unsupervised technique
then uses a database of "region sets" to annotate the variation
among samples. A region set is a set of genomic regions that
share a biological annotation, for instance transcription
factor (TF) binding regions, histone modification regions, or
open chromatin regions. COCOA can identify region sets that are
associated with epigenetic variation between samples and
increase understanding of variation in your data.
biocViews: Epigenetics, DNAMethylation, ATACSeq, DNaseSeq, MethylSeq,
MethylationArray, PrincipalComponent, GenomicVariation,
GeneRegulation, GenomeAnnotation, SystemsBiology,
FunctionalGenomics, ChIPSeq, Sequencing, ImmunoOncology
Author: John Lawson [aut, cre], Nathan Sheffield [aut]
(http://www.databio.org), Jason Smith [ctb]
Maintainer: John Lawson <jtl2hk@virginia.edu>
URL: http://code.databio.org/COCOA/
VignetteBuilder: knitr
BugReports: https://github.com/databio/COCOA
git_url: https://git.bioconductor.org/packages/COCOA
git_branch: RELEASE_3_13
git_last_commit: 81a954d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/COCOA_2.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/COCOA_2.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/COCOA_2.6.0.tgz
vignettes: vignettes/COCOA/inst/doc/IntroToCOCOA.html
vignetteTitles: Introduction to Coordinate Covariation Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/COCOA/inst/doc/IntroToCOCOA.R
dependencyCount: 114
Package: codelink
Version: 1.60.0
Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>=
2.17.8), limma
Imports: annotate
Suggests: genefilter, parallel, knitr
License: GPL-2
MD5sum: 65431df61323f9e1355fb13fc7e0848b
NeedsCompilation: no
Title: Manipulation of Codelink microarray data
Description: This package facilitates reading, preprocessing and
manipulating Codelink microarray data. The raw data must be
exported as text file using the Codelink software.
biocViews: Microarray, OneChannel, DataImport, Preprocessing
Author: Diego Diez
Maintainer: Diego Diez <diego10ruiz@gmail.com>
URL: https://github.com/ddiez/codelink
VignetteBuilder: knitr
BugReports: https://github.com/ddiez/codelink/issues
git_url: https://git.bioconductor.org/packages/codelink
git_branch: RELEASE_3_13
git_last_commit: 73c3522
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/codelink_1.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/codelink_1.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/codelink_1.60.0.tgz
vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf,
vignettes/codelink/inst/doc/Codelink_Legacy.pdf
vignetteTitles: Codelink Intruction, Codelink Legacy
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/codelink/inst/doc/Codelink_Introduction.R,
vignettes/codelink/inst/doc/Codelink_Legacy.R
suggestsMe: MAQCsubset
dependencyCount: 50
Package: CODEX
Version: 1.24.0
Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb,
BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors
Suggests: WES.1KG.WUGSC
License: GPL-2
MD5sum: b7b6fc3bf1b000d743eac4f3ea671cb8
NeedsCompilation: no
Title: A Normalization and Copy Number Variation Detection Method for
Whole Exome Sequencing
Description: A normalization and copy number variation calling
procedure for whole exome DNA sequencing data. CODEX relies on
the availability of multiple samples processed using the same
sequencing pipeline for normalization, and does not require
matched controls. The normalization model in CODEX includes
terms that specifically remove biases due to GC content, exon
length and targeting and amplification efficiency, and latent
systemic artifacts. CODEX also includes a Poisson
likelihood-based recursive segmentation procedure that
explicitly models the count-based exome sequencing data.
biocViews: ImmunoOncology, ExomeSeq, Normalization, QualityControl,
CopyNumberVariation
Author: Yuchao Jiang, Nancy R. Zhang
Maintainer: Yuchao Jiang <yuchaoj@wharton.upenn.edu>
git_url: https://git.bioconductor.org/packages/CODEX
git_branch: RELEASE_3_13
git_last_commit: 0051ee2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CODEX_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CODEX_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CODEX_1.24.0.tgz
vignettes: vignettes/CODEX/inst/doc/CODEX_vignettes.pdf
vignetteTitles: Using CODEX
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CODEX/inst/doc/CODEX_vignettes.R
dependsOnMe: iCNV
dependencyCount: 46
Package: coexnet
Version: 1.14.0
Depends: R (>= 3.6)
Imports: affy, siggenes, GEOquery, vsn, igraph, acde, Biobase, limma,
graphics, stats, utils, STRINGdb, SummarizedExperiment, minet,
rmarkdown
Suggests: RUnit, BiocGenerics, knitr
License: LGPL
MD5sum: b5996a22fabd204c3b63e5dcb01d2c60
NeedsCompilation: no
Title: coexnet: An R package to build CO-EXpression NETworks from
Microarray Data
Description: Extracts the gene expression matrix from GEO DataSets
(.CEL files) as a AffyBatch object. Additionally, can make the
normalization process using two different methods (vsn and
rma). The summarization (pass from multi-probe to one gene)
uses two different criteria (Maximum value and Median of the
samples expression data) and the process of gene differentially
expressed analisys using two methods (sam and acde). The
construction of the co-expression network can be conduced using
two different methods, Pearson Correlation Coefficient (PCC) or
Mutual Information (MI) and choosing a threshold value using a
graph theory approach.
biocViews: GeneExpression, Microarray, DifferentialExpression,
GraphAndNetwork, NetworkInference, SystemsBiology,
Normalization, Network
Author: Juan David Henao [aut,cre], Liliana Lopez-Kleine [aut], Andres
Pinzon-Velasco [aut]
Maintainer: Juan David Henao <judhenaosa@unal.edu.co>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/coexnet
git_branch: RELEASE_3_13
git_last_commit: bf5b2c4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/coexnet_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/coexnet_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/coexnet_1.14.0.tgz
vignettes: vignettes/coexnet/inst/doc/coexnet.pdf
vignetteTitles: The title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/coexnet/inst/doc/coexnet.R
dependencyCount: 128
Package: CoGAPS
Version: 3.12.0
Depends: R (>= 3.5.0)
Imports: BiocParallel, cluster, methods, gplots, graphics, grDevices,
RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats,
SummarizedExperiment, tools, utils, rhdf5
LinkingTo: Rcpp, BH
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: BSD_3_clause + file LICENSE
MD5sum: 03776f9fbc37f0a92fed9d3451927042
NeedsCompilation: yes
Title: Coordinated Gene Activity in Pattern Sets
Description: Coordinated Gene Activity in Pattern Sets (CoGAPS)
implements a Bayesian MCMC matrix factorization algorithm,
GAPS, and links it to gene set statistic methods to infer
biological process activity. It can be used to perform sparse
matrix factorization on any data, and when this data represents
biomolecules, to do gene set analysis.
biocViews: GeneExpression, Transcription, GeneSetEnrichment,
DifferentialExpression, Bayesian, Clustering, TimeCourse,
RNASeq, Microarray, MultipleComparison, DimensionReduction,
ImmunoOncology
Author: Thomas Sherman, Wai-shing Lee, Conor Kelton, Ondrej Maxian,
Jacob Carey, Genevieve Stein-O'Brien, Michael Considine, Maggie
Wodicka, John Stansfield, Shawn Sivy, Carlo Colantuoni,
Alexander Favorov, Mike Ochs, Elana Fertig
Maintainer: Elana J. Fertig <ejfertig@jhmi.edu>, Thomas D. Sherman
<tomsherman159@gmail.com>, Melanie L. Loth <mloth1@jhmi.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CoGAPS
git_branch: RELEASE_3_13
git_last_commit: c297d7e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CoGAPS_3.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CoGAPS_3.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CoGAPS_3.12.0.tgz
vignettes: vignettes/CoGAPS/inst/doc/CoGAPS.html
vignetteTitles: CoGAPS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CoGAPS/inst/doc/CoGAPS.R
importsMe: projectR
dependencyCount: 44
Package: cogena
Version: 1.26.0
Depends: R (>= 3.6), cluster, ggplot2, kohonen
Imports: methods, class, gplots, mclust, amap, apcluster, foreach,
parallel, doParallel, fastcluster, corrplot, biwt, Biobase,
reshape2, stringr, tibble, tidyr, dplyr, devtools
Suggests: knitr, rmarkdown (>= 2.1)
License: LGPL-3
MD5sum: c3522a366794c95f89d3cff5d6f3b07f
NeedsCompilation: no
Title: co-expressed gene-set enrichment analysis
Description: cogena is a workflow for co-expressed gene-set enrichment
analysis. It aims to discovery smaller scale, but highly
correlated cellular events that may be of great biological
relevance. A novel pipeline for drug discovery and drug
repositioning based on the cogena workflow is proposed.
Particularly, candidate drugs can be predicted based on the
gene expression of disease-related data, or other similar drugs
can be identified based on the gene expression of drug-related
data. Moreover, the drug mode of action can be disclosed by the
associated pathway analysis. In summary, cogena is a flexible
workflow for various gene set enrichment analysis for
co-expressed genes, with a focus on pathway/GO analysis and
drug repositioning.
biocViews: Clustering, GeneSetEnrichment, GeneExpression,
Visualization, Pathways, KEGG, GO, Microarray, Sequencing,
SystemsBiology, DataRepresentation, DataImport
Author: Zhilong Jia [aut, cre], Michael Barnes [aut]
Maintainer: Zhilong Jia <zhilongjia@gmail.com>
URL: https://github.com/zhilongjia/cogena
VignetteBuilder: knitr
BugReports: https://github.com/zhilongjia/cogena/issues
git_url: https://git.bioconductor.org/packages/cogena
git_branch: RELEASE_3_13
git_last_commit: 62d798c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cogena_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cogena_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cogena_1.26.0.tgz
vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf,
vignettes/cogena/inst/doc/cogena-vignette_html.html
vignetteTitles: a workflow of cogena, cogena,, a workflow for gene set
enrichment analysis of co-expressed genes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cogena/inst/doc/cogena-vignette_html.R,
vignettes/cogena/inst/doc/cogena-vignette_pdf.R
dependencyCount: 128
Package: coGPS
Version: 1.36.0
Depends: R (>= 2.13.0)
Imports: graphics, grDevices
Suggests: limma
License: GPL-2
Archs: i386, x64
MD5sum: d898d1c27e10ef91524f436d2f825de8
NeedsCompilation: no
Title: cancer outlier Gene Profile Sets
Description: Gene Set Enrichment Analysis of P-value based statistics
for outlier gene detection in dataset merged from multiple
studies
biocViews: Microarray, DifferentialExpression
Author: Yingying Wei, Michael Ochs
Maintainer: Yingying Wei <ywei@jhsph.edu>
git_url: https://git.bioconductor.org/packages/coGPS
git_branch: RELEASE_3_13
git_last_commit: cba5151
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/coGPS_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/coGPS_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/coGPS_1.36.0.tgz
vignettes: vignettes/coGPS/inst/doc/coGPS.pdf
vignetteTitles: coGPS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/coGPS/inst/doc/coGPS.R
dependencyCount: 2
Package: COHCAP
Version: 1.38.0
Depends: WriteXLS, COHCAPanno, RColorBrewer, gplots
Imports: Rcpp, RcppArmadillo, BH
LinkingTo: Rcpp, BH
License: GPL-3
MD5sum: e6db005c50c99797c85756b25711ef24
NeedsCompilation: yes
Title: CpG Island Analysis Pipeline for Illumina Methylation Array and
Targeted BS-Seq Data
Description: COHCAP (pronounced "co-cap") provides a pipeline to
analyze single-nucleotide resolution methylation data (Illumina
450k/EPIC methylation array, targeted BS-Seq, etc.). It
provides differential methylation for CpG Sites, differential
methylation for CpG Islands, integration with gene expression
data, with visualizaton options. Discussion Group:
https://sourceforge.net/p/cohcap/discussion/bioconductor/
biocViews: DNAMethylation, Microarray, MethylSeq, Epigenetics,
DifferentialMethylation
Author: Charles Warden <cwarden@coh.org>, Yate-Ching Yuan
<yyuan@coh.org>, Xiwei Wu <xwu@coh.org>
Maintainer: Charles Warden <cwarden@coh.org>
SystemRequirements: Perl
git_url: https://git.bioconductor.org/packages/COHCAP
git_branch: RELEASE_3_13
git_last_commit: 706e19c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/COHCAP_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/COHCAP_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/COHCAP_1.38.0.tgz
vignettes: vignettes/COHCAP/inst/doc/COHCAP.pdf
vignetteTitles: COHCAP Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/COHCAP/inst/doc/COHCAP.R
dependencyCount: 14
Package: cola
Version: 1.8.1
Depends: R (>= 3.6.0)
Imports: grDevices, graphics, grid, stats, utils, ComplexHeatmap (>=
2.5.4), matrixStats, GetoptLong, circlize (>= 0.4.7),
GlobalOptions (>= 0.1.0), clue, parallel, RColorBrewer,
cluster, skmeans, png, mclust, crayon, methods, xml2,
microbenchmark, httr, knitr, markdown, digest, impute, brew,
Rcpp (>= 0.11.0), BiocGenerics, eulerr, foreach, doParallel,
irlba
LinkingTo: Rcpp
Suggests: genefilter, mvtnorm, testthat (>= 0.3), samr, pamr, kohonen,
NMF, WGCNA, Rtsne, umap, clusterProfiler, ReactomePA, DOSE,
AnnotationDbi, gplots, hu6800.db, BiocManager, data.tree,
dendextend, Polychrome, rmarkdown, simplifyEnrichment, cowplot,
flexclust
License: MIT + file LICENSE
MD5sum: f52c7fd88e3475eea17199908b34e589
NeedsCompilation: yes
Title: A Framework for Consensus Partitioning
Description: Subgroup classification is a basic task in genomic data
analysis, especially for gene expression and DNA methylation
data analysis. It can also be used to test the agreement to
known clinical annotations, or to test whether there exist
significant batch effects. The cola package provides a general
framework for subgroup classification by consensus
partitioning. It has the following features: 1. It modularizes
the consensus partitioning processes that various methods can
be easily integrated. 2. It provides rich visualizations for
interpreting the results. 3. It allows running multiple methods
at the same time and provides functionalities to
straightforward compare results. 4. It provides a new method to
extract features which are more efficient to separate
subgroups. 5. It automatically generates detailed reports for
the complete analysis. 6. It allows applying consensus
partitioning in a hierarchical manner.
biocViews: Clustering, GeneExpression, Classification, Software
Author: Zuguang Gu
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/cola,
https://jokergoo.github.io/cola_collection/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cola
git_branch: RELEASE_3_13
git_last_commit: ffd39a8
git_last_commit_date: 2021-07-15
Date/Publication: 2021-07-18
source.ver: src/contrib/cola_1.8.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cola_1.8.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/cola_1.8.1.tgz
vignettes: vignettes/cola/inst/doc/cola.html
vignetteTitles: Use of cola
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
suggestsMe: InteractiveComplexHeatmap, simplifyEnrichment
dependencyCount: 65
Package: combi
Version: 1.4.0
Depends: R (>= 3.5.0)
Imports: ggplot2, nleqslv, phyloseq, tensor, stats, limma, Matrix, BB,
reshape2, alabama, cobs, Biobase, vegan, grDevices, graphics,
methods, SummarizedExperiment
Suggests: knitr, rmarkdown, testthat
License: GPL-2
Archs: i386, x64
MD5sum: f60c30036e3a7f9bced73fdd1cdd943f
NeedsCompilation: no
Title: Compositional omics model based visual integration
Description: Combine quasi-likelihood estimation, compositional
regression models and latent variable models for integrative
visualization of several omics datasets. Both unconstrained and
constrained integration is available, the results are shown as
interpretable multiplots.
biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization,
Metabolomics
Author: Stijn Hawinkel <stijn.hawinkel@ugent.be>
Maintainer: Joris Meys <joris.meys@ugent.be>
VignetteBuilder: knitr
BugReports: https://github.com/CenterForStatistics-UGent/combi/issues
git_url: https://git.bioconductor.org/packages/combi
git_branch: RELEASE_3_13
git_last_commit: c7a1fa5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/combi_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/combi_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/combi_1.4.0.tgz
vignettes: vignettes/combi/inst/doc/combi.html
vignetteTitles: Manual for the combi pacakage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/combi/inst/doc/combi.R
dependencyCount: 95
Package: coMET
Version: 1.24.0
Depends: R (>= 3.7.0), grid, utils, biomaRt, Gviz, psych
Imports: colortools, hash,grDevices, gridExtra, rtracklayer, IRanges,
S4Vectors, GenomicRanges, stats, corrplot
Suggests: BiocStyle, knitr, RUnit, BiocGenerics
License: GPL (>= 2)
Archs: i386, x64
MD5sum: c01e3fcb9feeaf846b89adb2e582df4e
NeedsCompilation: no
Title: coMET: visualisation of regional epigenome-wide association scan
(EWAS) results and DNA co-methylation patterns
Description: Visualisation of EWAS results in a genomic region. In
addition to phenotype-association P-values, coMET also
generates plots of co-methylation patterns and provides a
series of annotation tracks. It can be used to other omic-wide
association scans as long as the data can be translated to
genomic level and for any species.
biocViews: Software, DifferentialMethylation, Visualization,
Sequencing, Genetics, FunctionalGenomics, Microarray,
MethylationArray, MethylSeq, ChIPSeq, DNASeq, RiboSeq, RNASeq,
ExomeSeq, DNAMethylation, GenomeWideAssociation,
MotifAnnotation
Author: Tiphaine C. Martin [aut,cre], Thomas Hardiman [aut], Idil Yet
[aut], Pei-Chien Tsai [aut], Jordana T. Bell [aut]
Maintainer: Tiphaine Martin <tiphaine.martin@mssm.edu>
URL: http://epigen.kcl.ac.uk/comet
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/coMET
git_branch: RELEASE_3_13
git_last_commit: fb8047d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/coMET_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/coMET_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/coMET_1.24.0.tgz
vignettes: vignettes/coMET/inst/doc/coMET.pdf
vignetteTitles: coMET users guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/coMET/inst/doc/coMET.R
dependencyCount: 148
Package: compartmap
Version: 1.10.0
Depends: R (>= 4.1.0), SummarizedExperiment, RaggedExperiment,
BiocSingular, HDF5Array
Imports: GenomicRanges, parallel, grid, ggplot2, reshape2, scales,
DelayedArray, rtracklayer, DelayedMatrixStats, Matrix, RMTstat
Suggests: covr, testthat, knitr, Rcpp, rmarkdown, markdown
License: GPL-3 + file LICENSE
MD5sum: 00a55eb2feabce29511d21d49cb5f27f
NeedsCompilation: no
Title: Higher-order chromatin domain inference in single cells from
scRNA-seq and scATAC-seq
Description: Compartmap performs direct inference of higher-order
chromatin from scRNA-seq and scATAC-seq. This package
implements a James-Stein estimator for computing single-cell
level higher-order chromatin domains. Further, we utilize
random matrix theory as a method to de-noise correlation
matrices to achieve a similar "plaid-like" patterning as
observed in Hi-C and scHi-C data.
biocViews: Genetics, Epigenetics, ATACSeq, RNASeq, SingleCell
Author: Benjamin Johnson [aut, cre], Tim Triche [aut], Hui Shen [aut],
Kasper Hansen [aut], Jean-Philippe Fortin [aut]
Maintainer: Benjamin Johnson <ben.johnson@vai.org>
URL: https://github.com/biobenkj/compartmap
VignetteBuilder: knitr
BugReports: https://github.com/biobenkj/compartmap/issues
git_url: https://git.bioconductor.org/packages/compartmap
git_branch: RELEASE_3_13
git_last_commit: 4c305de
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/compartmap_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/compartmap_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/compartmap_1.10.0.tgz
vignettes: vignettes/compartmap/inst/doc/compartmap_vignette.html
vignetteTitles: Higher-order chromatin inference with compartmap
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/compartmap/inst/doc/compartmap_vignette.R
dependencyCount: 91
Package: COMPASS
Version: 1.30.0
Depends: R (>= 3.0.3)
Imports: methods, Rcpp, data.table, RColorBrewer, scales, grid, plyr,
knitr, abind, clue, grDevices, utils, pdist, magrittr,
reshape2, dplyr, tidyr, rlang, BiocStyle, rmarkdown, foreach,
coda
LinkingTo: Rcpp (>= 0.11.0)
Suggests: flowWorkspace (>= 3.33.1), flowCore, ncdfFlow, shiny,
testthat, devtools, flowWorkspaceData, ggplot2, progress
License: Artistic-2.0
MD5sum: 0810fd8857b75e7dffcdfa1c8a7cd5a0
NeedsCompilation: yes
Title: Combinatorial Polyfunctionality Analysis of Single Cells
Description: COMPASS is a statistical framework that enables unbiased
analysis of antigen-specific T-cell subsets. COMPASS uses a
Bayesian hierarchical framework to model all observed
cell-subsets and select the most likely to be antigen-specific
while regularizing the small cell counts that often arise in
multi-parameter space. The model provides a posterior
probability of specificity for each cell subset and each
sample, which can be used to profile a subject's immune
response to external stimuli such as infection or vaccination.
biocViews: ImmunoOncology, FlowCytometry
Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap)
Maintainer: Greg Finak <gfinak@fhcrc.org>
VignetteBuilder: knitr
BugReports: https://github.com/RGLab/COMPASS/issues
git_url: https://git.bioconductor.org/packages/COMPASS
git_branch: RELEASE_3_13
git_last_commit: 00c36c3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/COMPASS_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/COMPASS_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/COMPASS_1.30.0.tgz
vignettes: vignettes/COMPASS/inst/doc/SimpleCOMPASS.pdf,
vignettes/COMPASS/inst/doc/COMPASS.html
vignetteTitles: SimpleCOMPASS, COMPASS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R,
vignettes/COMPASS/inst/doc/SimpleCOMPASS.R
dependencyCount: 65
Package: compcodeR
Version: 1.28.0
Depends: sm
Imports: tcltk, knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16),
gplots, gtools, caTools, grid, KernSmooth, MASS, ggplot2,
stringr, modeest, edgeR, limma, vioplot, methods, utils, stats,
grDevices, graphics
Suggests: BiocStyle, EBSeq, DESeq2 (>= 1.1.31), baySeq (>= 2.2.0),
genefilter, NOISeq, TCC, NBPSeq (>= 0.3.0), rmarkdown, testthat
Enhances: rpanel, DSS
License: GPL (>= 2)
MD5sum: 619bd78f2a05dbef48e1c25e4f500872
NeedsCompilation: no
Title: RNAseq data simulation, differential expression analysis and
performance comparison of differential expression methods
Description: This package provides extensive functionality for
comparing results obtained by different methods for
differential expression analysis of RNAseq data. It also
contains functions for simulating count data. Finally, it
provides convenient interfaces to several packages for
performing the differential expression analysis. These can also
be used as templates for setting up and running a user-defined
differential analysis workflow within the framework of the
package.
biocViews: ImmunoOncology, RNASeq, DifferentialExpression
Author: Charlotte Soneson [aut, cre]
(<https://orcid.org/0000-0003-3833-2169>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/csoneson/compcodeR
VignetteBuilder: knitr
BugReports: https://github.com/csoneson/compcodeR/issues
git_url: https://git.bioconductor.org/packages/compcodeR
git_branch: RELEASE_3_13
git_last_commit: 16b062e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/compcodeR_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/compcodeR_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/compcodeR_1.28.0.tgz
vignettes: vignettes/compcodeR/inst/doc/compcodeR.html
vignetteTitles: compcodeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R
dependencyCount: 75
Package: compEpiTools
Version: 1.26.0
Depends: R (>= 3.1.1), methods, topGO, GenomicRanges
Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel,
grDevices, gplots, IRanges, GenomicFeatures, XVector,
methylPipe, GO.db, S4Vectors, GenomeInfoDb
Suggests: BSgenome.Mmusculus.UCSC.mm9,
TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr,
rtracklayer
License: GPL
Archs: i386, x64
MD5sum: 9a7b0f246a06641578a53264bd2ba84b
NeedsCompilation: no
Title: Tools for computational epigenomics
Description: Tools for computational epigenomics developed for the
analysis, integration and simultaneous visualization of various
(epi)genomics data types across multiple genomic regions in
multiple samples.
biocViews: GeneExpression, Sequencing, Visualization, GenomeAnnotation,
Coverage
Author: Mattia Pelizzola [aut], Kamal Kishore [aut, cre]
Maintainer: Kamal Kishore <kamal.fartiyal84@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/compEpiTools
git_branch: RELEASE_3_13
git_last_commit: cefdb04
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/compEpiTools_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/compEpiTools_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/compEpiTools_1.26.0.tgz
vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf
vignetteTitles: compEpiTools.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/compEpiTools/inst/doc/compEpiTools.R
dependencyCount: 153
Package: CompGO
Version: 1.28.0
Depends: RDAVIDWebService
Imports: rtracklayer, Rgraphviz, ggplot2, GenomicFeatures,
TxDb.Mmusculus.UCSC.mm9.knownGene, pcaMethods, reshape2,
pathview
License: GPL-2
MD5sum: e4179b0a1093bf2c10942c757e5b104b
NeedsCompilation: no
Title: An R pipeline for .bed file annotation, comparing GO term
enrichment between gene sets and data visualisation
Description: This package contains functions to accomplish several
tasks. It is able to download full genome databases from UCSC,
import .bed files easily, annotate these .bed file regions with
genes (plus distance) from aforementioned database dumps,
interface with DAVID to create functional annotation and gene
ontology enrichment charts based on gene lists (such as those
generated from input .bed files) and finally visualise and
compare these enrichments using either directed acyclic graphs
or scatterplots.
biocViews: GeneSetEnrichment, MultipleComparison, GO, Visualization
Author: Sam D. Bassett [aut], Ashley J. Waardenberg [aut, cre]
Maintainer: Ashley J. Waardenberg <A.Waardenberg@victorchang.edu.au>
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/CompGO
git_branch: RELEASE_3_13
git_last_commit: e8e77da
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CompGO_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CompGO_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CompGO_1.28.0.tgz
vignettes: vignettes/CompGO/inst/doc/CompGO-Intro.pdf
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CompGO/inst/doc/CompGO-Intro.R
dependencyCount: 120
Package: ComplexHeatmap
Version: 2.8.0
Depends: R (>= 3.5.0), methods, grid, graphics, stats, grDevices
Imports: circlize (>= 0.4.5), GetoptLong, colorspace, clue,
RColorBrewer, GlobalOptions (>= 0.1.0), png, Cairo, digest,
IRanges, matrixStats, foreach, doParallel
Suggests: testthat (>= 1.0.0), knitr, markdown, dendsort, jpeg, tiff,
fastcluster, EnrichedHeatmap, dendextend (>= 1.0.1), grImport,
grImport2, glue, GenomicRanges, gridtext, pheatmap (>= 1.0.12),
gridGraphics, gplots
License: MIT + file LICENSE
MD5sum: 0bee5e12ddbda1d89536cc63238c13c9
NeedsCompilation: no
Title: Make Complex Heatmaps
Description: Complex heatmaps are efficient to visualize associations
between different sources of data sets and reveal potential
patterns. Here the ComplexHeatmap package provides a highly
flexible way to arrange multiple heatmaps and supports various
annotation graphics.
biocViews: Software, Visualization, Sequencing
Author: Zuguang Gu
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/ComplexHeatmap,
https://jokergoo.github.io/ComplexHeatmap-reference/book/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ComplexHeatmap
git_branch: RELEASE_3_13
git_last_commit: 1bd0c3b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ComplexHeatmap_2.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ComplexHeatmap_2.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ComplexHeatmap_2.8.0.tgz
vignettes: vignettes/ComplexHeatmap/inst/doc/complex_heatmap.html,
vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.html
vignetteTitles: complex_heatmap.html, Most probably asked questions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.R
dependsOnMe: AMARETTO, EnrichedHeatmap, recoup, countToFPKM
importsMe: airpart, BiocOncoTK, BioNERO, blacksheepr, BloodGen3Module,
CATALYST, celda, CeTF, COCOA, cola, DEComplexDisease,
DEGreport, DEP, diffcyt, diffUTR, ELMER, fCCAC, GeneTonic,
GenomicSuperSignature, gmoviz, InteractiveComplexHeatmap,
InterCellar, iSEE, LineagePulse, MatrixQCvis, MesKit, MOMA,
muscat, musicatk, MWASTools, PathoStat, PeacoQC, pipeComp,
POMA, profileplyr, sechm, SEtools, simplifyEnrichment,
singleCellTK, TBSignatureProfiler, Xeva, YAPSA, TCGAWorkflow,
armada, conos, MKomics, pkgndep, rKOMICS, RVA, sigQC,
tidyHeatmap, wilson
suggestsMe: ALPS, artMS, bambu, BrainSABER, clustifyr, CNVRanger,
dittoSeq, EnrichmentBrowser, gtrellis, HilbertCurve, msImpute,
projectR, scDblFinder, TCGAbiolinks, TCGAutils,
TimeSeriesExperiment, weitrix, NanoporeRNASeq, circlize,
eclust, i2dash, IOHanalyzer, MOSS, multipanelfigure, spiralize
dependencyCount: 29
Package: ComPrAn
Version: 1.0.0
Imports: data.table, dplyr, forcats, ggplot2, magrittr, purrr, tidyr,
rlang, stringr, shiny, DT, RColorBrewer, VennDiagram, rio,
scales, shinydashboard, shinyjs, stats, tibble, grid
Suggests: testthat (>= 2.1.0), knitr
License: MIT + file LICENSE
MD5sum: eca97ab303d89863edf18e32331c4af4
NeedsCompilation: no
Title: Complexome Profiling Analysis package
Description: This package is for analysis of SILAC labeled complexome
profiling data. It uses peptide table in tab-delimited format
as an input and produces ready-to-use tables and plots.
biocViews: MassSpectrometry, Proteomics, Visualization
Author: Rick Scavetta [aut], Petra Palenikova [aut, cre]
(<https://orcid.org/0000-0002-2465-4370>)
Maintainer: Petra Palenikova <pp451@cam.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ComPrAn
git_branch: RELEASE_3_13
git_last_commit: 838acf2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ComPrAn_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ComPrAn_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ComPrAn_1.0.0.tgz
vignettes: vignettes/ComPrAn/inst/doc/fileFormats.html,
vignettes/ComPrAn/inst/doc/proteinWorkflow.html,
vignettes/ComPrAn/inst/doc/SILACcomplexomics.html
vignetteTitles: fileFormats.html, Protein workflow, SILAC complexomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ComPrAn/inst/doc/fileFormats.R,
vignettes/ComPrAn/inst/doc/proteinWorkflow.R,
vignettes/ComPrAn/inst/doc/SILACcomplexomics.R
dependencyCount: 99
Package: conclus
Version: 1.0.0
Depends: R (>= 4.1)
Imports: dbscan, fpc, factoextra, Biobase, BiocFileCache, parallel,
doParallel, foreach, SummarizedExperiment, biomaRt,
AnnotationDbi, methods, dplyr, scran, scater, pheatmap,
ggplot2, gridExtra, SingleCellExperiment, stats, utils, scales,
grDevices, graphics, Rtsne, GEOquery, clusterProfiler, stringr,
tools
Suggests: knitr, rmarkdown, BiocStyle, S4Vectors, matrixStats,
org.Hs.eg.db, org.Mm.eg.db, dynamicTreeCut, testthat
License: GPL-3
MD5sum: c8dca1898d9b893cdf324b4eb94caa0d
NeedsCompilation: no
Title: ScRNA-seq Workflow CONCLUS - From CONsensus CLUSters To A
Meaningful CONCLUSion
Description: CONCLUS is a tool for robust clustering and positive
marker features selection of single-cell RNA-seq (sc-RNA-seq)
datasets. It takes advantage of a consensus clustering approach
that greatly simplify sc-RNA-seq data analysis for the user. Of
note, CONCLUS does not cover the preprocessing steps of
sequencing files obtained following next-generation sequencing.
CONCLUS is organized into the following steps: Generation of
multiple t-SNE plots with a range of parameters including
different selection of genes extracted from PCA. Use the
Density-based spatial clustering of applications with noise
(DBSCAN) algorithm for idenfication of clusters in each
generated t-SNE plot. All DBSCAN results are combined into a
cell similarity matrix. The cell similarity matrix is used to
define "CONSENSUS" clusters conserved accross the previously
defined clustering solutions. Identify marker genes for each
concensus cluster.
biocViews: Software, Technology, SingleCell, Sequencing, Clustering,
ATACSeq, Classification
Author: Ilyess Rachedi [cre], Nicolas Descostes [aut], Polina Pavlovich
[aut], Christophe Lancrin [aut]
Maintainer: Ilyess Rachedi <ilyessr@hotmail.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/conclus
git_branch: RELEASE_3_13
git_last_commit: 1336595
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/conclus_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/conclus_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/conclus_1.0.0.tgz
vignettes: vignettes/conclus/inst/doc/conclus_vignette.pdf
vignetteTitles: conclus
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/conclus/inst/doc/conclus_vignette.R
dependencyCount: 243
Package: condiments
Version: 1.0.0
Depends: R (>= 4.1)
Imports: slingshot (>= 1.9), mgcv, RANN, stats, SingleCellExperiment,
SummarizedExperiment, utils, magrittr, dplyr (>= 1.0), Ecume
(>= 0.9.1), methods, pbapply, matrixStats, BiocParallel,
TrajectoryUtils, igraph
Suggests: knitr, testthat, rmarkdown, covr, viridis, ggplot2,
RColorBrewer, randomForest, tidyr, TSCAN
License: MIT + file LICENSE
MD5sum: 2d88881dfec5c93b4917e025e825a1dd
NeedsCompilation: no
Title: Differential Topology, Progression and Differentiation
Description: This package encapsulate many functions to conduct a
differential topology analysis. It focuses on analyzing an
'omic dataset with multiple conditions. While the package is
mostly geared toward scRNASeq, it does not place any
restriction on the actual input format.
biocViews: RNASeq, Sequencing, Software, SingleCell, Transcriptomics,
MultipleComparison, Visualization
Author: Hector Roux de Bezieux [aut, cre]
(<https://orcid.org/0000-0002-1489-8339>), Koen Van den Berge
[aut, ctb], Kelly Street [aut, ctb]
Maintainer: Hector Roux de Bezieux <hector.rouxdebezieux@berkeley.edu>
URL: https://hectorrdb.github.io/condiments/index.html
VignetteBuilder: knitr
BugReports: https://github.com/HectorRDB/condiments/issues
git_url: https://git.bioconductor.org/packages/condiments
git_branch: RELEASE_3_13
git_last_commit: f66736c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/condiments_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/condiments_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/condiments_1.0.0.tgz
vignettes: vignettes/condiments/inst/doc/condiments.html,
vignettes/condiments/inst/doc/controls.html,
vignettes/condiments/inst/doc/examples.html
vignetteTitles: The condiments workflow, Using condiments, Generating
more examples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/condiments/inst/doc/condiments.R,
vignettes/condiments/inst/doc/controls.R,
vignettes/condiments/inst/doc/examples.R
dependencyCount: 126
Package: CONFESS
Version: 1.20.0
Depends: R (>= 3.3),grDevices,utils,stats,graphics
Imports: methods,changepoint,cluster,contrast,data.table(>=
1.9.7),ecp,EBImage,flexmix,flowCore,flowClust,flowMeans,flowMerge,flowPeaks,foreach,ggplot2,grid,limma,MASS,moments,outliers,parallel,plotrix,raster,readbitmap,reshape2,SamSPECTRAL,waveslim,wavethresh,zoo
Suggests: BiocStyle, knitr, rmarkdown, CONFESSdata
License: GPL-2
MD5sum: 5423a7f0339ee95fb91344e3ecf17223
NeedsCompilation: no
Title: Cell OrderiNg by FluorEScence Signal
Description: Single Cell Fluidigm Spot Detector.
biocViews: ImmunoOncology,
GeneExpression,DataImport,CellBiology,Clustering,RNASeq,QualityControl,Visualization,TimeCourse,Regression,Classification
Author: Diana LOW and Efthimios MOTAKIS
Maintainer: Diana LOW <lowdiana@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CONFESS
git_branch: RELEASE_3_13
git_last_commit: 5f6120a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CONFESS_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CONFESS_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CONFESS_1.20.0.tgz
vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf,
vignettes/CONFESS/inst/doc/vignette.html
vignetteTitles: CONFESS, CONFESS Walkthrough
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R,
vignettes/CONFESS/inst/doc/vignette.R
dependencyCount: 152
Package: consensus
Version: 1.10.0
Depends: R (>= 3.5), RColorBrewer
Imports: matrixStats, gplots, grDevices, methods, graphics, stats,
utils
Suggests: knitr, RUnit, rmarkdown, BiocGenerics
License: BSD_3_clause + file LICENSE
MD5sum: 4493b38da76218f75dbc2291e966ccc9
NeedsCompilation: no
Title: Cross-platform consensus analysis of genomic measurements via
interlaboratory testing method
Description: An implementation of the American Society for Testing and
Materials (ASTM) Standard E691 for interlaboratory testing
procedures, designed for cross-platform genomic measurements.
Given three (3) or more genomic platforms or laboratory
protocols, this package provides interlaboratory testing
procedures giving per-locus comparisons for sensitivity and
precision between platforms.
biocViews: QualityControl, Regression, DataRepresentation,
GeneExpression, Microarray, RNASeq
Author: Tim Peters
Maintainer: Tim Peters <t.peters@garvan.org.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/consensus
git_branch: RELEASE_3_13
git_last_commit: 9601d93
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/consensus_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/consensus_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/consensus_1.10.0.tgz
vignettes: vignettes/consensus/inst/doc/consensus.pdf
vignetteTitles: Fitting and visualising row-linear models with
\texttt{consensus}
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/consensus/inst/doc/consensus.R
dependencyCount: 12
Package: ConsensusClusterPlus
Version: 1.56.0
Imports: Biobase, ALL, graphics, stats, utils, cluster
License: GPL version 2
MD5sum: 039e4a35086ad8b170c512c08c38653e
NeedsCompilation: no
Title: ConsensusClusterPlus
Description: algorithm for determining cluster count and membership by
stability evidence in unsupervised analysis
biocViews: Software, Clustering
Author: Matt Wilkerson <mdwilkerson@outlook.com>, Peter Waltman
<waltman@soe.ucsc.edu>
Maintainer: Matt Wilkerson <mdwilkerson@outlook.com>
git_url: https://git.bioconductor.org/packages/ConsensusClusterPlus
git_branch: RELEASE_3_13
git_last_commit: 82a7165
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ConsensusClusterPlus_1.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ConsensusClusterPlus_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ConsensusClusterPlus_1.56.0.tgz
vignettes:
vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf
vignetteTitles: ConsensusClusterPlus Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.R
importsMe: CancerSubtypes, CATALYST, ChromSCape, DEGreport, FlowSOM,
PDATK, DeSousa2013, iSubGen, neatmaps, scRNAtools
suggestsMe: TCGAbiolinks
dependencyCount: 10
Package: consensusDE
Version: 1.10.0
Depends: R (>= 3.5), BiocGenerics
Imports: airway, AnnotationDbi, BiocParallel, Biobase, Biostrings,
data.table, dendextend, DESeq2 (>= 1.20.0), EDASeq, ensembldb,
edgeR, EnsDb.Hsapiens.v86, GenomicAlignments, GenomicFeatures,
limma, org.Hs.eg.db, pcaMethods, RColorBrewer, Rsamtools,
RUVSeq, S4Vectors, stats, SummarizedExperiment,
TxDb.Dmelanogaster.UCSC.dm3.ensGene, utils
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: a0fcbb2e9066a1052381e48aac3a3fa4
NeedsCompilation: no
Title: RNA-seq analysis using multiple algorithms
Description: This package allows users to perform DE analysis using
multiple algorithms. It seeks consensus from multiple methods.
Currently it supports "Voom", "EdgeR" and "DESeq". It uses
RUV-seq (optional) to remove unwanted sources of variation.
biocViews: Transcriptomics, MultipleComparison, Clustering, Sequencing,
Software
Author: Ashley J. Waardenberg [aut, cre], Martha M. Cooper [ctb]
Maintainer: Ashley J. Waardenberg <a.waardenberg@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/consensusDE
git_branch: RELEASE_3_13
git_last_commit: 1b434d7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/consensusDE_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/consensusDE_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/consensusDE_1.10.0.tgz
vignettes: vignettes/consensusDE/inst/doc/consensusDE.html
vignetteTitles: consensusDE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/consensusDE/inst/doc/consensusDE.R
dependencyCount: 145
Package: consensusOV
Version: 1.14.0
Depends: R (>= 3.6)
Imports: Biobase, GSVA, gdata, genefu, limma, matrixStats,
randomForest, stats, utils, methods
Suggests: BiocStyle, ggplot2, knitr, rmarkdown
License: Artistic-2.0
MD5sum: d109afc26430e3203d1753f8c67255a7
NeedsCompilation: no
Title: Gene expression-based subtype classification for high-grade
serous ovarian cancer
Description: This package implements four major subtype classifiers for
high-grade serous (HGS) ovarian cancer as described by Helland
et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012),
Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J
Natl Cancer Inst, 2014). In addition, the package implements a
consensus classifier, which consolidates and improves on the
robustness of the proposed subtype classifiers, thereby
providing reliable stratification of patients with HGS ovarian
tumors of clearly defined subtype.
biocViews: Classification, Clustering, DifferentialExpression,
GeneExpression, Microarray, Transcriptomics
Author: Gregory M Chen, Lavanya Kannan, Ludwig Geistlinger, Victor
Kofia, Levi Waldron, Benjamin Haibe-Kains
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
URL: http://www.pmgenomics.ca/bhklab/software/consensusOV
VignetteBuilder: knitr
BugReports: https://github.com/bhklab/consensusOV/issues
git_url: https://git.bioconductor.org/packages/consensusOV
git_branch: RELEASE_3_13
git_last_commit: 1ff128e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/consensusOV_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/consensusOV_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/consensusOV_1.14.0.tgz
vignettes: vignettes/consensusOV/inst/doc/consensusOV.html
vignetteTitles: Molecular subtyping for ovarian cancer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/consensusOV/inst/doc/consensusOV.R
dependencyCount: 143
Package: consensusSeekeR
Version: 1.20.0
Depends: R (>= 2.10), BiocGenerics, IRanges, GenomicRanges,
BiocParallel
Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors, methods
Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit
License: Artistic-2.0
MD5sum: c5c98bd5d561f748be6213556b5f5516
NeedsCompilation: no
Title: Detection of consensus regions inside a group of experiences
using genomic positions and genomic ranges
Description: This package compares genomic positions and genomic ranges
from multiple experiments to extract common regions. The size
of the analyzed region is adjustable as well as the number of
experiences in which a feature must be present in a potential
region to tag this region as a consensus region.
biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison,
Transcription, PeakDetection, Sequencing, Coverage
Author: Astrid Deschenes [cre, aut], Fabien Claude Lamaze [ctb], Pascal
Belleau [aut], Arnaud Droit [aut]
Maintainer: Astrid Deschenes <adeschen@hotmail.com>
URL: https://github.com/ArnaudDroitLab/consensusSeekeR
VignetteBuilder: knitr
BugReports: https://github.com/ArnaudDroitLab/consensusSeekeR/issues
git_url: https://git.bioconductor.org/packages/consensusSeekeR
git_branch: RELEASE_3_13
git_last_commit: e7f757a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/consensusSeekeR_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/consensusSeekeR_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/consensusSeekeR_1.20.0.tgz
vignettes: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html
vignetteTitles: Detection of consensus regions inside a group of
experiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R
importsMe: RJMCMCNucleosomes
dependencyCount: 48
Package: CONSTANd
Version: 1.0.0
Depends: R (>= 4.1)
Suggests: BiocStyle, knitr, rmarkdown, tidyr, ggplot2, gridExtra,
magick, Cairo, limma
License: file LICENSE
MD5sum: 71921e25b4fa3cfef5f2821910c75408
NeedsCompilation: no
Title: Data normalization by matrix raking
Description: Normalizes a data matrix `data` by raking (using the RAS
method by Bacharach, see references) the Nrows by Ncols matrix
such that the row means and column means equal 1. The result is
a normalized data matrix `K=RAS`, a product of row mulipliers
`R` and column multipliers `S` with the original matrix `A`.
Missing information needs to be presented as `NA` values and
not as zero values, because CONSTANd is able to ignore missing
values when calculating the mean. Using CONSTANd normalization
allows for the direct comparison of values between samples
within the same and even across different CONSTANd-normalized
data matrices.
biocViews: MassSpectrometry, Cheminformatics, Normalization,
Preprocessing, DifferentialExpression, Genetics,
Transcriptomics, Proteomics
Author: Joris Van Houtven [aut, trl], Geert Jan Bex [trl], Dirk
Valkenborg [aut, cre]
Maintainer: Dirk Valkenborg <dirk.valkenborg@uhasselt.be>
URL: qcquan.net/constand
VignetteBuilder: knitr
BugReports: https://github.com/PDiracDelta/CONSTANd/issues
git_url: https://git.bioconductor.org/packages/CONSTANd
git_branch: RELEASE_3_13
git_last_commit: 3bac955
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CONSTANd_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CONSTANd_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CONSTANd_1.0.0.tgz
vignettes: vignettes/CONSTANd/inst/doc/CONSTANd.html
vignetteTitles: CONSTANd
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CONSTANd/inst/doc/CONSTANd.R
dependencyCount: 0
Package: contiBAIT
Version: 1.20.0
Depends: BH (>= 1.51.0-3), Rsamtools (>= 1.21)
Imports: data.table, grDevices, clue, cluster, gplots, BiocGenerics (>=
0.31.6), S4Vectors, IRanges, GenomicRanges, Rcpp, TSP,
GenomicFiles, gtools, rtracklayer, BiocParallel, DNAcopy,
colorspace, reshape2, ggplot2, methods, exomeCopy,
GenomicAlignments, diagram
LinkingTo: Rcpp, BH
Suggests: BiocStyle
License: BSD_2_clause + file LICENSE
MD5sum: f844041b4d77a99ca16bc8e4c41ddbf9
NeedsCompilation: yes
Title: Improves Early Build Genome Assemblies using Strand-Seq Data
Description: Using strand inheritance data from multiple single cells
from the organism whose genome is to be assembled, contiBAIT
can cluster unbridged contigs together into putative
chromosomes, and order the contigs within those chromosomes.
biocViews: ImmunoOncology, CellBasedAssays, QualityControl,
WholeGenome, Genetics, GenomeAssembly
Author: Kieran O'Neill, Mark Hills, Mike Gottlieb
Maintainer: Kieran O'Neill <koneill@bccrc.ca>
git_url: https://git.bioconductor.org/packages/contiBAIT
git_branch: RELEASE_3_13
git_last_commit: 42094e1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/contiBAIT_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/contiBAIT_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/contiBAIT_1.20.0.tgz
vignettes: vignettes/contiBAIT/inst/doc/contiBAIT.pdf
vignetteTitles: flowBi
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/contiBAIT/inst/doc/contiBAIT.R
dependencyCount: 130
Package: conumee
Version: 1.26.0
Depends: R (>= 3.0), minfi,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylationEPICanno.ilm10b2.hg19,
IlluminaHumanMethylationEPICmanifest
Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges,
GenomeInfoDb
Suggests: BiocStyle, knitr, rmarkdown, minfiData, RCurl
License: GPL (>= 2)
MD5sum: 5a015a04e5edc62a32b98239223b5432
NeedsCompilation: no
Title: Enhanced copy-number variation analysis using Illumina DNA
methylation arrays
Description: This package contains a set of processing and plotting
methods for performing copy-number variation (CNV) analysis
using Illumina 450k or EPIC methylation arrays.
biocViews: CopyNumberVariation, DNAMethylation, MethylationArray,
Microarray, Normalization, Preprocessing, QualityControl,
Software
Author: Volker Hovestadt, Marc Zapatka
Maintainer: Volker Hovestadt <conumee@hovestadt.bio>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/conumee
git_branch: RELEASE_3_13
git_last_commit: f49dd58
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/conumee_1.26.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/conumee_1.26.0.tgz
vignettes: vignettes/conumee/inst/doc/conumee.html
vignetteTitles: conumee
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/conumee/inst/doc/conumee.R
dependencyCount: 144
Package: convert
Version: 1.68.0
Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray,
utils, methods
License: LGPL
MD5sum: 938e70253370992e1c050c9b04b079d3
NeedsCompilation: no
Title: Convert Microarray Data Objects
Description: Define coerce methods for microarray data objects.
biocViews: Infrastructure, Microarray, TwoChannel
Author: Gordon Smyth <smyth@wehi.edu.au>, James Wettenhall
<wettenhall@wehi.edu.au>, Yee Hwa (Jean Yang)
<jean@biostat.ucsf.edu>, Martin Morgan
<Martin.Morgan@RoswellPark.org>
Maintainer: Yee Hwa (Jean) Yang <jean@biostat.ucsf.edu>
URL: http://bioinf.wehi.edu.au/limma/convert.html
git_url: https://git.bioconductor.org/packages/convert
git_branch: RELEASE_3_13
git_last_commit: 4c67da1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/convert_1.68.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/convert_1.68.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/convert_1.68.0.tgz
vignettes: vignettes/convert/inst/doc/convert.pdf
vignetteTitles: Converting Between Microarray Data Classes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: maigesPack, TurboNorm
suggestsMe: dyebias, OLIN, dyebiasexamples, maGUI
dependencyCount: 10
Package: copa
Version: 1.60.0
Depends: Biobase, methods
Suggests: colonCA
License: Artistic-2.0
Archs: i386, x64
MD5sum: 8b97df11811842e5c7d5f507c20b63fc
NeedsCompilation: yes
Title: Functions to perform cancer outlier profile analysis.
Description: COPA is a method to find genes that undergo recurrent
fusion in a given cancer type by finding pairs of genes that
have mutually exclusive outlier profiles.
biocViews: OneChannel, TwoChannel, DifferentialExpression,
Visualization
Author: James W. MacDonald
Maintainer: James W. MacDonald <jmacdon@u.washington.edu>
git_url: https://git.bioconductor.org/packages/copa
git_branch: RELEASE_3_13
git_last_commit: 6eea48b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/copa_1.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/copa_1.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/copa_1.60.0.tgz
vignettes: vignettes/copa/inst/doc/copa.pdf
vignetteTitles: copa Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/copa/inst/doc/copa.R
dependencyCount: 7
Package: copynumber
Version: 1.32.0
Depends: R (>= 2.10), BiocGenerics
Imports: S4Vectors, IRanges, GenomicRanges
License: Artistic-2.0
MD5sum: 44339f7100d9bc942a592d40e8be6407
NeedsCompilation: no
Title: Segmentation of single- and multi-track copy number data by
penalized least squares regression.
Description: Penalized least squares regression is applied to fit
piecewise constant curves to copy number data to locate genomic
regions of constant copy number. Procedures are available for
individual segmentation of each sample, joint segmentation of
several samples and joint segmentation of the two data tracks
from SNP-arrays. Several plotting functions are available for
visualization of the data and the segmentation results.
biocViews: aCGH, SNP, CopyNumberVariation, Genetics, Visualization
Author: Gro Nilsen, Knut Liestoel and Ole Christian Lingjaerde.
Maintainer: Gro Nilsen <gronilse@ifi.uio.no>
git_url: https://git.bioconductor.org/packages/copynumber
git_branch: RELEASE_3_13
git_last_commit: 7a14096
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/copynumber_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/copynumber_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/copynumber_1.32.0.tgz
vignettes: vignettes/copynumber/inst/doc/copynumber.pdf
vignetteTitles: copynumber.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/copynumber/inst/doc/copynumber.R
importsMe: sequenza
suggestsMe: PureCN, sigminer
dependencyCount: 17
Package: CopyNumberPlots
Version: 1.8.0
Depends: R (>= 3.6), karyoploteR
Imports: regioneR, IRanges, Rsamtools, SummarizedExperiment,
VariantAnnotation, methods, stats, GenomeInfoDb, GenomicRanges,
cn.mops, rhdf5, utils
Suggests: BiocStyle, knitr, panelcn.mops,
BSgenome.Hsapiens.UCSC.hg19.masked, DNAcopy, testthat
License: Artistic-2.0
Archs: i386, x64
MD5sum: 413d710191dcc92b57a67fbb0747649f
NeedsCompilation: no
Title: Create Copy-Number Plots using karyoploteR functionality
Description: CopyNumberPlots have a set of functions extending
karyoploteRs functionality to create beautiful, customizable
and flexible plots of copy-number related data.
biocViews: Visualization, CopyNumberVariation, Coverage, OneChannel,
DataImport, Sequencing, DNASeq
Author: Bernat Gel <bgel@igtp.cat> and Miriam Magallon
<mmagallon@igtp.cat>
Maintainer: Bernat Gel <bgel@igtp.cat>
URL: https://github.com/bernatgel/CopyNumberPlots
VignetteBuilder: knitr
BugReports: https://github.com/bernatgel/CopyNumberPlots/issues
git_url: https://git.bioconductor.org/packages/CopyNumberPlots
git_branch: RELEASE_3_13
git_last_commit: 474e67e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CopyNumberPlots_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CopyNumberPlots_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CopyNumberPlots_1.8.0.tgz
vignettes: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.html
vignetteTitles: CopyNumberPlots vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.R
importsMe: CNVfilteR, CNViz
dependencyCount: 150
Package: CopywriteR
Version: 2.24.0
Depends: R(>= 3.2), BiocParallel
Imports: matrixStats, gtools, data.table, S4Vectors, chipseq, IRanges,
Rsamtools, DNAcopy, GenomicAlignments, GenomicRanges,
CopyhelpeR, GenomeInfoDb, futile.logger
Suggests: BiocStyle, SCLCBam, snow
License: GPL-2
MD5sum: c60a71dea168c07050395a626ac0ad7f
NeedsCompilation: no
Title: Copy number information from targeted sequencing using
off-target reads
Description: CopywriteR extracts DNA copy number information from
targeted sequencing by utiizing off-target reads. It allows for
extracting uniformly distributed copy number information, can
be used without reference, and can be applied to sequencing
data obtained from various techniques including chromatin
immunoprecipitation and target enrichment on small gene panels.
Thereby, CopywriteR constitutes a widely applicable alternative
to available copy number detection tools.
biocViews: ImmunoOncology, TargetedResequencing, ExomeSeq,
CopyNumberVariation, Preprocessing, Visualization, Coverage
Author: Thomas Kuilman
Maintainer: Oscar Krijgsman <o.krijgsman@nki.nl>
URL: https://github.com/PeeperLab/CopywriteR
git_url: https://git.bioconductor.org/packages/CopywriteR
git_branch: RELEASE_3_13
git_last_commit: 5957876
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CopywriteR_2.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CopywriteR_2.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CopywriteR_2.24.0.tgz
vignettes: vignettes/CopywriteR/inst/doc/CopywriteR.pdf
vignetteTitles: CopywriteR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CopywriteR/inst/doc/CopywriteR.R
dependencyCount: 49
Package: coRdon
Version: 1.10.0
Depends: R (>= 3.5)
Imports: methods, stats, utils, Biostrings, Biobase, dplyr, stringr,
purrr, ggplot2, data.table
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 020e4de07d783f641eeb6c048d5ca9ef
NeedsCompilation: no
Title: Codon Usage Analysis and Prediction of Gene Expressivity
Description: Tool for analysis of codon usage in various unannotated or
KEGG/COG annotated DNA sequences. Calculates different measures
of CU bias and CU-based predictors of gene expressivity, and
performs gene set enrichment analysis for annotated sequences.
Implements several methods for visualization of CU and
enrichment analysis results.
biocViews: Software, Metagenomics, GeneExpression, GeneSetEnrichment,
GenePrediction, Visualization, KEGG, Pathways, Genetics
CellBiology, BiomedicalInformatics, ImmunoOncology
Author: Anamaria Elek [cre, aut], Maja Kuzman [aut], Kristian
Vlahovicek [aut]
Maintainer: Anamaria Elek <anamariaelek@gmail.com>
URL: https://github.com/BioinfoHR/coRdon
VignetteBuilder: knitr
BugReports: https://github.com/BioinfoHR/coRdon/issues
git_url: https://git.bioconductor.org/packages/coRdon
git_branch: RELEASE_3_13
git_last_commit: 9387b5f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/coRdon_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/coRdon_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/coRdon_1.10.0.tgz
vignettes: vignettes/coRdon/inst/doc/coRdon.html
vignetteTitles: coRdon
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/coRdon/inst/doc/coRdon.R
importsMe: vhcub
dependencyCount: 59
Package: CoRegNet
Version: 1.30.0
Depends: R (>= 2.14), igraph, shiny, arules, methods
Suggests: RColorBrewer, gplots, BiocStyle, knitr
License: GPL-3
MD5sum: 0904b0cecc828e18c12ba99cda344ac3
NeedsCompilation: yes
Title: CoRegNet : reconstruction and integrated analysis of
co-regulatory networks
Description: This package provides methods to identify active
transcriptional programs. Methods and classes are provided to
import or infer large scale co-regulatory network from
transcriptomic data. The specificity of the encoded networks is
to model Transcription Factor cooperation. External regulation
evidences (TFBS, ChIP,...) can be integrated to assess the
inferred network and refine it if necessary. Transcriptional
activity of the regulators in the network can be estimated
using an measure of their influence in a given sample. Finally,
an interactive UI can be used to navigate through the network
of cooperative regulators and to visualize their activity in a
specific sample or subgroup sample. The proposed visualization
tool can be used to integrate gene expression, transcriptional
activity, copy number status, sample classification and a
transcriptional network including co-regulation information.
biocViews: NetworkInference, NetworkEnrichment, GeneRegulation,
GeneExpression, GraphAndNetwork,SystemsBiology, Network,
Visualization, Transcription
Author: Remy Nicolle, Thibault Venzac and Mohamed Elati
Maintainer: Remy Nicolle <remy.c.nicolle@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CoRegNet
git_branch: RELEASE_3_13
git_last_commit: 340f25d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CoRegNet_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CoRegNet_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CoRegNet_1.30.0.tgz
vignettes: vignettes/CoRegNet/inst/doc/CoRegNet.html
vignetteTitles: Custom Print Methods
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CoRegNet/inst/doc/CoRegNet.R
dependencyCount: 41
Package: CoreGx
Version: 1.4.2
Depends: R (>= 4.1), BiocGenerics, SummarizedExperiment
Imports: Biobase, S4Vectors, MultiAssayExperiment, MatrixGenerics,
piano, BiocParallel, methods, stats, utils, graphics,
grDevices, lsa, data.table, crayon, glue, rlang
Suggests: pander, markdown, BiocStyle, rmarkdown, knitr, formatR,
testthat
License: GPL-3
MD5sum: 0cfe3b00d11e2b300f821c593df70205
NeedsCompilation: no
Title: Classes and Functions to Serve as the Basis for Other 'Gx'
Packages
Description: A collection of functions and classes which serve as the
foundation for our lab's suite of R packages, such as
'PharmacoGx' and 'RadioGx'. This package was created to
abstract shared functionality from other lab package releases
to increase ease of maintainability and reduce code repetition
in current and future 'Gx' suite programs. Major features
include a 'CoreSet' class, from which 'RadioSet' and
'PharmacoSet' are derived, along with get and set methods for
each respective slot. Additional functions related to fitting
and plotting dose response curves, quantifying statistical
correlation and calculating area under the curve (AUC) or
survival fraction (SF) are included. For more details please
see the included documentation, as well as: Smirnov, P.,
Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C.,
Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A.,
Aerts, H., Lupien, M., Goldenberg, A. (2015)
<doi:10.1093/bioinformatics/btv723>. Manem, V., Labie, M.,
Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed,
M., Haibe-Kains, B., Bratman, S. (2018) <doi:10.1101/449793>.
biocViews: Software, Pharmacogenomics, Classification, Survival
Author: Petr Smirnov [aut], Ian Smith [aut], Christopher Eeles [aut],
Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CoreGx
git_branch: RELEASE_3_13
git_last_commit: 32dc73a
git_last_commit_date: 2021-09-28
Date/Publication: 2021-09-30
source.ver: src/contrib/CoreGx_1.4.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CoreGx_1.4.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/CoreGx_1.4.2.tgz
vignettes: vignettes/CoreGx/inst/doc/coreGx.html,
vignettes/CoreGx/inst/doc/LongTable.html
vignetteTitles: CoreGx: Class and Function Abstractions, The LongTable
Class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CoreGx/inst/doc/coreGx.R,
vignettes/CoreGx/inst/doc/LongTable.R
dependsOnMe: PharmacoGx, RadioGx, ToxicoGx
importsMe: PDATK
dependencyCount: 116
Package: Cormotif
Version: 1.38.0
Depends: R (>= 2.12.0), affy, limma
Imports: affy, graphics, grDevices
License: GPL-2
Archs: i386, x64
MD5sum: ca6b1f08d8c2941a8793c9984e0007a3
NeedsCompilation: no
Title: Correlation Motif Fit
Description: It fits correlation motif model to multiple studies to
detect study specific differential expression patterns.
biocViews: Microarray, DifferentialExpression
Author: Hongkai Ji, Yingying Wei
Maintainer: Yingying Wei <ywei@jhsph.edu>
git_url: https://git.bioconductor.org/packages/Cormotif
git_branch: RELEASE_3_13
git_last_commit: 48a8008
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Cormotif_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Cormotif_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Cormotif_1.38.0.tgz
vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf
vignetteTitles: Cormotif Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Cormotif/inst/doc/CormotifVignette.R
dependencyCount: 14
Package: corral
Version: 1.2.0
Imports: ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix,
methods, MultiAssayExperiment, pals, SingleCellExperiment,
SummarizedExperiment, transport
Suggests: ade4, BiocStyle, CellBench, DuoClustering2018, knitr,
testthat
License: GPL-2
MD5sum: 4e734c0d4e1d9d314cf61b94d12d6fef
NeedsCompilation: no
Title: Correspondence Analysis for Single Cell Data
Description: Correspondence analysis (CA) is a matrix factorization
method, and is similar to principal components analysis (PCA).
Whereas PCA is designed for application to continuous,
approximately normally distributed data, CA is appropriate for
non-negative, count-based data that are in the same additive
scale. The corral package implements CA for dimensionality
reduction of a single matrix of single-cell data, as well as a
multi-table adaptation of CA that leverages data-optimized
scaling to align data generated from different sequencing
platforms by projecting into a shared latent space. corral
utilizes sparse matrices and a fast implementation of SVD, and
can be called directly on Bioconductor objects (e.g.,
SingleCellExperiment) for easy pipeline integration. The
package also includes the option to apply CA-style processing
to continuous data (e.g., proteomic TOF intensities) with the
Hellinger distance adaptation of CA.
biocViews: BatchEffect, DimensionReduction, Preprocessing,
PrincipalComponent, Sequencing, SingleCell, Software,
Visualization
Author: Lauren Hsu [aut, cre]
(<https://orcid.org/0000-0002-6035-7381>), Aedin Culhane [aut]
(<https://orcid.org/0000-0002-1395-9734>)
Maintainer: Lauren Hsu <lrnshoe@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/corral
git_branch: RELEASE_3_13
git_last_commit: 1c730af
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/corral_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/corral_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/corral_1.2.0.tgz
vignettes: vignettes/corral/inst/doc/corral_dimred.html,
vignettes/corral/inst/doc/corralm_alignment.html
vignetteTitles: dim reduction with corral, alignment with corralm
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/corral/inst/doc/corral_dimred.R,
vignettes/corral/inst/doc/corralm_alignment.R
dependsOnMe: OSCA.advanced
dependencyCount: 77
Package: CORREP
Version: 1.58.0
Imports: e1071, stats
Suggests: cluster, MASS
License: GPL (>= 2)
MD5sum: 6cfcebb8a2762215cf3d8c4a73c7b9b2
NeedsCompilation: no
Title: Multivariate Correlation Estimator and Statistical Inference
Procedures.
Description: Multivariate correlation estimation and statistical
inference. See package vignette.
biocViews: Microarray, Clustering, GraphAndNetwork
Author: Dongxiao Zhu and Youjuan Li
Maintainer: Dongxiao Zhu <doz@stowers-institute.org>
git_url: https://git.bioconductor.org/packages/CORREP
git_branch: RELEASE_3_13
git_last_commit: d6cf444
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CORREP_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CORREP_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CORREP_1.58.0.tgz
vignettes: vignettes/CORREP/inst/doc/CORREP.pdf
vignetteTitles: Multivariate Correlation Estimator
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CORREP/inst/doc/CORREP.R
dependencyCount: 9
Package: coseq
Version: 1.16.0
Depends: R (>= 4.0.0), SummarizedExperiment, S4Vectors
Imports: edgeR, DESeq2, capushe, Rmixmod, e1071, BiocParallel, ggplot2,
scales, HTSFilter, corrplot, HTSCluster, grDevices, graphics,
stats, methods, compositions, mvtnorm
Suggests: Biobase, knitr, rmarkdown, testthat, BiocStyle
License: GPL-3
MD5sum: 74ab6897b3f500fd6f66d6db97fc9f4c
NeedsCompilation: no
Title: Co-Expression Analysis of Sequencing Data
Description: Co-expression analysis for expression profiles arising
from high-throughput sequencing data. Feature (e.g., gene)
profiles are clustered using adapted transformations and
mixture models or a K-means algorithm, and model selection
criteria (to choose an appropriate number of clusters) are
provided.
biocViews: GeneExpression, RNASeq, Sequencing, Software, ImmunoOncology
Author: Andrea Rau [cre, aut]
(<https://orcid.org/0000-0001-6469-488X>), Cathy
Maugis-Rabusseau [ctb], Antoine Godichon-Baggioni [ctb]
Maintainer: Andrea Rau <andrea.rau@inrae.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/coseq
git_branch: RELEASE_3_13
git_last_commit: e1d4441
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/coseq_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/coseq_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/coseq_1.16.0.tgz
vignettes: vignettes/coseq/inst/doc/coseq.html
vignetteTitles: coseq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/coseq/inst/doc/coseq.R
dependencyCount: 111
Package: cosmiq
Version: 1.26.0
Depends: R (>= 3.6), Rcpp
Imports: pracma, xcms, MassSpecWavelet, faahKO
Suggests: RUnit, BiocGenerics, BiocStyle
License: GPL-3
MD5sum: ddfcc872924bf5b2ecdf30ef8e6e9396
NeedsCompilation: yes
Title: cosmiq - COmbining Single Masses Into Quantities
Description: cosmiq is a tool for the preprocessing of liquid- or gas -
chromatography mass spectrometry (LCMS/GCMS) data with a focus
on metabolomics or lipidomics applications. To improve the
detection of low abundant signals, cosmiq generates master maps
of the mZ/RT space from all acquired runs before a peak
detection algorithm is applied. The result is a more robust
identification and quantification of low-intensity MS signals
compared to conventional approaches where peak picking is
performed in each LCMS/GCMS file separately. The cosmiq package
builds on the xcmsSet object structure and can be therefore
integrated well with the package xcms as an alternative
preprocessing step.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics
Author: David Fischer [aut, cre], Christian Panse [aut]
(<https://orcid.org/0000-0003-1975-3064>), Endre Laczko [ctb]
Maintainer: David Fischer <dajofischer@googlemail.com>
URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html
git_url: https://git.bioconductor.org/packages/cosmiq
git_branch: RELEASE_3_13
git_last_commit: 4491dc8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cosmiq_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cosmiq_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cosmiq_1.26.0.tgz
vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf
vignetteTitles: cosmiq primer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R
dependencyCount: 96
Package: cosmosR
Version: 1.0.1
Depends: R (>= 4.1)
Imports: CARNIVAL, dorothea, igraph, dplyr, utils, stringr, readr,
rlang, tibble, purrr, AnnotationDbi, biomaRt, org.Hs.eg.db,
visNetwork
Suggests: testthat, knitr, rmarkdown
License: GPL-3
MD5sum: 6c1996d8903551932896e3326d407103
NeedsCompilation: no
Title: COSMOS (Causal Oriented Search of Multi-Omic Space)
Description: COSMOS (Causal Oriented Search of Multi-Omic Space) is a
method that integrates phosphoproteomics, transcriptomics, and
metabolomics data sets based on prior knowledge of signaling,
metabolic, and gene regulatory networks. It estimated the
activities of transcrption factors and kinases and finds a
network-level causal reasoning. Thereby, COSMOS provides
mechanistic hypotheses for experimental observations across
mulit-omics datasets.
biocViews: CellBiology, Pathways, Network, Proteomics, Metabolomics,
Transcriptomics, GeneSignaling
Author: Aurélien Dugourd [aut]
(<https://orcid.org/0000-0002-0714-028X>), Attila Gabor [aut]
(<https://orcid.org/0000-0002-0776-1182>), Katharina Zirngibl
[cre, aut] (<https://orcid.org/0000-0002-7518-0339>)
Maintainer: Katharina Zirngibl <katharina.zirngibl@uni-heidelberg.de>
URL: https://github.com/saezlab/COSMOSR
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/COSMOSR/issues
git_url: https://git.bioconductor.org/packages/cosmosR
git_branch: RELEASE_3_13
git_last_commit: 6d16ee7
git_last_commit_date: 2021-06-22
Date/Publication: 2021-06-24
source.ver: src/contrib/cosmosR_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cosmosR_1.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/cosmosR_1.0.1.tgz
vignettes: vignettes/cosmosR/inst/doc/tutorial.html
vignetteTitles: cosmosR tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cosmosR/inst/doc/tutorial.R
dependencyCount: 110
Package: COSNet
Version: 1.26.0
Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics
License: GPL (>= 2)
Archs: i386, x64
MD5sum: fb11722b842014444c92e4e350ba5fa2
NeedsCompilation: yes
Title: Cost Sensitive Network for node label prediction on graphs with
highly unbalanced labelings
Description: Package that implements the COSNet classification
algorithm. The algorithm predicts node labels in partially
labeled graphs where few positives are available for the class
being predicted.
biocViews: GraphAndNetwork, Classification,Network, NeuralNetwork
Author: Marco Frasca and Giorgio Valentini -- Universita' degli Studi
di Milano
Maintainer: Marco Frasca<frasca@di.unimi.it>
URL: https://github.com/m1frasca/COSNet_GitHub
git_url: https://git.bioconductor.org/packages/COSNet
git_branch: RELEASE_3_13
git_last_commit: 7f28084
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/COSNet_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/COSNet_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/COSNet_1.26.0.tgz
vignettes: vignettes/COSNet/inst/doc/COSNet_v.pdf
vignetteTitles: An R Package for Predicting Binary Labels in
Partially-Labeled Graphs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/COSNet/inst/doc/COSNet_v.R
dependencyCount: 0
Package: CountClust
Version: 1.20.0
Depends: R (>= 3.4), ggplot2 (>= 2.1.0)
Imports: SQUAREM, slam, maptpx, plyr(>= 1.7.1), cowplot, gtools,
flexmix, picante, limma, parallel, reshape2, stats, utils,
graphics, grDevices
Suggests: knitr, kableExtra, BiocStyle, Biobase, roxygen2,
RColorBrewer, devtools, xtable
License: GPL (>= 2)
MD5sum: c80e7ef12f8158eb285a9cc6570c844a
NeedsCompilation: no
Title: Clustering and Visualizing RNA-Seq Expression Data using Grade
of Membership Models
Description: Fits grade of membership models (GoM, also known as
admixture models) to cluster RNA-seq gene expression count
data, identifies characteristic genes driving cluster
memberships, and provides a visual summary of the cluster
memberships.
biocViews: ImmunoOncology, RNASeq, GeneExpression, Clustering,
Sequencing, StatisticalMethod, Software, Visualization
Author: Kushal Dey [aut, cre], Joyce Hsiao [aut], Matthew Stephens
[aut]
Maintainer: Kushal Dey <kkdey@uchicago.edu>
URL: https://github.com/kkdey/CountClust
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CountClust
git_branch: RELEASE_3_13
git_last_commit: 8a70b18
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CountClust_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CountClust_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CountClust_1.20.0.tgz
vignettes: vignettes/CountClust/inst/doc/count-clust.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CountClust/inst/doc/count-clust.R
dependencyCount: 60
Package: countsimQC
Version: 1.10.0
Depends: R (>= 3.5)
Imports: rmarkdown (>= 2.5), edgeR, DESeq2 (>= 1.16.0), dplyr, tidyr,
ggplot2, grDevices, tools, SummarizedExperiment, genefilter,
DT, GenomeInfoDbData, caTools, randtests, stats, utils, methods
Suggests: knitr, testthat
License: GPL (>=2)
Archs: i386, x64
MD5sum: 47dd82d9d7f425d3759ac3a38c5e7cbd
NeedsCompilation: no
Title: Compare Characteristic Features of Count Data Sets
Description: countsimQC provides functionality to create a
comprehensive report comparing a broad range of characteristics
across a collection of count matrices. One important use case
is the comparison of one or more synthetic count matrices to a
real count matrix, possibly the one underlying the simulations.
However, any collection of count matrices can be compared.
biocViews: Microbiome, RNASeq, SingleCell, ExperimentalDesign,
QualityControl, ReportWriting, Visualization, ImmunoOncology
Author: Charlotte Soneson [aut, cre]
(<https://orcid.org/0000-0003-3833-2169>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/csoneson/countsimQC
VignetteBuilder: knitr
BugReports: https://github.com/csoneson/countsimQC/issues
git_url: https://git.bioconductor.org/packages/countsimQC
git_branch: RELEASE_3_13
git_last_commit: a1da5e8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/countsimQC_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/countsimQC_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/countsimQC_1.10.0.tgz
vignettes: vignettes/countsimQC/inst/doc/countsimQC.html
vignetteTitles: countsimQC User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/countsimQC/inst/doc/countsimQC.R
suggestsMe: muscat
dependencyCount: 121
Package: covEB
Version: 1.18.0
Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats,
LaplacesDemon, Matrix
Suggests: curatedBladderData
License: GPL-3
MD5sum: 96fd0b6a714d040595bcb9e465d5c984
NeedsCompilation: no
Title: Empirical Bayes estimate of block diagonal covariance matrices
Description: Using bayesian methods to estimate correlation matrices
assuming that they can be written and estimated as block
diagonal matrices. These block diagonal matrices are determined
using shrinkage parameters that values below this parameter to
zero.
biocViews: ImmunoOncology, Bayesian, Microarray, RNASeq, Preprocessing,
Software, GeneExpression, StatisticalMethod
Author: C. Pacini
Maintainer: C. Pacini <clarepacini@gmail.com>
git_url: https://git.bioconductor.org/packages/covEB
git_branch: RELEASE_3_13
git_last_commit: 7598cd6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/covEB_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/covEB_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/covEB_1.18.0.tgz
vignettes: vignettes/covEB/inst/doc/covEB.pdf
vignetteTitles: covEB
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/covEB/inst/doc/covEB.R
dependencyCount: 17
Package: CoverageView
Version: 1.30.0
Depends: R (>= 2.10), methods, Rsamtools (>= 1.19.17), rtracklayer
Imports: S4Vectors (>= 0.7.21), IRanges(>= 2.3.23), GenomicRanges,
GenomicAlignments, parallel, tools
License: Artistic-2.0
MD5sum: da8b2cfc7790747d4be43156434e9953
NeedsCompilation: no
Title: Coverage visualization package for R
Description: This package provides a framework for the visualization of
genome coverage profiles. It can be used for ChIP-seq
experiments, but it can be also used for genome-wide nucleosome
positioning experiments or other experiment types where it is
important to have a framework in order to inspect how the
coverage distributed across the genome
biocViews: ImmunoOncology,
Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software
Author: Ernesto Lowy
Maintainer: Ernesto Lowy <ernestolowy@gmail.com>
git_url: https://git.bioconductor.org/packages/CoverageView
git_branch: RELEASE_3_13
git_last_commit: f760e2e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CoverageView_1.30.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/CoverageView_1.30.0.tgz
vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf
vignetteTitles: Easy visualization of the read coverage
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CoverageView/inst/doc/CoverageView.R
dependencyCount: 44
Package: covRNA
Version: 1.18.0
Depends: ade4, Biobase
Imports: parallel, genefilter, grDevices, stats, graphics
Suggests: BiocStyle, knitr, rmarkdown
License: GPL (>= 2)
MD5sum: 27431112d8c45e5c86fe441224253868
NeedsCompilation: no
Title: Multivariate Analysis of Transcriptomic Data
Description: This package provides the analysis methods fourthcorner
and RLQ analysis for large-scale transcriptomic data.
biocViews: GeneExpression, Transcription
Author: Lara Urban <lara.h.urban@ebi.ac.uk>
Maintainer: Lara Urban <lara.h.urban@ebi.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/covRNA
git_branch: RELEASE_3_13
git_last_commit: 05e7528
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/covRNA_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/covRNA_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/covRNA_1.18.0.tgz
vignettes: vignettes/covRNA/inst/doc/covRNA.html
vignetteTitles: An Introduction to covRNA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/covRNA/inst/doc/covRNA.R
dependencyCount: 59
Package: cpvSNP
Version: 1.24.0
Depends: R (>= 2.10), GenomicFeatures, GSEABase (>= 1.24.0)
Imports: methods, corpcor, BiocParallel, ggplot2, plyr
Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics,
ReportingTools, BiocStyle
License: Artistic-2.0
MD5sum: 5f5f3589c0b2ba218802a2aa9c9f6c06
NeedsCompilation: no
Title: Gene set analysis methods for SNP association p-values that lie
in genes in given gene sets
Description: Gene set analysis methods exist to combine SNP-level
association p-values into gene sets, calculating a single
association p-value for each gene set. This package implements
two such methods that require only the calculated SNP p-values,
the gene set(s) of interest, and a correlation matrix (if
desired). One method (GLOSSI) requires independent SNPs and the
other (VEGAS) can take into account correlation (LD) among the
SNPs. Built-in plotting functions are available to help users
visualize results.
biocViews: Genetics, StatisticalMethod, Pathways, GeneSetEnrichment,
GenomicVariation
Author: Caitlin McHugh, Jessica Larson, and Jason Hackney
Maintainer: Caitlin McHugh <mchughc@uw.edu>
git_url: https://git.bioconductor.org/packages/cpvSNP
git_branch: RELEASE_3_13
git_last_commit: 7b0ad2e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cpvSNP_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cpvSNP_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cpvSNP_1.24.0.tgz
vignettes: vignettes/cpvSNP/inst/doc/cpvSNP.pdf
vignetteTitles: Running gene set analyses with the "cpvSNP" package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cpvSNP/inst/doc/cpvSNP.R
dependencyCount: 116
Package: cqn
Version: 1.38.0
Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore,
splines, quantreg
Imports: splines
Suggests: scales, edgeR
License: Artistic-2.0
MD5sum: e6d5389540e0ac33917e35ee65a7c658
NeedsCompilation: no
Title: Conditional quantile normalization
Description: A normalization tool for RNA-Seq data, implementing the
conditional quantile normalization method.
biocViews: ImmunoOncology, RNASeq, Preprocessing,
DifferentialExpression
Author: Jean (Zhijin) Wu, Kasper Daniel Hansen
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
git_url: https://git.bioconductor.org/packages/cqn
git_branch: RELEASE_3_13
git_last_commit: 0d15ecc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cqn_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cqn_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cqn_1.38.0.tgz
vignettes: vignettes/cqn/inst/doc/cqn.pdf
vignetteTitles: CQN (Conditional Quantile Normalization)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cqn/inst/doc/cqn.R
dependsOnMe: KnowSeq
importsMe: tweeDEseq, GeoTcgaData
dependencyCount: 19
Package: CRImage
Version: 1.40.0
Depends: EBImage, DNAcopy, aCGH
Imports: MASS, e1071, foreach, sgeostat
License: Artistic-2.0
MD5sum: 06b30311e1f4225949e17b0a1d4fdec2
NeedsCompilation: no
Title: CRImage a package to classify cells and calculate tumour
cellularity
Description: CRImage provides functionality to process and analyze
images, in particular to classify cells in biological images.
Furthermore, in the context of tumor images, it provides
functionality to calculate tumour cellularity.
biocViews: CellBiology, Classification
Author: Henrik Failmezger <failmezger@mpipz.mpg.de>, Yinyin Yuan
<Yinyin.Yuan@cancer.org.uk>, Oscar Rueda
<oscar.rueda@cancer.org.uk>, Florian Markowetz
<Florian.Markowetz@cancer.org.uk>
Maintainer: Henrik Failmezger <failmezger@mpipz.mpg.de>, Yinyin Yuan
<Yinyin.Yuan@cancer.org.uk>
git_url: https://git.bioconductor.org/packages/CRImage
git_branch: RELEASE_3_13
git_last_commit: d8013b0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CRImage_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CRImage_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CRImage_1.40.0.tgz
vignettes: vignettes/CRImage/inst/doc/CRImage.pdf
vignetteTitles: CRImage Manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CRImage/inst/doc/CRImage.R
dependencyCount: 43
Package: CRISPRseek
Version: 1.32.0
Depends: R (>= 3.0.1), BiocGenerics, Biostrings
Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges,
BSgenome, BiocParallel, hash, methods,reticulate,rhdf5
Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db
License: GPL (>= 2)
MD5sum: 730398427f664014d2dc8cb350d4b9d6
NeedsCompilation: no
Title: Design of target-specific guide RNAs in CRISPR-Cas9,
genome-editing systems
Description: The package includes functions to find potential guide
RNAs for the CRISPR editing system including Base Editors and
the Prime Editor for input target sequences, optionally filter
guide RNAs without restriction enzyme cut site, or without
paired guide RNAs, genome-wide search for off-targets, score,
rank, fetch flank sequence and indicate whether the target and
off-targets are located in exon region or not. Potential guide
RNAs are annotated with total score of the top5 and topN
off-targets, detailed topN mismatch sites, restriction enzyme
cut sites, and paired guide RNAs. The package also output
indels and their frequencies for Cas9 targeted sites.
biocViews: ImmunoOncology, GeneRegulation, SequenceMatching, CRISPR
Author: Lihua Julie Zhu, Benjamin R. Holmes, Hervé Pagès, Hui Mao,
Michael Lawrence, Isana Veksler-Lublinsky, Victor Ambros, Neil
Aronin and Michael Brodsky
Maintainer: Lihua Julie Zhu <julie.zhu@umassmed.edu>
git_url: https://git.bioconductor.org/packages/CRISPRseek
git_branch: RELEASE_3_13
git_last_commit: b64de86
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CRISPRseek_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CRISPRseek_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CRISPRseek_1.32.0.tgz
vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.pdf
vignetteTitles: CRISPRseek Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R
dependsOnMe: crisprseekplus
importsMe: GUIDEseq, multicrispr
dependencyCount: 64
Package: crisprseekplus
Version: 1.18.0
Depends: R (>= 3.3.0), shiny, shinyjs, CRISPRseek
Imports: DT, utils, GUIDEseq, GenomicRanges, GenomicFeatures,
BiocManager, BSgenome, AnnotationDbi, hash
Suggests: testthat, rmarkdown, knitr, R.rsp
License: GPL-3 + file LICENSE
MD5sum: 130ed922b88db70cc057628b13235773
NeedsCompilation: no
Title: crisprseekplus
Description: Bioinformatics platform containing interface to work with
offTargetAnalysis and compare2Sequences in the CRISPRseek
package, and GUIDEseqAnalysis.
biocViews: GeneRegulation, SequenceMatching, Software
Author: Sophie Wigmore <Sophie.Wigmore@umassmed.edu>, Alper Kucukural
<alper.kucukural@umassmed.edu>, Lihua Julie Zhu
<julie.zhu@umassmed.edu>, Michael Brodsky
<Michael.Brodsky@umassmed.edu>, Manuel Garber
<Manuel.Garber@umassmed.edu>
Maintainer: Alper Kucukural <alper.kucukural@umassmed.edu>
URL: https://github.com/UMMS-Biocore/crisprseekplus
VignetteBuilder: knitr, R.rsp
BugReports: https://github.com/UMMS-Biocore/crisprseekplus/issues/new
git_url: https://git.bioconductor.org/packages/crisprseekplus
git_branch: RELEASE_3_13
git_last_commit: bd34985
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/crisprseekplus_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/crisprseekplus_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/crisprseekplus_1.18.0.tgz
vignettes: vignettes/crisprseekplus/inst/doc/crisprseekplus.html
vignetteTitles: DEBrowser Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/crisprseekplus/inst/doc/crisprseekplus.R
dependencyCount: 158
Package: CrispRVariants
Version: 1.20.0
Depends: R (>= 3.5), ggplot2 (>= 2.2.0)
Imports: AnnotationDbi, BiocParallel, Biostrings, methods,
GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices,
grid, gridExtra, IRanges, reshape2, Rsamtools, S4Vectors (>=
0.9.38), utils
Suggests: BiocStyle, gdata, GenomicFeatures, knitr, rmarkdown,
rtracklayer, sangerseqR, testthat, VariantAnnotation
License: GPL-2
MD5sum: a82c8d51a6ea1dfe76077d28bdf323a9
NeedsCompilation: no
Title: Tools for counting and visualising mutations in a target
location
Description: CrispRVariants provides tools for analysing the results of
a CRISPR-Cas9 mutagenesis sequencing experiment, or other
sequencing experiments where variants within a given region are
of interest. These tools allow users to localize variant allele
combinations with respect to any genomic location (e.g. the
Cas9 cut site), plot allele combinations and calculate mutation
rates with flexible filtering of unrelated variants.
biocViews: ImmunoOncology, CRISPR, GenomicVariation, VariantDetection,
GeneticVariability, DataRepresentation, Visualization
Author: Helen Lindsay [aut, cre]
Maintainer: Helen Lindsay <helen.lindsay@uzh.ch>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CrispRVariants
git_branch: RELEASE_3_13
git_last_commit: 6789cce
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CrispRVariants_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CrispRVariants_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CrispRVariants_1.20.0.tgz
vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf
vignetteTitles: CrispRVariants
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R
dependencyCount: 92
Package: crlmm
Version: 1.50.0
Depends: R (>= 2.14.0), oligoClasses (>= 1.21.12), preprocessCore (>=
1.17.7)
Imports: methods, Biobase (>= 2.15.4), BiocGenerics, affyio (>=
1.23.2), illuminaio, ellipse, mvtnorm, splines, stats, utils,
lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats,
VGAM, parallel, graphics, limma, beanplot
LinkingTo: preprocessCore (>= 1.17.7)
Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), snpStats, RUnit
License: Artistic-2.0
MD5sum: 44462f213cda05ab424fea107ed02de2
NeedsCompilation: yes
Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for
Affymetrix SNP 5.0 and 6.0 and Illumina arrays
Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0
arrays, as well as a copy number tool specific to 5.0, 6.0, and
Illumina platforms.
biocViews: Microarray, Preprocessing, SNP, CopyNumberVariation
Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo
Ruczinski, Rafael A Irizarry
Maintainer: Benilton S Carvalho <benilton@unicamp.br>, Robert Scharpf
<rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.EDU.AU>
git_url: https://git.bioconductor.org/packages/crlmm
git_branch: RELEASE_3_13
git_last_commit: c3560d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/crlmm_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/crlmm_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/crlmm_1.50.0.tgz
vignettes: vignettes/crlmm/inst/doc/AffyGW.pdf,
vignettes/crlmm/inst/doc/CopyNumberOverview.pdf,
vignettes/crlmm/inst/doc/genotyping.pdf,
vignettes/crlmm/inst/doc/gtypeDownstream.pdf,
vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf,
vignettes/crlmm/inst/doc/Infrastructure.pdf
vignetteTitles: Copy number estimation, Overview of copy number
vignettes, crlmm Vignette - Genotyping, crlmm Vignette -
Downstream Analysis, Preprocessing and genotyping Illumina
arrays for copy number analysis, Infrastructure for copy number
analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/crlmm/inst/doc/genotyping.R
dependsOnMe: MAGAR
importsMe: VanillaICE
suggestsMe: oligoClasses, hapmap370k
dependencyCount: 63
Package: crossmeta
Version: 1.18.0
Depends: R (>= 4.0)
Imports: affy (>= 1.52.0), affxparser (>= 1.46.0), AnnotationDbi (>=
1.36.2), Biobase (>= 2.34.0), BiocGenerics (>= 0.20.0),
BiocManager (>= 1.30.4), DT (>= 0.2), DBI (>= 1.0.0), DESeq2,
data.table (>= 1.10.4), edgeR, fdrtool (>= 1.2.15), GEOquery
(>= 2.40.0), limma (>= 3.30.13), matrixStats (>= 0.51.0),
metaMA (>= 3.1.2), miniUI (>= 0.1.1), methods, oligo (>=
1.38.0), reader(>= 1.0.6), RColorBrewer (>= 1.1.2), RCurl (>=
1.95.4.11), RSQLite (>= 2.1.1), stringr (>= 1.2.0), sva (>=
3.22.0), shiny (>= 1.0.0), shinyjs (>= 2.0.0), shinyBS (>=
0.61), shinyWidgets (>= 0.5.3), shinypanel (>= 0.1.0), statmod
(>= 1.4.34), SummarizedExperiment, tibble, XML (>= 3.98.1.17),
readxl (>= 1.3.1)
Suggests: knitr, rmarkdown, lydata, org.Hs.eg.db, testthat,
tximportData
License: MIT + file LICENSE
MD5sum: 15b23ba1aca4e9ef3853f3213d96f13a
NeedsCompilation: no
Title: Cross Platform Meta-Analysis of Microarray Data
Description: Implements cross-platform and cross-species meta-analyses
of Affymentrix, Illumina, and Agilent microarray data. This
package automates common tasks such as downloading,
normalizing, and annotating raw GEO data. The user then selects
control and treatment samples in order to perform differential
expression analyses for all comparisons. After analysing each
contrast seperately, the user can select tissue sources for
each contrast and specify any tissue sources that should be
grouped for the subsequent meta-analyses.
biocViews: GeneExpression, Transcription, DifferentialExpression,
Microarray, TissueMicroarray, OneChannel, Annotation,
BatchEffect, Preprocessing, GUI
Author: Alex Pickering
Maintainer: Alex Pickering <alexvpickering@gmail.com>
SystemRequirements: libxml2: libxml2-dev (deb), libxml2-devel (rpm)
libcurl: libcurl4-openssl-dev (deb), libcurl-devel (rpm)
openssl: libssl-dev (deb), openssl-devel (rpm), libssl_dev
(csw), openssl@1.1 (brew)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/crossmeta
git_branch: RELEASE_3_13
git_last_commit: 8e42dd1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/crossmeta_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/crossmeta_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/crossmeta_1.18.0.tgz
vignettes: vignettes/crossmeta/inst/doc/crossmeta-vignette.html
vignetteTitles: crossmeta vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/crossmeta/inst/doc/crossmeta-vignette.R
suggestsMe: ccmap
dependencyCount: 159
Package: CSAR
Version: 1.44.0
Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges
Imports: stats, utils
Suggests: ShortRead, Biostrings
License: Artistic-2.0
Archs: i386, x64
MD5sum: 6a2968c445a2b0b152c6e4f5fde8ee77
NeedsCompilation: yes
Title: Statistical tools for the analysis of ChIP-seq data
Description: Statistical tools for ChIP-seq data analysis. The package
includes the statistical method described in Kaufmann et al.
(2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average
DNA fragment size subjected to sequencing into account, the
software calculates genomic single-nucleotide read-enrichment
values. After normalization, sample and control are compared
using a test based on the Poisson distribution. Test statistic
thresholds to control the false discovery rate are obtained
through random permutation.
biocViews: ChIPSeq, Transcription, Genetics
Author: Jose M Muino
Maintainer: Jose M Muino <jose.muino@live.com>
git_url: https://git.bioconductor.org/packages/CSAR
git_branch: RELEASE_3_13
git_last_commit: 3eef45f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CSAR_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CSAR_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CSAR_1.44.0.tgz
vignettes: vignettes/CSAR/inst/doc/CSAR.pdf
vignetteTitles: CSAR Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CSAR/inst/doc/CSAR.R
dependencyCount: 17
Package: csaw
Version: 1.26.0
Depends: GenomicRanges, SummarizedExperiment
Imports: Rcpp, Matrix, BiocGenerics, Rsamtools, edgeR, limma, methods,
S4Vectors, IRanges, GenomeInfoDb, stats, BiocParallel, metapod,
utils
LinkingTo: Rhtslib, zlibbioc, Rcpp
Suggests: AnnotationDbi, org.Mm.eg.db,
TxDb.Mmusculus.UCSC.mm10.knownGene, testthat, GenomicFeatures,
GenomicAlignments, knitr, BiocStyle, rmarkdown, BiocManager
License: GPL-3
MD5sum: 138378af65a80e9d09c57fdc0e7fa85e
NeedsCompilation: yes
Title: ChIP-Seq Analysis with Windows
Description: Detection of differentially bound regions in ChIP-seq data
with sliding windows, with methods for normalization and proper
FDR control.
biocViews: MultipleComparison, ChIPSeq, Normalization, Sequencing,
Coverage, Genetics, Annotation, DifferentialPeakCalling
Author: Aaron Lun [aut, cre], Gordon Smyth [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/csaw
git_branch: RELEASE_3_13
git_last_commit: d3850f3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/csaw_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/csaw_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/csaw_1.26.0.tgz
vignettes: vignettes/csaw/inst/doc/csaw.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/csaw/inst/doc/csaw.R
dependsOnMe: csawBook
importsMe: diffHic, epigraHMM, icetea, NADfinder, vulcan, BinQuasi
suggestsMe: chipseqDB
dependencyCount: 42
Package: CSSP
Version: 1.30.0
Imports: methods, splines, stats, utils
Suggests: testthat
License: GPL-2
Archs: i386, x64
MD5sum: 5209df478a7964b218add26ab9266a30
NeedsCompilation: yes
Title: ChIP-Seq Statistical Power
Description: Power computation for ChIP-Seq data based on Bayesian
estimation for local poisson counting process.
biocViews: ChIPSeq, Sequencing, QualityControl, Bayesian
Author: Chandler Zuo, Sunduz Keles
Maintainer: Chandler Zuo<zuo@stat.wisc.edu>
git_url: https://git.bioconductor.org/packages/CSSP
git_branch: RELEASE_3_13
git_last_commit: b0c8d53
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CSSP_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CSSP_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CSSP_1.30.0.tgz
vignettes: vignettes/CSSP/inst/doc/cssp.pdf
vignetteTitles: cssp.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CSSP/inst/doc/cssp.R
dependencyCount: 4
Package: CSSQ
Version: 1.4.1
Depends: SummarizedExperiment, GenomicRanges, IRanges, S4Vectors,
rtracklayer
Imports: GenomicAlignments, GenomicFeatures, Rsamtools, ggplot2,
grDevices, stats, utils
Suggests: BiocStyle, knitr, rmarkdown, markdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: 3057f150c2626fc286a785d0e92e2855
NeedsCompilation: no
Title: Chip-seq Signal Quantifier Pipeline
Description: This package is desgined to perform statistical analysis
to identify statistically significant differentially bound
regions between multiple groups of ChIP-seq dataset.
biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Normalization
Author: Ashwath Kumar [aut], Michael Y Hu [aut], Yajun Mei [aut],
Yuhong Fan [aut]
Maintainer: Fan Lab at Georgia Institute of Technology
<yuhong.fan@biology.gatech.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CSSQ
git_branch: RELEASE_3_13
git_last_commit: d067a65
git_last_commit_date: 2021-07-28
Date/Publication: 2021-07-29
source.ver: src/contrib/CSSQ_1.4.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CSSQ_1.4.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/CSSQ_1.4.1.tgz
vignettes: vignettes/CSSQ/inst/doc/CSSQ.html
vignetteTitles: Introduction to CSSQ
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CSSQ/inst/doc/CSSQ.R
dependencyCount: 110
Package: ctc
Version: 1.66.0
Depends: amap
License: GPL-2
Archs: i386, x64
MD5sum: ddca457d1a093c896383117a877417ac
NeedsCompilation: no
Title: Cluster and Tree Conversion.
Description: Tools for export and import classification trees and
clusters to other programs
biocViews: Microarray, Clustering, Classification, DataImport,
Visualization
Author: Antoine Lucas <antoinelucas@gmail.com>, Laurent Gautier
<laurent@cbs.dtu.dk>
Maintainer: Antoine Lucas <antoinelucas@gmail.com>
URL: http://antoinelucas.free.fr/ctc
git_url: https://git.bioconductor.org/packages/ctc
git_branch: RELEASE_3_13
git_last_commit: 21b8569
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ctc_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ctc_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ctc_1.66.0.tgz
vignettes: vignettes/ctc/inst/doc/ctc.pdf
vignetteTitles: Introduction to ctc
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ctc/inst/doc/ctc.R
importsMe: miRLAB, multiClust
dependencyCount: 1
Package: CTDquerier
Version: 2.0.0
Depends: R (>= 4.1)
Imports: RCurl, stringr, S4Vectors, stringdist, ggplot2, igraph, utils,
grid, gridExtra, methods, stats, BiocFileCache
Suggests: BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 26b27bf06dbdcaa104eada5ae7a87ee0
NeedsCompilation: no
Title: Package for CTDbase data query, visualization and downstream
analysis
Description: Package to retrieve and visualize data from the
Comparative Toxicogenomics Database (http://ctdbase.org/). The
downloaded data is formated as DataFrames for further
downstream analyses.
biocViews: Software, BiomedicalInformatics, Infrastructure, DataImport,
DataRepresentation, GeneSetEnrichment, NetworkEnrichment,
Pathways, Network, GO, KEGG
Author: Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut], Xavier
Escribà -Montagut [cre]
Maintainer: Xavier Escribà -Montagut <xavier.escriba@isglobal.org>
VignetteBuilder: rmarkdown
git_url: https://git.bioconductor.org/packages/CTDquerier
git_branch: RELEASE_3_13
git_last_commit: 54a0a69
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CTDquerier_2.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CTDquerier_2.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CTDquerier_2.0.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependencyCount: 76
Package: ctgGEM
Version: 1.4.0
Depends: monocle, SummarizedExperiment,
Imports: Biobase, BiocGenerics, graphics, grDevices, igraph, methods,
utils, sincell, TSCAN, destiny, HSMMSingleCell
Suggests: BiocStyle, biomaRt, irlba, knitr, VGAM
License: GPL(>=2)
MD5sum: 127670ec33a2dfd070076a72bb9a1a46
NeedsCompilation: no
Title: Generating Tree Hierarchy Visualizations from Gene Expression
Data
Description: Cell Tree Generator for Gene Expression Matrices (ctgGEM)
streamlines the building of cell-state hierarchies from
single-cell gene expression data across multiple existing tools
for improved comparability and reproducibility. It supports
pseudotemporal ordering algorithms and visualization tools from
monocle, cellTree, TSCAN, sincell, and destiny, and provides a
unified output format for integration with downstream data
analysis workflows and Cytoscape.
biocViews: GeneExpression, Visualization, Sequencing, SingleCell,
Clustering, RNASeq, ImmunoOncology, DifferentialExpression,
MultipleComparison, QualityControl, DataImport
Author: Mark Block and Carrie Minette
Maintainer: USD Biomedical Engineering <bicbioeng@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ctgGEM
git_branch: RELEASE_3_13
git_last_commit: 0e246e8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ctgGEM_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ctgGEM_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ctgGEM_1.4.0.tgz
vignettes: vignettes/ctgGEM/inst/doc/ctgGEM-Vignette.html
vignetteTitles: ctgGEM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ctgGEM/inst/doc/ctgGEM-Vignette.R
dependencyCount: 136
Package: cTRAP
Version: 1.10.1
Depends: R (>= 4.0)
Imports: biomaRt, binr, cowplot, data.table, dplyr, DT, fastmatch,
fgsea, ggplot2, ggrepel, graphics, highcharter, httr, limma,
methods, parallel, pbapply, R.utils, readxl, reshape2, rhdf5,
scales, shiny, stats, tibble, tools, utils
Suggests: testthat, knitr, covr, rmarkdown, spelling
License: MIT + file LICENSE
MD5sum: b16a1df4c30086abe254597a116b5e19
NeedsCompilation: no
Title: Identification of candidate causal perturbations from
differential gene expression data
Description: Compare differential gene expression results with those
from known cellular perturbations (such as gene knock-down,
overexpression or small molecules) derived from the
Connectivity Map. Such analyses allow not only to infer the
molecular causes of the observed difference in gene expression
but also to identify small molecules that could drive or revert
specific transcriptomic alterations.
biocViews: DifferentialExpression, GeneExpression, RNASeq,
Transcriptomics, Pathways, ImmunoOncology, GeneSetEnrichment
Author: Bernardo P. de Almeida [aut], Nuno Saraiva-Agostinho [aut,
cre], Nuno L. Barbosa-Morais [aut, led]
Maintainer: Nuno Saraiva-Agostinho <nunodanielagostinho@gmail.com>
URL: https://nuno-agostinho.github.io/cTRAP,
https://github.com/nuno-agostinho/cTRAP
VignetteBuilder: knitr
BugReports: https://github.com/nuno-agostinho/cTRAP/issues
git_url: https://git.bioconductor.org/packages/cTRAP
git_branch: RELEASE_3_13
git_last_commit: 089d9ff
git_last_commit_date: 2021-10-04
Date/Publication: 2021-10-07
source.ver: src/contrib/cTRAP_1.10.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cTRAP_1.10.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/cTRAP_1.10.1.tgz
vignettes: vignettes/cTRAP/inst/doc/cTRAP.html
vignetteTitles: cTRAP: identifying candidate causal perturbations from
differential gene expression data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cTRAP/inst/doc/cTRAP.R
dependencyCount: 148
Package: ctsGE
Version: 1.18.0
Depends: R (>= 3.2)
Imports: ccaPP, ggplot2, limma, reshape2, shiny, stats, stringr, utils
Suggests: BiocStyle, dplyr, DT, GEOquery, knitr, pander, rmarkdown,
testthat
License: GPL-2
MD5sum: 0acb477022c094530dd91ae9dc6f0096
NeedsCompilation: no
Title: Clustering of Time Series Gene Expression data
Description: Methodology for supervised clustering of potentially many
predictor variables, such as genes etc., in time series
datasets Provides functions that help the user assigning genes
to predefined set of model profiles.
biocViews: ImmunoOncology, GeneExpression, Transcription,
DifferentialExpression, GeneSetEnrichment, Genetics, Bayesian,
Clustering, TimeCourse, Sequencing, RNASeq
Author: Michal Sharabi-Schwager [aut, cre], Ron Ophir [aut]
Maintainer: Michal Sharabi-Schwager <michalsharabi@gmail.com>
URL: https://github.com/michalsharabi/ctsGE
VignetteBuilder: knitr
BugReports: https://github.com/michalsharabi/ctsGE/issues
git_url: https://git.bioconductor.org/packages/ctsGE
git_branch: RELEASE_3_13
git_last_commit: ad3b72d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ctsGE_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ctsGE_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ctsGE_1.18.0.tgz
vignettes: vignettes/ctsGE/inst/doc/ctsGE.html
vignetteTitles: ctsGE Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ctsGE/inst/doc/ctsGE.R
dependencyCount: 71
Package: cummeRbund
Version: 2.34.0
Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite, ggplot2,
reshape2, fastcluster, rtracklayer, Gviz
Imports: methods, plyr, BiocGenerics, S4Vectors (>= 0.9.25), Biobase
Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges,
rjson
License: Artistic-2.0
MD5sum: 552bb5f8cfdc6a8e97e2faf702f28385
NeedsCompilation: no
Title: Analysis, exploration, manipulation, and visualization of
Cufflinks high-throughput sequencing data.
Description: Allows for persistent storage, access, exploration, and
manipulation of Cufflinks high-throughput sequencing data. In
addition, provides numerous plotting functions for commonly
used visualizations.
biocViews: HighThroughputSequencing, HighThroughputSequencingData,
RNAseq, RNAseqData, GeneExpression, DifferentialExpression,
Infrastructure, DataImport, DataRepresentation, Visualization,
Bioinformatics, Clustering, MultipleComparisons, QualityControl
Author: L. Goff, C. Trapnell, D. Kelley
Maintainer: Loyal A. Goff <lgoff@csail.mit.edu>
git_url: https://git.bioconductor.org/packages/cummeRbund
git_branch: RELEASE_3_13
git_last_commit: e2ae106
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cummeRbund_2.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cummeRbund_2.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cummeRbund_2.34.0.tgz
vignettes:
vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf,
vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf
vignetteTitles: Sample cummeRbund workflow, CummeRbund User Guide
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.R,
vignettes/cummeRbund/inst/doc/cummeRbund-manual.R
importsMe: meshr
dependencyCount: 145
Package: customCMPdb
Version: 1.2.0
Depends: R (>= 4.0)
Imports: AnnotationHub, RSQLite, XML, utils, ChemmineR, methods, stats,
rappdirs, BiocFileCache
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: Artistic-2.0
MD5sum: fa73484d13805c13ae7fa9b749f87020
NeedsCompilation: no
Title: Customize and Query Compound Annotation Database
Description: This package serves as a query interface for important
community collections of small molecules, while also allowing
users to include custom compound collections.
biocViews: Software, Cheminformatics,AnnotationHubSoftware
Author: Yuzhu Duan [aut, cre], Thomas Girke [aut]
Maintainer: Yuzhu Duan <yduan004@ucr.edu>
URL: https://github.com/yduan004/customCMPdb/
VignetteBuilder: knitr
BugReports: https://github.com/yduan004/customCMPdb/issues
git_url: https://git.bioconductor.org/packages/customCMPdb
git_branch: RELEASE_3_13
git_last_commit: 3357c4d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/customCMPdb_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/customCMPdb_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/customCMPdb_1.2.0.tgz
vignettes: vignettes/customCMPdb/inst/doc/customCMPdb.html
vignetteTitles: customCMPdb
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/customCMPdb/inst/doc/customCMPdb.R
dependencyCount: 110
Package: customProDB
Version: 1.32.0
Depends: R (>= 3.0.1), IRanges, AnnotationDbi, biomaRt(>= 2.17.1)
Imports: S4Vectors (>= 0.9.25), DBI, GenomeInfoDb, GenomicRanges,
Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>=
2.26.3), GenomicFeatures (>= 1.32.0), stringr, RCurl, plyr,
VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite,
AhoCorasickTrie, methods
Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19
License: Artistic-2.0
MD5sum: 7819a4ec151dc2891cebd1a52a8ae6a4
NeedsCompilation: no
Title: Generate customized protein database from NGS data, with a focus
on RNA-Seq data, for proteomics search
Description: Database search is the most widely used approach for
peptide and protein identification in mass spectrometry-based
proteomics studies. Our previous study showed that
sample-specific protein databases derived from RNA-Seq data can
better approximate the real protein pools in the samples and
thus improve protein identification. More importantly, single
nucleotide variations, short insertion and deletions and novel
junctions identified from RNA-Seq data make protein database
more complete and sample-specific. Here, we report an R package
customProDB that enables the easy generation of customized
databases from RNA-Seq data for proteomics search. This work
bridges genomics and proteomics studies and facilitates
cross-omics data integration.
biocViews: ImmunoOncology, Sequencing, MassSpectrometry, Proteomics,
SNP, RNASeq, Software, Transcription, AlternativeSplicing,
FunctionalGenomics
Author: Xiaojing Wang
Maintainer: Xiaojing Wang <xwang.research@gmail.com> Bo Wen
<wenbostar@gmail.com>
git_url: https://git.bioconductor.org/packages/customProDB
git_branch: RELEASE_3_13
git_last_commit: 6c740d6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/customProDB_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/customProDB_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/customProDB_1.32.0.tgz
vignettes: vignettes/customProDB/inst/doc/customProDB.pdf
vignetteTitles: Introduction to customProDB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/customProDB/inst/doc/customProDB.R
dependencyCount: 100
Package: cyanoFilter
Version: 1.0.0
Depends: R(>= 4.1.0)
Imports: Biobase, flowCore, flowDensity, ggplot2, GGally, graphics,
grDevices, methods, stats, utils
Suggests: magrittr, dplyr, purrr, knitr, stringr, rmarkdown, tidyr
License: MIT + file LICENSE
MD5sum: a8bccabda62c56737171fba85bf1532a
NeedsCompilation: no
Title: Phytoplankton Population Identification using Cell Pigmentation
and/or Complexity
Description: An approach to filter out and/or identify phytoplankton
cells from all particles measured via flow cytometry pigment
and cell complexity information. It does this using a sequence
of one-dimensional gates on pre-defined channels measuring
certain pigmentation and complexity. The package is especially
tuned for cyanobacteria, but will work fine for phytoplankton
communities where there is at least one cell characteristic
that differentiates every phytoplankton in the community.
biocViews: FlowCytometry, Clustering, OneChannel
Author: Oluwafemi Olusoji [cre, aut], Aerts Marc [ctb], Delaender
Frederik [ctb], Neyens Thomas [ctb], Spaak jurg [aut]
Maintainer: Oluwafemi Olusoji <oluwafemi.olusoji@uhasselt.be>
URL: https://github.com/fomotis/cyanoFilter
VignetteBuilder: knitr
BugReports: https://github.com/fomotis/cyanoFilter/issues
git_url: https://git.bioconductor.org/packages/cyanoFilter
git_branch: RELEASE_3_13
git_last_commit: 3160dc7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cyanoFilter_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cyanoFilter_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cyanoFilter_1.0.0.tgz
vignettes: vignettes/cyanoFilter/inst/doc/cyanoFilter.html
vignetteTitles: cyanoFilter: A Semi-Automated Framework for Identifying
Phytplanktons and Cyanobacteria Population in Flow Cytometry
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cyanoFilter/inst/doc/cyanoFilter.R
dependencyCount: 154
Package: cycle
Version: 1.46.0
Depends: R (>= 2.10.0), Mfuzz
Imports: Biobase, stats
License: GPL-2
MD5sum: 59f988e8ff012a2988230f22ede46776
NeedsCompilation: no
Title: Significance of periodic expression pattern in time-series data
Description: Package for assessing the statistical significance of
periodic expression based on Fourier analysis and comparison
with data generated by different background models
biocViews: Microarray, TimeCourse
Author: Matthias Futschik <mfutschik@ualg.pt>
Maintainer: Matthias Futschik <mfutschik@ualg.pt>
URL: http://cycle.sysbiolab.eu
git_url: https://git.bioconductor.org/packages/cycle
git_branch: RELEASE_3_13
git_last_commit: 50faf16
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cycle_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cycle_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cycle_1.46.0.tgz
vignettes: vignettes/cycle/inst/doc/cycle.pdf
vignetteTitles: Introduction to cycle
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cycle/inst/doc/cycle.R
dependencyCount: 18
Package: cydar
Version: 1.16.0
Depends: SingleCellExperiment
Imports: viridis, methods, shiny, graphics, stats, grDevices, utils,
BiocGenerics, S4Vectors, BiocParallel, SummarizedExperiment,
flowCore, Biobase, Rcpp, BiocNeighbors
LinkingTo: Rcpp
Suggests: ncdfFlow, testthat, rmarkdown, knitr, edgeR, limma, glmnet,
BiocStyle, flowStats
License: GPL-3
MD5sum: f5583939f42a01ae34449bde0e553f76
NeedsCompilation: yes
Title: Using Mass Cytometry for Differential Abundance Analyses
Description: Identifies differentially abundant populations between
samples and groups in mass cytometry data. Provides methods for
counting cells into hyperspheres, controlling the spatial false
discovery rate, and visualizing changes in abundance in the
high-dimensional marker space.
biocViews: ImmunoOncology, FlowCytometry, MultipleComparison,
Proteomics, SingleCell
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cydar
git_branch: RELEASE_3_13
git_last_commit: 38926f1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cydar_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cydar_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cydar_1.16.0.tgz
vignettes: vignettes/cydar/inst/doc/cydar.html
vignetteTitles: Detecting differential abundance
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cydar/inst/doc/cydar.R
dependencyCount: 94
Package: CytoDx
Version: 1.12.0
Depends: R (>= 3.5)
Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats,
flowCore,grDevices, graphics, utils
Suggests: knitr
License: GPL-2
MD5sum: abf64d8662121768a0325284b4036313
NeedsCompilation: no
Title: Robust prediction of clinical outcomes using cytometry data
without cell gating
Description: This package provides functions that predict clinical
outcomes using single cell data (such as flow cytometry data,
RNA single cell sequencing data) without the requirement of
cell gating or clustering.
biocViews: ImmunoOncology, CellBiology, FlowCytometry,
StatisticalMethod, Software, CellBasedAssays, Regression,
Classification, Survival
Author: Zicheng Hu
Maintainer: Zicheng Hu <zicheng.hu@ucsf.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CytoDx
git_branch: RELEASE_3_13
git_last_commit: 32996cb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CytoDx_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CytoDx_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CytoDx_1.12.0.tgz
vignettes: vignettes/CytoDx/inst/doc/CytoDx_Vignette.pdf
vignetteTitles: Introduction to CytoDx
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CytoDx/inst/doc/CytoDx_Vignette.R
dependencyCount: 50
Package: CytoGLMM
Version: 1.0.0
Imports: stats, methods, BiocParallel, RColorBrewer, cowplot,
doParallel, dplyr, factoextra, flexmix, ggplot2, magrittr,
mbest, pheatmap, speedglm, stringr, strucchange, tibble,
ggrepel, MASS, logging, Matrix, tidyr, caret, rlang, grDevices
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: LGPL-3
Archs: i386, x64
MD5sum: 1d144c35f5eed47f40264cdede613187
NeedsCompilation: no
Title: Conditional Differential Analysis for Flow and Mass Cytometry
Experiments
Description: The CytoGLMM R package implements two multiple regression
strategies: A bootstrapped generalized linear model (GLM) and a
generalized linear mixed model (GLMM). Most current data
analysis tools compare expressions across many computationally
discovered cell types. CytoGLMM focuses on just one cell type.
Our narrower field of application allows us to define a more
specific statistical model with easier to control statistical
guarantees. As a result, CytoGLMM finds differential proteins
in flow and mass cytometry data while reducing biases arising
from marker correlations and safeguarding against false
discoveries induced by patient heterogeneity.
biocViews: FlowCytometry, Proteomics, SingleCell, CellBasedAssays,
CellBiology, ImmunoOncology, Regression, StatisticalMethod,
Software
Author: Christof Seiler [aut, cre]
(<https://orcid.org/0000-0001-8802-3642>)
Maintainer: Christof Seiler <christof.seiler@maastrichtuniversity.nl>
URL: https://christofseiler.github.io/CytoGLMM,
https://github.com/ChristofSeiler/CytoGLMM
VignetteBuilder: knitr
BugReports: https://github.com/ChristofSeiler/CytoGLMM/issues
git_url: https://git.bioconductor.org/packages/CytoGLMM
git_branch: RELEASE_3_13
git_last_commit: 6da96d2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CytoGLMM_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CytoGLMM_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CytoGLMM_1.0.0.tgz
vignettes: vignettes/CytoGLMM/inst/doc/CytoGLMM.html
vignetteTitles: CytoGLMM Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CytoGLMM/inst/doc/CytoGLMM.R
dependencyCount: 173
Package: cytolib
Version: 2.4.0
Depends: R (>= 3.4)
Imports: RcppParallel, RProtoBufLib
LinkingTo: Rcpp, BH(>= 1.75.0.0), RProtoBufLib(>= 2.3.5),Rhdf5lib,
RcppArmadillo, RcppParallel(>= 4.4.2-1)
Suggests: knitr
License: file LICENSE
License_restricts_use: yes
MD5sum: 4d396b2150c70ec73e7ce9f0101e1433
NeedsCompilation: yes
Title: C++ infrastructure for representing and interacting with the
gated cytometry data
Description: This package provides the core data structure and API to
represent and interact with the gated cytometry data.
biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing,
DataRepresentation
Author: Mike Jiang
Maintainer: Mike Jiang <mike@ozette.ai>
SystemRequirements: GNU make, C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cytolib
git_branch: RELEASE_3_13
git_last_commit: 652ea2c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/cytolib_2.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cytolib_2.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/cytolib_2.4.0.tgz
vignettes: vignettes/cytolib/inst/doc/cytolib.html
vignetteTitles: Using cytolib
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cytolib/inst/doc/cytolib.R
importsMe: CytoML, flowCore, flowWorkspace
linksToMe: CytoML, flowCore, flowWorkspace
dependencyCount: 9
Package: cytomapper
Version: 1.4.1
Depends: R (>= 4.0), EBImage, SingleCellExperiment, methods
Imports: S4Vectors, BiocParallel, HDF5Array, DelayedArray,
RColorBrewer, viridis, utils, SummarizedExperiment, tools,
graphics, raster, grDevices, stats, ggplot2, ggbeeswarm,
svgPanZoom, svglite, shiny, shinydashboard, matrixStats, rhdf5
Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, shinytest
License: GPL (>= 2)
MD5sum: aa2375e4b8d82d978d06754d4d59c3cf
NeedsCompilation: no
Title: Visualization of highly multiplexed imaging data in R
Description: Highly multiplexed imaging acquires the single-cell
expression of selected proteins in a spatially-resolved
fashion. These measurements can be visualised across multiple
length-scales. First, pixel-level intensities represent the
spatial distributions of feature expression with highest
resolution. Second, after segmentation, expression values or
cell-level metadata (e.g. cell-type information) can be
visualised on segmented cell areas. This package contains
functions for the visualisation of multiplexed read-outs and
cell-level information obtained by multiplexed imaging
technologies. The main functions of this package allow 1. the
visualisation of pixel-level information across multiple
channels, 2. the display of cell-level information (expression
and/or metadata) on segmentation masks and 3. gating and
visualisation of single cells.
biocViews: ImmunoOncology, Software, SingleCell, OneChannel,
TwoChannel, MultipleComparison, Normalization, DataImport
Author: Nils Eling [aut, cre]
(<https://orcid.org/0000-0002-4711-1176>), Nicolas Damond [aut]
(<https://orcid.org/0000-0003-3027-8989>), Tobias Hoch [ctb]
Maintainer: Nils Eling <nils.eling@dqbm.uzh.ch>
URL: https://github.com/BodenmillerGroup/cytomapper
VignetteBuilder: knitr
BugReports: https://github.com/BodenmillerGroup/cytomapper/issues
git_url: https://git.bioconductor.org/packages/cytomapper
git_branch: RELEASE_3_13
git_last_commit: edd9a70
git_last_commit_date: 2021-05-21
Date/Publication: 2021-05-21
source.ver: src/contrib/cytomapper_1.4.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/cytomapper_1.4.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/cytomapper_1.4.1.tgz
vignettes: vignettes/cytomapper/inst/doc/cytomapper_ondisk.html,
vignettes/cytomapper/inst/doc/cytomapper.html
vignetteTitles: "On disk storage of images", "Visualization of imaging
cytometry data in R"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cytomapper/inst/doc/cytomapper_ondisk.R,
vignettes/cytomapper/inst/doc/cytomapper.R
dependencyCount: 110
Package: CytoML
Version: 2.4.0
Depends: R (>= 3.5.0)
Imports: cytolib(>= 2.3.9), flowCore (>= 1.99.10), flowWorkspace (>=
4.1.8), openCyto (>= 1.99.2), XML, data.table, jsonlite, RBGL,
Rgraphviz, Biobase, methods, graph, graphics, utils, base64enc,
plyr, dplyr, grDevices, methods, ggcyto (>= 1.11.4), yaml,
lattice, stats, corpcor, RUnit, tibble, RcppParallel, xml2
LinkingTo: Rcpp, BH(>= 1.62.0-1), RProtoBufLib, cytolib, Rhdf5lib,
RcppArmadillo, RcppParallel(>= 4.4.2-1), flowWorkspace
Suggests: testthat, flowWorkspaceData , knitr, parallel
License: file LICENSE
License_restricts_use: yes
Archs: i386, x64
MD5sum: 21bf73aa629cae06bd45f6ae6b7eb3c6
NeedsCompilation: yes
Title: A GatingML Interface for Cross Platform Cytometry Data Sharing
Description: Uses platform-specific implemenations of the GatingML2.0
standard to exchange gated cytometry data with other software
platforms.
biocViews: ImmunoOncology, FlowCytometry, DataImport,
DataRepresentation
Author: Mike Jiang, Jake Wagner
Maintainer: Mike Jiang <wjiang2@fhcrc.org>
URL: https://github.com/RGLab/CytoML
SystemRequirements: xml2, GNU make, C++11
VignetteBuilder: knitr
BugReports: https://github.com/RGLab/CytoML/issues
git_url: https://git.bioconductor.org/packages/CytoML
git_branch: RELEASE_3_13
git_last_commit: 6e64d69
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CytoML_2.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CytoML_2.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CytoML_2.4.0.tgz
vignettes: vignettes/CytoML/inst/doc/cytobank2GatingSet.html,
vignettes/CytoML/inst/doc/flowjo_to_gatingset.html,
vignettes/CytoML/inst/doc/HowToExportGatingSet.html
vignetteTitles: How to import Cytobank into a GatingSet, flowJo parser,
How to export a GatingSet to GatingML
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CytoML/inst/doc/cytobank2GatingSet.R,
vignettes/CytoML/inst/doc/flowjo_to_gatingset.R,
vignettes/CytoML/inst/doc/HowToExportGatingSet.R
importsMe: FlowSOM
suggestsMe: flowWorkspace, openCyto
dependencyCount: 127
Package: CytoTree
Version: 1.2.0
Depends: R (>= 4.0), igraph
Imports: FlowSOM, Rtsne, ggplot2, destiny, gmodels, flowUtils, Biobase,
Matrix, flowCore, sva, matrixStats, methods, mclust, prettydoc,
RANN(>= 2.5), Rcpp (>= 0.12.0), BiocNeighbors, cluster,
pheatmap, scatterpie, umap, scatterplot3d, limma, stringr,
grDevices, grid, stats
LinkingTo: Rcpp
Suggests: BiocGenerics, knitr, RColorBrewer, rmarkdown, testthat,
BiocStyle
License: GPL-3
MD5sum: 96f3edfd49f1d4155e1b42d672c1674b
NeedsCompilation: yes
Title: A Toolkit for Flow And Mass Cytometry Data
Description: A trajectory inference toolkit for flow and mass cytometry
data. CytoTree is a valuable tool to build a tree-shaped
trajectory using flow and mass cytometry data. The application
of CytoTree ranges from clustering and dimensionality reduction
to trajectory reconstruction and pseudotime estimation. It
offers complete analyzing workflow for flow and mass cytometry
data.
biocViews: CellBiology, Clustering, Visualization, Software,
CellBasedAssays, FlowCytometry, NetworkInference, Network
Author: Yuting Dai [aut, cre]
Maintainer: Yuting Dai <forlynna@sjtu.edu.cn>
URL: http://www.r-project.org, https://github.com/JhuangLab/CytoTree
VignetteBuilder: knitr
BugReports: https://github.com/JhuangLab/CytoTree/issues
git_url: https://git.bioconductor.org/packages/CytoTree
git_branch: RELEASE_3_13
git_last_commit: a4311cc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/CytoTree_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/CytoTree_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/CytoTree_1.2.0.tgz
vignettes: vignettes/CytoTree/inst/doc/Tutorial.html
vignetteTitles: Quick_start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CytoTree/inst/doc/Tutorial.R
dependencyCount: 241
Package: dada2
Version: 1.20.0
Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods (>= 3.4.0)
Imports: Biostrings (>= 2.42.1), ggplot2 (>= 2.1.0), reshape2 (>=
1.4.1), ShortRead (>= 1.32.0), RcppParallel (>= 4.3.0),
parallel (>= 3.2.0), IRanges (>= 2.6.0), XVector (>= 0.16.0),
BiocGenerics (>= 0.22.0)
LinkingTo: Rcpp, RcppParallel
Suggests: BiocStyle, knitr, rmarkdown
License: LGPL-2
Archs: i386, x64
MD5sum: 4540dd8d746695739da07a08e3c98f7f
NeedsCompilation: yes
Title: Accurate, high-resolution sample inference from amplicon
sequencing data
Description: The dada2 package infers exact amplicon sequence variants
(ASVs) from high-throughput amplicon sequencing data, replacing
the coarser and less accurate OTU clustering approach. The
dada2 pipeline takes as input demultiplexed fastq files, and
outputs the sequence variants and their sample-wise abundances
after removing substitution and chimera errors. Taxonomic
classification is available via a native implementation of the
RDP naive Bayesian classifier, and species-level assignment to
16S rRNA gene fragments by exact matching.
biocViews: ImmunoOncology, Microbiome, Sequencing, Classification,
Metagenomics
Author: Benjamin Callahan <benjamin.j.callahan@gmail.com>, Paul
McMurdie, Susan Holmes
Maintainer: Benjamin Callahan <benjamin.j.callahan@gmail.com>
URL: http://benjjneb.github.io/dada2/
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/benjjneb/dada2/issues
git_url: https://git.bioconductor.org/packages/dada2
git_branch: RELEASE_3_13
git_last_commit: b8a796b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dada2_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dada2_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dada2_1.20.0.tgz
vignettes: vignettes/dada2/inst/doc/dada2-intro.html
vignetteTitles: Introduction to dada2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dada2/inst/doc/dada2-intro.R
importsMe: Rbec, microbial
suggestsMe: mia
dependencyCount: 78
Package: dagLogo
Version: 1.30.0
Depends: R (>= 3.0.1), methods, grid
Imports: pheatmap, Biostrings, UniProt.ws, BiocGenerics, utils,
biomaRt, motifStack
Suggests: XML, grImport, grImport2, BiocStyle, knitr, rmarkdown,
testthat
License: GPL (>=2)
Archs: i386, x64
MD5sum: 609bb8577e05a3de8de45e36164ecd2d
NeedsCompilation: no
Title: dagLogo: a Bioconductor package for visualizing conserved amino
acid sequence pattern in groups based on probability theory
Description: Visualize significant conserved amino acid sequence
pattern in groups based on probability theory.
biocViews: SequenceMatching, Visualization
Author: Jianhong Ou, Haibo Liu, Alexey Stukalov, Niraj Nirala, Usha
Acharya, Lihua Julie Zhu
Maintainer: Jianhong Ou <jianhong.ou@duke.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dagLogo
git_branch: RELEASE_3_13
git_last_commit: 603fae3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dagLogo_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dagLogo_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dagLogo_1.30.0.tgz
vignettes: vignettes/dagLogo/inst/doc/dagLogo.html
vignetteTitles: dagLogo Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R
dependencyCount: 99
Package: daMA
Version: 1.64.0
Imports: MASS, stats
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 0222202e8da1ed6bf205ba6822684c6a
NeedsCompilation: no
Title: Efficient design and analysis of factorial two-colour microarray
data
Description: This package contains functions for the efficient design
of factorial two-colour microarray experiments and for the
statistical analysis of factorial microarray data. Statistical
details are described in Bretz et al. (2003, submitted)
biocViews: Microarray, TwoChannel, DifferentialExpression
Author: Jobst Landgrebe <jlandgr1@gwdg.de> and Frank Bretz
<bretz@bioinf.uni-hannover.de>
Maintainer: Jobst Landgrebe <jlandgr1@gwdg.de>
URL: http://www.microarrays.med.uni-goettingen.de
git_url: https://git.bioconductor.org/packages/daMA
git_branch: RELEASE_3_13
git_last_commit: 6a0fc3a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/daMA_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/daMA_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/daMA_1.64.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 6
Package: DAMEfinder
Version: 1.4.0
Depends: R (>= 4.0)
Imports: stats, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, readr,
SummarizedExperiment, GenomicAlignments, stringr, plyr,
VariantAnnotation, parallel, ggplot2, Rsamtools, BiocGenerics,
methods, limma, bumphunter, Biostrings, reshape2, cowplot,
utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer,
BSgenome.Hsapiens.UCSC.hg19
License: MIT + file LICENSE
MD5sum: ae56dd9c8405ab51eddd211d7da829ad
NeedsCompilation: no
Title: Finds DAMEs - Differential Allelicly MEthylated regions
Description: 'DAMEfinder' offers functionality for taking methtuple or
bismark outputs to calculate ASM scores and compute DAMEs. It
also offers nice visualization of methyl-circle plots.
biocViews: DNAMethylation, DifferentialMethylation, Coverage
Author: Stephany Orjuela [aut, cre]
(<https://orcid.org/0000-0002-1508-461X>), Dania Machlab [aut],
Mark Robinson [aut]
Maintainer: Stephany Orjuela <sorjuelal@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/markrobinsonuzh/DAMEfinder/issues
git_url: https://git.bioconductor.org/packages/DAMEfinder
git_branch: RELEASE_3_13
git_last_commit: a0da475
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DAMEfinder_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DAMEfinder_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DAMEfinder_1.4.0.tgz
vignettes: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.html
vignetteTitles: DAMEfinder Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.R
dependencyCount: 128
Package: DaMiRseq
Version: 2.4.3
Depends: R (>= 3.4), SummarizedExperiment, ggplot2
Imports: DESeq2, limma, EDASeq, RColorBrewer, sva, Hmisc, pheatmap,
FactoMineR, corrplot, randomForest, e1071, caret, MASS,
lubridate, plsVarSel, kknn, FSelector, methods, stats, utils,
graphics, grDevices, reshape2, ineq, arm, pls, RSNNS, edgeR,
plyr
Suggests: BiocStyle, knitr, testthat
License: GPL (>= 2)
MD5sum: 0452ae1e29276486edb6439d6f9dc571
NeedsCompilation: no
Title: Data Mining for RNA-seq data: normalization, feature selection
and classification
Description: The DaMiRseq package offers a tidy pipeline of data mining
procedures to identify transcriptional biomarkers and exploit
them for both binary and multi-class classification purposes.
The package accepts any kind of data presented as a table of
raw counts and allows including both continous and factorial
variables that occur with the experimental setting. A series of
functions enable the user to clean up the data by filtering
genomic features and samples, to adjust data by identifying and
removing the unwanted source of variation (i.e. batches and
confounding factors) and to select the best predictors for
modeling. Finally, a "stacking" ensemble learning technique is
applied to build a robust classification model. Every step
includes a checkpoint that the user may exploit to assess the
effects of data management by looking at diagnostic plots, such
as clustering and heatmaps, RLE boxplots, MDS or correlation
plot.
biocViews: Sequencing, RNASeq, Classification, ImmunoOncology
Author: Mattia Chiesa <mattia.chiesa@cardiologicomonzino.it>, Luca
Piacentini <luca.piacentini@cardiologicomonzino.it>
Maintainer: Mattia Chiesa <mattia.chiesa@cardiologicomonzino.it>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DaMiRseq
git_branch: RELEASE_3_13
git_last_commit: 6c00662
git_last_commit_date: 2021-08-12
Date/Publication: 2021-08-12
source.ver: src/contrib/DaMiRseq_2.4.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DaMiRseq_2.4.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/DaMiRseq_2.4.3.tgz
vignettes: vignettes/DaMiRseq/inst/doc/DaMiRseq.pdf
vignetteTitles: Data Mining for RNA-seq data: normalization,, features
selection and classification - DaMiRseq package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DaMiRseq/inst/doc/DaMiRseq.R
importsMe: GARS
dependencyCount: 242
Package: DAPAR
Version: 1.24.8
Depends: R (>= 4.1.0)
Imports: Biobase, MSnbase, tibble, RColorBrewer,stats,preprocessCore,
Cairo,png, lattice,reshape2,gplots,pcaMethods,ggplot2,
limma,knitr,tmvtnorm,norm,impute, stringr, grDevices, graphics,
openxlsx, utils, cp4p (>= 0.3.5), scales, Matrix, vioplot,
imp4p (>= 1.1), forcats, methods, DAPARdata (>= 1.22.2),
siggenes, graph, lme4, readxl, highcharter, clusterProfiler,
dplyr, tidyr,AnnotationDbi, tidyverse, vsn, FactoMineR,
factoextra, multcomp, purrr, visNetwork, foreach, parallel,
doParallel, igraph, dendextend, Mfuzz, apcluster, diptest,
cluster
Suggests: BiocGenerics, testthat, BiocStyle
License: Artistic-2.0
MD5sum: cbb5442b2ee3f0c2de0465d87c9d64ed
NeedsCompilation: no
Title: Tools for the Differential Analysis of Proteins Abundance with R
Description: This package contains a collection of functions for the
visualisation and the statistical analysis of proteomic data.
biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry,
QualityControl, GO, DataImport
Author: Samuel Wieczorek [cre, aut], Florence Combes [aut], Thomas
Burger [aut], Cosmin Lazar [ctb], Alexia Dorffer [ctb], Anais
Courtier [ctb], Helene Borges [ctb], Enora Fremy [ctb]
Maintainer: Samuel Wieczorek <samuel.wieczorek@cea.fr>
URL: http://www.prostar-proteomics.org/
VignetteBuilder: knitr
BugReports: https://github.com/samWieczorek/DAPAR/issues
git_url: https://git.bioconductor.org/packages/DAPAR
git_branch: RELEASE_3_13
git_last_commit: 4b52ffd
git_last_commit_date: 2021-08-19
Date/Publication: 2021-08-22
source.ver: src/contrib/DAPAR_1.24.8.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DAPAR_1.24.8.zip
mac.binary.ver: bin/macosx/contrib/4.1/DAPAR_1.24.8.tgz
vignettes: vignettes/DAPAR/inst/doc/Prostar_UserManual.pdf
vignetteTitles: Prostar user manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DAPAR/inst/doc/Prostar_UserManual.R
importsMe: Prostar, mi4p
suggestsMe: DAPARdata
dependencyCount: 294
Package: DART
Version: 1.40.0
Depends: R (>= 2.10.0), igraph (>= 0.6.0)
Suggests: breastCancerVDX, breastCancerMAINZ, Biobase
License: GPL-2
MD5sum: f98cb3db55121e31639563fa499e465f
NeedsCompilation: no
Title: Denoising Algorithm based on Relevance network Topology
Description: Denoising Algorithm based on Relevance network Topology
(DART) is an algorithm designed to evaluate the consistency of
prior information molecular signatures (e.g in-vitro
perturbation expression signatures) in independent molecular
data (e.g gene expression data sets). If consistent, a pruning
network strategy is then used to infer the activation status of
the molecular signature in individual samples.
biocViews: GeneExpression, DifferentialExpression, GraphAndNetwork,
Pathways
Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff, Charles
Shijie Zheng
Maintainer: Charles Shijie Zheng <charles_zheng@live.com>
git_url: https://git.bioconductor.org/packages/DART
git_branch: RELEASE_3_13
git_last_commit: ebf6ae6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DART_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DART_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DART_1.40.0.tgz
vignettes: vignettes/DART/inst/doc/DART.pdf
vignetteTitles: DART Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DART/inst/doc/DART.R
dependencyCount: 11
Package: dasper
Version: 1.2.0
Depends: R (>= 4.0)
Imports: basilisk, BiocFileCache, BiocParallel, data.table, dplyr,
GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges,
magrittr, megadepth, methods, plyranges, readr, reticulate,
S4Vectors, stringr, SummarizedExperiment, tidyr
Suggests: BiocStyle, covr, testthat, GenomicState, ggplot2, ggpubr,
ggrepel, grid, knitcitations, knitr, recount, rmarkdown,
sessioninfo, rtracklayer, tibble
License: Artistic-2.0
MD5sum: 07a4332113458a5634d46752ad6c8257
NeedsCompilation: no
Title: Detecting abberant splicing events from RNA-sequencing data
Description: The aim of dasper is to detect aberrant splicing events
from RNA-seq data. dasper will use as input both junction and
coverage data from RNA-seq to calculate the deviation of each
splicing event in a patient from a set of user-defined
controls. dasper uses an unsupervised outlier detection
algorithm to score each splicing event in the patient with an
outlier score representing the degree to which that splicing
event looks abnormal.
biocViews: Software, RNASeq, Transcriptomics, AlternativeSplicing,
Coverage, Sequencing
Author: David Zhang [aut, cre]
(<https://orcid.org/0000-0003-2382-8460>), Leonardo
Collado-Torres [ctb] (<https://orcid.org/0000-0003-2140-308X>)
Maintainer: David Zhang <david.zhang.12@ucl.ac.uk>
URL: https://github.com/dzhang32/dasper
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/dasper
git_url: https://git.bioconductor.org/packages/dasper
git_branch: RELEASE_3_13
git_last_commit: 9bf018b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dasper_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dasper_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dasper_1.2.0.tgz
vignettes: vignettes/dasper/inst/doc/dasper.html
vignetteTitles: Introduction to dasper
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dasper/inst/doc/dasper.R
dependencyCount: 138
Package: dcanr
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: igraph, foreach, plyr, stringr, reshape2, methods, Matrix,
graphics, stats, RColorBrewer, circlize, doRNG
Suggests: EBcoexpress, testthat, EBarrays, GeneNet, COSINE, mclust,
minqa, SummarizedExperiment, Biobase, knitr, rmarkdown,
BiocStyle, edgeR
Enhances: parallel, doSNOW, doParallel
License: GPL-3
MD5sum: 1bdda7118a9c6e896f94efc007c415ce
NeedsCompilation: no
Title: Differential co-expression/association network analysis
Description: Methods and an evaluation framework for the inference of
differential co-expression/association networks.
biocViews: NetworkInference, GraphAndNetwork, DifferentialExpression,
Network
Author: Dharmesh D. Bhuva [aut, cre]
(<https://orcid.org/0000-0002-6398-9157>)
Maintainer: Dharmesh D. Bhuva <bhuva.d@wehi.edu.au>
URL: https://davislaboratory.github.io/dcanr/,
https://github.com/DavisLaboratory/dcanr
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/dcanr/issues
git_url: https://git.bioconductor.org/packages/dcanr
git_branch: RELEASE_3_13
git_last_commit: ca50fbf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dcanr_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dcanr_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dcanr_1.8.0.tgz
vignettes: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.html,
vignettes/dcanr/inst/doc/dcanr_vignette.html
vignetteTitles: 2. DC method evaluation, 1. Differential co-expression
analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.R,
vignettes/dcanr/inst/doc/dcanr_vignette.R
importsMe: SingscoreAMLMutations
dependencyCount: 30
Package: dce
Version: 1.0.0
Depends: R (>= 4.1)
Imports: stats, methods, assertthat, graph, pcalg, purrr, tidyverse,
Matrix, ggraph, tidygraph, ggplot2, rlang, expm, MASS,
CombinePValue, edgeR, epiNEM, igraph, metap, mnem, naturalsort,
ppcor, glm2, graphite, reshape2, dplyr, glue, Rgraphviz,
harmonicmeanp, org.Hs.eg.db, logger
Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR,
cowplot, dagitty, lmtest, sandwich, devtools, curatedTCGAData,
TCGAutils, SummarizedExperiment
License: GPL-3
MD5sum: 84a72ddae24812ca80b5c3af07d9cd2b
NeedsCompilation: no
Title: Pathway Enrichment Based on Differential Causal Effects
Description: Compute differential causal effects (dce) on (biological)
networks. Given observational samples from a control experiment
and non-control (e.g., cancer) for two genes A and B, we can
compute differential causal effects with a (generalized) linear
regression. If the causal effect of gene A on gene B in the
control samples is different from the causal effect in the
non-control samples the dce will differ from zero. We
regularize the dce computation by the inclusion of prior
network information from pathway databases such as KEGG.
biocViews: Software, StatisticalMethod, GraphAndNetwork, Regression,
GeneExpression, DifferentialExpression, NetworkEnrichment,
Network, KEGG
Author: Kim Philipp Jablonski [aut, cre]
(<https://orcid.org/0000-0002-4166-4343>), Martin Pirkl [aut]
Maintainer: Kim Philipp Jablonski <kim.philipp.jablonski@gmail.com>
URL: https://github.com/cbg-ethz/dce
VignetteBuilder: knitr
BugReports: https://github.com/cbg-ethz/dce/issues
git_url: https://git.bioconductor.org/packages/dce
git_branch: RELEASE_3_13
git_last_commit: a66589d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dce_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dce_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dce_1.0.0.tgz
vignettes: vignettes/dce/inst/doc/dce.html,
vignettes/dce/inst/doc/pathway_databases.html
vignetteTitles: Get started, Overview of pathway network databases
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dce/inst/doc/dce.R,
vignettes/dce/inst/doc/pathway_databases.R
dependencyCount: 234
Package: dcGSA
Version: 1.20.0
Depends: R (>= 3.3), Matrix
Imports: BiocParallel
Suggests: knitr
License: GPL-2
MD5sum: 82aadb34484631987b7a8ac2062ba829
NeedsCompilation: no
Title: Distance-correlation based Gene Set Analysis for longitudinal
gene expression profiles
Description: Distance-correlation based Gene Set Analysis for
longitudinal gene expression profiles. In longitudinal studies,
the gene expression profiles were collected at each visit from
each subject and hence there are multiple measurements of the
gene expression profiles for each subject. The dcGSA package
could be used to assess the associations between gene sets and
clinical outcomes of interest by fully taking advantage of the
longitudinal nature of both the gene expression profiles and
clinical outcomes.
biocViews: ImmunoOncology, GeneSetEnrichment,Microarray,
StatisticalMethod, Sequencing, RNASeq, GeneExpression
Author: Jiehuan Sun [aut, cre], Jose Herazo-Maya [aut], Xiu Huang
[aut], Naftali Kaminski [aut], and Hongyu Zhao [aut]
Maintainer: Jiehuan sun <jiehuan.sun@yale.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dcGSA
git_branch: RELEASE_3_13
git_last_commit: 3c660d9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dcGSA_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dcGSA_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dcGSA_1.20.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 16
Package: ddCt
Version: 1.48.0
Depends: R (>= 2.3.0), methods
Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice,
BiocGenerics
Suggests: RUnit
License: LGPL-3
MD5sum: 28790fa3bfed64f83cef15a48b386f20
NeedsCompilation: no
Title: The ddCt Algorithm for the Analysis of Quantitative Real-Time
PCR (qRT-PCR)
Description: The Delta-Delta-Ct (ddCt) Algorithm is an approximation
method to determine relative gene expression with quantitative
real-time PCR (qRT-PCR) experiments. Compared to other
approaches, it requires no standard curve for each
primer-target pair, therefore reducing the working load and yet
returning accurate enough results as long as the assumptions of
the amplification efficiency hold. The ddCt package implements
a pipeline to collect, analyse and visualize qRT-PCR results,
for example those from TaqMan SDM software, mainly using the
ddCt method. The pipeline can be either invoked by a script in
command-line or through the API consisting of S4-Classes,
methods and functions.
biocViews: GeneExpression, DifferentialExpression,
MicrotitrePlateAssay, qPCR
Author: Jitao David Zhang, Rudolf Biczok, and Markus Ruschhaupt
Maintainer: Jitao David Zhang <jitao_david.zhang@roche.com>
git_url: https://git.bioconductor.org/packages/ddCt
git_branch: RELEASE_3_13
git_last_commit: 7456707
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ddCt_1.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ddCt_1.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ddCt_1.48.0.tgz
vignettes: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf,
vignettes/ddCt/inst/doc/rtPCR-usage.pdf,
vignettes/ddCt/inst/doc/rtPCR.pdf
vignetteTitles: How to apply the ddCt method, Analyse RT-PCR data with
the end-to-end script in ddCt package, Introduction to the ddCt
method for qRT-PCR data analysis: background,, algorithm and
example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R,
vignettes/ddCt/inst/doc/rtPCR-usage.R,
vignettes/ddCt/inst/doc/rtPCR.R
dependencyCount: 12
Package: ddPCRclust
Version: 1.12.0
Depends: R (>= 3.5)
Imports: plotrix, clue, parallel, ggplot2, openxlsx, R.utils, flowCore,
flowDensity (>= 1.13.3), SamSPECTRAL, flowPeaks
Suggests: BiocStyle
License: Artistic-2.0
MD5sum: fc72ecedd74a5349756efe23e34a7dd0
NeedsCompilation: no
Title: Clustering algorithm for ddPCR data
Description: The ddPCRclust algorithm can automatically quantify the
CPDs of non-orthogonal ddPCR reactions with up to four targets.
In order to determine the correct droplet count for each
target, it is crucial to both identify all clusters and label
them correctly based on their position. For more information on
what data can be analyzed and how a template needs to be
formatted, please check the vignette.
biocViews: ddPCR, Clustering
Author: Benedikt G. Brink [aut, cre], Justin Meskas [ctb], Ryan R.
Brinkman [ctb]
Maintainer: Benedikt G. Brink <bbrink@cebitec.uni-bielefeld.de>
URL: https://github.com/bgbrink/ddPCRclust
BugReports: https://github.com/bgbrink/ddPCRclust/issues
git_url: https://git.bioconductor.org/packages/ddPCRclust
git_branch: RELEASE_3_13
git_last_commit: edc91db
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ddPCRclust_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ddPCRclust_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ddPCRclust_1.12.0.tgz
vignettes: vignettes/ddPCRclust/inst/doc/ddPCRclust.pdf
vignetteTitles: Bioconductor LaTeX Style
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ddPCRclust/inst/doc/ddPCRclust.R
dependencyCount: 159
Package: dearseq
Version: 1.4.0
Depends: R (>= 3.6.0)
Imports: CompQuadForm, ggplot2, KernSmooth, matrixStats, methods,
parallel, pbapply, stats, statmod
Suggests: Biobase, BiocManager, BiocSet, edgeR, DESeq2, GEOquery, GSA,
knitr, limma, readxl, rmarkdown, S4Vectors,
SummarizedExperiment, testthat, covr
License: GPL-2 | file LICENSE
MD5sum: b0c0828bea8f7737358dec0ca3155b3a
NeedsCompilation: no
Title: Differential Expression Analysis for RNA-seq data through a
robust variance component test
Description: Differential Expression Analysis RNA-seq data with
variance component score test accounting for data
heteroscedasticity through precision weights. Perform both
gene-wise and gene set analyses, and can deal with repeated or
longitudinal data. Methods are detailed in: Agniel D & Hejblum
BP (2017) Variance component score test for time-course gene
set analysis of longitudinal RNA-seq data, Biostatistics,
18(4):589-604. and Gauthier M, Agniel D, Thiébaut R & Hejblum
BP (2019). dearseq: a variance component score test for RNA-Seq
differential analysis that effectively controls the false
discovery rate, *bioRxiv* 635714.
biocViews: BiomedicalInformatics, CellBiology, DifferentialExpression,
DNASeq, GeneExpression, Genetics, GeneSetEnrichment,
ImmunoOncology, KEGG, Regression, RNASeq, Sequencing,
SystemsBiology, TimeCourse, Transcription, Transcriptomics
Author: Denis Agniel [aut], Boris P. Hejblum [aut, cre], Marine
Gauthier [aut]
Maintainer: Boris P. Hejblum <boris.hejblum@u-bordeaux.fr>
VignetteBuilder: knitr
BugReports: https://github.com/borishejblum/dearseq/issues
git_url: https://git.bioconductor.org/packages/dearseq
git_branch: RELEASE_3_13
git_last_commit: 738a4a7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dearseq_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dearseq_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dearseq_1.4.0.tgz
vignettes: vignettes/dearseq/inst/doc/dearseqUserguide.html
vignetteTitles: dearseqUserguide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dearseq/inst/doc/dearseqUserguide.R
dependencyCount: 44
Package: debCAM
Version: 1.10.0
Depends: R (>= 3.5)
Imports: methods, rJava, BiocParallel, stats, Biobase,
SummarizedExperiment, corpcor, geometry, NMF, nnls, DMwR2,
pcaPP, apcluster, graphics
Suggests: knitr, rmarkdown, BiocStyle, testthat, GEOquery, rgl
License: GPL-2
MD5sum: 7123accf3a3735a530f6dda097fc10c1
NeedsCompilation: no
Title: Deconvolution by Convex Analysis of Mixtures
Description: An R package for fully unsupervised deconvolution of
complex tissues. It provides basic functions to perform
unsupervised deconvolution on mixture expression profiles by
Convex Analysis of Mixtures (CAM) and some auxiliary functions
to help understand the subpopulation-specific results. It also
implements functions to perform supervised deconvolution based
on prior knowledge of molecular markers, S matrix or A matrix.
Combining molecular markers from CAM and from prior knowledge
can achieve semi-supervised deconvolution of mixtures.
biocViews: Software, CellBiology, GeneExpression
Author: Lulu Chen <luluchen@vt.edu>
Maintainer: Lulu Chen <luluchen@vt.edu>
SystemRequirements: Java (>= 1.8)
VignetteBuilder: knitr
BugReports: https://github.com/Lululuella/debCAM/issues
git_url: https://git.bioconductor.org/packages/debCAM
git_branch: RELEASE_3_13
git_last_commit: 86afedd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/debCAM_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/debCAM_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/debCAM_1.10.0.tgz
vignettes: vignettes/debCAM/inst/doc/debcam.html
vignetteTitles: debCAM User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/debCAM/inst/doc/debcam.R
dependencyCount: 116
Package: debrowser
Version: 1.20.1
Depends: R (>= 3.5.0),
Imports: shiny, jsonlite, shinyjs, shinydashboard, shinyBS, gplots, DT,
ggplot2, RColorBrewer, annotate, AnnotationDbi, DESeq2, DOSE,
igraph, grDevices, graphics, stats, utils, GenomicRanges,
IRanges, S4Vectors, SummarizedExperiment, stringi, reshape2,
org.Hs.eg.db, org.Mm.eg.db, limma, edgeR, clusterProfiler,
methods, sva, RCurl, enrichplot, colourpicker, plotly,
heatmaply, Harman, pathview, apeglm, ashr
Suggests: testthat, rmarkdown, knitr
License: GPL-3 + file LICENSE
MD5sum: 501243e26cd9048b0789201ab52897c2
NeedsCompilation: no
Title: Interactive Differential Expresion Analysis Browser
Description: Bioinformatics platform containing interactive plots and
tables for differential gene and region expression studies.
Allows visualizing expression data much more deeply in an
interactive and faster way. By changing the parameters, users
can easily discover different parts of the data that like never
have been done before. Manually creating and looking these
plots takes time. With DEBrowser users can prepare plots
without writing any code. Differential expression, PCA and
clustering analysis are made on site and the results are shown
in various plots such as scatter, bar, box, volcano, ma plots
and Heatmaps.
biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression,
GeneExpression, Clustering, ImmunoOncology
Author: Alper Kucukural <alper.kucukural@umassmed.edu>, Onur Yukselen
<onur.yukselen@umassmed.edu>, Manuel Garber
<manuel.garber@umassmed.edu>
Maintainer: Alper Kucukural <alper.kucukural@umassmed.edu>
URL: https://github.com/UMMS-Biocore/debrowser
VignetteBuilder: knitr, rmarkdown
BugReports: https://github.com/UMMS-Biocore/debrowser/issues/new
git_url: https://git.bioconductor.org/packages/debrowser
git_branch: RELEASE_3_13
git_last_commit: 3379462
git_last_commit_date: 2021-08-11
Date/Publication: 2021-08-12
source.ver: src/contrib/debrowser_1.20.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/debrowser_1.20.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/debrowser_1.20.1.tgz
vignettes: vignettes/debrowser/inst/doc/DEBrowser.html
vignetteTitles: DEBrowser Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R
dependencyCount: 210
Package: DECIPHER
Version: 2.20.0
Depends: R (>= 3.5.0), Biostrings (>= 2.59.1), RSQLite (>= 1.1), stats,
parallel
Imports: methods, DBI, S4Vectors, IRanges, XVector
LinkingTo: Biostrings, S4Vectors, IRanges, XVector
License: GPL-3
MD5sum: 2fbd3feae7aee1a8e9fd2c7dae596408
NeedsCompilation: yes
Title: Tools for curating, analyzing, and manipulating biological
sequences
Description: A toolset for deciphering and managing biological
sequences.
biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization,
Microarray, QualityControl, qPCR, Alignment, WholeGenome,
Microbiome, ImmunoOncology, GenePrediction
Author: Erik Wright
Maintainer: Erik Wright <eswright@pitt.edu>
git_url: https://git.bioconductor.org/packages/DECIPHER
git_branch: RELEASE_3_13
git_last_commit: bcd35a8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DECIPHER_2.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DECIPHER_2.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DECIPHER_2.20.0.tgz
vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf,
vignettes/DECIPHER/inst/doc/ClassifySequences.pdf,
vignettes/DECIPHER/inst/doc/DECIPHERing.pdf,
vignettes/DECIPHER/inst/doc/DesignMicroarray.pdf,
vignettes/DECIPHER/inst/doc/DesignPrimers.pdf,
vignettes/DECIPHER/inst/doc/DesignProbes.pdf,
vignettes/DECIPHER/inst/doc/DesignSignatures.pdf,
vignettes/DECIPHER/inst/doc/FindChimeras.pdf,
vignettes/DECIPHER/inst/doc/FindingGenes.pdf,
vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.pdf
vignetteTitles: The Art of Multiple Sequence Alignment in R, Classify
Sequences, Getting Started DECIPHERing, Design Microarray
Probes, Design Group-Specific Primers, Design Group-Specific
FISH Probes, Design Primers That Yield Group-Specific
Signatures, Finding Chimeric Sequences, The Magic of Gene
Finding, The Double Life of RNA: Uncovering Non-Coding RNAs
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.R,
vignettes/DECIPHER/inst/doc/ClassifySequences.R,
vignettes/DECIPHER/inst/doc/DECIPHERing.R,
vignettes/DECIPHER/inst/doc/DesignMicroarray.R,
vignettes/DECIPHER/inst/doc/DesignPrimers.R,
vignettes/DECIPHER/inst/doc/DesignProbes.R,
vignettes/DECIPHER/inst/doc/DesignSignatures.R,
vignettes/DECIPHER/inst/doc/FindChimeras.R,
vignettes/DECIPHER/inst/doc/FindingGenes.R,
vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.R
dependsOnMe: AssessORF, sangeranalyseR, SynExtend
importsMe: mia, openPrimeR, AssessORFData, ensembleTax, microbial
suggestsMe: MicrobiotaProcess, pagoo
dependencyCount: 34
Package: deco
Version: 1.8.0
Depends: R (>= 3.5.0), AnnotationDbi, BiocParallel,
SummarizedExperiment, limma
Imports: stats, methods, ggplot2, foreign, graphics, BiocStyle,
Biobase, cluster, gplots, RColorBrewer, locfit, made4, ade4,
sfsmisc, scatterplot3d, gdata, grDevices, utils, reshape2,
gridExtra
Suggests: knitr, curatedTCGAData, MultiAssayExperiment, Homo.sapiens
License: GPL (>=3)
MD5sum: 4402e842eba0843545aee4e2ef3dc591
NeedsCompilation: no
Title: Decomposing Heterogeneous Cohorts using Omic Data Profiling
Description: This package discovers differential features in hetero-
and homogeneous omic data by a two-step method including
subsampling LIMMA and NSCA. DECO reveals feature associations
to hidden subclasses not exclusively related to higher
deregulation levels.
biocViews: Software, FeatureExtraction, Clustering, MultipleComparison,
DifferentialExpression, Transcriptomics, BiomedicalInformatics,
Proteomics, Bayesian, GeneExpression, Transcription,
Sequencing, Microarray, ExonArray, RNASeq, MicroRNAArray,
mRNAMicroarray
Author: Francisco Jose Campos-Laborie, Jose Manuel Sanchez-Santos and
Javier De Las Rivas. Bioinformatics and Functional Genomics
Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL).
Salamanca. Spain.
Maintainer: Francisco Jose Campos Laborie <fjcamlab@gmail.com>
URL: https://github.com/fjcamlab/deco
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/deco
git_branch: RELEASE_3_13
git_last_commit: b3606bb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/deco_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/deco_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/deco_1.8.0.tgz
vignettes: vignettes/deco/inst/doc/DECO.html
vignetteTitles: deco
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/deco/inst/doc/DECO.R
dependencyCount: 117
Package: DEComplexDisease
Version: 1.12.0
Depends: R (>= 3.3.3)
Imports: Rcpp (>= 0.12.7), DESeq2, edgeR, SummarizedExperiment,
ComplexHeatmap, grid, parallel, BiocParallel, grDevices,
graphics, stats, methods, utils
LinkingTo: Rcpp
Suggests: knitr
License: GPL-3
Archs: i386, x64
MD5sum: 02faa33dc0bf7f8c43a0ec95d74270e4
NeedsCompilation: yes
Title: A tool for differential expression analysis and DEGs based
investigation to complex diseases by bi-clustering analysis
Description: It is designed to find the differential expressed genes
(DEGs) for complex disease, which is characterized by the
heterogeneous genomic expression profiles. Different from the
established DEG analysis tools, it does not assume the patients
of complex diseases to share the common DEGs. By applying a
bi-clustering algorithm, DECD finds the DEGs shared by as many
patients. In this way, DECD describes the DEGs of complex
disease in a novel syntax, e.g. a gene list composed of 200
genes are differentially expressed in 30% percent of studied
complex disease. Applying the DECD analysis results, users are
possible to find the patients affected by the same mechanism
based on the shared signatures.
biocViews: DNASeq, WholeGenome, FunctionalGenomics,
DifferentialExpression,GeneExpression, Clustering
Author: Guofeng Meng
Maintainer: Guofeng Meng <menggf@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DEComplexDisease
git_branch: RELEASE_3_13
git_last_commit: 33dd72a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEComplexDisease_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DEComplexDisease_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DEComplexDisease_1.12.0.tgz
vignettes: vignettes/DEComplexDisease/inst/doc/vignettes.pdf,
vignettes/DEComplexDisease/inst/doc/decd.html
vignetteTitles: DEComplexDisease: a R package for DE analysis,
DEComplexDisease: a R package for DE analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEComplexDisease/inst/doc/decd.R
dependencyCount: 108
Package: decompTumor2Sig
Version: 2.8.0
Depends: R(>= 4.0), ggplot2
Imports: methods, Matrix, quadprog(>= 1.5-5), GenomicRanges, stats,
GenomicFeatures, Biostrings, BiocGenerics, S4Vectors, plyr,
utils, graphics, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation,
SummarizedExperiment, ggseqlogo, gridExtra, data.table,
GenomeInfoDb, readxl
Suggests: knitr, rmarkdown, BiocStyle
License: GPL-2
MD5sum: 903588aafee014039343824a74154dfb
NeedsCompilation: no
Title: Decomposition of individual tumors into mutational signatures by
signature refitting
Description: Uses quadratic programming for signature refitting, i.e.,
to decompose the mutation catalog from an individual tumor
sample into a set of given mutational signatures (either
Alexandrov-model signatures or Shiraishi-model signatures),
computing weights that reflect the contributions of the
signatures to the mutation load of the tumor.
biocViews: Software, SNP, Sequencing, DNASeq, GenomicVariation,
SomaticMutation, BiomedicalInformatics, Genetics,
BiologicalQuestion, StatisticalMethod
Author: Rosario M. Piro [aut, cre], Sandra Krueger [ctb]
Maintainer: Rosario M. Piro <rmpiro@gmail.com>
URL: http://rmpiro.net/decompTumor2Sig/,
https://github.com/rmpiro/decompTumor2Sig
VignetteBuilder: knitr
BugReports: https://github.com/rmpiro/decompTumor2Sig/issues
git_url: https://git.bioconductor.org/packages/decompTumor2Sig
git_branch: RELEASE_3_13
git_last_commit: 974c2b7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/decompTumor2Sig_2.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/decompTumor2Sig_2.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/decompTumor2Sig_2.8.0.tgz
vignettes: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.html
vignetteTitles: A brief introduction to decompTumor2Sig
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.R
importsMe: musicatk
dependencyCount: 122
Package: DeconRNASeq
Version: 1.34.0
Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid
License: GPL-2
Archs: i386, x64
MD5sum: ed8ad80c671c11ddbbb05d3f6ca03dba
NeedsCompilation: no
Title: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data
Description: DeconSeq is an R package for deconvolution of
heterogeneous tissues based on mRNA-Seq data. It modeled
expression levels from heterogeneous cell populations in
mRNA-Seq as the weighted average of expression from different
constituting cell types and predicted cell type proportions of
single expression profiles.
biocViews: DifferentialExpression
Author: Ting Gong <tinggong@gmail.com> Joseph D. Szustakowski
<joseph.szustakowski@novartis.com>
Maintainer: Ting Gong <tinggong@gmail.com>
git_url: https://git.bioconductor.org/packages/DeconRNASeq
git_branch: RELEASE_3_13
git_last_commit: 69e542f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DeconRNASeq_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DeconRNASeq_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DeconRNASeq_1.34.0.tgz
vignettes: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf
vignetteTitles: DeconRNASeq Demo
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.R
suggestsMe: ADAPTS
dependencyCount: 46
Package: decontam
Version: 1.12.0
Depends: R (>= 3.4.1), methods (>= 3.4.1)
Imports: ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), stats
Suggests: BiocStyle, knitr, rmarkdown, phyloseq
License: Artistic-2.0
MD5sum: 32521a613d636569c1685f6234ee5f5d
NeedsCompilation: no
Title: Identify Contaminants in Marker-gene and Metagenomics Sequencing
Data
Description: Simple statistical identification of contaminating
sequence features in marker-gene or metagenomics data. Works on
any kind of feature derived from environmental sequencing data
(e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA
quantitation data or sequenced negative control samples.
biocViews: ImmunoOncology, Microbiome, Sequencing, Classification,
Metagenomics
Author: Benjamin Callahan [aut, cre], Nicole Marie Davis [aut], Felix
G.M. Ernst [ctb] (<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Benjamin Callahan <benjamin.j.callahan@gmail.com>
URL: https://github.com/benjjneb/decontam
VignetteBuilder: knitr
BugReports: https://github.com/benjjneb/decontam/issues
git_url: https://git.bioconductor.org/packages/decontam
git_branch: RELEASE_3_13
git_last_commit: 20b09b7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/decontam_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/decontam_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/decontam_1.12.0.tgz
vignettes: vignettes/decontam/inst/doc/decontam_intro.html
vignetteTitles: Introduction to dada2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/decontam/inst/doc/decontam_intro.R
importsMe: mia
dependencyCount: 44
Package: decoupleR
Version: 1.0.0
Depends: R (>= 4.0)
Imports: broom, dplyr, GSVA, magrittr, Matrix, purrr, rlang, speedglm,
stats, stringr, tibble, tidyr, tidyselect, viper, withr
Suggests: BiocStyle, covr, knitr, pkgdown, RefManageR, rmarkdown,
roxygen2, sessioninfo, testthat
License: GPL-3
MD5sum: 839daedb272bf0444bb11463bbe70330
NeedsCompilation: no
Title: Package to decouple gene sets from statistics
Description: Transcriptome profiling followed by differential gene
expression analysis often leads to lists of genes that are hard
to analyze and interpret. Downstream analysis tools can be used
to summarize deregulation events into a smaller set of
biologically interpretable features. In particular, methods
that estimate the activity of transcription factors (TFs) from
gene expression are commonly used. It has been shown that the
transcriptional targets of a TF yield a much more robust
estimation of the TF activity than observing the expression of
the TF itself. Consequently, for the estimation of
transcription factor activities, a network of transcriptional
regulation is required in combination with a statistical
algorithm that summarizes the expression of the target genes
into a single activity score. Over the years, many different
regulatory networks and statistical algorithms have been
developed, mostly in a fixed combination of one network and one
algorithm. To systematically evaluate both networks and
algorithms, we developed decoupleR , an R package that allows
users to apply efficiently any combination provided.
biocViews: DifferentialExpression, FunctionalGenomics, GeneExpression,
GeneRegulation, Network, Software, StatisticalMethod,
Transcription,
Author: Jesús Vélez [cre, aut]
(<https://orcid.org/0000-0001-5128-3838>), Christian H. Holland
[aut] (<https://orcid.org/0000-0002-3060-5786>)
Maintainer: Jesús Vélez <jvelezmagic@gmail.com>
URL: https://saezlab.github.io/decoupleR/
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/decoupleR/issues
git_url: https://git.bioconductor.org/packages/decoupleR
git_branch: RELEASE_3_13
git_last_commit: 2d54fd6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/decoupleR_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/decoupleR_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/decoupleR_1.0.0.tgz
vignettes: vignettes/decoupleR/inst/doc/decoupleR.html
vignetteTitles: Introduction to decoupleR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/decoupleR/inst/doc/decoupleR.R
dependencyCount: 109
Package: DeepBlueR
Version: 1.18.0
Depends: R (>= 3.3), XML, RCurl
Imports: GenomicRanges, data.table, stringr, diffr, dplyr, methods,
rjson, utils, R.utils, foreach, withr, rtracklayer,
GenomeInfoDb, settings, filehash
Suggests: knitr, rmarkdown, LOLA, Gviz, gplots, ggplot2, tidyr,
RColorBrewer, matrixStats
License: GPL (>=2.0)
MD5sum: 587ed5c34f8eef9523cc078a9d934ef1
NeedsCompilation: no
Title: DeepBlueR
Description: Accessing the DeepBlue Epigenetics Data Server through R.
biocViews: DataImport, DataRepresentation, ThirdPartyClient,
GeneRegulation, GenomeAnnotation, CpGIsland, DNAMethylation,
Epigenetics, Annotation, Preprocessing, ImmunoOncology
Author: Felipe Albrecht, Markus List
Maintainer: Felipe Albrecht <felipe.albrecht@mpi-inf.mpg.de>, Markus
List <markus.list@wzw.tum.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DeepBlueR
git_branch: RELEASE_3_13
git_last_commit: 96a5591
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DeepBlueR_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DeepBlueR_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DeepBlueR_1.18.0.tgz
vignettes: vignettes/DeepBlueR/inst/doc/DeepBlueR.html
vignetteTitles: The DeepBlue epigenomic data server - R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DeepBlueR/inst/doc/DeepBlueR.R
dependencyCount: 80
Package: DeepPINCS
Version: 1.0.2
Depends: keras, tensorflow, R (>= 4.1)
Imports: CatEncoders, matlab, rcdk, stringdist, tokenizers, webchem,
purrr, ttgsea, PRROC, reticulate, stats
Suggests: knitr, testthat, rmarkdown
License: Artistic-2.0
MD5sum: f90cccb077ff206d4241f219fb60d531
NeedsCompilation: no
Title: Protein Interactions and Networks with Compounds based on
Sequences using Deep Learning
Description: The identification of novel compound-protein interaction
(CPI) is important in drug discovery. Revealing unknown
compound-protein interactions is useful to design a new drug
for a target protein by screening candidate compounds. The
accurate CPI prediction assists in effective drug discovery
process. To identify potential CPI effectively, prediction
methods based on machine learning and deep learning have been
developed. Data for sequences are provided as discrete symbolic
data. In the data, compounds are represented as SMILES
(simplified molecular-input line-entry system) strings and
proteins are sequences in which the characters are amino acids.
The outcome is defined as a variable that indicates how strong
two molecules interact with each other or whether there is an
interaction between them. In this package, a deep-learning
based model that takes only sequence information of both
compounds and proteins as input and the outcome as output is
used to predict CPI. The model is implemented by using compound
and protein encoders with useful features. The CPI model also
supports other modeling tasks, including protein-protein
interaction (PPI), chemical-chemical interaction (CCI), or
single compounds and proteins. Although the model is designed
for proteins, DNA and RNA can be used if they are represented
as sequences.
biocViews: Software, Network, GraphAndNetwork, NeuralNetwork
Author: Dongmin Jung [cre, aut]
(<https://orcid.org/0000-0001-7499-8422>)
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DeepPINCS
git_branch: RELEASE_3_13
git_last_commit: 71f9817
git_last_commit_date: 2021-08-27
Date/Publication: 2021-08-29
source.ver: src/contrib/DeepPINCS_1.0.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DeepPINCS_1.0.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/DeepPINCS_1.0.2.tgz
vignettes: vignettes/DeepPINCS/inst/doc/DeepPINCS.html
vignetteTitles: DeepPINCS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DeepPINCS/inst/doc/DeepPINCS.R
dependencyCount: 143
Package: deepSNV
Version: 1.38.0
Depends: R (>= 2.13.0), methods, graphics, parallel, IRanges,
GenomicRanges, SummarizedExperiment, Biostrings, VGAM,
VariantAnnotation (>= 1.13.44),
Imports: Rhtslib
LinkingTo: Rhtslib (>= 1.13.1)
Suggests: RColorBrewer, knitr, rmarkdown
License: GPL-3
MD5sum: dcf6cfd4d1808e6913f51624d502ba01
NeedsCompilation: yes
Title: Detection of subclonal SNVs in deep sequencing data.
Description: This package provides provides quantitative variant
callers for detecting subclonal mutations in ultra-deep (>=100x
coverage) sequencing experiments. The deepSNV algorithm is used
for a comparative setup with a control experiment of the same
loci and uses a beta-binomial model and a likelihood ratio test
to discriminate sequencing errors and subclonal SNVs. The
shearwater algorithm computes a Bayes classifier based on a
beta-binomial model for variant calling with multiple samples
for precisely estimating model parameters - such as local error
rates and dispersion - and prior knowledge, e.g. from variation
data bases such as COSMIC.
biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport
Author: Niko Beerenwinkel [ths], Raul Alcantara [ctb], David Jones
[ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre]
Maintainer: Moritz Gerstung <moritz.gerstung@ebi.ac.uk>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/deepSNV
git_branch: RELEASE_3_13
git_last_commit: c542997
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/deepSNV_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/deepSNV_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/deepSNV_1.38.0.tgz
vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf,
vignettes/deepSNV/inst/doc/shearwater.pdf,
vignettes/deepSNV/inst/doc/shearwaterML.html
vignetteTitles: An R package for detecting low frequency variants in
deep sequencing experiments, Subclonal variant calling with
multiple samples and prior knowledge using shearwater,
Shearwater ML
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R,
vignettes/deepSNV/inst/doc/shearwater.R,
vignettes/deepSNV/inst/doc/shearwaterML.R
suggestsMe: GenomicFiles
dependencyCount: 100
Package: DEFormats
Version: 1.20.0
Imports: checkmate, data.table, DESeq2, edgeR (>= 3.13.4),
GenomicRanges, methods, S4Vectors, stats, SummarizedExperiment
Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat
License: GPL-3
MD5sum: d71a0023d81a9e2419629b0822d91341
NeedsCompilation: no
Title: Differential gene expression data formats converter
Description: Convert between different data formats used by
differential gene expression analysis tools.
biocViews: ImmunoOncology, DifferentialExpression, GeneExpression,
RNASeq, Sequencing, Transcription
Author: Andrzej OleÅ›
Maintainer: Andrzej OleÅ› <andrzej.oles@embl.de>
URL: https://github.com/aoles/DEFormats
VignetteBuilder: knitr
BugReports: https://github.com/aoles/DEFormats/issues
git_url: https://git.bioconductor.org/packages/DEFormats
git_branch: RELEASE_3_13
git_last_commit: ae1b30e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEFormats_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DEFormats_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DEFormats_1.20.0.tgz
vignettes: vignettes/DEFormats/inst/doc/DEFormats.html
vignetteTitles: Differential gene expression data formats converter
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R
importsMe: regionReport
suggestsMe: ideal
dependencyCount: 98
Package: DegNorm
Version: 1.2.0
Depends: R (>= 4.0.0), methods
Imports: Rcpp (>= 1.0.2),GenomicFeatures, parallel, foreach, S4Vectors,
doParallel, Rsamtools (>= 1.31.2), GenomicAlignments,
heatmaply, data.table, stats, ggplot2, GenomicRanges, IRanges,
plyr, plotly, utils,viridis
LinkingTo: Rcpp, RcppArmadillo,S4Vectors,IRanges
Suggests: knitr,rmarkdown,formatR
License: LGPL (>= 3)
Archs: i386, x64
MD5sum: aa4d4eda6d86fcf9b6b05689891a8ca5
NeedsCompilation: yes
Title: DegNorm: degradation normalization for RNA-seq data
Description: This package performs degradation normalization in bulk
RNA-seq data to improve differential expression analysis
accuracy.
biocViews: RNASeq, Normalization, GeneExpression, Alignment,Coverage,
DifferentialExpression, BatchEffect,Software,Sequencing,
ImmunoOncology, QualityControl, DataImport
Author: Bin Xiong and Ji-Ping Wang
Maintainer: Ji-Ping Wang <jzwang@northwestern.edu>
VignetteBuilder: knitr
BugReports: https://github.com/jipingw/DegNorm/issues
git_url: https://git.bioconductor.org/packages/DegNorm
git_branch: RELEASE_3_13
git_last_commit: 4194859
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DegNorm_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DegNorm_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DegNorm_1.2.0.tgz
vignettes: vignettes/DegNorm/inst/doc/DegNorm.html
vignetteTitles: DegNorm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DegNorm/inst/doc/DegNorm.R
dependencyCount: 144
Package: DEGraph
Version: 1.44.0
Depends: R (>= 2.10.0), R.utils
Imports: graph, KEGGgraph, lattice, mvtnorm, R.methodsS3, RBGL,
Rgraphviz, rrcov, NCIgraph
Suggests: corpcor, fields, graph, KEGGgraph, lattice, marray, RBGL,
rrcov, Rgraphviz, NCIgraph
License: GPL-3
MD5sum: d76bef855a9f02c439417e67c48989be
NeedsCompilation: no
Title: Two-sample tests on a graph
Description: DEGraph implements recent hypothesis testing methods which
directly assess whether a particular gene network is
differentially expressed between two conditions. This is to be
contrasted with the more classical two-step approaches which
first test individual genes, then test gene sets for enrichment
in differentially expressed genes. These recent methods take
into account the topology of the network to yield more powerful
detection procedures. DEGraph provides methods to easily test
all KEGG pathways for differential expression on any gene
expression data set and tools to visualize the results.
biocViews: Microarray, DifferentialExpression, GraphAndNetwork,
Network, NetworkEnrichment, DecisionTree
Author: Laurent Jacob, Pierre Neuvial and Sandrine Dudoit
Maintainer: Laurent Jacob <laurent.jacob@gmail.com>
git_url: https://git.bioconductor.org/packages/DEGraph
git_branch: RELEASE_3_13
git_last_commit: 49e7998
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEGraph_1.44.0.tar.gz
vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf
vignetteTitles: DEGraph: differential expression testing for gene
networks
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEGraph/inst/doc/DEGraph.R
dependencyCount: 75
Package: DEGreport
Version: 1.28.0
Depends: R (>= 3.6.0)
Imports: utils, methods, Biobase, BiocGenerics, broom, circlize,
ComplexHeatmap, cowplot, ConsensusClusterPlus, cluster, DESeq2,
dplyr, edgeR, ggplot2, ggdendro, grid, ggrepel, grDevices,
knitr, logging, lasso2, magrittr, Nozzle.R1, psych,
RColorBrewer, reshape, rlang, scales, stats, stringr,
S4Vectors, SummarizedExperiment, tidyr, tibble
Suggests: BiocStyle, AnnotationDbi, limma, pheatmap, rmarkdown,
statmod, testthat
License: MIT + file LICENSE
MD5sum: 83052fb100579c58c49429a5dfbbb9ab
NeedsCompilation: no
Title: Report of DEG analysis
Description: Creation of a HTML report of differential expression
analyses of count data. It integrates some of the code
mentioned in DESeq2 and edgeR vignettes, and report a ranked
list of genes according to the fold changes mean and
variability for each selected gene.
biocViews: DifferentialExpression, Visualization, RNASeq,
ReportWriting, GeneExpression, ImmunoOncology
Author: Lorena Pantano [aut, cre], John Hutchinson [ctb], Victor
Barrera [ctb], Mary Piper [ctb], Radhika Khetani [ctb], Kenneth
Daily [ctb], Thanneer Malai Perumal [ctb], Rory Kirchner [ctb],
Michael Steinbaugh [ctb]
Maintainer: Lorena Pantano <lorena.pantano@gmail.com>
URL: http://lpantano.github.io/DEGreport/
VignetteBuilder: knitr
BugReports: https://github.com/lpantano/DEGreport/issues
git_url: https://git.bioconductor.org/packages/DEGreport
git_branch: RELEASE_3_13
git_last_commit: c9d2eef
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEGreport_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DEGreport_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DEGreport_1.28.0.tgz
vignettes: vignettes/DEGreport/inst/doc/DEGreport.html
vignetteTitles: QC and downstream analysis for differential expression
RNA-seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R
importsMe: isomiRs
dependencyCount: 136
Package: DEGseq
Version: 1.46.0
Depends: R (>= 2.8.0), qvalue, methods
Imports: graphics, grDevices, methods, stats, utils
License: LGPL (>=2)
Archs: i386, x64
MD5sum: 2531f9d60f03a21f66375e52f739ae85
NeedsCompilation: yes
Title: Identify Differentially Expressed Genes from RNA-seq data
Description: DEGseq is an R package to identify differentially
expressed genes from RNA-Seq data.
biocViews: RNASeq, Preprocessing, GeneExpression,
DifferentialExpression, ImmunoOncology
Author: Likun Wang <wanglk@hsc.pku.edu.cn> and Xi Wang
<wang-xi05@mails.tsinghua.edu.cn>.
Maintainer: Likun Wang <wanglk@hsc.pku.edu.cn>
git_url: https://git.bioconductor.org/packages/DEGseq
git_branch: RELEASE_3_13
git_last_commit: f56cc1c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEGseq_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DEGseq_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DEGseq_1.46.0.tgz
vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf
vignetteTitles: DEGseq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEGseq/inst/doc/DEGseq.R
dependencyCount: 45
Package: DelayedArray
Version: 0.18.0
Depends: R (>= 4.0.0), methods, stats4, Matrix, BiocGenerics (>=
0.37.0), MatrixGenerics (>= 1.1.3), S4Vectors (>= 0.27.2),
IRanges (>= 2.17.3)
Imports: stats
LinkingTo: S4Vectors
Suggests: BiocParallel, HDF5Array (>= 1.17.12), genefilter,
SummarizedExperiment, airway, lobstr, DelayedMatrixStats,
knitr, rmarkdown, BiocStyle, RUnit
License: Artistic-2.0
MD5sum: 51b4b685e8d0962a1cc3a66775630543
NeedsCompilation: yes
Title: A unified framework for working transparently with on-disk and
in-memory array-like datasets
Description: Wrapping an array-like object (typically an on-disk
object) in a DelayedArray object allows one to perform common
array operations on it without loading the object in memory. In
order to reduce memory usage and optimize performance,
operations on the object are either delayed or executed using a
block processing mechanism. Note that this also works on
in-memory array-like objects like DataFrame objects (typically
with Rle columns), Matrix objects, ordinary arrays and, data
frames.
biocViews: Infrastructure, DataRepresentation, Annotation,
GenomeAnnotation
Author: Hervé Pagès <hpages.on.github@gmail.com>, with contributions
from Peter Hickey <peter.hickey@gmail.com> and Aaron Lun
<infinite.monkeys.with.keyboards@gmail.com>
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/DelayedArray
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/DelayedArray/issues
git_url: https://git.bioconductor.org/packages/DelayedArray
git_branch: RELEASE_3_13
git_last_commit: 4e18a4f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DelayedArray_0.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DelayedArray_0.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DelayedArray_0.18.0.tgz
vignettes:
vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.pdf,
vignettes/DelayedArray/inst/doc/03-DelayedArray_HDF5Array_update.pdf,
vignettes/DelayedArray/inst/doc/02-Implementing_a_backend.html
vignetteTitles: Working with large arrays in R, DelayedArray /
HDF5Array update, Implementing A DelayedArray Backend
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.R,
vignettes/DelayedArray/inst/doc/03-DelayedArray_HDF5Array_update.R
dependsOnMe: DelayedDataFrame, DelayedMatrixStats, DelayedRandomArray,
GDSArray, HDF5Array, rhdf5client, SCArray, singleCellTK,
TileDBArray, VCFArray
importsMe: batchelor, beachmat, bigPint, BiocSingular, bsseq, CAGEr,
celaref, celda, ChromSCape, clusterExperiment, compartmap,
cytomapper, DEScan2, DropletUtils, DSS, ELMER, flowWorkspace,
FRASER, GenoGAM, GenomicScores, glmGamPoi, GSVA, hipathia,
LoomExperiment, mbkmeans, MethReg, methrix, methylSig, mia,
miaViz, minfi, MOFA2, mumosa, netSmooth, NewWave, PCAtools,
ResidualMatrix, RTCGAToolbox, ScaledMatrix, scater,
scDblFinder, scMerge, scmeth, scPCA, scran, scry, scuttle,
signatureSearch, SingleCellExperiment, SingleR,
SummarizedExperiment, TSCAN, VariantExperiment, velociraptor,
weitrix, zellkonverter, celldex, imcdatasets, scDiffCom
suggestsMe: BiocGenerics, ChIPpeakAnno, CNVgears, gwascat, iSEE, MAST,
S4Vectors, satuRn, SQLDataFrame, TrajectoryUtils,
digitalDLSorteR
dependencyCount: 15
Package: DelayedDataFrame
Version: 1.8.0
Depends: R (>= 3.6), S4Vectors (>= 0.23.19), DelayedArray (>= 0.7.5)
Imports: methods, stats, BiocGenerics
Suggests: testthat, knitr, rmarkdown, SeqArray, GDSArray
License: GPL-3
Archs: i386, x64
MD5sum: 32fad8ed32a58b573d97ebd5367d6e0f
NeedsCompilation: no
Title: Delayed operation on DataFrame using standard DataFrame metaphor
Description: Based on the standard DataFrame metaphor, we are trying to
implement the feature of delayed operation on the
DelayedDataFrame, with a slot of lazyIndex, which saves the
mapping indexes for each column of DelayedDataFrame. Methods
like show, validity check, [/[[ subsetting, rbind/cbind are
implemented for DelayedDataFrame to be operated around
lazyIndex. The listData slot stays untouched until a
realization call e.g., DataFrame constructor OR as.list() is
invoked.
biocViews: Infrastructure, DataRepresentation
Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut]
Maintainer: Qian Liu <Qian.Liu@roswellpark.org>
URL: https://github.com/Bioconductor/DelayedDataFrame
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/DelayedDataFrame/issues
git_url: https://git.bioconductor.org/packages/DelayedDataFrame
git_branch: RELEASE_3_13
git_last_commit: df21f97
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DelayedDataFrame_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DelayedDataFrame_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DelayedDataFrame_1.8.0.tgz
vignettes: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.html
vignetteTitles: DelayedDataFrame
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.R
dependsOnMe: VariantExperiment
dependencyCount: 16
Package: DelayedMatrixStats
Version: 1.14.3
Depends: MatrixGenerics (>= 1.4.3), DelayedArray (>= 0.17.6)
Imports: methods, matrixStats (>= 0.60.0), sparseMatrixStats, Matrix,
S4Vectors (>= 0.17.5), IRanges (>= 2.25.10)
Suggests: testthat, knitr, rmarkdown, covr, BiocStyle, microbenchmark,
profmem, HDF5Array
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: c6992f7cb6c853595e18e9328ec863a9
NeedsCompilation: no
Title: Functions that Apply to Rows and Columns of 'DelayedMatrix'
Objects
Description: A port of the 'matrixStats' API for use with DelayedMatrix
objects from the 'DelayedArray' package. High-performing
functions operating on rows and columns of DelayedMatrix
objects, e.g. col / rowMedians(), col / rowRanks(), and col /
rowSds(). Functions optimized per data type and for subsetted
calculations such that both memory usage and processing time is
minimized.
biocViews: Infrastructure, DataRepresentation, Software
Author: Peter Hickey [aut, cre], Hervé Pagès [ctb], Aaron Lun [ctb]
Maintainer: Peter Hickey <peter.hickey@gmail.com>
URL: https://github.com/PeteHaitch/DelayedMatrixStats
VignetteBuilder: knitr
BugReports: https://github.com/PeteHaitch/DelayedMatrixStats/issues
git_url: https://git.bioconductor.org/packages/DelayedMatrixStats
git_branch: RELEASE_3_13
git_last_commit: b8bf535
git_last_commit_date: 2021-08-25
Date/Publication: 2021-08-26
source.ver: src/contrib/DelayedMatrixStats_1.14.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DelayedMatrixStats_1.14.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/DelayedMatrixStats_1.14.3.tgz
vignettes:
vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.html
vignetteTitles: Overview of DelayedMatrixStats
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.R
importsMe: batchelor, biscuiteer, bsseq, compartmap, dmrseq,
DropletUtils, FRASER, glmGamPoi, GSVA, methrix, methylSig, mia,
minfi, mumosa, PCAtools, scater, scMerge, scran, scuttle,
singleCellTK, SingleR, weitrix, celldex
suggestsMe: DelayedArray, MatrixGenerics, mbkmeans, SCArray, scPCA,
TrajectoryUtils, digitalDLSorteR
dependencyCount: 18
Package: DelayedRandomArray
Version: 1.0.0
Depends: DelayedArray
Imports: methods, dqrng, Rcpp
LinkingTo: dqrng, BH, Rcpp
Suggests: testthat, knitr, BiocStyle, rmarkdown, Matrix
License: GPL-3
MD5sum: fc90d4c433ee1a7efa0a101576edaccb
NeedsCompilation: yes
Title: Delayed Arrays of Random Values
Description: Implements a DelayedArray of random values where the
realization of the sampled values is delayed until they are
needed. Reproducible sampling within any subarray is achieved
by chunking where each chunk is initialized with a different
random seed and stream. The usual distributions in the stats
package are supported, along with scalar, vector and arrays for
the parameters.
biocViews: DataRepresentation
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/DelayedRandomArray
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/DelayedRandomArray/issues
git_url: https://git.bioconductor.org/packages/DelayedRandomArray
git_branch: RELEASE_3_13
git_last_commit: 03b918c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DelayedRandomArray_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DelayedRandomArray_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DelayedRandomArray_1.0.0.tgz
vignettes: vignettes/DelayedRandomArray/inst/doc/userguide.html
vignetteTitles: User's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DelayedRandomArray/inst/doc/userguide.R
dependencyCount: 20
Package: deltaCaptureC
Version: 1.6.0
Depends: R (>= 3.6)
Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2
Suggests: knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 66138e764239603c681749485d0b7462
NeedsCompilation: no
Title: This Package Discovers Meso-scale Chromatin Remodeling from 3C
Data
Description: This package discovers meso-scale chromatin remodelling
from 3C data. 3C data is local in nature. It givens
interaction counts between restriction enzyme digestion
fragments and a preferred 'viewpoint' region. By binning this
data and using permutation testing, this package can test
whether there are statistically significant changes in the
interaction counts between the data from two cell types or two
treatments.
biocViews: BiologicalQuestion, StatisticalMethod
Author: Michael Shapiro [aut, cre]
(<https://orcid.org/0000-0002-2769-9320>)
Maintainer: Michael Shapiro <sifka@earthlink.net>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/deltaCaptureC
git_branch: RELEASE_3_13
git_last_commit: 1bc5aff
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/deltaCaptureC_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/deltaCaptureC_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/deltaCaptureC_1.6.0.tgz
vignettes: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.html
vignetteTitles: Delta Capture-C
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.R
dependencyCount: 93
Package: deltaGseg
Version: 1.32.0
Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh,
tseries, pvclust, fBasics, grid, reshape, scales
Suggests: knitr
License: GPL-2
Archs: i386, x64
MD5sum: 351c37b10d87a51091b2ee07772c707a
NeedsCompilation: no
Title: deltaGseg
Description: Identifying distinct subpopulations through multiscale
time series analysis
biocViews: Proteomics, TimeCourse, Visualization, Clustering
Author: Diana Low, Efthymios Motakis
Maintainer: Diana Low <lowdiana@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/deltaGseg
git_branch: RELEASE_3_13
git_last_commit: 59cd572
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/deltaGseg_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/deltaGseg_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/deltaGseg_1.32.0.tgz
vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf
vignetteTitles: deltaGseg
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/deltaGseg/inst/doc/deltaGseg.R
dependencyCount: 57
Package: DeMAND
Version: 1.22.0
Depends: R (>= 2.14.0), KernSmooth, methods
License: file LICENSE
MD5sum: e809281ffb7e06d6d818cc84702b9e1e
NeedsCompilation: no
Title: DeMAND
Description: DEMAND predicts Drug MoA by interrogating a cell context
specific regulatory network with a small number (N >= 6) of
compound-induced gene expression signatures, to elucidate
specific proteins whose interactions in the network is
dysregulated by the compound.
biocViews: SystemsBiology, NetworkEnrichment, GeneExpression,
StatisticalMethod, Network
Author: Jung Hoon Woo <jw2853@columbia.edu>, Yishai Shimoni
<ys2559@columbia.edu>
Maintainer: Jung Hoon Woo <jw2853@columbia.edu>, Mariano Alvarez
<reef103@gmail.com>
git_url: https://git.bioconductor.org/packages/DeMAND
git_branch: RELEASE_3_13
git_last_commit: 3803d5d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DeMAND_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DeMAND_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DeMAND_1.22.0.tgz
vignettes: vignettes/DeMAND/inst/doc/DeMAND.pdf
vignetteTitles: Using DeMAND
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DeMAND/inst/doc/DeMAND.R
dependencyCount: 3
Package: DeMixT
Version: 1.8.0
Depends: R (>= 3.6.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment,
knitr, KernSmooth, matrixcalc
Imports: matrixStats, stats, truncdist, base64enc, ggplot2
LinkingTo: Rcpp
License: GPL-3
MD5sum: f714922ad87c7dbb85966167eb6c1e6d
NeedsCompilation: yes
Title: Cell type-specific deconvolution of heterogeneous tumor samples
with two or three components using expression data from RNAseq
or microarray platforms
Description: DeMixT is a software package that performs deconvolution
on transcriptome data from a mixture of two or three
components.
biocViews: Software, StatisticalMethod, Classification, GeneExpression,
Sequencing, Microarray, TissueMicroarray, Coverage
Author: Zeya Wang <zw17.rice@gmail.com>, Shaolong
Cao<scao@mdanderson.org>, Wenyi Wang <wwang7@@mdanderson.org>
Maintainer: Shaolong Cao<scao@mdanderson.org>, Peng Yang
<pyang7@mdanderson.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DeMixT
git_branch: RELEASE_3_13
git_last_commit: bea3935
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DeMixT_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DeMixT_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DeMixT_1.8.0.tgz
vignettes: vignettes/DeMixT/inst/doc/demixt.html
vignetteTitles: DeMixT.Rmd
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DeMixT/inst/doc/demixt.R
dependencyCount: 69
Package: densvis
Version: 1.2.0
Imports: Rcpp, basilisk, assertthat, reticulate
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, ggplot2, Rtsne, uwot, testthat
License: MIT + file LICENSE
MD5sum: c235d7bc954577dc1ea12fa7148ba14f
NeedsCompilation: yes
Title: Density-Preserving Data Visualization via Non-Linear
Dimensionality Reduction
Description: Implements the density-preserving modification to t-SNE
and UMAP described by Narayan et al. (2020)
<doi:10.1101/2020.05.12.077776>. The non-linear dimensionality
reduction techniques t-SNE and UMAP enable users to summarise
complex high-dimensional sequencing data such as single cell
RNAseq using lower dimensional representations. These lower
dimensional representations enable the visualisation of
discrete transcriptional states, as well as continuous
trajectory (for example, in early development). However, these
methods focus on the local neighbourhood structure of the data.
In some cases, this results in misleading visualisations, where
the density of cells in the low-dimensional embedding does not
represent the transcriptional heterogeneity of data in the
original high-dimensional space. den-SNE and densMAP aim to
enable more accurate visual interpretation of high-dimensional
datasets by producing lower-dimensional embeddings that
accurately represent the heterogeneity of the original
high-dimensional space, enabling the identification of
homogeneous and heterogeneous cell states. This accuracy is
accomplished by including in the optimisation process a term
which considers the local density of points in the original
high-dimensional space. This can help to create visualisations
that are more representative of heterogeneity in the original
high-dimensional space.
biocViews: DimensionReduction, Visualization, Software, SingleCell,
Sequencing
Author: Alan O'Callaghan [aut, cre], Ashwinn Narayan [aut], Hyunghoon
Cho [aut]
Maintainer: Alan O'Callaghan <alan.ocallaghan@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/Alanocallaghan/densvis/issues
git_url: https://git.bioconductor.org/packages/densvis
git_branch: RELEASE_3_13
git_last_commit: ceda85d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/densvis_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/densvis_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/densvis_1.2.0.tgz
vignettes: vignettes/densvis/inst/doc/densvis.html
vignetteTitles: Introduction to densvis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/densvis/inst/doc/densvis.R
dependsOnMe: OSCA.advanced
dependencyCount: 23
Package: DEP
Version: 1.14.0
Depends: R (>= 3.5)
Imports: ggplot2, dplyr, purrr, readr, tibble, tidyr,
SummarizedExperiment (>= 1.11.5), MSnbase, limma, vsn, fdrtool,
ggrepel, ComplexHeatmap, RColorBrewer, circlize, shiny,
shinydashboard, DT, rmarkdown, assertthat, gridExtra, grid,
stats, imputeLCMD, cluster
Suggests: testthat, enrichR, knitr, BiocStyle
License: Artistic-2.0
Archs: i386, x64
MD5sum: 1fdb7a402180c74fd246ca7b9c5ab1a4
NeedsCompilation: no
Title: Differential Enrichment analysis of Proteomics data
Description: This package provides an integrated analysis workflow for
robust and reproducible analysis of mass spectrometry
proteomics data for differential protein expression or
differential enrichment. It requires tabular input (e.g. txt
files) as generated by quantitative analysis softwares of raw
mass spectrometry data, such as MaxQuant or IsobarQuant.
Functions are provided for data preparation, filtering,
variance normalization and imputation of missing values, as
well as statistical testing of differentially enriched /
expressed proteins. It also includes tools to check
intermediate steps in the workflow, such as normalization and
missing values imputation. Finally, visualization tools are
provided to explore the results, including heatmap, volcano
plot and barplot representations. For scientists with limited
experience in R, the package also contains wrapper functions
that entail the complete analysis workflow and generate a
report. Even easier to use are the interactive Shiny apps that
are provided by the package.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry,
DifferentialExpression, DataRepresentation
Author: Arne Smits [cre, aut], Wolfgang Huber [aut]
Maintainer: Arne Smits <smits.arne@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DEP
git_branch: RELEASE_3_13
git_last_commit: 200efdf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEP_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DEP_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DEP_1.14.0.tgz
vignettes: vignettes/DEP/inst/doc/DEP.html,
vignettes/DEP/inst/doc/MissingValues.html
vignetteTitles: DEP: Introduction, DEP: Missing value handling
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEP/inst/doc/DEP.R,
vignettes/DEP/inst/doc/MissingValues.R
suggestsMe: proDA, RforProteomics
dependencyCount: 155
Package: DepecheR
Version: 1.8.0
Depends: R (>= 4.0)
Imports: ggplot2 (>= 3.1.0), MASS (>= 7.3.51), Rcpp (>= 1.0.0), dplyr
(>= 0.7.8), gplots (>= 3.0.1), viridis (>= 0.5.1), foreach (>=
1.4.4), doSNOW (>= 1.0.16), matrixStats (>= 0.54.0), mixOmics
(>= 6.6.1), moments (>= 0.14), grDevices (>= 3.5.2), graphics
(>= 3.5.2), stats (>= 3.5.2), utils (>= 3.5), methods (>= 3.5),
parallel (>= 3.5.2), reshape2 (>= 1.4.3), beanplot (>= 1.2),
FNN (>= 1.1.3), robustbase (>= 0.93.5), gmodels (>= 2.18.1)
LinkingTo: Rcpp, RcppEigen
Suggests: uwot, reshape2, testthat, knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
Archs: x64
MD5sum: 9724b43faa1db33fd28efb1eb86db16f
NeedsCompilation: yes
Title: Determination of essential phenotypic elements of clusters in
high-dimensional entities
Description: The purpose of this package is to identify traits in a
dataset that can separate groups. This is done on two levels.
First, clustering is performed, using an implementation of
sparse K-means. Secondly, the generated clusters are used to
predict outcomes of groups of individuals based on their
distribution of observations in the different clusters. As
certain clusters with separating information will be
identified, and these clusters are defined by a sparse number
of variables, this method can reduce the complexity of data, to
only emphasize the data that actually matters.
biocViews:
Software,CellBasedAssays,Transcription,DifferentialExpression,
DataRepresentation,ImmunoOncology,Transcriptomics,Classification,Clustering,
DimensionReduction,FeatureExtraction,FlowCytometry,RNASeq,SingleCell,
Visualization
Author: Jakob Theorell [aut, cre], Axel Theorell [aut]
Maintainer: Jakob Theorell <jakob.theorell@ndcn.ox.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DepecheR
git_branch: RELEASE_3_13
git_last_commit: 7eee7d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DepecheR_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DepecheR_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DepecheR_1.8.0.tgz
vignettes: vignettes/DepecheR/inst/doc/DepecheR_test.html,
vignettes/DepecheR/inst/doc/GroupProbPlot_usage.html
vignetteTitles: Example of a cytometry data analysis with DepecheR,
Using the groupProbPlot plot function for single-cell
probability display
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DepecheR/inst/doc/DepecheR_test.R,
vignettes/DepecheR/inst/doc/GroupProbPlot_usage.R
suggestsMe: flowSpecs
dependencyCount: 85
Package: DEqMS
Version: 1.10.0
Depends: R(>= 3.5),graphics,stats,ggplot2,limma(>= 3.34)
Suggests:
BiocStyle,knitr,rmarkdown,plyr,matrixStats,reshape2,farms,utils,ggrepel,ExperimentHub,LSD
License: LGPL
MD5sum: 9b3d664a2c8c3d8223f8bd8b14ce8258
NeedsCompilation: no
Title: a tool to perform statistical analysis of differential protein
expression for quantitative proteomics data.
Description: DEqMS is developped on top of Limma. However, Limma
assumes same prior variance for all genes. In proteomics, the
accuracy of protein abundance estimates varies by the number of
peptides/PSMs quantified in both label-free and labelled data.
Proteins quantification by multiple peptides or PSMs are more
accurate. DEqMS package is able to estimate different prior
variances for proteins quantified by different number of
PSMs/peptides, therefore acchieving better accuracy. The
package can be applied to analyze both label-free and labelled
proteomics data.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Preprocessing,
DifferentialExpression,
MultipleComparison,Normalization,Bayesian
Author: Yafeng Zhu
Maintainer: Yafeng Zhu <yafeng.zhu@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/yafeng/DEqMS/issues
git_url: https://git.bioconductor.org/packages/DEqMS
git_branch: RELEASE_3_13
git_last_commit: fde4809
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEqMS_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DEqMS_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DEqMS_1.10.0.tgz
vignettes: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.html
vignetteTitles: DEqMS R Markdown vignettes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.R
dependencyCount: 39
Package: derfinder
Version: 1.26.0
Depends: R (>= 3.5.0)
Imports: BiocGenerics (>= 0.25.1), AnnotationDbi (>= 1.27.9),
BiocParallel (>= 1.15.15), bumphunter (>= 1.9.2),
derfinderHelper (>= 1.1.0), GenomeInfoDb (>= 1.3.3),
GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges
(>= 1.17.40), Hmisc, IRanges (>= 2.3.23), methods, qvalue (>=
1.99.0), Rsamtools (>= 1.25.0), rtracklayer, S4Vectors (>=
0.23.19), stats, utils
Suggests: BiocStyle (>= 2.5.19), sessioninfo, derfinderData (>=
0.99.0), derfinderPlot, DESeq2, ggplot2, knitr (>= 1.6), limma,
RefManageR, rmarkdown (>= 0.3.3), testthat (>= 2.1.0),
TxDb.Hsapiens.UCSC.hg19.knownGene, covr
License: Artistic-2.0
Archs: i386, x64
MD5sum: 8031467f2ec2ab0750d84289dfa8fa5d
NeedsCompilation: no
Title: Annotation-agnostic differential expression analysis of RNA-seq
data at base-pair resolution via the DER Finder approach
Description: This package provides functions for annotation-agnostic
differential expression analysis of RNA-seq data. Two
implementations of the DER Finder approach are included in this
package: (1) single base-level F-statistics and (2) DER
identification at the expressed regions-level. The DER Finder
approach can also be used to identify differentially bounded
ChIP-seq peaks.
biocViews: DifferentialExpression, Sequencing, RNASeq, ChIPSeq,
DifferentialPeakCalling, Software, ImmunoOncology, Coverage
Author: Leonardo Collado-Torres [aut, cre]
(<https://orcid.org/0000-0003-2140-308X>), Alyssa C. Frazee
[ctb], Andrew E. Jaffe [aut]
(<https://orcid.org/0000-0001-6886-1454>), Jeffrey T. Leek
[aut, ths] (<https://orcid.org/0000-0002-2873-2671>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/lcolladotor/derfinder
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/derfinder/
git_url: https://git.bioconductor.org/packages/derfinder
git_branch: RELEASE_3_13
git_last_commit: 7fc343a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/derfinder_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/derfinder_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/derfinder_1.26.0.tgz
vignettes: vignettes/derfinder/inst/doc/derfinder-quickstart.html,
vignettes/derfinder/inst/doc/derfinder-users-guide.html
vignetteTitles: derfinder quick start guide, derfinder users guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R,
vignettes/derfinder/inst/doc/derfinder-users-guide.R
importsMe: brainflowprobes, derfinderPlot, recount, regionReport,
GenomicState, recountWorkflow
suggestsMe: megadepth
dependencyCount: 148
Package: derfinderHelper
Version: 1.26.0
Depends: R(>= 3.2.2)
Imports: IRanges (>= 1.99.27), Matrix, methods, S4Vectors (>= 0.2.2)
Suggests: sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19),
RefManageR, rmarkdown (>= 0.3.3), testthat, covr
License: Artistic-2.0
MD5sum: f1256989686695066e60f1e89abaaf74
NeedsCompilation: no
Title: derfinder helper package
Description: Helper package for speeding up the derfinder package when
using multiple cores.
biocViews: DifferentialExpression, Sequencing, RNASeq, Software,
ImmunoOncology
Author: Leonardo Collado-Torres [aut, cre]
(<https://orcid.org/0000-0003-2140-308X>), Andrew E. Jaffe
[aut] (<https://orcid.org/0000-0001-6886-1454>), Jeffrey T.
Leek [aut, ths] (<https://orcid.org/0000-0002-2873-2671>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/leekgroup/derfinderHelper
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/derfinderHelper
git_url: https://git.bioconductor.org/packages/derfinderHelper
git_branch: RELEASE_3_13
git_last_commit: 3e8d7b1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/derfinderHelper_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/derfinderHelper_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/derfinderHelper_1.26.0.tgz
vignettes: vignettes/derfinderHelper/inst/doc/derfinderHelper.html
vignetteTitles: Introduction to derfinderHelper
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R
importsMe: derfinder
dependencyCount: 13
Package: derfinderPlot
Version: 1.26.0
Depends: R(>= 3.2)
Imports: derfinder (>= 1.1.0), GenomeInfoDb (>= 1.3.3),
GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>=
1.13.13), ggplot2, graphics, grDevices, IRanges (>= 1.99.28),
limma, methods, plyr, RColorBrewer, reshape2, S4Vectors (>=
0.9.38), scales, utils
Suggests: biovizBase (>= 1.27.2), bumphunter (>= 1.7.6), derfinderData
(>= 0.99.0), sessioninfo, knitr (>= 1.6), BiocStyle (>=
2.5.19), org.Hs.eg.db, RefManageR, rmarkdown (>= 0.3.3),
testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, covr
License: Artistic-2.0
MD5sum: 1b1bc007e7706f5e15705f83348f71f3
NeedsCompilation: no
Title: Plotting functions for derfinder
Description: This package provides plotting functions for results from
the derfinder package.
biocViews: DifferentialExpression, Sequencing, RNASeq, Software,
Visualization, ImmunoOncology
Author: Leonardo Collado-Torres [aut, cre]
(<https://orcid.org/0000-0003-2140-308X>), Andrew E. Jaffe
[aut] (<https://orcid.org/0000-0001-6886-1454>), Jeffrey T.
Leek [aut, ths] (<https://orcid.org/0000-0002-2873-2671>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/leekgroup/derfinderPlot
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/derfinderPlot
git_url: https://git.bioconductor.org/packages/derfinderPlot
git_branch: RELEASE_3_13
git_last_commit: 41d3249
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/derfinderPlot_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/derfinderPlot_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/derfinderPlot_1.26.0.tgz
vignettes: vignettes/derfinderPlot/inst/doc/derfinderPlot.html
vignetteTitles: Introduction to derfinderPlot
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R
importsMe: brainflowprobes, recountWorkflow
suggestsMe: derfinder, regionReport, GenomicState
dependencyCount: 165
Package: DEScan2
Version: 1.12.0
Depends: R (>= 3.5), GenomicRanges
Imports: BiocParallel, BiocGenerics, ChIPpeakAnno, data.table,
DelayedArray, GenomeInfoDb, GenomicAlignments, glue, IRanges,
plyr, Rcpp (>= 0.12.13), rtracklayer, S4Vectors (>= 0.23.19),
SummarizedExperiment, tools, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle, knitr, rmarkdown, testthat, edgeR, limma, EDASeq,
RUVSeq, RColorBrewer, statmod
License: Artistic-2.0
MD5sum: 8863e6b81d32ff90e84fec142d0fa0e5
NeedsCompilation: yes
Title: Differential Enrichment Scan 2
Description: Integrated peak and differential caller, specifically
designed for broad epigenomic signals.
biocViews: ImmunoOncology, PeakDetection, Epigenetics, Software,
Sequencing, Coverage
Author: Dario Righelli [aut, cre], John Koberstein [aut], Bruce Gomes
[aut], Nancy Zhang [aut], Claudia Angelini [aut], Lucia Peixoto
[aut], Davide Risso [aut]
Maintainer: Dario Righelli <dario.righelli@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DEScan2
git_branch: RELEASE_3_13
git_last_commit: 004c7f5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEScan2_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DEScan2_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DEScan2_1.12.0.tgz
vignettes: vignettes/DEScan2/inst/doc/DEScan2.html
vignetteTitles: DEScan2 Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEScan2/inst/doc/DEScan2.R
dependencyCount: 126
Package: DESeq2
Version: 1.32.0
Depends: S4Vectors (>= 0.23.18), IRanges, GenomicRanges,
SummarizedExperiment (>= 1.1.6)
Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, genefilter,
methods, stats4, locfit, geneplotter, ggplot2, Rcpp (>= 0.11.0)
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown, vsn, pheatmap, RColorBrewer,
apeglm, ashr, tximport, tximeta, tximportData, readr, pbapply,
airway, pasilla (>= 0.2.10), glmGamPoi, BiocManager
License: LGPL (>= 3)
MD5sum: cf6f5468280b2282d587bec61fc9cb5c
NeedsCompilation: yes
Title: Differential gene expression analysis based on the negative
binomial distribution
Description: Estimate variance-mean dependence in count data from
high-throughput sequencing assays and test for differential
expression based on a model using the negative binomial
distribution.
biocViews: Sequencing, RNASeq, ChIPSeq, GeneExpression, Transcription,
Normalization, DifferentialExpression, Bayesian, Regression,
PrincipalComponent, Clustering, ImmunoOncology
Author: Michael Love [aut, cre], Constantin Ahlmann-Eltze [ctb], Kwame
Forbes [ctb], Simon Anders [aut, ctb], Wolfgang Huber [aut,
ctb], RADIANT EU FP7 [fnd], NIH NHGRI [fnd], CZI [fnd]
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
URL: https://github.com/mikelove/DESeq2
VignetteBuilder: knitr, rmarkdown
git_url: https://git.bioconductor.org/packages/DESeq2
git_branch: RELEASE_3_13
git_last_commit: d2820e0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DESeq2_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DESeq2_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DESeq2_1.32.0.tgz
vignettes: vignettes/DESeq2/inst/doc/DESeq2.html
vignetteTitles: Analyzing RNA-seq data with DESeq2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R
dependsOnMe: DEWSeq, DEXSeq, metaseqR2, rgsepd, SeqGSEA, TCC,
tRanslatome, rnaseqDTU, rnaseqGene, Brundle, DRomics
importsMe: Anaquin, animalcules, APAlyzer, BioNERO, BRGenomics, CeTF,
circRNAprofiler, consensusDE, coseq, countsimQC, crossmeta,
DaMiRseq, debrowser, DEComplexDisease, DEFormats, DEGreport,
deltaCaptureC, DEsubs, DiffBind, EBSEA, eegc, ERSSA,
GDCRNATools, GeneTonic, GenoGAM, Glimma, HTSFilter, icetea,
ideal, INSPEcT, IntEREst, isomiRs, kissDE, microbiomeExplorer,
MLSeq, multiSight, muscat, NBAMSeq, ORFik, OUTRIDER, PathoStat,
pcaExplorer, phantasus, proActiv, RegEnrich, regionReport,
ReportingTools, RiboDiPA, Rmmquant, RNASeqR, scBFA, scGPS,
SEtools, singleCellTK, SNPhood, spatialHeatmap, srnadiff,
systemPipeR, systemPipeTools, TBSignatureProfiler,
TimeSeriesExperiment, UMI4Cats, vidger, vulcan,
BloodCancerMultiOmics2017, FieldEffectCrc, IHWpaper,
ExpHunterSuite, recountWorkflow, cinaR, HeritSeq, HTSSIP,
intePareto, MetaLonDA, microbial, wilson
suggestsMe: aggregateBioVar, apeglm, bambu, biobroom, BiocGenerics,
BioCor, BiocSet, CAGEr, compcodeR, dearseq, derfinder,
diffloop, dittoSeq, EDASeq, EnhancedVolcano, EnrichmentBrowser,
fishpond, gage, GenomicAlignments, GenomicRanges, glmGamPoi,
HiCDCPlus, IHW, InteractiveComplexHeatmap, miRmine, OPWeight,
PCAtools, phyloseq, progeny, recount, RUVSeq, scran, subSeq,
SummarizedBenchmark, systemPipeShiny, TFEA.ChIP, tidybulk,
topconfects, tximeta, tximport, variancePartition, Wrench,
zinbwave, curatedAdipoChIP, curatedAdipoRNA, RegParallel,
Single.mTEC.Transcriptomes, CAGEWorkflow, fluentGenomics,
conos, FateID, GeoTcgaData, metaRNASeq, RaceID, seqgendiff,
Seurat
dependencyCount: 92
Package: DEsingle
Version: 1.12.0
Depends: R (>= 3.4.0)
Imports: stats, Matrix (>= 1.2-14), MASS (>= 7.3-45), VGAM (>= 1.0-2),
bbmle (>= 1.0.18), gamlss (>= 4.4-0), maxLik (>= 1.3-4), pscl
(>= 1.4.9), BiocParallel (>= 1.12.0),
Suggests: knitr, rmarkdown, SingleCellExperiment
License: GPL-2
Archs: i386, x64
MD5sum: 92260524abc8a71cc3bcfa088f178038
NeedsCompilation: no
Title: DEsingle for detecting three types of differential expression in
single-cell RNA-seq data
Description: DEsingle is an R package for differential expression (DE)
analysis of single-cell RNA-seq (scRNA-seq) data. It defines
and detects 3 types of differentially expressed genes between
two groups of single cells, with regard to different expression
status (DEs), differential expression abundance (DEa), and
general differential expression (DEg). DEsingle employs
Zero-Inflated Negative Binomial model to estimate the
proportion of real and dropout zeros and to define and detect
the 3 types of DE genes. Results showed that DEsingle
outperforms existing methods for scRNA-seq DE analysis, and can
reveal different types of DE genes that are enriched in
different biological functions.
biocViews: DifferentialExpression, GeneExpression, SingleCell,
ImmunoOncology, RNASeq, Transcriptomics, Sequencing,
Preprocessing, Software
Author: Zhun Miao <miaoz13@tsinghua.org.cn>
Maintainer: Zhun Miao <miaoz13@tsinghua.org.cn>
URL: https://miaozhun.github.io/DEsingle/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DEsingle
git_branch: RELEASE_3_13
git_last_commit: 48b1689
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEsingle_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DEsingle_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DEsingle_1.12.0.tgz
vignettes: vignettes/DEsingle/inst/doc/DEsingle.html
vignetteTitles: DEsingle
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEsingle/inst/doc/DEsingle.R
dependencyCount: 37
Package: DEsubs
Version: 1.18.0
Depends: R (>= 3.3), locfit
Imports: graph, igraph, RBGL, circlize, limma, edgeR, EBSeq, NBPSeq,
stats, grDevices, graphics, pheatmap, utils, ggplot2, Matrix,
jsonlite, tools, DESeq2, methods
Suggests: RUnit, BiocGenerics, knitr
License: GPL-3
Archs: i386, x64
MD5sum: 65e2d2236be9011e4d19cb7fb66dcd59
NeedsCompilation: no
Title: DEsubs: an R package for flexible identification of
differentially expressed subpathways using RNA-seq expression
experiments
Description: DEsubs is a network-based systems biology package that
extracts disease-perturbed subpathways within a pathway network
as recorded by RNA-seq experiments. It contains an extensive
and customizable framework covering a broad range of operation
modes at all stages of the subpathway analysis, enabling a
case-specific approach. The operation modes refer to the
pathway network construction and processing, the subpathway
extraction, visualization and enrichment analysis with regard
to various biological and pharmacological features. Its
capabilities render it a tool-guide for both the modeler and
experimentalist for the identification of more robust
systems-level biomarkers for complex diseases.
biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG,
GeneExpression, NetworkEnrichment, Network, RNASeq,
DifferentialExpression, Normalization, ImmunoOncology
Author: Aristidis G. Vrahatis and Panos Balomenos
Maintainer: Aristidis G. Vrahatis <agvrahatis@upatras.gr>, Panos
Balomenos <balomenos@upatras.gr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DEsubs
git_branch: RELEASE_3_13
git_last_commit: 9c8fa85
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEsubs_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DEsubs_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DEsubs_1.18.0.tgz
vignettes: vignettes/DEsubs/inst/doc/DEsubs.pdf
vignetteTitles: DEsubs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEsubs/inst/doc/DEsubs.R
dependencyCount: 128
Package: DEWSeq
Version: 1.6.0
Depends: R(>= 4.0.0), R.utils, DESeq2, BiocParallel
Imports: BiocGenerics, data.table(>= 1.11.8), GenomeInfoDb,
GenomicRanges, methods, S4Vectors, SummarizedExperiment, stats,
utils
Suggests: knitr, rmarkdown, testthat, BiocStyle, IHW
License: LGPL (>= 3)
MD5sum: 015bc489373d0bc0335c40225e91d29d
NeedsCompilation: no
Title: Differential Expressed Windows Based on Negative Binomial
Distribution
Description: DEWSeq is a sliding window approach for the analysis of
differentially enriched binding regions eCLIP or iCLIP next
generation sequencing data.
biocViews: Sequencing, GeneRegulation, FunctionalGenomics,
DifferentialExpression
Author: Sudeep Sahadevan [aut], Thomas Schwarzl [aut], bioinformatics
team Hentze [aut, cre]
Maintainer: bioinformatics team Hentze <biohentze@embl.de>
URL: https://github.com/EMBL-Hentze-group/DEWSeq/
VignetteBuilder: knitr
BugReports: https://github.com/EMBL-Hentze-group/DEWSeq/issues
git_url: https://git.bioconductor.org/packages/DEWSeq
git_branch: RELEASE_3_13
git_last_commit: 34f4829
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEWSeq_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DEWSeq_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DEWSeq_1.6.0.tgz
vignettes: vignettes/DEWSeq/inst/doc/DEWSeq.html
vignetteTitles: Analyzing eCLIP/iCLIP data with DEWSeq
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEWSeq/inst/doc/DEWSeq.R
dependencyCount: 97
Package: DExMA
Version: 1.0.2
Depends: R (>= 4.1), DExMAdata
Imports: Biobase, GEOquery, impute, limma, pheatmap, plyr, scales,
snpStats, sva, swamp, stats, methods, utils
Suggests: BiocStyle, qpdf, BiocGenerics, RUnit
License: GPL-2
Archs: i386, x64
MD5sum: b592375040f6b78da0ac0ac1dade0a84
NeedsCompilation: no
Title: Differential Expression Meta-Analysis
Description: performing all the steps of gene expression meta-analysis
without eliminating those genes that are presented in at least
two datasets. It provides the necessary functions to be able to
perform the different methods of gene expression meta-analysis.
In addition, it contains functions to apply quality controls,
download GEO data sets and show graphical representations of
the results.
biocViews: DifferentialExpression, GeneExpression, StatisticalMethod,
QualityControl
Author: Juan Antonio Villatoro-GarcÃa [aut, cre], Pedro Carmona-Sáez
[aut]
Maintainer: Juan Antonio Villatoro-GarcÃa
<juanantoniovillatorogarcia@gmail.com>
git_url: https://git.bioconductor.org/packages/DExMA
git_branch: RELEASE_3_13
git_last_commit: 2f24d41
git_last_commit_date: 2021-07-26
Date/Publication: 2021-07-27
source.ver: src/contrib/DExMA_1.0.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DExMA_1.0.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/DExMA_1.0.2.tgz
vignettes: vignettes/DExMA/inst/doc/DExMA.pdf
vignetteTitles: Differential Expression Meta-Analysis with DExMA
package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DExMA/inst/doc/DExMA.R
dependencyCount: 112
Package: DEXSeq
Version: 1.38.0
Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>=
2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.9.11),
AnnotationDbi, RColorBrewer, S4Vectors (>= 0.23.18)
Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools,
statmod, geneplotter, genefilter
Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22),
parathyroidSE, BiocStyle, knitr, rmarkdown, testthat
License: GPL (>= 3)
MD5sum: dd2484b9e02e3de6623c359c66adae4a
NeedsCompilation: no
Title: Inference of differential exon usage in RNA-Seq
Description: The package is focused on finding differential exon usage
using RNA-seq exon counts between samples with different
experimental designs. It provides functions that allows the
user to make the necessary statistical tests based on a model
that uses the negative binomial distribution to estimate the
variance between biological replicates and generalized linear
models for testing. The package also provides functions for the
visualization and exploration of the results.
biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression,
AlternativeSplicing, DifferentialSplicing, GeneExpression,
Visualization
Author: Simon Anders <sanders@fs.tum.de> and Alejandro Reyes
<alejandro.reyes.ds@gmail.com>
Maintainer: Alejandro Reyes <alejandro.reyes.ds@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DEXSeq
git_branch: RELEASE_3_13
git_last_commit: 62dc651
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DEXSeq_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DEXSeq_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DEXSeq_1.38.0.tgz
vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.html
vignetteTitles: Inferring differential exon usage in RNA-Seq data with
the DEXSeq package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R
dependsOnMe: IsoformSwitchAnalyzeR, rnaseqDTU
importsMe: diffUTR, IntEREst
suggestsMe: bambu, GenomicRanges, satuRn, stageR, subSeq, pasilla
dependencyCount: 113
Package: DFP
Version: 1.50.0
Depends: methods, Biobase (>= 2.5.5)
License: GPL-2
MD5sum: 9ba0cbf35e01b2b70e92cb49c496ac7b
NeedsCompilation: no
Title: Gene Selection
Description: This package provides a supervised technique able to
identify differentially expressed genes, based on the
construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns
are built by means of applying 3 Membership Functions to
discretized gene expression values.
biocViews: Microarray, DifferentialExpression
Author: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola
Maintainer: Rodrigo Alvarez-Glez <rodrigo.djv@uvigo.es>
git_url: https://git.bioconductor.org/packages/DFP
git_branch: RELEASE_3_13
git_last_commit: 2f2f1c8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DFP_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DFP_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DFP_1.50.0.tgz
vignettes: vignettes/DFP/inst/doc/DFP.pdf
vignetteTitles: Howto: Discriminat Fuzzy Pattern
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DFP/inst/doc/DFP.R
dependencyCount: 7
Package: DIAlignR
Version: 2.0.0
Depends: methods, stats, R (>= 4.0)
Imports: zoo (>= 1.8-3), data.table, magrittr, dplyr, tidyr, rlang, mzR
(>= 2.18), signal, bit64, reticulate, ggplot2, RSQLite, DBI,
ape, phangorn, pracma, RMSNumpress, Rcpp
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, akima, lattice, scales, gridExtra, latticeExtra,
rmarkdown, BiocStyle, BiocParallel, testthat (>= 2.1.0)
License: GPL-3
MD5sum: e583e7393d5404dd729d1402e55bbc8b
NeedsCompilation: yes
Title: Dynamic Programming Based Alignment of MS2 Chromatograms
Description: To obtain unbiased proteome coverage from a biological
sample, mass-spectrometer is operated in Data Independent
Acquisition (DIA) mode. Alignment of these DIA runs establishes
consistency and less missing values in complete data-matrix.
This package implements dynamic programming with affine gap
penalty based approach for pair-wise alignment of analytes. A
hybrid approach of global alignment (through MS2 features) and
local alignment (with MS2 chromatograms) is implemented in this
tool.
biocViews: MassSpectrometry, Metabolomics, Proteomics, Alignment,
Software
Author: Shubham Gupta <shubham.1637@gmail.com>, Hannes Rost
<hannes.rost@utoronto.ca>
Maintainer: Shubham Gupta <shubham.1637@gmail.com>
SystemRequirements: C++14
VignetteBuilder: knitr
BugReports: https://github.com/shubham1637/DIAlignR/issues
git_url: https://git.bioconductor.org/packages/DIAlignR
git_branch: RELEASE_3_13
git_last_commit: fe5d1ad
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DIAlignR_2.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DIAlignR_2.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DIAlignR_2.0.0.tgz
vignettes: vignettes/DIAlignR/inst/doc/DIAlignR-vignette.html
vignetteTitles: MS2 chromatograms based alignment of targeted
mass-spectrometry runs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DIAlignR/inst/doc/DIAlignR-vignette.R
dependencyCount: 80
Package: DiffBind
Version: 3.2.7
Depends: R (>= 4.0), GenomicRanges, SummarizedExperiment
Imports: RColorBrewer, amap, gplots, grDevices, limma,
GenomicAlignments, locfit, stats, utils, IRanges, lattice,
systemPipeR, tools, Rcpp, dplyr, ggplot2, BiocParallel,
parallel, S4Vectors, Rsamtools (>= 2.0), DESeq2, methods,
graphics, ggrepel, apeglm, ashr, GreyListChIP
LinkingTo: Rhtslib (>= 1.15.3), Rcpp
Suggests: BiocStyle, testthat, xtable
Enhances: rgl, XLConnect, edgeR, csaw, BSgenome, GenomeInfoDb,
profileplyr, rtracklayer, grid
License: Artistic-2.0
MD5sum: 77c955f5339904da9e13dd04e20f95d2
NeedsCompilation: yes
Title: Differential Binding Analysis of ChIP-Seq Peak Data
Description: Compute differentially bound sites from multiple ChIP-seq
experiments using affinity (quantitative) data. Also enables
occupancy (overlap) analysis and plotting functions.
biocViews: Sequencing, ChIPSeq,ATACSeq, DNaseSeq, MethylSeq, RIPSeq,
DifferentialPeakCalling, DifferentialMethylation,
GeneRegulation, HistoneModification, PeakDetection,
BiomedicalInformatics, CellBiology, MultipleComparison,
Normalization, ReportWriting, Epigenetics, FunctionalGenomics
Author: Rory Stark [aut, cre], Gord Brown [aut]
Maintainer: Rory Stark <rory.stark@cruk.cam.ac.uk>
URL:
https://www.cruk.cam.ac.uk/core-facilities/bioinformatics-core/software/DiffBind
SystemRequirements: GNU make
git_url: https://git.bioconductor.org/packages/DiffBind
git_branch: RELEASE_3_13
git_last_commit: bf9b04a
git_last_commit_date: 2021-09-13
Date/Publication: 2021-09-14
source.ver: src/contrib/DiffBind_3.2.7.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DiffBind_3.2.7.zip
mac.binary.ver: bin/macosx/contrib/4.1/DiffBind_3.2.7.tgz
vignettes: vignettes/DiffBind/inst/doc/DiffBind.pdf
vignetteTitles: DiffBind: Differential binding analysis of ChIP-Seq
peak data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DiffBind/inst/doc/DiffBind.R
dependsOnMe: ChIPQC, vulcan, Brundle
dependencyCount: 184
Package: diffcoexp
Version: 1.12.0
Depends: R (>= 3.5), WGCNA, SummarizedExperiment
Imports: stats, DiffCorr, psych, igraph, BiocGenerics
Suggests: GEOquery
License: GPL (>2)
MD5sum: e5448303ad10e07712d5ee6f96902c2a
NeedsCompilation: no
Title: Differential Co-expression Analysis
Description: A tool for the identification of differentially
coexpressed links (DCLs) and differentially coexpressed genes
(DCGs). DCLs are gene pairs with significantly different
correlation coefficients under two conditions. DCGs are genes
with significantly more DCLs than by chance.
biocViews: GeneExpression, DifferentialExpression, Transcription,
Microarray, OneChannel, TwoChannel, RNASeq, Sequencing,
Coverage, ImmunoOncology
Author: Wenbin Wei, Sandeep Amberkar, Winston Hide
Maintainer: Wenbin Wei <wenbin.wei2@durham.ac.uk>
URL: https://github.com/hidelab/diffcoexp
git_url: https://git.bioconductor.org/packages/diffcoexp
git_branch: RELEASE_3_13
git_last_commit: 6ec7796
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/diffcoexp_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/diffcoexp_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/diffcoexp_1.12.0.tgz
vignettes: vignettes/diffcoexp/inst/doc/diffcoexp.pdf
vignetteTitles: About diffcoexp
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/diffcoexp/inst/doc/diffcoexp.R
importsMe: ExpHunterSuite
dependencyCount: 121
Package: diffcyt
Version: 1.12.0
Depends: R (>= 3.4.0)
Imports: flowCore, FlowSOM, SummarizedExperiment, S4Vectors, limma,
edgeR, lme4, multcomp, dplyr, tidyr, reshape2, magrittr, stats,
methods, utils, grDevices, graphics, ComplexHeatmap, circlize,
grid
Suggests: BiocStyle, knitr, rmarkdown, testthat, HDCytoData, CATALYST
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: c81fb484f9cc406c5981cc387d036ca7
NeedsCompilation: no
Title: Differential discovery in high-dimensional cytometry via
high-resolution clustering
Description: Statistical methods for differential discovery analyses in
high-dimensional cytometry data (including flow cytometry, mass
cytometry or CyTOF, and oligonucleotide-tagged cytometry),
based on a combination of high-resolution clustering and
empirical Bayes moderated tests adapted from transcriptomics.
biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell,
CellBasedAssays, CellBiology, Clustering, FeatureExtraction,
Software
Author: Lukas M. Weber [aut, cre]
(<https://orcid.org/0000-0002-3282-1730>)
Maintainer: Lukas M. Weber <lukas.weber.edu@gmail.com>
URL: https://github.com/lmweber/diffcyt
VignetteBuilder: knitr
BugReports: https://github.com/lmweber/diffcyt/issues
git_url: https://git.bioconductor.org/packages/diffcyt
git_branch: RELEASE_3_13
git_last_commit: 7e8f4d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/diffcyt_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/diffcyt_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/diffcyt_1.12.0.tgz
vignettes: vignettes/diffcyt/inst/doc/diffcyt_workflow.html
vignetteTitles: diffcyt workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/diffcyt/inst/doc/diffcyt_workflow.R
dependsOnMe: censcyt, cytofWorkflow
suggestsMe: CATALYST
dependencyCount: 218
Package: diffGeneAnalysis
Version: 1.74.0
Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils
License: GPL
Archs: i386, x64
MD5sum: 41fa2a635f1ecc0868249890406397ff
NeedsCompilation: no
Title: Performs differential gene expression Analysis
Description: Analyze microarray data
biocViews: Microarray, DifferentialExpression
Author: Choudary Jagarlamudi
Maintainer: Choudary Jagarlamudi <choudary.jagar@swosu.edu>
git_url: https://git.bioconductor.org/packages/diffGeneAnalysis
git_branch: RELEASE_3_13
git_last_commit: 1a4c3f1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/diffGeneAnalysis_1.74.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/diffGeneAnalysis_1.74.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/diffGeneAnalysis_1.74.0.tgz
vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf
vignetteTitles: Documentation on diffGeneAnalysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.R
dependencyCount: 5
Package: diffHic
Version: 1.24.1
Depends: R (>= 3.5), GenomicRanges, InteractionSet,
SummarizedExperiment
Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma,
csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb,
BiocGenerics, grDevices, graphics, stats, utils, Rcpp,
rtracklayer
LinkingTo: Rhtslib (>= 1.13.1), zlibbioc, Rcpp
Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix, testthat
License: GPL-3
MD5sum: 393f3167ea25e5dc11bfc45398b61616
NeedsCompilation: yes
Title: Differential Analyis of Hi-C Data
Description: Detects differential interactions across biological
conditions in a Hi-C experiment. Methods are provided for read
alignment and data pre-processing into interaction counts.
Statistical analysis is based on edgeR and supports
normalization and filtering. Several visualization options are
also available.
biocViews: MultipleComparison, Preprocessing, Sequencing, Coverage,
Alignment, Normalization, Clustering, HiC
Author: Aaron Lun [aut, cre], Gordon Smyth [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11, GNU make
git_url: https://git.bioconductor.org/packages/diffHic
git_branch: RELEASE_3_13
git_last_commit: a505f6d
git_last_commit_date: 2021-07-15
Date/Publication: 2021-07-18
source.ver: src/contrib/diffHic_1.24.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/diffHic_1.24.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/diffHic_1.24.1.tgz
vignettes: vignettes/diffHic/inst/doc/diffHic.pdf,
vignettes/diffHic/inst/doc/diffHicUsersGuide.pdf
vignetteTitles: diffHic Vignette, diffHicUsersGuide.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 55
Package: DiffLogo
Version: 2.16.0
Depends: R (>= 3.4), stats, cba
Imports: grDevices, graphics, utils, tools
Suggests: knitr, testthat, seqLogo, MotifDb
License: GPL (>= 2)
MD5sum: ae9b311cd45114d4ac4476912d25f976
NeedsCompilation: no
Title: DiffLogo: A comparative visualisation of biooligomer motifs
Description: DiffLogo is an easy-to-use tool to visualize motif
differences.
biocViews: Software, SequenceMatching, MultipleComparison,
MotifAnnotation, Visualization, Alignment
Author: c( person("Martin", "Nettling", role = c("aut", "cre"), email =
"martin.nettling@informatik.uni-halle.de"), person("Hendrik",
"Treutler", role = c("aut", "cre"), email =
"hendrik.treutler@ipb-halle.de"), person("Jan", "Grau", role =
c("aut", "ctb"), email = "grau@informatik.uni-halle.de"),
person("Andrey", "Lando", role = c("aut", "ctb"), email =
"dronte@autosome.ru"), person("Jens", "Keilwagen", role =
c("aut", "ctb"), email = "jens.keilwagen@julius-kuehn.de"),
person("Stefan", "Posch", role = "aut", email =
"posch@informatik.uni-halle.de"), person("Ivo", "Grosse", role
= "aut", email = "grosse@informatik.uni-halle.de"))
Maintainer: Hendrik Treutler<hendrik.treutler@gmail.com>
URL: https://github.com/mgledi/DiffLogo/
BugReports: https://github.com/mgledi/DiffLogo/issues
git_url: https://git.bioconductor.org/packages/DiffLogo
git_branch: RELEASE_3_13
git_last_commit: f4c40e5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DiffLogo_2.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DiffLogo_2.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DiffLogo_2.16.0.tgz
vignettes: vignettes/DiffLogo/inst/doc/DiffLogoBasics.pdf
vignetteTitles: Basics of the DiffLogo package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DiffLogo/inst/doc/DiffLogoBasics.R
dependencyCount: 9
Package: diffloop
Version: 1.20.0
Imports: methods, GenomicRanges, foreach, plyr, dplyr, reshape2,
ggplot2, matrixStats, Sushi, edgeR, locfit, statmod, biomaRt,
GenomeInfoDb, S4Vectors, IRanges, grDevices, graphics, stats,
utils, Biobase, readr, data.table, rtracklayer, pbapply, limma
Suggests: DESeq2, diffloopdata, ggrepel, knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: ba2fad9e05f0fd334b286639ec53d41d
NeedsCompilation: no
Title: Identifying differential DNA loops from chromatin topology data
Description: A suite of tools for subsetting, visualizing, annotating,
and statistically analyzing the results of one or more ChIA-PET
experiments or other assays that infer chromatin loops.
biocViews: Preprocessing, QualityControl, Visualization, DataImport,
DataRepresentation, GO
Author: Caleb Lareau [aut, cre], Martin Aryee [aut]
Maintainer: Caleb Lareau <caleblareau@g.harvard.edu>
URL: https://github.com/aryeelab/diffloop
VignetteBuilder: knitr
BugReports: https://github.com/aryeelab/diffloop/issues
git_url: https://git.bioconductor.org/packages/diffloop
git_branch: RELEASE_3_13
git_last_commit: 900a5e1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/diffloop_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/diffloop_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/diffloop_1.20.0.tgz
vignettes: vignettes/diffloop/inst/doc/diffloop.html
vignetteTitles: diffloop: Identifying differential DNA loops from
chromatin topology data.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/diffloop/inst/doc/diffloop.R
dependencyCount: 127
Package: diffuStats
Version: 1.12.0
Depends: R (>= 3.4)
Imports: grDevices, stats, methods, Matrix, MASS, checkmate, expm,
igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata,
BiocStyle, reshape2, utils
License: GPL-3
Archs: i386, x64
MD5sum: 2da801d2978c88088124fe423d5280df
NeedsCompilation: yes
Title: Diffusion scores on biological networks
Description: Label propagation approaches are a widely used procedure
in computational biology for giving context to molecular
entities using network data. Node labels, which can derive from
gene expression, genome-wide association studies, protein
domains or metabolomics profiling, are propagated to their
neighbours in the network, effectively smoothing the scores
through prior annotated knowledge and prioritising novel
candidates. The R package diffuStats contains a collection of
diffusion kernels and scoring approaches that facilitates their
computation, characterisation and benchmarking.
biocViews: Network, GeneExpression, GraphAndNetwork, Metabolomics,
Transcriptomics, Proteomics, Genetics, GenomeWideAssociation,
Normalization
Author: Sergio Picart-Armada [aut, cre], Alexandre Perera-Lluna [aut]
Maintainer: Sergio Picart-Armada <sergi.picart@upc.edu>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/diffuStats
git_branch: RELEASE_3_13
git_last_commit: 2996ef5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/diffuStats_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/diffuStats_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/diffuStats_1.12.0.tgz
vignettes: vignettes/diffuStats/inst/doc/diffuStats.pdf,
vignettes/diffuStats/inst/doc/intro.html
vignetteTitles: Case study: predicting protein function, Quick start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/diffuStats/inst/doc/diffuStats.R,
vignettes/diffuStats/inst/doc/intro.R
dependencyCount: 51
Package: diffUTR
Version: 1.0.0
Depends: R (>= 4.0)
Imports: S4Vectors, SummarizedExperiment, limma, edgeR, DEXSeq,
GenomicRanges, Rsubread, ggplot2, rtracklayer, ComplexHeatmap,
ggrepel, stringi, methods, stats, GenomeInfoDb, dplyr,
matrixStats, IRanges, ensembldb, viridisLite
Suggests: BiocStyle, knitr
License: GPL-3
MD5sum: 3bfeb267299c336d7322aea6858c825c
NeedsCompilation: no
Title: diffUTR: Streamlining differential exon and 3' UTR usage
Description: The diffUTR package provides a uniform interface and
plotting functions for limma/edgeR/DEXSeq -powered differential
bin/exon usage. It includes in addition an improved version of
the limma::diffSplice method. Most importantly, diffUTR further
extends the application of these frameworks to differential UTR
usage analysis using poly-A site databases.
biocViews: GeneExpression
Author: Pierre-Luc Germain [cre, aut]
(<https://orcid.org/0000-0003-3418-4218>), Stefan Gerber [aut]
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
VignetteBuilder: knitr
BugReports: https://github.com/ETHZ-INS/diffUTR
git_url: https://git.bioconductor.org/packages/diffUTR
git_branch: RELEASE_3_13
git_last_commit: 948e593
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/diffUTR_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/diffUTR_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/diffUTR_1.0.0.tgz
vignettes: vignettes/diffUTR/inst/doc/diffSplice2.html,
vignettes/diffUTR/inst/doc/diffUTR.html
vignetteTitles: diffUTR_diffSplice2, 1_diffUTR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/diffUTR/inst/doc/diffSplice2.R,
vignettes/diffUTR/inst/doc/diffUTR.R
dependencyCount: 141
Package: diggit
Version: 1.24.0
Depends: R (>= 3.0.2), Biobase, methods
Imports: ks, viper(>= 1.3.1), parallel
Suggests: diggitdata
License: file LICENSE
MD5sum: 3d6618b9d307b117bad69dfbca533229
NeedsCompilation: no
Title: Inference of Genetic Variants Driving Cellular Phenotypes
Description: Inference of Genetic Variants Driving Cellullar Phenotypes
by the DIGGIT algorithm
biocViews: SystemsBiology, NetworkEnrichment, GeneExpression,
FunctionalPrediction, GeneRegulation
Author: Mariano J Alvarez <reef103@gmail.com>
Maintainer: Mariano J Alvarez <reef103@gmail.com>
git_url: https://git.bioconductor.org/packages/diggit
git_branch: RELEASE_3_13
git_last_commit: ad4d03a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/diggit_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/diggit_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/diggit_1.24.0.tgz
vignettes: vignettes/diggit/inst/doc/diggit.pdf
vignetteTitles: Using DIGGIT
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/diggit/inst/doc/diggit.R
dependencyCount: 34
Package: dir.expiry
Version: 1.0.0
Imports: utils, filelock
Suggests: rmarkdown, knitr, testthat, BiocStyle
License: GPL-3
Archs: i386, x64
MD5sum: 0b1ab8b211eacc2dbc3d93db4dc5592a
NeedsCompilation: no
Title: Managing Expiration for Cache Directories
Description: Implements an expiration system for access to versioned
directories. Directories that have not been accessed by a
registered function within a certain time frame are deleted.
This aims to reduce disk usage by eliminating obsolete caches
generated by old versions of packages.
biocViews: Software, Infrastructure
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dir.expiry
git_branch: RELEASE_3_13
git_last_commit: 251926f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dir.expiry_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dir.expiry_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dir.expiry_1.0.0.tgz
vignettes: vignettes/dir.expiry/inst/doc/userguide.html
vignetteTitles: Managing directory expiration
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dir.expiry/inst/doc/userguide.R
importsMe: basilisk, basilisk.utils, rebook
dependencyCount: 2
Package: Director
Version: 1.18.0
Depends: R (>= 4.0)
Imports: htmltools, utils, grDevices
License: GPL-3 + file LICENSE
MD5sum: c3a3f317d770ca43d94c2466a8cc1c79
NeedsCompilation: no
Title: A dynamic visualization tool of multi-level data
Description: Director is an R package designed to streamline the
visualization of molecular effects in regulatory cascades. It
utilizes the R package htmltools and a modified Sankey plugin
of the JavaScript library D3 to provide a fast and easy,
browser-enabled solution to discovering potentially interesting
downstream effects of regulatory and/or co-expressed molecules.
The diagrams are robust, interactive, and packaged as
highly-portable HTML files that eliminate the need for
third-party software to view. This enables a straightforward
approach for scientists to interpret the data produced, and
bioinformatics developers an alternative means to present
relevant data.
biocViews: Visualization
Author: Katherine Icay [aut, cre]
Maintainer: Katherine Icay <kat.icay@gmail.com>
URL: https://github.com/kzouchka/Director
BugReports: https://github.com/kzouchka/Director/issues
git_url: https://git.bioconductor.org/packages/Director
git_branch: RELEASE_3_13
git_last_commit: 54d58a4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Director_1.18.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/Director_1.18.0.tgz
vignettes: vignettes/Director/inst/doc/vignette.pdf
vignetteTitles: Using Director
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Director/inst/doc/vignette.R
dependencyCount: 7
Package: DirichletMultinomial
Version: 1.34.0
Depends: S4Vectors, IRanges
Imports: stats4, methods, BiocGenerics
Suggests: lattice, parallel, MASS, RColorBrewer, xtable
License: LGPL-3
MD5sum: 9eb271cd44ecb237637dcb734ae5ac73
NeedsCompilation: yes
Title: Dirichlet-Multinomial Mixture Model Machine Learning for
Microbiome Data
Description: Dirichlet-multinomial mixture models can be used to
describe variability in microbial metagenomic data. This
package is an interface to code originally made available by
Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as
discussed further in the man page for this package,
?DirichletMultinomial.
biocViews: ImmunoOncology, Microbiome, Sequencing, Clustering,
Classification, Metagenomics
Author: Martin Morgan <martin.morgan@roswellpark.org>
Maintainer: Martin Morgan <martin.morgan@roswellpark.org>
SystemRequirements: gsl
git_url: https://git.bioconductor.org/packages/DirichletMultinomial
git_branch: RELEASE_3_13
git_last_commit: 75a199d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DirichletMultinomial_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DirichletMultinomial_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DirichletMultinomial_1.34.0.tgz
vignettes:
vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf
vignetteTitles: An introduction to DirichletMultinomial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.R
importsMe: mia, miaViz, TFBSTools
dependencyCount: 9
Package: discordant
Version: 1.16.0
Depends: R (>= 3.4)
Imports: Biobase, stats, biwt, gtools, MASS, tools
Suggests: BiocStyle, knitr
License: GPL (>= 2)
MD5sum: f8f617d7b336870f507f2940b74999d1
NeedsCompilation: yes
Title: The Discordant Method: A Novel Approach for Differential
Correlation
Description: Discordant is a method to determine differential
correlation of molecular feature pairs from -omics data using
mixture models. Algorithm is explained further in Siska et al.
biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod,
mRNAMicroarray, Microarray, Genetics, RNASeq
Author: Charlotte Siska [cre,aut], Katerina Kechris [aut]
Maintainer: Charlotte Siska <siska.charlotte@gmail.com>
URL: https://github.com/siskac/discordant
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/discordant
git_branch: RELEASE_3_13
git_last_commit: c57e96b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/discordant_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/discordant_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/discordant_1.16.0.tgz
vignettes: vignettes/discordant/inst/doc/Discordant_vignette.pdf
vignetteTitles: Discordant
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/discordant/inst/doc/Discordant_vignette.R
dependencyCount: 20
Package: DiscoRhythm
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: matrixTests, matrixStats, MetaCycle (>= 1.2.0), data.table,
ggplot2, ggExtra, dplyr, broom, shiny, shinyBS,
shinycssloaders, shinydashboard, shinyjs, BiocStyle, rmarkdown,
knitr, kableExtra, magick, VennDiagram, UpSetR, heatmaply,
viridis, plotly, DT, gridExtra, methods, stats,
SummarizedExperiment, BiocGenerics, S4Vectors, zip, reshape2
Suggests: testthat
License: GPL-3
MD5sum: 3fd336f36dc1674722d52659a967081d
NeedsCompilation: no
Title: Interactive Workflow for Discovering Rhythmicity in Biological
Data
Description: Set of functions for estimation of cyclical
characteristics, such as period, phase, amplitude, and
statistical significance in large temporal datasets. Supporting
functions are available for quality control, dimensionality
reduction, spectral analysis, and analysis of experimental
replicates. Contains a R Shiny web interface to execute all
workflow steps.
biocViews: Software, TimeCourse, QualityControl, Visualization, GUI,
PrincipalComponent
Author: Matthew Carlucci [aut, cre], Algimantas KriÅ¡ÄiÅ«nas [aut],
Haohan Li [aut], Povilas Gibas [aut], Karolis KonceviÄius
[aut], Art Petronis [aut], Gabriel Oh [aut]
Maintainer: Matthew Carlucci <Matthew.Carlucci@camh.ca>
URL: https://github.com/matthewcarlucci/DiscoRhythm
SystemRequirements: To generate html reports pandoc
(http://pandoc.org/installing.html) is required.
VignetteBuilder: knitr
BugReports: https://github.com/matthewcarlucci/DiscoRhythm/issues
git_url: https://git.bioconductor.org/packages/DiscoRhythm
git_branch: RELEASE_3_13
git_last_commit: 1ea451c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DiscoRhythm_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DiscoRhythm_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DiscoRhythm_1.8.0.tgz
vignettes: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.html
vignetteTitles: Introduction to DiscoRhythm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.R
dependencyCount: 157
Package: distinct
Version: 1.4.1
Depends: R (>= 4.0)
Imports: Rcpp, stats, SummarizedExperiment, SingleCellExperiment,
methods, Matrix, foreach, parallel, doParallel, doRNG, ggplot2,
limma, scater
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, UpSetR
License: GPL (>= 3)
Archs: i386, x64
MD5sum: 14286487f256b7a3317fcb80e26f757e
NeedsCompilation: yes
Title: distinct: a method for differential analyses via hierarchical
permutation tests
Description: distinct is a statistical method to perform differential
testing between two or more groups of distributions;
differential testing is performed via hierarchical
non-parametric permutation tests on the cumulative distribution
functions (cdfs) of each sample. While most methods for
differential expression target differences in the mean
abundance between conditions, distinct, by comparing full cdfs,
identifies, both, differential patterns involving changes in
the mean, as well as more subtle variations that do not involve
the mean (e.g., unimodal vs. bi-modal distributions with the
same mean). distinct is a general and flexible tool: due to its
fully non-parametric nature, which makes no assumptions on how
the data was generated, it can be applied to a variety of
datasets. It is particularly suitable to perform differential
state analyses on single cell data (i.e., differential analyses
within sub-populations of cells), such as single cell RNA
sequencing (scRNA-seq) and high-dimensional flow or mass
cytometry (HDCyto) data. To use distinct one needs data from
two or more groups of samples (i.e., experimental conditions),
with at least 2 samples (i.e., biological replicates) per
group.
biocViews: Genetics, RNASeq, Sequencing, DifferentialExpression,
GeneExpression, MultipleComparison, Software, Transcription,
StatisticalMethod, Visualization, SingleCell, FlowCytometry,
GeneTarget
Author: Simone Tiberi [aut, cre], Mark D. Robinson [aut].
Maintainer: Simone Tiberi <simone.tiberi@uzh.ch>
URL: https://github.com/SimoneTiberi/distinct
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/SimoneTiberi/distinct/issues
git_url: https://git.bioconductor.org/packages/distinct
git_branch: RELEASE_3_13
git_last_commit: 53f064d
git_last_commit_date: 2021-08-19
Date/Publication: 2021-08-22
source.ver: src/contrib/distinct_1.4.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/distinct_1.4.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/distinct_1.4.1.tgz
vignettes: vignettes/distinct/inst/doc/distinct.html
vignetteTitles: distinct: a method for differential analyses via
hierarchical permutation tests
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/distinct/inst/doc/distinct.R
dependencyCount: 90
Package: dittoSeq
Version: 1.4.4
Depends: ggplot2
Imports: methods, colorspace (>= 1.4), gridExtra, cowplot, reshape2,
pheatmap, grDevices, ggrepel, ggridges, stats, utils,
SummarizedExperiment, SingleCellExperiment, S4Vectors
Suggests: plotly, testthat, Seurat (>= 2.2), DESeq2, edgeR,
ggplot.multistats, knitr, rmarkdown, BiocStyle, scRNAseq,
ggrastr (>= 0.2.0), ComplexHeatmap, bluster, scater, scran
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 855c626af1c6635b1ed8ab4d4542a3fc
NeedsCompilation: no
Title: User Friendly Single-Cell and Bulk RNA Sequencing Visualization
Description: A universal, user friendly, single-cell and bulk RNA
sequencing visualization toolkit that allows highly
customizable creation of color blindness friendly,
publication-quality figures. dittoSeq accepts both
SingleCellExperiment (SCE) and Seurat objects, as well as the
import and usage, via conversion to an SCE, of
SummarizedExperiment or DGEList bulk data. Visualizations
include dimensionality reduction plots, heatmaps, scatterplots,
percent composition or expression across groups, and more.
Customizations range from size and title adjustments to
automatic generation of annotations for heatmaps, overlay of
trajectory analysis onto any dimensionality reduciton plot,
hidden data overlay upon cursor hovering via ggplotly
conversion, and many more. All with simple, discrete inputs.
Color blindness friendliness is powered by legend adjustments
(enlarged keys), and by allowing the use of shapes or
letter-overlay in addition to the carefully selected
dittoColors().
biocViews: Software, Visualization, RNASeq, SingleCell, GeneExpression,
Transcriptomics, DataImport
Author: Daniel Bunis [aut, cre], Jared Andrews [aut, ctb]
Maintainer: Daniel Bunis <daniel.bunis@ucsf.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dittoSeq
git_branch: RELEASE_3_13
git_last_commit: 922ef4d
git_last_commit_date: 2021-10-11
Date/Publication: 2021-10-12
source.ver: src/contrib/dittoSeq_1.4.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dittoSeq_1.4.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/dittoSeq_1.4.4.tgz
vignettes: vignettes/dittoSeq/inst/doc/dittoSeq.html
vignetteTitles: Annotating scRNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dittoSeq/inst/doc/dittoSeq.R
suggestsMe: escape, tidySingleCellExperiment, magmaR
dependencyCount: 67
Package: divergence
Version: 1.8.0
Depends: R (>= 3.6), SummarizedExperiment
Suggests: knitr, rmarkdown
License: GPL-2
MD5sum: 06227281f8b74de46a679c2236c6f5a7
NeedsCompilation: no
Title: Divergence: Functionality for assessing omics data by divergence
with respect to a baseline
Description: This package provides functionality for performing
divergence analysis as presented in Dinalankara et al,
"Digitizing omics profiles by divergence from a baseline", PANS
2018. This allows the user to simplify high dimensional omics
data into a binary or ternary format which encapsulates how the
data is divergent from a specified baseline group with the same
univariate or multivariate features.
biocViews: Software, StatisticalMethod
Author: Wikum Dinalankara <wdinala1@jhmi.edu>, Luigi Marchionni
<marchion@jhu.edu>, Qian Ke <qke1@jhu.edu>
Maintainer: Wikum Dinalankara <wdinala1@jhmi.edu>, Luigi Marchionni
<marchion@jhu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/divergence
git_branch: RELEASE_3_13
git_last_commit: e13dfb8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/divergence_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/divergence_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/divergence_1.8.0.tgz
vignettes: vignettes/divergence/inst/doc/divergence.html
vignetteTitles: Performing Divergence Analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/divergence/inst/doc/divergence.R
dependencyCount: 26
Package: dks
Version: 1.38.0
Depends: R (>= 2.8)
Imports: cubature
License: GPL
Archs: i386, x64
MD5sum: 6d348e8347f14d1456f9a6ca4791035c
NeedsCompilation: no
Title: The double Kolmogorov-Smirnov package for evaluating multiple
testing procedures.
Description: The dks package consists of a set of diagnostic functions
for multiple testing methods. The functions can be used to
determine if the p-values produced by a multiple testing
procedure are correct. These functions are designed to be
applied to simulated data. The functions require the entire set
of p-values from multiple simulated studies, so that the joint
distribution can be evaluated.
biocViews: MultipleComparison, QualityControl
Author: Jeffrey T. Leek <jleek@jhsph.edu>
Maintainer: Jeffrey T. Leek <jleek@jhsph.edu>
git_url: https://git.bioconductor.org/packages/dks
git_branch: RELEASE_3_13
git_last_commit: 445de0a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dks_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dks_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dks_1.38.0.tgz
vignettes: vignettes/dks/inst/doc/dks.pdf
vignetteTitles: dksTutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dks/inst/doc/dks.R
dependencyCount: 4
Package: DMCFB
Version: 1.6.0
Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors,
BiocParallel, GenomicRanges, IRanges
Imports: utils, stats, speedglm, MASS, data.table, splines, arm,
rtracklayer, benchmarkme, tibble, matrixStats, fastDummies,
graphics
Suggests: testthat, knitr, rmarkdown
License: GPL-3
MD5sum: c037f6d7748f0327ff4ab32a4896d454
NeedsCompilation: no
Title: Differentially Methylated Cytosines via a Bayesian Functional
Approach
Description: DMCFB is a pipeline for identifying differentially
methylated cytosines using a Bayesian functional regression
model in bisulfite sequencing data. By using a functional
regression data model, it tries to capture position-specific,
group-specific and other covariates-specific methylation
patterns as well as spatial correlation patterns and unknown
underlying models of methylation data. It is robust and
flexible with respect to the true underlying models and
inclusion of any covariates, and the missing values are imputed
using spatial correlation between positions and samples. A
Bayesian approach is adopted for estimation and inference in
the proposed method.
biocViews: DifferentialMethylation, Sequencing, Coverage, Bayesian,
Regression
Author: Farhad Shokoohi [aut, cre]
(<https://orcid.org/0000-0002-6224-2609>)
Maintainer: Farhad Shokoohi <shokoohi@icloud.com>
VignetteBuilder: knitr
BugReports: https://github.com/shokoohi/DMCFB/issues
git_url: https://git.bioconductor.org/packages/DMCFB
git_branch: RELEASE_3_13
git_last_commit: 802cb16
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DMCFB_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DMCFB_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DMCFB_1.6.0.tgz
vignettes: vignettes/DMCFB/inst/doc/DMCFB.html
vignetteTitles: Identifying DMCs using Bayesian functional regressions
in BS-Seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DMCFB/inst/doc/DMCFB.R
dependencyCount: 92
Package: DMCHMM
Version: 1.14.0
Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors,
BiocParallel, GenomicRanges, IRanges, fdrtool
Imports: utils, stats, grDevices, rtracklayer, multcomp, calibrate,
graphics
Suggests: testthat, knitr
License: GPL-3
MD5sum: 1c3e4861b53cc3748358a90fbfecd4a6
NeedsCompilation: no
Title: Differentially Methylated CpG using Hidden Markov Model
Description: A pipeline for identifying differentially methylated CpG
sites using Hidden Markov Model in bisulfite sequencing data.
biocViews: DifferentialMethylation, Sequencing, HiddenMarkovModel,
Coverage
Author: Farhad Shokoohi [aut, cre]
(<https://orcid.org/0000-0002-6224-2609>)
Maintainer: Farhad Shokoohi <shokoohi@icloud.com>
VignetteBuilder: knitr
BugReports: https://github.com/shokoohi/DMCHMM/issues
git_url: https://git.bioconductor.org/packages/DMCHMM
git_branch: RELEASE_3_13
git_last_commit: d913bcf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DMCHMM_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DMCHMM_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DMCHMM_1.14.0.tgz
vignettes: vignettes/DMCHMM/inst/doc/DMCHMM.html
vignetteTitles: Sending Messages With Gmailr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DMCHMM/inst/doc/DMCHMM.R
dependencyCount: 55
Package: DMRcaller
Version: 1.24.0
Depends: R (>= 3.5), GenomicRanges, IRanges, S4Vectors (>= 0.23.10)
Imports: parallel, Rcpp, RcppRoll, betareg, grDevices, graphics,
methods, stats, utils
Suggests: knitr, RUnit, BiocGenerics
License: GPL-3
MD5sum: c66bc404c52b29769ee7445c59d1af11
NeedsCompilation: no
Title: Differentially Methylated Regions caller
Description: Uses Bisulfite sequencing data in two conditions and
identifies differentially methylated regions between the
conditions in CG and non-CG context. The input is the CX report
files produced by Bismark and the output is a list of DMRs
stored as GRanges objects.
biocViews: DifferentialMethylation, DNAMethylation, Software,
Sequencing, Coverage
Author: Nicolae Radu Zabet <n.r.zabet@gen.cam.ac.uk>, Jonathan Michael
Foonlan Tsang <jmft2@cam.ac.uk>, Alessandro Pio Greco
<apgrec@essex.ac.uk> and Ryan Merritt <rmerri@essex.ac.uk>
Maintainer: Nicolae Radu Zabet <n.r.zabet@gen.cam.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DMRcaller
git_branch: RELEASE_3_13
git_last_commit: 828312c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DMRcaller_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DMRcaller_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DMRcaller_1.24.0.tgz
vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.pdf
vignetteTitles: DMRcaller
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DMRcaller/inst/doc/DMRcaller.R
dependencyCount: 30
Package: DMRcate
Version: 2.6.0
Depends: R (>= 3.6.0), minfi, SummarizedExperiment
Imports: ExperimentHub, bsseq, GenomeInfoDb, limma, edgeR, DSS,
missMethyl, GenomicRanges, methods, graphics, plyr, Gviz,
IRanges, stats, utils, S4Vectors
Suggests: knitr, RUnit, BiocGenerics,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19
License: file LICENSE
MD5sum: 24f8ec5101b41377068b4734ab432945
NeedsCompilation: no
Title: Methylation array and sequencing spatial analysis methods
Description: De novo identification and extraction of differentially
methylated regions (DMRs) from the human genome using Whole
Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array
(450K and EPIC) data. Provides functionality for filtering
probes possibly confounded by SNPs and cross-hybridisation.
Includes GRanges generation and plotting functions.
biocViews: DifferentialMethylation, GeneExpression, Microarray,
MethylationArray, Genetics, DifferentialExpression,
GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel,
MultipleComparison, QualityControl, TimeCourse, Sequencing,
WholeGenome, Epigenetics, Coverage, Preprocessing, DataImport
Author: Tim Peters
Maintainer: Tim Peters <t.peters@garvan.org.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DMRcate
git_branch: RELEASE_3_13
git_last_commit: d7de4ca
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DMRcate_2.6.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/DMRcate_2.5.1.tgz
vignettes: vignettes/DMRcate/inst/doc/DMRcate.pdf
vignetteTitles: The DMRcate package user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DMRcate/inst/doc/DMRcate.R
dependsOnMe: methylationArrayAnalysis
suggestsMe: missMethyl
dependencyCount: 220
Package: DMRforPairs
Version: 1.28.0
Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1),
GenomicRanges (>= 1.10.7), parallel
License: GPL (>= 2)
MD5sum: 58c0500b1f16ca91db44b393ee2c1c57
NeedsCompilation: no
Title: DMRforPairs: identifying Differentially Methylated Regions
between unique samples using array based methylation profiles
Description: DMRforPairs (formerly DMR2+) allows researchers to compare
n>=2 unique samples with regard to their methylation profile.
The (pairwise) comparison of n unique single samples
distinguishes DMRforPairs from other existing pipelines as
these often compare groups of samples in either single CpG
locus or region based analysis. DMRforPairs defines regions of
interest as genomic ranges with sufficient probes located in
close proximity to each other. Probes in one region are
optionally annotated to the same functional class(es).
Differential methylation is evaluated by comparing the
methylation values within each region between individual
samples and (if the difference is sufficiently large), testing
this difference formally for statistical significance.
biocViews: Microarray, DNAMethylation, DifferentialMethylation,
ReportWriting, Visualization, Annotation
Author: Martin Rijlaarsdam [aut, cre], Yvonne vd Zwan [aut], Lambert
Dorssers [aut], Leendert Looijenga [aut]
Maintainer: Martin Rijlaarsdam <m.a.rijlaarsdam@gmail.com>
URL: http://www.martinrijlaarsdam.nl,
http://www.erasmusmc.nl/pathologie/research/lepo/3898639/
git_url: https://git.bioconductor.org/packages/DMRforPairs
git_branch: RELEASE_3_13
git_last_commit: 4f0c493
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DMRforPairs_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DMRforPairs_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DMRforPairs_1.28.0.tgz
vignettes: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.pdf
vignetteTitles: DMRforPairs_vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.R
dependencyCount: 143
Package: DMRScan
Version: 1.14.0
Depends: R (>= 3.6.0)
Imports: Matrix, MASS, RcppRoll,GenomicRanges, IRanges, GenomeInfoDb,
methods, mvtnorm, stats, parallel
Suggests: knitr, rmarkdown, BiocStyle, BiocManager
License: GPL-3
MD5sum: e62a43759bd3f08da551a97ab2ba4045
NeedsCompilation: no
Title: Detection of Differentially Methylated Regions
Description: This package detects significant differentially methylated
regions (for both qualitative and quantitative traits), using a
scan statistic with underlying Poisson heuristics. The scan
statistic will depend on a sequence of window sizes (# of CpGs
within each window) and on a threshold for each window size.
This threshold can be calculated by three different means: i)
analytically using Siegmund et.al (2012) solution (preferred),
ii) an important sampling as suggested by Zhang (2008), and a
iii) full MCMC modeling of the data, choosing between a number
of different options for modeling the dependency between each
CpG.
biocViews: Software, Technology, Sequencing, WholeGenome
Author: Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge
[ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut]
Maintainer: Christian M Page <page.ntnu@gmail.com>
URL: https://github.com/christpa/DMRScan
VignetteBuilder: knitr
BugReports: https://github.com/christpa/DMRScan/issues
PackageStatus: Active
git_url: https://git.bioconductor.org/packages/DMRScan
git_branch: RELEASE_3_13
git_last_commit: 8fa94c3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DMRScan_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DMRScan_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DMRScan_1.14.0.tgz
vignettes: vignettes/DMRScan/inst/doc/DMRScan_vignette.html
vignetteTitles: DMR Scan Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DMRScan/inst/doc/DMRScan_vignette.R
dependencyCount: 25
Package: dmrseq
Version: 1.12.0
Depends: R (>= 3.5), bsseq
Imports: GenomicRanges, nlme, ggplot2, S4Vectors, RColorBrewer,
bumphunter, DelayedMatrixStats (>= 1.1.13), matrixStats,
BiocParallel, outliers, methods, locfit, IRanges, grDevices,
graphics, stats, utils, annotatr, AnnotationHub, rtracklayer,
GenomeInfoDb, splines
Suggests: knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: b51537a45ee027dfa422a3aad11065be
NeedsCompilation: no
Title: Detection and inference of differentially methylated regions
from Whole Genome Bisulfite Sequencing
Description: This package implements an approach for scanning the
genome to detect and perform accurate inference on
differentially methylated regions from Whole Genome Bisulfite
Sequencing data. The method is based on comparing detected
regions to a pooled null distribution, that can be implemented
even when as few as two samples per population are available.
Region-level statistics are obtained by fitting a generalized
least squares (GLS) regression model with a nested
autoregressive correlated error structure for the effect of
interest on transformed methylation proportions.
biocViews: ImmunoOncology, DNAMethylation, Epigenetics,
MultipleComparison, Software, Sequencing,
DifferentialMethylation, WholeGenome, Regression,
FunctionalGenomics
Author: Keegan Korthauer [cre, aut]
(<https://orcid.org/0000-0002-4565-1654>), Rafael Irizarry
[aut] (<https://orcid.org/0000-0002-3944-4309>), Yuval
Benjamini [aut], Sutirtha Chakraborty [aut]
Maintainer: Keegan Korthauer <keegan@stat.ubc.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dmrseq
git_branch: RELEASE_3_13
git_last_commit: e8485f3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dmrseq_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dmrseq_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dmrseq_1.12.0.tgz
vignettes: vignettes/dmrseq/inst/doc/dmrseq.html
vignetteTitles: Analyzing Bisulfite-seq data with dmrseq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dmrseq/inst/doc/dmrseq.R
importsMe: biscuiteer
dependencyCount: 165
Package: DNABarcodeCompatibility
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: dplyr, tidyr, numbers, purrr, stringr, DNABarcodes, stats,
utils, methods
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: file LICENSE
MD5sum: 28245292f77807568bb3a10a7df815e0
NeedsCompilation: no
Title: A Tool for Optimizing Combinations of DNA Barcodes Used in
Multiplexed Experiments on Next Generation Sequencing Platforms
Description: The package allows one to obtain optimised combinations of
DNA barcodes to be used for multiplex sequencing. In each
barcode combination, barcodes are pooled with respect to
Illumina chemistry constraints. Combinations can be filtered to
keep those that are robust against substitution and
insertion/deletion errors thereby facilitating the
demultiplexing step. In addition, the package provides an
optimiser function to further favor the selection of barcode
combinations with least heterogeneity in barcode usage.
biocViews: Preprocessing, Sequencing
Author: Céline Trébeau [cre] (<https://orcid.org/0000-0001-6795-5379>),
Jacques Boutet de Monvel [aut]
(<https://orcid.org/0000-0001-6182-3527>), Fabienne Wong Jun
Tai [ctb], Raphaël Etournay [aut]
(<https://orcid.org/0000-0002-2441-9274>)
Maintainer: Céline Trébeau <ctrebeau@pasteur.fr>
VignetteBuilder: knitr
BugReports:
https://github.com/comoto-pasteur-fr/DNABarcodeCompatibility/issues
git_url: https://git.bioconductor.org/packages/DNABarcodeCompatibility
git_branch: RELEASE_3_13
git_last_commit: 4f760bf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DNABarcodeCompatibility_1.8.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/DNABarcodeCompatibility_1.8.0.zip
mac.binary.ver:
bin/macosx/contrib/4.1/DNABarcodeCompatibility_1.8.0.tgz
vignettes: vignettes/DNABarcodeCompatibility/inst/doc/introduction.html
vignetteTitles: Introduction to DNABarcodeCompatibility
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DNABarcodeCompatibility/inst/doc/introduction.R
dependencyCount: 36
Package: DNABarcodes
Version: 1.22.0
Depends: Matrix, parallel
Imports: Rcpp (>= 0.11.2), BH
LinkingTo: Rcpp, BH
Suggests: knitr, BiocStyle, rmarkdown
License: GPL-2
MD5sum: cb64630dcc51a446dec6275194df9ac8
NeedsCompilation: yes
Title: A tool for creating and analysing DNA barcodes used in Next
Generation Sequencing multiplexing experiments
Description: The package offers a function to create DNA barcode sets
capable of correcting insertion, deletion, and substitution
errors. Existing barcodes can be analysed regarding their
minimal, maximal and average distances between barcodes.
Finally, reads that start with a (possibly mutated) barcode can
be demultiplexed, i.e., assigned to their original reference
barcode.
biocViews: Preprocessing, Sequencing
Author: Tilo Buschmann <tilo.buschmann.ac@gmail.com>
Maintainer: Tilo Buschmann <tilo.buschmann.ac@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DNABarcodes
git_branch: RELEASE_3_13
git_last_commit: 985d1f4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DNABarcodes_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DNABarcodes_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DNABarcodes_1.22.0.tgz
vignettes: vignettes/DNABarcodes/inst/doc/DNABarcodes.html
vignetteTitles: DNABarcodes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R
importsMe: DNABarcodeCompatibility
dependencyCount: 11
Package: DNAcopy
Version: 1.66.0
License: GPL (>= 2)
MD5sum: 03c2e5385a6560bb47443cce13ff2761
NeedsCompilation: yes
Title: DNA copy number data analysis
Description: Implements the circular binary segmentation (CBS)
algorithm to segment DNA copy number data and identify genomic
regions with abnormal copy number.
biocViews: Microarray, CopyNumberVariation
Author: Venkatraman E. Seshan, Adam Olshen
Maintainer: Venkatraman E. Seshan <seshanv@mskcc.org>
git_url: https://git.bioconductor.org/packages/DNAcopy
git_branch: RELEASE_3_13
git_last_commit: d6ba71e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DNAcopy_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DNAcopy_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DNAcopy_1.66.0.tgz
vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf
vignetteTitles: DNAcopy
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R
dependsOnMe: CGHcall, cghMCR, Clonality, CRImage, PureCN, CSclone,
ParDNAcopy, saasCNV
importsMe: ADaCGH2, AneuFinder, ChAMP, cn.farms, CNAnorm, CNVrd2,
contiBAIT, conumee, CopywriteR, GWASTools, MDTS, MEDIPS,
MethCP, MinimumDistance, QDNAseq, Repitools, SCOPE, sesame,
snapCGH, cghRA, jointseg, PSCBS
suggestsMe: beadarraySNP, cn.mops, CopyNumberPlots, fastseg, ACNE,
aroma.cn, aroma.core, bcp, calmate
dependencyCount: 0
Package: DNAshapeR
Version: 1.20.0
Depends: R (>= 3.4), GenomicRanges
Imports: Rcpp (>= 0.12.1), Biostrings, fields
LinkingTo: Rcpp
Suggests: AnnotationHub, knitr, rmarkdown, testthat,
BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Hsapiens.UCSC.hg19,
caret
License: GPL-2
MD5sum: 85d33ea1332b766c92cec1c8087342b7
NeedsCompilation: yes
Title: High-throughput prediction of DNA shape features
Description: DNAhapeR is an R/BioConductor package for ultra-fast,
high-throughput predictions of DNA shape features. The package
allows to predict, visualize and encode DNA shape features for
statistical learning.
biocViews: StructuralPrediction, DNA3DStructure, Software
Author: Tsu-Pei Chiu and Federico Comoglio
Maintainer: Tsu-Pei Chiu <tsupeich@usc.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DNAshapeR
git_branch: RELEASE_3_13
git_last_commit: 20c156a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DNAshapeR_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DNAshapeR_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DNAshapeR_1.20.0.tgz
vignettes: vignettes/DNAshapeR/inst/doc/DNAshapeR.html
vignetteTitles: DNAshapeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R
dependencyCount: 59
Package: DominoEffect
Version: 1.12.0
Depends: R(>= 3.5)
Imports: biomaRt, data.table, utils, stats, Biostrings,
SummarizedExperiment, VariantAnnotation, AnnotationDbi,
GenomeInfoDb, IRanges, GenomicRanges, methods
Suggests: knitr, testthat, rmarkdown
License: GPL (>= 3)
Archs: i386, x64
MD5sum: e1d830013d92fad3c46f2f823e590865
NeedsCompilation: no
Title: Identification and Annotation of Protein Hotspot Residues
Description: The functions support identification and annotation of
hotspot residues in proteins. These are individual amino acids
that accumulate mutations at a much higher rate than their
surrounding regions.
biocViews: Software, SomaticMutation, Proteomics, SequenceMatching,
Alignment
Author: Marija Buljan and Peter Blattmann
Maintainer: Marija Buljan <marija.buljan.2@gmail.com>, Peter Blattmann
<blattmann@imsb.biol.ethz.ch>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DominoEffect
git_branch: RELEASE_3_13
git_last_commit: bce8fe8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DominoEffect_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DominoEffect_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DominoEffect_1.12.0.tgz
vignettes: vignettes/DominoEffect/inst/doc/Vignette.html
vignetteTitles: Vignette for DominoEffect package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DominoEffect/inst/doc/Vignette.R
dependencyCount: 99
Package: doppelgangR
Version: 1.20.0
Depends: R (>= 3.5.0), Biobase, BiocParallel
Imports: sva, impute, digest, mnormt, methods, grDevices, graphics,
stats, SummarizedExperiment, utils
Suggests: BiocStyle, knitr, rmarkdown, curatedOvarianData, testthat
License: GPL (>=2.0)
Archs: i386, x64
MD5sum: a08bb41600a550aeae6fffb5ac8aa792
NeedsCompilation: no
Title: Identify likely duplicate samples from genomic or meta-data
Description: The main function is doppelgangR(), which takes as minimal
input a list of ExpressionSet object, and searches all list
pairs for duplicated samples. The search is based on the
genomic data (exprs(eset)), phenotype/clinical data
(pData(eset)), and "smoking guns" - supposedly unique
identifiers found in pData(eset).
biocViews: ImmunoOncology, RNASeq, Microarray, GeneExpression,
QualityControl
Author: Levi Waldron [aut, cre], Markus Reister [aut, ctb], Marcel
Ramos [ctb]
Maintainer: Levi Waldron <lwaldron.research@gmail.com>
URL: https://github.com/lwaldron/doppelgangR
VignetteBuilder: knitr
BugReports: https://github.com/lwaldron/doppelgangR/issues
git_url: https://git.bioconductor.org/packages/doppelgangR
git_branch: RELEASE_3_13
git_last_commit: 94fcf60
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/doppelgangR_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/doppelgangR_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/doppelgangR_1.20.0.tgz
vignettes: vignettes/doppelgangR/inst/doc/doppelgangR.html
vignetteTitles: doppelgangR vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R
dependencyCount: 77
Package: Doscheda
Version: 1.14.0
Depends: R (>= 3.4)
Imports: methods, drc, stats, httr, jsonlite, reshape2 , vsn, affy,
limma, stringr, ggplot2, graphics, grDevices, calibrate,
corrgram, gridExtra, DT, shiny, shinydashboard, readxl,
prodlim, matrixStats
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
Archs: i386, x64
MD5sum: 32f489719b05a63dfbe151b30703cfd9
NeedsCompilation: no
Title: A DownStream Chemo-Proteomics Analysis Pipeline
Description: Doscheda focuses on quantitative chemoproteomics used to
determine protein interaction profiles of small molecules from
whole cell or tissue lysates using Mass Spectrometry data. The
package provides a shiny application to run the pipeline,
several visualisations and a downloadable report of an
experiment.
biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry,
QualityControl, DataImport, Regression
Author: Bruno Contrino, Piero Ricchiuto
Maintainer: Bruno Contrino <br1contrino@yahoo.co.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Doscheda
git_branch: RELEASE_3_13
git_last_commit: 8a69504
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Doscheda_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Doscheda_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Doscheda_1.14.0.tgz
vignettes: vignettes/Doscheda/inst/doc/Doscheda.html
vignetteTitles: Doscheda
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Doscheda/inst/doc/Doscheda.R
dependencyCount: 157
Package: DOSE
Version: 3.18.3
Depends: R (>= 3.5.0)
Imports: AnnotationDbi, BiocParallel, DO.db, fgsea, ggplot2, GOSemSim
(>= 2.0.0), methods, qvalue, reshape2, stats, utils
Suggests: prettydoc, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db,
testthat
License: Artistic-2.0
MD5sum: addf6e4e031d5a36b996aee655d8468f
NeedsCompilation: no
Title: Disease Ontology Semantic and Enrichment analysis
Description: This package implements five methods proposed by Resnik,
Schlicker, Jiang, Lin and Wang respectively for measuring
semantic similarities among DO terms and gene products.
Enrichment analyses including hypergeometric model and gene set
enrichment analysis are also implemented for discovering
disease associations of high-throughput biological data.
biocViews: Annotation, Visualization, MultipleComparison,
GeneSetEnrichment, Pathways, Software
Author: Guangchuang Yu [aut, cre], Li-Gen Wang [ctb], Vladislav Petyuk
[ctb], Giovanni Dall'Olio [ctb], Erqiang Hu [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/biomedical-knowledge-mining-book/
VignetteBuilder: knitr
BugReports: https://github.com/GuangchuangYu/DOSE/issues
git_url: https://git.bioconductor.org/packages/DOSE
git_branch: RELEASE_3_13
git_last_commit: f01fef4
git_last_commit_date: 2021-09-30
Date/Publication: 2021-10-03
source.ver: src/contrib/DOSE_3.18.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DOSE_3.18.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/DOSE_3.18.3.tgz
vignettes: vignettes/DOSE/inst/doc/DOSE.html
vignetteTitles: DOSE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DOSE/inst/doc/DOSE.R
importsMe: bioCancer, clusterProfiler, debrowser, eegc, enrichplot,
GDCRNATools, meshes, miRspongeR, MoonlightR, ReactomePA,
RegEnrich, RNASeqR, scTensor, signatureSearch
suggestsMe: cola, GOSemSim, MAGeCKFlute, rrvgo, scGPS,
simplifyEnrichment, genekitr
dependencyCount: 91
Package: doseR
Version: 1.8.0
Depends: R (>= 3.6)
Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4,
RUnit, SummarizedExperiment, digest, S4Vectors
Suggests: BiocStyle, knitr, rmarkdown
License: GPL
MD5sum: cbde374d37a1b25549721cdd139d7811
NeedsCompilation: no
Title: doseR
Description: doseR package is a next generation sequencing package for
sex chromosome dosage compensation which can be applied broadly
to detect shifts in gene expression among an arbitrary number
of pre-defined groups of loci. doseR is a differential gene
expression package for count data, that detects directional
shifts in expression for multiple, specific subsets of genes,
broad utility in systems biology research. doseR has been
prepared to manage the nature of the data and the desired set
of inferences. doseR uses S4 classes to store count data from
sequencing experiment. It contains functions to normalize and
filter count data, as well as to plot and calculate statistics
of count data. It contains a framework for linear modeling of
count data. The package has been tested using real and
simulated data.
biocViews: Infrastructure, Software, DataRepresentation, Sequencing,
GeneExpression, SystemsBiology, DifferentialExpression
Author: AJ Vaestermark, JR Walters.
Maintainer: ake.vastermark <ake.vastermark@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/doseR
git_branch: RELEASE_3_13
git_last_commit: ddab3b7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/doseR_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/doseR_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/doseR_1.8.0.tgz
vignettes: vignettes/doseR/inst/doc/doseR.html
vignetteTitles: "doseR"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/doseR/inst/doc/doseR.R
dependencyCount: 41
Package: dpeak
Version: 1.4.0
Depends: R (>= 4.0.0), methods, stats, utils, graphics, Rcpp
Imports: MASS, IRanges, BSgenome, grDevices, parallel
LinkingTo: Rcpp
Suggests: BSgenome.Ecoli.NCBI.20080805
License: GPL (>= 2)
MD5sum: 9cd66574eca19b9967748aaf8bbc2fd6
NeedsCompilation: yes
Title: dPeak (Deconvolution of Peaks in ChIP-seq Analysis)
Description: dPeak is a statistical framework for the high resolution
identification of protein-DNA interaction sites using PET and
SET ChIP-Seq and ChIP-exo data. It provides computationally
efficient and user friendly interface to process ChIP-seq and
ChIP-exo data, implement exploratory analysis, fit dPeak model,
and export list of predicted binding sites for downstream
analysis.
biocViews: ChIPSeq, Genetics, Sequencing, Software, Transcription
Author: Dongjun Chung, Carter Allen
Maintainer: Dongjun Chung <dongjun.chung@gmail.com>
SystemRequirements: GNU make, meme, fimo
BugReports: https://github.com/dongjunchung/dpeak/issues
git_url: https://git.bioconductor.org/packages/dpeak
git_branch: RELEASE_3_13
git_last_commit: 893adb0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dpeak_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dpeak_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dpeak_1.4.0.tgz
vignettes: vignettes/dpeak/inst/doc/dpeak-example.pdf
vignetteTitles: dPeak
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dpeak/inst/doc/dpeak-example.R
dependencyCount: 47
Package: drawProteins
Version: 1.12.0
Depends: R (>= 4.0)
Imports: ggplot2, httr, dplyr, readr, tidyr
Suggests: covr, testthat, knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: d85b30def3647793ab1b50758eabfdba
NeedsCompilation: no
Title: Package to Draw Protein Schematics from Uniprot API output
Description: This package draws protein schematics from Uniprot API
output. From the JSON returned by the GET command, it creates a
dataframe from the Uniprot Features API. This dataframe can
then be used by geoms based on ggplot2 and base R to draw
protein schematics.
biocViews: Visualization, FunctionalPrediction, Proteomics
Author: Paul Brennan [aut, cre]
Maintainer: Paul Brennan <brennanpincardiff@gmail.com>
URL: https://github.com/brennanpincardiff/drawProteins
VignetteBuilder: knitr
BugReports:
https://github.com/brennanpincardiff/drawProteins/issues/new
git_url: https://git.bioconductor.org/packages/drawProteins
git_branch: RELEASE_3_13
git_last_commit: f3e45e2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/drawProteins_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/drawProteins_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/drawProteins_1.12.0.tgz
vignettes: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.html,
vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.html
vignetteTitles: Using drawProteins, Using extract_transcripts in
drawProteins
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.R,
vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.R
dependencyCount: 61
Package: DRIMSeq
Version: 1.20.0
Depends: R (>= 3.4.0)
Imports: utils, stats, MASS, GenomicRanges, IRanges, S4Vectors,
BiocGenerics, methods, BiocParallel, limma, edgeR, ggplot2,
reshape2
Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid,
BiocStyle, knitr, testthat
License: GPL (>= 3)
MD5sum: c2828c0104e50c651d036c34fddde6cf
NeedsCompilation: no
Title: Differential transcript usage and tuQTL analyses with
Dirichlet-multinomial model in RNA-seq
Description: The package provides two frameworks. One for the
differential transcript usage analysis between different
conditions and one for the tuQTL analysis. Both are based on
modeling the counts of genomic features (i.e., transcripts)
with the Dirichlet-multinomial distribution. The package also
makes available functions for visualization and exploration of
the data and results.
biocViews: ImmunoOncology, SNP, AlternativeSplicing,
DifferentialSplicing, Genetics, RNASeq, Sequencing,
WorkflowStep, MultipleComparison, GeneExpression,
DifferentialExpression
Author: Malgorzata Nowicka [aut, cre]
Maintainer: Malgorzata Nowicka <gosia.nowicka.uzh@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DRIMSeq
git_branch: RELEASE_3_13
git_last_commit: 27f2619
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DRIMSeq_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DRIMSeq_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DRIMSeq_1.20.0.tgz
vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf
vignetteTitles: Differential transcript usage and transcript usage QTL
analyses in RNA-seq with the DRIMSeq package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R
dependsOnMe: rnaseqDTU
importsMe: BANDITS, IsoformSwitchAnalyzeR
dependencyCount: 66
Package: DriverNet
Version: 1.32.0
Depends: R (>= 2.10), methods
License: GPL-3
Archs: i386, x64
MD5sum: 1b21984ba9872e6a8f1d1c9e8c52c0ae
NeedsCompilation: no
Title: Drivernet: uncovering somatic driver mutations modulating
transcriptional networks in cancer
Description: DriverNet is a package to predict functional important
driver genes in cancer by integrating genome data (mutation and
copy number variation data) and transcriptome data (gene
expression data). The different kinds of data are combined by
an influence graph, which is a gene-gene interaction network
deduced from pathway data. A greedy algorithm is used to find
the possible driver genes, which may mutated in a larger number
of patients and these mutations will push the gene expression
values of the connected genes to some extreme values.
biocViews: Network
Author: Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth
Liu, Jamie Rosner and Sohrab Shah
Maintainer: Jiarui Ding <jiaruid@cs.ubc.ca>
git_url: https://git.bioconductor.org/packages/DriverNet
git_branch: RELEASE_3_13
git_last_commit: 91602de
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DriverNet_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DriverNet_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DriverNet_1.32.0.tgz
vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf
vignetteTitles: An introduction to DriverNet
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DriverNet/inst/doc/DriverNet-Overview.R
dependencyCount: 1
Package: DropletUtils
Version: 1.12.3
Depends: SingleCellExperiment
Imports: utils, stats, methods, Matrix, Rcpp, BiocGenerics, S4Vectors,
SummarizedExperiment, BiocParallel, DelayedArray,
DelayedMatrixStats, HDF5Array, rhdf5, edgeR, R.utils, dqrng,
beachmat, scuttle
LinkingTo: Rcpp, beachmat, Rhdf5lib, BH, dqrng, scuttle
Suggests: testthat, knitr, BiocStyle, rmarkdown, jsonlite,
DropletTestFiles
License: GPL-3
MD5sum: 4ddb9fbd03d3450a724a0ee6648c2d6d
NeedsCompilation: yes
Title: Utilities for Handling Single-Cell Droplet Data
Description: Provides a number of utility functions for handling
single-cell (RNA-seq) data from droplet technologies such as
10X Genomics. This includes data loading from count matrices or
molecule information files, identification of cells from empty
droplets, removal of barcode-swapped pseudo-cells, and
downsampling of the count matrix.
biocViews: ImmunoOncology, SingleCell, Sequencing, RNASeq,
GeneExpression, Transcriptomics, DataImport, Coverage
Author: Aaron Lun [aut, cre], Jonathan Griffiths [ctb], Davis McCarthy
[ctb]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DropletUtils
git_branch: RELEASE_3_13
git_last_commit: 3f2d0d0
git_last_commit_date: 2021-09-18
Date/Publication: 2021-09-19
source.ver: src/contrib/DropletUtils_1.12.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DropletUtils_1.12.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/DropletUtils_1.12.3.tgz
vignettes: vignettes/DropletUtils/inst/doc/DropletUtils.html
vignetteTitles: Utilities for handling droplet-based single-cell
RNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DropletUtils/inst/doc/DropletUtils.R
dependsOnMe: OSCA.advanced, OSCA.intro, OSCA.multisample,
OSCA.workflows
importsMe: scCB2, singleCellTK, Spaniel, SpatialExperiment
suggestsMe: mumosa, Nebulosa, DropletTestFiles, muscData, SoupX
dependencyCount: 51
Package: drugTargetInteractions
Version: 1.0.2
Depends: methods, R (>= 4.1)
Imports: utils, RSQLite, UniProt.ws, biomaRt,ensembldb,
BiocFileCache,dplyr,rappdirs, AnnotationFilter, S4Vectors
Suggests: RUnit, BiocStyle, knitr, rmarkdown, ggplot2, reshape2, DT,
EnsDb.Hsapiens.v86
License: Artistic-2.0
MD5sum: 1d31b5709c6d7e23c68737a049358817
NeedsCompilation: no
Title: Drug-Target Interactions
Description: Provides utilities for identifying drug-target
interactions for sets of small molecule or gene/protein
identifiers. The required drug-target interaction information
is obained from a local SQLite instance of the ChEMBL database.
ChEMBL has been chosen for this purpose, because it provides
one of the most comprehensive and best annotatated knowledge
resources for drug-target information available in the public
domain.
biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics,
Pharmacogenomics, Proteomics, Metabolomics
Author: Thomas Girke [cre, aut]
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://github.com/girke-lab/drugTargetInteractions
VignetteBuilder: knitr
BugReports: https://github.com/girke-lab/drugTargetInteractions
git_url: https://git.bioconductor.org/packages/drugTargetInteractions
git_branch: RELEASE_3_13
git_last_commit: dd1665a
git_last_commit_date: 2021-08-26
Date/Publication: 2021-08-29
source.ver: src/contrib/drugTargetInteractions_1.0.2.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/drugTargetInteractions_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/drugTargetInteractions_1.0.2.tgz
vignettes:
vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.html
vignetteTitles: Drug-Target Interactions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.R
dependencyCount: 101
Package: DrugVsDisease
Version: 2.34.0
Depends: R (>= 2.10), affy, limma, biomaRt, ArrayExpress, GEOquery,
DrugVsDiseasedata, cMap2data, qvalue
Imports: annotate, hgu133a.db, hgu133a2.db, hgu133plus2.db, RUnit,
BiocGenerics, xtable
License: GPL-3
MD5sum: 6707ad82891dc9996e52ef44550337b3
NeedsCompilation: no
Title: Comparison of disease and drug profiles using Gene set
Enrichment Analysis
Description: This package generates ranked lists of differential gene
expression for either disease or drug profiles. Input data can
be downloaded from Array Express or GEO, or from local CEL
files. Ranked lists of differential expression and associated
p-values are calculated using Limma. Enrichment scores
(Subramanian et al. PNAS 2005) are calculated to a reference
set of default drug or disease profiles, or a set of custom
data supplied by the user. Network visualisation of significant
scores are output in Cytoscape format.
biocViews: Microarray, GeneExpression, Clustering
Author: C. Pacini
Maintainer: j. Saez-Rodriguez <saezrodriguez@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/DrugVsDisease
git_branch: RELEASE_3_13
git_last_commit: 4d15037
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DrugVsDisease_2.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DrugVsDisease_2.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DrugVsDisease_2.34.0.tgz
vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf
vignetteTitles: DrugVsDisease
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.R
dependencyCount: 126
Package: DSS
Version: 2.40.0
Depends: R (>= 3.3), methods, Biobase, BiocParallel, bsseq
Imports: utils, graphics, stats, splines, DelayedArray
Suggests: BiocStyle, knitr, rmarkdown
License: GPL
Archs: i386, x64
MD5sum: 49b18e5a7460b262c010696d1537802c
NeedsCompilation: yes
Title: Dispersion shrinkage for sequencing data
Description: DSS is an R library performing differntial analysis for
count-based sequencing data. It detectes differentially
expressed genes (DEGs) from RNA-seq, and differentially
methylated loci or regions (DML/DMRs) from bisulfite sequencing
(BS-seq). The core of DSS is a new dispersion shrinkage method
for estimating the dispersion parameter from Gamma-Poisson or
Beta-Binomial distributions.
biocViews: Sequencing, RNASeq, DNAMethylation,GeneExpression,
DifferentialExpression,DifferentialMethylation
Author: Hao Wu<hao.wu@emory.edu>, Hao Feng<hxf155@case.edu>
Maintainer: Hao Wu <hao.wu@emory.edu>, Hao Feng <hxf155@case.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DSS
git_branch: RELEASE_3_13
git_last_commit: 7189a27
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DSS_2.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DSS_2.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DSS_2.40.0.tgz
vignettes: vignettes/DSS/inst/doc/DSS.html
vignetteTitles: The DSS User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DSS/inst/doc/DSS.R
importsMe: DMRcate, kissDE, metaseqR2, MethCP, methylSig
suggestsMe: biscuiteer, methrix, NanoMethViz
dependencyCount: 74
Package: DTA
Version: 2.38.0
Depends: R (>= 2.10), LSD
Imports: scatterplot3d
License: Artistic-2.0
MD5sum: 0659ba815a70b57a499d82cbbd87e24e
NeedsCompilation: no
Title: Dynamic Transcriptome Analysis
Description: Dynamic Transcriptome Analysis (DTA) can monitor the
cellular response to perturbations with higher sensitivity and
temporal resolution than standard transcriptomics. The package
implements the underlying kinetic modeling approach capable of
the precise determination of synthesis- and decay rates from
individual microarray or RNAseq measurements.
biocViews: Microarray, DifferentialExpression, GeneExpression,
Transcription
Author: Bjoern Schwalb, Benedikt Zacher, Sebastian Duemcke, Achim
Tresch
Maintainer: Bjoern Schwalb <schwalb@lmb.uni-muenchen.de>
git_url: https://git.bioconductor.org/packages/DTA
git_branch: RELEASE_3_13
git_last_commit: 31af82a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DTA_2.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DTA_2.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DTA_2.38.0.tgz
vignettes: vignettes/DTA/inst/doc/DTA.pdf
vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA)
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DTA/inst/doc/DTA.R
dependencyCount: 5
Package: dualKS
Version: 1.52.0
Depends: R (>= 2.6.0), Biobase (>= 1.15.0), affy, methods
Imports: graphics
License: LGPL (>= 2.0)
MD5sum: 5552c482522095dc916b8b7a7cd9f5dc
NeedsCompilation: no
Title: Dual KS Discriminant Analysis and Classification
Description: This package implements a Kolmogorov Smirnov rank-sum
based algorithm for training (i.e. discriminant
analysis--identification of genes that discriminate between
classes) and classification of gene expression data sets. One
of the chief strengths of this approach is that it is amenable
to the "multiclass" problem. That is, it can discriminate
between more than 2 classes.
biocViews: Microarray, Classification
Author: Eric J. Kort, Yarong Yang
Maintainer: Eric J. Kort <ericjkort@gmail.com>, Yarong Yang
<yarong.yang@ndsu.edu>
git_url: https://git.bioconductor.org/packages/dualKS
git_branch: RELEASE_3_13
git_last_commit: ee599fd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dualKS_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dualKS_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dualKS_1.52.0.tgz
vignettes: vignettes/dualKS/inst/doc/dualKS.pdf
vignetteTitles: dualKS.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dualKS/inst/doc/dualKS.R
dependencyCount: 13
Package: Dune
Version: 1.4.0
Depends: R (>= 3.6)
Imports: BiocParallel, SummarizedExperiment, utils, ggplot2, dplyr,
tidyr, RColorBrewer, magrittr, gganimate, purrr, aricode
Suggests: knitr, rmarkdown, testthat (>= 2.1.0)
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 8f252152a7a015db21c416edfc9a1a30
NeedsCompilation: no
Title: Improving replicability in single-cell RNA-Seq cell type
discovery
Description: Given a set of clustering labels, Dune merges pairs of
clusters to increase mean ARI between labels, improving
replicability.
biocViews: Clustering, GeneExpression, RNASeq, Software, SingleCell,
Transcriptomics, Visualization
Author: Hector Roux de Bezieux [aut, cre]
(<https://orcid.org/0000-0002-1489-8339>), Kelly Street [aut]
Maintainer: Hector Roux de Bezieux <hector.rouxdebezieux@berkeley.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Dune
git_branch: RELEASE_3_13
git_last_commit: b65b866
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Dune_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Dune_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Dune_1.4.0.tgz
vignettes: vignettes/Dune/inst/doc/Dune.html
vignetteTitles: Dune Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Dune/inst/doc/Dune.R
dependencyCount: 78
Package: dupRadar
Version: 1.22.0
Depends: R (>= 3.2.0)
Imports: Rsubread (>= 1.14.1)
Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub
License: GPL-3
MD5sum: 1fbf8e56d03ddf5e96c11b1833202cf5
NeedsCompilation: no
Title: Assessment of duplication rates in RNA-Seq datasets
Description: Duplication rate quality control for RNA-Seq datasets.
biocViews: Technology, Sequencing, RNASeq, QualityControl,
ImmunoOncology
Author: Sergi Sayols <sergisayolspuig@gmail.com>, Holger Klein
<holger.klein@gmail.com>
Maintainer: Sergi Sayols <sergisayolspuig@gmail.com>, Holger Klein
<holger.klein@gmail.com>
URL: https://www.bioconductor.org/packages/dupRadar,
https://ssayols.github.io/dupRadar/index.html
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dupRadar
git_branch: RELEASE_3_13
git_last_commit: f2c89b9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dupRadar_1.22.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/dupRadar_1.22.0.tgz
vignettes: vignettes/dupRadar/inst/doc/dupRadar.html
vignetteTitles: Using dupRadar
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R
dependencyCount: 9
Package: dyebias
Version: 1.52.0
Depends: R (>= 1.4.1), marray, Biobase
Suggests: limma, convert, GEOquery, dyebiasexamples, methods
License: GPL-3
MD5sum: 34abebe6cc2e361d04960942a81aaee4
NeedsCompilation: no
Title: The GASSCO method for correcting for slide-dependent
gene-specific dye bias
Description: Many two-colour hybridizations suffer from a dye bias that
is both gene-specific and slide-specific. The former depends on
the content of the nucleotide used for labeling; the latter
depends on the labeling percentage. The slide-dependency was
hitherto not recognized, and made addressing the artefact
impossible. Given a reasonable number of dye-swapped pairs of
hybridizations, or of same vs. same hybridizations, both the
gene- and slide-biases can be estimated and corrected using the
GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009),
doi:10.1038/msb.2009.21)
biocViews: Microarray, TwoChannel, QualityControl, Preprocessing
Author: Philip Lijnzaad and Thanasis Margaritis
Maintainer: Philip Lijnzaad <plijnzaad@gmail.com>
URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad
git_url: https://git.bioconductor.org/packages/dyebias
git_branch: RELEASE_3_13
git_last_commit: 90e5fe4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/dyebias_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/dyebias_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/dyebias_1.52.0.tgz
vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf
vignetteTitles: dye bias correction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dyebias/inst/doc/dyebias-vignette.R
suggestsMe: dyebiasexamples
dependencyCount: 10
Package: DynDoc
Version: 1.70.0
Depends: methods, utils
Imports: methods
License: Artistic-2.0
MD5sum: c2185987a98dc4915298021b094d3037
NeedsCompilation: no
Title: Dynamic document tools
Description: A set of functions to create and interact with dynamic
documents and vignettes.
biocViews: ReportWriting, Infrastructure
Author: R. Gentleman, Jeff Gentry
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/DynDoc
git_branch: RELEASE_3_13
git_last_commit: fcb8530
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/DynDoc_1.70.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/DynDoc_1.70.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/DynDoc_1.70.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: tkWidgets
dependencyCount: 2
Package: easyreporting
Version: 1.4.0
Depends: R (>= 3.5.0)
Imports: rmarkdown, methods, tools, shiny, rlang
Suggests: distill, BiocStyle, knitr, readxl, edgeR, limma, EDASeq,
statmod
License: Artistic-2.0
MD5sum: c84cf0fd5fec279839514a8139207f7b
NeedsCompilation: no
Title: Helps creating report for improving Reproducible Computational
Research
Description: An S4 class for facilitating the automated creation of
rmarkdown files inside other packages/software even without
knowing rmarkdown language. Best if implemented in functions as
"recursive" style programming.
biocViews: ReportWriting
Author: Dario Righelli [cre, aut]
Maintainer: Dario Righelli <dario.righelli@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/drighelli/easyreporting/issues
git_url: https://git.bioconductor.org/packages/easyreporting
git_branch: RELEASE_3_13
git_last_commit: b56b250
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/easyreporting_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/easyreporting_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/easyreporting_1.4.0.tgz
vignettes: vignettes/easyreporting/inst/doc/bio_usage.html,
vignettes/easyreporting/inst/doc/standard_usage.html
vignetteTitles: bio_usage.html, standard_usage.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/easyreporting/inst/doc/bio_usage.R,
vignettes/easyreporting/inst/doc/standard_usage.R
dependencyCount: 44
Package: easyRNASeq
Version: 2.28.0
Imports: Biobase (>= 2.50.0), BiocFileCache (>= 1.14.0), BiocGenerics
(>= 0.36.0), BiocParallel (>= 1.24.1), biomaRt (>= 2.46.0),
Biostrings (>= 2.58.0), edgeR (>= 3.32.0), GenomeInfoDb (>=
1.26.0), genomeIntervals (>= 1.46.0), GenomicAlignments (>=
1.26.0), GenomicRanges (>= 1.42.0), SummarizedExperiment (>=
1.20.0), graphics, IRanges (>= 2.24.0), LSD (>= 4.1-0), locfit,
methods, parallel, rappdirs (>= 0.3.1), Rsamtools (>= 2.6.0),
S4Vectors (>= 0.28.0), ShortRead (>= 1.48.0), utils
Suggests: BiocStyle (>= 2.18.0), BSgenome (>= 1.58.0),
BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, knitr,
rmarkdown, RUnit (>= 0.4.32)
License: Artistic-2.0
MD5sum: b0b5d6410437b124d0b4b51eaf24271e
NeedsCompilation: no
Title: Count summarization and normalization for RNA-Seq data
Description: Calculates the coverage of high-throughput short-reads
against a genome of reference and summarizes it per feature of
interest (e.g. exon, gene, transcript). The data can be
normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package.
biocViews: GeneExpression, RNASeq, Genetics, Preprocessing,
ImmunoOncology
Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler,
Niklas Maehler
Maintainer: Nicolas Delhomme <nicolas.delhomme@umu.se>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/easyRNASeq
git_branch: RELEASE_3_13
git_last_commit: 6d18522
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/easyRNASeq_2.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/easyRNASeq_2.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/easyRNASeq_2.28.0.tgz
vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf,
vignettes/easyRNASeq/inst/doc/simpleRNASeq.html
vignetteTitles: R / Bioconductor for High Throughput Sequence Analysis,
geneNetworkR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R,
vignettes/easyRNASeq/inst/doc/simpleRNASeq.R
importsMe: msgbsR
dependencyCount: 101
Package: EBarrays
Version: 2.56.0
Depends: R (>= 1.8.0), Biobase, lattice, methods
Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats
License: GPL (>= 2)
MD5sum: d5de3251a9863a5501ac17a59c5725c9
NeedsCompilation: yes
Title: Unified Approach for Simultaneous Gene Clustering and
Differential Expression Identification
Description: EBarrays provides tools for the analysis of
replicated/unreplicated microarray data.
biocViews: Clustering, DifferentialExpression
Author: Ming Yuan, Michael Newton, Deepayan Sarkar and Christina
Kendziorski
Maintainer: Ming Yuan <myuan@isye.gatech.edu>
git_url: https://git.bioconductor.org/packages/EBarrays
git_branch: RELEASE_3_13
git_last_commit: 097b73e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EBarrays_2.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EBarrays_2.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EBarrays_2.56.0.tgz
vignettes: vignettes/EBarrays/inst/doc/vignette.pdf
vignetteTitles: Introduction to EBarrays
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EBarrays/inst/doc/vignette.R
dependsOnMe: EBcoexpress, gaga, geNetClassifier
importsMe: casper
suggestsMe: Category, dcanr
dependencyCount: 11
Package: EBcoexpress
Version: 1.36.0
Depends: EBarrays, mclust, minqa
Suggests: graph, igraph, colorspace
License: GPL (>= 2)
MD5sum: 11020d3f190e66c6e891878d11362e01
NeedsCompilation: yes
Title: EBcoexpress for Differential Co-Expression Analysis
Description: An Empirical Bayesian Approach to Differential
Co-Expression Analysis at the Gene-Pair Level
biocViews: Bayesian
Author: John A. Dawson
Maintainer: John A. Dawson <jadawson@wisc.edu>
git_url: https://git.bioconductor.org/packages/EBcoexpress
git_branch: RELEASE_3_13
git_last_commit: 3d7103b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EBcoexpress_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EBcoexpress_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EBcoexpress_1.36.0.tgz
vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf
vignetteTitles: EBcoexpress Demo
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R
suggestsMe: dcanr
dependencyCount: 15
Package: EBImage
Version: 4.34.0
Depends: methods
Imports: BiocGenerics (>= 0.7.1), graphics, grDevices, stats, abind,
tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils,
htmltools, htmlwidgets, RCurl
Suggests: BiocStyle, digest, knitr, rmarkdown, shiny
License: LGPL
MD5sum: 050d351fb103eb5b12f36266abe9d1c4
NeedsCompilation: yes
Title: Image processing and analysis toolbox for R
Description: EBImage provides general purpose functionality for image
processing and analysis. In the context of (high-throughput)
microscopy-based cellular assays, EBImage offers tools to
segment cells and extract quantitative cellular descriptors.
This allows the automation of such tasks using the R
programming language and facilitates the use of other tools in
the R environment for signal processing, statistical modeling,
machine learning and visualization with image data.
biocViews: Visualization
Author: Andrzej OleÅ›, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang
Huber, with contributions from Joseph Barry and Philip A.
Marais
Maintainer: Andrzej OleÅ› <andrzej.oles@gmail.com>
URL: https://github.com/aoles/EBImage
VignetteBuilder: knitr
BugReports: https://github.com/aoles/EBImage/issues
git_url: https://git.bioconductor.org/packages/EBImage
git_branch: RELEASE_3_13
git_last_commit: b4e3a23
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EBImage_4.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EBImage_4.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EBImage_4.34.0.tgz
vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.html
vignetteTitles: Introduction to EBImage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R
dependsOnMe: Cardinal, CRImage, cytomapper, flowcatchR, imageHTS,
DonaPLLP2013, furrowSeg, GiNA, nucim, ShinyImage
importsMe: bnbc, flowCHIC, heatmaps, yamss, BioImageDbs, bioimagetools,
CropDetectR, ExpImage, GoogleImage2Array, LFApp, pliman,
RockFab, SAFARI, trackter
suggestsMe: HilbertVis, tofsims, DmelSGI, aroma.core, ijtiff, juicr,
lidR, metagear, ProFound
dependencyCount: 25
Package: EBSEA
Version: 1.20.0
Depends: R (>= 4.0.0)
Imports: DESeq2, graphics, stats, EmpiricalBrownsMethod
Suggests: knitr, rmarkdown
License: GPL-2
Archs: i386, x64
MD5sum: c9213bc13f17c856620b611159817061
NeedsCompilation: no
Title: Exon Based Strategy for Expression Analysis of genes
Description: Calculates differential expression of genes based on exon
counts of genes obtained from RNA-seq sequencing data.
biocViews: Software, DifferentialExpression, GeneExpression, Sequencing
Author: Arfa Mehmood, Asta Laiho, Laura L. Elo
Maintainer: Arfa Mehmood <arfa.mehmood@utu.fi>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EBSEA
git_branch: RELEASE_3_13
git_last_commit: 72eef5e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EBSEA_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EBSEA_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EBSEA_1.20.0.tgz
vignettes: vignettes/EBSEA/inst/doc/EBSEA.html
vignetteTitles: EBSEA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EBSEA/inst/doc/EBSEA.R
dependencyCount: 94
Package: EBSeq
Version: 1.32.0
Depends: blockmodeling, gplots, testthat, R (>= 3.0.0)
License: Artistic-2.0
Archs: i386, x64
MD5sum: 19f459f3de57052ccfde1233b2160675
NeedsCompilation: no
Title: An R package for gene and isoform differential expression
analysis of RNA-seq data
Description: Differential Expression analysis at both gene and isoform
level using RNA-seq data
biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression,
MultipleComparison, RNASeq, Sequencing
Author: Ning Leng, Christina Kendziorski
Maintainer: Ning Leng <lengning1@gmail.com>
git_url: https://git.bioconductor.org/packages/EBSeq
git_branch: RELEASE_3_13
git_last_commit: c92c0b9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EBSeq_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EBSeq_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EBSeq_1.32.0.tgz
vignettes: vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf
vignetteTitles: EBSeq Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EBSeq/inst/doc/EBSeq_Vignette.R
dependsOnMe: EBSeqHMM, Oscope
importsMe: DEsubs, scDD
suggestsMe: compcodeR
dependencyCount: 47
Package: EBSeqHMM
Version: 1.26.0
Depends: EBSeq
License: Artistic-2.0
Archs: i386, x64
MD5sum: 0da1eaba0ddb29ecd1f87222e1ea5c90
NeedsCompilation: no
Title: Bayesian analysis for identifying gene or isoform expression
changes in ordered RNA-seq experiments
Description: The EBSeqHMM package implements an auto-regressive hidden
Markov model for statistical analysis in ordered RNA-seq
experiments (e.g. time course or spatial course data). The
EBSeqHMM package provides functions to identify genes and
isoforms that have non-constant expression profile over the
time points/positions, and cluster them into expression paths.
biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression,
MultipleComparison, RNASeq, Sequencing, GeneExpression,
Bayesian, HiddenMarkovModel, TimeCourse
Author: Ning Leng, Christina Kendziorski
Maintainer: Ning Leng <lengning1@gmail.com>
git_url: https://git.bioconductor.org/packages/EBSeqHMM
git_branch: RELEASE_3_13
git_last_commit: 1ee1381
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EBSeqHMM_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EBSeqHMM_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EBSeqHMM_1.26.0.tgz
vignettes: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.pdf
vignetteTitles: HMM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.R
dependencyCount: 48
Package: ecolitk
Version: 1.64.0
Depends: R (>= 2.10)
Imports: Biobase, graphics, methods
Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy
License: GPL (>= 2)
MD5sum: f9a34386b1ca4880f92546d9a75faa07
NeedsCompilation: no
Title: Meta-data and tools for E. coli
Description: Meta-data and tools to work with E. coli. The tools are
mostly plotting functions to work with circular genomes. They
can used with other genomes/plasmids.
biocViews: Annotation, Visualization
Author: Laurent Gautier
Maintainer: Laurent Gautier <lgautier@gmail.com>
git_url: https://git.bioconductor.org/packages/ecolitk
git_branch: RELEASE_3_13
git_last_commit: 27ed302
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ecolitk_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ecolitk_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ecolitk_1.64.0.tgz
vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf
vignetteTitles: ecolitk
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ecolitk/inst/doc/ecolitk.R
dependencyCount: 7
Package: EDASeq
Version: 2.26.1
Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42)
Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9),
aroma.light, Rsamtools (>= 1.5.75), biomaRt, Biostrings,
AnnotationDbi, GenomicFeatures, GenomicRanges, BiocManager
Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR,
KernSmooth, testthat, DESeq2, rmarkdown
License: Artistic-2.0
MD5sum: 42c199c4f749298ab4d6633de535a4c0
NeedsCompilation: no
Title: Exploratory Data Analysis and Normalization for RNA-Seq
Description: Numerical and graphical summaries of RNA-Seq read data.
Within-lane normalization procedures to adjust for GC-content
effect (or other gene-level effects) on read counts: loess
robust local regression, global-scaling, and full-quantile
normalization (Risso et al., 2011). Between-lane normalization
procedures to adjust for distributional differences between
lanes (e.g., sequencing depth): global-scaling and
full-quantile normalization (Bullard et al., 2010).
biocViews: ImmunoOncology, Sequencing, RNASeq, Preprocessing,
QualityControl, DifferentialExpression
Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Ludwig
Geistlinger [ctb]
Maintainer: Davide Risso <risso.davide@gmail.com>
URL: https://github.com/drisso/EDASeq
VignetteBuilder: knitr
BugReports: https://github.com/drisso/EDASeq/issues
git_url: https://git.bioconductor.org/packages/EDASeq
git_branch: RELEASE_3_13
git_last_commit: 49df07d
git_last_commit_date: 2021-06-18
Date/Publication: 2021-06-20
source.ver: src/contrib/EDASeq_2.26.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EDASeq_2.26.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/EDASeq_2.26.1.tgz
vignettes: vignettes/EDASeq/inst/doc/EDASeq.html
vignetteTitles: EDASeq Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R
dependsOnMe: RUVSeq
importsMe: consensusDE, DaMiRseq, metaseqR2, ribosomeProfilingQC
suggestsMe: awst, bigPint, DEScan2, easyreporting, HTSFilter,
TCGAbiolinks
dependencyCount: 106
Package: edge
Version: 2.24.0
Depends: R(>= 3.1.0), Biobase
Imports: methods, splines, sva, snm, jackstraw, qvalue(>= 1.99.0), MASS
Suggests: testthat, knitr, ggplot2, reshape2
License: MIT + file LICENSE
MD5sum: 8ce0c207b741d174e0e151f41eccc0ec
NeedsCompilation: yes
Title: Extraction of Differential Gene Expression
Description: The edge package implements methods for carrying out
differential expression analyses of genome-wide gene expression
studies. Significance testing using the optimal discovery
procedure and generalized likelihood ratio tests (equivalent to
F-tests and t-tests) are implemented for general study designs.
Special functions are available to facilitate the analysis of
common study designs, including time course experiments. Other
packages such as snm, sva, and qvalue are integrated in edge to
provide a wide range of tools for gene expression analysis.
biocViews: MultipleComparison, DifferentialExpression, TimeCourse,
Regression, GeneExpression, DataImport
Author: John D. Storey, Jeffrey T. Leek and Andrew J. Bass
Maintainer: John D. Storey <jstorey@princeton.edu>, Andrew J. Bass
<ajbass@princeton.edu>
URL: https://github.com/jdstorey/edge
VignetteBuilder: knitr
BugReports: https://github.com/jdstorey/edge/issues
git_url: https://git.bioconductor.org/packages/edge
git_branch: RELEASE_3_13
git_last_commit: 29ac248
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/edge_2.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/edge_2.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/edge_2.24.0.tgz
vignettes: vignettes/edge/inst/doc/edge.pdf
vignetteTitles: edge Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/edge/inst/doc/edge.R
dependencyCount: 110
Package: edgeR
Version: 3.34.1
Depends: R (>= 3.6.0), limma (>= 3.41.5)
Imports: methods, graphics, stats, utils, locfit, Rcpp
LinkingTo: Rcpp
Suggests: jsonlite, readr, rhdf5, splines, Biobase, AnnotationDbi,
SummarizedExperiment, org.Hs.eg.db
License: GPL (>=2)
MD5sum: b8ffb43e7c9b6221361da5683f60d289
NeedsCompilation: yes
Title: Empirical Analysis of Digital Gene Expression Data in R
Description: Differential expression analysis of RNA-seq expression
profiles with biological replication. Implements a range of
statistical methodology based on the negative binomial
distributions, including empirical Bayes estimation, exact
tests, generalized linear models and quasi-likelihood tests. As
well as RNA-seq, it be applied to differential signal analysis
of other types of genomic data that produce read counts,
including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE.
biocViews: GeneExpression, Transcription, AlternativeSplicing,
Coverage, DifferentialExpression, DifferentialSplicing,
DifferentialMethylation, GeneSetEnrichment, Pathways, Genetics,
DNAMethylation, Bayesian, Clustering, ChIPSeq, Regression,
TimeCourse, Sequencing, RNASeq, BatchEffect, SAGE,
Normalization, QualityControl, MultipleComparison,
BiomedicalInformatics, CellBiology, FunctionalGenomics,
Epigenetics, Genetics, ImmunoOncology, SystemsBiology,
Transcriptomics
Author: Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Matthew E
Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D
Robinson, Gordon K Smyth
Maintainer: Yunshun Chen <yuchen@wehi.edu.au>, Gordon Smyth
<smyth@wehi.edu.au>, Aaron Lun
<infinite.monkeys.with.keyboards@gmail.com>, Mark Robinson
<mark.robinson@imls.uzh.ch>
URL: http://bioinf.wehi.edu.au/edgeR,
https://bioconductor.org/packages/edgeR
SystemRequirements: C++11
git_url: https://git.bioconductor.org/packages/edgeR
git_branch: RELEASE_3_13
git_last_commit: 0a0c62a
git_last_commit_date: 2021-09-03
Date/Publication: 2021-09-05
source.ver: src/contrib/edgeR_3.34.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/edgeR_3.34.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/edgeR_3.34.1.tgz
vignettes: vignettes/edgeR/inst/doc/edgeR.pdf,
vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf
vignetteTitles: edgeR Vignette, edgeRUsersGuide.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: ASpli, IntEREst, methylMnM, miloR, RNASeqR, RUVSeq, TCC,
tRanslatome, ReactomeGSA.data, EGSEA123, RNAseq123, rnaseqDTU,
RnaSeqGeneEdgeRQL, csawBook, OSCA.advanced, OSCA.multisample,
OSCA.workflows, babel, BALLI, BioInsight, edgeRun, GSAgm
importsMe: affycoretools, ArrayExpressHTS, ATACseqQC, autonomics,
AWFisher, baySeq, BioQC, censcyt, ChromSCape, circRNAprofiler,
clusterExperiment, CNVRanger, compcodeR, consensusDE, coseq,
countsimQC, crossmeta, csaw, DaMiRseq, dce, debrowser,
DEComplexDisease, DEFormats, DEGreport, DEsubs, diffcyt,
diffHic, diffloop, diffUTR, DMRcate, doseR, DRIMSeq,
DropletUtils, easyRNASeq, eegc, EGSEA, eisaR,
EnrichmentBrowser, erccdashboard, ERSSA, GDCRNATools, Glimma,
GSEABenchmarkeR, HTSFilter, icetea, infercnv,
IsoformSwitchAnalyzeR, KnowSeq, Maaslin2, MEDIPS, metaseqR2,
MIGSA, MLSeq, moanin, msgbsR, msmsTests, multiHiCcompare,
muscat, NBSplice, PathoStat, PhIPData, ppcseq, PROPER,
psichomics, RCM, regsplice, Repitools, ROSeq, scCB2, scde,
scone, scran, SEtools, SIMD, SingleCellSignalR, singscore,
spatialHeatmap, splatter, SPsimSeq, srnadiff, STATegRa, sva,
systemPipeR, TBSignatureProfiler, TCseq, TimeSeriesExperiment,
tradeSeq, tweeDEseq, vidger, yarn, zinbwave, emtdata,
ExpHunterSuite, recountWorkflow, SingscoreAMLMutations,
BinQuasi, cinaR, DGEobj.utils, digitalDLSorteR, HTSCluster,
MetaLonDA, microbial, myTAI, QuasiSeq, RVA, scRNAtools,
SPUTNIK, ssizeRNA, TSGS
suggestsMe: ABSSeq, bigPint, biobroom, ClassifyR, clonotypeR, cqn,
cydar, dcanr, dearseq, DEScan2, dittoSeq, easyreporting,
EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges,
glmGamPoi, goseq, groHMM, GSAR, GSVA, ideal, iSEEu, missMethyl,
multiMiR, recount, regionReport, ribosomeProfilingQC, satuRn,
SeqGate, stageR, subSeq, SummarizedBenchmark, TCGAbiolinks,
tidybulk, topconfects, tximeta, tximport, variancePartition,
weitrix, Wrench, zFPKM, leeBamViews, CAGEWorkflow, chipseqDB,
DGEobj, DiPALM, GeoTcgaData, glmmSeq, seqgendiff, SIBERG
dependencyCount: 10
Package: eegc
Version: 1.18.0
Depends: R (>= 3.4.0)
Imports: R.utils, gplots, sna, wordcloud, igraph, pheatmap, edgeR,
DESeq2, clusterProfiler, S4Vectors, ggplot2, org.Hs.eg.db,
org.Mm.eg.db, limma, DOSE, AnnotationDbi
Suggests: knitr
License: GPL-2
MD5sum: 51928b81b4630d44f5e54d326ae67c30
NeedsCompilation: no
Title: Engineering Evaluation by Gene Categorization (eegc)
Description: This package has been developed to evaluate cellular
engineering processes for direct differentiation of stem cells
or conversion (transdifferentiation) of somatic cells to
primary cells based on high throughput gene expression data
screened either by DNA microarray or RNA sequencing. The
package takes gene expression profiles as inputs from three
types of samples: (i) somatic or stem cells to be
(trans)differentiated (input of the engineering process), (ii)
induced cells to be evaluated (output of the engineering
process) and (iii) target primary cells (reference for the
output). The package performs differential gene expression
analysis for each pair-wise sample comparison to identify and
evaluate the transcriptional differences among the 3 types of
samples (input, output, reference). The ideal goal is to have
induced and primary reference cell showing overlapping
profiles, both very different from the original cells.
biocViews: ImmunoOncology, Microarray, Sequencing, RNASeq,
DifferentialExpression, GeneRegulation, GeneSetEnrichment,
GeneExpression, GeneTarget
Author: Xiaoyuan Zhou, Guofeng Meng, Christine Nardini, Hongkang Mei
Maintainer: Xiaoyuan Zhou <zhouxiaoyuan@picb.ac.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/eegc
git_branch: RELEASE_3_13
git_last_commit: d0acc6a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/eegc_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/eegc_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/eegc_1.18.0.tgz
vignettes: vignettes/eegc/inst/doc/eegc.pdf
vignetteTitles: Engineering Evaluation by Gene Categorization (eegc)
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/eegc/inst/doc/eegc.R
dependencyCount: 156
Package: EGAD
Version: 1.20.0
Depends: R(>= 3.5)
Imports: gplots, Biobase, GEOquery, limma, impute, RColorBrewer, zoo,
igraph, plyr, MASS, RCurl, methods
Suggests: knitr, testthat, rmarkdown, markdown
License: GPL-2
Archs: i386, x64
MD5sum: e53f37f6414a59d7c9fa7009fc669d57
NeedsCompilation: no
Title: Extending guilt by association by degree
Description: The package implements a series of highly efficient tools
to calculate functional properties of networks based on guilt
by association methods.
biocViews: Software, FunctionalGenomics, SystemsBiology,
GenePrediction, FunctionalPrediction, NetworkEnrichment,
GraphAndNetwork, Network
Author: Sara Ballouz [aut, cre], Melanie Weber [aut, ctb], Paul
Pavlidis [aut], Jesse Gillis [aut, ctb]
Maintainer: Sara Ballouz <sarahballouz@gmail.com>
VignetteBuilder: rmarkdown
git_url: https://git.bioconductor.org/packages/EGAD
git_branch: RELEASE_3_13
git_last_commit: 948910f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EGAD_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EGAD_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EGAD_1.20.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 65
Package: EGSEA
Version: 1.20.0
Depends: R (>= 3.5), Biobase, gage (>= 2.14.4), AnnotationDbi, topGO
(>= 2.16.0), pathview (>= 1.4.2)
Imports: PADOG (>= 1.6.0), GSVA (>= 1.12.0), globaltest (>= 5.18.0),
limma (>= 3.20.9), edgeR (>= 3.6.8), HTMLUtils (>= 0.1.5),
hwriter (>= 1.2.2), gplots (>= 2.14.2), ggplot2 (>= 1.0.0),
safe (>= 3.4.0), stringi (>= 0.5.0), parallel, stats, metap,
grDevices, graphics, utils, org.Hs.eg.db, org.Mm.eg.db,
org.Rn.eg.db, RColorBrewer, methods, EGSEAdata (>= 1.3.1),
Glimma (>= 1.4.0), htmlwidgets, plotly, DT
Suggests: BiocStyle, knitr, testthat
License: GPL-3
Archs: i386, x64
MD5sum: 60a5a089cdfe6c4ae0eaac82b3c21aa7
NeedsCompilation: no
Title: Ensemble of Gene Set Enrichment Analyses
Description: This package implements the Ensemble of Gene Set
Enrichment Analyses (EGSEA) method for gene set testing.
biocViews: ImmunoOncology, DifferentialExpression, GO, GeneExpression,
GeneSetEnrichment, Genetics, Microarray, MultipleComparison,
OneChannel, Pathways, RNASeq, Sequencing, Software,
SystemsBiology, TwoChannel,Metabolomics, Proteomics, KEGG,
GraphAndNetwork, GeneSignaling, GeneTarget, NetworkEnrichment,
Network, Classification
Author: Monther Alhamdoosh, Luyi Tian, Milica Ng and Matthew Ritchie
Maintainer: Monther Alhamdoosh <m.hamdoosh@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EGSEA
git_branch: RELEASE_3_13
git_last_commit: 77593a7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EGSEA_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EGSEA_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EGSEA_1.20.0.tgz
vignettes: vignettes/EGSEA/inst/doc/EGSEA.pdf
vignetteTitles: EGSEA vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EGSEA/inst/doc/EGSEA.R
dependsOnMe: EGSEA123
suggestsMe: EGSEAdata
dependencyCount: 180
Package: eiR
Version: 1.32.0
Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI
Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl,
digest, BiocGenerics, RcppAnnoy (>= 0.0.9)
Suggests: BiocStyle, knitcitations, knitr, knitrBootstrap,rmarkdown
License: Artistic-2.0
MD5sum: 72534477ae7303314111a0168dcc472a
NeedsCompilation: yes
Title: Accelerated similarity searching of small molecules
Description: The eiR package provides utilities for accelerated
structure similarity searching of very large small molecule
data sets using an embedding and indexing approach.
biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics,
Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays,
Visualization, Infrastructure, DataImport, Clustering,
Proteomics, Metabolomics
Author: Kevin Horan, Yiqun Cao and Tyler Backman
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://github.com/girke-lab/eiR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/eiR
git_branch: RELEASE_3_13
git_last_commit: b291e60
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/eiR_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/eiR_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/eiR_1.32.0.tgz
vignettes: vignettes/eiR/inst/doc/eiR.html
vignetteTitles: eiR: Accelerated Similarity Searching of Small
Molecules
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: TRUE
Rfiles: vignettes/eiR/inst/doc/eiR.R
dependencyCount: 67
Package: eisaR
Version: 1.4.0
Depends: R (>= 4.0.0)
Imports: graphics, stats, GenomicRanges, S4Vectors, IRanges, limma,
edgeR, methods, SummarizedExperiment, BiocGenerics, utils
Suggests: knitr, rmarkdown, testthat, BiocStyle, QuasR, Rbowtie,
Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg38, ensembldb,
AnnotationDbi, GenomicFeatures, rtracklayer
License: GPL-3
MD5sum: c95be545537ec4abb6036e54297973d5
NeedsCompilation: no
Title: Exon-Intron Split Analysis (EISA) in R
Description: Exon-intron split analysis (EISA) uses ordinary RNA-seq
data to measure changes in mature RNA and pre-mRNA reads across
different experimental conditions to quantify transcriptional
and post-transcriptional regulation of gene expression. For
details see Gaidatzis et al., Nat Biotechnol 2015. doi:
10.1038/nbt.3269. eisaR implements the major steps of EISA in
R.
biocViews: Transcription, GeneExpression, GeneRegulation,
FunctionalGenomics, Transcriptomics, Regression, RNASeq
Author: Michael Stadler [aut, cre], Dimos Gaidatzis [aut], Lukas Burger
[aut], Charlotte Soneson [aut]
Maintainer: Michael Stadler <michael.stadler@fmi.ch>
URL: https://github.com/fmicompbio/eisaR
VignetteBuilder: knitr
BugReports: https://github.com/fmicompbio/eisaR/issues
git_url: https://git.bioconductor.org/packages/eisaR
git_branch: RELEASE_3_13
git_last_commit: 907e2ad
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/eisaR_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/eisaR_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/eisaR_1.4.0.tgz
vignettes: vignettes/eisaR/inst/doc/eisaR.html,
vignettes/eisaR/inst/doc/rna-velocity.html
vignetteTitles: Using eisaR for Exon-Intron Split Analysis (EISA),
Generating reference files for spliced and unspliced abundance
estimation with alignment-free methods
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/eisaR/inst/doc/eisaR.R,
vignettes/eisaR/inst/doc/rna-velocity.R
dependencyCount: 30
Package: ELMER
Version: 2.16.0
Depends: R (>= 3.4.0), ELMER.data (>= 2.9.3)
Imports: GenomicRanges, ggplot2, reshape, grid, grDevices, graphics,
methods, parallel, stats, utils, IRanges, GenomeInfoDb,
S4Vectors, GenomicFeatures, TCGAbiolinks (>= 2.9.2), plyr,
Matrix, dplyr, Gviz, ComplexHeatmap, circlize,
MultiAssayExperiment, SummarizedExperiment, biomaRt,
doParallel, downloader, ggrepel, lattice, magrittr, readr,
scales, rvest, xml2, plotly, gridExtra, rmarkdown, stringr,
tibble, tidyr, progress, purrr, reshape2, ggpubr, rtracklayer,
DelayedArray
Suggests: BiocStyle, knitr, testthat, data.table, DT,
GenomicInteractions, webshot, R.utils, covr, sesameData
License: GPL-3
MD5sum: 01b19571f4a76f7840705f3730691c9a
NeedsCompilation: no
Title: Inferring Regulatory Element Landscapes and Transcription Factor
Networks Using Cancer Methylomes
Description: ELMER is designed to use DNA methylation and gene
expression from a large number of samples to infere regulatory
element landscape and transcription factor network in primary
tissue.
biocViews: DNAMethylation, GeneExpression, MotifAnnotation, Software,
GeneRegulation, Transcription, Network
Author: Tiago Chedraoui Silva [aut, cre], Lijing Yao [aut], Simon
Coetzee [aut], Nicole Gull [ctb], Hui Shen [ctb], Peter Laird
[ctb], Peggy Farnham [aut], Dechen Li [ctb], Benjamin Berman
[aut]
Maintainer: Tiago Chedraoui Silva <tiagochst@usp.br>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ELMER
git_branch: RELEASE_3_13
git_last_commit: 31e996d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ELMER_2.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ELMER_2.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ELMER_2.16.0.tgz
vignettes: vignettes/ELMER/inst/doc/analysis_data_input.html,
vignettes/ELMER/inst/doc/analysis_diff_meth.html,
vignettes/ELMER/inst/doc/analysis_get_pair.html,
vignettes/ELMER/inst/doc/analysis_gui.html,
vignettes/ELMER/inst/doc/analysis_motif_enrichment.html,
vignettes/ELMER/inst/doc/analysis_regulatory_tf.html,
vignettes/ELMER/inst/doc/index.html,
vignettes/ELMER/inst/doc/input.html,
vignettes/ELMER/inst/doc/pipe.html,
vignettes/ELMER/inst/doc/plots_heatmap.html,
vignettes/ELMER/inst/doc/plots_motif_enrichment.html,
vignettes/ELMER/inst/doc/plots_scatter.html,
vignettes/ELMER/inst/doc/plots_schematic.html,
vignettes/ELMER/inst/doc/plots_TF.html,
vignettes/ELMER/inst/doc/usecase.html
vignetteTitles: "3.1 - Data input - Creating MAE object", "3.2 -
Identifying differentially methylated probes", "3.3 -
Identifying putative probe-gene pairs", 5 - Integrative
analysis workshop with TCGAbiolinks and ELMER - Analysis GUI,
"3.4 - Motif enrichment analysis on the selected probes", "3.5
- Identifying regulatory TFs", "1 - ELMER v.2: An
R/Bioconductor package to reconstruct gene regulatory networks
from DNA methylation and transcriptome profiles", "2 -
Introduction: Input data", "3.6 - TCGA.pipe: Running ELMER for
TCGA data in a compact way", "4.5 - Heatmap plots", "4.3 -
Motif enrichment plots", "4.1 - Scatter plots", "4.2 -
Schematic plots", "4.4 - Regulatory TF plots", "11 - ELMER: Use
case"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ELMER/inst/doc/analysis_data_input.R,
vignettes/ELMER/inst/doc/analysis_diff_meth.R,
vignettes/ELMER/inst/doc/analysis_get_pair.R,
vignettes/ELMER/inst/doc/analysis_gui.R,
vignettes/ELMER/inst/doc/analysis_motif_enrichment.R,
vignettes/ELMER/inst/doc/analysis_regulatory_tf.R,
vignettes/ELMER/inst/doc/index.R,
vignettes/ELMER/inst/doc/input.R,
vignettes/ELMER/inst/doc/pipe.R,
vignettes/ELMER/inst/doc/plots_heatmap.R,
vignettes/ELMER/inst/doc/plots_motif_enrichment.R,
vignettes/ELMER/inst/doc/plots_scatter.R,
vignettes/ELMER/inst/doc/plots_schematic.R,
vignettes/ELMER/inst/doc/plots_TF.R,
vignettes/ELMER/inst/doc/usecase.R
importsMe: TCGAbiolinksGUI, TCGAWorkflow
dependencyCount: 213
Package: EMDomics
Version: 2.22.0
Depends: R (>= 3.2.1)
Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt,
preprocessCore
Suggests: knitr
License: MIT + file LICENSE
Archs: x64
MD5sum: e0e1f7b37cdbe476909335ed157656a6
NeedsCompilation: no
Title: Earth Mover's Distance for Differential Analysis of Genomics
Data
Description: The EMDomics algorithm is used to perform a supervised
multi-class analysis to measure the magnitude and statistical
significance of observed continuous genomics data between
groups. Usually the data will be gene expression values from
array-based or sequence-based experiments, but data from other
types of experiments can also be analyzed (e.g. copy number
variation). Traditional methods like Significance Analysis of
Microarrays (SAM) and Linear Models for Microarray Data (LIMMA)
use significance tests based on summary statistics (mean and
standard deviation) of the distributions. This approach lacks
power to identify expression differences between groups that
show high levels of intra-group heterogeneity. The Earth
Mover's Distance (EMD) algorithm instead computes the "work"
needed to transform one distribution into another, thus
providing a metric of the overall difference in shape between
two distributions. Permutation of sample labels is used to
generate q-values for the observed EMD scores. This package
also incorporates the Komolgorov-Smirnov (K-S) test and the
Cramer von Mises test (CVM), which are both common distribution
comparison tests.
biocViews: Software, DifferentialExpression, GeneExpression, Microarray
Author: Sadhika Malladi [aut, cre], Daniel Schmolze [aut, cre], Andrew
Beck [aut], Sheida Nabavi [aut]
Maintainer: Sadhika Malladi <contact@sadhikamalladi.com> and Daniel
Schmolze <emd@schmolze.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EMDomics
git_branch: RELEASE_3_13
git_last_commit: 86b3e22
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EMDomics_2.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EMDomics_2.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EMDomics_2.22.0.tgz
vignettes: vignettes/EMDomics/inst/doc/EMDomics.html
vignetteTitles: EMDomics Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R
dependencyCount: 50
Package: EmpiricalBrownsMethod
Version: 1.20.0
Depends: R (>= 3.2.0)
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 0966bc14a84787c8f4f5cde8ba401f32
NeedsCompilation: no
Title: Uses Brown's method to combine p-values from dependent tests
Description: Combining P-values from multiple statistical tests is
common in bioinformatics. However, this procedure is
non-trivial for dependent P-values. This package implements an
empirical adaptation of Brown’s Method (an extension of
Fisher’s Method) for combining dependent P-values which is
appropriate for highly correlated data sets found in
high-throughput biological experiments.
biocViews: StatisticalMethod, GeneExpression, Pathways
Author: William Poole
Maintainer: David Gibbs <dgibbs@systemsbiology.org>
URL: https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.git
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EmpiricalBrownsMethod
git_branch: RELEASE_3_13
git_last_commit: d216c2c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EmpiricalBrownsMethod_1.20.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/EmpiricalBrownsMethod_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EmpiricalBrownsMethod_1.20.0.tgz
vignettes: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html
vignetteTitles: Empirical Browns Method
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R
dependsOnMe: poolVIM
importsMe: EBSEA
dependencyCount: 0
Package: EnhancedVolcano
Version: 1.10.0
Depends: ggplot2, ggrepel
Imports: ggalt, ggrastr
Suggests: RUnit, BiocGenerics, knitr, DESeq2, pasilla, airway,
org.Hs.eg.db, gridExtra, magrittr, rmarkdown
License: GPL-3
MD5sum: ecfd3f3fc1491027c38dc4d964ab8be7
NeedsCompilation: no
Title: Publication-ready volcano plots with enhanced colouring and
labeling
Description: Volcano plots represent a useful way to visualise the
results of differential expression analyses. Here, we present a
highly-configurable function that produces publication-ready
volcano plots. EnhancedVolcano will attempt to fit as many
point labels in the plot window as possible, thus avoiding
'clogging' up the plot with labels that could not otherwise
have been read. Other functionality allows the user to identify
up to 4 different types of attributes in the same plot space
via colour, shape, size, and shade parameter configurations.
biocViews: RNASeq, GeneExpression, Transcription,
DifferentialExpression, ImmunoOncology
Author: Kevin Blighe [aut, cre], Sharmila Rana [aut], Emir Turkes
[ctb], Benjamin Ostendorf [ctb], Andrea Grioni [ctb], Myles
Lewis [aut]
Maintainer: Kevin Blighe <kevin@clinicalbioinformatics.co.uk>
URL: https://github.com/kevinblighe/EnhancedVolcano
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EnhancedVolcano
git_branch: RELEASE_3_13
git_last_commit: f229674
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EnhancedVolcano_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EnhancedVolcano_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EnhancedVolcano_1.10.0.tgz
vignettes: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html
vignetteTitles: Publication-ready volcano plots with enhanced colouring
and labeling
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.R
dependencyCount: 82
Package: EnMCB
Version: 1.4.1
Depends: R (>= 4.0)
Imports: foreach, doParallel, parallel, stats, survivalROC, glmnet,
rms, mboost, survivalsvm, ggplot2,
IlluminaHumanMethylation450kanno.ilmn12.hg19, minfi, boot,
survival, utils
Suggests: SummarizedExperiment, testthat, Biobase, survminer,
affycoretools, knitr, plotROC, prognosticROC
License: GPL-2
MD5sum: debf194044e7f0034bcbcd681bd291e0
NeedsCompilation: no
Title: Predicting Disease Progression Based on Methylation Correlated
Blocks using Ensemble Models
Description: Creation of the correlated blocks using DNA methylation
profiles. A stacked ensemble of machine learning models, which
combined the cox, support vector machine and elastic-net
regression model, can be constructed to predict disease
progression.
biocViews: Normalization, DNAMethylation, MethylationArray,
SupportVectorMachine
Author: Xin Yu
Maintainer: Xin Yu <whirlsyu@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/whirlsyu/EnMCB/issues
git_url: https://git.bioconductor.org/packages/EnMCB
git_branch: RELEASE_3_13
git_last_commit: f4ab997
git_last_commit_date: 2021-09-20
Date/Publication: 2021-09-21
source.ver: src/contrib/EnMCB_1.4.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EnMCB_1.4.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/EnMCB_1.4.1.tgz
vignettes: vignettes/EnMCB/inst/doc/vignette.html
vignetteTitles: vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EnMCB/inst/doc/vignette.R
dependencyCount: 197
Package: ENmix
Version: 1.28.8
Depends: parallel,doParallel,foreach,SummarizedExperiment,stats
Imports:
grDevices,graphics,preprocessCore,matrixStats,methods,utils,irr,
geneplotter,impute,minfi,RPMM,illuminaio,dynamicTreeCut,IRanges,gtools,
Biobase,ExperimentHub,AnnotationHub,genefilter,gplots,quadprog,S4Vectors
Suggests: minfiData, RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: 828ee92c302037d1d971069c58f4caff
NeedsCompilation: no
Title: Quality control and analysis tools for Illumina DNA methylation
BeadChip
Description: Tool kits for quanlity control, analysis and visulization
of Illumina DNA methylation arrays.
biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel,
Microarray, OneChannel, MethylationArray, BatchEffect,
Normalization, DataImport, Regression,
PrincipalComponent,Epigenetics, MultiChannel,
DifferentialMethylation, ImmunoOncology
Author: Zongli Xu [cre, aut], Liang Niu [aut], Jack Taylor [ctb]
Maintainer: Zongli Xu <xuz@niehs.nih.gov>
git_url: https://git.bioconductor.org/packages/ENmix
git_branch: RELEASE_3_13
git_last_commit: e996e24
git_last_commit_date: 2021-10-05
Date/Publication: 2021-10-07
source.ver: src/contrib/ENmix_1.28.8.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ENmix_1.28.8.zip
mac.binary.ver: bin/macosx/contrib/4.1/ENmix_1.28.8.tgz
vignettes: vignettes/ENmix/inst/doc/ENmix.pdf
vignetteTitles: ENmix User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ENmix/inst/doc/ENmix.R
dependencyCount: 173
Package: EnrichedHeatmap
Version: 1.22.0
Depends: R (>= 3.1.2), methods, grid, ComplexHeatmap (>= 2.5.1),
GenomicRanges
Imports: matrixStats, stats, GetoptLong, Rcpp, utils, locfit, circlize
(>= 0.4.5), IRanges
LinkingTo: Rcpp
Suggests: testthat (>= 0.3), knitr, markdown, genefilter, RColorBrewer
License: MIT + file LICENSE
MD5sum: da9ba95b7ab1dfee9a194bc9cb2f9ad7
NeedsCompilation: yes
Title: Making Enriched Heatmaps
Description: Enriched heatmap is a special type of heatmap which
visualizes the enrichment of genomic signals on specific target
regions. Here we implement enriched heatmap by ComplexHeatmap
package. Since this type of heatmap is just a normal heatmap
but with some special settings, with the functionality of
ComplexHeatmap, it would be much easier to customize the
heatmap as well as concatenating to a list of heatmaps to show
correspondance between different data sources.
biocViews: Software, Visualization, Sequencing, GenomeAnnotation,
Coverage
Author: Zuguang Gu
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/EnrichedHeatmap
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EnrichedHeatmap
git_branch: RELEASE_3_13
git_last_commit: ff49b7f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EnrichedHeatmap_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EnrichedHeatmap_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EnrichedHeatmap_1.22.0.tgz
vignettes: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html,
vignettes/EnrichedHeatmap/inst/doc/roadmap.html,
vignettes/EnrichedHeatmap/inst/doc/row_odering.html,
vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.html
vignetteTitles: 1. Make Enriched Heatmaps, 4. Visualize Comprehensive
Associations in Roadmap dataset, 3. Compare row ordering
methods, 2. Visualize Categorical Signals
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.R,
vignettes/EnrichedHeatmap/inst/doc/roadmap.R,
vignettes/EnrichedHeatmap/inst/doc/row_odering.R,
vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.R
importsMe: profileplyr
suggestsMe: ComplexHeatmap, InteractiveComplexHeatmap
dependencyCount: 41
Package: EnrichmentBrowser
Version: 2.22.2
Depends: SummarizedExperiment, graph
Imports: AnnotationDbi, BiocFileCache, BiocManager, GSEABase, GO.db,
KEGGREST, KEGGgraph, Rgraphviz, S4Vectors, SPIA, edgeR,
graphite, hwriter, limma, methods, pathview, safe
Suggests: ALL, BiocStyle, ComplexHeatmap, DESeq2, ReportingTools,
airway, biocGraph, hgu95av2.db, geneplotter, knitr, msigdbr,
rmarkdown
License: Artistic-2.0
MD5sum: 6d0355aea7c6a6e10a992519e3b307d0
NeedsCompilation: no
Title: Seamless navigation through combined results of set-based and
network-based enrichment analysis
Description: The EnrichmentBrowser package implements essential
functionality for the enrichment analysis of gene expression
data. The analysis combines the advantages of set-based and
network-based enrichment analysis in order to derive
high-confidence gene sets and biological pathways that are
differentially regulated in the expression data under
investigation. Besides, the package facilitates the
visualization and exploration of such sets and pathways.
biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression,
DifferentialExpression, Pathways, GraphAndNetwork, Network,
GeneSetEnrichment, NetworkEnrichment, Visualization,
ReportWriting
Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara
Santarelli [ctb], Mirko Signorelli [ctb], Marcel Ramos [ctb],
Levi Waldron [ctb], Ralf Zimmer [aut]
Maintainer: Ludwig Geistlinger <Ludwig_Geistlinger@hms.harvard.edu>
VignetteBuilder: knitr
BugReports: https://github.com/lgeistlinger/EnrichmentBrowser/issues
git_url: https://git.bioconductor.org/packages/EnrichmentBrowser
git_branch: RELEASE_3_13
git_last_commit: e3ba414
git_last_commit_date: 2021-07-21
Date/Publication: 2021-07-22
source.ver: src/contrib/EnrichmentBrowser_2.22.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EnrichmentBrowser_2.22.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/EnrichmentBrowser_2.22.2.tgz
vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.pdf
vignetteTitles: EnrichmentBrowser Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R
importsMe: GSEABenchmarkeR
suggestsMe: GenomicSuperSignature
dependencyCount: 92
Package: enrichplot
Version: 1.12.3
Depends: R (>= 3.5.0)
Imports: cowplot, DOSE (>= 3.16.0), ggplot2, ggraph, graphics, grid,
igraph, methods, plyr, purrr, RColorBrewer, reshape2, stats,
utils, scatterpie, shadowtext, GOSemSim, magrittr, ggtree
Suggests: clusterProfiler, dplyr, europepmc, ggupset, knitr, rmarkdown,
org.Hs.eg.db, prettydoc, tibble, tidyr, ggforce, AnnotationDbi,
ggplotify, ggridges, grDevices, gridExtra, ggnewscale, ggrepel
(>= 0.9.0), ggstar, treeio, scales, tidytree
License: Artistic-2.0
MD5sum: b27db6550582409011532c8363944f7e
NeedsCompilation: no
Title: Visualization of Functional Enrichment Result
Description: The 'enrichplot' package implements several visualization
methods for interpreting functional enrichment results obtained
from ORA or GSEA analysis. All the visualization methods are
developed based on 'ggplot2' graphics.
biocViews: Annotation, GeneSetEnrichment, GO, KEGG, Pathways, Software,
Visualization
Author: Guangchuang Yu [aut, cre]
(<https://orcid.org/0000-0002-6485-8781>), Erqiang Hu [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/biomedical-knowledge-mining-book/
VignetteBuilder: knitr
BugReports: https://github.com/GuangchuangYu/enrichplot/issues
git_url: https://git.bioconductor.org/packages/enrichplot
git_branch: RELEASE_3_13
git_last_commit: 586391d
git_last_commit_date: 2021-10-08
Date/Publication: 2021-10-10
source.ver: src/contrib/enrichplot_1.12.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/enrichplot_1.12.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/enrichplot_1.12.3.tgz
vignettes: vignettes/enrichplot/inst/doc/enrichplot.html
vignetteTitles: enrichplot
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: maEndToEnd
importsMe: ChIPseeker, clusterProfiler, debrowser, MAGeCKFlute, meshes,
multiSight, ReactomePA, ExpHunterSuite
suggestsMe: methylGSA
dependencyCount: 123
Package: enrichTF
Version: 1.8.0
Depends: pipeFrame
Imports: BSgenome, rtracklayer, motifmatchr, TFBSTools, R.utils,
methods, JASPAR2018, GenomeInfoDb, GenomicRanges, IRanges,
BiocGenerics, S4Vectors, utils, parallel, stats, ggpubr,
heatmap3, ggplot2, clusterProfiler, rmarkdown, grDevices,
magrittr
Suggests: knitr, testthat, webshot
License: GPL-3
Archs: i386, x64
MD5sum: 91d272b46ea357e9081efac18379c306
NeedsCompilation: no
Title: Transcription Factors Enrichment Analysis
Description: As transcription factors (TFs) play a crucial role in
regulating the transcription process through binding on the
genome alone or in a combinatorial manner, TF enrichment
analysis is an efficient and important procedure to locate the
candidate functional TFs from a set of experimentally defined
regulatory regions. While it is commonly accepted that
structurally related TFs may have similar binding preference to
sequences (i.e. motifs) and one TF may have multiple motifs, TF
enrichment analysis is much more challenging than motif
enrichment analysis. Here we present a R package for TF
enrichment analysis which combine motif enrichment with the
PECA model.
biocViews: Software, GeneTarget, MotifAnnotation, GraphAndNetwork,
Transcription
Author: Zheng Wei, Zhana Duren, Shining Ma
Maintainer: Zheng Wei <wzwz@stanford.edu>
URL: https://github.com/wzthu/enrichTF
VignetteBuilder: knitr
BugReports: https://github.com/wzthu/enrichTF/issues
git_url: https://git.bioconductor.org/packages/enrichTF
git_branch: RELEASE_3_13
git_last_commit: 956c94b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/enrichTF_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/enrichTF_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/enrichTF_1.8.0.tgz
vignettes: vignettes/enrichTF/inst/doc/enrichTF.html
vignetteTitles: An Introduction to enrichTF
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/enrichTF/inst/doc/enrichTF.R
dependencyCount: 211
Package: ensembldb
Version: 2.16.4
Depends: BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.31.18),
GenomicFeatures (>= 1.29.10), AnnotationFilter (>= 1.5.2)
Imports: methods, RSQLite (>= 1.1), DBI, Biobase, GenomeInfoDb,
AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors (>=
0.23.10), Rsamtools, IRanges (>= 2.13.24), ProtGenerics,
Biostrings (>= 2.47.9), curl
Suggests: BiocStyle, knitr, EnsDb.Hsapiens.v86 (>= 0.99.8), testthat,
BSgenome.Hsapiens.NCBI.GRCh38, ggbio (>= 1.24.0), Gviz (>=
1.20.0), magrittr, rmarkdown, AnnotationHub
Enhances: RMariaDB, shiny
License: LGPL
MD5sum: 34403077a67040567ab005e3f28cdda7
NeedsCompilation: no
Title: Utilities to create and use Ensembl-based annotation databases
Description: The package provides functions to create and use
transcript centric annotation databases/packages. The
annotation for the databases are directly fetched from Ensembl
using their Perl API. The functionality and data is similar to
that of the TxDb packages from the GenomicFeatures package,
but, in addition to retrieve all gene/transcript models and
annotations from the database, ensembldb provides a filter
framework allowing to retrieve annotations for specific entries
like genes encoded on a chromosome region or transcript models
of lincRNA genes. EnsDb databases built with ensembldb contain
also protein annotations and mappings between proteins and
their encoding transcripts. Finally, ensembldb provides
functions to map between genomic, transcript and protein
coordinates.
biocViews: Genetics, AnnotationData, Sequencing, Coverage
Author: Johannes Rainer <johannes.rainer@eurac.edu> with contributions
from Tim Triche, Sebastian Gibb, Laurent Gatto and Christian
Weichenberger.
Maintainer: Johannes Rainer <johannes.rainer@eurac.edu>
URL: https://github.com/jorainer/ensembldb
VignetteBuilder: knitr
BugReports: https://github.com/jorainer/ensembldb/issues
git_url: https://git.bioconductor.org/packages/ensembldb
git_branch: RELEASE_3_13
git_last_commit: 130dbcc
git_last_commit_date: 2021-08-04
Date/Publication: 2021-08-05
source.ver: src/contrib/ensembldb_2.16.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ensembldb_2.16.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/ensembldb_2.16.4.tgz
vignettes:
vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.html,
vignettes/ensembldb/inst/doc/coordinate-mapping.html,
vignettes/ensembldb/inst/doc/ensembldb.html,
vignettes/ensembldb/inst/doc/MySQL-backend.html,
vignettes/ensembldb/inst/doc/proteins.html
vignetteTitles: Use cases for coordinate mapping with ensembldb,
Mapping between genome,, transcript and protein coordinates,
Generating an using Ensembl based annotation packages, Using a
MariaDB/MySQL server backend, Querying protein features
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.R,
vignettes/ensembldb/inst/doc/coordinate-mapping.R,
vignettes/ensembldb/inst/doc/ensembldb.R,
vignettes/ensembldb/inst/doc/MySQL-backend.R,
vignettes/ensembldb/inst/doc/proteins.R
dependsOnMe: chimeraviz, AHEnsDbs, EnsDb.Hsapiens.v75,
EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v75,
EnsDb.Mmusculus.v79, EnsDb.Rnorvegicus.v75,
EnsDb.Rnorvegicus.v79
importsMe: APAlyzer, biovizBase, BUSpaRse, ChIPpeakAnno, consensusDE,
diffUTR, epivizrData, ggbio, Gviz, ldblock, metagene, TVTB,
tximeta, GenomicDistributionsData, scRNAseq, utr.annotation
suggestsMe: alpine, CNVRanger, eisaR, EpiTxDb, GenomicFeatures,
multicrispr, satuRn, wiggleplotr
dependencyCount: 99
Package: ensemblVEP
Version: 1.34.0
Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation
Imports: S4Vectors (>= 0.9.25), Biostrings, SummarizedExperiment,
GenomeInfoDb, stats
Suggests: RUnit
License: Artistic-2.0
MD5sum: 0a2162c10aa913918af3dac1f2aabc06
NeedsCompilation: no
Title: R Interface to Ensembl Variant Effect Predictor
Description: Query the Ensembl Variant Effect Predictor via the perl
API.
biocViews: Annotation, VariantAnnotation, SNP
Author: Valerie Obenchain and Lori Shepherd
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
SystemRequirements: Ensembl VEP (API version 104) and the Perl modules
DBI and DBD::mysql must be installed. See the package README
and Ensembl installation instructions:
http://www.ensembl.org/info/docs/tools/vep/script/vep_download.html#installer
git_url: https://git.bioconductor.org/packages/ensemblVEP
git_branch: RELEASE_3_13
git_last_commit: 71a49c5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ensemblVEP_1.34.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/ensemblVEP_1.34.0.tgz
vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf,
vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.pdf
vignetteTitles: ensemblVEP, PreV90EnsemblVEP
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ensemblVEP/inst/doc/ensemblVEP.R,
vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.R
importsMe: MMAPPR2, TVTB
dependencyCount: 98
Package: epialleleR
Version: 1.0.0
Depends: R (>= 4.1)
Imports: stats, methods, utils, Rsamtools, GenomicRanges, BiocGenerics,
GenomeInfoDb, SummarizedExperiment, VariantAnnotation, stringi,
data.table
LinkingTo: Rcpp, BH
Suggests: RUnit, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 96984d5c93ffc2a060907648e015405b
NeedsCompilation: yes
Title: Fast, Epiallele-Aware Methylation Reporter
Description: Epialleles are specific DNA methylation patterns that are
mitotically and/or meiotically inherited. This package calls
hypermethylated epiallele frequencies at the level of genomic
regions or individual cytosines in next-generation sequencing
data using binary alignment map (BAM) files as an input. Other
functionality includes computing the empirical cumulative
distribution function for per-read beta values, and testing the
significance of the association between epiallele methylation
status and base frequencies at particular genomic positions
(SNPs).
biocViews: DNAMethylation, Epigenetics, MethylSeq
Author: Oleksii Nikolaienko [aut, cre]
(<https://orcid.org/0000-0002-5910-4934>)
Maintainer: Oleksii Nikolaienko <oleksii.nikolaienko@gmail.com>
URL: https://github.com/BBCG/epialleleR
VignetteBuilder: knitr
BugReports: https://github.com/BBCG/epialleleR/issues
git_url: https://git.bioconductor.org/packages/epialleleR
git_branch: RELEASE_3_13
git_last_commit: b01940d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/epialleleR_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/epialleleR_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/epialleleR_1.0.0.tgz
vignettes: vignettes/epialleleR/inst/doc/epialleleR.html
vignetteTitles: epialleleR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epialleleR/inst/doc/epialleleR.R
dependencyCount: 99
Package: epidecodeR
Version: 1.0.2
Depends: R (>= 3.1.0)
Imports: EnvStats, ggplot2, rtracklayer, GenomicRanges, IRanges,
rstatix, ggpubr, methods, stats, utils, dplyr
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 69afa31c303487ac5fc70e7805824d56
NeedsCompilation: no
Title: epidecodeR: a functional exploration tool for epigenetic and
epitranscriptomic regulation
Description: epidecodeR is a package capable of analysing impact of
degree of DNA/RNA epigenetic chemical modifications on
dysregulation of genes or proteins. This package integrates
chemical modification data generated from a host of epigenomic
or epitranscriptomic techniques such as ChIP-seq, ATAC-seq,
m6A-seq, etc. and dysregulated gene lists in the form of
differential gene expression, ribosome occupancy or
differential protein translation and identify impact of
dysregulation of genes caused due to varying degrees of
chemical modifications associated with the genes. epidecodeR
generates cumulative distribution function (CDF) plots showing
shifts in trend of overall log2FC between genes divided into
groups based on the degree of modification associated with the
genes. The tool also tests for significance of difference in
log2FC between groups of genes.
biocViews: DifferentialExpression, GeneRegulation, HistoneModification,
FunctionalPrediction, Transcription, GeneExpression,
Epitranscriptomics, Epigenetics, FunctionalGenomics,
SystemsBiology, Transcriptomics, ChipOnChip
Author: Kandarp Joshi [aut, cre], Dan Ohtan Wang [aut]
Maintainer: Kandarp Joshi <kandarpbioinfo@gmail.com>
URL: https://github.com/kandarpRJ/epidecodeR,
https://epidecoder.shinyapps.io/shinyapp/
VignetteBuilder: knitr
BugReports: https://github.com/kandarpRJ/epidecodeR/issues
git_url: https://git.bioconductor.org/packages/epidecodeR
git_branch: RELEASE_3_13
git_last_commit: ca8d4d8
git_last_commit_date: 2021-06-02
Date/Publication: 2021-06-03
source.ver: src/contrib/epidecodeR_1.0.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/epidecodeR_1.0.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/epidecodeR_1.0.2.tgz
vignettes: vignettes/epidecodeR/inst/doc/epidecodeR.html
vignetteTitles: epidecodeR: a functional exploration tool for
epigenetic and epitranscriptomic regulation
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epidecodeR/inst/doc/epidecodeR.R
dependencyCount: 137
Package: EpiDISH
Version: 2.8.0
Depends: R (>= 4.1)
Imports: MASS, e1071, quadprog, parallel, stats, matrixStats, stringr,
locfdr, Matrix
Suggests: roxygen2, GEOquery, BiocStyle, knitr, rmarkdown, Biobase,
testthat
License: GPL-2
MD5sum: 7e8c5a9ca756e13b7a120c733bb3ed3f
NeedsCompilation: no
Title: Epigenetic Dissection of Intra-Sample-Heterogeneity
Description: EpiDISH is a R package to infer the proportions of a
priori known cell-types present in a sample representing a
mixture of such cell-types. Right now, the package can be used
on DNAm data of whole blood, generic epithelial tissue and
breast tissue. Besides, the package provides a function that
allows the identification of differentially methylated
cell-types and their directionality of change in Epigenome-Wide
Association Studies.
biocViews: DNAMethylation, MethylationArray, Epigenetics,
DifferentialMethylation, ImmunoOncology
Author: Andrew E. Teschendorff [aut], Shijie C. Zheng [aut, cre]
Maintainer: Shijie C. Zheng <shijieczheng@gmail.com>
URL: https://github.com/sjczheng/EpiDISH
VignetteBuilder: knitr
BugReports: https://github.com/sjczheng/EpiDISH/issues
git_url: https://git.bioconductor.org/packages/EpiDISH
git_branch: RELEASE_3_13
git_last_commit: e034b98
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EpiDISH_2.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EpiDISH_2.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EpiDISH_2.8.0.tgz
vignettes: vignettes/EpiDISH/inst/doc/EpiDISH.html
vignetteTitles: Epigenetic Dissection of Intra-Sample-Heterogeneity
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EpiDISH/inst/doc/EpiDISH.R
dependsOnMe: TOAST
suggestsMe: planet, FlowSorted.Blood.EPIC
dependencyCount: 22
Package: epigenomix
Version: 1.32.0
Depends: R (>= 3.2.0), methods, Biobase, S4Vectors, IRanges,
GenomicRanges, SummarizedExperiment
Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb,
beadarray
License: LGPL-3
MD5sum: a23673fa33acbc98c91a0b3de5512608
NeedsCompilation: no
Title: Epigenetic and gene transcription data normalization and
integration with mixture models
Description: A package for the integrative analysis of RNA-seq or
microarray based gene transcription and histone modification
data obtained by ChIP-seq. The package provides methods for
data preprocessing and matching as well as methods for fitting
bayesian mixture models in order to detect genes with
differences in both data types.
biocViews: ChIPSeq, GeneExpression, DifferentialExpression,
Classification
Author: Hans-Ulrich Klein, Martin Schaefer
Maintainer: Hans-Ulrich Klein <h.klein@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/epigenomix
git_branch: RELEASE_3_13
git_last_commit: dc1b2d5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/epigenomix_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/epigenomix_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/epigenomix_1.32.0.tgz
vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf
vignetteTitles: epigenomix package vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epigenomix/inst/doc/epigenomix.R
dependencyCount: 104
Package: epigraHMM
Version: 1.0.8
Depends: R (>= 3.5.0)
Imports: Rcpp, magrittr, data.table, SummarizedExperiment, methods,
GenomeInfoDb, GenomicRanges, rtracklayer, IRanges, Rsamtools,
bamsignals, csaw, S4Vectors, limma, stats, Rhdf5lib, rhdf5,
Matrix, MASS, scales, ggpubr, ggplot2, GreyListChIP, pheatmap,
grDevices
LinkingTo: Rcpp, RcppArmadillo, Rhdf5lib
Suggests: testthat, knitr, rmarkdown, BiocStyle,
BSgenome.Rnorvegicus.UCSC.rn4, gcapc, chromstaRData
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 91f0c1325e73f8c35515805d41029d77
NeedsCompilation: yes
Title: Epigenomic R-based analysis with hidden Markov models
Description: epigraHMM provides a set of tools for the analysis of
epigenomic data based on hidden Markov Models. It contains two
separate peak callers, one for consensus peaks from biological
or technical replicates, and one for differential peaks from
multi-replicate multi-condition experiments. In differential
peak calling, epigraHMM provides window-specific posterior
probabilities associated with every possible combinatorial
pattern of read enrichment across conditions.
biocViews: ChIPSeq, ATACSeq, DNaseSeq, HiddenMarkovModel, Epigenetics
Author: Pedro Baldoni [aut, cre]
Maintainer: Pedro Baldoni <pedrobaldoni@gmail.com>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/epigraHMM
git_branch: RELEASE_3_13
git_last_commit: 3d5f844
git_last_commit_date: 2021-10-04
Date/Publication: 2021-10-07
source.ver: src/contrib/epigraHMM_1.0.8.tar.gz
win.binary.ver: bin/windows/contrib/4.1/epigraHMM_1.0.8.zip
mac.binary.ver: bin/macosx/contrib/4.1/epigraHMM_1.0.8.tgz
vignettes: vignettes/epigraHMM/inst/doc/epigraHMM.html
vignetteTitles: Consensus and Differential Peak Calling With epigraHMM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/epigraHMM/inst/doc/epigraHMM.R
dependencyCount: 147
Package: epihet
Version: 1.8.0
Depends: R(>= 3.6), GenomicRanges, IRanges, S4Vectors, ggplot2,
foreach, Rtsne, igraph
Imports: data.table, doParallel, EntropyExplorer, graphics, stats,
grDevices, pheatmap, utils, qvalue, WGCNA, ReactomePA
Suggests: knitr, clusterProfiler, ggfortify, org.Hs.eg.db, rmarkdown
License: Artistic-2.0
MD5sum: 2913ca46c4dca11282842f2dd3973624
NeedsCompilation: no
Title: Determining Epigenetic Heterogeneity from Bisulfite Sequencing
Data
Description: epihet is an R-package that calculates the epigenetic
heterogeneity between cancer cells and/or normal cells. The
functions establish a pipeline that take in bisulfite
sequencing data from multiple samples and use the data to track
similarities and differences in epipolymorphism,proportion of
discordantly methylated sequencing reads (PDR),and Shannon
entropy values at epialleles that are shared between the
samples.epihet can be used to perform analysis on the data by
creating pheatmaps, box plots, PCA plots, and t-SNE plots. MA
plots can also be created by calculating the differential
heterogeneity of the samples. And we construct co-epihet
network and perform network analysis.
biocViews: DNAMethylation, Epigenetics, MethylSeq, Sequencing, Software
Author: Xiaowen Chen [aut, cre], Haitham Ashoor [aut], Ryan Musich
[aut], Mingsheng Zhang [aut], Jiahui Wang [aut], Sheng Li [aut]
Maintainer: Xiaowen Chen <Xiaowen.Chen@jax.org>
URL: https://github.com/TheJacksonLaboratory/epihet
VignetteBuilder: knitr
BugReports: https://github.com/TheJacksonLaboratory/epihet/issues
git_url: https://git.bioconductor.org/packages/epihet
git_branch: RELEASE_3_13
git_last_commit: cffe2bb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/epihet_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/epihet_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/epihet_1.8.0.tgz
vignettes: vignettes/epihet/inst/doc/epihet.pdf
vignetteTitles: epihet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epihet/inst/doc/epihet.R
dependencyCount: 164
Package: epiNEM
Version: 1.16.0
Depends: R (>= 4.1)
Imports: BoolNet, e1071, gtools, stats, igraph, utils, lattice,
latticeExtra, RColorBrewer, pcalg, minet, grDevices, graph,
mnem, latex2exp
Suggests: knitr, RUnit, BiocGenerics, STRINGdb, devtools, rmarkdown,
GOSemSim, AnnotationHub, org.Sc.sgd.db
License: GPL-3
MD5sum: 7472f66c0e611abf0320e5043de7aec7
NeedsCompilation: no
Title: epiNEM
Description: epiNEM is an extension of the original Nested Effects
Models (NEM). EpiNEM is able to take into account double
knockouts and infer more complex network signalling pathways.
It is tailored towards large scale double knock-out screens.
biocViews: Pathways, SystemsBiology, NetworkInference, Network
Author: Madeline Diekmann & Martin Pirkl
Maintainer: Martin Pirkl <martinpirkl@yahoo.de>
URL: https://github.com/cbg-ethz/epiNEM/
VignetteBuilder: knitr
BugReports: https://github.com/cbg-ethz/epiNEM/issues
git_url: https://git.bioconductor.org/packages/epiNEM
git_branch: RELEASE_3_13
git_last_commit: 2383f66
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/epiNEM_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/epiNEM_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/epiNEM_1.16.0.tgz
vignettes: vignettes/epiNEM/inst/doc/epiNEM.html
vignetteTitles: epiNEM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epiNEM/inst/doc/epiNEM.R
importsMe: bnem, dce, nempi
suggestsMe: mnem
dependencyCount: 108
Package: EpiTxDb
Version: 1.4.0
Depends: R (>= 4.0), AnnotationDbi, Modstrings
Imports: methods, utils, httr, xml2, curl, GenomicFeatures,
GenomicRanges, GenomeInfoDb, BiocGenerics, BiocFileCache,
S4Vectors, IRanges, RSQLite, DBI, Biostrings, tRNAdbImport
Suggests: BiocStyle, knitr, rmarkdown, testthat, httptest,
AnnotationHub, ensembldb, ggplot2, EpiTxDb.Hs.hg38,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Scerevisiae.UCSC.sacCer3,
TxDb.Hsapiens.UCSC.hg38.knownGene
License: Artistic-2.0
MD5sum: 4c96f38e1ebe292b4c97120b59aae59b
NeedsCompilation: no
Title: Storing and accessing epitranscriptomic information using the
AnnotationDbi interface
Description: EpiTxDb facilitates the storage of epitranscriptomic
information. More specifically, it can keep track of
modification identity, position, the enzyme for introducing it
on the RNA, a specifier which determines the position on the
RNA to be modified and the literature references each
modification is associated with.
biocViews: Software, Epitranscriptomics
Author: Felix G.M. Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/EpiTxDb
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/EpiTxDb/issues
git_url: https://git.bioconductor.org/packages/EpiTxDb
git_branch: RELEASE_3_13
git_last_commit: 14681e3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EpiTxDb_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EpiTxDb_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EpiTxDb_1.4.0.tgz
vignettes: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.html,
vignettes/EpiTxDb/inst/doc/EpiTxDb.html
vignetteTitles: EpiTxDb-creation, EpiTxDb
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.R,
vignettes/EpiTxDb/inst/doc/EpiTxDb.R
dependsOnMe: EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3
dependencyCount: 114
Package: epivizr
Version: 2.22.0
Depends: R (>= 3.0), methods,
Imports: epivizrServer (>= 1.1.1), epivizrData (>= 1.3.4),
GenomicRanges, S4Vectors, IRanges, bumphunter, GenomeInfoDb
Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment,
antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle,
minfi
License: Artistic-2.0
Archs: i386, x64
MD5sum: 57c17eedb00dd887654efb0eef7bd677
NeedsCompilation: no
Title: R Interface to epiviz web app
Description: This package provides connections to the epiviz web app
(http://epiviz.cbcb.umd.edu) for interactive visualization of
genomic data. Objects in R/bioc interactive sessions can be
displayed in genome browser tracks or plots to be explored by
navigation through genomic regions. Fundamental Bioconductor
data structures are supported (e.g., GenomicRanges and
RangedSummarizedExperiment objects), while providing an easy
mechanism to support other data structures (through package
epivizrData). Visualizations (using d3.js) can be easily added
to the web app as well.
biocViews: Visualization, Infrastructure, GUI
Author: Hector Corrada Bravo, Florin Chelaru, Llewellyn Smith, Naomi
Goldstein, Jayaram Kancherla, Morgan Walter, Brian Gottfried
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
VignetteBuilder: knitr
Video: https://www.youtube.com/watch?v=099c4wUxozA
git_url: https://git.bioconductor.org/packages/epivizr
git_branch: RELEASE_3_13
git_last_commit: f93816c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/epivizr_2.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/epivizr_2.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/epivizr_2.22.0.tgz
vignettes: vignettes/epivizr/inst/doc/IntroToEpivizr.html
vignetteTitles: Introduction to epivizr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R
dependsOnMe: epivizrStandalone
importsMe: metavizr
dependencyCount: 117
Package: epivizrChart
Version: 1.14.0
Depends: R (>= 3.4.0)
Imports: epivizrData (>= 1.5.1), epivizrServer, htmltools, rjson,
methods, BiocGenerics
Suggests: testthat, roxygen2, knitr, Biobase, GenomicRanges, S4Vectors,
IRanges, SummarizedExperiment, antiProfilesData,
hgu133plus2.db, Mus.musculus, BiocStyle, Homo.sapiens, shiny,
minfi, Rsamtools, rtracklayer, RColorBrewer, magrittr,
AnnotationHub
License: Artistic-2.0
MD5sum: 53d95e01e8da4131f674d9456152cb69
NeedsCompilation: no
Title: R interface to epiviz web components
Description: This package provides an API for interactive visualization
of genomic data using epiviz web components. Objects in
R/BioConductor can be used to generate interactive R
markdown/notebook documents or can be visualized in the R
Studio's default viewer.
biocViews: Visualization, GUI
Author: Brian Gottfried [aut], Jayaram Kancherla [aut], Hector Corrada
Bravo [aut, cre]
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/epivizrChart
git_branch: RELEASE_3_13
git_last_commit: 5b50f3c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/epivizrChart_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/epivizrChart_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/epivizrChart_1.14.0.tgz
vignettes: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.html,
vignettes/epivizrChart/inst/doc/IntegrationWithShiny.html,
vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.html,
vignettes/epivizrChart/inst/doc/VisualizeSumExp.html
vignetteTitles: Visualizing Files with epivizrChart, Visualizing
genomic data in Shiny Apps using epivizrChart, Introduction to
epivizrChart, Visualizing `RangeSummarizedExperiment` objects
Shiny Apps using epivizrChart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.R,
vignettes/epivizrChart/inst/doc/IntegrationWithShiny.R,
vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.R,
vignettes/epivizrChart/inst/doc/VisualizeSumExp.R
dependencyCount: 111
Package: epivizrData
Version: 1.20.0
Depends: R (>= 3.4), methods, epivizrServer (>= 1.1.1), Biobase
Imports: S4Vectors, GenomicRanges, SummarizedExperiment (>= 0.2.0),
OrganismDbi, GenomicFeatures, GenomeInfoDb, IRanges, ensembldb
Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus,
TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown,
BiocStyle, EnsDb.Mmusculus.v79, AnnotationHub, rtracklayer,
utils, RMySQL, DBI, matrixStats
License: MIT + file LICENSE
MD5sum: 3a3a5be3d2e05ab495d890c0b3735857
NeedsCompilation: no
Title: Data Management API for epiviz interactive visualization app
Description: Serve data from Bioconductor Objects through a WebSocket
connection.
biocViews: Infrastructure, Visualization
Author: Hector Corrada Bravo [aut, cre], Florin Chelaru [aut]
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
URL: http://epiviz.github.io
VignetteBuilder: knitr
BugReports: https://github.com/epiviz/epivizrData/issues
git_url: https://git.bioconductor.org/packages/epivizrData
git_branch: RELEASE_3_13
git_last_commit: ac055c8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/epivizrData_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/epivizrData_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/epivizrData_1.20.0.tgz
vignettes: vignettes/epivizrData/inst/doc/epivizrData.html
vignetteTitles: epivizrData Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R
importsMe: epivizr, epivizrChart, metavizr
dependencyCount: 108
Package: epivizrServer
Version: 1.20.0
Depends: R (>= 3.2.3), methods
Imports: httpuv (>= 1.3.0), R6 (>= 2.0.0), rjson, mime (>= 0.2)
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 18eb570424d8ff0be96f366fcea5c67b
NeedsCompilation: no
Title: WebSocket server infrastructure for epivizr apps and packages
Description: This package provides objects to manage WebSocket
connections to epiviz apps. Other epivizr package use this
infrastructure.
biocViews: Infrastructure, Visualization
Author: Hector Corrada Bravo [aut, cre]
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
URL: https://epiviz.github.io
VignetteBuilder: knitr
BugReports: https://github.com/epiviz/epivizrServer
git_url: https://git.bioconductor.org/packages/epivizrServer
git_branch: RELEASE_3_13
git_last_commit: af3b113
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/epivizrServer_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/epivizrServer_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/epivizrServer_1.20.0.tgz
vignettes: vignettes/epivizrServer/inst/doc/epivizrServer.html
vignetteTitles: epivizrServer Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependsOnMe: epivizrData
importsMe: epivizr, epivizrChart, epivizrStandalone, metavizr
dependencyCount: 13
Package: epivizrStandalone
Version: 1.20.0
Depends: R (>= 3.2.3), epivizr (>= 2.3.6), methods
Imports: git2r, epivizrServer, GenomeInfoDb, BiocGenerics,
GenomicFeatures, S4Vectors
Suggests: testthat, knitr, rmarkdown, OrganismDbi (>= 1.13.9),
Mus.musculus, Biobase, BiocStyle
License: MIT + file LICENSE
MD5sum: 96d41d5ec7b7abd4c526bd8466bd0dc8
NeedsCompilation: no
Title: Run Epiviz Interactive Genomic Data Visualization App within R
Description: This package imports the epiviz visualization JavaScript
app for genomic data interactive visualization. The
'epivizrServer' package is used to provide a web server running
completely within R. This standalone version allows to browse
arbitrary genomes through genome annotations provided by
Bioconductor packages.
biocViews: Visualization, Infrastructure, GUI
Author: Hector Corrada Bravo, Jayaram Kancherla
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/epivizrStandalone
git_branch: RELEASE_3_13
git_last_commit: fd34eef
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/epivizrStandalone_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/epivizrStandalone_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/epivizrStandalone_1.20.0.tgz
vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html
vignetteTitles: Introduction to epivizrStandalone
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
importsMe: metavizr
dependencyCount: 119
Package: erccdashboard
Version: 1.26.0
Depends: R (>= 3.2), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0)
Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr,
qvalue, reshape2, ROCR, scales, stringr
License: GPL (>=2)
MD5sum: 64ac5264609ec7396505bc3ce452f63a
NeedsCompilation: no
Title: Assess Differential Gene Expression Experiments with ERCC
Controls
Description: Technical performance metrics for differential gene
expression experiments using External RNA Controls Consortium
(ERCC) spike-in ratio mixtures.
biocViews: ImmunoOncology, GeneExpression, Transcription,
AlternativeSplicing, DifferentialExpression,
DifferentialSplicing, Genetics, Microarray, mRNAMicroarray,
RNASeq, BatchEffect, MultipleComparison, QualityControl
Author: Sarah Munro, Steve Lund
Maintainer: Sarah Munro <sarah.munro@gmail.com>
URL: https://github.com/munrosa/erccdashboard,
http://tinyurl.com/erccsrm
BugReports: https://github.com/munrosa/erccdashboard/issues
git_url: https://git.bioconductor.org/packages/erccdashboard
git_branch: RELEASE_3_13
git_last_commit: cea65f1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/erccdashboard_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/erccdashboard_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/erccdashboard_1.26.0.tgz
vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.pdf
vignetteTitles: erccdashboard examples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R
dependencyCount: 55
Package: erma
Version: 1.8.0
Depends: R (>= 3.1), methods, Homo.sapiens, GenomicFiles (>= 1.5.2)
Imports: rtracklayer (>= 1.38.1), S4Vectors (>= 0.23.18), BiocGenerics,
GenomicRanges, SummarizedExperiment, ggplot2, GenomeInfoDb,
Biobase, shiny, BiocParallel, IRanges, AnnotationDbi
Suggests: rmarkdown, BiocStyle, knitr, GO.db, png, DT, doParallel
License: Artistic-2.0
MD5sum: 711c8d85bd43af3dc9336d1376a8674b
NeedsCompilation: no
Title: epigenomic road map adventures
Description: Software and data to support epigenomic road map
adventures.
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/erma
git_branch: RELEASE_3_13
git_last_commit: 6958ee9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/erma_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/erma_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/erma_1.8.0.tgz
vignettes: vignettes/erma/inst/doc/erma.html
vignetteTitles: ermaInteractive
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/erma/inst/doc/erma.R
dependencyCount: 135
Package: ERSSA
Version: 1.10.0
Depends: R (>= 4.0.0)
Imports: edgeR (>= 3.23.3), DESeq2 (>= 1.21.16), ggplot2 (>= 3.0.0),
RColorBrewer (>= 1.1-2), plyr (>= 1.8.4), BiocParallel (>=
1.15.8), grDevices, stats, utils
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3 | file LICENSE
MD5sum: 4cc400c8b6d366694467373d2a04538c
NeedsCompilation: no
Title: Empirical RNA-seq Sample Size Analysis
Description: The ERSSA package takes user supplied RNA-seq differential
expression dataset and calculates the number of differentially
expressed genes at varying biological replicate levels. This
allows the user to determine, without relying on any a priori
assumptions, whether sufficient differential detection has been
acheived with their RNA-seq dataset.
biocViews: ImmunoOncology, GeneExpression, Transcription,
DifferentialExpression, RNASeq, MultipleComparison,
QualityControl
Author: Zixuan Shao [aut, cre]
Maintainer: Zixuan Shao <Zixuanshao.zach@gmail.com>
URL: https://github.com/zshao1/ERSSA
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ERSSA
git_branch: RELEASE_3_13
git_last_commit: cdcef91
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ERSSA_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ERSSA_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ERSSA_1.10.0.tgz
vignettes: vignettes/ERSSA/inst/doc/ERSSA.html
vignetteTitles: ERSSA Package Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ERSSA/inst/doc/ERSSA.R
dependencyCount: 96
Package: esATAC
Version: 1.14.0
Depends: R (>= 3.5), Rsamtools, GenomicRanges, ShortRead, pipeFrame
Imports: Rcpp (>= 0.12.11), methods, knitr, Rbowtie2, rtracklayer,
ggplot2, Biostrings, ChIPseeker, clusterProfiler, igraph,
rJava, magrittr, digest, BSgenome, AnnotationDbi,
GenomicFeatures, R.utils, GenomeInfoDb, BiocGenerics,
S4Vectors, IRanges, rmarkdown, tools, VennDiagram, grid,
JASPAR2018, TFBSTools, grDevices, graphics, stats, utils,
parallel, corrplot, BiocManager, motifmatchr
LinkingTo: Rcpp
Suggests: BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat,
webshot
License: GPL-3 | file LICENSE
MD5sum: aa7b51fbb24d051dbf3437857f11c12d
NeedsCompilation: yes
Title: An Easy-to-use Systematic pipeline for ATACseq data analysis
Description: This package provides a framework and complete preset
pipeline for quantification and analysis of ATAC-seq Reads. It
covers raw sequencing reads preprocessing (FASTQ files), reads
alignment (Rbowtie2), aligned reads file operations (SAM, BAM,
and BED files), peak calling (F-seq), genome annotations
(Motif, GO, SNP analysis) and quality control report. The
package is managed by dataflow graph. It is easy for user to
pass variables seamlessly between processes and understand the
workflow. Users can process FASTQ files through end-to-end
preset pipeline which produces a pretty HTML report for quality
control and preliminary statistical results, or customize
workflow starting from any intermediate stages with esATAC
functions easily and flexibly.
biocViews: ImmunoOncology, Sequencing, DNASeq, QualityControl,
Alignment, Preprocessing, Coverage, ATACSeq, DNaseSeq
Author: Zheng Wei, Wei Zhang
Maintainer: Zheng Wei <wzweizheng@qq.com>
URL: https://github.com/wzthu/esATAC
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/wzthu/esATAC/issues
git_url: https://git.bioconductor.org/packages/esATAC
git_branch: RELEASE_3_13
git_last_commit: 2939dd0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/esATAC_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/esATAC_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/esATAC_1.14.0.tgz
vignettes: vignettes/esATAC/inst/doc/esATAC-Introduction.html
vignetteTitles: esATAC: an Easy-to-use Systematic pipeline for ATAC-seq
data analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/esATAC/inst/doc/esATAC-Introduction.R
dependencyCount: 199
Package: escape
Version: 1.2.0
Depends: R (>= 4.0)
Imports: grDevices, dplyr, ggplot2, GSEABase, GSVA,
SingleCellExperiment, limma, ggridges, msigdbr, stats,
BiocParallel, Matrix
Suggests: Seurat, SeuratObject, knitr, rmarkdown, BiocStyle, testthat,
dittoSeq (>= 1.1.2)
License: Apache License 2.0
MD5sum: f8d7152ea491949ec42ea370ff7a5ac5
NeedsCompilation: no
Title: Easy single cell analysis platform for enrichment
Description: A bridging R package to facilitate gene set enrichment
analysis (GSEA) in the context of single-cell RNA sequencing.
Using raw count information, Seurat objects, or
SingleCellExperiment format, users can perform and visualize
GSEA across individual cells.
biocViews: Software, SingleCell, Classification, Annotation,
GeneSetEnrichment, Sequencing, GeneSignaling, Pathways
Author: Nick Borcherding [aut, cre], Jared Andrews [aut]
Maintainer: Nick Borcherding <ncborch@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/escape
git_branch: RELEASE_3_13
git_last_commit: eac1d27
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/escape_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/escape_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/escape_1.2.0.tgz
vignettes: vignettes/escape/inst/doc/vignette.html
vignetteTitles: Escape-ingToWork
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/escape/inst/doc/vignette.R
dependencyCount: 111
Package: esetVis
Version: 1.18.0
Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils,
grDevices, methods
Suggests: ggplot2, ggvis, rbokeh, ggrepel, knitr, rmarkdown, ALL,
hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment
License: GPL-3
MD5sum: 749bfb9f06a69ab7973afe959e6addd7
NeedsCompilation: no
Title: Visualizations of expressionSet Bioconductor object
Description: Utility functions for visualization of expressionSet (or
SummarizedExperiment) Bioconductor object, including spectral
map, tsne and linear discriminant analysis. Static plot via the
ggplot2 package or interactive via the ggvis or rbokeh packages
are available.
biocViews: Visualization, DataRepresentation, DimensionReduction,
PrincipalComponent, Pathways
Author: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/esetVis
git_branch: RELEASE_3_13
git_last_commit: 3e8a9aa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/esetVis_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/esetVis_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/esetVis_1.18.0.tgz
vignettes: vignettes/esetVis/inst/doc/esetVis-vignette.html
vignetteTitles: esetVis package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/esetVis/inst/doc/esetVis-vignette.R
dependencyCount: 57
Package: eudysbiome
Version: 1.22.0
Depends: R (>= 3.1.0)
Imports: plyr, Rsamtools, R.utils, Biostrings
License: GPL-2
MD5sum: 0a108d616549e2a71e7e84fb43acd33c
NeedsCompilation: no
Title: Cartesian plot and contingency test on 16S Microbial data
Description: eudysbiome a package that permits to annotate the
differential genera as harmful/harmless based on their ability
to contribute to host diseases (as indicated in literature) or
unknown based on their ambiguous genus classification. Further,
the package statistically measures the eubiotic (harmless
genera increase or harmful genera decrease) or
dysbiotic(harmless genera decrease or harmful genera increase)
impact of a given treatment or environmental change on the
(gut-intestinal, GI) microbiome in comparison to the microbiome
of the reference condition.
Author: Xiaoyuan Zhou, Christine Nardini
Maintainer: Xiaoyuan Zhou <zhouxiaoyuan@picb.ac.cn>
git_url: https://git.bioconductor.org/packages/eudysbiome
git_branch: RELEASE_3_13
git_last_commit: 2309739
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/eudysbiome_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/eudysbiome_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/eudysbiome_1.22.0.tgz
vignettes: vignettes/eudysbiome/inst/doc/eudysbiome.pdf
vignetteTitles: eudysbiome User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/eudysbiome/inst/doc/eudysbiome.R
dependencyCount: 34
Package: evaluomeR
Version: 1.8.0
Depends: R (>= 3.6), SummarizedExperiment, MultiAssayExperiment,
cluster (>= 2.0.9), fpc (>= 2.2-3), randomForest (>= 4.6.14),
flexmix (>= 2.3.15)
Imports: corrplot (>= 0.84), grDevices, graphics, reshape2, ggplot2,
ggdendro, plotrix, stats, matrixStats, Rdpack, MASS, class,
prabclus, mclust, kableExtra
Suggests: BiocStyle, knitr, rmarkdown, magrittr
License: GPL-3
Archs: x64
MD5sum: 401d0c0b674a8699960fa422de9acf9e
NeedsCompilation: no
Title: Evaluation of Bioinformatics Metrics
Description: Evaluating the reliability of your own metrics and the
measurements done on your own datasets by analysing the
stability and goodness of the classifications of such metrics.
biocViews: Clustering, Classification, FeatureExtraction
Author: José Antonio Bernabé-DÃaz [aut, cre], Manuel Franco [aut],
Juana-MarÃa Vivo [aut], Manuel Quesada-MartÃnez [aut], Astrid
Duque-Ramos [aut], Jesualdo Tomás Fernández-Breis [aut]
Maintainer: José Antonio Bernabé-DÃaz <joseantonio.bernabe1@um.es>
URL: https://github.com/neobernad/evaluomeR
VignetteBuilder: knitr
BugReports: https://github.com/neobernad/evaluomeR/issues
git_url: https://git.bioconductor.org/packages/evaluomeR
git_branch: RELEASE_3_13
git_last_commit: b88f5c0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/evaluomeR_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/evaluomeR_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/evaluomeR_1.8.0.tgz
vignettes: vignettes/evaluomeR/inst/doc/manual.html
vignetteTitles: Evaluation of Bioinformatics Metrics with evaluomeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/evaluomeR/inst/doc/manual.R
dependencyCount: 115
Package: EventPointer
Version: 3.0.0
Depends: R (>= 3.5.0), SGSeq, Matrix, SummarizedExperiment
Imports: GenomicFeatures, stringr, GenomeInfoDb, igraph, MASS, nnls,
limma, matrixStats, RBGL, prodlim, graph, methods, utils,
stats, doParallel, foreach, affxparser, GenomicRanges,
S4Vectors, IRanges, qvalue, cobs, rhdf5, BSgenome, Biostrings,
glmnet, abind, iterators, lpSolve, poibin, speedglm, tximport
Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, dplyr,
kableExtra
License: Artistic-2.0
MD5sum: a7c141ef59c8ca9406a54cb5c35059b6
NeedsCompilation: yes
Title: An effective identification of alternative splicing events using
junction arrays and RNA-Seq data
Description: EventPointer is an R package to identify alternative
splicing events that involve either simple (case-control
experiment) or complex experimental designs such as time course
experiments and studies including paired-samples. The algorithm
can be used to analyze data from either junction arrays
(Affymetrix Arrays) or sequencing data (RNA-Seq). The software
returns a data.frame with the detected alternative splicing
events: gene name, type of event (cassette, alternative
3',...,etc), genomic position, statistical significance and
increment of the percent spliced in (Delta PSI) for all the
events. The algorithm can generate a series of files to
visualize the detected alternative splicing events in IGV. This
eases the interpretation of results and the design of primers
for standard PCR validation.
biocViews: AlternativeSplicing, DifferentialSplicing, mRNAMicroarray,
RNASeq, Transcription, Sequencing, TimeCourse, ImmunoOncology
Author: Juan Pablo Romero [aut], Juan A. Ferrer-Bonsoms [aut, cre]
Maintainer: Juan A. Ferrer-Bonsoms <jafhernandez@tecnun.es>
VignetteBuilder: knitr
BugReports: https://github.com/jpromeror/EventPointer/issues
git_url: https://git.bioconductor.org/packages/EventPointer
git_branch: RELEASE_3_13
git_last_commit: e4f2123
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/EventPointer_3.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EventPointer_3.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/EventPointer_3.0.0.tgz
vignettes: vignettes/EventPointer/inst/doc/EventPointer.html
vignetteTitles: EventPointer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EventPointer/inst/doc/EventPointer.R
dependencyCount: 154
Package: EWCE
Version: 1.0.1
Depends: R(>= 4.1), RNOmni (>= 1.0)
Imports: AnnotationHub, ewceData, ExperimentHub, ggplot2, grDevices,
grid, reshape2, biomaRt, limma, stringr, cowplot, HGNChelper,
ggdendro, gridExtra, Matrix, methods, parallel, future, scales,
SummarizedExperiment, stats, utils
Suggests: devtools, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0),
data.table, sctransform, readxl, SingleCellExperiment, memoise,
markdown
License: Artistic-2.0
MD5sum: 332d18de204a132f273b6deb7255ea2e
NeedsCompilation: no
Title: Expression Weighted Celltype Enrichment
Description: Used to determine which cell types are enriched within
gene lists. The package provides tools for testing enrichments
within simple gene lists (such as human disease associated
genes) and those resulting from differential expression
studies. The package does not depend upon any particular Single
Cell Transcriptome dataset and user defined datasets can be
loaded in and used in the analyses.
biocViews: GeneExpression, Transcription, DifferentialExpression,
GeneSetEnrichment, Genetics, Microarray, mRNAMicroarray,
OneChannel, RNASeq, BiomedicalInformatics, Proteomics,
Visualization, FunctionalGenomics, SingleCell
Author: Alan Murphy [cre] (<https://orcid.org/0000-0002-2487-8753>),
Nathan Skene [aut] (<https://orcid.org/0000-0002-6807-3180>)
Maintainer: Alan Murphy <alanmurph94@hotmail.com>
URL: https://github.com/NathanSkene/EWCE
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EWCE
git_branch: RELEASE_3_13
git_last_commit: 680dd7d
git_last_commit_date: 2021-06-17
Date/Publication: 2021-06-20
source.ver: src/contrib/EWCE_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/EWCE_1.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/EWCE_1.0.1.tgz
vignettes: vignettes/EWCE/inst/doc/EWCE.html
vignetteTitles: Expression Weighted Celltype Enrichment with EWCE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EWCE/inst/doc/EWCE.R
dependencyCount: 133
Package: ExCluster
Version: 1.10.0
Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges
Imports: stats, methods, grDevices, graphics, utils
License: GPL-3
MD5sum: 403c8d320794692b48b2f5b186b3e687
NeedsCompilation: no
Title: ExCluster robustly detects differentially expressed exons
between two conditions of RNA-seq data, requiring at least two
independent biological replicates per condition
Description: ExCluster flattens Ensembl and GENCODE GTF files into GFF
files, which are used to count reads per non-overlapping exon
bin from BAM files. This read counting is done using the
function featureCounts from the package Rsubread. Library sizes
are normalized across all biological replicates, and ExCluster
then compares two different conditions to detect signifcantly
differentially spliced genes. This process requires at least
two independent biological repliates per condition, and
ExCluster accepts only exactly two conditions at a time.
ExCluster ultimately produces false discovery rates (FDRs) per
gene, which are used to detect significance. Exon log2 fold
change (log2FC) means and variances may be plotted for each
significantly differentially spliced gene, which helps
scientists develop hypothesis and target differential splicing
events for RT-qPCR validation in the wet lab.
biocViews: ImmunoOncology, DifferentialSplicing, RNASeq, Software
Author: R. Matthew Tanner, William L. Stanford, and Theodore J. Perkins
Maintainer: R. Matthew Tanner <rtann038@uottawa.ca>
git_url: https://git.bioconductor.org/packages/ExCluster
git_branch: RELEASE_3_13
git_last_commit: e1c1647
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ExCluster_1.10.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/ExCluster_1.10.0.tgz
vignettes: vignettes/ExCluster/inst/doc/ExCluster.pdf
vignetteTitles: ExCluster Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ExCluster/inst/doc/ExCluster.R
dependencyCount: 45
Package: ExiMiR
Version: 2.34.0
Depends: R (>= 2.10), Biobase (>= 2.5.5), affy (>= 1.26.1), limma
Imports: affyio(>= 1.13.3), Biobase(>= 2.5.5), preprocessCore(>=
1.10.0)
Suggests: mirna10cdf
License: GPL-2
MD5sum: e04cd03d39fedce4a3a8525c5583b754
NeedsCompilation: no
Title: R functions for the normalization of Exiqon miRNA array data
Description: This package contains functions for reading raw data in
ImaGene TXT format obtained from Exiqon miRCURY LNA arrays,
annotating them with appropriate GAL files, and normalizing
them using a spike-in probe-based method. Other platforms and
data formats are also supported.
biocViews: Microarray, OneChannel, TwoChannel, Preprocessing,
GeneExpression, Transcription
Author: Sylvain Gubian <DL.RSupport@pmi.com>, Alain Sewer
<DL.RSupport@pmi.com>, PMP SA
Maintainer: Sylvain Gubian <DL.RSupport@pmi.com>
git_url: https://git.bioconductor.org/packages/ExiMiR
git_branch: RELEASE_3_13
git_last_commit: 8830d07
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ExiMiR_2.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ExiMiR_2.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ExiMiR_2.34.0.tgz
vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf
vignetteTitles: Description of ExiMiR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.R
dependencyCount: 14
Package: exomeCopy
Version: 1.38.0
Depends: IRanges (>= 2.5.27), GenomicRanges (>= 1.23.16), Rsamtools
Imports: stats4, methods, GenomeInfoDb
Suggests: Biostrings
License: GPL (>= 2)
MD5sum: e9e221dfa2a3bdd95490634d192e6be2
NeedsCompilation: yes
Title: Copy number variant detection from exome sequencing read depth
Description: Detection of copy number variants (CNV) from exome
sequencing samples, including unpaired samples. The package
implements a hidden Markov model which uses positional
covariates, such as background read depth and GC-content, to
simultaneously normalize and segment the samples into regions
of constant copy count.
biocViews: CopyNumberVariation, Sequencing, Genetics
Author: Michael Love
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
git_url: https://git.bioconductor.org/packages/exomeCopy
git_branch: RELEASE_3_13
git_last_commit: 4f9c531
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/exomeCopy_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/exomeCopy_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/exomeCopy_1.38.0.tgz
vignettes: vignettes/exomeCopy/inst/doc/exomeCopy.pdf
vignetteTitles: Copy number variant detection in exome sequencing data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/exomeCopy/inst/doc/exomeCopy.R
importsMe: cn.mops, CNVPanelizer, contiBAIT
dependencyCount: 29
Package: exomePeak2
Version: 1.4.2
Depends: SummarizedExperiment,cqn
Imports:
Rsamtools,GenomicAlignments,GenomicRanges,GenomicFeatures,DESeq2,ggplot2,mclust,genefilter,Biostrings,BSgenome,BiocParallel,IRanges,S4Vectors,reshape2,rtracklayer,apeglm,methods,stats,utils,Biobase,GenomeInfoDb,BiocGenerics
Suggests: knitr, rmarkdown, RMariaDB
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 60fb6521f21b22fa3917d8a4eac7a6d9
NeedsCompilation: no
Title: Bias-aware Peak Calling and Quantification for MeRIP-Seq
Description: exomePeak2 provides bias-aware quantification and peak
detection for Methylated RNA immunoprecipitation sequencing
data (MeRIP-Seq). MeRIP-Seq is a commonly applied sequencing
technology that can measure the location and abundance of RNA
modification sites under given cell line conditions. However,
quantification and peak calling in MeRIP-Seq are sensitive to
PCR amplification biases, which generally present in
next-generation sequencing (NGS) technologies. In addition, the
count data generated by RNA-Seq exhibits significant biological
variations between biological replicates. exomePeak2
collectively address the challenges by introducing a series of
robust data science tools tailored for MeRIP-Seq. Using
exomePeak2, users can perform peak calling, modification site
quantification and differential analysis through a
straightforward single-step function. Alternatively, multi-step
functions can be used to generate diagnostic plots and perform
customized analyses.
biocViews: Sequencing, MethylSeq, RNASeq, ExomeSeq, Coverage,
Normalization, Preprocessing, DifferentialExpression
Author: Zhen Wei [aut, cre]
Maintainer: Zhen Wei <zhen.wei10@icloud.com>
VignetteBuilder: knitr
BugReports: https://github.com/ZW-xjtlu/exomePeak2/issues
git_url: https://git.bioconductor.org/packages/exomePeak2
git_branch: RELEASE_3_13
git_last_commit: d8d75f9
git_last_commit_date: 2021-09-07
Date/Publication: 2021-09-09
source.ver: src/contrib/exomePeak2_1.4.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/exomePeak2_1.4.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/exomePeak2_1.4.2.tgz
vignettes: vignettes/exomePeak2/inst/doc/Vignette_V_1.00.html
vignetteTitles: The exomePeak2 user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/exomePeak2/inst/doc/Vignette_V_1.00.R
dependencyCount: 138
Package: ExperimentHub
Version: 2.0.0
Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 2.19.3),
BiocFileCache (>= 1.5.1)
Imports: utils, S4Vectors, BiocManager, curl, rappdirs
Suggests: knitr, BiocStyle, rmarkdown
Enhances: ExperimentHubData
License: Artistic-2.0
MD5sum: 764124b06c2f9c00bce62f59f0a1fc18
NeedsCompilation: no
Title: Client to access ExperimentHub resources
Description: This package provides a client for the Bioconductor
ExperimentHub web resource. ExperimentHub provides a central
location where curated data from experiments, publications or
training courses can be accessed. Each resource has associated
metadata, tags and date of modification. The client creates and
manages a local cache of files retrieved enabling quick and
reproducible access.
biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient
Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut],
Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb],
Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd
[aut]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/ExperimentHub/issues
git_url: https://git.bioconductor.org/packages/ExperimentHub
git_branch: RELEASE_3_13
git_last_commit: a899441
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ExperimentHub_2.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ExperimentHub_2.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ExperimentHub_2.0.0.tgz
vignettes: vignettes/ExperimentHub/inst/doc/ExperimentHub.html
vignetteTitles: ExperimentHub: Access the ExperimentHub Web Service
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ExperimentHub/inst/doc/ExperimentHub.R
dependsOnMe: adductomicsR, LRcell, SeqSQC, alpineData,
BeadSorted.Saliva.EPIC, benchmarkfdrData2019, biscuiteerData,
bodymapRat, brainImageRdata, CellMapperData, clustifyrdatahub,
curatedAdipoChIP, DMRcatedata, ewceData, FlowSorted.Blood.EPIC,
FlowSorted.CordBloodCombined.450k, HDCytoData,
HighlyReplicatedRNASeq, HumanAffyData, mcsurvdata,
MetaGxBreast, MetaGxOvarian, MetaGxPancreas, muscData,
NanoporeRNASeq, NestLink, ObMiTi, restfulSEData, RNAmodR.Data,
SCATEData, scpdata, sesameData, SimBenchData, STexampleData,
tartare, tcgaWGBSData.hg19, TENxVisiumData
importsMe: BloodGen3Module, DMRcate, EWCE, ExperimentHubData,
GSEABenchmarkeR, MACSr, PhyloProfile, restfulSE,
signatureSearch, singleCellTK, adductData, BioImageDbs,
celldex, chipseqDBData, CLLmethylation, curatedMetagenomicData,
curatedTCGAData, depmap, DropletTestFiles, DuoClustering2018,
emtdata, FieldEffectCrc, GenomicDistributionsData,
HarmonizedTCGAData, HCAData, HMP16SData, HMP2Data, imcdatasets,
LRcellTypeMarkers, methylclockData, MethylSeqData,
microbiomeDataSets, MouseGastrulationData, MouseThymusAgeing,
msigdb, PhyloProfileData, preciseTADhub, pwrEWAS.data,
scRNAseq, signatureSearchData, SingleCellMultiModal,
SingleMoleculeFootprintingData, spatialLIBD, TabulaMurisData,
TENxBrainData, TENxBUSData, TENxPBMCData
suggestsMe: ANF, AnnotationHub, bambu, celaref, CellMapper, HDF5Array,
missMethyl, muscat, quantiseqr, rawrr, recountmethylation,
SingleMoleculeFootprinting, celarefData, curatedAdipoArray,
GSE13015, GSE62944, tissueTreg
dependencyCount: 87
Package: ExperimentHubData
Version: 1.18.0
Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData
(>= 1.21.3)
Imports: methods, ExperimentHub, BiocManager, DBI, httr, curl
Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle, rmarkdown
License: Artistic-2.0
MD5sum: 30ce148573af2a3a62ceae0bd3e41b45
NeedsCompilation: no
Title: Add resources to ExperimentHub
Description: Functions to add metadata to ExperimentHub db and resource
files to AWS S3 buckets.
biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient
Author: Bioconductor Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ExperimentHubData
git_branch: RELEASE_3_13
git_last_commit: 2706bf5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ExperimentHubData_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ExperimentHubData_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ExperimentHubData_1.18.0.tgz
vignettes: vignettes/ExperimentHubData/inst/doc/ExperimentHubData.html
vignetteTitles: Introduction to ExperimentHubData
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: RNAmodR.Data
importsMe: methylclockData
suggestsMe: HubPub
dependencyCount: 135
Package: ExperimentSubset
Version: 1.2.0
Depends: R (>= 4.0.0), SummarizedExperiment, SingleCellExperiment,
SpatialExperiment, TreeSummarizedExperiment
Imports: methods, Matrix, S4Vectors
Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, stats, scran,
scater, scds, TENxPBMCData, airway
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 20eb1c88f4c077fb178634127d7126ec
NeedsCompilation: no
Title: Manages subsets of data with Bioconductor Experiment objects
Description: Experiment objects such as the SummarizedExperiment or
SingleCellExperiment are data containers for one or more
matrix-like assays along with the associated row and column
data. Often only a subset of the original data is needed for
down-stream analysis. For example, filtering out poor quality
samples will require excluding some columns before analysis.
The ExperimentSubset object is a container to efficiently
manage different subsets of the same data without having to
make separate objects for each new subset.
biocViews: Infrastructure, Software, DataImport, DataRepresentation
Author: Irzam Sarfraz [aut, cre]
(<https://orcid.org/0000-0001-8121-792X>), Muhammad Asif [aut,
ths] (<https://orcid.org/0000-0003-1839-2527>), Joshua D.
Campbell [aut] (<https://orcid.org/0000-0003-0780-8662>)
Maintainer: Irzam Sarfraz <irzam9095@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ExperimentSubset
git_branch: RELEASE_3_13
git_last_commit: f9c08bb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ExperimentSubset_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ExperimentSubset_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ExperimentSubset_1.2.0.tgz
vignettes: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.html
vignetteTitles: An introduction to ExperimentSubset class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.R
dependencyCount: 104
Package: ExploreModelMatrix
Version: 1.4.0
Imports: shiny (>= 1.5.0), shinydashboard, DT, cowplot, utils, dplyr,
magrittr, tidyr, ggplot2, stats, methods, rintrojs, scales,
tibble, MASS, limma, S4Vectors, shinyjs
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, htmltools, BiocStyle
License: MIT + file LICENSE
MD5sum: 2b85533c9714cf15e7af6744b91640b8
NeedsCompilation: no
Title: Graphical Exploration of Design Matrices
Description: Given a sample data table and a design formula,
ExploreModelMatrix generates an interactive application for
exploration of the resulting design matrix. This can be helpful
for interpreting model coefficients and constructing
appropriate contrasts in (generalized) linear models. Static
visualizations can also be generated.
biocViews: ExperimentalDesign, Regression, DifferentialExpression
Author: Charlotte Soneson [aut, cre]
(<https://orcid.org/0000-0003-3833-2169>), Federico Marini
[aut] (<https://orcid.org/0000-0003-3252-7758>), Michael Love
[aut] (<https://orcid.org/0000-0001-8401-0545>), Florian Geier
[aut] (<https://orcid.org/0000-0002-9076-9264>), Michael
Stadler [aut] (<https://orcid.org/0000-0002-2269-4934>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/csoneson/ExploreModelMatrix
VignetteBuilder: knitr
BugReports: https://github.com/csoneson/ExploreModelMatrix/issues
git_url: https://git.bioconductor.org/packages/ExploreModelMatrix
git_branch: RELEASE_3_13
git_last_commit: 72ccfe4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ExploreModelMatrix_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ExploreModelMatrix_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ExploreModelMatrix_1.4.0.tgz
vignettes: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.html,
vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.html
vignetteTitles: ExploreModelMatrix-deploy, ExploreModelMatrix
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.R,
vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.R
dependencyCount: 79
Package: ExpressionAtlas
Version: 1.20.0
Depends: R (>= 3.2.0), methods, Biobase, SummarizedExperiment, limma,
S4Vectors, xml2
Imports: utils, XML, httr
Suggests: knitr, testthat, rmarkdown
License: GPL (>= 3)
MD5sum: cae510f42f7b488ce88491aa1d31de7d
NeedsCompilation: no
Title: Download datasets from EMBL-EBI Expression Atlas
Description: This package is for searching for datasets in EMBL-EBI
Expression Atlas, and downloading them into R for further
analysis. Each Expression Atlas dataset is represented as a
SimpleList object with one element per platform. Sequencing
data is contained in a SummarizedExperiment object, while
microarray data is contained in an ExpressionSet or MAList
object.
biocViews: ExpressionData, ExperimentData, SequencingData,
MicroarrayData, ArrayExpress
Author: Maria Keays
Maintainer: Suhaib Mohammed <suhaib@ebi.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ExpressionAtlas
git_branch: RELEASE_3_13
git_last_commit: d1a0181
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ExpressionAtlas_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ExpressionAtlas_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ExpressionAtlas_1.20.0.tgz
vignettes: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html
vignetteTitles: ExpressionAtlas
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R
suggestsMe: spatialHeatmap
dependencyCount: 37
Package: fabia
Version: 2.38.0
Depends: R (>= 3.6.0), Biobase
Imports: methods, graphics, grDevices, stats, utils
License: LGPL (>= 2.1)
MD5sum: f5e75566976f6eab225bd37e6ba8073f
NeedsCompilation: yes
Title: FABIA: Factor Analysis for Bicluster Acquisition
Description: Biclustering by "Factor Analysis for Bicluster
Acquisition" (FABIA). FABIA is a model-based technique for
biclustering, that is clustering rows and columns
simultaneously. Biclusters are found by factor analysis where
both the factors and the loading matrix are sparse. FABIA is a
multiplicative model that extracts linear dependencies between
samples and feature patterns. It captures realistic
non-Gaussian data distributions with heavy tails as observed in
gene expression measurements. FABIA utilizes well understood
model selection techniques like the EM algorithm and
variational approaches and is embedded into a Bayesian
framework. FABIA ranks biclusters according to their
information content and separates spurious biclusters from true
biclusters. The code is written in C.
biocViews: StatisticalMethod, Microarray, DifferentialExpression,
MultipleComparison, Clustering, Visualization
Author: Sepp Hochreiter <hochreit@bioinf.jku.at>
Maintainer: Andreas Mitterecker <mitterecker@bioinf.jku.at>
URL: http://www.bioinf.jku.at/software/fabia/fabia.html
git_url: https://git.bioconductor.org/packages/fabia
git_branch: RELEASE_3_13
git_last_commit: 7a5d2e0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fabia_2.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fabia_2.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fabia_2.38.0.tgz
vignettes: vignettes/fabia/inst/doc/fabia.pdf
vignetteTitles: FABIA: Manual for the R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fabia/inst/doc/fabia.R
dependsOnMe: hapFabia, RcmdrPlugin.BiclustGUI, superbiclust
importsMe: miRSM, BcDiag, CSFA
suggestsMe: fabiaData
dependencyCount: 8
Package: factDesign
Version: 1.68.0
Depends: Biobase (>= 2.5.5)
Imports: stats
Suggests: affy, genefilter, multtest
License: LGPL
MD5sum: c8121e48d2a0efdb3589c0674e2b33c3
NeedsCompilation: no
Title: Factorial designed microarray experiment analysis
Description: This package provides a set of tools for analyzing data
from a factorial designed microarray experiment, or any
microarray experiment for which a linear model is appropriate.
The functions can be used to evaluate tests of contrast of
biological interest and perform single outlier detection.
biocViews: Microarray, DifferentialExpression
Author: Denise Scholtens
Maintainer: Denise Scholtens <dscholtens@northwestern.edu>
git_url: https://git.bioconductor.org/packages/factDesign
git_branch: RELEASE_3_13
git_last_commit: c4ad08e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/factDesign_1.68.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/factDesign_1.68.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/factDesign_1.68.0.tgz
vignettes: vignettes/factDesign/inst/doc/factDesign.pdf
vignetteTitles: factDesign
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/factDesign/inst/doc/factDesign.R
dependencyCount: 7
Package: FamAgg
Version: 1.20.0
Depends: methods, kinship2, igraph
Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey
Suggests: BiocStyle, knitr, RUnit, rmarkdown
License: MIT + file LICENSE
MD5sum: 2b53af2b66372d047d0c86a5e7b8a9a8
NeedsCompilation: no
Title: Pedigree Analysis and Familial Aggregation
Description: Framework providing basic pedigree analysis and plotting
utilities as well as a variety of methods to evaluate familial
aggregation of traits in large pedigrees.
biocViews: Genetics
Author: J. Rainer, D. Taliun, C.X. Weichenberger
Maintainer: Johannes Rainer <johannes.rainer@eurac.edu>
URL: https://github.com/EuracBiomedicalResearch/FamAgg
VignetteBuilder: knitr
BugReports: https://github.com/EuracBiomedicalResearch/FamAgg/issues
git_url: https://git.bioconductor.org/packages/FamAgg
git_branch: RELEASE_3_13
git_last_commit: b92025e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FamAgg_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FamAgg_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FamAgg_1.20.0.tgz
vignettes: vignettes/FamAgg/inst/doc/FamAgg.html
vignetteTitles: Pedigree Analysis and Familial Aggregation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R
dependencyCount: 24
Package: famat
Version: 1.2.1
Depends: R (>= 4.0)
Imports: KEGGREST, MPINet, dplyr, gprofiler2, rWikiPathways,
reactome.db, stringr, GO.db, ontologyIndex, tidyr, shiny,
shinydashboard, shinyBS, plotly, magrittr, DT, clusterProfiler,
org.Hs.eg.db
Suggests: BiocStyle, knitr, rmarkdown, testthat, BiocManager
License: GPL-3
MD5sum: 4167fb34f2b660a09aface39f4df9014
NeedsCompilation: no
Title: Functional analysis of metabolic and transcriptomic data
Description: Famat is made to collect data about lists of genes and
metabolites provided by user, and to visualize it through a
Shiny app. Information collected is: - Pathways containing some
of the user's genes and metabolites (obtained using a pathway
enrichment analysis). - Direct interactions between user's
elements inside pathways. - Information about elements (their
identifiers and descriptions). - Go terms enrichment analysis
performed on user's genes. The Shiny app is composed of: -
information about genes, metabolites, and direct interactions
between them inside pathways. - an heatmap showing which
elements from the list are in pathways (pathways are structured
in hierarchies). - hierarchies of enriched go terms using
Molecular Function and Biological Process.
biocViews: FunctionalPrediction, GeneSetEnrichment, Pathways, GO,
Reactome, KEGG
Author: Mathieu Charles [aut, cre]
(<https://orcid.org/0000-0001-5343-6324>)
Maintainer: Mathieu Charles <mathieu.charles@inrae.fr>
URL: https://github.com/emiliesecherre/famat
VignetteBuilder: knitr
BugReports: https://github.com/emiliesecherre/famat/issues
git_url: https://git.bioconductor.org/packages/famat
git_branch: RELEASE_3_13
git_last_commit: f97cf00
git_last_commit_date: 2021-10-13
Date/Publication: 2021-10-14
source.ver: src/contrib/famat_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/famat_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/famat_1.2.1.tgz
vignettes: vignettes/famat/inst/doc/famat.html
vignetteTitles: famat
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/famat/inst/doc/famat.R
dependencyCount: 157
Package: farms
Version: 1.44.0
Depends: R (>= 2.8), affy (>= 1.20.0), MASS, methods
Imports: affy, MASS, Biobase (>= 1.13.41), methods, graphics
Suggests: affydata, Biobase, utils
License: LGPL (>= 2.1)
Archs: i386, x64
MD5sum: ad8a9a58915131a7ec16f0c03749df28
NeedsCompilation: no
Title: FARMS - Factor Analysis for Robust Microarray Summarization
Description: The package provides the summarization algorithm called
Factor Analysis for Robust Microarray Summarization (FARMS) and
a novel unsupervised feature selection criterion called
"I/NI-calls"
biocViews: GeneExpression, Microarray, Preprocessing, QualityControl
Author: Djork-Arne Clevert <okko@clevert.de>
Maintainer: Djork-Arne Clevert <okko@clevert.de>
URL: http://www.bioinf.jku.at/software/farms/farms.html
git_url: https://git.bioconductor.org/packages/farms
git_branch: RELEASE_3_13
git_last_commit: 9cf9a17
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/farms_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/farms_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/farms_1.44.0.tgz
vignettes: vignettes/farms/inst/doc/farms.pdf
vignetteTitles: Using farms
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/farms/inst/doc/farms.R
dependencyCount: 14
Package: fastLiquidAssociation
Version: 1.28.0
Depends: methods, LiquidAssociation, parallel, doParallel, stats,
Hmisc, utils
Imports: WGCNA, impute, preprocessCore
Suggests: GOstats, yeastCC, org.Sc.sgd.db
License: GPL-2
MD5sum: 5bd4dc9aabf1e12094aec321de1473ff
NeedsCompilation: no
Title: functions for genome-wide application of Liquid Association
Description: This package extends the function of the LiquidAssociation
package for genome-wide application. It integrates a screening
method into the LA analysis to reduce the number of triplets to
be examined for a high LA value and provides code for use in
subsequent significance analyses.
biocViews: Software, GeneExpression, Genetics, Pathways, CellBiology
Author: Tina Gunderson
Maintainer: Tina Gunderson <gunderson.tina@gmail.com>
git_url: https://git.bioconductor.org/packages/fastLiquidAssociation
git_branch: RELEASE_3_13
git_last_commit: a7ffa47
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fastLiquidAssociation_1.28.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/fastLiquidAssociation_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fastLiquidAssociation_1.28.0.tgz
vignettes:
vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf
vignetteTitles: fastLiquidAssociation Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.R
dependencyCount: 120
Package: FastqCleaner
Version: 1.10.0
Imports: methods, shiny, stats, IRanges, Biostrings, ShortRead, DT,
S4Vectors, graphics, htmltools, shinyBS, Rcpp (>= 0.12.12)
LinkingTo: Rcpp
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: aded4c4f79045ba406c4c5c3a7e74687
NeedsCompilation: yes
Title: A Shiny Application for Quality Control, Filtering and Trimming
of FASTQ Files
Description: An interactive web application for quality control,
filtering and trimming of FASTQ files. This user-friendly tool
combines a pipeline for data processing based on Biostrings and
ShortRead infrastructure, with a cutting-edge visual
environment. Single-Read and Paired-End files can be locally
processed. Diagnostic interactive plots (CG content, per-base
sequence quality, etc.) are provided for both the input and
output files.
biocViews:
QualityControl,Sequencing,Software,SangerSeq,SequenceMatching
Author: Leandro Roser [aut, cre], Fernán Agüero [aut], Daniel Sánchez
[aut]
Maintainer: Leandro Roser <learoser@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FastqCleaner
git_branch: RELEASE_3_13
git_last_commit: 6704c61
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FastqCleaner_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FastqCleaner_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FastqCleaner_1.10.0.tgz
vignettes: vignettes/FastqCleaner/inst/doc/Overview.pdf
vignetteTitles: An Introduction to FastqCleaner
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/FastqCleaner/inst/doc/Overview.R
dependencyCount: 78
Package: fastseg
Version: 1.38.0
Depends: R (>= 2.13), GenomicRanges, Biobase
Imports: methods, graphics, stats, BiocGenerics, S4Vectors, IRanges
Suggests: DNAcopy, oligo
License: LGPL (>= 2.0)
MD5sum: 999e079f094df90dae22ca0daf924bea
NeedsCompilation: yes
Title: fastseg - a fast segmentation algorithm
Description: fastseg implements a very fast and efficient segmentation
algorithm. It has similar functionality as DNACopy (Olshen and
Venkatraman 2004), but is considerably faster and more
flexible. fastseg can segment data from DNA microarrays and
data from next generation sequencing for example to detect copy
number segments. Further it can segment data from RNA
microarrays like tiling arrays to identify transcripts. Most
generally, it can segment data given as a matrix or as a
vector. Various data formats can be used as input to fastseg
like expression set objects for microarrays or GRanges for
sequencing data. The segmentation criterion of fastseg is based
on a statistical test in a Bayesian framework, namely the cyber
t-test (Baldi 2001). The speed-up arises from the facts, that
sampling is not necessary in for fastseg and that a dynamic
programming approach is used for calculation of the segments'
first and higher order moments.
biocViews: Classification, CopyNumberVariation
Author: Guenter Klambauer
Maintainer: Guenter Klambauer <fastseg@bioinf.jku.at>
URL: http://www.bioinf.jku.at/software/fastseg/fastseg.html
git_url: https://git.bioconductor.org/packages/fastseg
git_branch: RELEASE_3_13
git_last_commit: 48225db
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fastseg_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fastseg_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fastseg_1.38.0.tgz
vignettes: vignettes/fastseg/inst/doc/fastseg.pdf
vignetteTitles: fastseg: Manual for the R package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fastseg/inst/doc/fastseg.R
importsMe: methylKit
dependencyCount: 18
Package: FCBF
Version: 2.0.0
Depends: R (>= 4.1)
Imports: ggplot2, gridExtra, pbapply, parallel, SummarizedExperiment,
stats, mclust
Suggests: caret, mlbench, SingleCellExperiment, knitr, rmarkdown,
testthat, BiocManager
License: MIT + file LICENSE
MD5sum: d5f13ebb2e43fec151f3c581d3ec2532
NeedsCompilation: no
Title: Fast Correlation Based Filter for Feature Selection
Description: This package provides a simple R implementation for the
Fast Correlation Based Filter described in Yu, L. and Liu, H.;
Feature Selection for High-Dimensional Data: A Fast Correlation
Based Filter Solution,Proc. 20th Intl. Conf. Mach. Learn.
(ICML-2003), Washington DC, 2003 The current package is an
intent to make easier for bioinformaticians to use FCBF for
feature selection, especially regarding transcriptomic
data.This implies discretizing expression (function
discretize_exprs) before calculating the features that explain
the class, but are not predictable by other features. The
functions are implemented based on the algorithm of Yu and Liu,
2003 and Rajarshi Guha's implementation from 13/05/2005
available (as of 26/08/2018) at
http://www.rguha.net/code/R/fcbf.R .
biocViews: GeneTarget, FeatureExtraction, Classification,
GeneExpression, SingleCell, ImmunoOncology
Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths]
Maintainer: Tiago Lubiana <tiago.lubiana.alves@usp.br>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FCBF
git_branch: RELEASE_3_13
git_last_commit: 3eb0809
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FCBF_2.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FCBF_2.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FCBF_2.0.0.tgz
vignettes: vignettes/FCBF/inst/doc/FCBF-Vignette.html
vignetteTitles: FCBF : Fast Correlation Based Filter for Feature
Selection
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/FCBF/inst/doc/FCBF-Vignette.R
importsMe: fcoex
suggestsMe: PubScore
dependencyCount: 59
Package: fCCAC
Version: 1.18.0
Depends: R (>= 3.3.0), S4Vectors, IRanges, GenomicRanges, grid
Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap,
grDevices, stats, utils
Suggests: RUnit, BiocGenerics, BiocStyle
License: Artistic-2.0
MD5sum: 4db63f8697305b627ad47eed801b855d
NeedsCompilation: no
Title: functional Canonical Correlation Analysis to evaluate Covariance
between nucleic acid sequencing datasets
Description: An application of functional canonical correlation
analysis to assess covariance of nucleic acid sequencing
datasets such as chromatin immunoprecipitation followed by deep
sequencing (ChIP-seq). The package can be used as well with
other types of sequencing data such as neMeRIP-seq (see PMID:
29489750).
biocViews: Transcription, Genetics, Sequencing, Coverage
Author: Pedro Madrigal <bioinformatics.engineer@gmail.com>
Maintainer: Pedro Madrigal <bioinformatics.engineer@gmail.com>
git_url: https://git.bioconductor.org/packages/fCCAC
git_branch: RELEASE_3_13
git_last_commit: 9de3dbd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fCCAC_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fCCAC_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fCCAC_1.18.0.tgz
vignettes: vignettes/fCCAC/inst/doc/fCCAC.pdf
vignetteTitles: fCCAC Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fCCAC/inst/doc/fCCAC.R
dependencyCount: 129
Package: fCI
Version: 1.22.0
Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram
Suggests: knitr, rmarkdown, BiocStyle
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 9f321c2be0500454610077f00dcb62c6
NeedsCompilation: no
Title: f-divergence Cutoff Index for Differential Expression Analysis
in Transcriptomics and Proteomics
Description: (f-divergence Cutoff Index), is to find DEGs in the
transcriptomic & proteomic data, and identify DEGs by computing
the difference between the distribution of fold-changes for the
control-control and remaining (non-differential) case-control
gene expression ratio data. fCI provides several advantages
compared to existing methods.
biocViews: Proteomics
Author: Shaojun Tang
Maintainer: Shaojun Tang <tangshao2008@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/fCI
git_branch: RELEASE_3_13
git_last_commit: 0a5f069
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fCI_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fCI_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fCI_1.22.0.tgz
vignettes: vignettes/fCI/inst/doc/fCI.html
vignetteTitles: fCI
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fCI/inst/doc/fCI.R
dependencyCount: 41
Package: fcoex
Version: 1.6.0
Depends: R (>= 4.1)
Imports: FCBF, parallel, progress, dplyr, ggplot2, ggrepel, igraph,
grid, intergraph, stringr, clusterProfiler, data.table,
grDevices, methods, network, scales, sna, utils, stats,
SingleCellExperiment, pathwayPCA, Matrix
Suggests: testthat (>= 2.1.0), devtools, BiocManager, TENxPBMCData,
scater, schex, gridExtra, scran, Seurat, knitr
License: GPL-3
Archs: i386, x64
MD5sum: 19bd35aab36bc21061e7d8d9485cac64
NeedsCompilation: no
Title: FCBF-based Co-Expression Networks for Single Cells
Description: The fcoex package implements an easy-to use interface to
co-expression analysis based on the FCBF (Fast
Correlation-Based Filter) algorithm. it was implemented
especifically to deal with single-cell data. The modules found
can be used to redefine cell populations, unrevel novel gene
associations and predict gene function by guilt-by-association.
The package structure is adapted from the CEMiTool package,
relying on visualizations and code designed and written by
CEMiTool's authors.
biocViews: GeneExpression, Transcriptomics, GraphAndNetwork,
mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways,
ImmunoOncology, SingleCell
Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths]
Maintainer: Tiago Lubiana <tiago.lubiana.alves@usp.br>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/fcoex
git_branch: RELEASE_3_13
git_last_commit: f4e35ed
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fcoex_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fcoex_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fcoex_1.6.0.tgz
vignettes: vignettes/fcoex/inst/doc/fcoex.html
vignetteTitles: fcoex: co-expression for single-cell data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fcoex/inst/doc/fcoex.R
dependencyCount: 146
Package: fcScan
Version: 1.6.0
Imports: stats, plyr, VariantAnnotation, SummarizedExperiment,
rtracklayer, GenomicRanges, methods, IRanges
Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 4d86d14e5766b4dbbcfa627455e5b69c
NeedsCompilation: no
Title: fcScan for detecting clusters of coordinates with user defined
options
Description: This package is used to detect combination of genomic
coordinates falling within a user defined window size along
with user defined overlap between identified neighboring
clusters. It can be used for genomic data where the clusters
are built on a specific chromosome or specific strand.
Clustering can be performed with a "greedy" option allowing
thus the presence of additional sites within the allowed window
size.
biocViews: GenomeAnnotation, Clustering
Author: Abdullah El-Kurdi <ak161@aub.edu.lb> Ghiwa khalil
<gk39@aub.edu.lb> Georges Khazen <gkhazen@lau.edu.lb> Pierre
Khoueiry <pk17@aub.edu.lb>
Maintainer: Pierre Khoueiry <pk17@aub.edu.lb> Abdullah El-Kurdi
<ak161@aub.edu.lb>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/fcScan
git_branch: RELEASE_3_13
git_last_commit: 12424b4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fcScan_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fcScan_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fcScan_1.6.0.tgz
vignettes: vignettes/fcScan/inst/doc/fcScan_vignette.html
vignetteTitles: fcScan
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fcScan/inst/doc/fcScan_vignette.R
dependencyCount: 99
Package: fdrame
Version: 1.64.0
Imports: tcltk, graphics, grDevices, stats, utils
License: GPL (>= 2)
MD5sum: 91110a9757d76a174566e78331b2924d
NeedsCompilation: yes
Title: FDR adjustments of Microarray Experiments (FDR-AME)
Description: This package contains two main functions. The first is
fdr.ma which takes normalized expression data array,
experimental design and computes adjusted p-values It returns
the fdr adjusted p-values and plots, according to the methods
described in (Reiner, Yekutieli and Benjamini 2002). The
second, is fdr.gui() which creates a simple graphic user
interface to access fdr.ma
biocViews: Microarray, DifferentialExpression, MultipleComparison
Author: Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli
Maintainer: Effi Kenigsberg <effiken.fdrame@gmail.com>
git_url: https://git.bioconductor.org/packages/fdrame
git_branch: RELEASE_3_13
git_last_commit: a59c8a8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fdrame_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fdrame_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fdrame_1.64.0.tgz
vignettes: vignettes/fdrame/inst/doc/fdrame.pdf
vignetteTitles: Annotation Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 5
Package: FEAST
Version: 1.0.0
Depends: R (>= 4.1), mclust, BiocParallel, SummarizedExperiment
Imports: SingleCellExperiment, methods, stats, utils, irlba, TSCAN,
SC3, matrixStats
Suggests: rmarkdown, Seurat, ggpubr, knitr, testthat (>= 3.0.0),
BiocStyle
License: GPL-2
MD5sum: 4dfc32ee4a4fdc55ae626914af72619f
NeedsCompilation: yes
Title: FEAture SelcTion (FEAST) for Single-cell clustering
Description: Cell clustering is one of the most important and commonly
performed tasks in single-cell RNA sequencing (scRNA-seq) data
analysis. An important step in cell clustering is to select a
subset of genes (referred to as “featuresâ€), whose expression
patterns will then be used for downstream clustering. A good
set of features should include the ones that distinguish
different cell types, and the quality of such set could have
significant impact on the clustering accuracy. FEAST is an R
library for selecting most representative features before
performing the core of scRNA-seq clustering. It can be used as
a plug-in for the etablished clustering algorithms such as SC3,
TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm
includes three steps: 1. consensus clustering; 2. gene-level
significance inference; 3. validation of an optimized feature
set.
biocViews: Sequencing, SingleCell, Clustering, FeatureExtraction
Author: Kenong Su [aut, cre], Hao Wu [aut]
Maintainer: Kenong Su <kenong.su@emory.edu>
VignetteBuilder: knitr
BugReports: https://github.com/suke18/FEAST/issues
git_url: https://git.bioconductor.org/packages/FEAST
git_branch: RELEASE_3_13
git_last_commit: f91b6e1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FEAST_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FEAST_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FEAST_1.0.0.tgz
vignettes: vignettes/FEAST/inst/doc/FEAST.html
vignetteTitles: The FEAST User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FEAST/inst/doc/FEAST.R
dependencyCount: 116
Package: fedup
Version: 1.0.0
Depends: R (>= 4.1)
Imports: openxlsx, tibble, dplyr, data.table, ggplot2, ggthemes,
forcats, RColorBrewer, RCy3, utils, stats
Suggests: biomaRt, tidyr, testthat, knitr, rmarkdown, devtools, covr
License: MIT + file LICENSE
MD5sum: 07b18908d560ebbff8f9f9eaf470b5d2
NeedsCompilation: no
Title: Fisher's Test for Enrichment and Depletion of User-Defined
Pathways
Description: An R package that tests for enrichment and depletion of
user-defined pathways using a Fisher's exact test. The method
is designed for versatile pathway annotation formats (eg. gmt,
txt, xlsx) to allow the user to run pathway analysis on custom
annotations. This package is also integrated with Cytoscape to
provide network-based pathway visualization that enhances the
interpretability of the results.
biocViews: GeneSetEnrichment, Pathways, NetworkEnrichment, Network
Author: Catherine Ross [aut, cre]
Maintainer: Catherine Ross <catherinem.ross@mail.utoronto.ca>
URL: https://github.com/rosscm/fedup
VignetteBuilder: knitr
BugReports: https://github.com/rosscm/fedup/issues
git_url: https://git.bioconductor.org/packages/fedup
git_branch: RELEASE_3_13
git_last_commit: 5bbca3c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fedup_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fedup_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fedup_1.0.0.tgz
vignettes: vignettes/fedup/inst/doc/fedup_doubleTest.html,
vignettes/fedup/inst/doc/fedup_mutliTest.html,
vignettes/fedup/inst/doc/fedup_singleTest.html
vignetteTitles: fedup_doubleTest.html, fedup_mutliTest.html,
fedup_singleTest.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/fedup/inst/doc/fedup_doubleTest.R,
vignettes/fedup/inst/doc/fedup_mutliTest.R,
vignettes/fedup/inst/doc/fedup_singleTest.R
dependencyCount: 88
Package: FELLA
Version: 1.12.0
Depends: R (>= 3.5.0)
Imports: methods, igraph, Matrix, KEGGREST, plyr, stats, graphics,
utils
Suggests: shiny, DT, magrittr, visNetwork, knitr, BiocStyle, rmarkdown,
testthat, biomaRt, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi,
GOSemSim
License: GPL-3
MD5sum: 45ac501b0bf99b3cab0abdb5b22ba8d9
NeedsCompilation: no
Title: Interpretation and enrichment for metabolomics data
Description: Enrichment of metabolomics data using KEGG entries. Given
a set of affected compounds, FELLA suggests affected reactions,
enzymes, modules and pathways using label propagation in a
knowledge model network. The resulting subnetwork can be
visualised and exported.
biocViews: Software, Metabolomics, GraphAndNetwork, KEGG, GO, Pathways,
Network, NetworkEnrichment
Author: Sergio Picart-Armada [aut, cre], Francesc Fernandez-Albert
[aut], Alexandre Perera-Lluna [aut]
Maintainer: Sergio Picart-Armada <sergi.picart@upc.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FELLA
git_branch: RELEASE_3_13
git_last_commit: a4fcd75
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FELLA_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FELLA_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FELLA_1.12.0.tgz
vignettes: vignettes/FELLA/inst/doc/FELLA.pdf,
vignettes/FELLA/inst/doc/musmusculus.pdf,
vignettes/FELLA/inst/doc/zebrafish.pdf,
vignettes/FELLA/inst/doc/quickstart.html
vignetteTitles: FELLA, Example: a fatty liver study on Mus musculus,
Example: oxybenzone exposition in gilt-head bream, Quick start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FELLA/inst/doc/FELLA.R,
vignettes/FELLA/inst/doc/musmusculus.R,
vignettes/FELLA/inst/doc/quickstart.R,
vignettes/FELLA/inst/doc/zebrafish.R
dependencyCount: 37
Package: ffpe
Version: 1.36.0
Depends: R (>= 2.10.0), TTR, methods
Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc
Suggests: genefilter, ffpeExampleData
License: GPL (>2)
Archs: i386, x64
MD5sum: 496a8d68f02467551fc345aa34e6b76a
NeedsCompilation: no
Title: Quality assessment and control for FFPE microarray expression
data
Description: Identify low-quality data using metrics developed for
expression data derived from Formalin-Fixed, Paraffin-Embedded
(FFPE) data. Also a function for making Concordance at the Top
plots (CAT-plots).
biocViews: Microarray, GeneExpression, QualityControl
Author: Levi Waldron
Maintainer: Levi Waldron <lwaldron.research@gmail.com>
git_url: https://git.bioconductor.org/packages/ffpe
git_branch: RELEASE_3_13
git_last_commit: b02bf4a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ffpe_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ffpe_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ffpe_1.36.0.tgz
vignettes: vignettes/ffpe/inst/doc/ffpe.pdf
vignetteTitles: ffpe package user guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ffpe/inst/doc/ffpe.R
dependencyCount: 164
Package: fgga
Version: 1.0.0
Depends: R (>= 4.1), RBGL
Imports: graph, stats, e1071, methods, gRbase, jsonlite, BiocFileCache,
curl
Suggests: knitr, rmarkdown, GOstats, PerfMeas, GO.db, BiocGenerics
License: GPL-3
Archs: i386, x64
MD5sum: 6045b89228ce41833449e74a641d2e67
NeedsCompilation: no
Title: Hierarchical ensemble method based on factor graph
Description: Package that implements the FGGA algorithm. This package
provides a hierarchical ensemble method based ob factor graphs
for the consistent GO annotation of protein coding genes. FGGA
embodies elements of predicate logic, communication theory,
supervised learning and inference in graphical models.
biocViews: Software, StatisticalMethod, Classification, Network,
NetworkInference, SupportVectorMachine, GraphAndNetwork, GO
Author: Spetale Flavio [aut, cre], Elizabeth Tapia [aut, ctb]
Maintainer: Spetale Flavio <spetale@cifasis-conicet.gov.ar>
URL: https://github.com/fspetale/fgga
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/fgga
git_branch: RELEASE_3_13
git_last_commit: aefd8a8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fgga_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fgga_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fgga_1.0.0.tgz
vignettes: vignettes/fgga/inst/doc/fgga.html
vignetteTitles: FGGA: Factor Graph GO Annotation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fgga/inst/doc/fgga.R
dependencyCount: 65
Package: FGNet
Version: 3.26.0
Depends: R (>= 2.15)
Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2,
RColorBrewer, png, methods, stats, utils, graphics, grDevices
Suggests: RCurl, gage, topGO, GO.db, reactome.db, RUnit, BiocGenerics,
org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi, RGtk2,
BiocManager
License: GPL (>= 2)
MD5sum: 4f0eb2ca870f16d87d098f49e72950a1
NeedsCompilation: no
Title: Functional Gene Networks derived from biological enrichment
analyses
Description: Build and visualize functional gene and term networks from
clustering of enrichment analyses in multiple annotation
spaces. The package includes a graphical user interface (GUI)
and functions to perform the functional enrichment analysis
through DAVID, GeneTerm Linker, gage (GSEA) and topGO.
biocViews: Annotation, GO, Pathways, GeneSetEnrichment, Network,
Visualization, FunctionalGenomics, NetworkEnrichment,
Clustering
Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las
Rivas.
Maintainer: Sara Aibar <saibar@usal.es>
URL: http://www.cicancer.org
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FGNet
git_branch: RELEASE_3_13
git_last_commit: 11b6029
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FGNet_3.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FGNet_3.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FGNet_3.26.0.tgz
vignettes: vignettes/FGNet/inst/doc/FGNet.html
vignetteTitles: FGNet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FGNet/inst/doc/FGNet.R
importsMe: IntramiRExploreR
dependencyCount: 26
Package: fgsea
Version: 1.18.0
Depends: R (>= 3.3)
Imports: Rcpp, data.table, BiocParallel, stats, ggplot2 (>= 2.2.0),
gridExtra, grid, fastmatch, Matrix, utils
LinkingTo: Rcpp, BH
Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi,
parallel, org.Mm.eg.db, limma, GEOquery
License: MIT + file LICENCE
MD5sum: ec7032b1402576136e219873ca483fc0
NeedsCompilation: yes
Title: Fast Gene Set Enrichment Analysis
Description: The package implements an algorithm for fast gene set
enrichment analysis. Using the fast algorithm allows to make
more permutations and get more fine grained p-values, which
allows to use accurate stantard approaches to multiple
hypothesis correction.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
Pathways
Author: Gennady Korotkevich [aut], Vladimir Sukhov [aut], Nikolay Budin
[ctb], Alexey Sergushichev [aut, cre]
Maintainer: Alexey Sergushichev <alsergbox@gmail.com>
URL: https://github.com/ctlab/fgsea/
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/ctlab/fgsea/issues
git_url: https://git.bioconductor.org/packages/fgsea
git_branch: RELEASE_3_13
git_last_commit: 9b6e7d0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fgsea_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fgsea_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fgsea_1.18.0.tgz
vignettes: vignettes/fgsea/inst/doc/fgsea-tutorial.html
vignetteTitles: Using fgsea package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R
dependsOnMe: gsean, PPInfer
importsMe: ASpediaFI, CelliD, CEMiTool, clustifyr, cTRAP, DOSE,
fobitools, lipidr, mCSEA, multiGSEA, phantasus, piano,
RegEnrich, signatureSearch, ViSEAGO, cinaR
suggestsMe: mdp, Pi, ttgsea, rliger
dependencyCount: 50
Package: FilterFFPE
Version: 1.2.0
Imports: foreach, doParallel, GenomicRanges, IRanges, Rsamtools,
parallel, S4Vectors
Suggests: BiocStyle
License: LGPL-3
MD5sum: f8bc451b716e49cec6182f81a637ba45
NeedsCompilation: no
Title: FFPE Artificial Chimeric Read Filter for NGS data
Description: This package finds and filters artificial chimeric reads
specifically generated in next-generation sequencing (NGS)
process of formalin-fixed paraffin-embedded (FFPE) tissues.
These artificial chimeric reads can lead to a large number of
false positive structural variation (SV) calls. The required
input is an indexed BAM file of a FFPE sample.
biocViews: StructuralVariation, Sequencing, Alignment, QualityControl,
Preprocessing
Author: Lanying Wei [aut, cre]
(<https://orcid.org/0000-0002-4281-8017>)
Maintainer: Lanying Wei <lanying.wei@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/FilterFFPE
git_branch: RELEASE_3_13
git_last_commit: 567bb26
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FilterFFPE_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FilterFFPE_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FilterFFPE_1.2.0.tgz
vignettes: vignettes/FilterFFPE/inst/doc/FilterFFPE.pdf
vignetteTitles: An introduction to FilterFFPE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FilterFFPE/inst/doc/FilterFFPE.R
dependencyCount: 33
Package: FindMyFriends
Version: 1.22.0
Imports: methods, BiocGenerics, Biobase, tools, dplyr, IRanges,
Biostrings, S4Vectors, kebabs, igraph, Matrix, digest,
filehash, Rcpp, ggplot2, gtable, grid, reshape2, ggdendro,
BiocParallel, utils, stats
LinkingTo: Rcpp
Suggests: BiocStyle, testthat, knitr, rmarkdown, reutils
License: GPL (>=2)
MD5sum: c6a295e68fe061694a38f29cb5e7038b
NeedsCompilation: yes
Title: Microbial Comparative Genomics in R
Description: A framework for doing microbial comparative genomics in R.
The main purpose of the package is assisting in the creation of
pangenome matrices where genes from related organisms are
grouped by similarity, as well as the analysis of these data.
FindMyFriends provides many novel approaches to doing pangenome
analysis and supports a gene grouping algorithm that scales
linearly, thus making the creation of huge pangenomes feasible.
biocViews: ComparativeGenomics, Clustering, DataRepresentation,
GenomicVariation, SequenceMatching, GraphAndNetwork
Author: Thomas Lin Pedersen
Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com>
URL: https://github.com/thomasp85/FindMyFriends
VignetteBuilder: knitr
BugReports: https://github.com/thomasp85/FindMyFriends/issues
git_url: https://git.bioconductor.org/packages/FindMyFriends
git_branch: RELEASE_3_13
git_last_commit: 526b1e4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FindMyFriends_1.22.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/FindMyFriends_1.22.0.tgz
vignettes: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.html
vignetteTitles: Creating pangenomes using FindMyFriends
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.R
importsMe: PanVizGenerator
dependencyCount: 78
Package: FISHalyseR
Version: 1.26.0
Depends: EBImage,abind
Suggests: knitr
License: Artistic-2.0
MD5sum: 01359c93ea838a357e1e0af5dd1ba21a
NeedsCompilation: no
Title: FISHalyseR a package for automated FISH quantification
Description: FISHalyseR provides functionality to process and analyse
digital cell culture images, in particular to quantify FISH
probes within nuclei. Furthermore, it extract the spatial
location of each nucleus as well as each probe enabling spatial
co-localisation analysis.
biocViews: CellBiology
Author: Karesh Arunakirinathan <akaresh88@gmail.com>, Andreas Heindl
<andreas.heindl@icr.ac.uk>
Maintainer: Karesh Arunakirinathan <akaresh88@gmail.com>, Andreas
Heindl <andreas.heindl@icr.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FISHalyseR
git_branch: RELEASE_3_13
git_last_commit: 1ac9d07
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FISHalyseR_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FISHalyseR_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FISHalyseR_1.26.0.tgz
vignettes: vignettes/FISHalyseR/inst/doc/FISHalyseR.pdf
vignetteTitles: FISHAlyseR Automated fluorescence in situ hybridisation
quantification in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FISHalyseR/inst/doc/FISHalyseR.R
dependencyCount: 26
Package: fishpond
Version: 1.8.0
Imports: graphics, stats, utils, methods, abind, gtools, qvalue,
S4Vectors, SummarizedExperiment, matrixStats, svMisc, Rcpp,
Matrix
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, macrophage, tximeta,
org.Hs.eg.db, samr, DESeq2, apeglm, tximportData,
SingleCellExperiment, limma
License: GPL-2
MD5sum: 7112c4eb391dd1e1cf4ec2aa52e9baf4
NeedsCompilation: yes
Title: Fishpond: differential transcript and gene expression with
inferential replicates
Description: Fishpond contains methods for differential transcript and
gene expression analysis of RNA-seq data using inferential
replicates for uncertainty of abundance quantification, as
generated by Gibbs sampling or bootstrap sampling. Also the
package contains utilities for working with Salmon and Alevin
quantification files.
biocViews: Sequencing, RNASeq, GeneExpression, Transcription,
Normalization, Regression, MultipleComparison, BatchEffect,
Visualization, DifferentialExpression, DifferentialSplicing,
AlternativeSplicing, SingleCell
Author: Anzi Zhu [aut, ctb], Michael Love [aut, cre], Avi Srivastava
[aut, ctb], Rob Patro [aut, ctb], Joseph Ibrahim [aut, ctb],
Hirak Sarkar [ctb], Scott Van Buren [ctb]
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
URL: https://github.com/mikelove/fishpond
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/fishpond
git_branch: RELEASE_3_13
git_last_commit: 38a320c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fishpond_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fishpond_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fishpond_1.8.0.tgz
vignettes: vignettes/fishpond/inst/doc/swish.html
vignetteTitles: DTE and DGE with inferential replicates
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fishpond/inst/doc/swish.R
importsMe: singleCellTK
suggestsMe: tximport
dependencyCount: 65
Package: FitHiC
Version: 1.18.0
Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 2d33a8d5c1c1ce373888456b8ff0de29
NeedsCompilation: yes
Title: Confidence estimation for intra-chromosomal contact maps
Description: Fit-Hi-C is a tool for assigning statistical confidence
estimates to intra-chromosomal contact maps produced by
genome-wide genome architecture assays such as Hi-C.
biocViews: DNA3DStructure, Software
Author: Ferhat Ay [aut] (Python original,
https://noble.gs.washington.edu/proj/fit-hi-c/), Timothy L.
Bailey [aut], William S. Noble [aut], Ruyu Tan [aut, cre, trl]
(R port)
Maintainer: Ruyu Tan <rut003@ucsd.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FitHiC
git_branch: RELEASE_3_13
git_last_commit: 62f951d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FitHiC_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FitHiC_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FitHiC_1.18.0.tgz
vignettes: vignettes/FitHiC/inst/doc/fithic.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FitHiC/inst/doc/fithic.R
dependencyCount: 8
Package: flagme
Version: 1.48.0
Depends: gcspikelite, xcms, CAMERA
Imports: gplots, graphics, MASS, methods, SparseM, stats, utils
License: LGPL (>= 2)
MD5sum: 7dfcd3f44b0a4e346533964a5381cc71
NeedsCompilation: yes
Title: Analysis of Metabolomics GC/MS Data
Description: Fragment-level analysis of gas chromatography - mass
spectrometry metabolomics data
biocViews: ImmunoOncology, DifferentialExpression, MassSpectrometry
Author: Mark Robinson <mark.robinson@imls.uzh.ch>, Riccardo Romoli
<riccardo.romoli@unifi.it>
Maintainer: Mark Robinson <mark.robinson@imls.uzh.ch>, Riccardo Romoli
<riccardo.romoli@unifi.it>
git_url: https://git.bioconductor.org/packages/flagme
git_branch: RELEASE_3_13
git_last_commit: 31bd595
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flagme_1.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flagme_1.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flagme_1.48.0.tgz
vignettes: vignettes/flagme/inst/doc/flagme.pdf
vignetteTitles: Using flagme -- Fragment-level analysis of GC-MS-based
metabolomics data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flagme/inst/doc/flagme.R
dependencyCount: 132
Package: flowAI
Version: 1.22.0
Depends: R (>= 3.6)
Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2,
RColorBrewer, scales, methods, graphics, stats, utils,
rmarkdown
Suggests: testthat, shiny, BiocStyle
License: GPL (>= 2)
MD5sum: a146ce5cbfce13a8ab2bafa1bbe0d743
NeedsCompilation: no
Title: Automatic and interactive quality control for flow cytometry
data
Description: The package is able to perform an automatic or interactive
quality control on FCS data acquired using flow cytometry
instruments. By evaluating three different properties: 1) flow
rate, 2) signal acquisition, 3) dynamic range, the quality
control enables the detection and removal of anomalies.
biocViews: FlowCytometry, QualityControl, BiomedicalInformatics,
ImmunoOncology
Author: Gianni Monaco, Hao Chen
Maintainer: Gianni Monaco <mongianni1@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowAI
git_branch: RELEASE_3_13
git_last_commit: a517a94
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowAI_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowAI_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowAI_1.22.0.tgz
vignettes: vignettes/flowAI/inst/doc/flowAI.html
vignetteTitles: Automatic and GUI methods to do quality control on Flow
cytometry Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowAI/inst/doc/flowAI.R
dependencyCount: 71
Package: flowBeads
Version: 1.30.0
Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore
Imports: flowCore, rrcov, knitr, xtable
Suggests: flowViz
License: Artistic-2.0
Archs: i386, x64
MD5sum: 64f94f8bc1a076e4b39e124e6c49b8dc
NeedsCompilation: no
Title: flowBeads: Analysis of flow bead data
Description: This package extends flowCore to provide functionality
specific to bead data. One of the goals of this package is to
automate analysis of bead data for the purpose of
normalisation.
biocViews: ImmunoOncology, Infrastructure, FlowCytometry,
CellBasedAssays
Author: Nikolas Pontikos
Maintainer: Nikolas Pontikos <n.pontikos@gmail.com>
git_url: https://git.bioconductor.org/packages/flowBeads
git_branch: RELEASE_3_13
git_last_commit: 57c2789
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowBeads_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowBeads_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowBeads_1.30.0.tgz
vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf
vignetteTitles: Analysis of Flow Cytometry Bead Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowBeads/inst/doc/HowTo-flowBeads.R
dependencyCount: 37
Package: flowBin
Version: 1.28.0
Depends: methods, flowCore, flowFP, R (>= 2.10)
Imports: class, limma, snow, BiocGenerics
Suggests: parallel
License: Artistic-2.0
Archs: i386, x64
MD5sum: 9145376a7390a2763cf7023d77c3d954
NeedsCompilation: no
Title: Combining multitube flow cytometry data by binning
Description: Software to combine flow cytometry data that has been
multiplexed into multiple tubes with common markers between
them, by establishing common bins across tubes in terms of the
common markers, then determining expression within each tube
for each bin in terms of the tube-specific markers.
biocViews: ImmunoOncology, CellBasedAssays, FlowCytometry
Author: Kieran O'Neill
Maintainer: Kieran O'Neill <koneill@bccrc.ca>
git_url: https://git.bioconductor.org/packages/flowBin
git_branch: RELEASE_3_13
git_last_commit: 7e5e658
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowBin_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowBin_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowBin_1.28.0.tgz
vignettes: vignettes/flowBin/inst/doc/flowBin.pdf
vignetteTitles: flowBin
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowBin/inst/doc/flowBin.R
dependencyCount: 34
Package: flowcatchR
Version: 1.26.0
Depends: R (>= 2.10), methods, EBImage
Imports: colorRamps, abind, BiocParallel, graphics, stats, utils,
plotly, shiny
Suggests: BiocStyle, knitr, rmarkdown
License: BSD_3_clause + file LICENSE
Archs: i386, x64
MD5sum: c0acc9b3570a53503e50e19ef471e192
NeedsCompilation: no
Title: Tools to analyze in vivo microscopy imaging data focused on
tracking flowing blood cells
Description: flowcatchR is a set of tools to analyze in vivo microscopy
imaging data, focused on tracking flowing blood cells. It
guides the steps from segmentation to calculation of features,
filtering out particles not of interest, providing also a set
of utilities to help checking the quality of the performed
operations (e.g. how good the segmentation was). It allows
investigating the issue of tracking flowing cells such as in
blood vessels, to categorize the particles in flowing, rolling
and adherent. This classification is applied in the study of
phenomena such as hemostasis and study of thrombosis
development. Moreover, flowcatchR presents an integrated
workflow solution, based on the integration with a Shiny App
and Jupyter notebooks, which is delivered alongside the
package, and can enable fully reproducible bioimage analysis in
the R environment.
biocViews: Software, Visualization, CellBiology, Classification,
Infrastructure, GUI
Author: Federico Marini [aut, cre]
(<https://orcid.org/0000-0003-3252-7758>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
URL: https://github.com/federicomarini/flowcatchR,
https://federicomarini.github.io/flowcatchR/
SystemRequirements: ImageMagick
VignetteBuilder: knitr
BugReports: https://github.com/federicomarini/flowcatchR/issues
git_url: https://git.bioconductor.org/packages/flowcatchR
git_branch: RELEASE_3_13
git_last_commit: 33b6f14
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowcatchR_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowcatchR_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowcatchR_1.26.0.tgz
vignettes: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.html
vignetteTitles: flowcatchR: tracking and analyzing cells in time lapse
microscopy images
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.R
dependencyCount: 95
Package: flowCHIC
Version: 1.26.0
Depends: R (>= 3.1.0)
Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid
License: GPL-2
MD5sum: 3f341bc9080165fda378029034dd4961
NeedsCompilation: no
Title: Analyze flow cytometric data using histogram information
Description: A package to analyze flow cytometric data of complex
microbial communities based on histogram images
biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry,
Software, Visualization
Author: Joachim Schumann <joachim.schumann@ufz.de>, Christin Koch
<christin.koch@ufz.de>, Ingo Fetzer
<info.fetzer@stockholmresilience.su.se>, Susann Müller
<susann.mueller@ufz.de>
Maintainer: Author: Joachim Schumann <joachim.schumann@ufz.de>
URL: http://www.ufz.de/index.php?en=16773
git_url: https://git.bioconductor.org/packages/flowCHIC
git_branch: RELEASE_3_13
git_last_commit: 16676cf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowCHIC_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowCHIC_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowCHIC_1.26.0.tgz
vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf
vignetteTitles: Analyze flow cytometric data using histogram
information
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowCHIC/inst/doc/flowCHICmanual.R
dependencyCount: 71
Package: flowCL
Version: 1.30.0
Depends: R (>= 3.4), Rgraphviz, SPARQL
Imports: methods, grDevices, utils, graph
Suggests: RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: 402775cbb0ad31930b7f345762a00438
NeedsCompilation: no
Title: Semantic labelling of flow cytometric cell populations
Description: Semantic labelling of flow cytometric cell populations.
biocViews: FlowCytometry, ImmunoOncology
Author: Justin Meskas, Radina Droumeva
Maintainer: Justin Meskas <justinmeskas@gmail.com>
git_url: https://git.bioconductor.org/packages/flowCL
git_branch: RELEASE_3_13
git_last_commit: f0a1525
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowCL_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowCL_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowCL_1.30.0.tgz
vignettes: vignettes/flowCL/inst/doc/flowCL.pdf
vignetteTitles: flowCL package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 15
Package: flowClean
Version: 1.30.0
Depends: R (>= 2.15.0), flowCore
Imports: bit, changepoint, sfsmisc
Suggests: flowViz, grid, gridExtra
License: Artistic-2.0
MD5sum: c71ae0c9853448470d0661e98879a146
NeedsCompilation: no
Title: flowClean
Description: A quality control tool for flow cytometry data based on
compositional data analysis.
biocViews: FlowCytometry, QualityControl, ImmunoOncology
Author: Kipper Fletez-Brant
Maintainer: Kipper Fletez-Brant <cafletezbrant@gmail.com>
git_url: https://git.bioconductor.org/packages/flowClean
git_branch: RELEASE_3_13
git_last_commit: 5e882c9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowClean_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowClean_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowClean_1.30.0.tgz
vignettes: vignettes/flowClean/inst/doc/flowClean.pdf
vignetteTitles: flowClean
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowClean/inst/doc/flowClean.R
dependencyCount: 26
Package: flowClust
Version: 3.30.0
Depends: R(>= 2.5.0)
Imports: BiocGenerics, methods, Biobase, graph, ellipse, flowViz,
flowCore, clue, corpcor, mnormt, parallel
Suggests: testthat, flowWorkspace, flowWorkspaceData, knitr, rmarkdown,
openCyto
License: Artistic-2.0
MD5sum: 4a0016e67160417cf7ec4ab21e157e6a
NeedsCompilation: yes
Title: Clustering for Flow Cytometry
Description: Robust model-based clustering using a t-mixture model with
Box-Cox transformation. Note: users should have GSL installed.
Windows users: 'consult the README file available in the inst
directory of the source distribution for necessary
configuration instructions'.
biocViews: ImmunoOncology, Clustering, Visualization, FlowCytometry
Author: Raphael Gottardo <raph@stat.ubc.ca>, Kenneth Lo
<c.lo@stat.ubc.ca>, Greg Finak <gfinak@fhcrc.org>
Maintainer: Greg Finak <gfinak@fhcrc.org>, Mike Jiang
<wjiang2@fhcrc.org>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowClust
git_branch: RELEASE_3_13
git_last_commit: c52585b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowClust_3.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowClust_3.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowClust_3.30.0.tgz
vignettes: vignettes/flowClust/inst/doc/flowClust.html
vignetteTitles: Robust Model-based Clustering of Flow Cytometry Data\\
The flowClust package
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowClust/inst/doc/flowClust.R
importsMe: flowTrans
suggestsMe: BiocGenerics, flowTime, segmenTier
dependencyCount: 37
Package: flowCore
Version: 2.4.0
Depends: R (>= 3.5.0)
Imports: Biobase, BiocGenerics (>= 0.29.2), grDevices, graphics,
methods, stats, utils, stats4, Rcpp, matrixStats, cytolib (>=
2.3.4), S4Vectors
LinkingTo: Rcpp, RcppArmadillo, BH(>= 1.65.0.1), cytolib, RProtoBufLib
Suggests: Rgraphviz, flowViz, flowStats (>= 3.43.4), testthat,
flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto,
gridExtra
License: Artistic-2.0
MD5sum: 278d033fc84e1dc292538889a0c154f3
NeedsCompilation: yes
Title: flowCore: Basic structures for flow cytometry data
Description: Provides S4 data structures and basic functions to deal
with flow cytometry data.
biocViews: ImmunoOncology, Infrastructure, FlowCytometry,
CellBasedAssays
Author: B Ellis [aut], Perry Haaland [aut], Florian Hahne [aut],
Nolwenn Le Meur [aut], Nishant Gopalakrishnan [aut], Josef
Spidlen [aut], Mike Jiang [aut, cre], Greg Finak [aut], Samuel
Granjeaud [ctb]
Maintainer: Mike Jiang <mike@ozette.ai>
SystemRequirements: GNU make, C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowCore
git_branch: RELEASE_3_13
git_last_commit: 1f5f4b6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowCore_2.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowCore_2.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowCore_2.4.0.tgz
vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf,
vignettes/flowCore/inst/doc/fcs3.html,
vignettes/flowCore/inst/doc/hyperlog.notice.html
vignetteTitles: Basic Functions for Flow Cytometry Data, fcs3.html,
hyperlog.notice.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R
dependsOnMe: flowBeads, flowBin, flowClean, flowCut, flowFP, flowMatch,
flowTime, flowTrans, flowViz, flowVS, ggcyto, immunoClust,
infinityFlow, ncdfFlow, HDCytoData, healthyFlowData,
highthroughputassays
importsMe: CATALYST, cmapR, cyanoFilter, cydar, CytoML, CytoTree,
ddPCRclust, diffcyt, flowAI, flowBeads, flowCHIC, flowClust,
flowDensity, flowMeans, flowPloidy, FlowSOM, flowSpecs,
flowStats, flowTrans, flowUtils, flowViz, flowWorkspace,
GateFinder, ImmuneSpaceR, MetaCyto, oneSENSE, PeacoQC,
scDataviz, Sconify
suggestsMe: COMPASS, FlowRepositoryR, flowPloidyData, hypergate,
segmenTier
dependencyCount: 18
Package: flowCut
Version: 1.2.0
Depends: R (>= 3.4), flowCore
Imports: flowDensity (>= 1.13.1), Cairo, e1071, grDevices, graphics,
stats,methods
Suggests: RUnit, BiocGenerics, knitr
License: Artistic-2.0
MD5sum: 9a572c44c8d40e4d34ebcfef9021ddc9
NeedsCompilation: no
Title: Precise and Accurate Automated Removal of Outlier Events and
Flagging of Files Based on Time Versus Fluorescence Analysis
Description: Common techinical complications such as clogging can
result in spurious events and fluorescence intensity shifting,
flowCut is designed to detect and remove technical artifacts
from your data by removing segments that show statistical
differences from other segments.
biocViews: FlowCytometry, Preprocessing, QualityControl,
CellBasedAssays
Author: Justin Meskas [cre, aut], Sherrie Wang [aut]
Maintainer: Justin Meskas <justinmeskas@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowCut
git_branch: RELEASE_3_13
git_last_commit: 3edb4be
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowCut_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowCut_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowCut_1.2.0.tgz
vignettes: vignettes/flowCut/inst/doc/flowCut.html
vignetteTitles: _**flowCut**_: Precise and Accurate Automated Removal
of Outlier Events and Flagging of Files Based on Time Versus
Fluorescence Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowCut/inst/doc/flowCut.R
dependencyCount: 155
Package: flowCyBar
Version: 1.28.0
Depends: R (>= 3.0.0)
Imports: gplots, vegan, methods
License: GPL-2
MD5sum: e12d67586a089b3e438ced3625cf8354
NeedsCompilation: no
Title: Analyze flow cytometric data using gate information
Description: A package to analyze flow cytometric data using gate
information to follow population/community dynamics
biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry,
Software, Visualization
Author: Joachim Schumann <joachim.schumann@ufz.de>, Christin Koch
<christin.koch@ufz.de>, Susanne Günther
<susanne.guenther@ufz.de>, Ingo Fetzer
<ingo.fetzer@stockholmresilience.su.se>, Susann Müller
<susann.mueller@ufz.de>
Maintainer: Joachim Schumann <joachim.schumann@ufz.de>
URL: http://www.ufz.de/index.php?de=16773
git_url: https://git.bioconductor.org/packages/flowCyBar
git_branch: RELEASE_3_13
git_last_commit: 1cec105
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowCyBar_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowCyBar_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowCyBar_1.28.0.tgz
vignettes: vignettes/flowCyBar/inst/doc/flowCyBar-manual.pdf
vignetteTitles: Analyze flow cytometric data using gate information
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowCyBar/inst/doc/flowCyBar-manual.R
dependencyCount: 20
Package: flowDensity
Version: 1.26.0
Imports: flowCore, graphics, flowViz (>= 1.46.1), car, sp, rgeos,
gplots, RFOC, flowWorkspace (>= 3.33.1), methods, stats,
grDevices
Suggests: knitr
License: Artistic-2.0
Archs: i386, x64
MD5sum: 1fa498c3a8442991693352da0e8a8b03
NeedsCompilation: no
Title: Sequential Flow Cytometry Data Gating
Description: This package provides tools for automated sequential
gating analogous to the manual gating strategy based on the
density of the data.
biocViews: Bioinformatics, FlowCytometry, CellBiology, Clustering,
Cancer, FlowCytData, DataRepresentation, StemCell,
DensityGating
Author: Mehrnoush Malek,M. Jafar Taghiyar
Maintainer: Mehrnoush Malek <mmalekes@bccrc.ca>
SystemRequirements: xml2, GNU make, C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowDensity
git_branch: RELEASE_3_13
git_last_commit: 6559b9f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowDensity_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowDensity_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowDensity_1.26.0.tgz
vignettes: vignettes/flowDensity/inst/doc/flowDensity.html
vignetteTitles: Introduction to automated gating
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R
importsMe: cyanoFilter, ddPCRclust, flowCut
dependencyCount: 150
Package: flowFP
Version: 1.50.0
Depends: R (>= 2.10), flowCore, flowViz
Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices,
methods, stats, stats4
Suggests: RUnit
License: Artistic-2.0
Archs: i386, x64
MD5sum: 7f0f26448743300fc9dfdaf09cb0c258
NeedsCompilation: yes
Title: Fingerprinting for Flow Cytometry
Description: Fingerprint generation of flow cytometry data, used to
facilitate the application of machine learning and datamining
tools for flow cytometry.
biocViews: FlowCytometry, CellBasedAssays, Clustering, Visualization
Author: Herb Holyst <holyst@mail.med.upenn.edu>, Wade Rogers
<rogersw@mail.med.upenn.edu>
Maintainer: Herb Holyst <holyst@mail.med.upenn.edu>
git_url: https://git.bioconductor.org/packages/flowFP
git_branch: RELEASE_3_13
git_last_commit: 4749abd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowFP_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowFP_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowFP_1.50.0.tgz
vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf
vignetteTitles: Fingerprinting for Flow Cytometry
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowFP/inst/doc/flowFP_HowTo.R
dependsOnMe: flowBin
importsMe: GateFinder
dependencyCount: 30
Package: flowGraph
Version: 1.0.0
Depends: R (>= 4.1)
Imports: effsize, furrr, future, purrr, ggiraph, ggrepel, ggplot2,
igraph, Matrix, matrixStats, stats, utils, visNetwork,
htmlwidgets, grDevices, methods, stringr, stringi, Rdpack,
data.table (>= 1.9.5), gridExtra,
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0)
License: Artistic-2.0
MD5sum: fd5b95e6b09c4bb28c2beacc65a20959
NeedsCompilation: no
Title: Identifying differential cell populations in flow cytometry data
accounting for marker frequency
Description: Identifies maximal differential cell populations in flow
cytometry data taking into account dependencies between cell
populations; flowGraph calculates and plots SpecEnr abundance
scores given cell population cell counts.
biocViews: FlowCytometry, StatisticalMethod, ImmunoOncology, Software,
CellBasedAssays, Visualization
Author: Alice Yue [aut, cre]
Maintainer: Alice Yue <aya43@sfu.ca>
URL: https://github.com/aya49/flowGraph
VignetteBuilder: knitr
BugReports: https://github.com/aya49/flowGraph/issues
git_url: https://git.bioconductor.org/packages/flowGraph
git_branch: RELEASE_3_13
git_last_commit: 00d2dcb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowGraph_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowGraph_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowGraph_1.0.0.tgz
vignettes: vignettes/flowGraph/inst/doc/flowGraph.html
vignetteTitles: flowGraph
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowGraph/inst/doc/flowGraph.R
dependencyCount: 69
Package: flowMap
Version: 1.30.0
Depends: R (>= 3.0.1), ade4(>= 1.5-2), doParallel(>= 1.0.3), abind(>=
1.4.0), reshape2(>= 1.2.2), scales(>= 0.2.3), Matrix(>= 1.1-4),
methods (>= 2.14)
Suggests: BiocStyle, knitr
License: GPL (>=2)
MD5sum: 43bf2eb0add987c7b00669b6fa92de0b
NeedsCompilation: no
Title: Mapping cell populations in flow cytometry data for cross-sample
comparisons using the Friedman-Rafsky Test
Description: flowMap quantifies the similarity of cell populations
across multiple flow cytometry samples using a nonparametric
multivariate statistical test. The method is able to map cell
populations of different size, shape, and proportion across
multiple flow cytometry samples. The algorithm can be
incorporate in any flow cytometry work flow that requires
accurat quantification of similarity between cell populations.
biocViews: ImmunoOncology, MultipleComparison, FlowCytometry
Author: Chiaowen Joyce Hsiao, Yu Qian, and Richard H. Scheuermann
Maintainer: Chiaowen Joyce Hsiao <joyce.hsiao1@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowMap
git_branch: RELEASE_3_13
git_last_commit: 040845e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowMap_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowMap_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowMap_1.30.0.tgz
vignettes: vignettes/flowMap/inst/doc/flowMap.pdf
vignetteTitles: Mapping cell populations in flow cytometry data
flowMap-FR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowMap/inst/doc/flowMap.R
dependencyCount: 36
Package: flowMatch
Version: 1.28.0
Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore
Imports: Biobase
LinkingTo: Rcpp
Suggests: healthyFlowData
License: Artistic-2.0
MD5sum: 8ad623bf470412bb97ad5ac9fd8b5dd1
NeedsCompilation: yes
Title: Matching and meta-clustering in flow cytometry
Description: Matching cell populations and building meta-clusters and
templates from a collection of FC samples.
biocViews: ImmunoOncology, Clustering, FlowCytometry
Author: Ariful Azad
Maintainer: Ariful Azad <azad@lbl.gov>
git_url: https://git.bioconductor.org/packages/flowMatch
git_branch: RELEASE_3_13
git_last_commit: 9bf49d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowMatch_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowMatch_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowMatch_1.28.0.tgz
vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf
vignetteTitles: flowMatch: Cell population matching and meta-clustering
in Flow Cytometry
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R
dependencyCount: 19
Package: flowMeans
Version: 1.52.0
Depends: R (>= 2.10.0)
Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature,
flowCore
License: Artistic-2.0
Archs: i386, x64
MD5sum: f005ac1b9db4a9b0c6282b14de8f4238
NeedsCompilation: no
Title: Non-parametric Flow Cytometry Data Gating
Description: Identifies cell populations in Flow Cytometry data using
non-parametric clustering and segmented-regression-based change
point detection. Note: R 2.11.0 or newer is required.
biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering
Author: Nima Aghaeepour
Maintainer: Nima Aghaeepour <naghaeep@gmail.com>
git_url: https://git.bioconductor.org/packages/flowMeans
git_branch: RELEASE_3_13
git_last_commit: eaf6792
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowMeans_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowMeans_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowMeans_1.52.0.tgz
vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf
vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R
importsMe: optimalFlow
dependencyCount: 41
Package: flowMerge
Version: 2.40.0
Depends: graph,feature,flowClust,Rgraphviz,foreach,snow
Imports: rrcov,flowCore, graphics, methods, stats, utils
Suggests: knitr, rmarkdown
Enhances: doMC, multicore
License: Artistic-2.0
MD5sum: 08443b9702c260df0415fbaa54d3eb7f
NeedsCompilation: no
Title: Cluster Merging for Flow Cytometry Data
Description: Merging of mixture components for model-based automated
gating of flow cytometry data using the flowClust framework.
Note: users should have a working copy of flowClust 2.0
installed.
biocViews: ImmunoOncology, Clustering, FlowCytometry
Author: Greg Finak <gfinak@fhcrc.org>, Raphael Gottardo
<rgottard@fhcrc.org>
Maintainer: Greg Finak <gfinak@fhcrc.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowMerge
git_branch: RELEASE_3_13
git_last_commit: beb6e37
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowMerge_2.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowMerge_2.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowMerge_2.40.0.tgz
vignettes: vignettes/flowMerge/inst/doc/flowmerge.html
vignetteTitles: Merging Mixture Components for Cell Population
Identification in Flow Cytometry Data The flowMerge Package.
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowMerge/inst/doc/flowmerge.R
suggestsMe: segmenTier
dependencyCount: 62
Package: flowPeaks
Version: 1.38.0
Depends: R (>= 2.12.0)
Enhances: flowCore
License: Artistic-1.0
MD5sum: e9f07b362494a00a5ab14351da420cbf
NeedsCompilation: yes
Title: An R package for flow data clustering
Description: A fast and automatic clustering to classify the cells into
subpopulations based on finding the peaks from the overall
density function generated by K-means.
biocViews: ImmunoOncology, FlowCytometry, Clustering, Gating
Author: Yongchao Ge<yongchao.ge@gmail.com>
Maintainer: Yongchao Ge<yongchao.ge@gmail.com>
SystemRequirements: gsl
git_url: https://git.bioconductor.org/packages/flowPeaks
git_branch: RELEASE_3_13
git_last_commit: 214f2c7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowPeaks_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowPeaks_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowPeaks_1.38.0.tgz
vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf
vignetteTitles: Tutorial of flowPeaks package
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowPeaks/inst/doc/flowPeaks-guide.R
importsMe: ddPCRclust
dependencyCount: 0
Package: flowPloidy
Version: 1.18.0
Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny,
methods, graphics, stats, utils
Suggests: flowPloidyData, testthat
License: GPL-3
Archs: i386, x64
MD5sum: 9d7c100bdb9b4ad1df7f901e9286e970
NeedsCompilation: no
Title: Analyze flow cytometer data to determine sample ploidy
Description: Determine sample ploidy via flow cytometry histogram
analysis. Reads Flow Cytometry Standard (FCS) files via the
flowCore bioconductor package, and provides functions for
determining the DNA ploidy of samples based on internal
standards.
biocViews: FlowCytometry, GUI, Regression, Visualization
Author: Tyler Smith <tyler@plantarum.ca>
Maintainer: Tyler Smith <tyler@plantarum.ca>
URL: https://github.com/plantarum/flowPloidy
VignetteBuilder: knitr
BugReports: https://github.com/plantarum/flowPloidy/issues
git_url: https://git.bioconductor.org/packages/flowPloidy
git_branch: RELEASE_3_13
git_last_commit: 565690e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowPloidy_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowPloidy_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowPloidy_1.18.0.tgz
vignettes: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.pdf,
vignettes/flowPloidy/inst/doc/histogram-tour.pdf
vignetteTitles: flowPloidy: Getting Started, flowPloidy: FCM Histograms
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.R,
vignettes/flowPloidy/inst/doc/histogram-tour.R
dependencyCount: 119
Package: flowPlots
Version: 1.40.0
Depends: R (>= 2.13.0), methods
Suggests: vcd
License: Artistic-2.0
MD5sum: 422de928885d653973b33babc12b1812
NeedsCompilation: no
Title: flowPlots: analysis plots and data class for gated flow
cytometry data
Description: Graphical displays with embedded statistical tests for
gated ICS flow cytometry data, and a data class which stores
"stacked" data and has methods for computing summary measures
on stacked data, such as marginal and polyfunctional degree
data.
biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays,
Visualization, DataRepresentation
Author: N. Hawkins, S. Self
Maintainer: N. Hawkins <hawkins@fhcrc.org>
git_url: https://git.bioconductor.org/packages/flowPlots
git_branch: RELEASE_3_13
git_last_commit: 81be8c2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowPlots_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowPlots_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowPlots_1.40.0.tgz
vignettes: vignettes/flowPlots/inst/doc/flowPlots.pdf
vignetteTitles: Plots with Embedded Tests for Gated Flow Cytometry Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowPlots/inst/doc/flowPlots.R
dependencyCount: 1
Package: FlowRepositoryR
Version: 1.23.0
Depends: R (>= 3.2)
Imports: XML, RCurl, tools, utils, jsonlite
Suggests: RUnit, BiocGenerics, flowCore, methods
License: Artistic-2.0
MD5sum: 0718e29c9f61615b249a342c6adacca0
NeedsCompilation: no
Title: FlowRepository R Interface
Description: This package provides an interface to search and download
data and annotations from FlowRepository (flowrepository.org).
It uses the FlowRepository programming interface to communicate
with a FlowRepository server.
biocViews: ImmunoOncology, Infrastructure, FlowCytometry
Author: Josef Spidlen [aut, cre]
Maintainer: Josef Spidlen <jspidlen@gmail.com>
git_url: https://git.bioconductor.org/packages/FlowRepositoryR
git_branch: master
git_last_commit: 83c2a9f
git_last_commit_date: 2020-10-27
Date/Publication: 2021-03-20
source.ver: src/contrib/FlowRepositoryR_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FlowRepositoryR_1.23.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FlowRepositoryR_1.23.0.tgz
vignettes: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.pdf
vignetteTitles: FlowRepository R Interface
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.R
dependencyCount: 7
Package: FlowSOM
Version: 2.0.0
Depends: R (>= 4.0), igraph
Imports: stats, utils, BiocGenerics, colorRamps, ConsensusClusterPlus,
CytoML, dplyr, flowCore, flowWorkspace, ggforce, ggnewscale,
ggplot2, ggpointdensity, ggpubr, ggrepel, grDevices, magrittr,
methods, pheatmap, RColorBrewer, rlang, Rtsne, tidyr, XML,
scattermore
Suggests: BiocStyle, testthat
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 4f6840f09ab161355c5f1f8c10f77877
NeedsCompilation: yes
Title: Using self-organizing maps for visualization and interpretation
of cytometry data
Description: FlowSOM offers visualization options for cytometry data,
by using Self-Organizing Map clustering and Minimal Spanning
Trees.
biocViews: CellBiology, FlowCytometry, Clustering, Visualization,
Software, CellBasedAssays
Author: Sofie Van Gassen [aut, cre], Artuur Couckuyt [aut], Katrien
Quintelier [aut], Annelies Emmaneel [aut], Britt Callebaut
[aut], Yvan Saeys [aut]
Maintainer: Sofie Van Gassen <sofie.vangassen@ugent.be>
URL: http://www.r-project.org, http://dambi.ugent.be
git_url: https://git.bioconductor.org/packages/FlowSOM
git_branch: RELEASE_3_13
git_last_commit: 663dedc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FlowSOM_2.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FlowSOM_2.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FlowSOM_2.0.0.tgz
vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf
vignetteTitles: FlowSOM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R
importsMe: CATALYST, CytoTree, diffcyt
suggestsMe: HDCytoData
dependencyCount: 193
Package: flowSpecs
Version: 1.6.0
Depends: R (>= 4.0)
Imports: ggplot2 (>= 3.1.0), BiocGenerics (>= 0.30.0), BiocParallel (>=
1.18.1), Biobase (>= 2.48.0), reshape2 (>= 1.4.3), flowCore (>=
1.50.0), zoo (>= 1.8.6), stats (>= 3.6.0), methods (>= 3.6.0)
Suggests: testthat, knitr, rmarkdown, BiocStyle, DepecheR
License: MIT + file LICENSE
MD5sum: 037334f156cdd29cee2c50c4f22e35a0
NeedsCompilation: no
Title: Tools for processing of high-dimensional cytometry data
Description: This package is intended to fill the role of conventional
cytometry pre-processing software, for spectral decomposition,
transformation, visualization and cleanup, and to aid further
downstream analyses, such as with DepecheR, by enabling
transformation of flowFrames and flowSets to dataframes.
Functions for flowCore-compliant automatic 1D-gating/filtering
are in the pipe line. The package name has been chosen both as
it will deal with spectral cytometry and as it will hopefully
give the user a nice pair of spectacles through which to view
their data.
biocViews: Software,CellBasedAssays,DataRepresentation,ImmunoOncology,
FlowCytometry,SingleCell,Visualization,Normalization,DataImport
Author: Jakob Theorell [aut, cre]
Maintainer: Jakob Theorell <jakob.theorell@ndcn.ox.ac.uk>
VignetteBuilder: knitr
BugReports: https://github.com/jtheorell/flowSpecs/issues
git_url: https://git.bioconductor.org/packages/flowSpecs
git_branch: RELEASE_3_13
git_last_commit: e8a3b4e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowSpecs_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowSpecs_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowSpecs_1.6.0.tgz
vignettes: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.html
vignetteTitles: Example workflow for processing of raw spectral
cytometry files
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.R
dependencyCount: 64
Package: flowStats
Version: 4.4.0
Depends: R (>= 3.0.2)
Imports: BiocGenerics, MASS, flowCore (>= 1.99.6), flowWorkspace,
ncdfFlow(>= 2.19.5), flowViz, fda (>= 2.2.6), Biobase, methods,
grDevices, graphics, stats, cluster, utils, KernSmooth,
lattice, ks, RColorBrewer, rrcov
Suggests: xtable, testthat, openCyto
Enhances: RBGL,graph
License: Artistic-2.0
MD5sum: 8396614b53bfc338bd1ba5562e039cfa
NeedsCompilation: no
Title: Statistical methods for the analysis of flow cytometry data
Description: Methods and functionality to analyse flow data that is
beyond the basic infrastructure provided by the flowCore
package.
biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays
Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj
Khodabakhshi, Chao-Jen Wong, Kyongryun Lee
Maintainer: Greg Finak <gfinak@fhcrc.org>, Mike Jiang
<wjiang2@fhcrc.org>, Jake Wagner <jpwagner@fhcrc.org>
URL: http://www.github.com/RGLab/flowStats
BugReports: http://www.github.com/RGLab/flowStats/issues
git_url: https://git.bioconductor.org/packages/flowStats
git_branch: RELEASE_3_13
git_last_commit: 5d3758e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowStats_4.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowStats_4.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowStats_4.4.0.tgz
vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf
vignetteTitles: flowStats Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.R
dependsOnMe: flowVS, highthroughputassays
suggestsMe: cydar, flowCore, flowTime, flowViz, ggcyto
dependencyCount: 110
Package: flowTime
Version: 1.16.1
Depends: R (>= 3.4), flowCore
Imports: utils, dplyr (>= 1.0.0), tibble, magrittr, plyr, rlang
Suggests: knitr, rmarkdown, flowViz, ggplot2, BiocGenerics, stats,
flowClust, openCyto, flowStats, ggcyto
License: Artistic-2.0
MD5sum: b16c4e26f6b48cd1910f68415998efca
NeedsCompilation: no
Title: Annotation and analysis of biological dynamical systems using
flow cytometry
Description: This package facilitates analysis of both timecourse and
steady state flow cytometry experiments. This package was
originially developed for quantifying the function of gene
regulatory networks in yeast (strain W303) expressing
fluorescent reporter proteins using BD Accuri C6 and SORP
cytometers. However, the functions are for the most part
general and may be adapted for analysis of other organisms
using other flow cytometers. Functions in this package
facilitate the annotation of flow cytometry data with
experimental metadata, as often required for publication and
general ease-of-reuse. Functions for creating, saving and
loading gate sets are also included. In the past, we have
typically generated summary statistics for each flowset for
each timepoint and then annotated and analyzed these summary
statistics. This method loses a great deal of the power that
comes from the large amounts of individual cell data generated
in flow cytometry, by essentially collapsing this data into a
bulk measurement after subsetting. In addition to these summary
functions, this package also contains functions to facilitate
annotation and analysis of steady-state or time-lapse data
utilizing all of the data collected from the thousands of
individual cells in each sample.
biocViews: FlowCytometry, TimeCourse, Visualization, DataImport,
CellBasedAssays, ImmunoOncology
Author: R. Clay Wright [aut, cre], Nick Bolten [aut], Edith
Pierre-Jerome [aut]
Maintainer: R. Clay Wright <wright.clay@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowTime
git_branch: RELEASE_3_13
git_last_commit: 580e1b8
git_last_commit_date: 2021-07-27
Date/Publication: 2021-07-29
source.ver: src/contrib/flowTime_1.16.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowTime_1.16.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowTime_1.16.1.tgz
vignettes: vignettes/flowTime/inst/doc/gating-vignette.html,
vignettes/flowTime/inst/doc/steady-state-vignette.html,
vignettes/flowTime/inst/doc/time-course-vignette.html
vignetteTitles: Yeast gating, Steady-state analysis of flow cytometry
data, Time course analysis of flow cytometry data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowTime/inst/doc/gating-vignette.R,
vignettes/flowTime/inst/doc/steady-state-vignette.R,
vignettes/flowTime/inst/doc/time-course-vignette.R
dependencyCount: 38
Package: flowTrans
Version: 1.44.0
Depends: R (>= 2.11.0), flowCore, flowViz,flowClust
Imports: flowCore, methods, flowViz, stats, flowClust
License: Artistic-2.0
Archs: i386, x64
MD5sum: 348699f440d4bd44be6d2221cc411a51
NeedsCompilation: no
Title: Parameter Optimization for Flow Cytometry Data Transformation
Description: Profile maximum likelihood estimation of parameters for
flow cytometry data transformations.
biocViews: ImmunoOncology, FlowCytometry
Author: Greg Finak <gfinak@fredhutch.org>, Juan Manuel-Perez
<jperez@ircm.qc.ca>, Raphael Gottardo <rgottard@fredhutch.org>
Maintainer: Greg Finak <gfinak@fredhutch.org>
git_url: https://git.bioconductor.org/packages/flowTrans
git_branch: RELEASE_3_13
git_last_commit: 6d79ae3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowTrans_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowTrans_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowTrans_1.44.0.tgz
vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf
vignetteTitles: flowTrans package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowTrans/inst/doc/flowTrans.R
dependencyCount: 38
Package: flowUtils
Version: 1.56.0
Depends: R (>= 2.2.0)
Imports: Biobase, graph, methods, stats, utils, corpcor, RUnit, XML,
flowCore (>= 1.32.0)
Suggests: gatingMLData
License: Artistic-2.0
MD5sum: dd39163bd7144dfd7c2ee6ca882a04fa
NeedsCompilation: no
Title: Utilities for flow cytometry
Description: Provides utilities for flow cytometry data.
biocViews: ImmunoOncology, Infrastructure, FlowCytometry,
CellBasedAssays, DecisionTree
Author: J. Spidlen., N. Gopalakrishnan, F. Hahne, B. Ellis, R.
Gentleman, M. Dalphin, N. Le Meur, B. Purcell, W. Jiang
Maintainer: Josef Spidlen <jspidlen@gmail.com>
URL: https://github.com/jspidlen/flowUtils
BugReports: https://github.com/jspidlen/flowUtils/issues
git_url: https://git.bioconductor.org/packages/flowUtils
git_branch: RELEASE_3_13
git_last_commit: ad93cd2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowUtils_1.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowUtils_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowUtils_1.56.0.tgz
vignettes: vignettes/flowUtils/inst/doc/HowTo-flowUtils.pdf
vignetteTitles: Gating-ML support in R
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowUtils/inst/doc/HowTo-flowUtils.R
importsMe: CytoTree
suggestsMe: gatingMLData
dependencyCount: 23
Package: flowViz
Version: 1.56.0
Depends: R (>= 2.7.0), flowCore(>= 1.41.9), lattice
Imports: stats4, Biobase, flowCore, graphics, grDevices, grid,
KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer,
stats, utils, hexbin,IDPmisc
Suggests: colorspace, flowStats, knitr, testthat
License: Artistic-2.0
MD5sum: 0a1a47cdacca2940b5836d4a14b23966
NeedsCompilation: no
Title: Visualization for flow cytometry
Description: Provides visualization tools for flow cytometry data.
biocViews: ImmunoOncology, Infrastructure, FlowCytometry,
CellBasedAssays, Visualization
Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar, M.
Jiang
Maintainer: Mike Jiang <wjiang2@fhcrc.org>, Jake Wagner
<jpwagner@fhcrc.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowViz
git_branch: RELEASE_3_13
git_last_commit: f90ec49
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowViz_1.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowViz_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowViz_1.56.0.tgz
vignettes: vignettes/flowViz/inst/doc/filters.html
vignetteTitles: Visualizing Gates with Flow Cytometry Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowViz/inst/doc/filters.R
dependsOnMe: flowFP, flowVS
importsMe: flowClust, flowDensity, flowStats, flowTrans
suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto
dependencyCount: 29
Package: flowVS
Version: 1.24.0
Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats
Suggests: knitr, vsn,
License: Artistic-2.0
MD5sum: 259d9e5f4823b8665572e8d2d67673ed
NeedsCompilation: no
Title: Variance stabilization in flow cytometry (and microarrays)
Description: Per-channel variance stabilization from a collection of
flow cytometry samples by Bertlett test for homogeneity of
variances. The approach is applicable to microarrays data as
well.
biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Microarray
Author: Ariful Azad
Maintainer: Ariful Azad <azad@iu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowVS
git_branch: RELEASE_3_13
git_last_commit: 095b0d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowVS_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowVS_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowVS_1.24.0.tgz
vignettes: vignettes/flowVS/inst/doc/flowVS.pdf
vignetteTitles: flowVS: Cell population matching and meta-clustering in
Flow Cytometry
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowVS/inst/doc/flowVS.R
dependencyCount: 111
Package: flowWorkspace
Version: 4.4.0
Depends: R (>= 3.5.0)
Imports: Biobase, BiocGenerics, cytolib (>= 2.3.9), lattice,
latticeExtra, XML, ggplot2, graph, graphics, grDevices,
methods, stats, stats4, utils, RBGL, tools, Rgraphviz,
data.table, dplyr, Rcpp, scales, matrixStats, RcppParallel,
RProtoBufLib, digest, aws.s3, aws.signature, flowCore(>=
2.1.1), ncdfFlow(>= 2.25.4), DelayedArray, S4Vectors
LinkingTo: Rcpp, BH(>= 1.62.0-1), RProtoBufLib(>= 1.99.4), cytolib (>=
2.3.7),Rhdf5lib, RcppArmadillo, RcppParallel(>= 4.4.2-1)
Suggests: testthat, flowWorkspaceData (>= 2.23.2), knitr, ggcyto,
parallel, CytoML, openCyto
License: file LICENSE
License_restricts_use: yes
MD5sum: 7c871faaf53cfca2e0d10ee9b6e81189
NeedsCompilation: yes
Title: Infrastructure for representing and interacting with gated and
ungated cytometry data sets.
Description: This package is designed to facilitate comparison of
automated gating methods against manual gating done in flowJo.
This package allows you to import basic flowJo workspaces into
BioConductor and replicate the gating from flowJo using the
flowCore functionality. Gating hierarchies, groups of samples,
compensation, and transformation are performed so that the
output matches the flowJo analysis.
biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing,
DataRepresentation
Author: Greg Finak, Mike Jiang
Maintainer: Greg Finak <greg@ozette.ai>, Mike Jiang <mike@ozette.ai>
SystemRequirements: GNU make, C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowWorkspace
git_branch: RELEASE_3_13
git_last_commit: 8b26faf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/flowWorkspace_4.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/flowWorkspace_4.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/flowWorkspace_4.4.0.tgz
vignettes:
vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.html,
vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html
vignetteTitles: flowWorkspace Introduction: A Package to store and
maninpulate gated flow data, How to merge GatingSets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: TRUE
Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.R,
vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R
dependsOnMe: ggcyto, highthroughputassays
importsMe: CytoML, flowDensity, FlowSOM, flowStats, ImmuneSpaceR,
PeacoQC
suggestsMe: CATALYST, COMPASS, flowClust, flowCore
linksToMe: CytoML
dependencyCount: 81
Package: fmcsR
Version: 1.34.0
Depends: R (>= 2.10.0), ChemmineR, methods
Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel
Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap,rmarkdown
License: Artistic-2.0
MD5sum: b938405424e87121ae4b87d823fc2a18
NeedsCompilation: yes
Title: Mismatch Tolerant Maximum Common Substructure Searching
Description: The fmcsR package introduces an efficient maximum common
substructure (MCS) algorithms combined with a novel matching
strategy that allows for atom and/or bond mismatches in the
substructures shared among two small molecules. The resulting
flexible MCSs (FMCSs) are often larger than strict MCSs,
resulting in the identification of more common features in
their source structures, as well as a higher sensitivity in
finding compounds with weak structural similarities. The fmcsR
package provides several utilities to use the FMCS algorithm
for pairwise compound comparisons, structure similarity
searching and clustering.
biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics,
Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays,
Visualization, Infrastructure, DataImport, Clustering,
Proteomics, Metabolomics
Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://github.com/girke-lab/fmcsR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/fmcsR
git_branch: RELEASE_3_13
git_last_commit: 9440089
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fmcsR_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fmcsR_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fmcsR_1.34.0.tgz
vignettes: vignettes/fmcsR/inst/doc/fmcsR.html
vignetteTitles: fmcsR
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R
importsMe: Rcpi, BioMedR
suggestsMe: ChemmineR, xnet
dependencyCount: 63
Package: fmrs
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: methods, survival, stats
Suggests: BiocGenerics, testthat, knitr, utils
License: GPL (>= 3)
MD5sum: 65c15515082a303aa26587a03a494da2
NeedsCompilation: yes
Title: Variable Selection in Finite Mixture of AFT Regression and FMR
Description: Provides parameter estimation as well as variable
selection in Finite Mixture of Accelerated Failure Time
Regression and Finite Mixture of Regression Models.
Furthermore, this package provides Ridge Regression and Elastic
Net.
biocViews: Survival, Regression, DimensionReduction
Author: Farhad Shokoohi [aut, cre]
(<https://orcid.org/0000-0002-6224-2609>)
Maintainer: Farhad Shokoohi <shokoohi@icloud.com>
VignetteBuilder: knitr
BugReports: https://github.com/shokoohi/fmrs/issues
git_url: https://git.bioconductor.org/packages/fmrs
git_branch: RELEASE_3_13
git_last_commit: 0aa7bb6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fmrs_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fmrs_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fmrs_1.2.0.tgz
vignettes: vignettes/fmrs/inst/doc/usingfmrs.html
vignetteTitles: Using fmrs package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fmrs/inst/doc/usingfmrs.R
dependencyCount: 10
Package: fobitools
Version: 1.0.0
Depends: R (>= 4.1)
Imports: clisymbols, crayon, dplyr, fgsea, ggplot2, ggraph, magrittr,
ontologyIndex, purrr, RecordLinkage, stringr, textclean,
tictoc, tidygraph, tidyr, vroom
Suggests: BiocStyle, covr, ggrepel, kableExtra, knitr,
metabolomicsWorkbenchR, POMA, rmarkdown, rvest,
SummarizedExperiment, testthat (>= 2.3.2), tidyverse
License: GPL-3
MD5sum: 571db4ec49549003ef7759653818ddf0
NeedsCompilation: no
Title: Tools For Manipulating FOBI Ontology
Description: A set of tools for interacting with Food-Biomarker
Ontology (FOBI). A collection of basic manipulation tools for
biological significance analysis, graphs, and text mining
strategies for annotating nutritional data.
biocViews: MassSpectrometry, Metabolomics, Software, Visualization,
BiomedicalInformatics, GraphAndNetwork, Annotation,
Cheminformatics, Pathways, GeneSetEnrichment
Author: Pol Castellano-Escuder [aut, cre]
(<https://orcid.org/0000-0001-6466-877X>), Cristina
Andrés-Lacueva [aut] (<https://orcid.org/0000-0002-8494-4978>),
Alex Sánchez-Pla [aut]
(<https://orcid.org/0000-0002-8673-7737>)
Maintainer: Pol Castellano-Escuder <polcaes@gmail.com>
URL: https://github.com/pcastellanoescuder/fobitools/
VignetteBuilder: knitr
BugReports: https://github.com/pcastellanoescuder/fobitools/issues
git_url: https://git.bioconductor.org/packages/fobitools
git_branch: RELEASE_3_13
git_last_commit: ad1d5ca
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/fobitools_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/fobitools_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/fobitools_1.0.0.tgz
vignettes: vignettes/fobitools/inst/doc/Dietary_data_annotation.html,
vignettes/fobitools/inst/doc/food_enrichment_analysis.html,
vignettes/fobitools/inst/doc/MW_ST000291_enrichment.html,
vignettes/fobitools/inst/doc/MW_ST000629_enrichment.html
vignetteTitles: Dietary text annotation, Simple food ORA, Use case
ST000291, Use case ST000629
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fobitools/inst/doc/Dietary_data_annotation.R,
vignettes/fobitools/inst/doc/food_enrichment_analysis.R,
vignettes/fobitools/inst/doc/MW_ST000291_enrichment.R,
vignettes/fobitools/inst/doc/MW_ST000629_enrichment.R
dependencyCount: 123
Package: FoldGO
Version: 1.10.0
Depends: R (>= 4.0)
Imports: topGO (>= 2.30.1), ggplot2 (>= 2.2.1), tidyr (>= 0.8.0),
stats, methods
Suggests: knitr, rmarkdown, devtools, kableExtra
License: GPL-3
Archs: i386, x64
MD5sum: 686728ba47a787737b7283e54df9c68a
NeedsCompilation: no
Title: Package for Fold-specific GO Terms Recognition
Description: FoldGO is a package designed to annotate gene sets derived
from expression experiments and identify fold-change-specific
GO terms.
biocViews: DifferentialExpression, GeneExpression, GO, Software
Author: Daniil Wiebe <daniil.wiebe@gmail.com> [aut, cre]
Maintainer: Daniil Wiebe <daniil.wiebe@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FoldGO
git_branch: RELEASE_3_13
git_last_commit: defd37f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FoldGO_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FoldGO_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FoldGO_1.10.0.tgz
vignettes: vignettes/FoldGO/inst/doc/vignette.html
vignetteTitles: FoldGO: a tool for fold-change-specific functional
enrichment analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FoldGO/inst/doc/vignette.R
dependencyCount: 83
Package: FRASER
Version: 1.4.0
Depends: BiocParallel, data.table, Rsamtools, SummarizedExperiment
Imports: AnnotationDbi, BBmisc, Biobase, BiocGenerics, biomaRt,
BSgenome, cowplot, DelayedArray (>= 0.5.11),
DelayedMatrixStats, extraDistr, generics, GenomeInfoDb,
GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges,
grDevices, ggplot2, ggrepel, HDF5Array, matrixStats, methods,
OUTRIDER, pcaMethods, pheatmap, plotly, PRROC, RColorBrewer,
rhdf5, Rsubread, R.utils, S4Vectors, stats, tibble, tools,
utils, VGAM
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle, knitr, rmarkdown, testthat, covr,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db,
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 554a5ead744a51a5e0f834d475b02e7e
NeedsCompilation: yes
Title: Find RAre Splicing Events in RNA-Seq Data
Description: Detection of rare aberrant splicing events in
transcriptome profiles. The workflow aims to assist the
diagnostics in the field of rare diseases where RNA-seq is
performed to identify aberrant splicing defects.
biocViews: RNASeq, AlternativeSplicing, Sequencing, Software, Genetics,
Coverage
Author: Christian Mertes [aut, cre], Ines Scheller [aut], Vicente Yepez
[ctb], Julien Gagneur [aut]
Maintainer: Christian Mertes <mertes@in.tum.de>
URL: https://github.com/gagneurlab/FRASER
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FRASER
git_branch: RELEASE_3_13
git_last_commit: 926f313
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FRASER_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FRASER_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FRASER_1.4.0.tgz
vignettes: vignettes/FRASER/inst/doc/FRASER.pdf
vignetteTitles: FRASER: Find RAre Splicing Evens in RNA-seq Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/FRASER/inst/doc/FRASER.R
dependencyCount: 172
Package: frenchFISH
Version: 1.4.0
Imports: utils, MCMCpack, NHPoisson
Suggests: knitr, rmarkdown, testthat
License: Artistic-2.0
Archs: i386, x64
MD5sum: a65d618d00555195248eeaa508fc7652
NeedsCompilation: no
Title: Poisson Models for Quantifying DNA Copy-number from FISH Images
of Tissue Sections
Description: FrenchFISH comprises a nuclear volume correction method
coupled with two types of Poisson models: either a Poisson
model for improved manual spot counting without the need for
control probes; or a homogenous Poisson Point Process model for
automated spot counting.
biocViews: Software, BiomedicalInformatics, CellBiology, Genetics,
HiddenMarkovModel, Preprocessing
Author: Adam Berman, Geoff Macintyre
Maintainer: Adam Berman <agb61@cam.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/frenchFISH
git_branch: RELEASE_3_13
git_last_commit: 1497b34
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/frenchFISH_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/frenchFISH_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/frenchFISH_1.4.0.tgz
vignettes: vignettes/frenchFISH/inst/doc/frenchFISH.html
vignetteTitles: Correcting FISH probe counts with frenchFISH
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/frenchFISH/inst/doc/frenchFISH.R
dependencyCount: 89
Package: FRGEpistasis
Version: 1.28.0
Depends: R (>= 2.15), MASS, fda, methods, stats
Imports: utils
License: GPL-2
MD5sum: 6f9efdfdb9287727bb23ff7dba6d5ce4
NeedsCompilation: no
Title: Epistasis Analysis for Quantitative Traits by Functional
Regression Model
Description: A Tool for Epistasis Analysis Based on Functional
Regression Model
biocViews: Genetics, NetworkInference, GeneticVariability, Software
Author: Futao Zhang
Maintainer: Futao Zhang <futoaz@gmail.com>
git_url: https://git.bioconductor.org/packages/FRGEpistasis
git_branch: RELEASE_3_13
git_last_commit: ed89200
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FRGEpistasis_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FRGEpistasis_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FRGEpistasis_1.28.0.tgz
vignettes: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.pdf
vignetteTitles: FRGEpistasis: A Tool for Epistasis Analysis Based on
Functional Regression Model
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.R
dependencyCount: 61
Package: frma
Version: 1.44.0
Depends: R (>= 2.10.0), Biobase (>= 2.6.0)
Imports: Biobase, MASS, DBI, affy, methods, oligo, oligoClasses,
preprocessCore, utils, BiocGenerics
Suggests: hgu133afrmavecs, frmaExampleData
License: GPL (>= 2)
MD5sum: e469fc86ed9a0935437ed932d3dd3ad0
NeedsCompilation: no
Title: Frozen RMA and Barcode
Description: Preprocessing and analysis for single microarrays and
microarray batches.
biocViews: Software, Microarray, Preprocessing
Author: Matthew N. McCall <mccallm@gmail.com>, Rafael A. Irizarry
<rafa@jhu.edu>, with contributions from Terry Therneau
Maintainer: Matthew N. McCall <mccallm@gmail.com>
URL: http://bioconductor.org
git_url: https://git.bioconductor.org/packages/frma
git_branch: RELEASE_3_13
git_last_commit: 6ef2453
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/frma_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/frma_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/frma_1.44.0.tgz
vignettes: vignettes/frma/inst/doc/frma.pdf
vignetteTitles: frma: Preprocessing for single arrays and array batches
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/frma/inst/doc/frma.R
importsMe: ChIPXpress, rat2302frmavecs, DeSousa2013
suggestsMe: frmaTools, antiProfilesData
dependencyCount: 56
Package: frmaTools
Version: 1.44.0
Depends: R (>= 2.10.0), affy
Imports: Biobase, DBI, methods, preprocessCore, stats, utils
Suggests: oligo, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, frma, affyPLM,
hgu133aprobe, hgu133atagprobe, hgu133plus2probe, hgu133acdf,
hgu133atagcdf, hgu133plus2cdf, hgu133afrmavecs, frmaExampleData
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 95f608947491aeeb4fbb14bdf2a67e34
NeedsCompilation: no
Title: Frozen RMA Tools
Description: Tools for advanced use of the frma package.
biocViews: Software, Microarray, Preprocessing
Author: Matthew N. McCall <mccallm@gmail.com>, Rafael A. Irizarry
<rafa@jhu.edu>
Maintainer: Matthew N. McCall <mccallm@gmail.com>
URL: http://bioconductor.org
git_url: https://git.bioconductor.org/packages/frmaTools
git_branch: RELEASE_3_13
git_last_commit: 619dae9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/frmaTools_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/frmaTools_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/frmaTools_1.44.0.tgz
vignettes: vignettes/frmaTools/inst/doc/frmaTools.pdf
vignetteTitles: frmaTools: Create packages containing the vectors used
by frma.
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/frmaTools/inst/doc/frmaTools.R
importsMe: DeSousa2013
dependencyCount: 14
Package: FScanR
Version: 1.2.0
Depends: R (>= 4.0)
Imports: stats
Suggests: knitr, rmarkdown
License: Artistic-2.0
MD5sum: d14e03beeef0b37d6c7c800e79bd8d70
NeedsCompilation: no
Title: Detect Programmed Ribosomal Frameshifting Events from mRNA/cDNA
BLASTX Output
Description: 'FScanR' identifies Programmed Ribosomal Frameshifting
(PRF) events from BLASTX homolog sequence alignment between
targeted genomic/cDNA/mRNA sequences against the peptide
library of the same species or a close relative. The output by
BLASTX or diamond BLASTX will be used as input of 'FScanR' and
should be in a tabular format with 14 columns. For BLASTX, the
output parameter should be: -outfmt '6 qseqid sseqid pident
length mismatch gapopen qstart qend sstart send evalue bitscore
qframe sframe'. For diamond BLASTX, the output parameter should
be: -outfmt 6 qseqid sseqid pident length mismatch gapopen
qstart qend sstart send evalue bitscore qframe qframe.
biocViews: Alignment, Annotation, Software
Author: Xiao Chen [aut, cre] (<https://orcid.org/0000-0001-5059-8846>)
Maintainer: Xiao Chen <seanchen607@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/seanchen607/FScanR/issues
git_url: https://git.bioconductor.org/packages/FScanR
git_branch: RELEASE_3_13
git_last_commit: 95f349c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FScanR_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FScanR_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FScanR_1.2.0.tgz
vignettes: vignettes/FScanR/inst/doc/FScanR.html
vignetteTitles: FScanR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FScanR/inst/doc/FScanR.R
dependencyCount: 1
Package: FunChIP
Version: 1.18.0
Depends: R (>= 3.2), GenomicRanges
Imports: shiny, fda, doParallel, GenomicAlignments, Rcpp, methods,
foreach, parallel, GenomeInfoDb, Rsamtools, grDevices,
graphics, stats, RColorBrewer
LinkingTo: Rcpp
License: Artistic-2.0
MD5sum: 4c2fcc5aa80f023252527c297076c479
NeedsCompilation: yes
Title: Clustering and Alignment of ChIP-Seq peaks based on their shapes
Description: Preprocessing and smoothing of ChIP-Seq peaks and
efficient implementation of the k-mean alignment algorithm to
classify them.
biocViews: StatisticalMethod, Clustering, ChIPSeq
Author: Alice Parodi [aut, cre], Marco Morelli [aut, cre], Laura M.
Sangalli [aut], Piercesare Secchi [aut], Simone Vantini [aut]
Maintainer: Alice Parodi <alicecarla.parodi@polimi.it>
git_url: https://git.bioconductor.org/packages/FunChIP
git_branch: RELEASE_3_13
git_last_commit: 4981a2b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/FunChIP_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/FunChIP_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/FunChIP_1.18.0.tgz
vignettes: vignettes/FunChIP/inst/doc/FunChIP.pdf
vignetteTitles: An introduction to FunChIP
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FunChIP/inst/doc/FunChIP.R
dependencyCount: 111
Package: funtooNorm
Version: 1.16.0
Depends: R(>= 3.4)
Imports: pls, matrixStats, minfi, methods,
IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomeInfoDb,
grDevices, graphics, stats
Suggests: prettydoc, minfiData, knitr, rmarkdown
License: GPL-3
MD5sum: 1e7baa5f935ebfa0f007e3f4b9d917d3
NeedsCompilation: no
Title: Normalization Procedure for Infinium HumanMethylation450
BeadChip Kit
Description: Provides a function to normalize Illumina Infinium Human
Methylation 450 BeadChip (Illumina 450K), correcting for tissue
and/or cell type.
biocViews: DNAMethylation, Preprocessing, Normalization
Author: Celia Greenwood <celia.greenwood@mcgill.ca>,Stepan Grinek
<stepan.grinek@ladydavis.ca>, Maxime Turgeon
<maxime.turgeon@mail.mcgill.ca>, Kathleen Klein
<kathleen.klein@mail.mcgill.ca>
Maintainer: Kathleen Klein <kathleen.klein@mail.mcgill.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/funtooNorm
git_branch: RELEASE_3_13
git_last_commit: 3256fea
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/funtooNorm_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/funtooNorm_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/funtooNorm_1.16.0.tgz
vignettes: vignettes/funtooNorm/inst/doc/funtooNorm.pdf
vignetteTitles: Normalizing Illumina Infinium Human Methylation 450k
for multiple cell types with funtooNorm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/funtooNorm/inst/doc/funtooNorm.R
dependencyCount: 142
Package: GA4GHclient
Version: 1.16.0
Depends: S4Vectors
Imports: BiocGenerics, Biostrings, dplyr, GenomeInfoDb, GenomicRanges,
httr, IRanges, jsonlite, methods, VariantAnnotation
Suggests: AnnotationDbi, BiocStyle, DT, knitr, org.Hs.eg.db, rmarkdown,
testthat, TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL (>= 2)
MD5sum: 38b916d73a6254df6daa32459ffe099a
NeedsCompilation: no
Title: A Bioconductor package for accessing GA4GH API data servers
Description: GA4GHclient provides an easy way to access public data
servers through Global Alliance for Genomics and Health (GA4GH)
genomics API. It provides low-level access to GA4GH API and
translates response data into Bioconductor-based class objects.
biocViews: DataRepresentation, ThirdPartyClient
Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane
Rocha [ctb]
Maintainer: Welliton Souza <well309@gmail.com>
URL: https://github.com/labbcb/GA4GHclient
VignetteBuilder: knitr
BugReports: https://github.com/labbcb/GA4GHclient/issues
git_url: https://git.bioconductor.org/packages/GA4GHclient
git_branch: RELEASE_3_13
git_last_commit: d3644e7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GA4GHclient_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GA4GHclient_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GA4GHclient_1.16.0.tgz
vignettes: vignettes/GA4GHclient/inst/doc/GA4GHclient.html
vignetteTitles: GA4GHclient
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GA4GHclient/inst/doc/GA4GHclient.R
dependsOnMe: GA4GHshiny
dependencyCount: 98
Package: GA4GHshiny
Version: 1.14.0
Depends: GA4GHclient
Imports: AnnotationDbi, BiocGenerics, dplyr, DT, GenomeInfoDb,
openxlsx, GenomicFeatures, methods, purrr, S4Vectors, shiny,
shinyjs, tidyr, shinythemes
Suggests: BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL-3
MD5sum: 345a18b4c923582fd8128fbc17b82288
NeedsCompilation: no
Title: Shiny application for interacting with GA4GH-based data servers
Description: GA4GHshiny package provides an easy way to interact with
data servers based on Global Alliance for Genomics and Health
(GA4GH) genomics API through a Shiny application. It also
integrates with Beacon Network.
biocViews: GUI
Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane
Rocha [ctb], Elizabeth Borgognoni [ctb]
Maintainer: Welliton Souza <well309@gmail.com>
URL: https://github.com/labbcb/GA4GHshiny
VignetteBuilder: knitr
BugReports: https://github.com/labbcb/GA4GHshiny/issues
git_url: https://git.bioconductor.org/packages/GA4GHshiny
git_branch: RELEASE_3_13
git_last_commit: 8c2cb31
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GA4GHshiny_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GA4GHshiny_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GA4GHshiny_1.14.0.tgz
vignettes: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.html
vignetteTitles: GA4GHshiny
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.R
dependencyCount: 123
Package: gaga
Version: 2.38.0
Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv
Enhances: parallel
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 0236bccc048ff84e802840e55c6bdd7d
NeedsCompilation: yes
Title: GaGa hierarchical model for high-throughput data analysis
Description: Implements the GaGa model for high-throughput data
analysis, including differential expression analysis,
supervised gene clustering and classification. Additionally, it
performs sequential sample size calculations using the GaGa and
LNNGV models (the latter from EBarrays package).
biocViews: ImmunoOncology, OneChannel, MassSpectrometry,
MultipleComparison, DifferentialExpression, Classification
Author: David Rossell <rosselldavid@gmail.com>.
Maintainer: David Rossell <rosselldavid@gmail.com>
git_url: https://git.bioconductor.org/packages/gaga
git_branch: RELEASE_3_13
git_last_commit: 91b7446
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gaga_2.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gaga_2.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gaga_2.38.0.tgz
vignettes: vignettes/gaga/inst/doc/gagamanual.pdf
vignetteTitles: Manual for the gaga library
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gaga/inst/doc/gagamanual.R
importsMe: casper
dependencyCount: 17
Package: gage
Version: 2.42.0
Depends: R (>= 3.5.0)
Imports: graph, KEGGREST, AnnotationDbi, GO.db
Suggests: pathview, gageData, org.Hs.eg.db, hgu133a.db, GSEABase,
Rsamtools, GenomicAlignments,
TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq2, edgeR, limma
License: GPL (>=2.0)
MD5sum: f8ea31b7de2e654192c808cb19b4b084
NeedsCompilation: no
Title: Generally Applicable Gene-set Enrichment for Pathway Analysis
Description: GAGE is a published method for gene set (enrichment or
GSEA) or pathway analysis. GAGE is generally applicable
independent of microarray or RNA-Seq data attributes including
sample sizes, experimental designs, assay platforms, and other
types of heterogeneity, and consistently achieves superior
performance over other frequently used methods. In gage
package, we provide functions for basic GAGE analysis, result
processing and presentation. We have also built pipeline
routines for of multiple GAGE analyses in a batch, comparison
between parallel analyses, and combined analysis of
heterogeneous data from different sources/studies. In addition,
we provide demo microarray data and commonly used gene set data
based on KEGG pathways and GO terms. These funtions and data
are also useful for gene set analysis using other methods.
biocViews: Pathways, GO, DifferentialExpression, Microarray,
OneChannel, TwoChannel, RNASeq, Genetics, MultipleComparison,
GeneSetEnrichment, GeneExpression, SystemsBiology, Sequencing
Author: Weijun Luo
Maintainer: Weijun Luo <luo_weijun@yahoo.com>
URL: https://github.com/datapplab/gage,
http://www.biomedcentral.com/1471-2105/10/161
git_url: https://git.bioconductor.org/packages/gage
git_branch: RELEASE_3_13
git_last_commit: 8b222d6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gage_2.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gage_2.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gage_2.42.0.tgz
vignettes: vignettes/gage/inst/doc/dataPrep.pdf,
vignettes/gage/inst/doc/gage.pdf,
vignettes/gage/inst/doc/RNA-seqWorkflow.pdf
vignetteTitles: Gene set and data preparation, Generally Applicable
Gene-set/Pathway Analysis, RNA-Seq Data Pathway and Gene-set
Analysis Workflows
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gage/inst/doc/dataPrep.R,
vignettes/gage/inst/doc/gage.R,
vignettes/gage/inst/doc/RNA-seqWorkflow.R
dependsOnMe: EGSEA
importsMe: exp2flux
suggestsMe: FGNet, pathview, SBGNview, gageData
dependencyCount: 48
Package: gaggle
Version: 1.60.0
Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>=
0.4.17)
License: GPL version 2 or newer
MD5sum: 47d9a991d7372940e9f2ae1fdb9d127b
NeedsCompilation: no
Title: Broadcast data between R and Gaggle
Description: This package contains functions enabling data exchange
between R and Gaggle enabled bioinformatics software, including
Cytoscape, Firegoose and Gaggle Genome Browser.
biocViews: ThirdPartyClient, Visualization, Annotation,
GraphAndNetwork, DataImport
Author: Paul Shannon <pshannon@systemsbiology.org>
Maintainer: Christopher Bare <cbare@systemsbiology.org>
URL: http://gaggle.systemsbiology.net/docs/geese/r/
git_url: https://git.bioconductor.org/packages/gaggle
git_branch: RELEASE_3_13
git_last_commit: 9d24cfd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gaggle_1.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gaggle_1.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gaggle_1.60.0.tgz
vignettes: vignettes/gaggle/inst/doc/gaggle.pdf
vignetteTitles: Gaggle Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gaggle/inst/doc/gaggle.R
dependencyCount: 10
Package: gaia
Version: 2.36.0
Depends: R (>= 2.10)
License: GPL-2
MD5sum: 774fc52ac1c55f63730c406fa114e5fa
NeedsCompilation: no
Title: GAIA: An R package for genomic analysis of significant
chromosomal aberrations.
Description: This package allows to assess the statistical significance
of chromosomal aberrations.
biocViews: aCGH, CopyNumberVariation
Author: Sandro Morganella et al.
Maintainer: S. Morganella <sandro@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/gaia
git_branch: RELEASE_3_13
git_last_commit: 4524f5d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gaia_2.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gaia_2.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gaia_2.36.0.tgz
vignettes: vignettes/gaia/inst/doc/gaia.pdf
vignetteTitles: gaia
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gaia/inst/doc/gaia.R
importsMe: TCGAWorkflow
dependencyCount: 0
Package: GAPGOM
Version: 1.8.0
Depends: R (>= 4.0)
Imports: stats, utils, methods, Matrix, fastmatch, plyr, dplyr,
magrittr, data.table, igraph, graph, RBGL, GO.db, org.Hs.eg.db,
org.Mm.eg.db, GOSemSim, GEOquery, AnnotationDbi, Biobase,
BiocFileCache, matrixStats
Suggests: org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Dr.eg.db,
org.Ce.eg.db, org.At.tair.db, org.EcK12.eg.db, org.Bt.eg.db,
org.Cf.eg.db, org.Ag.eg.db, org.EcSakai.eg.db, org.Gg.eg.db,
org.Pt.eg.db, org.Pf.plasmo.db, org.Mmu.eg.db, org.Ss.eg.db,
org.Xl.eg.db, testthat, pryr, knitr, rmarkdown, prettydoc,
ggplot2, kableExtra, profvis, reshape2
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 701fc46ae79289d4c314abb2873b8cb1
NeedsCompilation: no
Title: GAPGOM (novel Gene Annotation Prediction and other GO Metrics)
Description: Collection of various measures and tools for lncRNA
annotation prediction put inside a redistributable R package.
The package contains two main algorithms; lncRNA2GOA and
TopoICSim. lncRNA2GOA tries to annotate novel genes (in this
specific case lncRNAs) by using various correlation/geometric
scoring methods on correlated expression data. After
correlating/scoring, the results are annotated and enriched.
TopoICSim is a topologically based method, that compares gene
similarity based on the topology of the GO DAG by information
content (IC) between GO terms.
biocViews: GO, GeneExpression, GenePrediction
Author: Rezvan Ehsani [aut, cre], Casper van Mourik [aut], Finn Drabløs
[aut]
Maintainer: Rezvan Ehsani <rezvanehsani74@gmail.com>
URL: https://github.com/Berghopper/GAPGOM/
VignetteBuilder: knitr
BugReports: https://github.com/Berghopper/GAPGOM/issues/
git_url: https://git.bioconductor.org/packages/GAPGOM
git_branch: RELEASE_3_13
git_last_commit: e8e35ed
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GAPGOM_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GAPGOM_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GAPGOM_1.8.0.tgz
vignettes: vignettes/GAPGOM/inst/doc/benchmarks.html,
vignettes/GAPGOM/inst/doc/GAPGOM.html
vignetteTitles: Benchmarks and other GO similarity methods, An
Introduction to GAPGOM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GAPGOM/inst/doc/benchmarks.R,
vignettes/GAPGOM/inst/doc/GAPGOM.R
dependencyCount: 90
Package: GAprediction
Version: 1.18.0
Depends: R (>= 3.3)
Imports: glmnet, stats, utils, Matrix
Suggests: knitr, rmarkdown
License: GPL (>=2)
MD5sum: f18abfea6e9c8270875f41bc70dbce65
NeedsCompilation: no
Title: Prediction of gestational age with Illumina HumanMethylation450
data
Description: [GAprediction] predicts gestational age using Illumina
HumanMethylation450 CpG data.
biocViews: ImmunoOncology, DNAMethylation, Epigenetics, Regression,
BiomedicalInformatics
Author: Jon Bohlin
Maintainer: Jon Bohlin <jon.bohlin@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GAprediction
git_branch: RELEASE_3_13
git_last_commit: 5d46177
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GAprediction_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GAprediction_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GAprediction_1.18.0.tgz
vignettes: vignettes/GAprediction/inst/doc/GAprediction.html
vignetteTitles: GAprediction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GAprediction/inst/doc/GAprediction.R
dependencyCount: 15
Package: garfield
Version: 1.20.0
Suggests: knitr
License: GPL-3
MD5sum: dfce0c090510b7af183487a54b5dadf1
NeedsCompilation: yes
Title: GWAS Analysis of Regulatory or Functional Information Enrichment
with LD correction
Description: GARFIELD is a non-parametric functional enrichment
analysis approach described in the paper GARFIELD: GWAS
analysis of regulatory or functional information enrichment
with LD correction. Briefly, it is a method that leverages GWAS
findings with regulatory or functional annotations (primarily
from ENCODE and Roadmap epigenomics data) to find features
relevant to a phenotype of interest. It performs greedy pruning
of GWAS SNPs (LD r2 > 0.1) and then annotates them based on
functional information overlap. Next, it quantifies Fold
Enrichment (FE) at various GWAS significance cutoffs and
assesses them by permutation testing, while matching for minor
allele frequency, distance to nearest transcription start site
and number of LD proxies (r2 > 0.8).
biocViews: Software, StatisticalMethod, Annotation,
FunctionalPrediction, GenomeAnnotation
Author: Sandro Morganella <sm22@sanger.ac.uk>
Maintainer: Valentina Iotchkova <vi1@sanger.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/garfield
git_branch: RELEASE_3_13
git_last_commit: 67bb168
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/garfield_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/garfield_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/garfield_1.20.0.tgz
vignettes: vignettes/garfield/inst/doc/vignette.pdf
vignetteTitles: garfield Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 0
Package: GARS
Version: 1.12.0
Depends: R (>= 3.5), ggplot2, cluster
Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment
Suggests: BiocStyle, knitr, testthat
License: GPL (>= 2)
MD5sum: 32e3573772e4fb0cc2a63e39e7c6c442
NeedsCompilation: no
Title: GARS: Genetic Algorithm for the identification of Robust Subsets
of variables in high-dimensional and challenging datasets
Description: Feature selection aims to identify and remove redundant,
irrelevant and noisy variables from high-dimensional datasets.
Selecting informative features affects the subsequent
classification and regression analyses by improving their
overall performances. Several methods have been proposed to
perform feature selection: most of them relies on univariate
statistics, correlation, entropy measurements or the usage of
backward/forward regressions. Herein, we propose an efficient,
robust and fast method that adopts stochastic optimization
approaches for high-dimensional. GARS is an innovative
implementation of a genetic algorithm that selects robust
features in high-dimensional and challenging datasets.
biocViews: Classification, FeatureExtraction, Clustering
Author: Mattia Chiesa <mattia.chiesa@hotmail.it>, Luca Piacentini
<luca.piacentini@cardiologicomonzino.it>
Maintainer: Mattia Chiesa <mattia.chiesa@hotmail.it>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GARS
git_branch: RELEASE_3_13
git_last_commit: 4d2039e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GARS_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GARS_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GARS_1.12.0.tgz
vignettes: vignettes/GARS/inst/doc/GARS.pdf
vignetteTitles: GARS: a Genetic Algorithm for the identification of
Robust Subsets of variables in high-dimensional and challenging
datasets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GARS/inst/doc/GARS.R
dependencyCount: 246
Package: GateFinder
Version: 1.12.0
Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP
Suggests: RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: 16f5fb3288026bae5d59d592037ccb49
NeedsCompilation: no
Title: Projection-based Gating Strategy Optimization for Flow and Mass
Cytometry
Description: Given a vector of cluster memberships for a cell
population, identifies a sequence of gates (polygon filters on
2D scatter plots) for isolation of that cell type.
biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering
Author: Nima Aghaeepour <naghaeep@gmail.com>, Erin F. Simonds
<erin.simonds@gmail.com>
Maintainer: Nima Aghaeepour <naghaeep@gmail.com>
git_url: https://git.bioconductor.org/packages/GateFinder
git_branch: RELEASE_3_13
git_last_commit: fd719d2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GateFinder_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GateFinder_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GateFinder_1.12.0.tgz
vignettes: vignettes/GateFinder/inst/doc/GateFinder.pdf
vignetteTitles: GateFinder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GateFinder/inst/doc/GateFinder.R
dependencyCount: 38
Package: gcapc
Version: 1.16.0
Depends: R (>= 3.4)
Imports: BiocGenerics, GenomeInfoDb, S4Vectors, IRanges, Biostrings,
BSgenome, GenomicRanges, Rsamtools, GenomicAlignments,
matrixStats, MASS, splines, grDevices, graphics, stats, methods
Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm10
License: GPL-3
MD5sum: b6e014a1f85f383eb4aac4c188c8ba55
NeedsCompilation: no
Title: GC Aware Peak Caller
Description: Peak calling for ChIP-seq data with consideration of
potential GC bias in sequencing reads. GC bias is first
estimated with generalized linear mixture models using
effective GC strategy, then applied into peak significance
estimation.
biocViews: Sequencing, ChIPSeq, BatchEffect, PeakDetection
Author: Mingxiang Teng and Rafael A. Irizarry
Maintainer: Mingxiang Teng <tengmx@gmail.com>
URL: https://github.com/tengmx/gcapc
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gcapc
git_branch: RELEASE_3_13
git_last_commit: 38a05b9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gcapc_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gcapc_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gcapc_1.16.0.tgz
vignettes: vignettes/gcapc/inst/doc/gcapc.html
vignetteTitles: The gcapc user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gcapc/inst/doc/gcapc.R
suggestsMe: epigraHMM
dependencyCount: 47
Package: gcatest
Version: 1.22.0
Depends: R (>= 3.2)
Imports: lfa
Suggests: knitr, ggplot2
License: GPL-3
Archs: i386, x64
MD5sum: f1afbe1cdf1bb8c558694edda2f35c7c
NeedsCompilation: yes
Title: Genotype Conditional Association TEST
Description: GCAT is an association test for genome wide association
studies that controls for population structure under a general
class of trait. models.
biocViews: SNP, DimensionReduction, PrincipalComponent,
GenomeWideAssociation
Author: Wei Hao, Minsun Song, John D. Storey
Maintainer: Wei Hao <whao@princeton.edu>, John D. Storey
<jstorey@princeton.edu>
URL: https://github.com/StoreyLab/gcatest
VignetteBuilder: knitr
BugReports: https://github.com/StoreyLab/gcatest/issues
git_url: https://git.bioconductor.org/packages/gcatest
git_branch: RELEASE_3_13
git_last_commit: 9efedc7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gcatest_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gcatest_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gcatest_1.22.0.tgz
vignettes: vignettes/gcatest/inst/doc/gcatest.pdf
vignetteTitles: gcat Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gcatest/inst/doc/gcatest.R
dependencyCount: 3
Package: gCrisprTools
Version: 1.20.0
Depends: R (>= 3.6)
Imports: Biobase, limma, RobustRankAggreg, ggplot2, PANTHER.db,
rmarkdown, grDevices, graphics, stats, utils, parallel,
SummarizedExperiment
Suggests: edgeR, knitr, grid, AnnotationDbi, org.Mm.eg.db,
org.Hs.eg.db, RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: a35d60a9e376918c4e31ff076ee11f35
NeedsCompilation: no
Title: Suite of Functions for Pooled Crispr Screen QC and Analysis
Description: Set of tools for evaluating pooled high-throughput
screening experiments, typically employing CRISPR/Cas9 or shRNA
expression cassettes. Contains methods for interrogating
library and cassette behavior within an experiment, identifying
differentially abundant cassettes, aggregating signals to
identify candidate targets for empirical validation, hypothesis
testing, and comprehensive reporting.
biocViews: ImmunoOncology, CRISPR, PooledScreens, ExperimentalDesign,
BiomedicalInformatics, CellBiology, FunctionalGenomics,
Pharmacogenomics, Pharmacogenetics, SystemsBiology,
DifferentialExpression, GeneSetEnrichment, Genetics,
MultipleComparison, Normalization, Preprocessing,
QualityControl, RNASeq, Regression, Software, Visualization
Author: Russell Bainer, Dariusz Ratman, Steve Lianoglou, Peter Haverty
Maintainer: Russell Bainer <russ.bainer@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gCrisprTools
git_branch: RELEASE_3_13
git_last_commit: ea57239
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gCrisprTools_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gCrisprTools_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gCrisprTools_1.20.0.tgz
vignettes:
vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html,
vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html
vignetteTitles: Example_Workflow_gCrisprTools, gCrisprTools_Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R,
vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R
dependencyCount: 120
Package: gcrma
Version: 2.64.0
Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats,
utils
Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector,
Biostrings (>= 2.11.32), splines, BiocManager
Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe
License: LGPL
MD5sum: db8329cd91ac5ba2d25274000de3c947
NeedsCompilation: yes
Title: Background Adjustment Using Sequence Information
Description: Background adjustment using sequence information
biocViews: Microarray, OneChannel, Preprocessing
Author: Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James
MacDonald <jmacdon@med.umich.edu> Jeff Gentry
Maintainer: Z. Wu <zwu@stat.brown.edu>
git_url: https://git.bioconductor.org/packages/gcrma
git_branch: RELEASE_3_13
git_last_commit: 9914de3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gcrma_2.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gcrma_2.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gcrma_2.64.0.tgz
vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf
vignetteTitles: gcrma1.2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: affyILM, affyPLM, bgx, maskBAD, webbioc
importsMe: affycoretools, affylmGUI
suggestsMe: panp, aroma.affymetrix
dependencyCount: 25
Package: GCSConnection
Version: 1.4.0
Depends: R (>= 4.0.0)
Imports: Rcpp (>= 1.0.2), httr, googleAuthR, googleCloudStorageR,
methods, jsonlite, utils
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL (>= 2)
MD5sum: 7d74056b9d1501f9598075b7910172aa
NeedsCompilation: yes
Title: Creating R Connection with Google Cloud Storage
Description: Create R 'connection' objects to google cloud storage
buckets using the Google REST interface. Both read and write
connections are supported. The package also provides functions
to view and manage files on Google Cloud.
biocViews: Infrastructure
Author: Jiefei Wang [cre]
Maintainer: Jiefei Wang <szwjf08@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GCSConnection
git_branch: RELEASE_3_13
git_last_commit: 47d39d5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GCSConnection_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GCSConnection_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GCSConnection_1.4.0.tgz
vignettes: vignettes/GCSConnection/inst/doc/Introduction.html
vignetteTitles: quickStart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GCSConnection/inst/doc/Introduction.R
suggestsMe: GCSFilesystem
dependencyCount: 32
Package: GCSFilesystem
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: stats
Suggests: testthat, knitr, rmarkdown, BiocStyle, GCSConnection
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 858728581e30dba2ded7a79b2216cece
NeedsCompilation: no
Title: Mounting a Google Cloud bucket to a local directory
Description: Mounting a Google Cloud bucket to a local directory. The
files in the bucket can be viewed and read as if they are
locally stored. For using the package, you need to install
GCSDokan on Windows or gcsfuse on Linux and MacOs.
biocViews: Infrastructure
Author: Jiefei Wang [aut, cre]
Maintainer: Jiefei Wang <szwjf08@gmail.com>
SystemRequirements: GCSDokan for Windows, gcsfuse for Linux and macOs
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GCSFilesystem
git_branch: RELEASE_3_13
git_last_commit: 7594317
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GCSFilesystem_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GCSFilesystem_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GCSFilesystem_1.2.0.tgz
vignettes: vignettes/GCSFilesystem/inst/doc/Quick-Start-Guide.html
vignetteTitles: Quick-Start-Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 1
Package: GCSscore
Version: 1.6.0
Depends: R (>= 3.6)
Imports: BiocManager, Biobase, utils, methods, RSQLite, devtools, dplR,
stringr, graphics, stats, affxparser, data.table
Suggests: siggenes, GEOquery, R.utils
License: GPL (>=3)
MD5sum: b980acab414397876cfba022740f1773
NeedsCompilation: no
Title: GCSscore: an R package for microarray analysis for
Affymetrix/Thermo Fisher arrays
Description: For differential expression analysis of 3'IVT and WT-style
microarrays from Affymetrix/Thermo-Fisher. Based on S-score
algorithm originally described by Zhang et al 2002.
biocViews: DifferentialExpression, Microarray, OneChannel,
ProprietaryPlatforms, DataImport
Author: Guy M. Harris & Shahroze Abbas & Michael F. Miles
Maintainer: Guy M. Harris <harrisgm@vcu.edu>
git_url: https://git.bioconductor.org/packages/GCSscore
git_branch: RELEASE_3_13
git_last_commit: 8bfdd99
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GCSscore_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GCSscore_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GCSscore_1.6.0.tgz
vignettes: vignettes/GCSscore/inst/doc/GCSscore.pdf
vignetteTitles: SScore primer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GCSscore/inst/doc/GCSscore.R
dependencyCount: 103
Package: GDCRNATools
Version: 1.13.1
Depends: R (>= 3.5.0)
Imports: shiny, jsonlite, rjson, XML, limma, edgeR, DESeq2,
clusterProfiler, DOSE, org.Hs.eg.db, biomaRt, survival,
survminer, pathview, ggplot2, gplots, DT, GenomicDataCommons,
BiocParallel
Suggests: knitr, testthat, rmarkdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: bfbb746a6f08b1d2703505d279fd6235
NeedsCompilation: no
Title: GDCRNATools: an R/Bioconductor package for integrative analysis
of lncRNA, mRNA, and miRNA data in GDC
Description: This is an easy-to-use package for downloading,
organizing, and integrative analyzing RNA expression data in
GDC with an emphasis on deciphering the lncRNA-mRNA related
ceRNA regulatory network in cancer. Three databases of
lncRNA-miRNA interactions including spongeScan, starBase, and
miRcode, as well as three databases of mRNA-miRNA interactions
including miRTarBase, starBase, and miRcode are incorporated
into the package for ceRNAs network construction. limma, edgeR,
and DESeq2 can be used to identify differentially expressed
genes/miRNAs. Functional enrichment analyses including GO,
KEGG, and DO can be performed based on the clusterProfiler and
DO packages. Both univariate CoxPH and KM survival analyses of
multiple genes can be implemented in the package. Besides some
routine visualization functions such as volcano plot, bar plot,
and KM plot, a few simply shiny apps are developed to
facilitate visualization of results on a local webpage.
biocViews: ImmunoOncology, GeneExpression, DifferentialExpression,
GeneRegulation, GeneTarget, NetworkInference, Survival,
Visualization, GeneSetEnrichment, NetworkEnrichment, Network,
RNASeq, GO, KEGG
Author: Ruidong Li, Han Qu, Shibo Wang, Julong Wei, Le Zhang, Renyuan
Ma, Jianming Lu, Jianguo Zhu, Wei-De Zhong, Zhenyu Jia
Maintainer: Ruidong Li <rli012@ucr.edu>, Han Qu <hqu002@ucr.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GDCRNATools
git_branch: RELEASE_3_13
git_last_commit: 48bda50
git_last_commit_date: 2021-08-03
Date/Publication: 2021-08-03
source.ver: src/contrib/GDCRNATools_1.13.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GDCRNATools_1.13.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/GDCRNATools_1.13.1.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GDCRNATools/inst/doc/GDCRNATools.R
dependencyCount: 232
Package: GDSArray
Version: 1.12.0
Depends: R (>= 3.5), gdsfmt, methods, BiocGenerics, DelayedArray (>=
0.5.32)
Imports: tools, S4Vectors (>= 0.17.34), SNPRelate, SeqArray
Suggests: testthat, knitr, BiocStyle, BiocManager
License: GPL-3
Archs: i386, x64
MD5sum: 881a8dc6b05d40a441540814b5c58e2a
NeedsCompilation: no
Title: Representing GDS files as array-like objects
Description: GDS files are widely used to represent genotyping or
sequence data. The GDSArray package implements the `GDSArray`
class to represent nodes in GDS files in a matrix-like
representation that allows easy manipulation (e.g., subsetting,
mathematical transformation) in _R_. The data remains on disk
until needed, so that very large files can be processed.
biocViews: Infrastructure, DataRepresentation, Sequencing,
GenotypingArray
Author: Qian Liu [aut, cre], Martin Morgan [aut], Hervé Pagès [aut],
Xiuwen Zheng [aut]
Maintainer: Qian Liu <qliu7@buffalo.edu>
URL: https://github.com/Bioconductor/GDSArray
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/GDSArray/issues
git_url: https://git.bioconductor.org/packages/GDSArray
git_branch: RELEASE_3_13
git_last_commit: f2a3983
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GDSArray_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GDSArray_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GDSArray_1.12.0.tgz
vignettes: vignettes/GDSArray/inst/doc/GDSArray.html
vignetteTitles: GDSArray: Representing GDS files as array-like objects
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GDSArray/inst/doc/GDSArray.R
dependsOnMe: VariantExperiment
importsMe: CNVRanger
suggestsMe: DelayedDataFrame
dependencyCount: 29
Package: gdsfmt
Version: 1.28.1
Depends: R (>= 2.15.0), methods
Suggests: parallel, digest, Matrix, crayon, RUnit, knitr, markdown,
rmarkdown, BiocGenerics
License: LGPL-3
MD5sum: 961a142b23fb40cfcf191859784f5e13
NeedsCompilation: yes
Title: R Interface to CoreArray Genomic Data Structure (GDS) Files
Description: Provides a high-level R interface to CoreArray Genomic
Data Structure (GDS) data files. GDS is portable across
platforms with hierarchical structure to store multiple
scalable array-oriented data sets with metadata information. It
is suited for large-scale datasets, especially for data which
are much larger than the available random-access memory. The
gdsfmt package offers the efficient operations specifically
designed for integers of less than 8 bits, since a diploid
genotype, like single-nucleotide polymorphism (SNP), usually
occupies fewer bits than a byte. Data compression and
decompression are available with relatively efficient random
access. It is also allowed to read a GDS file in parallel with
multiple R processes supported by the package parallel.
biocViews: Infrastructure, DataImport
Author: Xiuwen Zheng [aut, cre]
(<https://orcid.org/0000-0002-1390-0708>), Stephanie Gogarten
[ctb], Jean-loup Gailly and Mark Adler [ctb] (for the included
zlib sources), Yann Collet [ctb] (for the included LZ4
sources), xz contributors [ctb] (for the included liblzma
sources)
Maintainer: Xiuwen Zheng <zhengx@u.washington.edu>
URL: http://github.com/zhengxwen/gdsfmt
VignetteBuilder: knitr
BugReports: http://github.com/zhengxwen/gdsfmt/issues
git_url: https://git.bioconductor.org/packages/gdsfmt
git_branch: RELEASE_3_13
git_last_commit: c897242
git_last_commit_date: 2021-09-15
Date/Publication: 2021-09-16
source.ver: src/contrib/gdsfmt_1.28.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gdsfmt_1.28.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/gdsfmt_1.28.1.tgz
vignettes: vignettes/gdsfmt/inst/doc/gdsfmt.html
vignetteTitles: Introduction to GDS Format
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt.R
dependsOnMe: bigmelon, GDSArray, SAIGEgds, SCArray, SeqArray, SNPRelate
importsMe: CNVRanger, GENESIS, GWASTools, SeqSQC, SeqVarTools,
VariantExperiment, EthSEQ, R.SamBada, simplePHENOTYPES
suggestsMe: AnnotationHub, HIBAG
linksToMe: SeqArray, SNPRelate
dependencyCount: 1
Package: GEM
Version: 1.18.0
Depends: R (>= 3.3)
Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils
Suggests: knitr, RUnit, testthat, BiocGenerics
License: Artistic-2.0
MD5sum: 22372e7518ac68318371912f6b81b405
NeedsCompilation: no
Title: GEM: fast association study for the interplay of Gene,
Environment and Methylation
Description: Tools for analyzing EWAS, methQTL and GxE genome widely.
biocViews: MethylSeq, MethylationArray, GenomeWideAssociation,
Regression, DNAMethylation, SNP, GeneExpression, GUI
Author: Hong Pan, Joanna D Holbrook, Neerja Karnani, Chee-Keong Kwoh
Maintainer: Hong Pan <pan_hong@sics.a-star.edu.sg>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GEM
git_branch: RELEASE_3_13
git_last_commit: 72dc859
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GEM_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GEM_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GEM_1.18.0.tgz
vignettes: vignettes/GEM/inst/doc/user_guide.html
vignetteTitles: The GEM User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GEM/inst/doc/user_guide.R
dependencyCount: 39
Package: gemini
Version: 1.6.0
Depends: R (>= 4.1.0)
Imports: dplyr, grDevices, ggplot2, magrittr, mixtools, scales,
pbmcapply, parallel, stats, utils
Suggests: knitr, rmarkdown, testthat
License: BSD_3_clause + file LICENSE
MD5sum: 619bbd6cbc7b6883cccef4810f492ffa
NeedsCompilation: no
Title: GEMINI: Variational inference approach to infer genetic
interactions from pairwise CRISPR screens
Description: GEMINI uses log-fold changes to model sample-dependent and
independent effects, and uses a variational Bayes approach to
infer these effects. The inferred effects are used to score and
identify genetic interactions, such as lethality and recovery.
More details can be found in Zamanighomi et al. 2019 (in
press).
biocViews: Software, CRISPR, Bayesian, DataImport
Author: Mahdi Zamanighomi [aut], Sidharth Jain [aut, cre]
Maintainer: Sidharth Jain <sidharthsjain@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/sellerslab/gemini/issues
git_url: https://git.bioconductor.org/packages/gemini
git_branch: RELEASE_3_13
git_last_commit: bdea931
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gemini_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gemini_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gemini_1.6.0.tgz
vignettes: vignettes/gemini/inst/doc/gemini-quickstart.html
vignetteTitles: QuickStart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/gemini/inst/doc/gemini-quickstart.R
dependencyCount: 48
Package: genArise
Version: 1.68.0
Depends: R (>= 1.7.1), locfit, tkrplot, methods
Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable
License: file LICENSE
License_restricts_use: yes
MD5sum: 0b4e06d3d0208e643418cc48eb9f3f41
NeedsCompilation: no
Title: Microarray Analysis tool
Description: genArise is an easy to use tool for dual color microarray
data. Its GUI-Tk based environment let any non-experienced user
performs a basic, but not simple, data analysis just following
a wizard. In addition it provides some tools for the developer.
biocViews: Microarray, TwoChannel, Preprocessing
Author: Ana Patricia Gomez Mayen <pgomez@ifc.unam.mx>,\\ Gustavo Corral
Guille <gcorral@ifc.unam.mx>, \\ Lina Riego Ruiz
<lriego@ifc.unam.mx>,\\ Gerardo Coello Coutino
<gcoello@ifc.unam.mx>
Maintainer: IFC Development Team <info-genarise@ifc.unam.mx>
URL: http://www.ifc.unam.mx/genarise
git_url: https://git.bioconductor.org/packages/genArise
git_branch: RELEASE_3_13
git_last_commit: b242c5b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/genArise_1.68.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/genArise_1.68.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/genArise_1.68.0.tgz
vignettes: vignettes/genArise/inst/doc/genArise.pdf
vignetteTitles: genAriseGUI Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/genArise/inst/doc/genArise.R
dependencyCount: 11
Package: genbankr
Version: 1.20.0
Depends: methods
Imports: BiocGenerics, IRanges (>= 2.13.15), GenomicRanges (>=
1.31.10), GenomicFeatures (>= 1.31.5), Biostrings,
VariantAnnotation, rtracklayer, S4Vectors (>= 0.17.28),
GenomeInfoDb, Biobase
Suggests: RUnit, rentrez, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
Archs: i386, x64
MD5sum: cffa22acf248d889d4725ffc0e57e61b
NeedsCompilation: no
Title: Parsing GenBank files into semantically useful objects
Description: Reads Genbank files.
biocViews: Infrastructure, DataImport
Author: Gabriel Becker [aut, cre], Michael Lawrence [aut]
Maintainer: Gabriel Becker <becker.gabriel@gene.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/genbankr
git_branch: RELEASE_3_13
git_last_commit: 968c425
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/genbankr_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/genbankr_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/genbankr_1.20.0.tgz
vignettes: vignettes/genbankr/inst/doc/genbankr.html
vignetteTitles: An introduction to genbankr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genbankr/inst/doc/genbankr.R
importsMe: PACVr
dependencyCount: 98
Package: GeneAccord
Version: 1.10.0
Depends: R (>= 3.5)
Imports: biomaRt, caTools, dplyr, ggplot2, graphics, grDevices, gtools,
ggpubr, magrittr, maxLik, RColorBrewer, reshape2, stats,
tibble, utils
Suggests: assertthat, BiocStyle, devtools, knitr, rmarkdown, testthat
License: file LICENSE
MD5sum: d4fb31c743ecd6b2592b90541142dd02
NeedsCompilation: no
Title: Detection of clonally exclusive gene or pathway pairs in a
cohort of cancer patients
Description: A statistical framework to examine the combinations of
clones that co-exist in tumors. More precisely, the algorithm
finds pairs of genes that are mutated in the same tumor but in
different clones, i.e. their subclonal mutation profiles are
mutually exclusive. We refer to this as clonally exclusive. It
means that the mutations occurred in different branches of the
tumor phylogeny, indicating parallel evolution of the clones.
Our statistical framework assesses whether a pattern of clonal
exclusivity occurs more often than expected by chance alone
across a cohort of patients. The required input data are the
mutated gene-to-clone assignments from a cohort of cancer
patients, which were obtained by running phylogenetic tree
inference methods. Reconstructing the evolutionary history of a
tumor and detecting the clones is challenging. For
nondeterministic algorithms, repeated tree inference runs may
lead to slightly different mutation-to-clone assignments.
Therefore, our algorithm was designed to allow the input of
multiple gene-to-clone assignments per patient. They may have
been generated by repeatedly performing the tree inference, or
by sampling from the posterior distribution of trees. The tree
inference methods designate the mutations to individual clones.
The mutations can then be mapped to genes or pathways. Hence
our statistical framework can be applied on the gene level, or
on the pathway level to detect clonally exclusive pairs of
pathways. If a pair is significantly clonally exclusive, it
points towards the fact that this specific clone configuration
confers a selective advantage, possibly through synergies
between the clones with these mutations.
biocViews: BiomedicalInformatics, GeneticVariability, GenomicVariation,
SomaticMutation, FunctionalGenomics, Genetics,
MathematicalBiology, SystemsBiology, FeatureExtraction,
PatternLogic, Pathways
Author: Ariane L. Moore, Jack Kuipers and Niko Beerenwinkel
Maintainer: Ariane L. Moore <ariane.moore@bsse.ethz.ch>
URL: https://github.com/cbg-ethz/GeneAccord
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GeneAccord
git_branch: RELEASE_3_13
git_last_commit: cfc8768
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneAccord_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeneAccord_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeneAccord_1.10.0.tgz
vignettes: vignettes/GeneAccord/inst/doc/GeneAccord.html
vignetteTitles: GeneAccord
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GeneAccord/inst/doc/GeneAccord.R
dependencyCount: 148
Package: geneAttribution
Version: 1.18.0
Imports: utils, GenomicRanges, org.Hs.eg.db, BiocGenerics,
GenomeInfoDb, GenomicFeatures, IRanges, rtracklayer
Suggests: TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: 9061ceee02c52b0f67799c3914683396
NeedsCompilation: no
Title: Identification of candidate genes associated with genetic
variation
Description: Identification of the most likely gene or genes through
which variation at a given genomic locus in the human genome
acts. The most basic functionality assumes that the closer gene
is to the input locus, the more likely the gene is to be
causative. Additionally, any empirical data that links genomic
regions to genes (e.g. eQTL or genome conformation data) can be
used if it is supplied in the UCSC .BED file format.
biocViews: SNP, GenePrediction, GenomeWideAssociation,
VariantAnnotation, GenomicVariation
Author: Arthur Wuster
Maintainer: Arthur Wuster <wustera@gene.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/geneAttribution
git_branch: RELEASE_3_13
git_last_commit: e7e4262
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/geneAttribution_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/geneAttribution_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/geneAttribution_1.18.0.tgz
vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 97
Package: GeneBreak
Version: 1.22.0
Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges
Imports: graphics, methods
License: GPL-2
MD5sum: 660e127ff979f9a565e21fbee35c1fc4
NeedsCompilation: no
Title: Gene Break Detection
Description: Recurrent breakpoint gene detection on copy number
aberration profiles.
biocViews: aCGH, CopyNumberVariation, DNASeq, Genetics, Sequencing,
WholeGenome, Visualization
Author: Evert van den Broek, Stef van Lieshout
Maintainer: Evert van den Broek <vandenbroek.evert@gmail.com>
URL: https://github.com/stefvanlieshout/GeneBreak
git_url: https://git.bioconductor.org/packages/GeneBreak
git_branch: RELEASE_3_13
git_last_commit: 4c84ab8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneBreak_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeneBreak_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeneBreak_1.22.0.tgz
vignettes: vignettes/GeneBreak/inst/doc/GeneBreak.pdf
vignetteTitles: GeneBreak
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneBreak/inst/doc/GeneBreak.R
dependencyCount: 49
Package: geneClassifiers
Version: 1.16.0
Depends: R (>= 3.6.0)
Imports: utils, methods, stats, Biobase, BiocGenerics
Suggests: testthat
License: GPL-2
MD5sum: 36b2ff9a6bc89a20ba1804f7a86c1760
NeedsCompilation: no
Title: Application of gene classifiers
Description: This packages aims for easy accessible application of
classifiers which have been published in literature using an
ExpressionSet as input.
biocViews: GeneExpression, BiomedicalInformatics, Classification,
Survival, Microarray
Author: R Kuiper [cre, aut] (<https://orcid.org/0000-0002-3703-1762>)
Maintainer: R Kuiper <r.kuiper.emc@gmail.com>
URL: https://doi.org/doi:10.18129/B9.bioc.geneClassifiers
BugReports: https://github.com/rkuiper/geneClassifiers/issues
git_url: https://git.bioconductor.org/packages/geneClassifiers
git_branch: RELEASE_3_13
git_last_commit: 479c49c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/geneClassifiers_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/geneClassifiers_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/geneClassifiers_1.16.0.tgz
vignettes: vignettes/geneClassifiers/inst/doc/geneClassifiers.pdf,
vignettes/geneClassifiers/inst/doc/MissingCovariates.pdf
vignetteTitles: geneClassifiers introduction, geneClassifiers and
missing probesets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geneClassifiers/inst/doc/geneClassifiers.R
dependencyCount: 7
Package: GeneExpressionSignature
Version: 1.38.0
Depends: R (>= 4.0)
Imports: Biobase, stats, methods
Suggests: apcluster, GEOquery, knitr, rmarkdown, BiocStyle
License: GPL-2
MD5sum: 3dfb793015e720c6ac6766f9d92a86e2
NeedsCompilation: no
Title: Gene Expression Signature based Similarity Metric
Description: This package gives the implementations of the gene
expression signature and its distance to each. Gene expression
signature is represented as a list of genes whose expression is
correlated with a biological state of interest. And its
distance is defined using a nonparametric, rank-based
pattern-matching strategy based on the Kolmogorov-Smirnov
statistic. Gene expression signature and its distance can be
used to detect similarities among the signatures of drugs,
diseases, and biological states of interest.
biocViews: GeneExpression
Author: Yang Cao [aut, cre], Fei Li [ctb], Lu Han [ctb]
Maintainer: Yang Cao <yiluheihei@gmail.com>
URL: https://github.com/yiluheihei/GeneExpressionSignature
VignetteBuilder: knitr
BugReports:
https://github.com/yiluheihei/GeneExpressionSignature/issues/
git_url: https://git.bioconductor.org/packages/GeneExpressionSignature
git_branch: RELEASE_3_13
git_last_commit: ed0ab84
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneExpressionSignature_1.38.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/GeneExpressionSignature_1.38.0.zip
mac.binary.ver:
bin/macosx/contrib/4.1/GeneExpressionSignature_1.38.0.tgz
vignettes:
vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.html
vignetteTitles: GeneExpressionSignature
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.R
dependencyCount: 7
Package: genefilter
Version: 1.74.1
Imports: BiocGenerics, AnnotationDbi, annotate, Biobase, graphics,
methods, stats, survival, grDevices
Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, RColorBrewer,
BiocStyle, knitr
License: Artistic-2.0
MD5sum: d91770f2bde1d21328067709c16e0b19
NeedsCompilation: yes
Title: genefilter: methods for filtering genes from high-throughput
experiments
Description: Some basic functions for filtering genes.
biocViews: Microarray
Author: Robert Gentleman, Vincent J. Carey, Wolfgang Huber, Florian
Hahne
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/genefilter
git_branch: RELEASE_3_13
git_last_commit: 3ca5b57
git_last_commit_date: 2021-08-19
Date/Publication: 2021-10-12
source.ver: src/contrib/genefilter_1.74.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/genefilter_1.74.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/genefilter_1.74.1.tgz
vignettes: vignettes/genefilter/inst/doc/howtogenefilter.pdf,
vignettes/genefilter/inst/doc/howtogenefinder.pdf,
vignettes/genefilter/inst/doc/independent_filtering_plots.pdf
vignetteTitles: 01 - Using the genefilter function to filter genes from
a microarray dataset, 02 - How to find genes whose expression
profile is similar to that of specified genes, 03 - Additional
plots for: Independent filtering increases power for detecting
differentially expressed genes,, Bourgon et al.,, PNAS (2010)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genefilter/inst/doc/howtogenefilter.R,
vignettes/genefilter/inst/doc/howtogenefinder.R,
vignettes/genefilter/inst/doc/independent_filtering_plots.R
dependsOnMe: cellHTS2, CNTools, GeneMeta, sva, FlowSorted.Blood.EPIC,
Hiiragi2013, maEndToEnd, rnaseqGene, lmQCM, orQA
importsMe: a4Base, ALPS, annmap, arrayQualityMetrics, Category, cbaf,
countsimQC, covRNA, DESeq2, DEXSeq, GISPA, GSRI, metaseqR2,
methyAnalysis, methylCC, methylumi, minfi, MLInterfaces, mogsa,
NBAMSeq, pcaExplorer, PECA, phenoTest, pwrEWAS, Ringo,
spatialHeatmap, tilingArray, XDE, zinbwave, IHWpaper,
RNAinteractMAPK, CoNI, dGAselID, INCATome, MiDA, netgsa,
oncoPredict, specmine
suggestsMe: annotate, BioNet, categoryCompare, ClassifyR, clusterStab,
codelink, cola, compcodeR, DelayedArray, EnrichedHeatmap,
factDesign, ffpe, GenoGAM, GenomicFiles, GOstats, GSAR, GSEAlm,
GSVA, logicFS, lumi, MMUPHin, npGSEA, oligo, phyloseq, pvac,
qpgraph, rtracklayer, siggenes, TCGAbiolinks, topGO,
BloodCancerMultiOmics2017, curatedBladderData, curatedCRCData,
curatedOvarianData, ffpeExampleData, gageData, MAQCsubset,
RforProteomics, rheumaticConditionWOLLBOLD,
Single.mTEC.Transcriptomes, maGUI, rknn, SuperLearner
dependencyCount: 54
Package: genefu
Version: 2.24.2
Depends: R (>= 4.1), survcomp, biomaRt, iC10, AIMS
Imports: amap, impute, mclust, limma, graphics, stats, utils
Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ,
breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT,
breastCancerNKI, rmeta, Biobase, xtable, knitr, caret,
survival, BiocStyle, magick, rmarkdown
License: Artistic-2.0
MD5sum: 83739ed85019450d7ee834240ac741e5
NeedsCompilation: no
Title: Computation of Gene Expression-Based Signatures in Breast Cancer
Description: This package contains functions implementing various tasks
usually required by gene expression analysis, especially in
breast cancer studies: gene mapping between different
microarray platforms, identification of molecular subtypes,
implementation of published gene signatures, gene selection,
and survival analysis.
biocViews: DifferentialExpression, GeneExpression, Visualization,
Clustering, Classification
Author: Deena M.A. Gendoo [aut], Natchar Ratanasirigulchai [aut],
Markus S. Schroeder [aut], Laia Pare [aut], Joel S Parker
[aut], Aleix Prat [aut], Nikta Feizi [ctb], Christopher Eeles
[ctb], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
URL: http://www.pmgenomics.ca/bhklab/software/genefu
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/genefu
git_branch: RELEASE_3_13
git_last_commit: 829a30e
git_last_commit_date: 2021-05-21
Date/Publication: 2021-05-23
source.ver: src/contrib/genefu_2.24.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/genefu_2.24.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/genefu_2.24.2.tgz
vignettes: vignettes/genefu/inst/doc/genefu.html
vignetteTitles: genefu: A Package For Breast Cancer Gene Expression
Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genefu/inst/doc/genefu.R
importsMe: consensusOV, PDATK
suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI,
breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP,
breastCancerVDX
dependencyCount: 110
Package: GeneGA
Version: 1.42.0
Depends: seqinr, hash, methods
License: GPL version 2
MD5sum: bef45aaeee0c715c264005645286e8f0
NeedsCompilation: no
Title: Design gene based on both mRNA secondary structure and codon
usage bias using Genetic algorithm
Description: R based Genetic algorithm for gene expression optimization
by considering both mRNA secondary structure and codon usage
bias, GeneGA includes the information of highly expressed genes
of almost 200 genomes. Meanwhile, Vienna RNA Package is needed
to ensure GeneGA to function properly.
biocViews: GeneExpression
Author: Zhenpeng Li and Haixiu Huang
Maintainer: Zhenpeng Li <zpli21@gmail.com>
URL: http://www.tbi.univie.ac.at/~ivo/RNA/
git_url: https://git.bioconductor.org/packages/GeneGA
git_branch: RELEASE_3_13
git_last_commit: afbbb4a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneGA_1.42.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/GeneGA_1.42.0.tgz
vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf
vignetteTitles: GeneGA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneGA/inst/doc/GeneGA.R
dependencyCount: 14
Package: GeneGeneInteR
Version: 1.18.0
Depends: R (>= 4.0)
Imports: snpStats, mvtnorm, Rsamtools, igraph, kernlab, FactoMineR,
IRanges, GenomicRanges, data.table,grDevices, graphics,stats,
utils, methods
License: GPL (>= 2)
MD5sum: 130dabb7324b4652fd8565c63a270272
NeedsCompilation: yes
Title: Tools for Testing Gene-Gene Interaction at the Gene Level
Description: The aim of this package is to propose several methods for
testing gene-gene interaction in case-control association
studies. Such a test can be done by aggregating SNP-SNP
interaction tests performed at the SNP level (SSI) or by using
gene-gene multidimensionnal methods (GGI) methods. The package
also proposes tools for a graphic display of the results.
<doi:10.18637/jss.v095.i12>.
biocViews: GenomeWideAssociation, SNP, Genetics, GeneticVariability
Author: Mathieu Emily [aut, cre], Nicolas Sounac [ctb], Florian Kroell
[ctb], Magalie Houee-Bigot [aut]
Maintainer: Mathieu Emily <mathieu.emily@agrocampus-ouest.fr>
git_url: https://git.bioconductor.org/packages/GeneGeneInteR
git_branch: RELEASE_3_13
git_last_commit: c3d7539
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneGeneInteR_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeneGeneInteR_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeneGeneInteR_1.18.0.tgz
vignettes: vignettes/GeneGeneInteR/inst/doc/GenePair.pdf,
vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.pdf
vignetteTitles: Pairwise interaction tests, GeneGeneInteR Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneGeneInteR/inst/doc/GenePair.R,
vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.R
dependencyCount: 139
Package: GeneMeta
Version: 1.64.0
Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter
Imports: methods, Biobase (>= 2.5.5)
Suggests: RColorBrewer
License: Artistic-2.0
MD5sum: 203e065b502a46c2476c7b4124b5c592
NeedsCompilation: no
Title: MetaAnalysis for High Throughput Experiments
Description: A collection of meta-analysis tools for analysing high
throughput experimental data
biocViews: Sequencing, GeneExpression, Microarray
Author: Lara Lusa <lusa@ifom-firc.it>, R. Gentleman, M. Ruschhaupt
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/GeneMeta
git_branch: RELEASE_3_13
git_last_commit: 24685d2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneMeta_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeneMeta_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeneMeta_1.64.0.tgz
vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf
vignetteTitles: GeneMeta Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneMeta/inst/doc/GeneMeta.R
importsMe: XDE
suggestsMe: genefu
dependencyCount: 55
Package: GeneNetworkBuilder
Version: 1.34.0
Depends: R (>= 2.15.1), Rcpp (>= 0.9.13)
Imports: plyr, graph, htmlwidgets, Rgraphviz, rjson, XML, methods,
grDevices, stats, graphics
LinkingTo: Rcpp
Suggests: RUnit, BiocGenerics, RBGL, knitr, simpIntLists, shiny,
STRINGdb, BiocStyle, magick, rmarkdown
License: GPL (>= 2)
Archs: i386, x64
MD5sum: ce3e74db5e3c292883abeaebf2ff228e
NeedsCompilation: yes
Title: GeneNetworkBuilder: a bioconductor package for building
regulatory network using ChIP-chip/ChIP-seq data and Gene
Expression Data
Description: Appliation for discovering direct or indirect targets of
transcription factors using ChIP-chip or ChIP-seq, and
microarray or RNA-seq gene expression data. Inputting a list of
genes of potential targets of one TF from ChIP-chip or
ChIP-seq, and the gene expression results, GeneNetworkBuilder
generates a regulatory network of the TF.
biocViews: Sequencing, Microarray, GraphAndNetwork
Author: Jianhong Ou, Haibo Liu, Heidi A Tissenbaum and Lihua Julie Zhu
Maintainer: Jianhong Ou <jianhong.ou@duke.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GeneNetworkBuilder
git_branch: RELEASE_3_13
git_last_commit: 2e9cc20
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneNetworkBuilder_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeneNetworkBuilder_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeneNetworkBuilder_1.34.0.tgz
vignettes:
vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html,
vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.html,
vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.html
vignetteTitles: GeneNetworkBuilder Vignette, Generate Network from a
list of gene, Working with BioGRID,, STRING
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.R,
vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.R,
vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.R
dependencyCount: 23
Package: GeneOverlap
Version: 1.28.0
Imports: stats, RColorBrewer, gplots, methods
Suggests: RUnit, BiocGenerics, BiocStyle
License: GPL-3
MD5sum: 56db0bcbac4b22d03a1ef391e1df86a7
NeedsCompilation: no
Title: Test and visualize gene overlaps
Description: Test two sets of gene lists and visualize the results.
biocViews: MultipleComparison, Visualization
Author: Li Shen, Icahn School of Medicine at Mount Sinai
<shenli.sam@gmail.com>
Maintainer: Ant<c3><b3>nio Miguel de Jesus Domingues, Max-Planck
Institute for Cell Biology and Genetics
<amjdomingues@gmail.com>
URL: http://shenlab-sinai.github.io/shenlab-sinai/
git_url: https://git.bioconductor.org/packages/GeneOverlap
git_branch: RELEASE_3_13
git_last_commit: 870f8c6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneOverlap_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeneOverlap_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeneOverlap_1.28.0.tgz
vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf
vignetteTitles: Testing and visualizing gene overlaps with the
"GeneOverlap" package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R
dependencyCount: 9
Package: geneplast
Version: 1.18.0
Depends: R (>= 3.3), methods
Imports: igraph, snow, ape, grDevices, graphics, stats, utils,
data.table
Suggests: RTN, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown,
Fletcher2013b, geneplast.data.string.v91, ggplot2, ggpubr, plyr
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 2f248f65322d91bbc9aa7d7981755e31
NeedsCompilation: no
Title: Evolutionary and plasticity analysis of orthologous groups
Description: Geneplast is designed for evolutionary and plasticity
analysis based on orthologous groups distribution in a given
species tree. It uses Shannon information theory and orthologs
abundance to estimate the Evolutionary Plasticity Index.
Additionally, it implements the Bridge algorithm to determine
the evolutionary root of a given gene based on its orthologs
distribution.
biocViews: Genetics, GeneRegulation, SystemsBiology
Author: Rodrigo Dalmolin, Mauro Castro
Maintainer: Mauro Castro <mauro.a.castro@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/geneplast
git_branch: RELEASE_3_13
git_last_commit: d932944
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/geneplast_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/geneplast_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/geneplast_1.18.0.tgz
vignettes: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.html,
vignettes/geneplast/inst/doc/geneplast.html
vignetteTitles: "Supporting Material for Trefflich2019.", "Geneplast:
evolutionary rooting and plasticity analysis."
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.R,
vignettes/geneplast/inst/doc/geneplast.R
suggestsMe: TreeAndLeaf
dependencyCount: 18
Package: geneplotter
Version: 1.70.0
Depends: R (>= 2.10), methods, Biobase, BiocGenerics, lattice, annotate
Imports: AnnotationDbi, graphics, grDevices, grid, RColorBrewer, stats,
utils
Suggests: Rgraphviz, fibroEset, hgu95av2.db, hu6800.db, hgu133a.db
License: Artistic-2.0
MD5sum: 3cebdd28df88565dc31a3e0d03c76023
NeedsCompilation: no
Title: Graphics related functions for Bioconductor
Description: Functions for plotting genomic data
biocViews: Visualization
Author: R. Gentleman, Biocore
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/geneplotter
git_branch: RELEASE_3_13
git_last_commit: ca484d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/geneplotter_1.70.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/geneplotter_1.70.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/geneplotter_1.70.0.tgz
vignettes: vignettes/geneplotter/inst/doc/byChroms.pdf,
vignettes/geneplotter/inst/doc/visualize.pdf
vignetteTitles: How to assemble a chromLocation object, Visualization
of Microarray Data
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geneplotter/inst/doc/byChroms.R,
vignettes/geneplotter/inst/doc/visualize.R
dependsOnMe: HD2013SGI, Hiiragi2013, maEndToEnd
importsMe: biocGraph, DESeq2, DEXSeq, IsoGeneGUI, MethylSeekR,
RNAinteract
suggestsMe: biocGraph, Category, EnrichmentBrowser, GOstats,
Single.mTEC.Transcriptomes
dependencyCount: 52
Package: geneRecommender
Version: 1.64.0
Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods
Imports: Biobase, methods, stats
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 89008ca69a800e78e66f6dcfc31f3046
NeedsCompilation: no
Title: A gene recommender algorithm to identify genes coexpressed with
a query set of genes
Description: This package contains a targeted clustering algorithm for
the analysis of microarray data. The algorithm can aid in the
discovery of new genes with similar functions to a given list
of genes already known to have closely related functions.
biocViews: Microarray, Clustering
Author: Gregory J. Hather <ghather@stat.berkeley.edu>, with
contributions from Art B. Owen <art@stat.stanford.edu> and
Terence P. Speed <terry@stat.berkeley.edu>
Maintainer: Greg Hather <ghather@stat.berkeley.edu>
git_url: https://git.bioconductor.org/packages/geneRecommender
git_branch: RELEASE_3_13
git_last_commit: 5e408e1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/geneRecommender_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/geneRecommender_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/geneRecommender_1.64.0.tgz
vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf
vignetteTitles: Using the geneRecommender Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geneRecommender/inst/doc/geneRecommender.R
dependencyCount: 7
Package: GeneRegionScan
Version: 1.48.0
Depends: methods, Biobase (>= 2.5.5), Biostrings
Imports: S4Vectors (>= 0.9.25), Biobase (>= 2.5.5), affxparser,
RColorBrewer, Biostrings
Suggests: BSgenome, affy, AnnotationDbi
License: GPL (>= 2)
MD5sum: 3aeba7b7bf14ffadea923bc2254fba5d
NeedsCompilation: no
Title: GeneRegionScan
Description: A package with focus on analysis of discrete regions of
the genome. This package is useful for investigation of one or
a few genes using Affymetrix data, since it will extract probe
level data using the Affymetrix Power Tools application and
wrap these data into a ProbeLevelSet. A ProbeLevelSet directly
extends the expressionSet, but includes additional information
about the sequence of each probe and the probe set it is
derived from. The package includes a number of functions used
for plotting these probe level data as a function of location
along sequences of mRNA-strands. This can be used for analysis
of variable splicing, and is especially well suited for use
with exon-array data.
biocViews: Microarray, DataImport, SNP, OneChannel, Visualization
Author: Lasse Folkersen, Diego Diez
Maintainer: Lasse Folkersen <lasfol@cbs.dtu.dk>
git_url: https://git.bioconductor.org/packages/GeneRegionScan
git_branch: RELEASE_3_13
git_last_commit: 8331f9a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneRegionScan_1.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeneRegionScan_1.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeneRegionScan_1.48.0.tgz
vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf
vignetteTitles: GeneRegionScan
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.R
dependencyCount: 22
Package: geneRxCluster
Version: 1.28.0
Depends: GenomicRanges,IRanges
Suggests: RUnit, BiocGenerics
License: GPL (>= 2)
MD5sum: e2ede86e7259a5ff313e8e2528f04210
NeedsCompilation: yes
Title: gRx Differential Clustering
Description: Detect Differential Clustering of Genomic Sites such as
gene therapy integrations. The package provides some functions
for exploring genomic insertion sites originating from two
different sources. Possibly, the two sources are two different
gene therapy vectors. Vectors are preferred that target
sensitive regions less frequently, motivating the search for
localized clusters of insertions and comparison of the clusters
formed by integration of different vectors. Scan statistics
allow the discovery of spatial differences in clustering and
calculation of False Discovery Rates (FDRs) providing
statistical methods for comparing retroviral vectors. A scan
statistic for comparing two vectors using multiple window
widths to detect clustering differentials and compute FDRs is
implemented here.
biocViews: Sequencing, Clustering, Genetics
Author: Charles Berry
Maintainer: Charles Berry <ccberry@ucsd.edu>
git_url: https://git.bioconductor.org/packages/geneRxCluster
git_branch: RELEASE_3_13
git_last_commit: b17ecd9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/geneRxCluster_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/geneRxCluster_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/geneRxCluster_1.28.0.tgz
vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf
vignetteTitles: Using geneRxCluster
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geneRxCluster/inst/doc/tutorial.R
dependencyCount: 17
Package: GeneSelectMMD
Version: 2.36.0
Depends: R (>= 2.13.2), Biobase
Imports: MASS, graphics, stats, limma
Suggests: ALL
License: GPL (>= 2)
MD5sum: a63f71c5f816e570416371e8fb5d281d
NeedsCompilation: yes
Title: Gene selection based on the marginal distributions of gene
profiles that characterized by a mixture of three-component
multivariate distributions
Description: Gene selection based on a mixture of marginal
distributions.
biocViews: DifferentialExpression
Author: Jarrett Morrow <remdj@channing.harvard.edu>, Weiliang Qiu
<weiliang.qiu@gmail.com>, Wenqing He <whe@stats.uwo.ca>,
Xiaogang Wang <stevenw@mathstat.yorku.ca>, Ross Lazarus
<ross.lazarus@channing.harvard.edu>.
Maintainer: Weiliang Qiu <weiliang.qiu@gmail.com>
git_url: https://git.bioconductor.org/packages/GeneSelectMMD
git_branch: RELEASE_3_13
git_last_commit: c265d5d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneSelectMMD_2.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeneSelectMMD_2.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeneSelectMMD_2.36.0.tgz
vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf
vignetteTitles: Gene Selection based on a mixture of marginal
distributions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R
importsMe: iCheck
dependencyCount: 10
Package: GENESIS
Version: 2.22.2
Imports: Biobase, BiocGenerics, GWASTools, gdsfmt, GenomicRanges,
IRanges, S4Vectors, SeqArray, SeqVarTools, SNPRelate,
data.table, foreach, graphics, grDevices, igraph, Matrix,
methods, reshape2, stats, utils
Suggests: CompQuadForm, COMPoissonReg, poibin, SPAtest, survey,
testthat, BiocStyle, knitr, rmarkdown, GWASdata, dplyr,
ggplot2, GGally, RColorBrewer,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL-3
MD5sum: 5704257e71e1ebbf547feb2ea23c6b7b
NeedsCompilation: yes
Title: GENetic EStimation and Inference in Structured samples
(GENESIS): Statistical methods for analyzing genetic data from
samples with population structure and/or relatedness
Description: The GENESIS package provides methodology for estimating,
inferring, and accounting for population and pedigree structure
in genetic analyses. The current implementation provides
functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and
PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a
Principal Components Analysis on genome-wide SNP data for the
detection of population structure in a sample that may contain
known or cryptic relatedness. Unlike standard PCA, PC-AiR
accounts for relatedness in the sample to provide accurate
ancestry inference that is not confounded by family structure.
PC-Relate uses ancestry representative principal components to
adjust for population structure/ancestry and accurately
estimate measures of recent genetic relatedness such as kinship
coefficients, IBD sharing probabilities, and inbreeding
coefficients. Additionally, functions are provided to perform
efficient variance component estimation and mixed model
association testing for both quantitative and binary
phenotypes.
biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod,
DimensionReduction, PrincipalComponent, GenomeWideAssociation,
QualityControl, BiocViews
Author: Matthew P. Conomos, Stephanie M. Gogarten, Lisa Brown, Han
Chen, Thomas Lumley, Kenneth Rice, Tamar Sofer, Adrienne Stilp,
Timothy Thornton, Chaoyu Yu
Maintainer: Stephanie M. Gogarten <sdmorris@uw.edu>
URL: https://github.com/UW-GAC/GENESIS
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GENESIS
git_branch: RELEASE_3_13
git_last_commit: df77222
git_last_commit_date: 2021-06-16
Date/Publication: 2021-06-17
source.ver: src/contrib/GENESIS_2.22.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GENESIS_2.22.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/GENESIS_2.22.2.tgz
vignettes: vignettes/GENESIS/inst/doc/assoc_test_seq.html,
vignettes/GENESIS/inst/doc/assoc_test.html,
vignettes/GENESIS/inst/doc/pcair.html
vignetteTitles: Analyzing Sequence Data using the GENESIS Package,
Genetic Association Testing using the GENESIS Package,
Population Structure and Relatedness Inference using the
GENESIS Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GENESIS/inst/doc/assoc_test_seq.R,
vignettes/GENESIS/inst/doc/assoc_test.R,
vignettes/GENESIS/inst/doc/pcair.R
dependencyCount: 89
Package: GeneStructureTools
Version: 1.12.0
Imports:
Biostrings,GenomicRanges,IRanges,data.table,plyr,stringdist,stringr,S4Vectors,BSgenome.Mmusculus.UCSC.mm10,stats,utils,Gviz,rtracklayer,methods
Suggests: BiocStyle, knitr, rmarkdown
License: BSD_3_clause + file LICENSE
MD5sum: 7c2e9c96c453738905d0d78a6d119e55
NeedsCompilation: no
Title: Tools for spliced gene structure manipulation and analysis
Description: GeneStructureTools can be used to create in silico
alternative splicing events, and analyse potential effects this
has on functional gene products.
biocViews: ImmunoOncology, Software, DifferentialSplicing,
FunctionalPrediction, Transcriptomics, AlternativeSplicing,
RNASeq
Author: Beth Signal
Maintainer: Beth Signal <b.signal@garvan.org.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GeneStructureTools
git_branch: RELEASE_3_13
git_last_commit: 1b05c47
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneStructureTools_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeneStructureTools_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeneStructureTools_1.12.0.tgz
vignettes: vignettes/GeneStructureTools/inst/doc/Vignette.html
vignetteTitles: Introduction to GeneStructureTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GeneStructureTools/inst/doc/Vignette.R
dependencyCount: 145
Package: geNetClassifier
Version: 1.32.0
Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods
Imports: e1071, graphics, grDevices
Suggests: leukemiasEset, RUnit, BiocGenerics
Enhances: RColorBrewer, igraph, infotheo
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 1c38e7c17af3edfeca098bacbf0e338d
NeedsCompilation: no
Title: Classify diseases and build associated gene networks using gene
expression profiles
Description: Comprehensive package to automatically train and validate
a multi-class SVM classifier based on gene expression data.
Provides transparent selection of gene markers, their
coexpression networks, and an interface to query the
classifier.
biocViews: Classification, DifferentialExpression, Microarray
Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas.
Bioinformatics and Functional Genomics Group. Cancer Research
Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain.
Maintainer: Sara Aibar <saibar@usal.es>
URL: http://www.cicancer.org
git_url: https://git.bioconductor.org/packages/geNetClassifier
git_branch: RELEASE_3_13
git_last_commit: 1f86845
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/geNetClassifier_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/geNetClassifier_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/geNetClassifier_1.32.0.tgz
vignettes:
vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf
vignetteTitles: geNetClassifier-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.R
importsMe: bioCancer, canceR
dependencyCount: 18
Package: GeneticsPed
Version: 1.54.0
Depends: R (>= 2.4.0), MASS
Imports: gdata, genetics
Suggests: RUnit, gtools
License: LGPL (>= 2.1) | file LICENSE
Archs: i386, x64
MD5sum: 39c0c97194d14cc2d206256a98280723
NeedsCompilation: yes
Title: Pedigree and genetic relationship functions
Description: Classes and methods for handling pedigree data. It also
includes functions to calculate genetic relationship measures
as relationship and inbreeding coefficients and other
utilities. Note that package is not yet stable. Use it with
care!
biocViews: Genetics
Author: Gregor Gorjanc and David A. Henderson <DNADavenator@GMail.Com>,
with code contributions by Brian Kinghorn and Andrew Percy (see
file COPYING)
Maintainer: David Henderson <DNADavenator@GMail.Com>
URL: http://rgenetics.org
git_url: https://git.bioconductor.org/packages/GeneticsPed
git_branch: RELEASE_3_13
git_last_commit: aa226f9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeneticsPed_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeneticsPed_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeneticsPed_1.54.0.tgz
vignettes: vignettes/GeneticsPed/inst/doc/geneticRelatedness.pdf,
vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf,
vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf
vignetteTitles: Calculation of genetic relatedness/relationship between
individuals in the pedigree, Pedigree handling, Quantitative
genetic (animal) model example in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GeneticsPed/inst/doc/geneticRelatedness.R,
vignettes/GeneticsPed/inst/doc/pedigreeHandling.R,
vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.R
importsMe: LRQMM
dependencyCount: 11
Package: GeneTonic
Version: 1.4.1
Depends: R (>= 4.0.0)
Imports: AnnotationDbi, backbone, bs4Dash (>= 2.0.0), circlize,
colorspace, colourpicker, ComplexHeatmap, dendextend, DESeq2,
dplyr, DT, dynamicTreeCut, expm, ggforce, ggplot2, ggrepel,
GO.db, graphics, grDevices, grid, igraph, matrixStats, methods,
plotly, RColorBrewer, rintrojs, rlang, rmarkdown, S4Vectors,
scales, shiny, shinycssloaders, shinyWidgets, stats,
SummarizedExperiment, tidyr, tools, utils, viridis, visNetwork
Suggests: knitr, BiocStyle, htmltools, clusterProfiler, macrophage,
org.Hs.eg.db, magrittr, testthat (>= 2.1.0)
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 8660d111876085abe4e210006e6d072a
NeedsCompilation: no
Title: Enjoy Analyzing And Integrating The Results From Differential
Expression Analysis And Functional Enrichment Analysis
Description: This package provides a Shiny application that aims to
combine at different levels the existing pieces of the
transcriptome data and results, in a way that makes it easier
to generate insightful observations and hypothesis - combining
the benefits of interactivity and reproducibility, e.g. by
capturing the features and gene sets of interest highlighted
during the live session, and creating an HTML report as an
artifact where text, code, and output coexist.
biocViews: GUI, GeneExpression, Software, Transcription,
Transcriptomics, Visualization, DifferentialExpression,
Pathways, ReportWriting, GeneSetEnrichment, Annotation, GO
Author: Federico Marini [aut, cre]
(<https://orcid.org/0000-0003-3252-7758>), Annekathrin Ludt
[aut] (<https://orcid.org/0000-0002-2475-4945>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
URL: https://github.com/federicomarini/GeneTonic
VignetteBuilder: knitr
BugReports: https://github.com/federicomarini/GeneTonic/issues
git_url: https://git.bioconductor.org/packages/GeneTonic
git_branch: RELEASE_3_13
git_last_commit: 32c4e77
git_last_commit_date: 2021-06-04
Date/Publication: 2021-06-06
source.ver: src/contrib/GeneTonic_1.4.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeneTonic_1.4.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeneTonic_1.4.1.tgz
vignettes: vignettes/GeneTonic/inst/doc/GeneTonic_manual.html
vignetteTitles: The GeneTonic User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GeneTonic/inst/doc/GeneTonic_manual.R
dependencyCount: 170
Package: geneXtendeR
Version: 1.18.0
Depends: rtracklayer, GO.db, R (>= 3.5.0)
Imports: data.table, dplyr, graphics, networkD3, RColorBrewer,
SnowballC, tm, utils, wordcloud, AnnotationDbi, BiocStyle,
org.Rn.eg.db
Suggests: knitr, rmarkdown, testthat, org.Ag.eg.db, org.Bt.eg.db,
org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db,
org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Pt.eg.db,
org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, rtracklayer
License: GPL (>= 3)
MD5sum: 1802c9c8061c290f84779ea54c623e37
NeedsCompilation: yes
Title: Optimized Functional Annotation Of ChIP-seq Data
Description: geneXtendeR optimizes the functional annotation of
ChIP-seq peaks by exploring relative differences in annotating
ChIP-seq peak sets to variable-length gene bodies. In contrast
to prior techniques, geneXtendeR considers peak annotations
beyond just the closest gene, allowing users to see peak
summary statistics for the first-closest gene, second-closest
gene, ..., n-closest gene whilst ranking the output according
to biologically relevant events and iteratively comparing the
fidelity of peak-to-gene overlap across a user-defined range of
upstream and downstream extensions on the original boundaries
of each gene's coordinates. Since different ChIP-seq peak
callers produce different differentially enriched peaks with a
large variance in peak length distribution and total peak
count, annotating peak lists with their nearest genes can often
be a noisy process. As such, the goal of geneXtendeR is to
robustly link differentially enriched peaks with their
respective genes, thereby aiding experimental follow-up and
validation in designing primers for a set of prospective gene
candidates during qPCR.
biocViews: ChIPSeq, Genetics, Annotation, GenomeAnnotation,
DifferentialPeakCalling, Coverage, PeakDetection, ChipOnChip,
HistoneModification, DataImport, NaturalLanguageProcessing,
Visualization, GO, Software
Author: Bohdan Khomtchouk [aut, cre], William Koehler [aut]
Maintainer: Bohdan Khomtchouk <khomtchoukmed@gmail.com>
URL: https://github.com/Bohdan-Khomtchouk/geneXtendeR
VignetteBuilder: knitr
BugReports: https://github.com/Bohdan-Khomtchouk/geneXtendeR/issues
git_url: https://git.bioconductor.org/packages/geneXtendeR
git_branch: RELEASE_3_13
git_last_commit: 5f7ff0e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/geneXtendeR_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/geneXtendeR_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/geneXtendeR_1.18.0.tgz
vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf
vignetteTitles: geneXtendeR.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 109
Package: GENIE3
Version: 1.14.0
Imports: stats, reshape2, dplyr
Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase,
SummarizedExperiment, testthat, methods
License: GPL (>= 2)
MD5sum: 025689b6eda7f51baf356ae78eb1c3a4
NeedsCompilation: yes
Title: GEne Network Inference with Ensemble of trees
Description: This package implements the GENIE3 algorithm for inferring
gene regulatory networks from expression data.
biocViews: NetworkInference, SystemsBiology, DecisionTree, Regression,
Network, GraphAndNetwork, GeneExpression
Author: Van Anh Huynh-Thu, Sara Aibar, Pierre Geurts
Maintainer: Van Anh Huynh-Thu <vahuynh@ulg.ac.be>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GENIE3
git_branch: RELEASE_3_13
git_last_commit: a186e5b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GENIE3_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GENIE3_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GENIE3_1.14.0.tgz
vignettes: vignettes/GENIE3/inst/doc/GENIE3.html
vignetteTitles: GENIE3
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GENIE3/inst/doc/GENIE3.R
importsMe: BioNERO, MetNet
dependencyCount: 28
Package: genoCN
Version: 1.44.0
Imports: graphics, stats, utils
License: GPL (>=2)
Archs: i386, x64
MD5sum: 4f9d0f5d356f87eea0903609f2428737
NeedsCompilation: yes
Title: genotyping and copy number study tools
Description: Simultaneous identification of copy number states and
genotype calls for regions of either copy number variations or
copy number aberrations
biocViews: Microarray, Genetics
Author: Wei Sun and ZhengZheng Tang
Maintainer: Wei Sun <wsun@bios.unc.edu>
git_url: https://git.bioconductor.org/packages/genoCN
git_branch: RELEASE_3_13
git_last_commit: 22f18c6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/genoCN_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/genoCN_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/genoCN_1.44.0.tgz
vignettes: vignettes/genoCN/inst/doc/genoCN.pdf
vignetteTitles: add stuff
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genoCN/inst/doc/genoCN.R
dependencyCount: 3
Package: GenoGAM
Version: 2.10.0
Depends: R (>= 3.5), SummarizedExperiment (>= 1.1.19), HDF5Array (>=
1.8.0), rhdf5 (>= 2.21.6), S4Vectors (>= 0.23.18), Matrix (>=
1.2-8), data.table (>= 1.9.4)
Imports: Rcpp (>= 0.12.14), sparseinv (>= 0.1.1), Rsamtools (>=
1.18.2), GenomicRanges (>= 1.23.16), BiocParallel (>= 1.5.17),
DESeq2 (>= 1.11.23), futile.logger (>= 1.4.1), GenomeInfoDb (>=
1.7.6), GenomicAlignments (>= 1.7.17), IRanges (>= 2.5.30),
Biostrings (>= 2.39.14), DelayedArray (>= 0.3.19), methods,
stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle, chipseq (>= 1.21.2), LSD (>= 3.0.0), genefilter
(>= 1.54.2), ggplot2 (>= 2.1.0), testthat, knitr, rmarkdown
License: GPL-2
MD5sum: d08d8bd8afaebfb5a0354bd852a82c73
NeedsCompilation: yes
Title: A GAM based framework for analysis of ChIP-Seq data
Description: This package allows statistical analysis of genome-wide
data with smooth functions using generalized additive models
based on the implementation from the R-package 'mgcv'. It
provides methods for the statistical analysis of ChIP-Seq data
including inference of protein occupancy, and pointwise and
region-wise differential analysis. Estimation of dispersion and
smoothing parameters is performed by cross-validation. Scaling
of generalized additive model fitting to whole chromosomes is
achieved by parallelization over overlapping genomic intervals.
biocViews: Regression, DifferentialPeakCalling, ChIPSeq,
DifferentialExpression, Genetics, Epigenetics, WholeGenome,
ChipOnChip, ImmunoOncology
Author: Georg Stricker [aut, cre], Alexander Engelhardt [aut], Julien
Gagneur [aut]
Maintainer: Georg Stricker <georg.stricker@protonmail.com>
URL: https://github.com/gstricker/GenoGAM
VignetteBuilder: knitr
BugReports: https://github.com/gstricker/GenoGAM/issues
git_url: https://git.bioconductor.org/packages/GenoGAM
git_branch: RELEASE_3_13
git_last_commit: 3b86b0c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GenoGAM_2.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenoGAM_2.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenoGAM_2.10.0.tgz
vignettes: vignettes/GenoGAM/inst/doc/GenoGAM.html
vignetteTitles: "Modeling ChIP-Seq data with GenoGAM 2.0: A Genome-wide
generalized additive model"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenoGAM/inst/doc/GenoGAM.R
dependencyCount: 104
Package: genomation
Version: 1.24.0
Depends: R (>= 3.0.0),grid
Imports: Biostrings (>= 2.47.6), BSgenome (>= 1.47.3), data.table,
GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>=
1.15.6), S4Vectors (>= 0.17.25), ggplot2, gridBase, impute,
IRanges (>= 2.13.12), matrixStats, methods, parallel, plotrix,
plyr, readr, reshape2, Rsamtools (>= 1.31.2), seqPattern,
rtracklayer (>= 1.39.7), Rcpp (>= 0.12.14)
LinkingTo: Rcpp
Suggests: BiocGenerics, genomationData, knitr, RColorBrewer, rmarkdown,
RUnit
License: Artistic-2.0
Archs: i386, x64
MD5sum: 520aa98cc953e739dabbeda4f3585672
NeedsCompilation: yes
Title: Summary, annotation and visualization of genomic data
Description: A package for summary and annotation of genomic intervals.
Users can visualize and quantify genomic intervals over
pre-defined functional regions, such as promoters, exons,
introns, etc. The genomic intervals represent regions with a
defined chromosome position, which may be associated with a
score, such as aligned reads from HT-seq experiments, TF
binding sites, methylation scores, etc. The package can use any
tabular genomic feature data as long as it has minimal
information on the locations of genomic intervals. In addition,
It can use BAM or BigWig files as input.
biocViews: Annotation, Sequencing, Visualization, CpGIsland
Author: Altuna Akalin [aut, cre], Vedran Franke [aut, cre], Katarzyna
Wreczycka [aut], Alexander Gosdschan [ctb], Liz Ing-Simmons
[ctb], Bozena Mika-Gospodorz [ctb]
Maintainer: Altuna Akalin <aakalin@gmail.com>, Vedran Franke
<vedran.franke@gmail.com>, Katarzyna Wreczycka
<katwre@gmail.com>
URL: http://bioinformatics.mdc-berlin.de/genomation/
VignetteBuilder: knitr
BugReports: https://github.com/BIMSBbioinfo/genomation/issues
git_url: https://git.bioconductor.org/packages/genomation
git_branch: RELEASE_3_13
git_last_commit: 94f0278
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/genomation_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/genomation_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/genomation_1.24.0.tgz
vignettes: vignettes/genomation/inst/doc/GenomationManual.html
vignetteTitles: genomation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genomation/inst/doc/GenomationManual.R
importsMe: fCCAC, RCAS
suggestsMe: methylKit
dependencyCount: 97
Package: GenomeInfoDb
Version: 1.28.4
Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>=
0.25.12), IRanges (>= 2.13.12)
Imports: stats, stats4, utils, RCurl, GenomeInfoDbData
Suggests: GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures,
TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome,
BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2,
BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocStyle, knitr
License: Artistic-2.0
Archs: i386, x64
MD5sum: cee24e8eb4d5dfb1f8ead38f9e1f2c3f
NeedsCompilation: no
Title: Utilities for manipulating chromosome names, including modifying
them to follow a particular naming style
Description: Contains data and functions that define and allow
translation between different chromosome sequence naming
conventions (e.g., "chr1" versus "1"), including a function
that attempts to place sequence names in their natural, rather
than lexicographic, order.
biocViews: Genetics, DataRepresentation, Annotation, GenomeAnnotation
Author: Sonali Arora, Martin Morgan, Marc Carlson, H. Pagès
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/GenomeInfoDb
VignetteBuilder: knitr
Video: http://youtu.be/wdEjCYSXa7w
BugReports: https://github.com/Bioconductor/GenomeInfoDb/issues
git_url: https://git.bioconductor.org/packages/GenomeInfoDb
git_branch: RELEASE_3_13
git_last_commit: 3de2a41
git_last_commit_date: 2021-09-03
Date/Publication: 2021-09-05
source.ver: src/contrib/GenomeInfoDb_1.28.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomeInfoDb_1.28.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomeInfoDb_1.28.4.tgz
vignettes:
vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.pdf,
vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf
vignetteTitles: GenomeInfoDb: Submitting your organism to GenomeInfoDb,
GenomeInfoDb: Introduction to GenomeInfoDb
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.R,
vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R
dependsOnMe: Biostrings, BRGenomics, BSgenome, bumphunter, CODEX, CSAR,
GenomicAlignments, GenomicFeatures, GenomicRanges,
GenomicTuples, gmapR, groHMM, HelloRanges, methyAnalysis,
Rsamtools, SCOPE, VariantAnnotation,
SNPlocs.Hsapiens.dbSNP141.GRCh38,
SNPlocs.Hsapiens.dbSNP142.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, RTIGER
importsMe: AllelicImbalance, alpine, amplican, AneuFinder,
AnnotationHubData, annotatr, ASpediaFI, ATACseqQC, BaalChIP,
ballgown, bambu, biovizBase, biscuiteer, BiSeq, bnbc,
branchpointer, breakpointR, BSgenome, bsseq, BUSpaRse,
CAGEfightR, CAGEr, casper, cBioPortalData, chimeraviz,
chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPseeker, chromstaR,
chromVAR, circRNAprofiler, cleanUpdTSeq, cn.mops, CNEr,
CNVfilteR, CNVPanelizer, CNVRanger, compEpiTools,
consensusSeekeR, conumee, CopyNumberPlots, CopywriteR,
CrispRVariants, csaw, customProDB, DAMEfinder, dasper,
decompTumor2Sig, DeepBlueR, derfinder, derfinderPlot, DEScan2,
DEWSeq, diffHic, diffloop, diffUTR, DMRcate, DMRScan, dmrseq,
DominoEffect, easyRNASeq, ELMER, enrichTF, ensembldb,
ensemblVEP, epialleleR, epigenomix, epigraHMM, EpiTxDb,
epivizr, epivizrData, epivizrStandalone, erma, esATAC,
EventPointer, exomeCopy, FRASER, FunChIP, funtooNorm,
GA4GHclient, GA4GHshiny, gcapc, genbankr, geneAttribution,
GenoGAM, genomation, genomeIntervals, GenomicFiles,
GenomicInteractions, GenomicOZone, GenomicScores, genotypeeval,
GenVisR, ggbio, gmoviz, GOTHiC, GreyListChIP, GUIDEseq, Gviz,
gwascat, h5vc, heatmaps, HiCBricks, HiTC, HTSeqGenie, idr2d,
IMAS, InPAS, INSPEcT, InteractionSet, IsoformSwitchAnalyzeR,
IVAS, karyoploteR, ldblock, MACPET, MADSEQ, maser, metagene,
metagene2, metaseqR2, metavizr, MethCP, methimpute,
methInheritSim, methylKit, methylPipe, methylSig, methylumi,
minfi, MinimumDistance, MMAPPR2, mosaics, motifbreakR,
motifmatchr, msgbsR, multicrispr, multiHiCcompare, musicatk,
MutationalPatterns, myvariant, NADfinder, nearBynding, normr,
nucleR, OMICsPCA, ORFik, Organism.dplyr, panelcn.mops,
periodicDNA, Pi, pipeFrame, plyranges, podkat, pram, prebs,
proActiv, profileplyr, ProteomicsAnnotationHubData, PureCN,
qpgraph, qsea, QuasR, R3CPET, r3Cseq, RaggedExperiment,
RareVariantVis, Rcade, RCAS, RcisTarget, recount, recoup,
regioneR, regionReport, REMP, Repitools, RiboProfiling,
riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, rnaEditr,
RNAmodR, roar, RTCGAToolbox, rtracklayer, scmeth, scruff,
segmentSeq, SeqArray, seqCAT, seqsetvis, sevenC, SGSeq,
ShortRead, signeR, SigsPack, SingleMoleculeFootprinting,
sitadela, SNPhood, soGGi, SomaticSignatures, SparseSignatures,
SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR,
SummarizedExperiment, TAPseq, TarSeqQC, TCGAutils, TFBSTools,
TitanCNA, TnT, trackViewer, transcriptR, tRNAscanImport,
TSRchitect, TVTB, tximeta, Ularcirc, UMI4Cats, VanillaICE,
VariantFiltering, VariantTools, VaSP, VplotR, wiggleplotr,
YAPSA, fitCons.UCSC.hg19, GenomicState, grasp2db,
MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5,
MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5,
MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5,
MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5,
MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5,
MafDb.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38,
MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19,
MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38,
MafH5.gnomAD.v3.1.1.GRCh38, phastCons100way.UCSC.hg19,
phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38,
SNPlocs.Hsapiens.dbSNP141.GRCh38,
SNPlocs.Hsapiens.dbSNP142.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP151.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP141.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data,
GenomicDistributionsData, MethylSeqData, TCGAWorkflow,
ActiveDriverWGS, crispRdesignR, deconstructSigs, driveR,
HiCfeat, ICAMS, intePareto, MAAPER, MicroSEC, Signac, simMP
suggestsMe: AnnotationForge, AnnotationHub, BiocOncoTK, chromswitch,
ExperimentHubData, megadepth, methrix, MungeSumstats, parglms,
QDNAseq, splatter, TFutils, gkmSVM, LDheatmap, polyRAD, Seurat
dependencyCount: 12
Package: genomeIntervals
Version: 1.48.0
Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics
(>= 0.15.2)
Imports: GenomeInfoDb (>= 1.5.8), GenomicRanges (>= 1.21.16),
IRanges(>= 2.3.14), S4Vectors (>= 0.7.10)
License: Artistic-2.0
Archs: i386, x64
MD5sum: 90d47a6399cd1fcb6386e1e66b6d6d41
NeedsCompilation: no
Title: Operations on genomic intervals
Description: This package defines classes for representing genomic
intervals and provides functions and methods for working with
these. Note: The package provides the basic infrastructure for
and is enhanced by the package 'girafe'.
biocViews: DataImport, Infrastructure, Genetics
Author: Julien Gagneur <gagneur@in.tum.de>, Joern Toedling, Richard
Bourgon, Nicolas Delhomme
Maintainer: Julien Gagneur <gagneur@in.tum.de>
git_url: https://git.bioconductor.org/packages/genomeIntervals
git_branch: RELEASE_3_13
git_last_commit: ab17e64
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/genomeIntervals_1.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/genomeIntervals_1.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/genomeIntervals_1.48.0.tgz
vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf
vignetteTitles: Overview of the genomeIntervals package.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genomeIntervals/inst/doc/genomeIntervals.R
dependsOnMe: girafe, ChIC.data
importsMe: ChIC, easyRNASeq
dependencyCount: 18
Package: genomes
Version: 3.22.0
Depends: readr, curl
License: GPL-3
Archs: i386, x64
MD5sum: 6191949054a209521bf925f35177108d
NeedsCompilation: no
Title: Genome sequencing project metadata
Description: Download genome and assembly reports from NCBI
biocViews: Annotation, Genetics
Author: Chris Stubben
Maintainer: Chris Stubben <stubben@lanl.gov>
git_url: https://git.bioconductor.org/packages/genomes
git_branch: RELEASE_3_13
git_last_commit: ea29173
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/genomes_3.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/genomes_3.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/genomes_3.22.0.tgz
vignettes: vignettes/genomes/inst/doc/genomes.pdf
vignetteTitles: Genome metadata
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genomes/inst/doc/genomes.R
dependencyCount: 33
Package: GenomicAlignments
Version: 1.28.0
Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>=
0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1),
GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.9.13),
Biostrings (>= 2.55.7), Rsamtools (>= 1.31.2)
Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges,
GenomicRanges, Biostrings, Rsamtools, BiocParallel
LinkingTo: S4Vectors, IRanges
Suggests: ShortRead, rtracklayer, BSgenome, GenomicFeatures,
RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Dmelanogaster.UCSC.dm3.ensGene,
BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19,
DESeq2, edgeR, RUnit, BiocStyle
License: Artistic-2.0
Archs: i386, x64
MD5sum: 620d245c87edd17f00b589bd3661040b
NeedsCompilation: yes
Title: Representation and manipulation of short genomic alignments
Description: Provides efficient containers for storing and manipulating
short genomic alignments (typically obtained by aligning short
reads to a reference genome). This includes read counting,
computing the coverage, junction detection, and working with
the nucleotide content of the alignments.
biocViews: Infrastructure, DataImport, Genetics, Sequencing, RNASeq,
SNP, Coverage, Alignment, ImmunoOncology
Author: Hervé Pagès, Valerie Obenchain, Martin Morgan
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/GenomicAlignments
Video: https://www.youtube.com/watch?v=2KqBSbkfhRo ,
https://www.youtube.com/watch?v=3PK_jx44QTs
BugReports: https://github.com/Bioconductor/GenomicAlignments/issues
git_url: https://git.bioconductor.org/packages/GenomicAlignments
git_branch: RELEASE_3_13
git_last_commit: e755dc1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GenomicAlignments_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomicAlignments_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomicAlignments_1.28.0.tgz
vignettes:
vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.pdf,
vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf,
vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf,
vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.pdf
vignetteTitles: An Introduction to the GenomicAlignments Package,
Overlap encodings, Counting reads with summarizeOverlaps,
Working with aligned nucleotides
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.R,
vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R,
vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R,
vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R
dependsOnMe: AllelicImbalance, Basic4Cseq, ChIPexoQual, groHMM,
HelloRanges, hiReadsProcessor, igvR, ORFik, prebs, recoup,
RiboDiPA, ShortRead, SplicingGraphs, alpineData, SCATEData,
sequencing
importsMe: alpine, AneuFinder, APAlyzer, ASpediaFI, ASpli, ATACseqQC,
BaalChIP, bambu, biovizBase, breakpointR, BRGenomics,
CAGEfightR, CAGEr, chimeraviz, ChIPpeakAnno, ChIPQC, chromstaR,
CNEr, consensusDE, contiBAIT, CopywriteR, CoverageView,
CrispRVariants, CSSQ, customProDB, DAMEfinder, DegNorm,
derfinder, DEScan2, DiffBind, easyRNASeq, FRASER, FunChIP,
gcapc, GenoGAM, genomation, GenomicFiles, ggbio, gmapR, gmoviz,
GreyListChIP, GUIDEseq, Gviz, HTSeqGenie, icetea, IMAS,
INSPEcT, IntEREst, MACPET, MADSEQ, MDTS, metagene, metagene2,
metaseqR2, methylPipe, mosaics, msgbsR, NADfinder, PICS,
plyranges, pram, proActiv, ramwas, Rcade, Repitools,
RiboProfiling, ribosomeProfilingQC, RNAmodR, roar, Rqc,
rtracklayer, SCATE, scruff, seqsetvis, SGSeq, soGGi,
SplicingGraphs, SPLINTER, srnadiff, strandCheckR, TAPseq,
TarSeqQC, TCseq, trackViewer, transcriptR, TSRchitect,
Ularcirc, UMI4Cats, VaSP, VplotR, leeBamViews, alakazam,
BinQuasi, ExomeDepth, intePareto, MAAPER, MicroSEC, PACVr,
pulseTD, RAPIDR, VALERIE
suggestsMe: amplican, BiocParallel, csaw, gage, GenomeInfoDb,
GenomicDataCommons, GenomicFeatures, GenomicRanges, IRanges,
QuasR, Rsamtools, similaRpeak, Streamer, systemPipeR,
alpineData, NanoporeRNASeq, parathyroidSE,
RNAseqData.HNRNPC.bam.chr14, seqmagick
dependencyCount: 37
Package: GenomicDataCommons
Version: 1.16.0
Depends: R (>= 3.4.0), magrittr
Imports: stats, httr, xml2, jsonlite, utils, rlang, readr,
GenomicRanges, IRanges, dplyr, rappdirs, SummarizedExperiment,
S4Vectors, tibble
Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer,
ggplot2, GenomicAlignments, Rsamtools
License: Artistic-2.0
MD5sum: a3e66a51579b75e25df9ea96adc2a989
NeedsCompilation: no
Title: NIH / NCI Genomic Data Commons Access
Description: Programmatically access the NIH / NCI Genomic Data Commons
RESTful service.
biocViews: DataImport, Sequencing
Author: Martin Morgan [aut], Sean Davis [aut, cre]
Maintainer: Sean Davis <seandavi@gmail.com>
URL: https://bioconductor.org/packages/GenomicDataCommons,
http://github.com/Bioconductor/GenomicDataCommons
VignetteBuilder: knitr
BugReports:
https://github.com/Bioconductor/GenomicDataCommons/issues/new
git_url: https://git.bioconductor.org/packages/GenomicDataCommons
git_branch: RELEASE_3_13
git_last_commit: d614d6e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GenomicDataCommons_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomicDataCommons_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomicDataCommons_1.16.0.tgz
vignettes: vignettes/GenomicDataCommons/inst/doc/overview.html
vignetteTitles: Introduction to Accessing the NCI Genomic Data Commons
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicDataCommons/inst/doc/overview.R
importsMe: GDCRNATools, TCGAutils
dependencyCount: 64
Package: GenomicDistributions
Version: 1.0.0
Depends: R (>= 4.0), IRanges, GenomicRanges
Imports: data.table, ggplot2, reshape2, methods, utils, Biostrings,
Suggests: AnnotationFilter, rtracklayer, testthat, knitr, BiocStyle,
rmarkdown
Enhances: BSgenome, extrafont, ensembldb, GenomicFeatures
License: BSD_2_clause + file LICENSE
MD5sum: c87f17315d1283d946a0108561b6bd6f
NeedsCompilation: no
Title: Produces Summaries and Plots of Features Distributed Across
Genomes
Description: If you have a set of genomic ranges, this package can help
you with visualization and comparison. It produces several
kinds of plots, for example: Chromosome distribution plots,
which visualize how your regions are distributed over
chromosomes; feature distance distribution plots, which
visualizes how your regions are distributed relative to a
feature of interest, like Transcription Start Sites (TSSs);
genomic partition plots, which visualize how your regions
overlap given genomic features such as promoters, introns,
exons, or intergenic regions. It also makes it easy to compare
one set of ranges to another.
biocViews: Software, GenomeAnnotation, GenomeAssembly,
DataRepresentation, Sequencing, Coverage, FunctionalGenomics,
Visualization
Author: Nathan C. Sheffield [aut], Kristyna Kupkova [aut, cre], Jose
Verdezoto [aut], Tessa Danehy [ctb], John Lawson [ctb], Jose
Verdezoto [ctb], Michal Stolarczyk [ctb], Jason Smith [ctb]
Maintainer: Kristyna Kupkova <kristynakupkova@gmail.com>
URL: http://code.databio.org/GenomicDistributions
VignetteBuilder: knitr
BugReports: http://github.com/databio/GenomicDistributions
git_url: https://git.bioconductor.org/packages/GenomicDistributions
git_branch: RELEASE_3_13
git_last_commit: 84c4876
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GenomicDistributions_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomicDistributions_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomicDistributions_1.0.0.tgz
vignettes: vignettes/GenomicDistributions/inst/doc/full-power.html,
vignettes/GenomicDistributions/inst/doc/intro.html
vignetteTitles: 2. Full power GenomicDistributions, 1. Getting started
with GenomicDistributions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GenomicDistributions/inst/doc/intro.R
dependencyCount: 58
Package: GenomicFeatures
Version: 1.44.2
Depends: BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.29), IRanges (>=
2.13.23), GenomeInfoDb (>= 1.25.7), GenomicRanges (>= 1.31.17),
AnnotationDbi (>= 1.41.4)
Imports: methods, utils, stats, tools, DBI, RSQLite (>= 2.0), RCurl,
XVector (>= 0.19.7), Biostrings (>= 2.47.6), BiocIO,
rtracklayer (>= 1.51.5), biomaRt (>= 2.17.1), Biobase (>=
2.15.1)
Suggests: RMariaDB, org.Mm.eg.db, org.Hs.eg.db, BSgenome,
BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17),
BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm3
(>= 1.3.17), mirbase.db, FDb.UCSC.tRNAs,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Celegans.UCSC.ce11.ensGene,
TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1),
TxDb.Mmusculus.UCSC.mm10.knownGene (>= 3.4.7),
TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts,
TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.4.6),
SNPlocs.Hsapiens.dbSNP144.GRCh38, Rsamtools, pasillaBamSubset
(>= 0.0.5), GenomicAlignments (>= 1.15.7), ensembldb, RUnit,
BiocStyle, knitr
License: Artistic-2.0
MD5sum: a61f95ef6763f05d3ee72eff5386580e
NeedsCompilation: no
Title: Conveniently import and query gene models
Description: A set of tools and methods for making and manipulating
transcript centric annotations. With these tools the user can
easily download the genomic locations of the transcripts, exons
and cds of a given organism, from either the UCSC Genome
Browser or a BioMart database (more sources will be supported
in the future). This information is then stored in a local
database that keeps track of the relationship between
transcripts, exons, cds and genes. Flexible methods are
provided for extracting the desired features in a convenient
format.
biocViews: Genetics, Infrastructure, Annotation, Sequencing,
GenomeAnnotation
Author: M. Carlson, H. Pagès, P. Aboyoun, S. Falcon, M. Morgan, D.
Sarkar, M. Lawrence, V. Obenchain
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/GenomicFeatures
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/GenomicFeatures/issues
git_url: https://git.bioconductor.org/packages/GenomicFeatures
git_branch: RELEASE_3_13
git_last_commit: 49566bc
git_last_commit_date: 2021-08-26
Date/Publication: 2021-08-26
source.ver: src/contrib/GenomicFeatures_1.44.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomicFeatures_1.44.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomicFeatures_1.44.2.tgz
vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.pdf
vignetteTitles: Making and Utilizing TxDb Objects
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R
dependsOnMe: cpvSNP, ensembldb, GSReg, Guitar, HelloRanges,
OrganismDbi, OUTRIDER, RareVariantVis, RiboDiPA,
SplicingGraphs, FDb.FANTOM4.promoters.hg19,
FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19,
FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19,
FDb.UCSC.tRNAs, Homo.sapiens, Mus.musculus, Rattus.norvegicus,
TxDb.Athaliana.BioMart.plantsmart22,
TxDb.Athaliana.BioMart.plantsmart25,
TxDb.Athaliana.BioMart.plantsmart28,
TxDb.Btaurus.UCSC.bosTau8.refGene,
TxDb.Btaurus.UCSC.bosTau9.refGene,
TxDb.Celegans.UCSC.ce11.ensGene,
TxDb.Celegans.UCSC.ce11.refGene,
TxDb.Celegans.UCSC.ce6.ensGene,
TxDb.Cfamiliaris.UCSC.canFam3.refGene,
TxDb.Dmelanogaster.UCSC.dm3.ensGene,
TxDb.Dmelanogaster.UCSC.dm6.ensGene,
TxDb.Drerio.UCSC.danRer10.refGene,
TxDb.Drerio.UCSC.danRer11.refGene,
TxDb.Ggallus.UCSC.galGal4.refGene,
TxDb.Ggallus.UCSC.galGal5.refGene,
TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis,
TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Hsapiens.UCSC.hg38.refGene,
TxDb.Mmulatta.UCSC.rheMac10.refGene,
TxDb.Mmulatta.UCSC.rheMac3.refGene,
TxDb.Mmulatta.UCSC.rheMac8.refGene,
TxDb.Mmusculus.UCSC.mm10.ensGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Mmusculus.UCSC.mm39.refGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,
TxDb.Ptroglodytes.UCSC.panTro4.refGene,
TxDb.Ptroglodytes.UCSC.panTro5.refGene,
TxDb.Ptroglodytes.UCSC.panTro6.refGene,
TxDb.Rnorvegicus.BioMart.igis,
TxDb.Rnorvegicus.UCSC.rn4.ensGene,
TxDb.Rnorvegicus.UCSC.rn5.refGene,
TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq,
TxDb.Rnorvegicus.UCSC.rn6.refGene,
TxDb.Scerevisiae.UCSC.sacCer2.sgdGene,
TxDb.Scerevisiae.UCSC.sacCer3.sgdGene,
TxDb.Sscrofa.UCSC.susScr11.refGene,
TxDb.Sscrofa.UCSC.susScr3.refGene, generegulation
importsMe: AllelicImbalance, alpine, AnnotationHubData, annotatr,
APAlyzer, appreci8R, ASpediaFI, ASpli, bambu, BgeeCall,
BiocOncoTK, biovizBase, bumphunter, BUSpaRse, CAGEfightR,
casper, ChIPpeakAnno, ChIPQC, ChIPseeker, compEpiTools, CompGO,
consensusDE, crisprseekplus, CSSQ, customProDB, dasper,
decompTumor2Sig, derfinder, derfinderPlot, EDASeq, ELMER,
EpiTxDb, epivizrData, epivizrStandalone, esATAC, EventPointer,
FRASER, GA4GHshiny, genbankr, geneAttribution, GenVisR, ggbio,
gmapR, gmoviz, Gviz, gwascat, HiLDA, HTSeqGenie, icetea, InPAS,
INSPEcT, IntEREst, karyoploteR, lumi, mCSEA, metagene,
metaseqR2, msgbsR, multicrispr, musicatk, ORFik,
Organism.dplyr, proActiv, proBAMr, PureCN, qpgraph, QuasR,
RCAS, recoup, Rhisat2, RiboProfiling, ribosomeProfilingQC,
RNAmodR, scruff, SGSeq, sitadela, SplicingGraphs, SPLINTER,
srnadiff, StructuralVariantAnnotation, systemPipeR, TAPseq,
TCGAutils, TFEA.ChIP, trackViewer, transcriptR, tximeta,
Ularcirc, UMI4Cats, VariantAnnotation, VariantFiltering,
VariantTools, wavClusteR, FDb.FANTOM4.promoters.hg19,
FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19,
FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19,
FDb.UCSC.tRNAs, GenomicState, Homo.sapiens, Mus.musculus,
Rattus.norvegicus, TxDb.Athaliana.BioMart.plantsmart22,
TxDb.Athaliana.BioMart.plantsmart25,
TxDb.Hsapiens.BioMart.igis, TxDb.Rnorvegicus.BioMart.igis,
DMRcatedata, geneLenDataBase, GenomicDistributionsData,
scRNAseq, systemPipeRdata, driveR, MAAPER, oncoPredict,
pathwayTMB, pulseTD, utr.annotation
suggestsMe: AnnotationHub, BANDITS, biomvRCNS, Biostrings, chipseq,
chromPlot, CrispRVariants, csaw, cummeRbund, DEXSeq, eisaR,
GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM,
HDF5Array, InteractiveComplexHeatmap, IRanges, MiRaGE,
MutationalPatterns, pageRank, recount, RNAmodR.ML, Rsamtools,
rtracklayer, ShortRead, SummarizedExperiment, TFutils, TnT,
VplotR, wiggleplotr, BSgenome.Btaurus.UCSC.bosTau3,
BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau6,
BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9,
BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11,
BSgenome.Celegans.UCSC.ce2, BSgenome.Cfamiliaris.UCSC.canFam2,
BSgenome.Cfamiliaris.UCSC.canFam3,
BSgenome.Dmelanogaster.UCSC.dm2,
BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10,
BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5,
BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer7,
BSgenome.Gaculeatus.UCSC.gasAcu1,
BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal4,
BSgenome.Hsapiens.UCSC.hg17, BSgenome.Mmulatta.UCSC.rheMac2,
BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmusculus.UCSC.mm8,
BSgenome.Ptroglodytes.UCSC.panTro2,
BSgenome.Ptroglodytes.UCSC.panTro3,
BSgenome.Rnorvegicus.UCSC.rn6, curatedAdipoChIP, ObMiTi,
parathyroidSE, Single.mTEC.Transcriptomes, CAGEWorkflow,
polyRAD
dependencyCount: 95
Package: GenomicFiles
Version: 1.28.0
Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2),
MatrixGenerics, GenomicRanges (>= 1.31.16),
SummarizedExperiment, BiocParallel (>= 1.1.0), Rsamtools (>=
1.17.29), rtracklayer (>= 1.25.3)
Imports: GenomicAlignments (>= 1.7.7), IRanges, S4Vectors (>= 0.9.25),
VariantAnnotation (>= 1.27.9), GenomeInfoDb
Suggests: BiocStyle, RUnit, genefilter, deepSNV, snpStats,
RNAseqData.HNRNPC.bam.chr14, Biostrings, Homo.sapiens
License: Artistic-2.0
Archs: i386, x64
MD5sum: 5906016debc92564a2124a8f4ddf6d24
NeedsCompilation: no
Title: Distributed computing by file or by range
Description: This package provides infrastructure for parallel
computations distributed 'by file' or 'by range'. User defined
MAPPER and REDUCER functions provide added flexibility for data
combination and manipulation.
biocViews: Genetics, Infrastructure, DataImport, Sequencing, Coverage
Author: Bioconductor Package Maintainer [aut, cre], Valerie Obenchain
[aut], Michael Love [aut], Lori Shepherd [aut], Martin Morgan
[aut]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
Video: https://www.youtube.com/watch?v=3PK_jx44QTs
git_url: https://git.bioconductor.org/packages/GenomicFiles
git_branch: RELEASE_3_13
git_last_commit: b7b64cb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GenomicFiles_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomicFiles_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomicFiles_1.28.0.tgz
vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.pdf
vignetteTitles: Introduction to GenomicFiles
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R
dependsOnMe: erma
importsMe: CAGEfightR, contiBAIT, derfinder, ldblock, QuasR, Rqc,
VCFArray
suggestsMe: TFutils
dependencyCount: 98
Package: GenomicInteractions
Version: 1.26.0
Depends: R (>= 3.5), InteractionSet
Imports: Rsamtools, rtracklayer, GenomicRanges (>= 1.29.6), IRanges,
BiocGenerics (>= 0.15.3), data.table, stringr, GenomeInfoDb,
ggplot2, grid, gridExtra, methods, igraph, S4Vectors (>=
0.13.13), dplyr, Gviz, Biobase, graphics, stats, utils,
grDevices
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: GPL-3
MD5sum: d887f24e83af924d141a31a843802d1d
NeedsCompilation: no
Title: Utilities for handling genomic interaction data
Description: Utilities for handling genomic interaction data such as
ChIA-PET or Hi-C, annotating genomic features with interaction
information, and producing plots and summary statistics.
biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC
Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard,
B.
Maintainer: Liz Ing-Simmons <liz.ingsimmons@gmail.com>
URL:
https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GenomicInteractions
git_branch: RELEASE_3_13
git_last_commit: 54f6f7e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GenomicInteractions_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomicInteractions_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomicInteractions_1.26.0.tgz
vignettes:
vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html,
vignettes/GenomicInteractions/inst/doc/hic_vignette.html
vignetteTitles: chiapet_vignette.html, GenomicInteractions-HiC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R,
vignettes/GenomicInteractions/inst/doc/hic_vignette.R
importsMe: CAGEfightR
suggestsMe: Chicago, ELMER, sevenC, chicane
dependencyCount: 144
Package: GenomicOZone
Version: 1.6.0
Depends: R (>= 4.0.0), Ckmeans.1d.dp (>= 4.3.0), GenomicRanges,
biomaRt, ggplot2
Imports: grDevices, stats, utils, plyr, gridExtra, lsr, parallel,
ggbio, S4Vectors, IRanges, GenomeInfoDb, Rdpack
Suggests: readxl, GEOquery, knitr, rmarkdown
License: LGPL (>=3)
MD5sum: d4fae8b0c8adb3a200caa388d321cd14
NeedsCompilation: no
Title: Delineate outstanding genomic zones of differential gene
activity
Description: The package clusters gene activity along chromosome into
zones, detects differential zones as outstanding, and
visualizes maps of outstanding zones across the genome. It
enables characterization of effects on multiple genes within
adaptive genomic neighborhoods, which could arise from genome
reorganization, structural variation, or epigenome alteration.
It guarantees cluster optimality, linear runtime to sample
size, and reproducibility. One can apply it on genome-wide
activity measurements such as copy number, transcriptomic,
proteomic, and methylation data.
biocViews: Software, GeneExpression, Transcription,
DifferentialExpression, FunctionalPrediction, GeneRegulation,
BiomedicalInformatics, CellBiology, FunctionalGenomics,
Genetics, SystemsBiology, Transcriptomics, Clustering,
Regression, RNASeq, Annotation, Visualization, Sequencing,
Coverage, DifferentialMethylation, GenomicVariation,
StructuralVariation, CopyNumberVariation
Author: Hua Zhong, Mingzhou Song
Maintainer: Hua Zhong<zh9118@gmail.com>, Mingzhou Song
<joemsong@cs.nmsu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GenomicOZone
git_branch: RELEASE_3_13
git_last_commit: a1e58fc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GenomicOZone_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomicOZone_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomicOZone_1.6.0.tgz
vignettes: vignettes/GenomicOZone/inst/doc/GenomicOZone.html
vignetteTitles: GenomicOZone
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicOZone/inst/doc/GenomicOZone.R
dependencyCount: 157
Package: GenomicRanges
Version: 1.44.0
Depends: R (>= 4.0.0), methods, stats4, BiocGenerics (>= 0.37.0),
S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>=
1.15.2)
Imports: utils, stats, XVector (>= 0.29.2)
LinkingTo: S4Vectors, IRanges
Suggests: Matrix, Biobase, AnnotationDbi, annotate, Biostrings (>=
2.25.3), SummarizedExperiment (>= 0.1.5), Rsamtools (>=
1.13.53), GenomicAlignments, rtracklayer, BSgenome,
GenomicFeatures, Gviz, VariantAnnotation, AnnotationHub,
DESeq2, DEXSeq, edgeR, KEGGgraph, RNAseqData.HNRNPC.bam.chr14,
pasillaBamSubset, KEGGREST, hgu95av2.db, hgu95av2probe,
BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm10,
TxDb.Athaliana.BioMart.plantsmart22,
TxDb.Dmelanogaster.UCSC.dm3.ensGene,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene, RUnit, digest, knitr,
rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 83b2127e657caef536d509904470cce6
NeedsCompilation: yes
Title: Representation and manipulation of genomic intervals
Description: The ability to efficiently represent and manipulate
genomic annotations and alignments is playing a central role
when it comes to analyzing high-throughput sequencing data
(a.k.a. NGS data). The GenomicRanges package defines general
purpose containers for storing and manipulating genomic
intervals and variables defined along a genome. More
specialized containers for representing and manipulating short
alignments against a reference genome, or a matrix-like
summarization of an experiment, are defined in the
GenomicAlignments and SummarizedExperiment packages,
respectively. Both packages build on top of the GenomicRanges
infrastructure.
biocViews: Genetics, Infrastructure, DataRepresentation, Sequencing,
Annotation, GenomeAnnotation, Coverage
Author: P. Aboyoun, H. Pagès, and M. Lawrence
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/GenomicRanges
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/GenomicRanges/issues
git_url: https://git.bioconductor.org/packages/GenomicRanges
git_branch: RELEASE_3_13
git_last_commit: d27fdc8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GenomicRanges_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomicRanges_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomicRanges_1.44.0.tgz
vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf,
vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf,
vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf,
vignettes/GenomicRanges/inst/doc/Ten_things_slides.pdf,
vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html
vignetteTitles: 5. Extending GenomicRanges, 2. GenomicRanges HOWTOs, 3.
A quick introduction to GRanges and GRangesList objects
(slides), 4. Ten Things You Didn't Know (slides from BioC
2016), 1. An Introduction to the GenomicRanges Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.R,
vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.R,
vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.R,
vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.R,
vignettes/GenomicRanges/inst/doc/Ten_things_slides.R
dependsOnMe: AllelicImbalance, AneuFinder, annmap, AnnotationHubData,
BaalChIP, Basic4Cseq, baySeq, biomvRCNS, BiSeq, bnbc, BPRMeth,
breakpointR, BSgenome, bsseq, BubbleTree, bumphunter, CAFE,
CAGEfightR, casper, chimeraviz, ChIPpeakAnno, ChIPQC, chipseq,
chromPlot, chromstaR, chromswitch, CINdex, cn.mops, cnvGSA,
CNVPanelizer, CNVRanger, COCOA, compEpiTools, consensusSeekeR,
CSAR, csaw, CSSQ, deepSNV, DEScan2, DESeq2, DEXSeq, DiffBind,
diffHic, DMCFB, DMCHMM, DMRcaller, DMRforPairs, DNAshapeR,
EnrichedHeatmap, ensembldb, ensemblVEP, epigenomix, epihet,
esATAC, ExCluster, exomeCopy, fastseg, fCCAC, FunChIP,
GeneBreak, GenomicAlignments, GenomicDistributions,
GenomicFeatures, GenomicFiles, GenomicOZone, GenomicScores,
GenomicTuples, gmapR, gmoviz, GOTHiC, GreyListChIP, groHMM,
gtrellis, GUIDEseq, Guitar, Gviz, HelloRanges, hiAnnotator,
HiTC, IdeoViz, igvR, InPAS, InTAD, intansv, InteractionSet,
IntEREst, IWTomics, karyoploteR, maser, MBASED, Melissa,
metagene, metagene2, methimpute, methyAnalysis, methylKit,
methylPipe, minfi, MotifDb, msgbsR, MutationalPatterns,
NADfinder, ORFik, periodicDNA, plyranges, podkat, QuasR,
r3Cseq, RaggedExperiment, ramr, Rcade, recoup, regioneR,
RepViz, rfPred, rGREAT, riboSeqR, ribosomeProfilingQC,
RJMCMCNucleosomes, RNAmodR, RnBeads, Rsamtools, RSVSim,
rtracklayer, Scale4C, SCOPE, segmentSeq, seqbias, seqCAT,
SeqGate, SGSeq, SICtools, SigFuge, SMITE, SNPhood,
SomaticSignatures, StructuralVariantAnnotation,
SummarizedExperiment, TarSeqQC, TnT, trackViewer, TransView,
tRNA, tRNAdbImport, tRNAscanImport, VanillaICE, VarCon,
VariantAnnotation, VariantExperiment, VariantTools, VplotR,
vtpnet, vulcan, wavClusteR, YAPSA, EuPathDB,
SNPlocs.Hsapiens.dbSNP.20101109,
SNPlocs.Hsapiens.dbSNP.20120608,
SNPlocs.Hsapiens.dbSNP141.GRCh38,
SNPlocs.Hsapiens.dbSNP142.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, ChAMPdata,
EatonEtAlChIPseq, RnBeads.hg19, RnBeads.hg38, RnBeads.mm10,
RnBeads.mm9, RnBeads.rn5, SCATEData, WGSmapp, liftOver,
sequencing, HiCfeat, PlasmaMutationDetector, RTIGER
importsMe: ACE, ALDEx2, alpine, ALPS, amplican, AnnotationFilter,
annotatr, APAlyzer, apeglm, appreci8R, ArrayExpressHTS,
ASpediaFI, ASpli, AssessORF, ATACseqQC, BadRegionFinder,
ballgown, bambu, bamsignals, BBCAnalyzer, beadarray, BEAT,
BiFET, BiocOncoTK, BioTIP, biovizBase, biscuiteer, BiSeq,
brainflowprobes, branchpointer, BRGenomics, BSgenome, BUSpaRse,
CAGEr, cBioPortalData, ChIC, chipenrich, ChIPexoQual,
ChIPseeker, chipseq, ChIPseqR, chromDraw, ChromHeatMap,
ChromSCape, chromVAR, cicero, circRNAprofiler, cleanUpdTSeq,
CNEr, CNVfilteR, CNViz, coMET, compartmap, contiBAIT, conumee,
copynumber, CopyNumberPlots, CopywriteR, CoverageView,
crisprseekplus, CrispRVariants, customProDB, DAMEfinder,
dasper, debrowser, decompTumor2Sig, DeepBlueR, DEFormats,
DegNorm, deltaCaptureC, derfinder, derfinderPlot, DEWSeq,
diffloop, diffUTR, DMRcate, dmrseq, DominoEffect, DRIMSeq,
easyRNASeq, EDASeq, eisaR, ELMER, enrichTF, epialleleR,
epidecodeR, epigraHMM, EpiTxDb, epivizr, epivizrData, erma,
EventPointer, fcScan, FilterFFPE, FRASER, GA4GHclient, gcapc,
genbankr, geneAttribution, GeneGeneInteR, GENESIS, GenoGAM,
genomation, genomeIntervals, GenomicAlignments,
GenomicDataCommons, GenomicInteractions, genotypeeval, GenVisR,
ggbio, GOfuncR, gpart, gwascat, h5vc, heatmaps, HiCBricks,
HiCcompare, HilbertCurve, HiLDA, hiReadsProcessor, HTSeqGenie,
hummingbird, icetea, ideal, idr2d, IMAS, INSPEcT, InterMineR,
ipdDb, IsoformSwitchAnalyzeR, isomiRs, iteremoval, IVAS,
karyoploteR, loci2path, LOLA, LoomExperiment, lumi, MACPET,
MADSEQ, mCSEA, MDTS, MEAL, MEDIPS, megadepth, memes, metaseqR2,
MethCP, methInheritSim, MethReg, methrix, methyAnalysis,
methylCC, methylInheritance, MethylSeekR, methylSig, methylumi,
MinimumDistance, MIRA, missMethyl, MMAPPR2, MMDiff2,
Modstrings, mosaics, motifbreakR, motifmatchr,
MultiAssayExperiment, multicrispr, MultiDataSet,
multiHiCcompare, MungeSumstats, musicatk, NanoMethViz,
ncRNAtools, nearBynding, normr, nucleR, oligoClasses, OmaDB,
openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, packFinder,
pageRank, panelcn.mops, PAST, pcaExplorer, pepStat, PhIPData,
Pi, PICS, PING, pqsfinder, pram, prebs, preciseTAD,
PrecisionTrialDrawer, primirTSS, proActiv, proBAMr,
profileplyr, PureCN, Pviz, pwOmics, QDNAseq, qpgraph, qsea,
Qtlizer, R3CPET, R453Plus1Toolbox, RareVariantVis, RCAS,
RcisTarget, recount, recount3, regioneR, regionReport,
regutools, REMP, Repitools, rGADEM, RGMQL, Rhisat2, RiboDiPA,
RiboProfiling, RIPAT, Rmmquant, rnaEditr,
RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar,
RTCGAToolbox, SCATE, scmeth, scoreInvHap, scPipe, scruff,
scuttle, seq2pathway, SeqArray, seqPattern, seqsetvis, SeqSQC,
SeqVarTools, sesame, sevenC, shinyepico, ShortRead, signeR,
SigsPack, SimFFPE, SingleCellExperiment,
SingleMoleculeFootprinting, sitadela, snapcount, soGGi,
SparseSignatures, SpectralTAD, SplicingGraphs, SPLINTER,
srnadiff, STAN, strandCheckR, systemPipeR, TAPseq, target,
TCGAbiolinks, TCGAutils, TCseq, TFARM, TFBSTools, TFEA.ChIP,
TFHAZ, TitanCNA, tLOH, tracktables, transcriptR, transite,
trena, tricycle, triplex, tscR, TSRchitect, TVTB, tximeta,
Ularcirc, UMI4Cats, uncoverappLib, Uniquorn, VariantFiltering,
VaSP, VCFArray, wiggleplotr, XNAString, fitCons.UCSC.hg19,
MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5,
MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5,
MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5,
MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5,
MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5,
MafDb.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38,
MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19,
MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38,
MafH5.gnomAD.v3.1.1.GRCh38, phastCons100way.UCSC.hg19,
phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38,
SNPlocs.Hsapiens.dbSNP.20101109,
SNPlocs.Hsapiens.dbSNP.20120608,
SNPlocs.Hsapiens.dbSNP141.GRCh38,
SNPlocs.Hsapiens.dbSNP142.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP151.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP141.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data,
COSMIC.67, ELMER.data, GenomicDistributionsData, leeBamViews,
MethylSeqData, pepDat, scRNAseq, SomaticCancerAlterations,
systemPipeRdata, VariantToolsData, recountWorkflow,
TCGAWorkflow, ActiveDriverWGS, BinQuasi, cinaR, crispRdesignR,
DGEobj, driveR, ExomeDepth, geno2proteo, hoardeR, ICAMS,
intePareto, LoopRig, MAAPER, noisyr, oncoPredict, PACVr, pagoo,
RapidoPGS, RAPIDR, Signac, simMP, utr.annotation, VALERIE
suggestsMe: AnnotationHub, autonomics, biobroom, BiocGenerics,
BiocParallel, Chicago, CNVgears, ComplexHeatmap, cummeRbund,
epivizrChart, GenomeInfoDb, Glimma, GSReg, GWASTools,
HDF5Array, InteractiveComplexHeatmap, interactiveDisplay,
IRanges, maftools, MiRaGE, omicsPrint, parglms,
recountmethylation, RTCGA, S4Vectors, SeqGSEA, splatter,
TFutils, universalmotif, alternativeSplicingEvents.hg19,
alternativeSplicingEvents.hg38, GenomicState,
BeadArrayUseCases, GeuvadisTranscriptExpr, nanotubes,
RNAmodR.Data, sesameData, Single.mTEC.Transcriptomes,
CAGEWorkflow, cancerTiming, chicane, gkmSVM, LDheatmap,
polyRAD, rliger, seqmagick, Seurat, sigminer, valr
dependencyCount: 16
Package: GenomicScores
Version: 2.4.0
Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges, methods,
BiocGenerics (>= 0.13.8)
Imports: stats, utils, XML, Biobase, BiocManager, BiocFileCache,
IRanges (>= 2.3.23), Biostrings, GenomeInfoDb, AnnotationHub,
rhdf5, DelayedArray, HDF5Array
Suggests: RUnit, BiocStyle, knitr, rmarkdown,
BSgenome.Hsapiens.UCSC.hg19, phastCons100way.UCSC.hg19,
MafDb.1Kgenomes.phase1.hs37d5,
SNPlocs.Hsapiens.dbSNP144.GRCh37, VariantAnnotation,
TxDb.Hsapiens.UCSC.hg19.knownGene, gwascat, RColorBrewer,
shiny, shinyjs, shinycustomloader, data.table, DT, magrittr,
shinydashboard
License: Artistic-2.0
MD5sum: 8a95a891e0b7f595aebe6f2cc452906a
NeedsCompilation: no
Title: Infrastructure to work with genomewide position-specific scores
Description: Provide infrastructure to store and access genomewide
position-specific scores within R and Bioconductor.
biocViews: Infrastructure, Genetics, Annotation, Sequencing, Coverage,
AnnotationHubSoftware
Author: Robert Castelo [aut, cre], Pau Puigdevall [ctb], Pablo
RodrÃguez [ctb]
Maintainer: Robert Castelo <robert.castelo@upf.edu>
URL: https://github.com/rcastelo/GenomicScores
VignetteBuilder: knitr
BugReports: https://github.com/rcastelo/GenomicScores/issues
git_url: https://git.bioconductor.org/packages/GenomicScores
git_branch: RELEASE_3_13
git_last_commit: 4b46561
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GenomicScores_2.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomicScores_2.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomicScores_2.4.0.tgz
vignettes: vignettes/GenomicScores/inst/doc/GenomicScores.html
vignetteTitles: An introduction to the GenomicScores package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicScores/inst/doc/GenomicScores.R
dependsOnMe: fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38,
MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38,
MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38,
MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38,
MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38,
MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomAD.r3.0.GRCh38,
MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5,
MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38,
MafH5.gnomAD.r3.0.GRCh38, MafH5.gnomAD.v3.1.1.GRCh38,
phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38,
phastCons30way.UCSC.hg38, phastCons7way.UCSC.hg38
importsMe: appreci8R, ATACseqQC, primirTSS, RareVariantVis,
VariantFiltering
suggestsMe: methrix
dependencyCount: 99
Package: GenomicSuperSignature
Version: 1.0.1
Depends: R (>= 4.0), SummarizedExperiment
Imports: ComplexHeatmap, ggplot2, methods, S4Vectors, Biobase, ggpubr,
dplyr, plotly, BiocFileCache, grid, flextable
Suggests: knitr, rmarkdown, devtools, roxygen2, pkgdown, usethis,
BiocStyle, testthat, forcats, stats, wordcloud, circlize,
EnrichmentBrowser, clusterProfiler, msigdbr, cluster,
RColorBrewer, reshape2, tibble, BiocManager, bcellViper, readr,
utils
License: Artistic-2.0
MD5sum: 56b9c9c0590fe04e944998875595f532
NeedsCompilation: no
Title: Interpretation of RNA-seq experiments through robust, efficient
comparison to public databases
Description: This package contains the index, which is the Replicable
and interpretable Axes of Variation (RAV) extracted from public
RNA sequencing datasets by clustering and averaging top PCs.
This index, named as RAVindex, is further annotated with MeSH
terms and GSEA. Functions to connect PCs from new datasets to
RAVs, extract interpretable annotations, and provide intuitive
visualization, are implemented in this package. Overall, this
package enables researchers to analyze new data in the context
of existing databases with minimal computing resources.
biocViews: Transcriptomics, SystemsBiology, PrincipalComponent, RNASeq,
Sequencing, Pathways, Clustering
Author: Sehyun Oh [aut, cre], Levi Waldron [aut], Sean Davis [aut]
Maintainer: Sehyun Oh <shbrief@gmail.com>
URL: https://github.com/shbrief/GenomicSuperSignature
VignetteBuilder: knitr
BugReports: https://github.com/shbrief/GenomicSuperSignature/issues
git_url: https://git.bioconductor.org/packages/GenomicSuperSignature
git_branch: RELEASE_3_13
git_last_commit: 56891d8
git_last_commit_date: 2021-05-27
Date/Publication: 2021-05-27
source.ver: src/contrib/GenomicSuperSignature_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomicSuperSignature_1.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomicSuperSignature_1.0.1.tgz
vignettes:
vignettes/GenomicSuperSignature/inst/doc/GenomicSuperSignature_Contents.html,
vignettes/GenomicSuperSignature/inst/doc/Quickstart.html
vignetteTitles: Introduction on RAVmodel, Quickstart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/GenomicSuperSignature/inst/doc/GenomicSuperSignature_Contents.R,
vignettes/GenomicSuperSignature/inst/doc/Quickstart.R
dependencyCount: 168
Package: GenomicTuples
Version: 1.26.0
Depends: R (>= 4.0), GenomicRanges (>= 1.37.4), GenomeInfoDb (>=
1.15.2), S4Vectors (>= 0.17.25)
Imports: methods, BiocGenerics (>= 0.21.2), Rcpp (>= 0.11.2), IRanges
(>= 2.19.13), data.table, stats4, stats, utils
LinkingTo: Rcpp
Suggests: testthat, knitr, BiocStyle, rmarkdown
License: Artistic-2.0
MD5sum: 3cae20797e55517ad4131529d965d34b
NeedsCompilation: yes
Title: Representation and Manipulation of Genomic Tuples
Description: GenomicTuples defines general purpose containers for
storing genomic tuples. It aims to provide functionality for
tuples of genomic co-ordinates that are analogous to those
available for genomic ranges in the GenomicRanges Bioconductor
package.
biocViews: Infrastructure, DataRepresentation, Sequencing
Author: Peter Hickey [aut, cre], Marcin Cieslik [ctb], Hervé Pagès
[ctb]
Maintainer: Peter Hickey <peter.hickey@gmail.com>
URL: www.github.com/PeteHaitch/GenomicTuples
VignetteBuilder: knitr
BugReports: https://github.com/PeteHaitch/GenomicTuples/issues
git_url: https://git.bioconductor.org/packages/GenomicTuples
git_branch: RELEASE_3_13
git_last_commit: c82b526
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GenomicTuples_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenomicTuples_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenomicTuples_1.26.0.tgz
vignettes:
vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html
vignetteTitles: GenomicTuplesIntroduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R
dependencyCount: 19
Package: genotypeeval
Version: 1.24.0
Depends: R (>= 3.4.0), VariantAnnotation
Imports: ggplot2, rtracklayer, BiocGenerics, GenomicRanges,
GenomeInfoDb, IRanges, methods, BiocParallel, graphics, stats
Suggests: rmarkdown, testthat, SNPlocs.Hsapiens.dbSNP141.GRCh38,
TxDb.Hsapiens.UCSC.hg38.knownGene
License: file LICENSE
Archs: i386, x64
MD5sum: 7416170799a7ed18d8cc7ad60d887bff
NeedsCompilation: no
Title: QA/QC of a gVCF or VCF file
Description: Takes in a gVCF or VCF and reports metrics to assess
quality of calls.
biocViews: Genetics, BatchEffect, Sequencing, SNP, VariantAnnotation,
DataImport
Author: Jennifer Tom [aut, cre]
Maintainer: Jennifer Tom <tom.jennifer@gene.com>
VignetteBuilder: rmarkdown
git_url: https://git.bioconductor.org/packages/genotypeeval
git_branch: RELEASE_3_13
git_last_commit: ddb6801
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/genotypeeval_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/genotypeeval_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/genotypeeval_1.24.0.tgz
vignettes: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.html
vignetteTitles: genotypeeval_vignette.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependencyCount: 112
Package: genphen
Version: 1.20.0
Depends: R (>= 3.5.0), Rcpp (>= 0.12.17), methods, stats, graphics
Imports: rstan (>= 2.17.3), ranger, parallel, foreach, doParallel,
e1071, Biostrings, rPref
Suggests: testthat, ggplot2, gridExtra, ape, ggrepel, knitr, reshape,
xtable
License: GPL (>= 2)
MD5sum: bf07f905cfe6b5d6240a063b1e4b6f9a
NeedsCompilation: no
Title: A tool for quantification of associations between genotypes and
phenotypes in genome wide association studies (GWAS) with
Bayesian inference and statistical learning
Description: Genetic association studies are an essential tool for
studying the relationship between genotypes and phenotypes.
With genphen we can jointly study multiple phenotypes of
different types, by quantifying the association between
different genotypes and each phenotype using a hybrid method
which uses statistical learning techniques such as random
forest and support vector machines, and Bayesian inference
using hierarchical models.
biocViews: GenomeWideAssociation, Regression, Classification,
SupportVectorMachine, Genetics, SequenceMatching, Bayesian,
FeatureExtraction, Sequencing
Author: Simo Kitanovski [aut, cre]
Maintainer: Simo Kitanovski <simo.kitanovski@uni-due.de>
BugReports: https://github.com/snaketron/genphen/issues
git_url: https://git.bioconductor.org/packages/genphen
git_branch: RELEASE_3_13
git_last_commit: 33793d8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/genphen_1.20.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/genphen_1.20.0.tgz
vignettes: vignettes/genphen/inst/doc/genphenManual.pdf
vignetteTitles: genphen overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genphen/inst/doc/genphenManual.R
dependencyCount: 88
Package: GenVisR
Version: 1.24.0
Depends: R (>= 3.3.0), methods
Imports: AnnotationDbi, biomaRt (>= 2.45.8), BiocGenerics, Biostrings,
DBI, FField, GenomicFeatures, GenomicRanges (>= 1.25.4),
ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0), gtable, gtools,
IRanges (>= 2.7.5), plyr (>= 1.8.3), reshape2, Rsamtools,
scales, viridis, data.table, BSgenome, GenomeInfoDb,
VariantAnnotation
Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL,
roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene,
rmarkdown, vdiffr, formatR, TxDb.Hsapiens.UCSC.hg38.knownGene,
BSgenome.Hsapiens.UCSC.hg38
License: GPL-3 + file LICENSE
MD5sum: 6107719fa59f2b1631511b0791b2cbee
NeedsCompilation: no
Title: Genomic Visualizations in R
Description: Produce highly customizable publication quality graphics
for genomic data primarily at the cohort level.
biocViews: Infrastructure, DataRepresentation, Classification, DNASeq
Author: Zachary Skidmore [aut, cre], Alex Wagner [aut], Robert Lesurf
[aut], Katie Campbell [aut], Jason Kunisaki [aut], Obi Griffith
[aut], Malachi Griffith [aut]
Maintainer: Zachary Skidmore <zlskidmore@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/griffithlab/GenVisR/issues
git_url: https://git.bioconductor.org/packages/GenVisR
git_branch: RELEASE_3_13
git_last_commit: 3b6abcf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GenVisR_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GenVisR_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GenVisR_1.24.0.tgz
vignettes: vignettes/GenVisR/inst/doc/Intro.html,
vignettes/GenVisR/inst/doc/Upcoming_Features.html,
vignettes/GenVisR/inst/doc/waterfall_introduction.html
vignetteTitles: GenVisR: An introduction, Visualizing Small Variants,
waterfall: function introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GenVisR/inst/doc/Intro.R,
vignettes/GenVisR/inst/doc/Upcoming_Features.R,
vignettes/GenVisR/inst/doc/waterfall_introduction.R
dependencyCount: 119
Package: GEOfastq
Version: 1.0.0
Imports: xml2, rvest, stringr, RCurl, doParallel, foreach, plyr
Suggests: BiocCheck, roxygen2, knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: c2e4b95297e2d0ac89118fef8e1f8551
NeedsCompilation: no
Title: Downloads ENA Fastqs With GEO Accessions
Description: GEOfastq is used to download fastq files from the European
Nucleotide Archive (ENA) starting with an accession from the
Gene Expression Omnibus (GEO). To do this, sample metadata is
retrieved from GEO and the Sequence Read Archive (SRA). SRA run
accessions are then used to construct FTP and aspera download
links for fastq files generated by the ENA.
biocViews: RNASeq, DataImport
Author: Alex Pickering [cre, aut]
(<https://orcid.org/0000-0002-0002-6759>)
Maintainer: Alex Pickering <alexvpickering@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/alexvpickering/GEOfastq/issues
git_url: https://git.bioconductor.org/packages/GEOfastq
git_branch: RELEASE_3_13
git_last_commit: 8f1f5a2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GEOfastq_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GEOfastq_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GEOfastq_1.0.0.tgz
vignettes: vignettes/GEOfastq/inst/doc/GEOfastq.html
vignetteTitles: Using the GEOfastq Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GEOfastq/inst/doc/GEOfastq.R
dependencyCount: 40
Package: GEOmetadb
Version: 1.54.0
Depends: GEOquery,RSQLite
Suggests: knitr, rmarkdown, dplyr, tm, wordcloud
License: Artistic-2.0
Archs: i386, x64
MD5sum: 5b86974bae4844367b4516b20fb0b405
NeedsCompilation: no
Title: A compilation of metadata from NCBI GEO
Description: The NCBI Gene Expression Omnibus (GEO) represents the
largest public repository of microarray data. However, finding
data of interest can be challenging using current tools.
GEOmetadb is an attempt to make access to the metadata
associated with samples, platforms, and datasets much more
feasible. This is accomplished by parsing all the NCBI GEO
metadata into a SQLite database that can be stored and queried
locally. GEOmetadb is simply a thin wrapper around the SQLite
database along with associated documentation. Finally, the
SQLite database is updated regularly as new data is added to
GEO and can be downloaded at will for the most up-to-date
metadata. GEOmetadb paper:
http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798
.
biocViews: Infrastructure
Author: Jack Zhu and Sean Davis
Maintainer: Jack Zhu <zhujack@mail.nih.gov>
URL: http://gbnci.abcc.ncifcrf.gov/geo/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GEOmetadb
git_branch: RELEASE_3_13
git_last_commit: db8e760
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GEOmetadb_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GEOmetadb_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GEOmetadb_1.54.0.tgz
vignettes: vignettes/GEOmetadb/inst/doc/GEOmetadb.html
vignetteTitles: GEOmetadb
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R
importsMe: MetaIntegrator
suggestsMe: antiProfilesData, maGUI
dependencyCount: 57
Package: GeomxTools
Version: 1.0.0
Depends: R (>= 3.6), NanoStringNCTools
Imports: Biobase, S4Vectors, rjson, readxl, EnvStats, reshape2,
methods, utils, stats, BiocGenerics
Suggests: knitr
License: Artistic-2.0
MD5sum: 051b01b21b078ecd52e8704e9ae79dc8
NeedsCompilation: no
Title: NanoString GeoMx Tools
Description: Tools for NanoString Technologies GeoMx Technology.
Package provides functions for reading in DCC and PKC files
based on an ExpressionSet derived object. Normalization and QC
functions are also included.
biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport,
Transcriptomics, Proteomics, mRNAMicroarray,
ProprietaryPlatforms, RNASeq, Sequencing, ExperimentalDesign,
Normalization
Author: Nicole Ortogero [cre, aut], Zhi Yang [aut]
Maintainer: Nicole Ortogero <nortogero@nanostring.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GeomxTools
git_branch: RELEASE_3_13
git_last_commit: 2462584
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GeomxTools_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GeomxTools_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GeomxTools_1.0.0.tgz
vignettes: vignettes/GeomxTools/inst/doc/Introduction.html
vignetteTitles: Introduction to the NanoStringGeomxSet Class
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeomxTools/inst/doc/Introduction.R
dependencyCount: 83
Package: GEOquery
Version: 2.60.0
Depends: methods, Biobase
Imports: httr, readr (>= 1.3.1), xml2, dplyr, tidyr, magrittr, limma
Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr
License: GPL-2
MD5sum: 461364700733ca25ce233a8df5dce4c5
NeedsCompilation: no
Title: Get data from NCBI Gene Expression Omnibus (GEO)
Description: The NCBI Gene Expression Omnibus (GEO) is a public
repository of microarray data. Given the rich and varied
nature of this resource, it is only natural to want to apply
BioConductor tools to these data. GEOquery is the bridge
between GEO and BioConductor.
biocViews: Microarray, DataImport, OneChannel, TwoChannel, SAGE
Author: Sean Davis <sdavis2@mail.nih.gov>
Maintainer: Sean Davis <sdavis2@mail.nih.gov>
URL: https://github.com/seandavi/GEOquery
VignetteBuilder: knitr
BugReports: https://github.com/seandavi/GEOquery/issues/new
git_url: https://git.bioconductor.org/packages/GEOquery
git_branch: RELEASE_3_13
git_last_commit: 028b84d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GEOquery_2.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GEOquery_2.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GEOquery_2.60.0.tgz
vignettes: vignettes/GEOquery/inst/doc/GEOquery.html
vignetteTitles: Using GEOquery
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R
dependsOnMe: DrugVsDisease, SCAN.UPC, dyebiasexamples, GSE13015,
GSE62944
importsMe: bigmelon, ChIPXpress, coexnet, conclus, crossmeta, DExMA,
EGAD, GAPGOM, MACPET, minfi, MoonlightR, phantasus, recount,
SRAdb, BeadArrayUseCases, GSE13015, geneExpressionFromGEO,
MetaIntegrator
suggestsMe: AUCell, autonomics, ctsGE, dearseq, debCAM, diffcoexp,
dyebias, EpiDISH, fgsea, GCSscore, GeneExpressionSignature,
GenomicOZone, multiClust, MultiDataSet, omicsPrint, PCAtools,
quantiseqr, RegEnrich, RGSEA, Rnits, runibic, skewr,
spatialHeatmap, TargetScore, zFPKM, airway, antiProfilesData,
muscData, parathyroidSE, prostateCancerCamcap,
prostateCancerGrasso, prostateCancerStockholm,
prostateCancerTaylor, prostateCancerVarambally, RegParallel,
AnnoProbe, BED, maGUI, metaMA, MLML2R, NACHO, TcGSA, tinyarray
dependencyCount: 48
Package: GEOsubmission
Version: 1.44.0
Imports: affy, Biobase, utils
License: GPL (>= 2)
MD5sum: 932c3ad78f0555729d76ebca99f3f774
NeedsCompilation: no
Title: Prepares microarray data for submission to GEO
Description: Helps to easily submit a microarray dataset and the
associated sample information to GEO by preparing a single file
for upload (direct deposit).
biocViews: Microarray
Author: Alexandre Kuhn <alexandre.m.kuhn@gmail.com>
Maintainer: Alexandre Kuhn <alexandre.m.kuhn@gmail.com>
git_url: https://git.bioconductor.org/packages/GEOsubmission
git_branch: RELEASE_3_13
git_last_commit: 13a400d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GEOsubmission_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GEOsubmission_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GEOsubmission_1.44.0.tgz
vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf
vignetteTitles: GEOsubmission Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GEOsubmission/inst/doc/GEOsubmission.R
dependencyCount: 13
Package: gep2pep
Version: 1.12.0
Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods,
Biobase, XML, rhdf5, digest, iterators
Suggests: WriteXLS, testthat, knitr, rmarkdown
License: GPL-3
MD5sum: adc093b16246abddcd4ed0e92ca96d64
NeedsCompilation: no
Title: Creation and Analysis of Pathway Expression Profiles (PEPs)
Description: Pathway Expression Profiles (PEPs) are based on the
expression of pathways (defined as sets of genes) as opposed to
individual genes. This package converts gene expression
profiles to PEPs and performs enrichment analysis of both
pathways and experimental conditions, such as "drug set
enrichment analysis" and "gene2drug" drug discovery analysis
respectively.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
DimensionReduction, Pathways, GO
Author: Francesco Napolitano <franapoli@gmail.com>
Maintainer: Francesco Napolitano <franapoli@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gep2pep
git_branch: RELEASE_3_13
git_last_commit: c8755ce
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gep2pep_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gep2pep_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gep2pep_1.12.0.tgz
vignettes: vignettes/gep2pep/inst/doc/vignette.html
vignetteTitles: Introduction to gep2pep
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gep2pep/inst/doc/vignette.R
dependencyCount: 59
Package: gespeR
Version: 1.24.0
Depends: methods, graphics, ggplot2, R(>= 2.10)
Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel,
parallel, foreach, reshape2, dplyr
Suggests: knitr
License: GPL-3
MD5sum: 6216e693b372313a4b4a51194cb738fd
NeedsCompilation: no
Title: Gene-Specific Phenotype EstimatoR
Description: Estimates gene-specific phenotypes from off-target
confounded RNAi screens. The phenotype of each siRNA is modeled
based on on-targeted and off-targeted genes, using a
regularized linear regression model.
biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, GeneTarget,
Regression, Visualization
Author: Fabian Schmich
Maintainer: Fabian Schmich <fabian.schmich@bsse.ethz.ch>
URL: http://www.cbg.ethz.ch/software/gespeR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gespeR
git_branch: RELEASE_3_13
git_last_commit: 59abb7c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gespeR_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gespeR_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gespeR_1.24.0.tgz
vignettes: vignettes/gespeR/inst/doc/gespeR.pdf
vignetteTitles: An R package for deconvoluting off-target confounded
RNAi screens
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gespeR/inst/doc/gespeR.R
dependencyCount: 116
Package: getDEE2
Version: 1.2.0
Depends: R (>= 4.0)
Imports: stats, utils, SummarizedExperiment, htm2txt
Suggests: knitr, testthat
License: GPL-3
MD5sum: 14e01f4f1e81fc59ae5e3dafe3b4d3d4
NeedsCompilation: no
Title: Programmatic access to the DEE2 RNA expression dataset
Description: Digital Expression Explorer 2 (or DEE2 for short) is a
repository of processed RNA-seq data in the form of counts. It
was designed so that researchers could undertake re-analysis
and meta-analysis of published RNA-seq studies quickly and
easily. As of April 2020, over 1 million SRA datasets have been
processed. This package provides an R interface to access these
expression data. More information about the DEE2 project can be
found at the project homepage (http://dee2.io) and main
publication (https://doi.org/10.1093/gigascience/giz022).
biocViews: GeneExpression, Transcriptomics, Sequencing
Author: Mark Ziemann [aut, cre], Antony Kaspi [aut]
Maintainer: Mark Ziemann <mark.ziemann@gmail.com>
URL: https://github.com/markziemann/getDEE2
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/getDEE2
git_branch: RELEASE_3_13
git_last_commit: 2536232
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/getDEE2_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/getDEE2_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/getDEE2_1.2.0.tgz
vignettes: vignettes/getDEE2/inst/doc/getDEE2.html
vignetteTitles: getDEE2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/getDEE2/inst/doc/getDEE2.R
dependencyCount: 27
Package: geva
Version: 1.0.0
Depends: R (>= 4.1)
Imports: grDevices, graphics, methods, stats, utils, dbscan,
fastcluster, matrixStats
Suggests: devtools, knitr, rmarkdown, roxygen2, limma, topGO, testthat
(>= 3.0.0)
License: LGPL-3
MD5sum: d9dfe0e9a52b3ae1659fb0156e18685c
NeedsCompilation: no
Title: Gene Expression Variation Analysis (GEVA)
Description: Statistic methods to evaluate variations of differential
expression (DE) between multiple biological conditions. It
takes into account the fold-changes and p-values from previous
differential expression (DE) results that use large-scale data
(*e.g.*, microarray and RNA-seq) and evaluates which genes
would react in response to the distinct experiments. This
evaluation involves an unique pipeline of statistical methods,
including weighted summarization, quantile detection, cluster
analysis, and ANOVA tests, in order to classify a subset of
relevant genes whose DE is similar or dependent to certain
biological factors.
biocViews: Classification, DifferentialExpression, GeneExpression,
Microarray, MultipleComparison, RNASeq, SystemsBiology,
Transcriptomics
Author: Itamar José Guimarães Nunes [aut, cre]
(<https://orcid.org/0000-0002-6246-4658>), Murilo Zanini David
[ctb], Bruno César Feltes [ctb]
(<https://orcid.org/0000-0002-2825-8295>), Marcio Dorn [ctb]
(<https://orcid.org/0000-0001-8534-3480>)
Maintainer: Itamar José Guimarães Nunes <nunesijg@gmail.com>
URL: https://github.com/sbcblab/geva
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/geva
git_branch: RELEASE_3_13
git_last_commit: 7da51a4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/geva_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/geva_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/geva_1.0.0.tgz
vignettes: vignettes/geva/inst/doc/geva.pdf
vignetteTitles: GEVA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geva/inst/doc/geva.R
dependencyCount: 9
Package: GEWIST
Version: 1.36.0
Depends: R (>= 2.10), car
License: GPL-2
MD5sum: 916f322c43b76fbbeec46b759eb7e22d
NeedsCompilation: no
Title: Gene Environment Wide Interaction Search Threshold
Description: This 'GEWIST' package provides statistical tools to
efficiently optimize SNP prioritization for gene-gene and
gene-environment interactions.
biocViews: MultipleComparison, Genetics
Author: Wei Q. Deng, Guillaume Pare
Maintainer: Wei Q. Deng <dengwq@mcmaster.ca>
git_url: https://git.bioconductor.org/packages/GEWIST
git_branch: RELEASE_3_13
git_last_commit: 7171d51
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GEWIST_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GEWIST_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GEWIST_1.36.0.tgz
vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf
vignetteTitles: GEWIST.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GEWIST/inst/doc/GEWIST.R
dependencyCount: 84
Package: ggbio
Version: 1.40.0
Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0)
Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales,
reshape2, gtable, Hmisc, biovizBase (>= 1.29.2), Biobase,
S4Vectors (>= 0.13.13), IRanges (>= 2.11.16), GenomeInfoDb (>=
1.1.3), GenomicRanges (>= 1.29.14), SummarizedExperiment,
Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>=
1.1.16), BSgenome, VariantAnnotation (>= 1.11.4), rtracklayer
(>= 1.25.16), GenomicFeatures (>= 1.29.11), OrganismDbi,
GGally, ensembldb (>= 1.99.13), AnnotationDbi,
AnnotationFilter, rlang
Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat,
EnsDb.Hsapiens.v75, tinytex
License: Artistic-2.0
MD5sum: 6bfe0650246f6202b346dab2235c17b3
NeedsCompilation: no
Title: Visualization tools for genomic data
Description: The ggbio package extends and specializes the grammar of
graphics for biological data. The graphics are designed to
answer common scientific questions, in particular those often
asked of high throughput genomics data. All core Bioconductor
data structures are supported, where appropriate. The package
supports detailed views of particular genomic regions, as well
as genome-wide overviews. Supported overviews include ideograms
and grand linear views. High-level plots include sequence
fragment length, edge-linked interval to data view, mismatch
pileup, and several splicing summaries.
biocViews: Infrastructure, Visualization
Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne
Cook [aut, ths], Johannes Rainer [ctb]
Maintainer: Michael Lawrence <michafla@gene.com>
URL: http://tengfei.github.com/ggbio/
VignetteBuilder: knitr
BugReports: https://github.com/tengfei/ggbio/issues
git_url: https://git.bioconductor.org/packages/ggbio
git_branch: RELEASE_3_13
git_last_commit: c084632
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ggbio_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ggbio_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ggbio_1.40.0.tgz
vignettes: vignettes/ggbio/inst/doc/ggbio.pdf
vignetteTitles: Part 0: Introduction and quick start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: CAFE, intansv
importsMe: derfinderPlot, GenomicOZone, msgbsR, R3CPET, ReportingTools,
RiboProfiling, scruff, SomaticSignatures
suggestsMe: bambu, beadarray, ensembldb, gwascat, interactiveDisplay,
NanoStringNCTools, Pi, regionReport, RnBeads,
StructuralVariantAnnotation, universalmotif, IHWpaper,
NanoporeRNASeq, Single.mTEC.Transcriptomes,
SomaticCancerAlterations
dependencyCount: 152
Package: ggcyto
Version: 1.20.0
Depends: methods, ggplot2(>= 3.3.0), flowCore(>= 1.41.5), ncdfFlow(>=
2.17.1), flowWorkspace(>= 3.33.1)
Imports: plyr, scales, hexbin, data.table, RColorBrewer, gridExtra,
rlang
Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats,
openCyto, flowViz, ggridges, vdiffr
License: Artistic-2.0
MD5sum: 648c64766259d7e55697236ed0ccf1a5
NeedsCompilation: no
Title: Visualize Cytometry data with ggplot
Description: With the dedicated fortify method implemented for flowSet,
ncdfFlowSet and GatingSet classes, both raw and gated flow
cytometry data can be plotted directly with ggplot. ggcyto
wrapper and some customed layers also make it easy to add gates
and population statistics to the plot.
biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays,
Infrastructure, Visualization
Author: Mike Jiang
Maintainer: Mike Jiang <wjiang2@fhcrc.org>,Jake Wagner
<jpwagner@fhcrc.org>
URL: https://github.com/RGLab/ggcyto/issues
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ggcyto
git_branch: RELEASE_3_13
git_last_commit: 0fbbab2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ggcyto_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ggcyto_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ggcyto_1.20.0.tgz
vignettes: vignettes/ggcyto/inst/doc/autoplot.html,
vignettes/ggcyto/inst/doc/ggcyto.flowSet.html,
vignettes/ggcyto/inst/doc/ggcyto.GatingSet.html,
vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.html
vignetteTitles: Quick plot for cytometry data, Visualize flowSet with
ggcyto, Visualize GatingSet with ggcyto, Feature summary of
ggcyto
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ggcyto/inst/doc/autoplot.R,
vignettes/ggcyto/inst/doc/ggcyto.flowSet.R,
vignettes/ggcyto/inst/doc/ggcyto.GatingSet.R,
vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.R
importsMe: CytoML
suggestsMe: CATALYST, flowCore, flowTime, flowWorkspace, openCyto
dependencyCount: 85
Package: GGPA
Version: 1.4.0
Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna,
scales, matrixStats
Imports: Rcpp (>= 0.11.3)
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle
License: GPL (>= 2)
MD5sum: 513113d9aecaa0cc2a94344a5d20780f
NeedsCompilation: yes
Title: graph-GPA: A graphical model for prioritizing GWAS results and
investigating pleiotropic architecture
Description: Genome-wide association studies (GWAS) is a widely used
tool for identification of genetic variants associated with
phenotypes and diseases, though complex diseases featuring many
genetic variants with small effects present difficulties for
traditional these studies. By leveraging pleiotropy, the
statistical power of a single GWAS can be increased. This
package provides functions for fitting graph-GPA, a statistical
framework to prioritize GWAS results by integrating pleiotropy.
'GGPA' package provides user-friendly interface to fit
graph-GPA models, implement association mapping, and generate a
phenotype graph.
biocViews: Software, StatisticalMethod, Classification,
GenomeWideAssociation, SNP, Genetics, Clustering,
MultipleComparison, Preprocessing, GeneExpression,
DifferentialExpression
Author: Dongjun Chung, Hang J. Kim, Carter Allen
Maintainer: Dongjun Chung <dongjun.chung@gmail.com>
URL: https://github.com/dongjunchung/GGPA/
SystemRequirements: GNU make
git_url: https://git.bioconductor.org/packages/GGPA
git_branch: RELEASE_3_13
git_last_commit: c9bd582
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GGPA_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GGPA_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GGPA_1.4.0.tgz
vignettes: vignettes/GGPA/inst/doc/GGPA-example.pdf
vignetteTitles: GGPA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GGPA/inst/doc/GGPA-example.R
dependencyCount: 60
Package: ggtree
Version: 3.0.4
Depends: R (>= 3.5.0)
Imports: ape, aplot (>= 0.0.4), dplyr, ggfun, ggplot2 (>= 3.0.0), grid,
magrittr, methods, purrr, rlang, scales, tidyr, tidytree (>=
0.2.6), treeio (>= 1.8.0), utils, yulab.utils
Suggests: emojifont, ggimage, ggplotify, grDevices, knitr, prettydoc,
rmarkdown, stats, testthat, tibble
License: Artistic-2.0
MD5sum: f5d673cd08c69902749f614e3df29117
NeedsCompilation: no
Title: an R package for visualization of tree and annotation data
Description: 'ggtree' extends the 'ggplot2' plotting system which
implemented the grammar of graphics. 'ggtree' is designed for
visualization and annotation of phylogenetic trees and other
tree-like structures with their annotation data.
biocViews: Alignment, Annotation, Clustering, DataImport,
MultipleSequenceAlignment, Phylogenetics, ReproducibleResearch,
Software, Visualization
Author: Guangchuang Yu [aut, cre, cph]
(<https://orcid.org/0000-0002-6485-8781>), Tommy Tsan-Yuk Lam
[aut, ths], Shuangbin Xu [aut]
(<https://orcid.org/0000-0003-3513-5362>), Yonghe Xia [ctb],
Justin Silverman [ctb], Bradley Jones [ctb], Watal M. Iwasaki
[ctb], Ruizhu Huang [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/treedata-book/
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/ggtree/issues
git_url: https://git.bioconductor.org/packages/ggtree
git_branch: RELEASE_3_13
git_last_commit: 7a83be2
git_last_commit_date: 2021-08-20
Date/Publication: 2021-08-22
source.ver: src/contrib/ggtree_3.0.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ggtree_3.0.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/ggtree_3.0.4.tgz
vignettes: vignettes/ggtree/inst/doc/ggtree.html
vignetteTitles: ggtree
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ggtree/inst/doc/ggtree.R
importsMe: enrichplot, ggtreeExtra, LymphoSeq, miaViz,
MicrobiotaProcess, philr, singleCellTK, sitePath,
systemPipeTools, treekoR, dowser, genBaRcode, harrietr,
RAINBOWR, RevGadgets, STraTUS
suggestsMe: systemPipeShiny, TreeAndLeaf, treeio,
TreeSummarizedExperiment, universalmotif, aplot, CoOL, DAISIE,
deeptime, ggimage, idiogramFISH, microeco, nosoi, oppr,
PCMBase, rhierbaps, tidytree, yatah
dependencyCount: 58
Package: ggtreeExtra
Version: 1.2.3
Imports: ggplot2, utils, rlang, ggnewscale, stats, ggtree
Suggests: treeio, ggstar, patchwork, knitr, rmarkdown, prettydoc,
markdown, testthat (>= 3.0.0)
License: GPL-3
MD5sum: bef4ad95448ba51cf6acc49034ead29c
NeedsCompilation: no
Title: An R Package To Add Geometric Layers On Circular Or Other Layout
Tree Of "ggtree"
Description: 'ggtreeExtra' extends the method for mapping and
visualizing associated data on phylogenetic tree using
'ggtree'. These associated data can be presented on the
external panels to circular layout, fan layout, or other
rectangular layout tree built by 'ggtree' with the grammar of
'ggplot2'.
biocViews: Software, Visualization, Phylogenetics, Annotation
Author: Shuangbin Xu [aut, cre]
(<https://orcid.org/0000-0003-3513-5362>), Guangchuang Yu [aut,
ctb] (<https://orcid.org/0000-0002-6485-8781>)
Maintainer: Shuangbin Xu <xshuangbin@163.com>
URL: https://github.com/YuLab-SMU/ggtreeExtra/
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/ggtreeExtra/issues
git_url: https://git.bioconductor.org/packages/ggtreeExtra
git_branch: RELEASE_3_13
git_last_commit: 759f5d0
git_last_commit_date: 2021-09-09
Date/Publication: 2021-09-12
source.ver: src/contrib/ggtreeExtra_1.2.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ggtreeExtra_1.2.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/ggtreeExtra_1.2.3.tgz
vignettes: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html
vignetteTitles: ggtreeExtra
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.R
suggestsMe: MicrobiotaProcess
dependencyCount: 60
Package: GIGSEA
Version: 1.10.0
Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils
Suggests: knitr, rmarkdown
License: LGPL-3
MD5sum: f66a2cafbc1fbddd6e2c5d66988af89a
NeedsCompilation: no
Title: Genotype Imputed Gene Set Enrichment Analysis
Description: We presented the Genotype-imputed Gene Set Enrichment
Analysis (GIGSEA), a novel method that uses
GWAS-and-eQTL-imputed trait-associated differential gene
expression to interrogate gene set enrichment for the
trait-associated SNPs. By incorporating eQTL from large gene
expression studies, e.g. GTEx, GIGSEA appropriately addresses
such challenges for SNP enrichment as gene size, gene boundary,
SNP distal regulation, and multiple-marker regulation. The
weighted linear regression model, taking as weights both
imputation accuracy and model completeness, was used to perform
the enrichment test, properly adjusting the bias due to
redundancy in different gene sets. The permutation test,
furthermore, is used to evaluate the significance of
enrichment, whose efficiency can be largely elevated by
expressing the computational intensive part in terms of large
matrix operation. We have shown the appropriate type I error
rates for GIGSEA (<5%), and the preliminary results also
demonstrate its good performance to uncover the real signal.
biocViews:
GeneSetEnrichment,SNP,VariantAnnotation,GeneExpression,GeneRegulation,Regression,DifferentialExpression
Author: Shijia Zhu
Maintainer: Shijia Zhu <shijia.zhu@mssm.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GIGSEA
git_branch: RELEASE_3_13
git_last_commit: ae9c9f8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GIGSEA_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GIGSEA_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GIGSEA_1.10.0.tgz
vignettes: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.pdf
vignetteTitles: GIGSEA: Genotype Imputed Gene Set Enrichment Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.R
suggestsMe: GIGSEAdata
dependencyCount: 11
Package: girafe
Version: 1.44.0
Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.8), S4Vectors
(>= 0.17.25), Rsamtools (>= 1.31.2), intervals (>= 0.13.1),
ShortRead (>= 1.37.1), genomeIntervals (>= 1.25.1), grid
Imports: methods, Biobase, Biostrings (>= 2.47.6), graphics, grDevices,
stats, utils, IRanges (>= 2.13.12)
Suggests: MASS, org.Mm.eg.db, RColorBrewer
Enhances: genomeIntervals
License: Artistic-2.0
Archs: i386, x64
MD5sum: 91a0c7ecac5cfe40a25870097cd646f7
NeedsCompilation: yes
Title: Genome Intervals and Read Alignments for Functional Exploration
Description: The package 'girafe' deals with the genome-level
representation of aligned reads from next-generation sequencing
data. It contains an object class for enabling a detailed
description of genome intervals with aligned reads and
functions for comparing, visualising, exporting and working
with such intervals and the aligned reads. As such, the package
interacts with and provides a link between the packages
ShortRead, IRanges and genomeIntervals.
biocViews: Sequencing
Author: Joern Toedling, with contributions from Constance Ciaudo,
Olivier Voinnet, Edith Heard, Emmanuel Barillot, and Wolfgang
Huber
Maintainer: J. Toedling <jtoedling@yahoo.de>
git_url: https://git.bioconductor.org/packages/girafe
git_branch: RELEASE_3_13
git_last_commit: e753ae6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/girafe_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/girafe_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/girafe_1.44.0.tgz
vignettes: vignettes/girafe/inst/doc/girafe.pdf
vignetteTitles: Genome intervals and read alignments for functional
exploration
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/girafe/inst/doc/girafe.R
dependencyCount: 46
Package: GISPA
Version: 1.16.0
Depends: R (>= 3.5)
Imports: Biobase, changepoint, data.table, genefilter, graphics,
GSEABase, HH, lattice, latticeExtra, plyr, scatterplot3d, stats
Suggests: knitr
License: GPL-2
MD5sum: 2d9b35c05d15df3c4800e153bc268c97
NeedsCompilation: no
Title: GISPA: Method for Gene Integrated Set Profile Analysis
Description: GISPA is a method intended for the researchers who are
interested in defining gene sets with similar, a priori
specified molecular profile. GISPA method has been previously
published in Nucleic Acid Research (Kowalski et al., 2016;
PMID: 26826710).
biocViews: StatisticalMethod,GeneSetEnrichment,GenomeWideAssociation
Author: Bhakti Dwivedi and Jeanne Kowalski
Maintainer: Bhakti Dwivedi <bhakti.dwivedi@emory.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GISPA
git_branch: RELEASE_3_13
git_last_commit: 5f33fd2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GISPA_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GISPA_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GISPA_1.16.0.tgz
vignettes: vignettes/GISPA/inst/doc/GISPA_manual.html
vignetteTitles: GISPA:Method for Gene Integrated Set Profile Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GISPA/inst/doc/GISPA_manual.R
dependencyCount: 133
Package: GLAD
Version: 2.56.0
Depends: R (>= 2.10)
Imports: aws
License: GPL-2
MD5sum: 08df9292594a8073b825fecd9b9ae3c5
NeedsCompilation: yes
Title: Gain and Loss Analysis of DNA
Description: Analysis of array CGH data : detection of breakpoints in
genomic profiles and assignment of a status (gain, normal or
loss) to each chromosomal regions identified.
biocViews: Microarray, CopyNumberVariation
Author: Philippe Hupe
Maintainer: Philippe Hupe <glad@curie.fr>
URL: http://bioinfo.curie.fr
SystemRequirements: gsl. Note: users should have GSL installed. Windows
users: 'consult the README file available in the inst directory
of the source distribution for necessary configuration
instructions'.
git_url: https://git.bioconductor.org/packages/GLAD
git_branch: RELEASE_3_13
git_last_commit: 10516c2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GLAD_2.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GLAD_2.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GLAD_2.56.0.tgz
vignettes: vignettes/GLAD/inst/doc/GLAD.pdf
vignetteTitles: GLAD
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GLAD/inst/doc/GLAD.R
dependsOnMe: ADaCGH2, ITALICS, seqCNA
importsMe: ITALICS, MANOR, snapCGH
suggestsMe: RnBeads, aroma.cn, aroma.core, cghRA
dependencyCount: 4
Package: GladiaTOX
Version: 1.8.0
Depends: R (>= 3.6.0), data.table (>= 1.9.4)
Imports: DBI, RMySQL, RSQLite, numDeriv, RColorBrewer, parallel, stats,
methods, graphics, grDevices, xtable, tools, brew, stringr,
RJSONIO, ggplot2, ggrepel, tidyr, utils, RCurl, XML
Suggests: roxygen2, knitr, rmarkdown, testthat, BiocStyle
License: GPL-2
MD5sum: d19940d6ccf69caf8acdd20c976a171a
NeedsCompilation: no
Title: R Package for Processing High Content Screening data
Description: GladiaTOX R package is an open-source, flexible solution
to high-content screening data processing and reporting in
biomedical research. GladiaTOX takes advantage of the tcpl core
functionalities and provides a number of extensions: it
provides a web-service solution to fetch raw data; it computes
severity scores and exports ToxPi formatted files; furthermore
it contains a suite of functionalities to generate pdf reports
for quality control and data processing.
biocViews: Software, WorkflowStep, Normalization, Preprocessing,
QualityControl
Author: Vincenzo Belcastro [aut, cre], Dayne L Filer [aut], Stephane
Cano [aut]
Maintainer: PMP S.A. R Support <DL.RSupport@pmi.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GladiaTOX
git_branch: RELEASE_3_13
git_last_commit: 53cee0f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GladiaTOX_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GladiaTOX_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GladiaTOX_1.8.0.tgz
vignettes: vignettes/GladiaTOX/inst/doc/GladiaTOX.html
vignetteTitles: GladiaTOX
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GladiaTOX/inst/doc/GladiaTOX.R
dependencyCount: 68
Package: Glimma
Version: 2.2.0
Depends: R (>= 4.0.0)
Imports: htmlwidgets, edgeR, DESeq2, limma, SummarizedExperiment,
stats, jsonlite, methods, S4Vectors
Suggests: testthat, knitr, rmarkdown, BiocStyle, IRanges,
GenomicRanges, pryr
License: GPL-3
Archs: i386, x64
MD5sum: f65228a08cf29e34d4e5023ed60d5856
NeedsCompilation: no
Title: Interactive HTML graphics
Description: This package generates interactive visualisations for
analysis of RNA-sequencing data using output from limma, edgeR
or DESeq2 packages in an HTML page. The interactions are built
on top of the popular static representations of analysis
results in order to provide additional information.
biocViews: DifferentialExpression, GeneExpression, Microarray,
ReportWriting, RNASeq, Sequencing, Visualization
Author: Shian Su [aut, cre], Hasaru Kariyawasam [aut], Oliver Voogd
[aut], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee
[ctb], Isaac Virshup [ctb]
Maintainer: Shian Su <su.s@wehi.edu.au>
URL: https://github.com/hasaru-k/GlimmaV2
VignetteBuilder: knitr
BugReports: https://github.com/hasaru-k/GlimmaV2/issues
git_url: https://git.bioconductor.org/packages/Glimma
git_branch: RELEASE_3_13
git_last_commit: 56d8c66
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Glimma_2.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Glimma_2.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Glimma_2.2.0.tgz
vignettes: vignettes/Glimma/inst/doc/DESeq2.html,
vignettes/Glimma/inst/doc/limma_edger.html
vignetteTitles: DESeq2, limma
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Glimma/inst/doc/DESeq2.R,
vignettes/Glimma/inst/doc/limma_edger.R
dependsOnMe: RNAseq123
importsMe: affycoretools, EGSEA
dependencyCount: 99
Package: glmGamPoi
Version: 1.4.0
Imports: Rcpp, DelayedMatrixStats, matrixStats, DelayedArray,
HDF5Array, SummarizedExperiment, BiocGenerics, methods, stats,
utils, splines
LinkingTo: Rcpp, RcppArmadillo, beachmat
Suggests: testthat (>= 2.1.0), zoo, DESeq2, edgeR, limma, beachmat,
MASS, statmod, ggplot2, bench, BiocParallel, knitr, rmarkdown,
BiocStyle, TENxPBMCData, muscData, scran
License: GPL-3
MD5sum: 6f716eda9f18a96d00c6ab5faee2c7a1
NeedsCompilation: yes
Title: Fit a Gamma-Poisson Generalized Linear Model
Description: Fit linear models to overdispersed count data. The package
can estimate the overdispersion and fit repeated models for
matrix input. It is designed to handle large input datasets as
they typically occur in single cell RNA-seq experiments.
biocViews: Regression, RNASeq, Software, SingleCell
Author: Constantin Ahlmann-Eltze [aut, cre]
(<https://orcid.org/0000-0002-3762-068X>), Michael Love [ctb]
Maintainer: Constantin Ahlmann-Eltze <artjom31415@googlemail.com>
URL: https://github.com/const-ae/glmGamPoi
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/const-ae/glmGamPoi/issues
git_url: https://git.bioconductor.org/packages/glmGamPoi
git_branch: RELEASE_3_13
git_last_commit: 908a0c3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/glmGamPoi_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/glmGamPoi_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/glmGamPoi_1.4.0.tgz
vignettes: vignettes/glmGamPoi/inst/doc/glmGamPoi.html
vignetteTitles: glmGamPoi Quickstart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/glmGamPoi/inst/doc/glmGamPoi.R
suggestsMe: DESeq2
dependencyCount: 36
Package: glmSparseNet
Version: 1.10.0
Depends: R (>= 4.1), Matrix, MultiAssayExperiment, glmnet
Imports: SummarizedExperiment, biomaRt, futile.logger, sparsebn,
sparsebnUtils, forcats, dplyr, glue, readr, httr, ggplot2,
survminer, reshape2, stringr, parallel, methods, loose.rock (>=
1.0.12)
Suggests: testthat, knitr, rmarkdown, survival, survcomp, pROC,
VennDiagram, BiocStyle, curatedTCGAData, TCGAutils
License: GPL-3
Archs: i386, x64
MD5sum: 2a1e7e6d2f124bbbf161da43f8c86cbc
NeedsCompilation: no
Title: Network Centrality Metrics for Elastic-Net Regularized Models
Description: glmSparseNet is an R-package that generalizes sparse
regression models when the features (e.g. genes) have a graph
structure (e.g. protein-protein interactions), by including
network-based regularizers. glmSparseNet uses the glmnet
R-package, by including centrality measures of the network as
penalty weights in the regularization. The current version
implements regularization based on node degree, i.e. the
strength and/or number of its associated edges, either by
promoting hubs in the solution or orphan genes in the solution.
All the glmnet distribution families are supported, namely
"gaussian", "poisson", "binomial", "multinomial", "cox", and
"mgaussian".
biocViews: Software, StatisticalMethod, DimensionReduction, Regression,
Classification, Survival, Network, GraphAndNetwork
Author: André VerÃssimo [aut, cre], Susana Vinga [aut], Eunice
Carrasquinha [ctb], Marta Lopes [ctb]
Maintainer: André VerÃssimo <andre.verissimo@tecnico.ulisboa.pt>
URL: https://www.github.com/sysbiomed/glmSparseNet
VignetteBuilder: knitr
BugReports: https://www.github.com/sysbiomed/glmSparseNet/issues
git_url: https://git.bioconductor.org/packages/glmSparseNet
git_branch: RELEASE_3_13
git_last_commit: 68fb0a1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/glmSparseNet_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/glmSparseNet_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/glmSparseNet_1.10.0.tgz
vignettes: vignettes/glmSparseNet/inst/doc/example_brca_logistic.html,
vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.html,
vignettes/glmSparseNet/inst/doc/example_brca_survival.html,
vignettes/glmSparseNet/inst/doc/example_prad_survival.html,
vignettes/glmSparseNet/inst/doc/example_skcm_survival.html,
vignettes/glmSparseNet/inst/doc/separate2GroupsCox.html
vignetteTitles: Example for Classification -- Breast Invasive
Carcinoma, Breast survival dataset using network from STRING
DB, Example for Survival Data -- Breast Invasive Carcinoma,
Example for Survival Data -- Prostate Adenocarcinoma, Example
for Survival Data -- Skin Melanoma, Separate 2 groups in Cox
regression
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/glmSparseNet/inst/doc/example_brca_logistic.R,
vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.R,
vignettes/glmSparseNet/inst/doc/example_brca_survival.R,
vignettes/glmSparseNet/inst/doc/example_prad_survival.R,
vignettes/glmSparseNet/inst/doc/example_skcm_survival.R,
vignettes/glmSparseNet/inst/doc/separate2GroupsCox.R
dependencyCount: 176
Package: GlobalAncova
Version: 4.10.0
Depends: methods, corpcor, globaltest
Imports: annotate, AnnotationDbi, Biobase, dendextend, GSEABase, VGAM
Suggests: GO.db, golubEsets, hu6800.db, vsn, Rgraphviz
License: GPL (>= 2)
MD5sum: a2d1285c323a4b0c116361f3d0194f0d
NeedsCompilation: yes
Title: Global test for groups of variables via model comparisons
Description: The association between a variable of interest (e.g. two
groups) and the global pattern of a group of variables (e.g. a
gene set) is tested via a global F-test. We give the following
arguments in support of the GlobalAncova approach: After
appropriate normalisation, gene-expression-data appear rather
symmetrical and outliers are no real problem, so least squares
should be rather robust. ANCOVA with interaction yields
saturated data modelling e.g. different means per group and
gene. Covariate adjustment can help to correct for possible
selection bias. Variance homogeneity and uncorrelated residuals
cannot be expected. Application of ordinary least squares gives
unbiased, but no longer optimal estimates
(Gauss-Markov-Aitken). Therefore, using the classical F-test is
inappropriate, due to correlation. The test statistic however
mirrors deviations from the null hypothesis. In combination
with a permutation approach, empirical significance levels can
be approximated. Alternatively, an approximation yields
asymptotic p-values. The framework is generalized to groups of
categorical variables or even mixed data by a likelihood ratio
approach. Closed and hierarchical testing procedures are
supported. This work was supported by the NGFN grant 01 GR
0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany.
biocViews: Microarray, OneChannel, DifferentialExpression, Pathways,
Regression
Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with
contributions from S. Knueppel
Maintainer: Manuela Hummel <manuela.hummel@web.de>
git_url: https://git.bioconductor.org/packages/GlobalAncova
git_branch: RELEASE_3_13
git_last_commit: 4854ac8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GlobalAncova_4.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GlobalAncova_4.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GlobalAncova_4.10.0.tgz
vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf,
vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf
vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R,
vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R
importsMe: miRtest
dependencyCount: 85
Package: globalSeq
Version: 1.20.0
Depends: R (>= 3.0.0)
Suggests: knitr, testthat, SummarizedExperiment, S4Vectors
License: GPL-3
MD5sum: 5951d09967083f56e10abc9464a399df
NeedsCompilation: no
Title: Global Test for Counts
Description: The method may be conceptualised as a test of overall
significance in regression analysis, where the response
variable is overdispersed and the number of explanatory
variables exceeds the sample size. Useful for testing for
association between RNA-Seq and high-dimensional data.
biocViews: GeneExpression, ExonArray, DifferentialExpression,
GenomeWideAssociation, Transcriptomics, DimensionReduction,
Regression, Sequencing, WholeGenome, RNASeq, ExomeSeq, miRNA,
MultipleComparison
Author: Armin Rauschenberger [aut, cre]
Maintainer: Armin Rauschenberger <armin.rauschenberger@uni.lu>
URL: https://github.com/rauschenberger/globalSeq
VignetteBuilder: knitr
BugReports: https://github.com/rauschenberger/globalSeq/issues
git_url: https://git.bioconductor.org/packages/globalSeq
git_branch: RELEASE_3_13
git_last_commit: d7fa5ae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/globalSeq_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/globalSeq_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/globalSeq_1.20.0.tgz
vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf,
vignettes/globalSeq/inst/doc/article.html,
vignettes/globalSeq/inst/doc/vignette.html
vignetteTitles: vignette source, article frame, vignette frame
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R
dependencyCount: 0
Package: globaltest
Version: 5.46.0
Depends: methods, survival
Imports: Biobase, AnnotationDbi, annotate, graphics
Suggests: vsn, golubEsets, KEGGREST, hu6800.db, Rgraphviz, GO.db,
lungExpression, org.Hs.eg.db, GSEABase, penalized, gss, MASS,
boot, rpart, mstate
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 466317f589d669ae7529b2ecbe85faf1
NeedsCompilation: no
Title: Testing Groups of Covariates/Features for Association with a
Response Variable, with Applications to Gene Set Testing
Description: The global test tests groups of covariates (or features)
for association with a response variable. This package
implements the test with diagnostic plots and multiple testing
utilities, along with several functions to facilitate the use
of this test for gene set testing of GO and KEGG terms.
biocViews: Microarray, OneChannel, Bioinformatics,
DifferentialExpression, GO, Pathways
Author: Jelle Goeman and Jan Oosting, with contributions by Livio
Finos, Aldo Solari, Dominic Edelmann
Maintainer: Jelle Goeman <j.j.goeman@lumc.nl>
git_url: https://git.bioconductor.org/packages/globaltest
git_branch: RELEASE_3_13
git_last_commit: 8595301
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/globaltest_5.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/globaltest_5.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/globaltest_5.46.0.tgz
vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf
vignetteTitles: Global Test
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/globaltest/inst/doc/GlobalTest.R
dependsOnMe: GlobalAncova
importsMe: BiSeq, EGSEA, SIM, miRtest, SlaPMEG
suggestsMe: topGO, maGUI, penalized
dependencyCount: 54
Package: gmapR
Version: 1.34.0
Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges
(>= 1.31.8), Rsamtools (>= 1.31.2)
Imports: S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), BiocGenerics (>=
0.25.1), rtracklayer (>= 1.39.7), GenomicFeatures (>= 1.31.3),
Biostrings, VariantAnnotation (>= 1.25.11), tools, Biobase,
BSgenome, GenomicAlignments (>= 1.15.6), BiocParallel
Suggests: RUnit, BSgenome.Dmelanogaster.UCSC.dm3,
BSgenome.Scerevisiae.UCSC.sacCer3, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19,
LungCancerLines
License: Artistic-2.0
MD5sum: 8193c61b9bc6e98f460473a43749e82c
NeedsCompilation: yes
Title: An R interface to the GMAP/GSNAP/GSTRUCT suite
Description: GSNAP and GMAP are a pair of tools to align short-read
data written by Tom Wu. This package provides convenience
methods to work with GMAP and GSNAP from within R. In addition,
it provides methods to tally alignment results on a
per-nucleotide basis using the bam_tally tool.
biocViews: Alignment
Author: Cory Barr, Thomas Wu, Michael Lawrence
Maintainer: Michael Lawrence <michafla@gene.com>
git_url: https://git.bioconductor.org/packages/gmapR
git_branch: RELEASE_3_13
git_last_commit: 3262a4e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gmapR_1.34.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/gmapR_1.34.0.tgz
vignettes: vignettes/gmapR/inst/doc/gmapR.pdf
vignetteTitles: gmapR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gmapR/inst/doc/gmapR.R
dependsOnMe: HTSeqGenie
importsMe: MMAPPR2
suggestsMe: VariantTools, VariantToolsData
dependencyCount: 98
Package: GmicR
Version: 1.6.0
Imports: AnnotationDbi, ape, bnlearn, Category, DT, doParallel,
foreach, gRbase, GSEABase, gRain, GOstats, org.Hs.eg.db,
org.Mm.eg.db, shiny, WGCNA, data.table, grDevices, graphics,
reshape2, stats, utils
Suggests: knitr, rmarkdown, testthat
License: GPL-2 + file LICENSE
MD5sum: cd1722cc6b0e0cba22a4db1e44fd0cb0
NeedsCompilation: no
Title: Combines WGCNA and xCell readouts with bayesian network
learrning to generate a Gene-Module Immune-Cell network (GMIC)
Description: This package uses bayesian network learning to detect
relationships between Gene Modules detected by WGCNA and immune
cell signatures defined by xCell. It is a hypothesis generating
tool.
biocViews: Software, SystemsBiology, GraphAndNetwork, Network,
NetworkInference, GUI, ImmunoOncology, GeneExpression,
QualityControl, Bayesian, Clustering
Author: Richard Virgen-Slane
Maintainer: Richard Virgen-Slane <RVS.BioTools@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GmicR
git_branch: RELEASE_3_13
git_last_commit: 1f79941
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GmicR_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GmicR_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GmicR_1.6.0.tgz
vignettes: vignettes/GmicR/inst/doc/GmicR_vignette.html
vignetteTitles: GmicR_vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GmicR/inst/doc/GmicR_vignette.R
dependencyCount: 147
Package: gmoviz
Version: 1.4.0
Depends: circlize, GenomicRanges, graphics, R (>= 4.0)
Imports: grid, gridBase, Rsamtools, ComplexHeatmap, BiocGenerics,
Biostrings, GenomeInfoDb, methods, GenomicAlignments,
GenomicFeatures, IRanges, rtracklayer, pracma, colorspace,
S4Vectors
Suggests: testthat, knitr, rmarkdown, pasillaBamSubset, BiocStyle,
BiocManager
License: GPL-3
Archs: i386, x64
MD5sum: 80ea379a6356969e3aafa8654cf2000b
NeedsCompilation: no
Title: Seamless visualization of complex genomic variations in GMOs and
edited cell lines
Description: Genetically modified organisms (GMOs) and cell lines are
widely used models in all kinds of biological research. As part
of characterising these models, DNA sequencing technology and
bioinformatics analyses are used systematically to study their
genomes. Therefore, large volumes of data are generated and
various algorithms are applied to analyse this data, which
introduces a challenge on representing all findings in an
informative and concise manner. `gmoviz` provides users with an
easy way to visualise and facilitate the explanation of complex
genomic editing events on a larger, biologically-relevant
scale.
biocViews: Visualization, Sequencing, GeneticVariability,
GenomicVariation, Coverage
Author: Kathleen Zeglinski [cre, aut], Arthur Hsu [aut], Monther
Alhamdoosh [aut] (<https://orcid.org/0000-0002-2411-1325>),
Constantinos Koutsakis [aut]
Maintainer: Kathleen Zeglinski <kathleen.zeglinski@csl.com.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gmoviz
git_branch: RELEASE_3_13
git_last_commit: 8eb5807
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gmoviz_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gmoviz_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gmoviz_1.4.0.tgz
vignettes: vignettes/gmoviz/inst/doc/gmoviz_advanced.html,
vignettes/gmoviz/inst/doc/gmoviz_overview.html
vignetteTitles: Advanced usage of gmoviz, Introduction to gmoviz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gmoviz/inst/doc/gmoviz_advanced.R,
vignettes/gmoviz/inst/doc/gmoviz_overview.R
dependencyCount: 112
Package: GMRP
Version: 1.20.0
Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix,
base,GenomicRanges
Suggests: BiocStyle, BiocGenerics
License: GPL (>= 2)
MD5sum: 7ea3012cd2f049b296f04b05b3727998
NeedsCompilation: no
Title: GWAS-based Mendelian Randomization and Path Analyses
Description: Perform Mendelian randomization analysis of multiple SNPs
to determine risk factors causing disease of study and to
exclude confounding variabels and perform path analysis to
construct path of risk factors to the disease.
biocViews: Sequencing, Regression, SNP
Author: Yuan-De Tan
Maintainer: Yuan-De Tan <tanyuande@gmail.com>
git_url: https://git.bioconductor.org/packages/GMRP
git_branch: RELEASE_3_13
git_last_commit: bfb1e34
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GMRP_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GMRP_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GMRP_1.20.0.tgz
vignettes: vignettes/GMRP/inst/doc/GMRP-manual.pdf,
vignettes/GMRP/inst/doc/GMRP.pdf
vignetteTitles: GMRP-manual.pdf, Causal Effect Analysis of Risk Factors
for Disease with the "GMRP" package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GMRP/inst/doc/GMRP.R
dependencyCount: 22
Package: GNET2
Version: 1.8.0
Depends: R (>= 3.6)
Imports:
ggplot2,xgboost,Rcpp,reshape2,grid,DiagrammeR,methods,stats,matrixStats,graphics,SummarizedExperiment,dplyr,igraph,
grDevices, utils
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
License: Apache License 2.0
MD5sum: 4570e4a155267f585ec66b05f2c41af0
NeedsCompilation: yes
Title: Constructing gene regulatory networks from expression data
through functional module inference
Description: Cluster genes to functional groups with E-M process.
Iteratively perform TF assigning and Gene assigning, until the
assignment of genes did not change, or max number of iterations
is reached.
biocViews: GeneExpression, Regression, Network, NetworkInference,
Software
Author: Chen Chen, Jie Hou and Jianlin Cheng
Maintainer: Chen Chen <ccm3x@mail.missouri.edu>
URL: https://github.com/chrischen1/GNET2
VignetteBuilder: knitr
BugReports: https://github.com/chrischen1/GNET2/issues
git_url: https://git.bioconductor.org/packages/GNET2
git_branch: RELEASE_3_13
git_last_commit: c9db7ec
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GNET2_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GNET2_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GNET2_1.8.0.tgz
vignettes: vignettes/GNET2/inst/doc/run_gnet2.html
vignetteTitles: Build functional gene modules with GNET2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GNET2/inst/doc/run_gnet2.R
dependencyCount: 92
Package: GOexpress
Version: 1.26.0
Depends: R (>= 3.4), grid, stats, graphics, Biobase (>= 2.22.0)
Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0),
RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>=
4.6), RCurl (>= 1.95)
Suggests: BiocStyle
License: GPL (>= 3)
MD5sum: a6348f5c6749a158fda3da8b2d4802ff
NeedsCompilation: no
Title: Visualise microarray and RNAseq data using gene ontology
annotations
Description: The package contains methods to visualise the expression
profile of genes from a microarray or RNA-seq experiment, and
offers a supervised clustering approach to identify GO terms
containing genes with expression levels that best classify two
or more predefined groups of samples. Annotations for the genes
present in the expression dataset may be obtained from Ensembl
through the biomaRt package, if not provided by the user. The
default random forest framework is used to evaluate the
capacity of each gene to cluster samples according to the
factor of interest. Finally, GO terms are scored by averaging
the rank (alternatively, score) of their respective gene sets
to cluster the samples. P-values may be computed to assess the
significance of GO term ranking. Visualisation function include
gene expression profile, gene ontology-based heatmaps, and
hierarchical clustering of experimental samples using gene
expression data.
biocViews: Software, GeneExpression, Transcription,
DifferentialExpression, GeneSetEnrichment, DataRepresentation,
Clustering, TimeCourse, Microarray, Sequencing, RNASeq,
Annotation, MultipleComparison, Pathways, GO, Visualization,
ImmunoOncology
Author: Kevin Rue-Albrecht [aut, cre], Tharvesh M.L. Ali [ctb], Paul A.
McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee
[ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen
V. Gordon [ths], David E. MacHugh [ths]
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/kevinrue/GOexpress
git_url: https://git.bioconductor.org/packages/GOexpress
git_branch: RELEASE_3_13
git_last_commit: 9aca228
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GOexpress_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GOexpress_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GOexpress_1.26.0.tgz
vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf
vignetteTitles: UsersGuide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.R
suggestsMe: InteractiveComplexHeatmap
dependencyCount: 94
Package: GOfuncR
Version: 1.12.0
Depends: R (>= 3.4), vioplot (>= 0.2),
Imports: Rcpp (>= 0.11.5), mapplots (>= 1.5), gtools (>= 3.5.0),
GenomicRanges (>= 1.28.4), IRanges, AnnotationDbi, utils,
grDevices, graphics, stats,
LinkingTo: Rcpp
Suggests: Homo.sapiens, BiocStyle, knitr, testthat
License: GPL (>= 2)
MD5sum: 95eec0e61159a74c57ac895a8e7dcf1f
NeedsCompilation: yes
Title: Gene ontology enrichment using FUNC
Description: GOfuncR performs a gene ontology enrichment analysis based
on the ontology enrichment software FUNC. GO-annotations are
obtained from OrganismDb or OrgDb packages ('Homo.sapiens' by
default); the GO-graph is included in the package and updated
regularly (23-Mar-2020). GOfuncR provides the standard
candidate vs. background enrichment analysis using the
hypergeometric test, as well as three additional tests: (i) the
Wilcoxon rank-sum test that is used when genes are ranked, (ii)
a binomial test that is used when genes are associated with two
counts and (iii) a Chi-square or Fisher's exact test that is
used in cases when genes are associated with four counts. To
correct for multiple testing and interdependency of the tests,
family-wise error rates are computed based on random
permutations of the gene-associated variables. GOfuncR also
provides tools for exploring the ontology graph and the
annotations, and options to take gene-length or spatial
clustering of genes into account. It is also possible to
provide custom gene coordinates, annotations and ontologies.
biocViews: GeneSetEnrichment, GO
Author: Steffi Grote
Maintainer: Steffi Grote <grote.steffi@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GOfuncR
git_branch: RELEASE_3_13
git_last_commit: 5a31fd1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GOfuncR_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GOfuncR_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GOfuncR_1.12.0.tgz
vignettes: vignettes/GOfuncR/inst/doc/GOfuncR.html
vignetteTitles: Introduction to GOfuncR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOfuncR/inst/doc/GOfuncR.R
importsMe: ABAEnrichment
dependencyCount: 54
Package: GOpro
Version: 1.18.0
Depends: R (>= 3.4)
Imports: AnnotationDbi, dendextend, doParallel, foreach, parallel,
org.Hs.eg.db, GO.db, Rcpp, stats, graphics,
MultiAssayExperiment, IRanges, S4Vectors
LinkingTo: Rcpp, BH
Suggests: knitr, rmarkdown, RTCGA.PANCAN12, BiocStyle, testthat
License: GPL-3
MD5sum: c8fccc76dcc6d66ee2e889f63f841bd7
NeedsCompilation: yes
Title: Find the most characteristic gene ontology terms for groups of
human genes
Description: Find the most characteristic gene ontology terms for
groups of human genes. This package was created as a part of
the thesis which was developed under the auspices of MI^2 Group
(http://mi2.mini.pw.edu.pl/,
https://github.com/geneticsMiNIng).
biocViews: Annotation, Clustering, GO, GeneExpression,
GeneSetEnrichment, MultipleComparison
Author: Lidia Chrabaszcz
Maintainer: Lidia Chrabaszcz <chrabaszcz.lidia@gmail.com>
URL: https://github.com/mi2-warsaw/GOpro
VignetteBuilder: knitr
BugReports: https://github.com/mi2-warsaw/GOpro/issues
git_url: https://git.bioconductor.org/packages/GOpro
git_branch: RELEASE_3_13
git_last_commit: b5688fa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GOpro_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GOpro_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GOpro_1.18.0.tgz
vignettes: vignettes/GOpro/inst/doc/GOpro_vignette.html
vignetteTitles: GOpro: Determine groups of genes and find their
characteristic GO term
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOpro/inst/doc/GOpro_vignette.R
dependencyCount: 95
Package: goProfiles
Version: 1.54.0
Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr
Suggests: org.Hs.eg.db
License: GPL-2
Archs: i386, x64
MD5sum: a38e734d545c2853e890a143b59e670c
NeedsCompilation: no
Title: goProfiles: an R package for the statistical analysis of
functional profiles
Description: The package implements methods to compare lists of genes
based on comparing the corresponding 'functional profiles'.
biocViews: Annotation, GO, GeneExpression, GeneSetEnrichment,
GraphAndNetwork, Microarray, MultipleComparison, Pathways,
Software
Author: Alex Sanchez, Jordi Ocana and Miquel Salicru
Maintainer: Alex Sanchez <asanchez@ub.edu>
git_url: https://git.bioconductor.org/packages/goProfiles
git_branch: RELEASE_3_13
git_last_commit: df63e70
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/goProfiles_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/goProfiles_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/goProfiles_1.54.0.tgz
vignettes: vignettes/goProfiles/inst/doc/goProfiles-comparevisual.pdf,
vignettes/goProfiles/inst/doc/goProfiles-plotProfileMF.pdf,
vignettes/goProfiles/inst/doc/goProfiles.pdf
vignetteTitles: goProfiles-comparevisual.pdf,
goProfiles-plotProfileMF.pdf, goProfiles Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R
dependencyCount: 51
Package: GOSemSim
Version: 2.18.1
Depends: R (>= 3.5.0)
Imports: AnnotationDbi, GO.db, methods, utils
LinkingTo: Rcpp
Suggests: AnnotationHub, BiocManager, clusterProfiler, DOSE, knitr,
rmarkdown, org.Hs.eg.db, prettydoc, testthat
License: Artistic-2.0
MD5sum: ca2ad5756076b0ac3ecdf19c6b6b83de
NeedsCompilation: yes
Title: GO-terms Semantic Similarity Measures
Description: The semantic comparisons of Gene Ontology (GO) annotations
provide quantitative ways to compute similarities between genes
and gene groups, and have became important basis for many
bioinformatics analysis approaches. GOSemSim is an R package
for semantic similarity computation among GO terms, sets of GO
terms, gene products and gene clusters. GOSemSim implemented
five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang
respectively.
biocViews: Annotation, GO, Clustering, Pathways, Network, Software
Author: Guangchuang Yu [aut, cre], Alexey Stukalov [ctb], Chuanle Xiao
[ctb], LluÃs Revilla Sancho [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/biomedical-knowledge-mining-book/
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/GOSemSim/issues
git_url: https://git.bioconductor.org/packages/GOSemSim
git_branch: RELEASE_3_13
git_last_commit: d2edfc5
git_last_commit_date: 2021-07-28
Date/Publication: 2021-07-29
source.ver: src/contrib/GOSemSim_2.18.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GOSemSim_2.18.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/GOSemSim_2.18.1.tgz
vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.html
vignetteTitles: GOSemSim
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R
dependsOnMe: tRanslatome, BiSEp
importsMe: clusterProfiler, DOSE, enrichplot, GAPGOM, meshes, Rcpi,
rrvgo, simplifyEnrichment, ViSEAGO, BioMedR, LANDD
suggestsMe: BioCor, epiNEM, FELLA, SemDist, protr, rDNAse
dependencyCount: 47
Package: goseq
Version: 1.44.0
Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase (>= 1.9.2)
Imports: mgcv, graphics, stats, utils, AnnotationDbi,
GO.db,BiocGenerics
Suggests: edgeR, org.Hs.eg.db, rtracklayer
License: LGPL (>= 2)
Archs: i386, x64
MD5sum: 51c9e92ff758d44d3c4a7e3593537c57
NeedsCompilation: no
Title: Gene Ontology analyser for RNA-seq and other length biased data
Description: Detects Gene Ontology and/or other user defined categories
which are over/under represented in RNA-seq data
biocViews: ImmunoOncology, Sequencing, GO, GeneExpression,
Transcription, RNASeq
Author: Matthew Young
Maintainer: Matthew Young <my4@sanger.ac.uk>, Nadia Davidson
<nadia.davidson@mcri.edu.au>
git_url: https://git.bioconductor.org/packages/goseq
git_branch: RELEASE_3_13
git_last_commit: 4868dfb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/goseq_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/goseq_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/goseq_1.44.0.tgz
vignettes: vignettes/goseq/inst/doc/goseq.pdf
vignetteTitles: goseq User's Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/goseq/inst/doc/goseq.R
dependsOnMe: rgsepd
importsMe: ideal, SMITE
dependencyCount: 102
Package: GOSim
Version: 1.30.0
Depends: GO.db, annotate
Imports: org.Hs.eg.db, AnnotationDbi, topGO, cluster, flexmix, RBGL,
graph, Matrix, corpcor, Rcpp
LinkingTo: Rcpp
Enhances: igraph
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 6847e670ab712e0f0ae06e0b631b5fb3
NeedsCompilation: yes
Title: Computation of functional similarities between GO terms and gene
products; GO enrichment analysis
Description: This package implements several functions useful for
computing similarities between GO terms and gene products based
on their GO annotation. Moreover it allows for computing a GO
enrichment analysis
biocViews: GO, Clustering, Software, Pathways
Author: Holger Froehlich <frohlich@bit.uni-bonn.de>
Maintainer: Holger Froehlich <frohlich@bit.uni-bonn.de>
git_url: https://git.bioconductor.org/packages/GOSim
git_branch: RELEASE_3_13
git_last_commit: cd2c2c0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GOSim_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GOSim_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GOSim_1.30.0.tgz
vignettes: vignettes/GOSim/inst/doc/GOSim.pdf
vignetteTitles: GOsim
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOSim/inst/doc/GOSim.R
dependencyCount: 65
Package: goSTAG
Version: 1.16.0
Depends: R (>= 3.4)
Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
MD5sum: 08863603ff6cda7d0294ce255cb4e167
NeedsCompilation: no
Title: A tool to use GO Subtrees to Tag and Annotate Genes within a set
Description: Gene lists derived from the results of genomic analyses
are rich in biological information. For instance,
differentially expressed genes (DEGs) from a microarray or
RNA-Seq analysis are related functionally in terms of their
response to a treatment or condition. Gene lists can vary in
size, up to several thousand genes, depending on the robustness
of the perturbations or how widely different the conditions are
biologically. Having a way to associate biological relatedness
between hundreds and thousands of genes systematically is
impractical by manually curating the annotation and function of
each gene. Over-representation analysis (ORA) of genes was
developed to identify biological themes. Given a Gene Ontology
(GO) and an annotation of genes that indicate the categories
each one fits into, significance of the over-representation of
the genes within the ontological categories is determined by a
Fisher's exact test or modeling according to a hypergeometric
distribution. Comparing a small number of enriched biological
categories for a few samples is manageable using Venn diagrams
or other means for assessing overlaps. However, with hundreds
of enriched categories and many samples, the comparisons are
laborious. Furthermore, if there are enriched categories that
are shared between samples, trying to represent a common theme
across them is highly subjective. goSTAG uses GO subtrees to
tag and annotate genes within a set. goSTAG visualizes the
similarities between the over-representation of DEGs by
clustering the p-values from the enrichment statistical tests
and labels clusters with the GO term that has the most paths to
the root within the subtree generated from all the GO terms in
the cluster.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
Clustering, Microarray, mRNAMicroarray, RNASeq, Visualization,
GO, ImmunoOncology
Author: Brian D. Bennett and Pierre R. Bushel
Maintainer: Brian D. Bennett <brian.bennett@nih.gov>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/goSTAG
git_branch: RELEASE_3_13
git_last_commit: 01e14e9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/goSTAG_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/goSTAG_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/goSTAG_1.16.0.tgz
vignettes: vignettes/goSTAG/inst/doc/goSTAG.html
vignetteTitles: The goSTAG User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/goSTAG/inst/doc/goSTAG.R
dependencyCount: 73
Package: GOstats
Version: 2.58.0
Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.43.2), graph
Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), GO.db (>=
1.13.0), RBGL, annotate (>= 1.13.2), AnnotationForge, Rgraphviz
Suggests: hgu95av2.db (>= 1.13.0), ALL, multtest, genefilter,
RColorBrewer, xtable, SparseM, GSEABase, geneplotter,
org.Hs.eg.db, RUnit, BiocGenerics
License: Artistic-2.0
Archs: i386, x64
MD5sum: 6f577abe48d5ad70c6d5f5b97dfe2d5a
NeedsCompilation: no
Title: Tools for manipulating GO and microarrays
Description: A set of tools for interacting with GO and microarray
data. A variety of basic manipulation tools for graphs,
hypothesis testing and other simple calculations.
biocViews: Annotation, GO, MultipleComparison, GeneExpression,
Microarray, Pathways, GeneSetEnrichment, GraphAndNetwork
Author: Robert Gentleman [aut], Seth Falcon [ctb], Robert Castelo
[ctb], Bioconductor Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/GOstats
git_branch: RELEASE_3_13
git_last_commit: d3406a6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GOstats_2.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GOstats_2.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GOstats_2.58.0.tgz
vignettes:
vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.pdf,
vignettes/GOstats/inst/doc/GOstatsHyperG.pdf,
vignettes/GOstats/inst/doc/GOvis.pdf
vignetteTitles: Hypergeometric tests for less common model organisms,
Hypergeometric Tests Using GOstats, Visualizing Data Using
GOstats
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.R,
vignettes/GOstats/inst/doc/GOstatsHyperG.R,
vignettes/GOstats/inst/doc/GOvis.R
dependsOnMe: MineICA, PloGO2
importsMe: affycoretools, attract, categoryCompare, GmicR, ideal,
MIGSA, miRLAB, pcaExplorer, scTensor, systemPipeR, DNLC, LANDD
suggestsMe: a4, Category, fastLiquidAssociation, fgga, GSEAlm,
interactiveDisplay, MineICA, MLP, qpgraph, RnBeads, safe, DGCA,
maGUI, sand
dependencyCount: 63
Package: GOsummaries
Version: 2.28.0
Depends: R (>= 2.15), Rcpp
Imports: plyr, grid, gProfileR, reshape2, limma, ggplot2, gtable
LinkingTo: Rcpp
Suggests: vegan
License: GPL (>= 2)
MD5sum: e3fec21e898ce1e39dd7c7f726039bad
NeedsCompilation: yes
Title: Word cloud summaries of GO enrichment analysis
Description: A package to visualise Gene Ontology (GO) enrichment
analysis results on gene lists arising from different analyses
such clustering or PCA. The significant GO categories are
visualised as word clouds that can be combined with different
plots summarising the underlying data.
biocViews: GeneExpression, Clustering, GO, Visualization
Author: Raivo Kolde <raivo.kolde@eesti.ee>
Maintainer: Raivo Kolde <raivo.kolde@eesti.ee>
URL: https://github.com/raivokolde/GOsummaries
git_url: https://git.bioconductor.org/packages/GOsummaries
git_branch: RELEASE_3_13
git_last_commit: 6b27e45
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GOsummaries_2.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GOsummaries_2.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GOsummaries_2.28.0.tgz
vignettes: vignettes/GOsummaries/inst/doc/GOsummaries-basics.pdf
vignetteTitles: GOsummaries basics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOsummaries/inst/doc/GOsummaries-basics.R
dependencyCount: 48
Package: GOTHiC
Version: 1.28.0
Depends: R (>= 3.5.0), methods, GenomicRanges, Biostrings, BSgenome,
data.table
Imports: BiocGenerics, S4Vectors (>= 0.9.38), IRanges, Rsamtools,
ShortRead, rtracklayer, ggplot2, BiocManager, grDevices, utils,
stats, GenomeInfoDb
Suggests: HiCDataLymphoblast
Enhances: parallel
License: GPL-3
Archs: i386, x64
MD5sum: 590c5a065016e1b2aa753f748113b8a0
NeedsCompilation: no
Title: Binomial test for Hi-C data analysis
Description: This is a Hi-C analysis package using a cumulative
binomial test to detect interactions between distal genomic
loci that have significantly more reads than expected by chance
in Hi-C experiments. It takes mapped paired NGS reads as input
and gives back the list of significant interactions for a given
bin size in the genome.
biocViews: ImmunoOncology, Sequencing, Preprocessing, Epigenetics, HiC
Author: Borbala Mifsud and Robert Sugar
Maintainer: Borbala Mifsud <b.mifsud@qmul.ac.uk>
git_url: https://git.bioconductor.org/packages/GOTHiC
git_branch: RELEASE_3_13
git_last_commit: 4898bfc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GOTHiC_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GOTHiC_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GOTHiC_1.28.0.tgz
vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf
vignetteTitles: package_vignettes.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOTHiC/inst/doc/package_vignettes.R
dependencyCount: 81
Package: goTools
Version: 1.66.0
Depends: GO.db
Imports: AnnotationDbi, GO.db, graphics, grDevices
Suggests: hgu133a.db
License: GPL-2
MD5sum: f1bc415ec826ec6e2ff6abaacb83b2d3
NeedsCompilation: no
Title: Functions for Gene Ontology database
Description: Wraper functions for description/comparison of oligo ID
list using Gene Ontology database
biocViews: Microarray,GO,Visualization
Author: Yee Hwa (Jean) Yang <jean@biostat.ucsf.edu>, Agnes Paquet
<paquetagnes@yahoo.com>
Maintainer: Agnes Paquet <paquetagnes@yahoo.com>
git_url: https://git.bioconductor.org/packages/goTools
git_branch: RELEASE_3_13
git_last_commit: cc7fafe
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/goTools_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/goTools_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/goTools_1.66.0.tgz
vignettes: vignettes/goTools/inst/doc/goTools.pdf
vignetteTitles: goTools overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/goTools/inst/doc/goTools.R
dependencyCount: 47
Package: GPA
Version: 1.4.0
Depends: R (>= 4.0.0), methods, graphics, Rcpp
Imports: parallel, ggplot2, ggrepel, plyr, vegan, DT, shiny, shinyBS,
stats, utils, grDevices
LinkingTo: Rcpp
Suggests: gpaExample
License: GPL (>= 2)
MD5sum: ab63acb2e9f7a78a9ad44db233323a5b
NeedsCompilation: yes
Title: GPA (Genetic analysis incorporating Pleiotropy and Annotation)
Description: This package provides functions for fitting GPA, a
statistical framework to prioritize GWAS results by integrating
pleiotropy information and annotation data. In addition, it
also includes ShinyGPA, an interactive visualization toolkit to
investigate pleiotropic architecture.
biocViews: Software, StatisticalMethod, Classification,
GenomeWideAssociation, SNP, Genetics, Clustering,
MultipleComparison, Preprocessing, GeneExpression,
DifferentialExpression
Author: Dongjun Chung, Emma Kortemeier, Carter Allen
Maintainer: Dongjun Chung <dongjun.chung@gmail.com>
URL: http://dongjunchung.github.io/GPA/
SystemRequirements: GNU make
BugReports: https://github.com/dongjunchung/GPA/issues
git_url: https://git.bioconductor.org/packages/GPA
git_branch: RELEASE_3_13
git_last_commit: df84978
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GPA_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GPA_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GPA_1.4.0.tgz
vignettes: vignettes/GPA/inst/doc/GPA-example.pdf
vignetteTitles: GPA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GPA/inst/doc/GPA-example.R
dependencyCount: 71
Package: gpart
Version: 1.10.0
Depends: R (>= 3.5.0), grid, Homo.sapiens,
TxDb.Hsapiens.UCSC.hg38.knownGene,
Imports: igraph, biomaRt, Rcpp, data.table, OrganismDbi, AnnotationDbi,
grDevices, stats, utils, GenomicRanges, IRanges
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: a4447db156abeda91e7ae9c39584aad6
NeedsCompilation: yes
Title: Human genome partitioning of dense sequencing data by
identifying haplotype blocks
Description: we provide a new SNP sequence partitioning method which
partitions the whole SNP sequence based on not only LD block
structures but also gene location information. The LD block
construction for GPART is performed using Big-LD algorithm,
with additional improvement from previous version reported in
Kim et al.(2017). We also add a visualization tool to show the
LD heatmap with the information of LD block boundaries and gene
locations in the package.
biocViews: Software, Clustering
Author: Sun Ah Kim [aut, cre, cph], Yun Joo Yoo [aut, cph]
Maintainer: Sun Ah Kim <sunnyeesl@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gpart
git_branch: RELEASE_3_13
git_last_commit: 39e0086
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gpart_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gpart_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gpart_1.10.0.tgz
vignettes: vignettes/gpart/inst/doc/gpart.html
vignetteTitles: Your Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/gpart/inst/doc/gpart.R
dependencyCount: 107
Package: gpls
Version: 1.64.0
Imports: stats
Suggests: MASS
License: Artistic-2.0
MD5sum: ee3a2a5a9504a6a75785d44e56c19d9e
NeedsCompilation: no
Title: Classification using generalized partial least squares
Description: Classification using generalized partial least squares for
two-group and multi-group (more than 2 group) classification.
biocViews: Classification, Microarray, Regression
Author: Beiying Ding
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/gpls
git_branch: RELEASE_3_13
git_last_commit: f8dc89b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gpls_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gpls_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gpls_1.64.0.tgz
vignettes: vignettes/gpls/inst/doc/gpls.pdf
vignetteTitles: gpls Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gpls/inst/doc/gpls.R
suggestsMe: MLInterfaces
dependencyCount: 1
Package: gprege
Version: 1.36.0
Depends: R (>= 2.10), gptk
Suggests: spam
License: AGPL-3
MD5sum: 7c90c2ca478bdfe36d8765f9d6c07fd2
NeedsCompilation: no
Title: Gaussian Process Ranking and Estimation of Gene Expression
time-series
Description: The gprege package implements the methodology described in
Kalaitzis & Lawrence (2011) "A simple approach to ranking
differentially expressed gene expression time-courses through
Gaussian process regression". The software fits two GPs with
the an RBF (+ noise diagonal) kernel on each profile. One GP
kernel is initialised wih a short lengthscale hyperparameter,
signal variance as the observed variance and a zero noise
variance. It is optimised via scaled conjugate gradients
(netlab). A second GP has fixed hyperparameters: zero
inverse-width, zero signal variance and noise variance as the
observed variance. The log-ratio of marginal likelihoods of the
two hypotheses acts as a score of differential expression for
the profile. Comparison via ROC curves is performed against
BATS (Angelini et.al, 2007). A detailed discussion of the
ranking approach and dataset used can be found in the paper
(http://www.biomedcentral.com/1471-2105/12/180).
biocViews: Microarray, Preprocessing, Bioinformatics,
DifferentialExpression, TimeCourse
Author: Alfredo Kalaitzis <alkalait@gmail.com>
Maintainer: Alfredo Kalaitzis <alkalait@gmail.com>
BugReports: alkalait@gmail.com
git_url: https://git.bioconductor.org/packages/gprege
git_branch: RELEASE_3_13
git_last_commit: 3db6999
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gprege_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gprege_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gprege_1.36.0.tgz
vignettes: vignettes/gprege/inst/doc/gprege_quick.pdf
vignetteTitles: gprege Quick Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gprege/inst/doc/gprege_quick.R
dependsOnMe: robin
dependencyCount: 45
Package: gpuMagic
Version: 1.8.0
Depends: R (>= 3.6.0), methods, utils
Imports: Deriv, DescTools, digest, pryr, stringr, BiocGenerics
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-3
Archs: i386, x64
MD5sum: 3f62c474bf6de46bd8a07c10685573d1
NeedsCompilation: yes
Title: An openCL compiler with the capacity to compile R functions and
run the code on GPU
Description: The package aims to help users write openCL code with
little or no effort. It is able to compile an user-defined R
function and run it on a device such as a CPU or a GPU. The
user can also write and run their openCL code directly by
calling .kernel function.
biocViews: Infrastructure
Author: Jiefei Wang
Maintainer: Jiefei Wang <szwjf08@gmail.com>
SystemRequirements: 1. C++11, 2. a graphic driver or a CPU SDK. 3. ICD
loader For Windows user, an ICD loader is required at
C:/windows/system32/OpenCL.dll (Usually it is installed by the
graphic driver). For Linux user (Except mac):
ocl-icd-opencl-dev package is required. For Mac user, no action
is needed for the system has installed the dependency. 4. GNU
make
VignetteBuilder: knitr
BugReports: https://github.com/Jiefei-Wang/gpuMagic/issues
git_url: https://git.bioconductor.org/packages/gpuMagic
git_branch: RELEASE_3_13
git_last_commit: b7bdd0f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gpuMagic_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gpuMagic_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gpuMagic_1.8.0.tgz
vignettes: vignettes/gpuMagic/inst/doc/Customized-openCL-code.html,
vignettes/gpuMagic/inst/doc/Quick_start_guide.html
vignetteTitles: Customized_opencl_code, quickStart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gpuMagic/inst/doc/Customized-openCL-code.R,
vignettes/gpuMagic/inst/doc/Quick_start_guide.R
dependencyCount: 39
Package: granulator
Version: 1.0.0
Depends: R (>= 4.1)
Imports: cowplot, e1071, epiR, dplyr, dtangle, ggplot2, ggplotify,
grDevices, limSolve, magrittr, MASS, nnls, parallel, pheatmap,
purrr, rlang, stats, tibble, tidyr, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
Archs: i386, x64
MD5sum: 66ef1163bd9609c466d12240ccad1990
NeedsCompilation: no
Title: Rapid benchmarking of methods for *in silico* deconvolution of
bulk RNA-seq data
Description: granulator is an R package for the cell type deconvolution
of heterogeneous tissues based on bulk RNA-seq data or single
cell RNA-seq expression profiles. The package provides a
unified testing interface to rapidly run and benchmark multiple
state-of-the-art deconvolution methods. Data for the
deconvolution of peripheral blood mononuclear cells (PBMCs)
into individual immune cell types is provided as well.
biocViews: RNASeq, GeneExpression, DifferentialExpression,
Transcriptomics, SingleCell, StatisticalMethod, Regression
Author: Sabina Pfister [aut, cre], Vincent Kuettel [aut], Enrico
Ferrero [aut]
Maintainer: Sabina Pfister <sabina.pfister@novartis.com>
URL: https://github.com/xanibas/granulator
VignetteBuilder: knitr
BugReports: https://github.com/xanibas/granulator/issues
git_url: https://git.bioconductor.org/packages/granulator
git_branch: RELEASE_3_13
git_last_commit: 5730b42
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/granulator_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/granulator_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/granulator_1.0.0.tgz
vignettes: vignettes/granulator/inst/doc/granulator.html
vignetteTitles: Deconvoluting bulk RNA-seq data with granulator
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/granulator/inst/doc/granulator.R
dependencyCount: 66
Package: graper
Version: 1.8.0
Depends: R (>= 3.6)
Imports: Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats
LinkingTo: Rcpp, RcppArmadillo, BH
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: GPL (>= 2)
MD5sum: 0c6173b3274db27c4b68a0d63ea31bdd
NeedsCompilation: yes
Title: Adaptive penalization in high-dimensional regression and
classification with external covariates using variational Bayes
Description: This package enables regression and classification on
high-dimensional data with different relative strengths of
penalization for different feature groups, such as different
assays or omic types. The optimal relative strengths are chosen
adaptively. Optimisation is performed using a variational Bayes
approach.
biocViews: Regression, Bayesian, Classification
Author: Britta Velten [aut, cre], Wolfgang Huber [aut]
Maintainer: Britta Velten <britta.velten@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/graper
git_branch: RELEASE_3_13
git_last_commit: f94ff44
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/graper_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/graper_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/graper_1.8.0.tgz
vignettes: vignettes/graper/inst/doc/example_linear.html,
vignettes/graper/inst/doc/example_logistic.html
vignetteTitles: example_linear, example_logistic
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/graper/inst/doc/example_linear.R,
vignettes/graper/inst/doc/example_logistic.R
dependencyCount: 43
Package: graph
Version: 1.70.0
Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11)
Imports: stats, stats4, utils
Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster
Enhances: Rgraphviz
License: Artistic-2.0
MD5sum: 4b13e3e8fce8fcaf1f7927181590edb1
NeedsCompilation: yes
Title: graph: A package to handle graph data structures
Description: A package that implements some simple graph handling
capabilities.
biocViews: GraphAndNetwork
Author: R. Gentleman, Elizabeth Whalen, W. Huber, S. Falcon
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/graph
git_branch: RELEASE_3_13
git_last_commit: 1c28350
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/graph_1.70.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/graph_1.70.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/graph_1.70.0.tgz
vignettes: vignettes/graph/inst/doc/clusterGraph.pdf,
vignettes/graph/inst/doc/graph.pdf,
vignettes/graph/inst/doc/graphAttributes.pdf,
vignettes/graph/inst/doc/GraphClass.pdf,
vignettes/graph/inst/doc/MultiGraphClass.pdf
vignetteTitles: clusterGraph and distGraph, Graph, Attributes for Graph
Objects, Graph Design, graphBAM and MultiGraph classes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/graph/inst/doc/clusterGraph.R,
vignettes/graph/inst/doc/graph.R,
vignettes/graph/inst/doc/graphAttributes.R,
vignettes/graph/inst/doc/GraphClass.R,
vignettes/graph/inst/doc/MultiGraphClass.R
dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, BLMA,
CellNOptR, clipper, CNORfeeder, EnrichmentBrowser, gaggle,
GOstats, GraphAT, GSEABase, hypergraph, maigesPack, MineICA,
pathRender, Pigengene, pkgDepTools, PoTRA, RbcBook1, RBGL,
RBioinf, RCyjs, Rgraphviz, ROntoTools, SRAdb, topGO, vtpnet,
ppiData, SNAData, yeastExpData, dlsem, geneNetBP, gridGraphviz,
GUIProfiler, hasseDiagram, msSurv, NFP, PairViz, PerfMeas,
QuACN, RSeed, SubpathwayLNCE
importsMe: alpine, AnnotationHubData, BgeeDB, BiocCheck, biocGraph,
BiocOncoTK, BiocPkgTools, biocViews, bnem, CAMERA, Category,
categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, CytoML,
DAPAR, dce, DEGraph, DEsubs, epiNEM, EventPointer, fgga,
flowCL, flowClust, flowUtils, flowWorkspace, gage, GAPGOM,
GeneNetworkBuilder, GOSim, GraphAT, graphite, hyperdraw,
KEGGgraph, keggorthology, MIGSA, mnem, NCIgraph, NeighborNet,
netresponse, OncoSimulR, ontoProc, oposSOM, OrganismDbi,
pathview, PFP, PhenStat, pkgDepTools, ppiStats, pwOmics,
qpgraph, RCy3, RGraph2js, RpsiXML, rsbml, Rtreemix,
SplicingGraphs, Streamer, trackViewer, VariantFiltering,
BayesNetBP, BiDAG, BNrich, ceg, CePa, classGraph, CodeDepends,
cogmapr, dnet, eulerian, ggm, GGRidge, gRain, gRbase,
gridDebug, gRim, HEMDAG, hmma, HydeNet, kpcalg, MetaClean,
net4pg, netgsa, NetPreProc, pcalg, pcgen, rags2ridges, RANKS,
rsolr, SEMgraph, SourceSet, stablespec, topologyGSA, unifDAG,
wiseR, zenplots
suggestsMe: AnnotationDbi, DEGraph, EBcoexpress, ecolitk, gwascat,
KEGGlincs, MLP, NetPathMiner, rBiopaxParser, rTRM, S4Vectors,
SPIA, VariantTools, arulesViz, bnclassify, bnlearn, bnstruct,
bsub, ccdrAlgorithm, ChoR, epoc, gbutils, GeneNet, gMCP,
igraph, lava, loon, maGUI, psych, rEMM, rPref, sisal, sparsebn,
sparsebnUtils, textplot, tidygraph
dependencyCount: 7
Package: GraphAlignment
Version: 1.56.0
License: file LICENSE
License_restricts_use: yes
Archs: i386, x64
MD5sum: 865ae515d976408dcab44d3688c11137
NeedsCompilation: yes
Title: GraphAlignment
Description: Graph alignment is an extension package for the R
programming environment which provides functions for finding an
alignment between two networks based on link and node
similarity scores. (J. Berg and M. Laessig, "Cross-species
analysis of biological networks by Bayesian alignment", PNAS
103 (29), 10967-10972 (2006))
biocViews: GraphAndNetwork, Network
Author: Joern P. Meier <mail@ionflux.org>, Michal Kolar, Ville
Mustonen, Michael Laessig, and Johannes Berg.
Maintainer: Joern P. Meier <mail@ionflux.org>
URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/
git_url: https://git.bioconductor.org/packages/GraphAlignment
git_branch: RELEASE_3_13
git_last_commit: 3b476a9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GraphAlignment_1.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GraphAlignment_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GraphAlignment_1.56.0.tgz
vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf
vignetteTitles: GraphAlignment
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GraphAlignment/inst/doc/GraphAlignment.R
dependencyCount: 0
Package: GraphAT
Version: 1.64.0
Depends: R (>= 2.10), graph, methods
Imports: graph, MCMCpack, methods, stats
License: LGPL
MD5sum: 32dae02ecd942edece6dcc76b198c14e
NeedsCompilation: no
Title: Graph Theoretic Association Tests
Description: Functions and data used in Balasubramanian, et al. (2004)
biocViews: Network, GraphAndNetwork
Author: R. Balasubramanian, T. LaFramboise, D. Scholtens
Maintainer: Thomas LaFramboise <tlaframb@hsph.harvard.edu>
git_url: https://git.bioconductor.org/packages/GraphAT
git_branch: RELEASE_3_13
git_last_commit: cf236c4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GraphAT_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GraphAT_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GraphAT_1.64.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 23
Package: graphite
Version: 1.38.0
Depends: R (>= 2.10), methods
Imports: AnnotationDbi, checkmate, graph (>= 1.67.1), httr, rappdirs,
stats, utils
Suggests: a4Preproc, ALL, BiocStyle, clipper, codetools,
hgu133plus2.db, hgu95av2.db, impute, knitr, org.Hs.eg.db,
parallel, R.rsp, RCy3, rmarkdown, SPIA (>= 2.2), testthat,
topologyGSA (>= 1.4.0)
License: AGPL-3
MD5sum: 474d53e4417610fcf159b2fa689c5ba2
NeedsCompilation: no
Title: GRAPH Interaction from pathway Topological Environment
Description: Graph objects from pathway topology derived from KEGG,
Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways
databases.
biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network,
Reactome, KEGG, Metabolomics
Author: Gabriele Sales <gabriele.sales@unipd.it>, Enrica Calura
<enrica.calura@gmail.com>, Chiara Romualdi
<chiara.romualdi@unipd.it>
Maintainer: Gabriele Sales <gabriele.sales@unipd.it>
VignetteBuilder: knitr, R.rsp
git_url: https://git.bioconductor.org/packages/graphite
git_branch: RELEASE_3_13
git_last_commit: 7517460
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/graphite_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/graphite_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/graphite_1.38.0.tgz
vignettes: vignettes/graphite/inst/doc/graphite.pdf,
vignettes/graphite/inst/doc/metabolites.pdf
vignetteTitles: GRAPH Interaction from pathway Topological Environment,
metabolites.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/graphite/inst/doc/graphite.R
dependsOnMe: PoTRA
importsMe: dce, EnrichmentBrowser, mogsa, multiGSEA, ReactomePA,
StarBioTrek, ICDS, netgsa
suggestsMe: clipper, InterCellar, metaboliteIDmapping, NFP, SEMgraph,
SourceSet
dependencyCount: 50
Package: GraphPAC
Version: 1.34.0
Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow
Suggests: RUnit, BiocGenerics
License: GPL-2
Archs: i386, x64
MD5sum: 25ec2e4272de63e0780588dbae594819
NeedsCompilation: no
Title: Identification of Mutational Clusters in Proteins via a Graph
Theoretical Approach.
Description: Identifies mutational clusters of amino acids in a protein
while utilizing the proteins tertiary structure via a graph
theoretical model.
biocViews: Clustering, Proteomics
Author: Gregory Ryslik, Hongyu Zhao
Maintainer: Gregory Ryslik <gregory.ryslik@yale.edu>
git_url: https://git.bioconductor.org/packages/GraphPAC
git_branch: RELEASE_3_13
git_last_commit: 763bd51
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GraphPAC_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GraphPAC_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GraphPAC_1.34.0.tgz
vignettes: vignettes/GraphPAC/inst/doc/GraphPAC.pdf
vignetteTitles: iPAC: identification of Protein Amino acid Mutations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GraphPAC/inst/doc/GraphPAC.R
dependsOnMe: QuartPAC
dependencyCount: 40
Package: GRENITS
Version: 1.44.0
Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8),
ggplot2 (>= 0.9.0)
Imports: graphics, grDevices, reshape2, stats, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: network
License: GPL (>= 2)
Archs: i386, x64
MD5sum: b8854e7d5340e3130e817bb69591ea32
NeedsCompilation: yes
Title: Gene Regulatory Network Inference Using Time Series
Description: The package offers four network inference statistical
models using Dynamic Bayesian Networks and Gibbs Variable
Selection: a linear interaction model, two linear interaction
models with added experimental noise (Gaussian and Student
distributed) for the case where replicates are available and a
non-linear interaction model.
biocViews: NetworkInference, GeneRegulation, TimeCourse,
GraphAndNetwork, GeneExpression, Network, Bayesian
Author: Edward Morrissey
Maintainer: Edward Morrissey <edward.morrissey@gmail.com>
git_url: https://git.bioconductor.org/packages/GRENITS
git_branch: RELEASE_3_13
git_last_commit: fa79c31
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GRENITS_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GRENITS_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GRENITS_1.44.0.tgz
vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf
vignetteTitles: GRENITS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GRENITS/inst/doc/GRENITS_package.R
dependencyCount: 45
Package: GreyListChIP
Version: 1.24.0
Depends: R (>= 4.0), methods, GenomicRanges
Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS,
parallel, GenomeInfoDb, SummarizedExperiment, stats, utils
Suggests: BiocStyle, BiocGenerics, RUnit
Enhances: BSgenome.Hsapiens.UCSC.hg19
License: Artistic-2.0
Archs: i386, x64
MD5sum: 2704de9e3625160e90a4f790c1424c1b
NeedsCompilation: no
Title: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs
Description: Identify regions of ChIP experiments with high signal in
the input, that lead to spurious peaks during peak calling.
Remove reads aligning to these regions prior to peak calling,
for cleaner ChIP analysis.
biocViews: ChIPSeq, Alignment, Preprocessing, DifferentialPeakCalling,
Sequencing, GenomeAnnotation, Coverage
Author: Gord Brown <gdbzork@gmail.com>
Maintainer: Gordon Brown <gordon.brown@cruk.cam.ac.uk>
git_url: https://git.bioconductor.org/packages/GreyListChIP
git_branch: RELEASE_3_13
git_last_commit: 2a86668
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GreyListChIP_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GreyListChIP_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GreyListChIP_1.24.0.tgz
vignettes: vignettes/GreyListChIP/inst/doc/GreyList-demo.pdf
vignetteTitles: Generating Grey Lists from Input Libraries
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GreyListChIP/inst/doc/GreyList-demo.R
importsMe: DiffBind, epigraHMM
dependencyCount: 46
Package: GRmetrics
Version: 1.18.0
Depends: R (>= 4.0), SummarizedExperiment
Imports: drc, plotly, ggplot2, S4Vectors, stats
Suggests: knitr, rmarkdown, BiocStyle, tinytex
License: GPL-3
MD5sum: 3527d7e31cbc65b566b3335416d4f8fa
NeedsCompilation: no
Title: Calculate growth-rate inhibition (GR) metrics
Description: Functions for calculating and visualizing growth-rate
inhibition (GR) metrics.
biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Software,
TimeCourse, Visualization
Author: Nicholas Clark
Maintainer: Nicholas Clark <nicholas.clark00@gmail.com>, Mario
Medvedovic <medvedm@ucmail.uc.edu>
URL: https://github.com/uc-bd2k/GRmetrics
VignetteBuilder: knitr
BugReports: https://github.com/uc-bd2k/GRmetrics/issues
git_url: https://git.bioconductor.org/packages/GRmetrics
git_branch: RELEASE_3_13
git_last_commit: 28eeed1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GRmetrics_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GRmetrics_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GRmetrics_1.18.0.tgz
vignettes: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html
vignetteTitles: GRmetrics: an R package for calculation and
visualization of dose-response metrics based on growth rate
inhibition
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.R
dependencyCount: 135
Package: groHMM
Version: 1.26.0
Depends: R (>= 3.0.2), MASS, parallel, S4Vectors (>= 0.17.25), IRanges
(>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8),
GenomicAlignments (>= 1.15.6), rtracklayer (>= 1.39.7)
Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL-3
MD5sum: 94c326b37c3ee2295fa75e1cffa8bbea
NeedsCompilation: yes
Title: GRO-seq Analysis Pipeline
Description: A pipeline for the analysis of GRO-seq data.
biocViews: Sequencing, Software
Author: Charles G. Danko, Minho Chae, Andre Martins, W. Lee Kraus
Maintainer: Anusha Nagari <anusha.nagari@utsouthwestern.edu>, Tulip
Nandu <tulip.nandu@utsouthwestern.edu>, W. Lee Kraus
<lee.kraus@utsouthwestern.edu>
URL: https://github.com/Kraus-Lab/groHMM
BugReports: https://github.com/Kraus-Lab/groHMM/issues
git_url: https://git.bioconductor.org/packages/groHMM
git_branch: RELEASE_3_13
git_last_commit: 6be33d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/groHMM_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/groHMM_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/groHMM_1.26.0.tgz
vignettes: vignettes/groHMM/inst/doc/groHMM.pdf
vignetteTitles: groHMM tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/groHMM/inst/doc/groHMM.R
dependencyCount: 45
Package: GRridge
Version: 1.16.0
Depends: R (>= 3.2), penalized, Iso, survival, methods,
graph,stats,glmnet,mvtnorm
Suggests: testthat
License: GPL-3
MD5sum: 4908bd4e8e82d58c75e8f1c693609c84
NeedsCompilation: no
Title: Better prediction by use of co-data: Adaptive group-regularized
ridge regression
Description: This package allows the use of multiple sources of co-data
(e.g. external p-values, gene lists, annotation) to improve
prediction of binary, continuous and survival response using
(logistic, linear or Cox) group-regularized ridge regression.
It also facilitates post-hoc variable selection and prediction
diagnostics by cross-validation using ROC curves and AUC.
biocViews: Classification, Regression, Survival, Bayesian, RNASeq,
GenePrediction, GeneExpression, Pathways, GeneSetEnrichment,
GO, KEGG, GraphAndNetwork, ImmunoOncology
Author: Mark A. van de Wiel <mark.vdwiel@vumc.nl>, Putri W. Novianti
<p.novianti@vumc.nl>
Maintainer: Mark A. van de Wiel <mark.vdwiel@vumc.nl>
git_url: https://git.bioconductor.org/packages/GRridge
git_branch: RELEASE_3_13
git_last_commit: 45dab71
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GRridge_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GRridge_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GRridge_1.16.0.tgz
vignettes: vignettes/GRridge/inst/doc/GRridge.pdf
vignetteTitles: GRridge
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GRridge/inst/doc/GRridge.R
dependencyCount: 24
Package: GSALightning
Version: 1.20.0
Depends: R (>= 3.3.0)
Imports: Matrix, data.table, stats
Suggests: knitr, rmarkdown
License: GPL (>=2)
Archs: i386, x64
MD5sum: 4a6afb19ec3b19d7fb0dac3275b2f903
NeedsCompilation: no
Title: Fast Permutation-based Gene Set Analysis
Description: GSALightning provides a fast implementation of
permutation-based gene set analysis for two-sample problem.
This package is particularly useful when testing simultaneously
a large number of gene sets, or when a large number of
permutations is necessary for more accurate p-values
estimation.
biocViews: Software, BiologicalQuestion, GeneSetEnrichment,
DifferentialExpression, GeneExpression, Transcription
Author: Billy Heung Wing Chang
Maintainer: Billy Heung Wing Chang <billyheungwing@gmail.com>
URL: https://github.com/billyhw/GSALightning
VignetteBuilder: knitr
BugReports: https://github.com/billyhw/GSALightning/issues
git_url: https://git.bioconductor.org/packages/GSALightning
git_branch: RELEASE_3_13
git_last_commit: 05ff5ee
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GSALightning_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GSALightning_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GSALightning_1.20.0.tgz
vignettes: vignettes/GSALightning/inst/doc/vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GSALightning/inst/doc/vignette.R
dependencyCount: 9
Package: GSAR
Version: 1.26.0
Depends: R (>= 3.0.1), igraph (>= 0.7.1)
Imports: stats, graphics
Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate,
org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR,
BiocStyle
License: GPL (>=2)
MD5sum: 9bdc740eccaae3abcfbd6451fba0ea57
NeedsCompilation: no
Title: Gene Set Analysis in R
Description: Gene set analysis using specific alternative hypotheses.
Tests for differential expression, scale and net correlation
structure.
biocViews: Software, StatisticalMethod, DifferentialExpression
Author: Yasir Rahmatallah <yrahmatallah@uams.edu>, Galina Glazko
<gvglazko@uams.edu>
Maintainer: Yasir Rahmatallah <yrahmatallah@uams.edu>, Galina Glazko
<gvglazko@uams.edu>
git_url: https://git.bioconductor.org/packages/GSAR
git_branch: RELEASE_3_13
git_last_commit: f3ae269
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GSAR_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GSAR_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GSAR_1.26.0.tgz
vignettes: vignettes/GSAR/inst/doc/GSAR.pdf
vignetteTitles: Gene Set Analysis in R -- the GSAR Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSAR/inst/doc/GSAR.R
dependencyCount: 11
Package: GSCA
Version: 2.22.0
Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5,
R(>= 2.10.0)
Imports: graphics
Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr,
Affyhgu133Plus2Expr
License: GPL(>=2)
MD5sum: 7318a5da26bb2f41ca152f84fb66cd6b
NeedsCompilation: no
Title: GSCA: Gene Set Context Analysis
Description: GSCA takes as input several lists of activated and
repressed genes. GSCA then searches through a compendium of
publicly available gene expression profiles for biological
contexts that are enriched with a specified pattern of gene
expression. GSCA provides both traditional R functions and
interactive, user-friendly user interface.
biocViews: GeneExpression, Visualization, GUI
Author: Zhicheng Ji, Hongkai Ji
Maintainer: Zhicheng Ji <zji4@jhu.edu>
git_url: https://git.bioconductor.org/packages/GSCA
git_branch: RELEASE_3_13
git_last_commit: 24c5884
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GSCA_2.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GSCA_2.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GSCA_2.22.0.tgz
vignettes: vignettes/GSCA/inst/doc/GSCA.pdf
vignetteTitles: GSCA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSCA/inst/doc/GSCA.R
dependencyCount: 72
Package: gscreend
Version: 1.6.0
Depends: R (>= 3.6)
Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel,
graphics
Suggests: knitr, testthat
License: GPL-3
Archs: i386, x64
MD5sum: 14b755df22c37703e5ce5ea44fe2efdb
NeedsCompilation: no
Title: Analysis of pooled genetic screens
Description: Package for the analysis of pooled genetic screens (e.g.
CRISPR-KO). The analysis of such screens is based on the
comparison of gRNA abundances before and after a cell
proliferation phase. The gscreend packages takes gRNA counts as
input and allows detection of genes whose knockout decreases or
increases cell proliferation.
biocViews: Software, StatisticalMethod, PooledScreens, CRISPR
Author: Katharina Imkeller [cre, aut], Wolfgang Huber [aut]
Maintainer: Katharina Imkeller <k.imkeller@dkfz.de>
URL: https://github.com/imkeller/gscreend
VignetteBuilder: knitr
BugReports: https://github.com/imkeller/gscreend/issues
git_url: https://git.bioconductor.org/packages/gscreend
git_branch: RELEASE_3_13
git_last_commit: eaa4f18
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gscreend_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gscreend_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gscreend_1.6.0.tgz
vignettes: vignettes/gscreend/inst/doc/gscreend_simulated_data.html
vignetteTitles: Example_simulated
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gscreend/inst/doc/gscreend_simulated_data.R
dependencyCount: 43
Package: GSEABase
Version: 1.54.0
Depends: R (>= 2.6.0), BiocGenerics (>= 0.13.8), Biobase (>= 2.17.8),
annotate (>= 1.45.3), methods, graph (>= 1.37.2)
Imports: AnnotationDbi, XML
Suggests: hgu95av2.db, GO.db, org.Hs.eg.db, Rgraphviz, ReportingTools,
testthat, BiocStyle, knitr
License: Artistic-2.0
MD5sum: 4767797b8fb23c8726cf976ef7694817
NeedsCompilation: no
Title: Gene set enrichment data structures and methods
Description: This package provides classes and methods to support Gene
Set Enrichment Analysis (GSEA).
biocViews: GeneExpression, GeneSetEnrichment, GraphAndNetwork, GO, KEGG
Author: Martin Morgan, Seth Falcon, Robert Gentleman
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GSEABase
git_branch: RELEASE_3_13
git_last_commit: 5b59f70
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GSEABase_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GSEABase_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GSEABase_1.54.0.tgz
vignettes: vignettes/GSEABase/inst/doc/GSEABase.pdf
vignetteTitles: An introduction to GSEABase
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R
dependsOnMe: AGDEX, BicARE, CCPROMISE, cpvSNP, npGSEA, PROMISE,
splineTimeR, TissueEnrich, GSVAdata, OSCA.basic
importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cellHTS2,
EnrichmentBrowser, escape, gep2pep, GISPA, GlobalAncova, GmicR,
GSRI, GSVA, MIGSA, miRSM, mogsa, oppar, phenoTest, PROMISE,
RcisTarget, ReportingTools, scTGIF, signatureSearch,
singleCellTK, singscore, slalom, TFutils, vissE, msigdb,
SingscoreAMLMutations, clustermole, immcp, RVA
suggestsMe: BiocSet, gage, globaltest, GOstats, GSAR, MAST, phenoTest,
TFEA.ChIP, BaseSet
dependencyCount: 50
Package: GSEABenchmarkeR
Version: 1.12.1
Depends: Biobase, SummarizedExperiment
Imports: AnnotationDbi, AnnotationHub, BiocFileCache, BiocParallel,
edgeR, EnrichmentBrowser, ExperimentHub, grDevices, graphics,
KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, methods,
S4Vectors, stats, utils
Suggests: BiocStyle, GSE62944, knitr, rmarkdown
License: Artistic-2.0
MD5sum: ab9e4124b0208a90cba53a3c343a6bb2
NeedsCompilation: no
Title: Reproducible GSEA Benchmarking
Description: The GSEABenchmarkeR package implements an extendable
framework for reproducible evaluation of set- and network-based
methods for enrichment analysis of gene expression data. This
includes support for the efficient execution of these methods
on comprehensive real data compendia (microarray and RNA-seq)
using parallel computation on standard workstations and
institutional computer grids. Methods can then be assessed with
respect to runtime, statistical significance, and relevance of
the results for the phenotypes investigated.
biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression,
DifferentialExpression, Pathways, GraphAndNetwork, Network,
GeneSetEnrichment, NetworkEnrichment, Visualization,
ReportWriting
Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara
Santarelli [ctb], Lucas Schiffer [ctb], Marcel Ramos [ctb],
Ralf Zimmer [aut], Levi Waldron [aut]
Maintainer: Ludwig Geistlinger <Ludwig_Geistlinger@hms.harvard.edu>
URL: https://github.com/waldronlab/GSEABenchmarkeR
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/GSEABenchmarkeR/issues
git_url: https://git.bioconductor.org/packages/GSEABenchmarkeR
git_branch: RELEASE_3_13
git_last_commit: 06d448c
git_last_commit_date: 2021-07-16
Date/Publication: 2021-07-18
source.ver: src/contrib/GSEABenchmarkeR_1.12.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GSEABenchmarkeR_1.12.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/GSEABenchmarkeR_1.12.1.tgz
vignettes: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.html
vignetteTitles: Reproducible GSEA Benchmarking
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.R
dependencyCount: 125
Package: GSEAlm
Version: 1.52.0
Depends: Biobase
Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db,
genefilter, GOstats, RColorBrewer
License: Artistic-2.0
MD5sum: 512767e6b0906042f4bd6384f07de72f
NeedsCompilation: no
Title: Linear Model Toolset for Gene Set Enrichment Analysis
Description: Models and methods for fitting linear models to gene
expression data, together with tools for computing and using
various regression diagnostics.
biocViews: Microarray
Author: Assaf Oron, Robert Gentleman (with contributions from S. Falcon
and Z. Jiang)
Maintainer: Assaf Oron <assaf@uw.edu>
git_url: https://git.bioconductor.org/packages/GSEAlm
git_branch: RELEASE_3_13
git_last_commit: 12cf851
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GSEAlm_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GSEAlm_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GSEAlm_1.52.0.tgz
vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf
vignetteTitles: Linear models in GSEA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSEAlm/inst/doc/GSEAlm.R
dependencyCount: 7
Package: GSEAmining
Version: 1.2.0
Depends: R (>= 4.0)
Imports: dplyr, tidytext, dendextend, tibble, ggplot2, ggwordcloud,
stringr, gridExtra, rlang, grDevices, graphics, stats, methods
Suggests: knitr, rmarkdown, BiocStyle, clusterProfiler, testthat
License: GPL-3 | file LICENSE
MD5sum: 53edb681143aedb193a2ffe9c6499cae
NeedsCompilation: no
Title: Make Biological Sense of Gene Set Enrichment Analysis Outputs
Description: Gene Set Enrichment Analysis is a very powerful and
interesting computational method that allows an easy
correlation between differential expressed genes and biological
processes. Unfortunately, although it was designed to help
researchers to interpret gene expression data it can generate
huge amounts of results whose biological meaning can be
difficult to interpret. Many available tools rely on the
hierarchically structured Gene Ontology (GO) classification to
reduce reundandcy in the results. However, due to the
popularity of GSEA many more gene set collections, such as
those in the Molecular Signatures Database are emerging. Since
these collections are not organized as those in GO, their usage
for GSEA do not always give a straightforward answer or, in
other words, getting all the meaninful information can be
challenging with the currently available tools. For these
reasons, GSEAmining was born to be an easy tool to create
reproducible reports to help researchers make biological sense
of GSEA outputs. Given the results of GSEA, GSEAmining clusters
the different gene sets collections based on the presence of
the same genes in the leadind edge (core) subset. Leading edge
subsets are those genes that contribute most to the enrichment
score of each collection of genes or gene sets. For this
reason, gene sets that participate in similar biological
processes should share genes in common and in turn cluster
together. After that, GSEAmining is able to identify and
represent for each cluster: - The most enriched terms in the
names of gene sets (as wordclouds) - The most enriched genes in
the leading edge subsets (as bar plots). In each case, positive
and negative enrichments are shown in different colors so it is
easy to distinguish biological processes or genes that may be
of interest in that particular study.
biocViews: GeneSetEnrichment, Clustering, Visualization
Author: Oriol Arqués [aut, cre]
Maintainer: Oriol Arqués <oriol.arques@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GSEAmining
git_branch: RELEASE_3_13
git_last_commit: 862eab4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GSEAmining_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GSEAmining_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GSEAmining_1.2.0.tgz
vignettes: vignettes/GSEAmining/inst/doc/GSEAmining.html
vignetteTitles: GSEAmining
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GSEAmining/inst/doc/GSEAmining.R
dependencyCount: 57
Package: gsean
Version: 1.12.0
Depends: R (>= 3.5), fgsea, PPInfer
Suggests: SummarizedExperiment, knitr, plotly, RANKS, WGCNA, rmarkdown
License: Artistic-2.0
MD5sum: a05c09a5727e1395c8b16937efcd44a9
NeedsCompilation: no
Title: Gene Set Enrichment Analysis with Networks
Description: Biological molecules in a living organism seldom work
individually. They usually interact each other in a cooperative
way. Biological process is too complicated to understand
without considering such interactions. Thus, network-based
procedures can be seen as powerful methods for studying complex
process. However, many methods are devised for analyzing
individual genes. It is said that techniques based on
biological networks such as gene co-expression are more precise
ways to represent information than those using lists of genes
only. This package is aimed to integrate the gene expression
and biological network. A biological network is constructed
from gene expression data and it is used for Gene Set
Enrichment Analysis.
biocViews: Software, StatisticalMethod, Network, GraphAndNetwork,
GeneSetEnrichment, GeneExpression, NetworkEnrichment, Pathways,
DifferentialExpression
Author: Dongmin Jung
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gsean
git_branch: RELEASE_3_13
git_last_commit: 211c760
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gsean_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gsean_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gsean_1.12.0.tgz
vignettes: vignettes/gsean/inst/doc/gsean.html
vignetteTitles: gsean
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gsean/inst/doc/gsean.R
dependencyCount: 117
Package: GSgalgoR
Version: 1.2.1
Imports: cluster, doParallel, foreach, matchingR, nsga2R, survival,
proxy, stats, methods,
Suggests: knitr, rmarkdown, ggplot2, BiocStyle, genefu, survcomp,
Biobase, survminer, breastCancerTRANSBIG, breastCancerUPP,
iC10TrainingData, pamr, testthat
License: MIT + file LICENSE
MD5sum: 87bf265e0ef4f9fbd8987f2edad6d3eb
NeedsCompilation: no
Title: An Evolutionary Framework for the Identification and Study of
Prognostic Gene Expression Signatures in Cancer
Description: A multi-objective optimization algorithm for disease
sub-type discovery based on a non-dominated sorting genetic
algorithm. The 'Galgo' framework combines the advantages of
clustering algorithms for grouping heterogeneous 'omics' data
and the searching properties of genetic algorithms for feature
selection. The algorithm search for the optimal number of
clusters determination considering the features that maximize
the survival difference between sub-types while keeping cluster
consistency high.
biocViews: GeneExpression, Transcription, Clustering, Classification,
Survival
Author: Martin Guerrero [aut], Carlos Catania [cre]
Maintainer: Carlos Catania <harpomaxx@gmail.com>
URL: https://github.com/harpomaxx/GSgalgoR
VignetteBuilder: knitr
BugReports: https://github.com/harpomaxx/GSgalgoR/issues
git_url: https://git.bioconductor.org/packages/GSgalgoR
git_branch: RELEASE_3_13
git_last_commit: aa54268
git_last_commit_date: 2021-05-21
Date/Publication: 2021-05-23
source.ver: src/contrib/GSgalgoR_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GSgalgoR_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/GSgalgoR_1.2.1.tgz
vignettes: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.html,
vignettes/GSgalgoR/inst/doc/GSgalgoR.html
vignetteTitles: GSgalgoR_callbacks.html, GSgalgoR.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.R,
vignettes/GSgalgoR/inst/doc/GSgalgoR.R
dependencyCount: 22
Package: GSReg
Version: 1.26.0
Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures,
AnnotationDbi
Suggests: GenomicRanges, GSBenchMark
License: GPL-2
MD5sum: 378920ab35082f0ac6eeb268962995a9
NeedsCompilation: yes
Title: Gene Set Regulation (GS-Reg)
Description: A package for gene set analysis based on the variability
of expressions as well as a method to detect Alternative
Splicing Events . It implements DIfferential RAnk Conservation
(DIRAC) and gene set Expression Variation Analysis (EVA)
methods. For detecting Differentially Spliced genes, it
provides an implementation of the Spliced-EVA (SEVA).
biocViews: GeneRegulation, Pathways, GeneExpression,
GeneticVariability, GeneSetEnrichment, AlternativeSplicing
Author: Bahman Afsari <bahman@jhu.edu>, Elana J. Fertig
<ejfertig@jhmi.edu>
Maintainer: Bahman Afsari <bahman@jhu.edu>, Elana J. Fertig
<ejfertig@jhmi.edu>
git_url: https://git.bioconductor.org/packages/GSReg
git_branch: RELEASE_3_13
git_last_commit: f0f3ac7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GSReg_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GSReg_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GSReg_1.26.0.tgz
vignettes: vignettes/GSReg/inst/doc/GSReg.pdf
vignetteTitles: Working with the GSReg package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSReg/inst/doc/GSReg.R
dependencyCount: 104
Package: GSRI
Version: 2.40.0
Depends: R (>= 2.14.2), fdrtool
Imports: methods, graphics, stats, utils, genefilter, Biobase,
GSEABase, les (>= 1.1.6)
Suggests: limma, hgu95av2.db
Enhances: parallel
License: GPL-3
MD5sum: a5210ffc87bdeea31d05f0e470be2d51
NeedsCompilation: no
Title: Gene Set Regulation Index
Description: The GSRI package estimates the number of differentially
expressed genes in gene sets, utilizing the concept of the Gene
Set Regulation Index (GSRI).
biocViews: Microarray, Transcription, DifferentialExpression,
GeneSetEnrichment, GeneRegulation
Author: Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer
Maintainer: Julian Gehring <jg-bioc@gmx.com>
git_url: https://git.bioconductor.org/packages/GSRI
git_branch: RELEASE_3_13
git_last_commit: 4bba992
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GSRI_2.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GSRI_2.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GSRI_2.40.0.tgz
vignettes: vignettes/GSRI/inst/doc/gsri.pdf
vignetteTitles: Introduction to the GSRI package: Estimating Regulatory
Effects utilizing the Gene Set Regulation Index
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSRI/inst/doc/gsri.R
dependencyCount: 65
Package: GSVA
Version: 1.40.1
Depends: R (>= 3.5.0)
Imports: methods, stats, utils, graphics, S4Vectors, IRanges, Biobase,
SummarizedExperiment, GSEABase, Matrix, parallel, BiocParallel,
SingleCellExperiment, sparseMatrixStats, DelayedArray,
DelayedMatrixStats, HDF5Array, BiocSingular
Suggests: BiocGenerics, RUnit, BiocStyle, knitr, rmarkdown, limma,
RColorBrewer, org.Hs.eg.db, genefilter, edgeR, GSVAdata, shiny,
shinydashboard, ggplot2, data.table, plotly, future, promises,
shinybusy, shinyjs
License: GPL (>= 2)
MD5sum: 4c5007a9e17263c377e0019b0d4ba9b3
NeedsCompilation: yes
Title: Gene Set Variation Analysis for microarray and RNA-seq data
Description: Gene Set Variation Analysis (GSVA) is a non-parametric,
unsupervised method for estimating variation of gene set
enrichment through the samples of a expression data set. GSVA
performs a change in coordinate systems, transforming the data
from a gene by sample matrix to a gene-set by sample matrix,
thereby allowing the evaluation of pathway enrichment for each
sample. This new matrix of GSVA enrichment scores facilitates
applying standard analytical methods like functional
enrichment, survival analysis, clustering, CNV-pathway analysis
or cross-tissue pathway analysis, in a pathway-centric manner.
biocViews: FunctionalGenomics, Microarray, RNASeq, Pathways,
GeneSetEnrichment
Author: Justin Guinney [aut, cre], Robert Castelo [aut], Alexey
Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb]
Maintainer: Justin Guinney <justin.guinney@sagebase.org>
URL: https://github.com/rcastelo/GSVA
VignetteBuilder: knitr
BugReports: https://github.com/rcastelo/GSVA/issues
git_url: https://git.bioconductor.org/packages/GSVA
git_branch: RELEASE_3_13
git_last_commit: 61b842e
git_last_commit_date: 2021-06-03
Date/Publication: 2021-06-06
source.ver: src/contrib/GSVA_1.40.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GSVA_1.40.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/GSVA_1.40.1.tgz
vignettes: vignettes/GSVA/inst/doc/GSVA.html
vignetteTitles: Gene set variation analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSVA/inst/doc/GSVA.R
dependsOnMe: MM2S
importsMe: consensusOV, decoupleR, EGSEA, escape, oppar, singleCellTK,
TBSignatureProfiler, TNBC.CMS, clustermole, immcp,
psSubpathway, scMappR, SIGN, sigQC, SMDIC
suggestsMe: MCbiclust
dependencyCount: 78
Package: gtrellis
Version: 1.24.0
Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges
Imports: circlize (>= 0.4.8), GetoptLong, grDevices, utils
Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown,
ComplexHeatmap (>= 1.99.0), Cairo, png, jpeg, tiff
License: MIT + file LICENSE
MD5sum: 7fb8b03642b8e75f9beecad9b571747c
NeedsCompilation: no
Title: Genome Level Trellis Layout
Description: Genome level Trellis graph visualizes genomic data
conditioned by genomic categories (e.g. chromosomes). For each
genomic category, multiple dimensional data which are
represented as tracks describe different features from
different aspects. This package provides high flexibility to
arrange genomic categories and to add self-defined graphics in
the plot.
biocViews: Software, Visualization, Sequencing
Author: Zuguang Gu
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/gtrellis
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gtrellis
git_branch: RELEASE_3_13
git_last_commit: d92f3f7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gtrellis_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gtrellis_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gtrellis_1.24.0.tgz
vignettes: vignettes/gtrellis/inst/doc/gtrellis.html
vignetteTitles: Make Genome-level Trellis Graph
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R
importsMe: YAPSA
dependencyCount: 26
Package: GUIDEseq
Version: 1.22.0
Depends: R (>= 3.2.0), GenomicRanges, BiocGenerics
Imports: BiocParallel, Biostrings, CRISPRseek, ChIPpeakAnno,
data.table, matrixStats, BSgenome, parallel, IRanges (>=
2.5.5), S4Vectors (>= 0.9.6), GenomicAlignments (>= 1.7.3),
GenomeInfoDb, Rsamtools, hash, limma,dplyr
Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db
License: GPL (>= 2)
MD5sum: a923a95cbf0ccfbb8f92f98159b3a517
NeedsCompilation: no
Title: GUIDE-seq analysis pipeline
Description: The package implements GUIDE-seq analysis workflow
including functions for obtaining unique insertion sites (proxy
of cleavage sites), estimating the locations of the insertion
sites, aka, peaks, merging estimated insertion sites from plus
and minus strand, and performing off target search of the
extended regions around insertion sites.
biocViews: ImmunoOncology, GeneRegulation, Sequencing, WorkflowStep,
CRISPR
Author: Lihua Julie Zhu, Michael Lawrence, Ankit Gupta, Hervé Pagès ,
Alper Kucukural, Manuel Garber, Scot A. Wolfe
Maintainer: Lihua Julie Zhu <julie.zhu@umassmed.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GUIDEseq
git_branch: RELEASE_3_13
git_last_commit: d6c83e7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GUIDEseq_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GUIDEseq_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GUIDEseq_1.22.0.tgz
vignettes: vignettes/GUIDEseq/inst/doc/GUIDEseq.pdf
vignetteTitles: GUIDEseq Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GUIDEseq/inst/doc/GUIDEseq.R
importsMe: crisprseekplus
dependencyCount: 138
Package: Guitar
Version: 2.8.0
Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges,
magrittr, ggplot2, methods, stats,utils ,knitr,dplyr
License: GPL-2
Archs: i386, x64
MD5sum: f92d747d23021cdaf0add7361da4a373
NeedsCompilation: no
Title: Guitar
Description: The package is designed for visualization of RNA-related
genomic features with respect to the landmarks of RNA
transcripts, i.e., transcription starting site, start codon,
stop codon and transcription ending site.
biocViews: Sequencing, SplicedAlignment, Alignment, DataImport, RNASeq,
MethylSeq, QualityControl, Transcription
Author: Xiao Du, Hui Liu, Lin Zhang, Jia Meng
Maintainer: Jia Meng <jia.meng@xjtlu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Guitar
git_branch: RELEASE_3_13
git_last_commit: c233129
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Guitar_2.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Guitar_2.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Guitar_2.8.0.tgz
vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf
vignetteTitles: Guitar
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Guitar/inst/doc/Guitar-Overview.R
dependencyCount: 114
Package: Gviz
Version: 1.36.2
Depends: R (>= 4.0), methods, S4Vectors (>= 0.9.25), IRanges (>=
1.99.18), GenomicRanges (>= 1.17.20), grid
Imports: XVector (>= 0.5.7), rtracklayer (>= 1.25.13), lattice,
RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5),
Biobase (>= 2.15.3), GenomicFeatures (>= 1.17.22), ensembldb
(>= 2.11.3), BSgenome (>= 1.33.1), Biostrings (>= 2.33.11),
biovizBase (>= 1.13.8), Rsamtools (>= 1.17.28), latticeExtra
(>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>=
1.1.16), GenomeInfoDb (>= 1.1.3), BiocGenerics (>= 0.11.3),
digest(>= 0.6.8), graphics, grDevices, stats, utils
Suggests: BSgenome.Hsapiens.UCSC.hg19, xml2, BiocStyle, knitr,
rmarkdown, testthat
License: Artistic-2.0
MD5sum: a4e111d7b1d0204c66dda6c2114960f4
NeedsCompilation: no
Title: Plotting data and annotation information along genomic
coordinates
Description: Genomic data analyses requires integrated visualization of
known genomic information and new experimental data. Gviz uses
the biomaRt and the rtracklayer packages to perform live
annotation queries to Ensembl and UCSC and translates this to
e.g. gene/transcript structures in viewports of the grid
graphics package. This results in genomic information plotted
together with your data.
biocViews: Visualization, Microarray, Sequencing
Author: Florian Hahne [aut], Steffen Durinck [aut], Robert Ivanek [aut,
cre] (<https://orcid.org/0000-0002-8403-056X>), Arne Mueller
[aut], Steve Lianoglou [aut], Ge Tan [aut], Lance Parsons
[aut], Shraddha Pai [aut], Thomas McCarthy [ctb], Felix Ernst
[ctb], Mike Smith [ctb]
Maintainer: Robert Ivanek <robert.ivanek@unibas.ch>
URL: https://github.com/ivanek/Gviz
VignetteBuilder: knitr
BugReports: https://github.com/ivanek/Gviz/issues
git_url: https://git.bioconductor.org/packages/Gviz
git_branch: RELEASE_3_13
git_last_commit: 742ed80
git_last_commit_date: 2021-07-02
Date/Publication: 2021-07-04
source.ver: src/contrib/Gviz_1.36.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Gviz_1.36.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/Gviz_1.36.2.tgz
vignettes: vignettes/Gviz/inst/doc/Gviz.html
vignetteTitles: The Gviz User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Gviz/inst/doc/Gviz.R
dependsOnMe: biomvRCNS, chimeraviz, cicero, coMET, cummeRbund,
DMRforPairs, Pviz, methylationArrayAnalysis, rnaseqGene,
csawBook
importsMe: AllelicImbalance, ALPS, ASpediaFI, ASpli, CAGEfightR,
DMRcate, ELMER, GenomicInteractions, maser, mCSEA, MEAL,
methyAnalysis, methylPipe, motifbreakR, PING, primirTSS,
proActiv, regutools, RNAmodR, RNAmodR.AlkAnilineSeq,
RNAmodR.RiboMethSeq, SPLINTER, srnadiff, STAN, trackViewer,
TVTB, uncoverappLib, VariantFiltering, DMRcatedata
suggestsMe: annmap, cellbaseR, CNEr, CNVRanger, DeepBlueR, ensembldb,
GenomicRanges, gwascat, interactiveDisplay, InterMineR, Pi,
pqsfinder, QuasR, RnBeads, SplicingGraphs, TFutils,
Single.mTEC.Transcriptomes, CAGEWorkflow, chipseqDB, chicane,
RTIGER
dependencyCount: 141
Package: GWAS.BAYES
Version: 1.1.0
Depends: R (>= 4.0), Rcpp (>= 1.0.3), RcppEigen (>= 0.3.3.7.0), GA (>=
3.2), caret (>= 6.0-86), ggplot2 (>= 3.3.0), doParallel (>=
1.0.15), memoise (>= 1.1.0), reshape2 (>= 1.4.4), Matrix (>=
1.2-18)
LinkingTo: RcppEigen (>= 0.3.3.7.0),Rcpp (>= 1.0.3)
Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP, qqman
License: GPL-2 | GPL-3
Archs: i386, x64
MD5sum: df57c2cb8bcbc44319609952c9140b6f
NeedsCompilation: yes
Title: GWAS for Selfing Species
Description: This package is built to perform GWAS analysis for selfing
species. The research related to this package was supported in
part by National Science Foundation Award 1853549.
biocViews: AssayDomain, SNP
Author: Jake Williams [aut, cre], Marco Ferreira [aut], Tieming Ji
[aut]
Maintainer: Jake Williams <jwilliams@vt.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GWAS.BAYES
git_branch: master
git_last_commit: fdf9102
git_last_commit_date: 2020-10-27
Date/Publication: 2021-03-19
source.ver: src/contrib/GWAS.BAYES_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GWAS.BAYES_1.1.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GWAS.BAYES_1.1.0.tgz
vignettes: vignettes/GWAS.BAYES/inst/doc/VignetteGWASBAYES.html
vignetteTitles: GWAS.BAYES
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GWAS.BAYES/inst/doc/VignetteGWASBAYES.R
dependencyCount: 88
Package: gwascat
Version: 2.24.0
Depends: R (>= 3.5.0), methods
Imports: S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges
(>= 1.29.6), GenomicFeatures, readr, Biostrings, AnnotationDbi,
BiocFileCache, snpStats, VariantAnnotation, AnnotationHub
Suggests: DO.db, DT, knitr, RBGL, testthat, rmarkdown, Gviz, Rsamtools,
IRanges, rtracklayer, graph, ggbio, DelayedArray,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BiocStyle
Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37
License: Artistic-2.0
MD5sum: 27e63da1f93065094222b2c0c4510ee6
NeedsCompilation: no
Title: representing and modeling data in the EMBL-EBI GWAS catalog
Description: Represent and model data in the EMBL-EBI GWAS catalog.
biocViews: Genetics
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gwascat
git_branch: RELEASE_3_13
git_last_commit: d251baa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gwascat_2.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gwascat_2.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gwascat_2.24.0.tgz
vignettes: vignettes/gwascat/inst/doc/gwascat.html,
vignettes/gwascat/inst/doc/gwascatOnt.html
vignetteTitles: gwascat: structuring and querying the NHGRI GWAS
catalog, gwascat -- GRanges for GWAS hits in EBI catalog
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gwascat/inst/doc/gwascat.R,
vignettes/gwascat/inst/doc/gwascatOnt.R
dependsOnMe: vtpnet, liftOver
importsMe: circRNAprofiler
suggestsMe: GenomicScores, hmdbQuery, ldblock, parglms, TFutils,
grasp2db
dependencyCount: 128
Package: GWASTools
Version: 1.38.0
Depends: Biobase
Imports: graphics, stats, utils, methods, gdsfmt, DBI, RSQLite,
GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf,
quantsmooth, data.table
Suggests: ncdf4, GWASdata, BiocGenerics, RUnit, Biostrings,
GenomicRanges, IRanges, SNPRelate, snpStats, S4Vectors,
VariantAnnotation, parallel
License: Artistic-2.0
MD5sum: 29be9f5c8e240db94dc2c76ef34cbc05
NeedsCompilation: no
Title: Tools for Genome Wide Association Studies
Description: Classes for storing very large GWAS data sets and
annotation, and functions for GWAS data cleaning and analysis.
biocViews: SNP, GeneticVariability, QualityControl, Microarray
Author: Stephanie M. Gogarten, Cathy Laurie, Tushar Bhangale, Matthew
P. Conomos, Cecelia Laurie, Michael Lawrence, Caitlin McHugh,
Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar, Adrienne
Stilp, Sarah Nelson, David Levine
Maintainer: Stephanie M. Gogarten <sdmorris@uw.edu>
URL: https://github.com/smgogarten/GWASTools
git_url: https://git.bioconductor.org/packages/GWASTools
git_branch: RELEASE_3_13
git_last_commit: a04d6d6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GWASTools_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GWASTools_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GWASTools_1.38.0.tgz
vignettes: vignettes/GWASTools/inst/doc/Affymetrix.pdf,
vignettes/GWASTools/inst/doc/DataCleaning.pdf,
vignettes/GWASTools/inst/doc/Formats.pdf
vignetteTitles: Preparing Affymetrix Data, GWAS Data Cleaning, Data
formats in GWASTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GWASTools/inst/doc/Affymetrix.R,
vignettes/GWASTools/inst/doc/DataCleaning.R,
vignettes/GWASTools/inst/doc/Formats.R
dependsOnMe: mBPCR, GWASdata
importsMe: GENESIS, gwasurvivr
suggestsMe: podkat
dependencyCount: 68
Package: gwasurvivr
Version: 1.10.0
Depends: R (>= 3.4.0)
Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats,
SummarizedExperiment, stats, utils, SNPRelate
Suggests: BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: a271468b981564e344a68fa3571f0f4d
NeedsCompilation: no
Title: gwasurvivr: an R package for genome wide survival analysis
Description: gwasurvivr is a package to perform survival analysis using
Cox proportional hazard models on imputed genetic data.
biocViews: GenomeWideAssociation, Survival, Regression, Genetics, SNP,
GeneticVariability, Pharmacogenomics, BiomedicalInformatics
Author: Abbas Rizvi, Ezgi Karaesmen, Martin Morgan, Lara
Sucheston-Campbell
Maintainer: Abbas Rizvi <aarizv@gmail.com>
URL: https://github.com/suchestoncampbelllab/gwasurvivr
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gwasurvivr
git_branch: RELEASE_3_13
git_last_commit: a325164
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/gwasurvivr_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/gwasurvivr_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gwasurvivr_1.10.0.tgz
vignettes: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.html
vignetteTitles: gwasurvivr Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.R
dependencyCount: 125
Package: GWENA
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: WGCNA (>= 1.67), dplyr (>= 0.8.3), dynamicTreeCut (>= 1.63-1),
ggplot2 (>= 3.1.1), gprofiler2 (>= 0.1.6), magrittr (>= 1.5),
tibble (>= 2.1.1), tidyr (>= 1.0.0), NetRep (>= 1.2.1), igraph
(>= 1.2.4.1), RColorBrewer (>= 1.1-2), purrr (>= 0.3.3), rlist
(>= 0.4.6.1), matrixStats (>= 0.55.0), SummarizedExperiment (>=
1.14.1), stringr (>= 1.4.0), cluster (>= 2.1.0), grDevices (>=
4.0.4), methods, graphics, stats, utils
Suggests: testthat (>= 2.1.0), knitr (>= 1.25), rmarkdown (>= 1.16),
prettydoc (>= 0.3.0), httr (>= 1.4.1), S4Vectors (>= 0.22.1),
BiocStyle (>= 2.15.8)
License: GPL-3
MD5sum: 9f3a7f61c5e0882fa9f8c11662ceca38
NeedsCompilation: no
Title: Pipeline for augmented co-expression analysis
Description: The development of high-throughput sequencing led to
increased use of co-expression analysis to go beyong single
feature (i.e. gene) focus. We propose GWENA (Gene Whole
co-Expression Network Analysis) , a tool designed to perform
gene co-expression network analysis and explore the results in
a single pipeline. It includes functional enrichment of modules
of co-expressed genes, phenotypcal association, topological
analysis and comparison of networks configuration between
conditions.
biocViews: Software, GeneExpression, Network, Clustering,
GraphAndNetwork, GeneSetEnrichment, Pathways, Visualization,
RNASeq, Transcriptomics, mRNAMicroarray, Microarray,
NetworkEnrichment, Sequencing, GO
Author: Gwenaëlle Lemoine [aut, cre]
(<https://orcid.org/0000-0003-4747-1937>), Marie-Pier
Scott-Boyer [ths], Arnaud Droit [fnd]
Maintainer: Gwenaëlle Lemoine <lemoine.gwenaelle@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/Kumquatum/GWENA/issues
git_url: https://git.bioconductor.org/packages/GWENA
git_branch: RELEASE_3_13
git_last_commit: a8f00b9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/GWENA_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/GWENA_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/GWENA_1.2.0.tgz
vignettes: vignettes/GWENA/inst/doc/GWENA_guide.html
vignetteTitles: GWENA-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GWENA/inst/doc/GWENA_guide.R
dependencyCount: 134
Package: h5vc
Version: 2.26.1
Depends: grid, gridExtra, ggplot2
Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>=
1.99.1), methods, GenomicRanges, abind, BiocParallel,
BatchJobs, h5vcData, GenomeInfoDb
LinkingTo: Rhtslib (>= 1.15.3)
Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, biomaRt,
BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics
License: GPL (>= 3)
Archs: i386, x64
MD5sum: 10553775e3c6a86054c9eb584f701fa0
NeedsCompilation: yes
Title: Managing alignment tallies using a hdf5 backend
Description: This package contains functions to interact with tally
data from NGS experiments that is stored in HDF5 files.
Author: Paul Theodor Pyl
Maintainer: Paul Theodor Pyl <paul.theodor.pyl@gmail.com>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/h5vc
git_branch: RELEASE_3_13
git_last_commit: 95d3f8b
git_last_commit_date: 2021-05-21
Date/Publication: 2021-05-23
source.ver: src/contrib/h5vc_2.26.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/h5vc_2.26.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/h5vc_2.26.1.tgz
vignettes: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html,
vignettes/h5vc/inst/doc/h5vc.tour.html
vignetteTitles: Building a minimal genome browser with h5vc and shiny,
h5vc -- Tour
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.R,
vignettes/h5vc/inst/doc/h5vc.tour.R
suggestsMe: h5vcData
dependencyCount: 88
Package: hapFabia
Version: 1.34.0
Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1)
Imports: methods, graphics, grDevices, stats, utils
License: LGPL (>= 2.1)
MD5sum: 5503ebf4ff6b0354c663819db01c2680
NeedsCompilation: yes
Title: hapFabia: Identification of very short segments of identity by
descent (IBD) characterized by rare variants in large
sequencing data
Description: A package to identify very short IBD segments in large
sequencing data by FABIA biclustering. Two haplotypes are
identical by descent (IBD) if they share a segment that both
inherited from a common ancestor. Current IBD methods reliably
detect long IBD segments because many minor alleles in the
segment are concordant between the two haplotypes. However,
many cohort studies contain unrelated individuals which share
only short IBD segments. This package provides software to
identify short IBD segments in sequencing data. Knowledge of
short IBD segments are relevant for phasing of genotyping data,
association studies, and for population genetics, where they
shed light on the evolutionary history of humans. The package
supports VCF formats, is based on sparse matrix operations, and
provides visualization of haplotype clusters in different
formats.
biocViews: Genetics, GeneticVariability, SNP, Sequencing, Sequencing,
Visualization, Clustering, SequenceMatching, Software
Author: Sepp Hochreiter <hochreit@bioinf.jku.at>
Maintainer: Andreas Mitterecker <mitterecker@bioinf.jku.at>
URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html
git_url: https://git.bioconductor.org/packages/hapFabia
git_branch: RELEASE_3_13
git_last_commit: 1e6972a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hapFabia_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hapFabia_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/hapFabia_1.34.0.tgz
vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf
vignetteTitles: hapFabia: Manual for the R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hapFabia/inst/doc/hapfabia.R
dependencyCount: 9
Package: Harman
Version: 1.20.0
Depends: R (>= 3.6)
Imports: Rcpp (>= 0.11.2), graphics, stats, methods
LinkingTo: Rcpp
Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit,
RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA,
affydata, minfiData, sva
License: GPL-3 + file LICENCE
MD5sum: bfa36891d3e30b637e6f848661b79b08
NeedsCompilation: yes
Title: The removal of batch effects from datasets using a PCA and
constrained optimisation based technique
Description: Harman is a PCA and constrained optimisation based
technique that maximises the removal of batch effects from
datasets, with the constraint that the probability of
overcorrection (i.e. removing genuine biological signal along
with batch noise) is kept to a fraction which is set by the
end-user.
biocViews: BatchEffect, Microarray, MultipleComparison,
PrincipalComponent, Normalization, Preprocessing,
DNAMethylation, Transcription, Software, StatisticalMethod
Author: Josh Bowden [aut], Jason Ross [aut, cre], Yalchin Oytam [aut]
Maintainer: Jason Ross <jason.ross@csiro.au>
URL: http://www.bioinformatics.csiro.au/harman/
VignetteBuilder: knitr
BugReports: https://github.com/JasonR055/Harman/issues
git_url: https://git.bioconductor.org/packages/Harman
git_branch: RELEASE_3_13
git_last_commit: 44013b7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Harman_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Harman_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Harman_1.20.0.tgz
vignettes: vignettes/Harman/inst/doc/IntroductionToHarman.html
vignetteTitles: IntroductionToHarman
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R
importsMe: debrowser
dependencyCount: 5
Package: Harshlight
Version: 1.64.0
Depends: R (>= 2.10)
Imports: affy, altcdfenvs, Biobase, stats, utils
License: GPL (>= 2)
MD5sum: 0d8a0ddfb1c4224cadda25728494a36e
NeedsCompilation: yes
Title: A "corrective make-up" program for microarray chips
Description: The package is used to detect extended, diffuse and
compact blemishes on microarray chips. Harshlight automatically
marks the areas in a collection of chips (affybatch objects)
and a corrected AffyBatch object is returned, in which the
defected areas are substituted with NAs or the median of the
values of the same probe in the other chips in the collection.
The new version handle the substitute value as whole matrix to
solve the memory problem.
biocViews: Microarray, QualityControl, Preprocessing, OneChannel,
ReportWriting
Author: Mayte Suarez-Farinas, Maurizio Pellegrino, Knut M. Wittkowski,
Marcelo O. Magnasco
Maintainer: Maurizio Pellegrino <mpellegri@berkeley.edu>
URL: http://asterion.rockefeller.edu/Harshlight/
git_url: https://git.bioconductor.org/packages/Harshlight
git_branch: RELEASE_3_13
git_last_commit: 0da8ef4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Harshlight_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Harshlight_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Harshlight_1.64.0.tgz
vignettes: vignettes/Harshlight/inst/doc/Harshlight.pdf
vignetteTitles: Harshlight
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Harshlight/inst/doc/Harshlight.R
dependencyCount: 28
Package: hca
Version: 1.0.3
Depends: R (>= 4.1)
Imports: httr, jsonlite, dplyr, tibble, tidyr, BiocFileCache, tools,
utils
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, LoomExperiment,
BiocStyle
License: MIT + file LICENSE
MD5sum: 8b488f1a6acfeb89453ff4a16ecdea16
NeedsCompilation: no
Title: Exploring the Human Cell Atlas Data Coordinating Platform
Description: This package provides users with the ability to query the
Human Cell Atlas data repository for single-cell experiment
data. The `projects()`, `files()`, `samples()` and `bundles()`
functions retrieve summary information on each of these
indexes; corresponding `*_details()` are available for
individual entries of each index. File-based resources can be
downloaded using `files_download()`. Advanced use of the
package allows the user to page through large result sets, and
to flexibly query the 'list-of-lists' structure representing
query responses.
biocViews: Software, SingleCell
Author: Maya McDaniel [aut, cre], Martin Morgan [aut]
(<https://orcid.org/0000-0002-5874-8148>)
Maintainer: Maya McDaniel <maya.mcdaniel@roswellpark.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hca
git_branch: RELEASE_3_13
git_last_commit: d0d0c9c
git_last_commit_date: 2021-08-20
Date/Publication: 2021-08-22
source.ver: src/contrib/hca_1.0.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hca_1.0.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/hca_1.0.3.tgz
vignettes: vignettes/hca/inst/doc/hca_vignette.html
vignetteTitles: Accessing Human Cell Atlas Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/hca/inst/doc/hca_vignette.R
dependencyCount: 49
Package: HDF5Array
Version: 1.20.0
Depends: R (>= 3.4), methods, DelayedArray (>= 0.15.16), rhdf5 (>=
2.31.6)
Imports: utils, stats, tools, Matrix, rhdf5filters, BiocGenerics (>=
0.31.5), S4Vectors, IRanges
LinkingTo: S4Vectors (>= 0.27.13), Rhdf5lib
Suggests: BiocParallel, GenomicRanges, SummarizedExperiment (>=
1.15.1), h5vcData, ExperimentHub, TENxBrainData, zellkonverter,
GenomicFeatures, RUnit, SingleCellExperiment
License: Artistic-2.0
Archs: i386, x64
MD5sum: 92f1968f6d70442bb1cf260b481ace51
NeedsCompilation: yes
Title: HDF5 backend for DelayedArray objects
Description: Implement the HDF5Array, H5SparseMatrix, H5ADMatrix, and
TENxMatrix classes, 4 convenient and memory-efficient
array-like containers for representing and manipulating either:
(1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5
sparse matrix (stored in CSR/CSC/Yale format), (3) the central
matrix of an h5ad file (or any matrix in the /layers group), or
(4) a 10x Genomics sparse matrix. All these containers are
DelayedArray extensions and thus support all operations
(delayed or block-processed) supported by DelayedArray objects.
biocViews: Infrastructure, DataRepresentation, DataImport, Sequencing,
RNASeq, Coverage, Annotation, GenomeAnnotation, SingleCell,
ImmunoOncology
Author: Hervé Pagès
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/HDF5Array
SystemRequirements: GNU make
BugReports: https://github.com/Bioconductor/HDF5Array/issues
git_url: https://git.bioconductor.org/packages/HDF5Array
git_branch: RELEASE_3_13
git_last_commit: 8804048
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HDF5Array_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HDF5Array_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HDF5Array_1.20.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: compartmap, GenoGAM, MAGAR, TENxBrainData, TENxPBMCData
importsMe: biscuiteer, bsseq, clusterExperiment, cytomapper,
DropletUtils, FRASER, GenomicScores, glmGamPoi, GSVA,
LoomExperiment, methrix, minfi, MOFA2, netSmooth,
recountmethylation, scmeth, scry, signatureSearch,
spatialHeatmap, MafH5.gnomAD.r3.0.GRCh38,
MafH5.gnomAD.v3.1.1.GRCh38, curatedTCGAData, HCAData,
imcdatasets, MethylSeqData, SingleCellMultiModal
suggestsMe: beachmat, BiocSklearn, DelayedArray, DelayedMatrixStats,
iSEE, MAST, mbkmeans, metabolomicsWorkbenchR,
MultiAssayExperiment, PDATK, QFeatures, scMerge, scran, sesame,
SummarizedExperiment, zellkonverter, digitalDLSorteR
dependencyCount: 20
Package: HDTD
Version: 1.26.0
Depends: R (>= 3.6)
Imports: stats, Rcpp (>= 1.0.1)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, markdown
License: GPL-3
Archs: i386, x64
MD5sum: 0632092ec4dd63bc71ae5c179ac30004
NeedsCompilation: yes
Title: Statistical Inference about the Mean Matrix and the Covariance
Matrices in High-Dimensional Transposable Data (HDTD)
Description: Characterization of intra-individual variability using
physiologically relevant measurements provides important
insights into fundamental biological questions ranging from
cell type identity to tumor development. For each individual,
the data measurements can be written as a matrix with the
different subsamples of the individual recorded in the columns
and the different phenotypic units recorded in the rows.
Datasets of this type are called high-dimensional transposable
data. The HDTD package provides functions for conducting
statistical inference for the mean relationship between the row
and column variables and for the covariance structure within
and between the row and column variables.
biocViews: DifferentialExpression, Genetics, GeneExpression,
Microarray, Sequencing, StatisticalMethod, Software
Author: Anestis Touloumis [cre, aut]
(<https://orcid.org/0000-0002-5965-1639>), John C. Marioni
[aut] (<https://orcid.org/0000-0001-9092-0852>), Simon
Tavar\'{e} [aut] (<https://orcid.org/0000-0002-3716-4952>)
Maintainer: Anestis Touloumis <A.Touloumis@brighton.ac.uk>
URL: http://github.com/AnestisTouloumis/HDTD
VignetteBuilder: knitr
BugReports: http://github.com/AnestisTouloumis/HDTD/issues
git_url: https://git.bioconductor.org/packages/HDTD
git_branch: RELEASE_3_13
git_last_commit: 253d71b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HDTD_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HDTD_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HDTD_1.26.0.tgz
vignettes: vignettes/HDTD/inst/doc/HDTD.html
vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HDTD/inst/doc/HDTD.R
dependencyCount: 5
Package: heatmaps
Version: 1.16.0
Depends: R (>= 3.4)
Imports: methods, grDevices, graphics, stats, Biostrings,
GenomicRanges, IRanges, KernSmooth, plotrix, Matrix, EBImage,
RColorBrewer, BiocGenerics, GenomeInfoDb
Suggests: BSgenome.Drerio.UCSC.danRer7, knitr, rmarkdown, testthat
License: Artistic-2.0
Archs: i386, x64
MD5sum: 48bfb95f7570b627edd353ba1aca4651
NeedsCompilation: no
Title: Flexible Heatmaps for Functional Genomics and Sequence Features
Description: This package provides functions for plotting heatmaps of
genome-wide data across genomic intervals, such as ChIP-seq
signals at peaks or across promoters. Many functions are also
provided for investigating sequence features.
biocViews: Visualization, SequenceMatching, FunctionalGenomics
Author: Malcolm Perry <mgperry32@gmail.com>
Maintainer: Malcolm Perry <mgperry32@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/heatmaps
git_branch: RELEASE_3_13
git_last_commit: c35acce
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/heatmaps_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/heatmaps_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/heatmaps_1.16.0.tgz
vignettes: vignettes/heatmaps/inst/doc/heatmaps.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/heatmaps/inst/doc/heatmaps.R
dependencyCount: 41
Package: Heatplus
Version: 3.0.0
Imports: graphics, grDevices, stats, RColorBrewer
Suggests: Biobase, hgu95av2.db, limma
License: GPL (>= 2)
Archs: i386, x64
MD5sum: add587e3742327c068014382687f482a
NeedsCompilation: no
Title: Heatmaps with row and/or column covariates and colored clusters
Description: Display a rectangular heatmap (intensity plot) of a data
matrix. By default, both samples (columns) and features (row)
of the matrix are sorted according to a hierarchical
clustering, and the corresponding dendrogram is plotted.
Optionally, panels with additional information about samples
and features can be added to the plot.
biocViews: Microarray, Visualization
Author: Alexander Ploner <Alexander.Ploner@ki.se>
Maintainer: Alexander Ploner <Alexander.Ploner@ki.se>
URL: https://github.com/alexploner/Heatplus
BugReports: https://github.com/alexploner/Heatplus/issues
git_url: https://git.bioconductor.org/packages/Heatplus
git_branch: RELEASE_3_13
git_last_commit: 0ab2a5a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Heatplus_3.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Heatplus_3.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Heatplus_3.0.0.tgz
vignettes: vignettes/Heatplus/inst/doc/annHeatmap.pdf,
vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf,
vignettes/Heatplus/inst/doc/oldHeatplus.pdf
vignetteTitles: Annotated and regular heatmaps, Commented package
source, Old functions (deprecated)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Heatplus/inst/doc/annHeatmap.R,
vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R,
vignettes/Heatplus/inst/doc/oldHeatplus.R
dependsOnMe: phenoTest, tRanslatome, heatmapFlex
suggestsMe: mtbls2, RforProteomics, RAM
dependencyCount: 4
Package: HelloRanges
Version: 1.18.0
Depends: methods, BiocGenerics, S4Vectors (>= 0.17.39), IRanges (>=
2.13.12), GenomicRanges (>= 1.31.10), Biostrings (>= 2.41.3),
BSgenome, GenomicFeatures (>= 1.31.5), VariantAnnotation (>=
1.19.3), Rsamtools, GenomicAlignments (>= 1.15.7), rtracklayer
(>= 1.33.8), GenomeInfoDb, SummarizedExperiment
Imports: docopt, stats, tools, utils
Suggests: HelloRangesData, BiocStyle
License: GPL (>= 2)
MD5sum: b8f75b406cd6bdf9405b1eab79a29992
NeedsCompilation: no
Title: Introduce *Ranges to bedtools users
Description: Translates bedtools command-line invocations to R code
calling functions from the Bioconductor *Ranges infrastructure.
This is intended to educate novice Bioconductor users and to
compare the syntax and semantics of the two frameworks.
biocViews: Sequencing, Annotation, Coverage, GenomeAnnotation,
DataImport, SequenceMatching, VariantAnnotation
Author: Michael Lawrence
Maintainer: Michael Lawrence <michafla@gene.com>
git_url: https://git.bioconductor.org/packages/HelloRanges
git_branch: RELEASE_3_13
git_last_commit: e9067d1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HelloRanges_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HelloRanges_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HelloRanges_1.18.0.tgz
vignettes: vignettes/HelloRanges/inst/doc/tutorial.pdf
vignetteTitles: HelloRanges Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HelloRanges/inst/doc/tutorial.R
importsMe: OMICsPCA
suggestsMe: plyranges
dependencyCount: 99
Package: HELP
Version: 1.50.0
Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 8944301d279d34914e6394d3781b20d1
NeedsCompilation: no
Title: Tools for HELP data analysis
Description: The package contains a modular pipeline for analysis of
HELP microarray data, and includes graphical and mathematical
tools with more general applications.
biocViews: CpGIsland, DNAMethylation, Microarray, TwoChannel,
DataImport, QualityControl, Preprocessing, Visualization
Author: Reid F. Thompson <reid.thompson@gmail.com>, John M. Greally
<john.greally@einstein.yu.edu>, with contributions from Mark
Reimers <mreimers@vcu.edu>
Maintainer: Reid F. Thompson <reid.thompson@gmail.com>
git_url: https://git.bioconductor.org/packages/HELP
git_branch: RELEASE_3_13
git_last_commit: 57092fb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HELP_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HELP_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HELP_1.50.0.tgz
vignettes: vignettes/HELP/inst/doc/HELP.pdf
vignetteTitles: 1. Primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HELP/inst/doc/HELP.R
dependencyCount: 8
Package: HEM
Version: 1.64.0
Depends: R (>= 2.1.0)
Imports: Biobase, grDevices, stats, utils
License: GPL (>= 2)
Archs: i386, x64
MD5sum: f34ba6e9c2284be5ab0daf3cb1fccfa1
NeedsCompilation: yes
Title: Heterogeneous error model for identification of differentially
expressed genes under multiple conditions
Description: This package fits heterogeneous error models for analysis
of microarray data
biocViews: Microarray, DifferentialExpression
Author: HyungJun Cho <hcho@virginia.edu> and Jae K. Lee
<jaeklee@virginia.edu>
Maintainer: HyungJun Cho <hcho@virginia.edu>
URL:
http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/
git_url: https://git.bioconductor.org/packages/HEM
git_branch: RELEASE_3_13
git_last_commit: 41dddd9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HEM_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HEM_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HEM_1.64.0.tgz
vignettes: vignettes/HEM/inst/doc/HEM.pdf
vignetteTitles: HEM Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 8
Package: Herper
Version: 1.2.0
Depends: R (>= 4.0), reticulate
Imports: utils, rjson, withr, stats
Suggests: BiocStyle, testthat, knitr, rmarkdown, seqCNA
License: GPL-3
MD5sum: 622d0da449cad34c6f6ecb8b95fcfedd
NeedsCompilation: no
Title: The Herper package is a simple toolset to install and manage
conda packages and environments from R
Description: Many tools for data analysis are not available in R, but
are present in public repositories like conda. The Herper
package provides a comprehensive set of functions to interact
with the conda package managament system. With Herper users can
install, manage and run conda packages from the comfort of
their R session. Herper also provides an ad-hoc approach to
handling external system requirements for R packages. For
people developing packages with python conda dependencies we
recommend using basilisk
(https://bioconductor.org/packages/release/bioc/html/basilisk.html)
to internally support these system requirments pre-hoc.
biocViews: Infrastructure, Software
Author: Matt Paul [aut] (<https://orcid.org/0000-0002-3020-7729>),
Thomas Carroll [aut, cre]
(<https://orcid.org/0000-0002-0073-1714>), Doug Barrows [aut],
Kathryn Rozen-Gagnon [ctb]
Maintainer: Thomas Carroll <tc.infomatics@gmail.com>
URL: https://github.com/RockefellerUniversity/Herper
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Herper
git_branch: RELEASE_3_13
git_last_commit: 98047e8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Herper_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Herper_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Herper_1.2.0.tgz
vignettes: vignettes/Herper/inst/doc/Herper.html,
vignettes/Herper/inst/doc/QuickStart.html
vignetteTitles: Herper, Quick Start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Herper/inst/doc/Herper.R,
vignettes/Herper/inst/doc/QuickStart.R
dependencyCount: 17
Package: HGC
Version: 1.0.3
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 1.0.0), RcppEigen(>= 0.3.2.0), Matrix, RANN, ape,
dendextend, ggplot2, mclust, patchwork, dplyr, grDevices,
methods, stats
LinkingTo: Rcpp, RcppEigen
Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 0d402ce5cf01db013784022e7bf088d2
NeedsCompilation: yes
Title: A fast hierarchical graph-based clustering method
Description: HGC (short for Hierarchical Graph-based Clustering) is an
R package for conducting hierarchical clustering on large-scale
single-cell RNA-seq (scRNA-seq) data. The key idea is to
construct a dendrogram of cells on their shared nearest
neighbor (SNN) graph. HGC provides functions for building
graphs and for conducting hierarchical clustering on the graph.
The users with old R version could visit
https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get
HGC package built for R 3.6.
biocViews: SingleCell, Software, Clustering, RNASeq, GraphAndNetwork,
DNASeq
Author: Zou Ziheng [aut], Hua Kui [aut], XGlab [cre, cph]
Maintainer: XGlab <xglab@mail.tsinghua.edu.cn>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HGC
git_branch: RELEASE_3_13
git_last_commit: 786b2ce
git_last_commit_date: 2021-07-06
Date/Publication: 2021-07-08
source.ver: src/contrib/HGC_1.0.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HGC_1.0.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/HGC_1.0.3.tgz
vignettes: vignettes/HGC/inst/doc/HGC.html
vignetteTitles: HGC package manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HGC/inst/doc/HGC.R
dependencyCount: 54
Package: hiAnnotator
Version: 1.26.0
Depends: GenomicRanges, R (>= 2.10)
Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2,
scales, methods
Suggests: knitr, doParallel, testthat, BiocGenerics
License: GPL (>= 2)
MD5sum: 7eb67247be479e65fa627add1ed94fa1
NeedsCompilation: no
Title: Functions for annotating GRanges objects
Description: hiAnnotator contains set of functions which allow users to
annotate a GRanges object with custom set of annotations. The
basic philosophy of this package is to take two GRanges objects
(query & subject) with common set of seqnames (i.e.
chromosomes) and return associated annotation per seqnames and
rows from the query matching seqnames and rows from the subject
(i.e. genes or cpg islands). The package comes with three types
of annotation functions which calculates if a position from
query is: within a feature, near a feature, or count features
in defined window sizes. Moreover, each function is equipped
with parallel backend to utilize the foreach package. In
addition, the package is equipped with wrapper functions, which
finds appropriate columns needed to make a GRanges object from
a common data frame.
biocViews: Software, Annotation
Author: Nirav V Malani <malnirav@gmail.com>
Maintainer: Nirav V Malani <malnirav@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hiAnnotator
git_branch: RELEASE_3_13
git_last_commit: 8c633cf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hiAnnotator_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hiAnnotator_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/hiAnnotator_1.26.0.tgz
vignettes: vignettes/hiAnnotator/inst/doc/Intro.html
vignetteTitles: Using hiAnnotator
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R
dependsOnMe: hiReadsProcessor
dependencyCount: 81
Package: HIBAG
Version: 1.28.0
Depends: R (>= 3.2.0)
Imports: methods, RcppParallel
LinkingTo: RcppParallel (>= 5.0.0)
Suggests: parallel, ggplot2, reshape2, gdsfmt, SNPRelate, SeqArray,
knitr, markdown, rmarkdown
License: GPL-3
MD5sum: 7055dae0fa379655f9ca92c43d951d3c
NeedsCompilation: yes
Title: HLA Genotype Imputation with Attribute Bagging
Description: Imputes HLA classical alleles using GWAS SNP data, and it
relies on a training set of HLA and SNP genotypes. HIBAG can be
used by researchers with published parameter estimates instead
of requiring access to large training sample datasets. It
combines the concepts of attribute bagging, an ensemble
classifier method, with haplotype inference for SNPs and HLA
types. Attribute bagging is a technique which improves the
accuracy and stability of classifier ensembles using bootstrap
aggregating and random variable selection.
biocViews: Genetics, StatisticalMethod
Author: Xiuwen Zheng [aut, cre, cph]
(<https://orcid.org/0000-0002-1390-0708>), Bruce Weir [ctb,
ths] (<https://orcid.org/0000-0002-4883-1247>)
Maintainer: Xiuwen Zheng <zhengx@u.washington.edu>
URL: http://github.com/zhengxwen/HIBAG,
https://hibag.s3.amazonaws.com/index.html
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HIBAG
git_branch: RELEASE_3_13
git_last_commit: dead91d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HIBAG_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HIBAG_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HIBAG_1.28.0.tgz
vignettes: vignettes/HIBAG/inst/doc/HIBAG.html,
vignettes/HIBAG/inst/doc/HLA_Association.html,
vignettes/HIBAG/inst/doc/Implementation.html
vignetteTitles: HIBAG vignette html, HLA association vignette html,
HIBAG algorithm implementation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HIBAG/inst/doc/HIBAG.R,
vignettes/HIBAG/inst/doc/HLA_Association.R,
vignettes/HIBAG/inst/doc/Implementation.R
dependencyCount: 2
Package: HiCBricks
Version: 1.10.0
Depends: R (>= 3.6), utils, curl, rhdf5, R6, grid
Imports: ggplot2, viridis, RColorBrewer, scales, reshape2, stringr,
data.table, GenomeInfoDb, GenomicRanges, stats, IRanges,
grDevices, S4Vectors, digest, tibble, jsonlite, BiocParallel,
R.utils, readr, methods
Suggests: BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 88d41e7dfb014fc45b9d2bbfb79aaeac
NeedsCompilation: no
Title: Framework for Storing and Accessing Hi-C Data Through HDF Files
Description: HiCBricks is a library designed for handling large
high-resolution Hi-C datasets. Over the years, the Hi-C field
has experienced a rapid increase in the size and complexity of
datasets. HiCBricks is meant to overcome the challenges related
to the analysis of such large datasets within the R
environment. HiCBricks offers user-friendly and efficient
solutions for handling large high-resolution Hi-C datasets. The
package provides an R/Bioconductor framework with the bricks to
build more complex data analysis pipelines and algorithms.
HiCBricks already incorporates example algorithms for calling
domain boundaries and functions for high quality data
visualization.
biocViews: DataImport, Infrastructure, Software, Technology,
Sequencing, HiC
Author: Koustav Pal [aut, cre], Carmen Livi [ctb], Ilario Tagliaferri
[ctb]
Maintainer: Koustav Pal <koustav.pal@ifom.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HiCBricks
git_branch: RELEASE_3_13
git_last_commit: f242bbd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HiCBricks_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HiCBricks_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HiCBricks_1.10.0.tgz
vignettes: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.html
vignetteTitles: IntroductionToHiCBricks.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.R
dependencyCount: 86
Package: HiCcompare
Version: 1.14.0
Depends: R (>= 3.4.0), dplyr
Imports: data.table, ggplot2, gridExtra, mgcv, stats, InteractionSet,
GenomicRanges, IRanges, S4Vectors, BiocParallel, QDNAseq,
KernSmooth, methods, utils, graphics, pheatmap, gtools, rhdf5
Suggests: knitr, rmarkdown, testthat, multiHiCcompare
License: MIT + file LICENSE
MD5sum: a4a729e161ae53cb79a35268ff373e2c
NeedsCompilation: no
Title: HiCcompare: Joint normalization and comparative analysis of
multiple Hi-C datasets
Description: HiCcompare provides functions for joint normalization and
difference detection in multiple Hi-C datasets. HiCcompare
operates on processed Hi-C data in the form of
chromosome-specific chromatin interaction matrices. It accepts
three-column tab-separated text files storing chromatin
interaction matrices in a sparse matrix format which are
available from several sources. HiCcompare is designed to give
the user the ability to perform a comparative analysis on the
3-Dimensional structure of the genomes of cells in different
biological states.`HiCcompare` differs from other packages that
attempt to compare Hi-C data in that it works on processed data
in chromatin interaction matrix format instead of pre-processed
sequencing data. In addition, `HiCcompare` provides a
non-parametric method for the joint normalization and removal
of biases between two Hi-C datasets for the purpose of
comparative analysis. `HiCcompare` also provides a simple yet
robust method for detecting differences between Hi-C datasets.
biocViews: Software, HiC, Sequencing, Normalization
Author: John Stansfield <stansfieldjc@vcu.edu>, Kellen Cresswell
<cresswellkg@vcu.edu>, Mikhail Dozmorov
<mikhail.dozmorov@vcuhealth.org>
Maintainer: John Stansfield <stansfieldjc@vcu.edu>, Mikhail Dozmorov
<mikhail.dozmorov@vcuhealth.org>
URL: https://github.com/dozmorovlab/HiCcompare
VignetteBuilder: knitr
BugReports: https://github.com/dozmorovlab/HiCcompare/issues
git_url: https://git.bioconductor.org/packages/HiCcompare
git_branch: RELEASE_3_13
git_last_commit: e1b41c3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HiCcompare_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HiCcompare_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HiCcompare_1.14.0.tgz
vignettes: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.html
vignetteTitles: HiCcompare Usage Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.R
importsMe: multiHiCcompare, SpectralTAD, TADCompare
dependencyCount: 97
Package: HiCDCPlus
Version: 1.0.0
Imports:
Rcpp,InteractionSet,GenomicInteractions,bbmle,pscl,BSgenome,data.table,dplyr,tidyr,GenomeInfoDb,rlang,splines,MASS,GenomicRanges,IRanges,tibble,R.utils,Biostrings,rtracklayer,methods,S4Vectors
LinkingTo: Rcpp
Suggests: BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10,
BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38,
RUnit, BiocGenerics, knitr, rmarkdown, HiTC, DESeq2, Matrix,
BiocFileCache, rappdirs
Enhances: parallel
License: GPL-3
Archs: i386, x64
MD5sum: b6cc24956b397154a8cd05f2c6be71d2
NeedsCompilation: yes
Title: Hi-C Direct Caller Plus
Description: Systematic 3D interaction calls and differential analysis
for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller
plus) package enables principled statistical analysis of Hi-C
and HiChIP data sets – including calling significant
interactions within a single experiment and performing
differential analysis between conditions given replicate
experiments – to facilitate global integrative studies. HiC-DC+
estimates significant interactions in a Hi-C or HiChIP
experiment directly from the raw contact matrix for each
chromosome up to a specified genomic distance, binned by
uniform genomic intervals or restriction enzyme fragments, by
training a background model to account for random polymer
ligation and systematic sources of read count variation.
biocViews: HiC, DNA3DStructure, Software, Normalization
Author: Merve Sahin [cre, aut]
(<https://orcid.org/0000-0003-3858-8332>)
Maintainer: Merve Sahin <merve.sahn@gmail.com>
SystemRequirements: JRE 8+
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HiCDCPlus
git_branch: RELEASE_3_13
git_last_commit: c666131
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HiCDCPlus_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HiCDCPlus_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HiCDCPlus_1.0.0.tgz
vignettes: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.html
vignetteTitles: Analyzing Hi-C and HiChIP data with HiCDCPlus
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.R
dependencyCount: 156
Package: hierGWAS
Version: 1.22.0
Depends: R (>= 3.2.0)
Imports: fastcluster,glmnet, fmsb
Suggests: BiocGenerics, RUnit, MASS
License: GPL-3
MD5sum: 2d2b582483f1f6adcc5fcb50666c22ce
NeedsCompilation: no
Title: Asessing statistical significance in predictive GWA studies
Description: Testing individual SNPs, as well as arbitrarily large
groups of SNPs in GWA studies, using a joint model of all SNPs.
The method controls the FWER, and provides an automatic,
data-driven refinement of the SNP clusters to smaller groups or
single markers.
biocViews: SNP, LinkageDisequilibrium, Clustering
Author: Laura Buzdugan
Maintainer: Laura Buzdugan <buzdugan@stat.math.ethz.ch>
git_url: https://git.bioconductor.org/packages/hierGWAS
git_branch: RELEASE_3_13
git_last_commit: 9d27218
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hierGWAS_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hierGWAS_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/hierGWAS_1.22.0.tgz
vignettes: vignettes/hierGWAS/inst/doc/hierGWAS.pdf
vignetteTitles: User manual for R-Package hierGWAS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hierGWAS/inst/doc/hierGWAS.R
dependencyCount: 17
Package: hierinf
Version: 1.10.0
Depends: R (>= 3.6.0)
Imports: fmsb, glmnet, methods, parallel, stats
Suggests: knitr, MASS, testthat
License: GPL-3 | file LICENSE
Archs: i386, x64
MD5sum: 1da6cd6175f15948f536bdb2ea641629
NeedsCompilation: no
Title: Hierarchical Inference
Description: Tools to perform hierarchical inference for one or
multiple studies / data sets based on high-dimensional
multivariate (generalised) linear models. A possible
application is to perform hierarchical inference for GWA
studies to find significant groups or single SNPs (if the
signal is strong) in a data-driven and automated procedure. The
method is based on an efficient hierarchical multiple testing
correction and controls the FWER. The functions can easily be
run in parallel.
biocViews: Clustering, GenomeWideAssociation, LinkageDisequilibrium,
Regression, SNP
Author: Claude Renaux [aut, cre], Laura Buzdugan [aut], Markus Kalisch
[aut], Peter Bühlmann [aut]
Maintainer: Claude Renaux <renaux@stat.math.ethz.ch>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hierinf
git_branch: RELEASE_3_13
git_last_commit: f13c995
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hierinf_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hierinf_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/hierinf_1.10.0.tgz
vignettes: vignettes/hierinf/inst/doc/vignette-hierinf.pdf
vignetteTitles: vignette-hierinf.Rnw
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/hierinf/inst/doc/vignette-hierinf.R
dependencyCount: 17
Package: HilbertCurve
Version: 1.22.0
Depends: R (>= 3.1.2), grid
Imports: methods, utils, HilbertVis, png, grDevices, circlize (>=
0.3.3), IRanges, GenomicRanges, polylabelr
Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.99.0),
markdown, RColorBrewer, RCurl, GetoptLong
License: MIT + file LICENSE
MD5sum: 52c1d07e85d0998def3dd2a244f37271
NeedsCompilation: no
Title: Making 2D Hilbert Curve
Description: Hilbert curve is a type of space-filling curves that fold
one dimensional axis into a two dimensional space, but with
still preserves the locality. This package aims to provide an
easy and flexible way to visualize data through Hilbert curve.
biocViews: Software, Visualization, Sequencing, Coverage,
GenomeAnnotation
Author: Zuguang Gu
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/HilbertCurve
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HilbertCurve
git_branch: RELEASE_3_13
git_last_commit: 32424fc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HilbertCurve_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HilbertCurve_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HilbertCurve_1.22.0.tgz
vignettes: vignettes/HilbertCurve/inst/doc/HilbertCurve.html
vignetteTitles: Making 2D Hilbert Curve
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HilbertCurve/inst/doc/HilbertCurve.R
suggestsMe: InteractiveComplexHeatmap
dependencyCount: 28
Package: HilbertVis
Version: 1.50.0
Depends: R (>= 2.6.0), grid, lattice
Suggests: IRanges, EBImage
License: GPL (>= 3)
MD5sum: e10e5aab114bdcb14805c31373006c4f
NeedsCompilation: yes
Title: Hilbert curve visualization
Description: Functions to visualize long vectors of integer data by
means of Hilbert curves
biocViews: Visualization
Author: Simon Anders <sanders@fs.tum.de>
Maintainer: Simon Anders <sanders@fs.tum.de>
URL: http://www.ebi.ac.uk/~anders/hilbert
git_url: https://git.bioconductor.org/packages/HilbertVis
git_branch: RELEASE_3_13
git_last_commit: 62cc9d1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HilbertVis_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HilbertVis_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HilbertVis_1.50.0.tgz
vignettes: vignettes/HilbertVis/inst/doc/HilbertVis.pdf
vignetteTitles: Visualising very long data vectors with the Hilbert
curve
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HilbertVis/inst/doc/HilbertVis.R
dependsOnMe: HilbertVisGUI
importsMe: ChIPseqR, HilbertCurve
dependencyCount: 6
Package: HilbertVisGUI
Version: 1.50.0
Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6)
Suggests: lattice, IRanges
License: GPL (>= 3)
MD5sum: 2ffc4f15c17b8bb0791f6473ba4c3671
NeedsCompilation: yes
Title: HilbertVisGUI
Description: An interactive tool to visualize long vectors of integer
data by means of Hilbert curves
biocViews: Visualization
Author: Simon Anders <sanders@fs.tum.de>
Maintainer: Simon Anders <sanders@fs.tum.de>
URL: http://www.ebi.ac.uk/~anders/hilbert
SystemRequirements: gtkmm-2.4, GNU make
git_url: https://git.bioconductor.org/packages/HilbertVisGUI
git_branch: RELEASE_3_13
git_last_commit: d4cb976
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HilbertVisGUI_1.50.0.tar.gz
vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf
vignetteTitles: See vignette in package HilbertVis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: TRUE
hasLICENSE: FALSE
dependencyCount: 7
Package: HiLDA
Version: 1.6.0
Depends: R(>= 3.6), ggplot2
Imports: R2jags, abind, cowplot, grid, forcats, stringr, GenomicRanges,
S4Vectors, XVector, Biostrings, GenomicFeatures,
BSgenome.Hsapiens.UCSC.hg19, BiocGenerics, tidyr, grDevices,
stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, methods, Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: GPL-3
Archs: i386, x64
MD5sum: fb726b23b6cd56e595459cf2d0d08433
NeedsCompilation: yes
Title: Conducting statistical inference on comparing the mutational
exposures of mutational signatures by using hierarchical latent
Dirichlet allocation
Description: A package built under the Bayesian framework of applying
hierarchical latent Dirichlet allocation to statistically test
whether the mutational exposures of mutational signatures
(Shiraishi-model signatures) are different between two groups.
biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod,
Bayesian
Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb]
Maintainer: Zhi Yang <zyang895@gmail.com>
URL: https://github.com/USCbiostats/HiLDA,
https://doi.org/10.1101/577452
SystemRequirements: JAGS 4.2.0
VignetteBuilder: knitr
BugReports: https://github.com/USCbiostats/HiLDA/issues
git_url: https://git.bioconductor.org/packages/HiLDA
git_branch: RELEASE_3_13
git_last_commit: f7ac306
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HiLDA_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HiLDA_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HiLDA_1.6.0.tgz
vignettes: vignettes/HiLDA/inst/doc/HiLDA.html
vignetteTitles: An introduction to HiLDA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/HiLDA/inst/doc/HiLDA.R
importsMe: selectKSigs
dependencyCount: 123
Package: hipathia
Version: 2.8.0
Depends: R (>= 3.6), igraph (>= 1.0.1), AnnotationHub(>= 2.6.5),
MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1)
Imports: coin, stats, limma, grDevices, utils, graphics,
preprocessCore, servr, DelayedArray, matrixStats, methods,
S4Vectors
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-2
MD5sum: 040a809dd118c686652ba204273b8b89
NeedsCompilation: no
Title: HiPathia: High-throughput Pathway Analysis
Description: Hipathia is a method for the computation of signal
transduction along signaling pathways from transcriptomic data.
The method is based on an iterative algorithm which is able to
compute the signal intensity passing through the nodes of a
network by taking into account the level of expression of each
gene and the intensity of the signal arriving to it. It also
provides a new approach to functional analysis allowing to
compute the signal arriving to the functions annotated to each
pathway.
biocViews: Pathways, GraphAndNetwork, GeneExpression, GeneSignaling, GO
Author: Marta R. Hidalgo [aut, cre], José Carbonell-Caballero [ctb],
Francisco Salavert [ctb], Alicia Amadoz [ctb], Çankut Cubuk
[ctb], Joaquin Dopazo [ctb]
Maintainer: Marta R. Hidalgo <marta.hidalgo@outlook.es>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hipathia
git_branch: RELEASE_3_13
git_last_commit: c46391b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hipathia_2.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hipathia_2.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/hipathia_2.8.0.tgz
vignettes: vignettes/hipathia/inst/doc/hipathia-vignette.pdf
vignetteTitles: Hipathia Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hipathia/inst/doc/hipathia-vignette.R
dependencyCount: 115
Package: HIPPO
Version: 1.4.0
Depends: R (>= 3.6.0)
Imports: ggplot2, graphics, stats, reshape2, gridExtra, Rtsne, umap,
dplyr, rlang, magrittr, irlba, Matrix, SingleCellExperiment,
ggrepel
Suggests: knitr, rmarkdown
License: GPL (>=2)
MD5sum: fe3aa754f869b46c65e011fa19420997
NeedsCompilation: no
Title: Heterogeneity-Induced Pre-Processing tOol
Description: For scRNA-seq data, it selects features and clusters the
cells simultaneously for single-cell UMI data. It has a novel
feature selection method using the zero inflation instead of
gene variance, and computationally faster than other existing
methods since it only relies on PCA+Kmeans rather than
graph-clustering or consensus clustering.
biocViews: Sequencing, SingleCell, GeneExpression,
DifferentialExpression, Clustering
Author: Tae Kim [aut, cre], Mengjie Chen [aut]
Maintainer: Tae Kim <tk382@uchicago.edu>
URL: https://github.com/tk382/HIPPO
VignetteBuilder: knitr
BugReports: https://github.com/tk382/HIPPO/issues
git_url: https://git.bioconductor.org/packages/HIPPO
git_branch: RELEASE_3_13
git_last_commit: c955c6c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HIPPO_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HIPPO_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HIPPO_1.4.0.tgz
vignettes: vignettes/HIPPO/inst/doc/example.html
vignetteTitles: Example analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HIPPO/inst/doc/example.R
dependencyCount: 82
Package: hiReadsProcessor
Version: 1.28.0
Depends: Biostrings, GenomicAlignments, BiocParallel, hiAnnotator, R
(>= 3.0)
Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, readxl,
methods
Suggests: knitr, testthat
License: GPL-3
MD5sum: 5d107f19a09e580584e3f419e9b406ec
NeedsCompilation: no
Title: Functions to process LM-PCR reads from 454/Illumina data
Description: hiReadsProcessor contains set of functions which allow
users to process LM-PCR products sequenced using any platform.
Given an excel/txt file containing parameters for
demultiplexing and sample metadata, the functions automate
trimming of adaptors and identification of the genomic product.
Genomic products are further processed for QC and abundance
quantification.
biocViews: Sequencing, Preprocessing
Author: Nirav V Malani <malnirav@gmail.com>
Maintainer: Nirav V Malani <malnirav@gmail.com>
SystemRequirements: BLAT, UCSC hg18 in 2bit format for BLAT
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hiReadsProcessor
git_branch: RELEASE_3_13
git_last_commit: daa41a6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hiReadsProcessor_1.28.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/hiReadsProcessor_1.28.0.tgz
vignettes: vignettes/hiReadsProcessor/inst/doc/Tutorial.html
vignetteTitles: Using hiReadsProcessor
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R
dependencyCount: 90
Package: HIREewas
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: quadprog, gplots, grDevices, stats
Suggests: BiocStyle, knitr, BiocGenerics
License: GPL (>= 2)
MD5sum: ae4babff694ebbf3498e5b76fd373196
NeedsCompilation: yes
Title: Detection of cell-type-specific risk-CpG sites in epigenome-wide
association studies
Description: In epigenome-wide association studies, the measured
signals for each sample are a mixture of methylation profiles
from different cell types. The current approaches to the
association detection only claim whether a
cytosine-phosphate-guanine (CpG) site is associated with the
phenotype or not, but they cannot determine the cell type in
which the risk-CpG site is affected by the phenotype. We
propose a solid statistical method, HIgh REsolution (HIRE),
which not only substantially improves the power of association
detection at the aggregated level as compared to the existing
methods but also enables the detection of risk-CpG sites for
individual cell types. The "HIREewas" R package is to implement
HIRE model in R.
biocViews: DNAMethylation, DifferentialMethylation, FeatureExtraction
Author: Xiangyu Luo <xyluo1991@gmail.com>, Can Yang <macyang@ust.hk>,
Yingying Wei <yweicuhk@gmail.com>
Maintainer: Xiangyu Luo <xyluo1991@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HIREewas
git_branch: RELEASE_3_13
git_last_commit: b1f38e4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HIREewas_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HIREewas_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HIREewas_1.10.0.tgz
vignettes: vignettes/HIREewas/inst/doc/HIREewas.pdf
vignetteTitles: HIREewas
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HIREewas/inst/doc/HIREewas.R
dependencyCount: 10
Package: HiTC
Version: 1.36.0
Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges
Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer,
Matrix, parallel, GenomeInfoDb
Suggests: BiocStyle, HiCDataHumanIMR90
License: Artistic-2.0
Archs: i386, x64
MD5sum: bfff7653d843930fcb2760ca3bc73ccc
NeedsCompilation: no
Title: High Throughput Chromosome Conformation Capture analysis
Description: The HiTC package was developed to explore high-throughput
'C' data such as 5C or Hi-C. Dedicated R classes as well as
standard methods for quality controls, normalization,
visualization, and further analysis are also provided.
biocViews: Sequencing, HighThroughputSequencing, HiC
Author: Nicolas Servant
Maintainer: Nicolas Servant <nicolas.servant@curie.fr>
git_url: https://git.bioconductor.org/packages/HiTC
git_branch: RELEASE_3_13
git_last_commit: 012dec8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HiTC_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HiTC_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HiTC_1.36.0.tgz
vignettes: vignettes/HiTC/inst/doc/HiC_analysis.pdf,
vignettes/HiTC/inst/doc/HiTC.pdf
vignetteTitles: Hi-C data analysis using HiTC, Introduction to HiTC
package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HiTC/inst/doc/HiC_analysis.R,
vignettes/HiTC/inst/doc/HiTC.R
suggestsMe: HiCDCPlus, HiCDataHumanIMR90, adjclust
dependencyCount: 45
Package: hmdbQuery
Version: 1.12.1
Depends: R (>= 3.5), XML
Imports: S4Vectors, methods, utils
Suggests: knitr, annotate, gwascat, testthat, rmarkdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: 2bcd9ff989592b72eeb5b5b25cc6c255
NeedsCompilation: no
Title: utilities for exploration of human metabolome database
Description: Define utilities for exploration of human metabolome
database, including functions to retrieve specific metabolite
entries and data snapshots with pairwise associations
(metabolite-gene,-protein,-disease).
biocViews: Metabolomics, Infrastructure
Author: Vince Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hmdbQuery
git_branch: RELEASE_3_13
git_last_commit: c26d2ce
git_last_commit_date: 2021-07-28
Date/Publication: 2021-07-29
source.ver: src/contrib/hmdbQuery_1.12.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hmdbQuery_1.12.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/hmdbQuery_1.12.1.tgz
vignettes: vignettes/hmdbQuery/inst/doc/hmdbQuery.html
vignetteTitles: hmdbQuery: working with Human Metabolome Database
(hmdb.ca)
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hmdbQuery/inst/doc/hmdbQuery.R
dependencyCount: 9
Package: HMMcopy
Version: 1.34.0
Depends: R (>= 2.10.0), data.table (>= 1.11.8)
License: GPL-3
MD5sum: e7e90fbc69a2f94307723700d383135f
NeedsCompilation: yes
Title: Copy number prediction with correction for GC and mappability
bias for HTS data
Description: Corrects GC and mappability biases for readcounts (i.e.
coverage) in non-overlapping windows of fixed length for single
whole genome samples, yielding a rough estimate of copy number
for furthur analysis. Designed for rapid correction of high
coverage whole genome tumour and normal samples.
biocViews: Sequencing, Preprocessing, Visualization,
CopyNumberVariation, Microarray
Author: Daniel Lai, Gavin Ha, Sohrab Shah
Maintainer: Daniel Lai <dalai@bccrc.ca>, Sohrab Shah <shahs3@mskcc.org>
git_url: https://git.bioconductor.org/packages/HMMcopy
git_branch: RELEASE_3_13
git_last_commit: dc240df
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HMMcopy_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HMMcopy_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HMMcopy_1.34.0.tgz
vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf
vignetteTitles: HMMcopy
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HMMcopy/inst/doc/HMMcopy.R
importsMe: qsea
dependencyCount: 2
Package: hopach
Version: 2.52.0
Depends: R (>= 2.11.0), cluster, Biobase, methods
Imports: graphics, grDevices, stats, utils, BiocGenerics
License: GPL (>= 2)
MD5sum: 348fc0c235b4b5583b29f33c07aefed7
NeedsCompilation: yes
Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)
Description: The HOPACH clustering algorithm builds a hierarchical tree
of clusters by recursively partitioning a data set, while
ordering and possibly collapsing clusters at each level. The
algorithm uses the Mean/Median Split Silhouette (MSS) criteria
to identify the level of the tree with maximally homogeneous
clusters. It also runs the tree down to produce a final ordered
list of the elements. The non-parametric bootstrap allows one
to estimate the probability that each element belongs to each
cluster (fuzzy clustering).
biocViews: Clustering
Author: Katherine S. Pollard, with Mark J. van der Laan
<laan@stat.berkeley.edu> and Greg Wall
Maintainer: Katherine S. Pollard <katherine.pollard@gladstone.ucsf.edu>
URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/
git_url: https://git.bioconductor.org/packages/hopach
git_branch: RELEASE_3_13
git_last_commit: 931d0dd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hopach_2.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hopach_2.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/hopach_2.52.0.tgz
vignettes: vignettes/hopach/inst/doc/hopach.pdf
vignetteTitles: hopach
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hopach/inst/doc/hopach.R
importsMe: phenoTest, scClassify, treekoR
dependencyCount: 9
Package: HPAanalyze
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: dplyr, openxlsx, ggplot2, tibble, xml2, stats, utils,
gridExtra
Suggests: knitr, rmarkdown, markdown, devtools, BiocStyle
License: GPL-3 + file LICENSE
Archs: i386, x64
MD5sum: 70f6df55c3948d96b6be4f8ea3c534c2
NeedsCompilation: no
Title: Retrieve and analyze data from the Human Protein Atlas
Description: Provide functions for retrieving, exploratory analyzing
and visualizing the Human Protein Atlas data.
biocViews: Proteomics, CellBiology, Visualization, Software
Author: Anh Nhat Tran [aut, cre]
Maintainer: Anh Nhat Tran <trannhatanh89@gmail.com>
URL: https://github.com/anhtr/HPAanalyze
VignetteBuilder: knitr
BugReports: https://github.com/anhtr/HPAanalyze/issues
git_url: https://git.bioconductor.org/packages/HPAanalyze
git_branch: RELEASE_3_13
git_last_commit: d9bcfdb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HPAanalyze_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HPAanalyze_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HPAanalyze_1.10.0.tgz
vignettes: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.html,
vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.html,
vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.html,
vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.html,
vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.html,
vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.html,
vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.html
vignetteTitles: "1. Quick-start guide: Acquire and visualize the Human
Protein Atlas (HPA) data in one function with HPAanalyze", "2.
In-depth: Working with Human Protein Atlas (HPA) data in R with
HPAanalyze", "3. Tutorial: Combine HPAanalyze with your Human
Protein Atlas (HPA) queries", "4. Tutorial: Working with Human
Protein Atlas (HPA) xml files offline", "5. Tutorial: Export
Human Protein Atlas (HPA) data as JSON", "6. Tutorial: Download
histology images from the Human Protein Atlas", "99. Code for
figures from HPAanalyze paper"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.R,
vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.R,
vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.R,
vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.R,
vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.R,
vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.R,
vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.R
dependencyCount: 49
Package: hpar
Version: 1.34.0
Depends: R (>= 3.5.0)
Imports: utils
Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle, testthat
License: Artistic-2.0
MD5sum: 34f5d57208ccf8870f3599f848f35790
NeedsCompilation: no
Title: Human Protein Atlas in R
Description: The hpar package provides a simple R interface to and data
from the Human Protein Atlas project.
biocViews: Proteomics, Homo_sapiens, CellBiology
Author: Laurent Gatto [cre, aut]
(<https://orcid.org/0000-0002-1520-2268>), Manon Martin [aut]
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hpar
git_branch: RELEASE_3_13
git_last_commit: 8e1b66d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hpar_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hpar_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/hpar_1.34.0.tgz
vignettes: vignettes/hpar/inst/doc/hpar.html
vignetteTitles: Human Protein Atlas in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hpar/inst/doc/hpar.R
dependsOnMe: proteomics
importsMe: MetaboSignal
suggestsMe: HPAStainR, pRoloc, RforProteomics
dependencyCount: 1
Package: HPAStainR
Version: 1.2.1
Depends: R (>= 4.0.0), dplyr, tidyr
Imports: utils, stats, scales, stringr, tibble, shiny, data.table
Suggests: knitr, BiocManager, qpdf, hpar, testthat
License: Artistic-2.0
MD5sum: f9f1a36e01263f2ca4b746092d8cfa4a
NeedsCompilation: no
Title: Queries the Human Protein Atlas Staining Data for Multiple
Proteins and Genes
Description: This package is built around the HPAStainR function. The
purpose of the HPAStainR function is to query the visual
staining data in the Human Protein Atlas to return a table of
staining ranked cell types. The function also has multiple
arguments to personalize to output as well to include cancer
data, csv readable names, modify the confidence levels of the
results and more. The other functions exist exclusively to
easily acquire the data required to run HPAStainR.
biocViews: GeneExpression, GeneSetEnrichment
Author: Tim O. Nieuwenhuis [aut, cre]
(<https://orcid.org/0000-0002-1995-3317>)
Maintainer: Tim O. Nieuwenhuis <tnieuwe1@jhmi.edu>
SystemRequirements: 4GB of RAM
VignetteBuilder: knitr
BugReports: https://github.com/tnieuwe/HPAstainR
git_url: https://git.bioconductor.org/packages/HPAStainR
git_branch: RELEASE_3_13
git_last_commit: 8333874
git_last_commit_date: 2021-06-09
Date/Publication: 2021-06-10
source.ver: src/contrib/HPAStainR_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HPAStainR_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/HPAStainR_1.2.1.tgz
vignettes: vignettes/HPAStainR/inst/doc/HPAStainR.html
vignetteTitles: HPAStainR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HPAStainR/inst/doc/HPAStainR.R
dependencyCount: 58
Package: HTqPCR
Version: 1.46.0
Depends: Biobase, RColorBrewer, limma
Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods,
RColorBrewer, stats, stats4, utils
Suggests: statmod
License: Artistic-2.0
Archs: i386, x64
MD5sum: febcc62bc288fe591d2665790a00f2a4
NeedsCompilation: no
Title: Automated analysis of high-throughput qPCR data
Description: Analysis of Ct values from high throughput quantitative
real-time PCR (qPCR) assays across multiple conditions or
replicates. The input data can be from spatially-defined
formats such ABI TaqMan Low Density Arrays or OpenArray;
LightCycler from Roche Applied Science; the CFX plates from
Bio-Rad Laboratories; conventional 96- or 384-well plates; or
microfluidic devices such as the Dynamic Arrays from Fluidigm
Corporation. HTqPCR handles data loading, quality assessment,
normalization, visualization and parametric or non-parametric
testing for statistical significance in Ct values between
features (e.g. genes, microRNAs).
biocViews: MicrotitrePlateAssay, DifferentialExpression,
GeneExpression, DataImport, QualityControl, Preprocessing,
Visualization, MultipleComparison, qPCR
Author: Heidi Dvinge, Paul Bertone
Maintainer: Heidi Dvinge <hdvinge@fredhutch.org>
URL: http://www.ebi.ac.uk/bertone/software
git_url: https://git.bioconductor.org/packages/HTqPCR
git_branch: RELEASE_3_13
git_last_commit: 3c75acb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HTqPCR_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HTqPCR_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HTqPCR_1.46.0.tgz
vignettes: vignettes/HTqPCR/inst/doc/HTqPCR.pdf
vignetteTitles: qPCR analysis in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HTqPCR/inst/doc/HTqPCR.R
importsMe: nondetects, unifiedWMWqPCR
dependencyCount: 21
Package: HTSeqGenie
Version: 4.22.0
Depends: R (>= 3.0.0), gmapR (>= 1.8.0), ShortRead (>= 1.19.13),
VariantAnnotation (>= 1.8.3)
Imports: BiocGenerics (>= 0.2.0), S4Vectors (>= 0.9.25), IRanges (>=
1.21.39), GenomicRanges (>= 1.23.21), Rsamtools (>= 1.8.5),
Biostrings (>= 2.24.1), chipseq (>= 1.6.1), hwriter (>= 1.3.0),
Cairo (>= 1.5.5), GenomicFeatures (>= 1.9.31), BiocParallel,
parallel, tools, rtracklayer (>= 1.17.19), GenomicAlignments,
VariantTools (>= 1.7.7), GenomeInfoDb, SummarizedExperiment,
methods
Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, LungCancerLines,
org.Hs.eg.db
License: Artistic-2.0
MD5sum: 03c881368a14f9ed9e70b2d9f31edb2a
NeedsCompilation: no
Title: A NGS analysis pipeline.
Description: Libraries to perform NGS analysis.
Author: Gregoire Pau, Jens Reeder
Maintainer: Jens Reeder <reeder.jens@gene.com>
git_url: https://git.bioconductor.org/packages/HTSeqGenie
git_branch: RELEASE_3_13
git_last_commit: ddd1eb5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HTSeqGenie_4.22.0.tar.gz
vignettes: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.pdf
vignetteTitles: HTSeqGenie
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.R
dependencyCount: 107
Package: HTSFilter
Version: 1.32.0
Depends: R (>= 4.0.0)
Imports: edgeR, DESeq2, BiocParallel, Biobase, utils, stats, grDevices,
graphics, methods
Suggests: EDASeq, testthat, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
Archs: i386, x64
MD5sum: 5583aa43a9f3db45772226c49698a434
NeedsCompilation: no
Title: Filter replicated high-throughput transcriptome sequencing data
Description: This package implements a filtering procedure for
replicated transcriptome sequencing data based on a global
Jaccard similarity index in order to identify genes with low,
constant levels of expression across one or more experimental
conditions.
biocViews: Sequencing, RNASeq, Preprocessing, DifferentialExpression,
GeneExpression, Normalization, ImmunoOncology
Author: Andrea Rau [cre, aut]
(<https://orcid.org/0000-0001-6469-488X>), Melina Gallopin
[ctb], Gilles Celeux [ctb], Florence Jaffrézic [ctb]
Maintainer: Andrea Rau <andrea.rau@inrae.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HTSFilter
git_branch: RELEASE_3_13
git_last_commit: bd32ecf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HTSFilter_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HTSFilter_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HTSFilter_1.32.0.tgz
vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.html
vignetteTitles: HTSFilter
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R
importsMe: coseq
suggestsMe: HTSCluster
dependencyCount: 95
Package: HubPub
Version: 1.0.0
Imports: available, usethis, biocthis, dplyr, aws.s3, fs, BiocManager,
utils
Suggests: AnnotationHubData, ExperimentHubData, testthat, knitr,
rmarkdown, BiocStyle,
License: Artistic-2.0
MD5sum: 037508820ed95d5ea09da5b1a87e8cd6
NeedsCompilation: no
Title: Utilities to create and use Bioconductor Hubs
Description: HubPub provides users with functionality to help with the
Bioconductor Hub structures. The package provides the ability
to create a skeleton of a Hub style package that the user can
then populate with the necessary information. There are also
functions to help add resources to the Hub package metadata
files as well as publish data to the Bioconductor S3 bucket.
biocViews: DataImport, Infrastructure, Software, ThirdPartyClient
Author: Kayla Interdonato [aut, cre], Martin Morgan [aut]
Maintainer: Kayla Interdonato <kayla.morrell@roswellpark.org>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/HubPub/issues
git_url: https://git.bioconductor.org/packages/HubPub
git_branch: RELEASE_3_13
git_last_commit: 52515fb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HubPub_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HubPub_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HubPub_1.0.0.tgz
vignettes: vignettes/HubPub/inst/doc/HubPub.html
vignetteTitles: HubPub: Help with publication of Hub packages
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HubPub/inst/doc/HubPub.R
dependencyCount: 82
Package: HumanTranscriptomeCompendium
Version: 1.8.3
Depends: R (>= 4.1)
Imports: shiny, ssrch, S4Vectors, SummarizedExperiment, utils
Suggests: knitr, BiocStyle, beeswarm, tximportData, DT, tximport,
dplyr, magrittr, BiocFileCache, testthat, rhdf5client,
rmarkdown
License: Artistic-2.0
MD5sum: 8e4843cd0e69b341e8ddc91427582878
NeedsCompilation: no
Title: Tools to work with a Compendium of 181000 human transcriptome
sequencing studies
Description: Provide tools for working with a compendium of human
transcriptome sequences (originally htxcomp).
biocViews: Transcriptomics, Infrastructure, Sequencing
Author: Sean Davis, Vince Carey
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url:
https://git.bioconductor.org/packages/HumanTranscriptomeCompendium
git_branch: RELEASE_3_13
git_last_commit: 860ed80
git_last_commit_date: 2021-10-04
Date/Publication: 2021-10-07
source.ver: src/contrib/HumanTranscriptomeCompendium_1.8.3.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/HumanTranscriptomeCompendium_1.8.3.zip
mac.binary.ver:
bin/macosx/contrib/4.1/HumanTranscriptomeCompendium_1.8.3.tgz
vignettes: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.html
vignetteTitles: HumanTranscriptomeCompendium -- a cloud-resident
collection of sequenced human transcriptomes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.R
dependencyCount: 61
Package: hummingbird
Version: 1.2.0
Depends: R (>= 4.0)
Imports: Rcpp, graphics, GenomicRanges, SummarizedExperiment, IRanges
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
License: GPL (>=2)
MD5sum: 0ca3b180c14b58dc4ff214514568fbb6
NeedsCompilation: yes
Title: Bayesian Hidden Markov Model for the detection of differentially
methylated regions
Description: A package for detecting differential methylation. It
exploits a Bayesian hidden Markov model that incorporates
location dependence among genomic loci, unlike most existing
methods that assume independence among observations. Bayesian
priors are applied to permit information sharing across an
entire chromosome for improved power of detection. The direct
output of our software package is the best sequence of
methylation states, eliminating the use of a subjective, and
most of the time an arbitrary, threshold of p-value for
determining significance. At last, our methodology does not
require replication in either or both of the two comparison
groups.
biocViews: HiddenMarkovModel, Bayesian, DNAMethylation,
BiomedicalInformatics, Sequencing, GeneExpression,
DifferentialExpression, DifferentialMethylation
Author: Eleni Adam [aut, cre], Tieming Ji [aut], Desh Ranjan [aut]
Maintainer: Eleni Adam <eadam002@odu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hummingbird
git_branch: RELEASE_3_13
git_last_commit: 1d22c8a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hummingbird_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hummingbird_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/hummingbird_1.2.0.tgz
vignettes: vignettes/hummingbird/inst/doc/hummingbird.html
vignetteTitles: hummingbird
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hummingbird/inst/doc/hummingbird.R
dependencyCount: 27
Package: HybridMTest
Version: 1.36.0
Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival
Imports: stats
License: GPL Version 2 or later
MD5sum: 75e84bbbd94d2eb1cda636c99cf9aab3
NeedsCompilation: no
Title: Hybrid Multiple Testing
Description: Performs hybrid multiple testing that incorporates method
selection and assumption evaluations into the analysis using
empirical Bayes probability (EBP) estimates obtained by
Grenander density estimation. For instance, for 3-group
comparison analysis, Hybrid Multiple testing considers EBPs as
weighted EBPs between F-test and H-test with EBPs from Shapiro
Wilk test of normality as weigth. Instead of just using EBPs
from F-test only or using H-test only, this methodology
combines both types of EBPs through EBPs from Shapiro Wilk test
of normality. This methodology uses then the law of total EBPs.
biocViews: GeneExpression, Genetics, Microarray
Author: Stan Pounds <stanley.pounds@stjude.org>, Demba Fofana
<demba.fofana@stjude.org>
Maintainer: Demba Fofana <demba.fofana@stjude.org>
git_url: https://git.bioconductor.org/packages/HybridMTest
git_branch: RELEASE_3_13
git_last_commit: b80cc7b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/HybridMTest_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/HybridMTest_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/HybridMTest_1.36.0.tgz
vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf
vignetteTitles: Hybrid Multiple Testing
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HybridMTest/inst/doc/HybridMTest.R
dependencyCount: 15
Package: hypeR
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: ggplot2, ggforce, R6, magrittr, dplyr, purrr, stats, stringr,
scales, rlang, httr, openxlsx, htmltools, reshape2, reactable,
msigdbr, kableExtra, rmarkdown, igraph, visNetwork, shiny
Suggests: tidyverse, devtools, testthat, knitr
License: GPL-3 + file LICENSE
MD5sum: cc6eb563605be1bda780bc30c51da677
NeedsCompilation: no
Title: An R Package For Geneset Enrichment Workflows
Description: An R Package for Geneset Enrichment Workflows.
biocViews: GeneSetEnrichment, Annotation, Pathways
Author: Anthony Federico [aut, cre], Stefano Monti [aut]
Maintainer: Anthony Federico <anfed@bu.edu>
URL: https://github.com/montilab/hypeR
VignetteBuilder: knitr
BugReports: https://github.com/montilab/hypeR/issues
git_url: https://git.bioconductor.org/packages/hypeR
git_branch: RELEASE_3_13
git_last_commit: 23b7217
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hypeR_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hypeR_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/hypeR_1.8.0.tgz
vignettes: vignettes/hypeR/inst/doc/hypeR.html
vignetteTitles: hypeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/hypeR/inst/doc/hypeR.R
dependencyCount: 104
Package: hyperdraw
Version: 1.44.0
Depends: R (>= 2.9.0)
Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4
License: GPL (>= 2)
MD5sum: 684e0e29a39b8f08bf93588fe360b338
NeedsCompilation: no
Title: Visualizing Hypergaphs
Description: Functions for visualizing hypergraphs.
biocViews: Visualization, GraphAndNetwork
Author: Paul Murrell
Maintainer: Paul Murrell <p.murrell@auckland.ac.nz>
SystemRequirements: graphviz
git_url: https://git.bioconductor.org/packages/hyperdraw
git_branch: RELEASE_3_13
git_last_commit: 35eda74
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hyperdraw_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hyperdraw_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/hyperdraw_1.44.0.tgz
vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf
vignetteTitles: Hyperdraw
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hyperdraw/inst/doc/hyperdraw.R
dependsOnMe: BiGGR
dependencyCount: 12
Package: hypergraph
Version: 1.64.0
Depends: R (>= 2.1.0), methods, utils, graph
Suggests: BiocGenerics, RUnit
License: Artistic-2.0
MD5sum: 0b5351e011a82124cc51d2000588ced0
NeedsCompilation: no
Title: A package providing hypergraph data structures
Description: A package that implements some simple capabilities for
representing and manipulating hypergraphs.
biocViews: GraphAndNetwork
Author: Seth Falcon, Robert Gentleman
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/hypergraph
git_branch: RELEASE_3_13
git_last_commit: d4e5e2f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/hypergraph_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/hypergraph_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/hypergraph_1.64.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: altcdfenvs
importsMe: BiGGR, hyperdraw, RpsiXML
dependencyCount: 8
Package: iASeq
Version: 1.36.0
Depends: R (>= 2.14.1)
Imports: graphics, grDevices
License: GPL-2
Archs: i386, x64
MD5sum: ccbf2e7f910b6d57577762102c35195f
NeedsCompilation: no
Title: iASeq: integrating multiple sequencing datasets for detecting
allele-specific events
Description: It fits correlation motif model to multiple RNAseq or
ChIPseq studies to improve detection of allele-specific events
and describe correlation patterns across studies.
biocViews: ImmunoOncology, SNP, RNASeq, ChIPSeq
Author: Yingying Wei, Hongkai Ji
Maintainer: Yingying Wei <ywei@jhsph.edu>
git_url: https://git.bioconductor.org/packages/iASeq
git_branch: RELEASE_3_13
git_last_commit: e0a6d24
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iASeq_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iASeq_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iASeq_1.36.0.tgz
vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf
vignetteTitles: iASeq Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iASeq/inst/doc/iASeqVignette.R
dependencyCount: 2
Package: iasva
Version: 1.10.0
Depends: R (>= 3.5),
Imports: irlba, stats, cluster, graphics, SummarizedExperiment,
BiocParallel
Suggests: knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot,
DescTools, RColorBrewer
License: GPL-2
MD5sum: be1ebca59cdc1d99b9b03dcae819a9c6
NeedsCompilation: no
Title: Iteratively Adjusted Surrogate Variable Analysis
Description: Iteratively Adjusted Surrogate Variable Analysis (IA-SVA)
is a statistical framework to uncover hidden sources of
variation even when these sources are correlated. IA-SVA
provides a flexible methodology to i) identify a hidden factor
for unwanted heterogeneity while adjusting for all known
factors; ii) test the significance of the putative hidden
factor for explaining the unmodeled variation in the data; and
iii), if significant, use the estimated factor as an additional
known factor in the next iteration to uncover further hidden
factors.
biocViews: Preprocessing, QualityControl, BatchEffect, RNASeq,
Software, StatisticalMethod, FeatureExtraction, ImmunoOncology
Author: Donghyung Lee [aut, cre], Anthony Cheng [aut], Nathan Lawlor
[aut], Duygu Ucar [aut]
Maintainer: Donghyung Lee <Donghyung.Lee@jax.org>, Anthony Cheng
<Anthony.Cheng@jax.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/iasva
git_branch: RELEASE_3_13
git_last_commit: 1fe32a3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iasva_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iasva_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iasva_1.10.0.tgz
vignettes:
vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.html
vignetteTitles: "Detecting hidden heterogeneity in single cell RNA-Seq
data"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.R
dependencyCount: 35
Package: iBBiG
Version: 1.36.0
Depends: biclust
Imports: stats4,xtable,ade4
Suggests: methods
License: Artistic-2.0
MD5sum: 35b81a1070de201790e0c81938bcaf3e
NeedsCompilation: yes
Title: Iterative Binary Biclustering of Genesets
Description: iBBiG is a bi-clustering algorithm which is optimizes for
binary data analysis. We apply it to meta-gene set analysis of
large numbers of gene expression datasets. The iterative
algorithm extracts groups of phenotypes from multiple studies
that are associated with similar gene sets. iBBiG does not
require prior knowledge of the number or scale of clusters and
allows discovery of clusters with diverse sizes
biocViews: Clustering, Annotation, GeneSetEnrichment
Author: Daniel Gusenleitner, Aedin Culhane
Maintainer: Aedin Culhane <aedin@jimmy.harvard.edu>
URL: http://bcb.dfci.harvard.edu/~aedin/publications/
git_url: https://git.bioconductor.org/packages/iBBiG
git_branch: RELEASE_3_13
git_last_commit: 28747ce
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iBBiG_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iBBiG_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iBBiG_1.36.0.tgz
vignettes: vignettes/iBBiG/inst/doc/tutorial.pdf
vignetteTitles: iBBiG User Manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iBBiG/inst/doc/tutorial.R
importsMe: miRSM
dependencyCount: 55
Package: ibh
Version: 1.40.0
Depends: simpIntLists
Suggests: yeastCC, stats
License: GPL (>= 2)
MD5sum: 54286485aee92b9b9922eb83258f2825
NeedsCompilation: no
Title: Interaction Based Homogeneity for Evaluating Gene Lists
Description: This package contains methods for calculating Interaction
Based Homogeneity to evaluate fitness of gene lists to an
interaction network which is useful for evaluation of
clustering results and gene list analysis. BioGRID interactions
are used in the calculation. The user can also provide their
own interactions.
biocViews: QualityControl, DataImport, GraphAndNetwork,
NetworkEnrichment
Author: Kircicegi Korkmaz, Volkan Atalay, Rengul Cetin Atalay.
Maintainer: Kircicegi Korkmaz <e102771@ceng.metu.edu.tr>
git_url: https://git.bioconductor.org/packages/ibh
git_branch: RELEASE_3_13
git_last_commit: e61603c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ibh_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ibh_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ibh_1.40.0.tgz
vignettes: vignettes/ibh/inst/doc/ibh.pdf
vignetteTitles: ibh
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ibh/inst/doc/ibh.R
dependencyCount: 1
Package: iBMQ
Version: 1.32.0
Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2)
License: Artistic-2.0
MD5sum: 7a74ef93615efdc889ffb1fc84f0c517
NeedsCompilation: yes
Title: integrated Bayesian Modeling of eQTL data
Description: integrated Bayesian Modeling of eQTL data
biocViews: Microarray, Preprocessing, GeneExpression, SNP
Author: Marie-Pier Scott-Boyer and Greg Imholte
Maintainer: Greg Imholte <gimholte@uw.edu>
URL: http://www.rglab.org
SystemRequirements: GSL and OpenMP
git_url: https://git.bioconductor.org/packages/iBMQ
git_branch: RELEASE_3_13
git_last_commit: 13d8d38
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iBMQ_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iBMQ_1.32.0.zip
vignettes: vignettes/iBMQ/inst/doc/iBMQ.pdf
vignetteTitles: iBMQ: An Integrated Hierarchical Bayesian Model for
Multivariate eQTL Mapping
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iBMQ/inst/doc/iBMQ.R
dependencyCount: 41
Package: iCARE
Version: 1.20.0
Depends: R (>= 3.3.0), plotrix, gtools, Hmisc
Suggests: RUnit, BiocGenerics
License: GPL-3 + file LICENSE
MD5sum: f057e817554a972fe95fec5663a58d3e
NeedsCompilation: yes
Title: A Tool for Individualized Coherent Absolute Risk Estimation
(iCARE)
Description: An R package to compute Individualized Coherent Absolute
Risk Estimators.
biocViews: Software, StatisticalMethod, GenomeWideAssociation
Author: Paige Maas, Parichoy Pal Choudhury, Nilanjan Chatterjee and
William Wheeler
Maintainer: Bill Wheeler <wheelerb@imsweb.com>
git_url: https://git.bioconductor.org/packages/iCARE
git_branch: RELEASE_3_13
git_last_commit: 9549cae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iCARE_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iCARE_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iCARE_1.20.0.tgz
vignettes: vignettes/iCARE/inst/doc/vignette_model_validation.pdf,
vignettes/iCARE/inst/doc/vignette.pdf
vignetteTitles: iCARE Vignette Model Validation, iCARE Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/iCARE/inst/doc/vignette_model_validation.R,
vignettes/iCARE/inst/doc/vignette.R
dependencyCount: 70
Package: Icens
Version: 1.64.0
Depends: survival
Imports: graphics
License: Artistic-2.0
MD5sum: 356a0559ac67b6d36fd022b1299baedb
NeedsCompilation: no
Title: NPMLE for Censored and Truncated Data
Description: Many functions for computing the NPMLE for censored and
truncated data.
biocViews: Infrastructure
Author: R. Gentleman and Alain Vandal
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/Icens
git_branch: RELEASE_3_13
git_last_commit: 8bd4876
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Icens_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Icens_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Icens_1.64.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: PROcess, icensBKL, interval
importsMe: PROcess, LTRCtrees
suggestsMe: ReIns
dependencyCount: 10
Package: icetea
Version: 1.10.0
Depends: R (>= 4.0)
Imports: stats, utils, methods, graphics, grDevices, ggplot2,
GenomicFeatures, ShortRead, BiocParallel, Biostrings,
S4Vectors, Rsamtools, BiocGenerics, IRanges, GenomicAlignments,
GenomicRanges, rtracklayer, SummarizedExperiment,
VariantAnnotation, limma, edgeR, csaw, DESeq2,
TxDb.Dmelanogaster.UCSC.dm6.ensGene
Suggests: knitr, rmarkdown, Rsubread (>= 1.29.0), testthat
License: GPL-3 + file LICENSE
Archs: i386, x64
MD5sum: ffbacf3208ea4205878386ad1b3d6a3d
NeedsCompilation: no
Title: Integrating Cap Enrichment with Transcript Expression Analysis
Description: icetea (Integrating Cap Enrichment with Transcript
Expression Analysis) provides functions for end-to-end analysis
of multiple 5'-profiling methods such as CAGE, RAMPAGE and
MAPCap, beginning from raw reads to detection of transcription
start sites using replicates. It also allows performing
differential TSS detection between group of samples, therefore,
integrating the mRNA cap enrichment information with transcript
expression analysis.
biocViews: ImmunoOncology, Transcription, GeneExpression, Sequencing,
RNASeq, Transcriptomics, DifferentialExpression
Author: Vivek Bhardwaj [aut, cre]
Maintainer: Vivek Bhardwaj <v.bhardwaj@hubrecht.eu>
URL: https://github.com/vivekbhr/icetea
VignetteBuilder: knitr
BugReports: https://github.com/vivekbhr/icetea/issues
git_url: https://git.bioconductor.org/packages/icetea
git_branch: RELEASE_3_13
git_last_commit: b536bec
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/icetea_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/icetea_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/icetea_1.10.0.tgz
vignettes: vignettes/icetea/inst/doc/mapcap_analysis.html
vignetteTitles: Analysing transcript 5'-profiling data using icetea
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/icetea/inst/doc/mapcap_analysis.R
dependencyCount: 129
Package: iCheck
Version: 1.22.0
Depends: R (>= 3.2.0), Biobase, lumi, gplots
Imports: stats, graphics, preprocessCore, grDevices, randomForest,
affy, limma, parallel, GeneSelectMMD, rgl, MASS, lmtest,
scatterplot3d, utils
License: GPL (>= 2)
MD5sum: 7643bbbd5bc32483649e59459f75b7f8
NeedsCompilation: no
Title: QC Pipeline and Data Analysis Tools for High-Dimensional
Illumina mRNA Expression Data
Description: QC pipeline and data analysis tools for high-dimensional
Illumina mRNA expression data.
biocViews: GeneExpression, DifferentialExpression, Microarray,
Preprocessing, DNAMethylation, OneChannel, TwoChannel,
QualityControl
Author: Weiliang Qiu [aut, cre], Brandon Guo [aut, ctb], Christopher
Anderson [aut, ctb], Barbara Klanderman [aut, ctb], Vincent
Carey [aut, ctb], Benjamin Raby [aut, ctb]
Maintainer: Weiliang Qiu <stwxq@channing.harvard.edu>
git_url: https://git.bioconductor.org/packages/iCheck
git_branch: RELEASE_3_13
git_last_commit: 400b77a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iCheck_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iCheck_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iCheck_1.22.0.tgz
vignettes: vignettes/iCheck/inst/doc/iCheck.pdf
vignetteTitles: iCheck
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iCheck/inst/doc/iCheck.R
dependencyCount: 178
Package: iChip
Version: 1.46.0
Depends: R (>= 2.10.0)
Imports: limma
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 1b3cfefd8a527cf206905392d5ff0aff
NeedsCompilation: yes
Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models
Description: Hidden Ising models are implemented to identify enriched
genomic regions in ChIP-chip data. They can be used to analyze
the data from multiple platforms (e.g., Affymetrix, Agilent,
and NimbleGen), and the data with single to multiple
replicates.
biocViews: ChIPchip, OneChannel, AgilentChip, Microarray
Author: Qianxing Mo
Maintainer: Qianxing Mo <qianxing.mo@moffitt.org>
git_url: https://git.bioconductor.org/packages/iChip
git_branch: RELEASE_3_13
git_last_commit: fd29faf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iChip_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iChip_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iChip_1.46.0.tgz
vignettes: vignettes/iChip/inst/doc/iChip.pdf
vignetteTitles: iChip
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iChip/inst/doc/iChip.R
dependencyCount: 6
Package: iClusterPlus
Version: 1.28.0
Depends: R (>= 3.3.0), parallel
Suggests: RUnit, BiocGenerics
License: GPL (>= 2)
Archs: i386, x64
MD5sum: fe6307505fe0e1a72663fc5fd3e21c92
NeedsCompilation: yes
Title: Integrative clustering of multi-type genomic data
Description: Integrative clustering of multiple genomic data using a
joint latent variable model.
biocViews: Microarray, Clustering
Author: Qianxing Mo, Ronglai Shen
Maintainer: Qianxing Mo <qianxing.mo@moffitt.org>, Ronglai Shen
<shenr@mskcc.org>
git_url: https://git.bioconductor.org/packages/iClusterPlus
git_branch: RELEASE_3_13
git_last_commit: fdec867
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iClusterPlus_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iClusterPlus_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iClusterPlus_1.28.0.tgz
vignettes: vignettes/iClusterPlus/inst/doc/iClusterPlus.pdf,
vignettes/iClusterPlus/inst/doc/iManual.pdf
vignetteTitles: iClusterPlus, iManual.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
suggestsMe: MultiDataSet
dependencyCount: 1
Package: iCNV
Version: 1.12.0
Depends: R (>= 3.3.1), CODEX
Imports: fields, ggplot2, truncnorm, tidyr, data.table, dplyr,
grDevices, graphics, stats, utils, rlang
Suggests: knitr, rmarkdown, WES.1KG.WUGSC
License: GPL-2
MD5sum: 68aadf45941df536792dc3ddb11c129d
NeedsCompilation: no
Title: Integrated Copy Number Variation detection
Description: Integrative copy number variation (CNV) detection from
multiple platform and experimental design.
biocViews: ImmunoOncology, ExomeSeq, WholeGenome, SNP,
CopyNumberVariation, HiddenMarkovModel
Author: Zilu Zhou, Nancy Zhang
Maintainer: Zilu Zhou <zhouzilu@pennmedicine.upenn.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/iCNV
git_branch: RELEASE_3_13
git_last_commit: 6539bcc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iCNV_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iCNV_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iCNV_1.12.0.tgz
vignettes: vignettes/iCNV/inst/doc/iCNV-vignette.html
vignetteTitles: iCNV Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iCNV/inst/doc/iCNV-vignette.R
dependencyCount: 90
Package: iCOBRA
Version: 1.20.0
Depends: R (>= 4.0)
Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2,
ggplot2 (>= 2.0.0), scales, ROCR, dplyr, DT, limma, methods,
UpSetR
Suggests: knitr, rmarkdown, testthat
License: GPL (>=2)
MD5sum: a6b442ea692e7f9f773f977ee7d56e12
NeedsCompilation: no
Title: Comparison and Visualization of Ranking and Assignment Methods
Description: This package provides functions for calculation and
visualization of performance metrics for evaluation of ranking
and binary classification (assignment) methods. Various types
of performance plots can be generated programmatically. The
package also contains a shiny application for interactive
exploration of results.
biocViews: Classification, Visualization
Author: Charlotte Soneson [aut, cre]
(<https://orcid.org/0000-0003-3833-2169>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/csoneson/iCOBRA
VignetteBuilder: knitr
BugReports: https://github.com/csoneson/iCOBRA/issues
git_url: https://git.bioconductor.org/packages/iCOBRA
git_branch: RELEASE_3_13
git_last_commit: 6f2c176
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iCOBRA_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iCOBRA_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iCOBRA_1.20.0.tgz
vignettes: vignettes/iCOBRA/inst/doc/iCOBRA.html
vignetteTitles: iCOBRA User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R
suggestsMe: muscat, SummarizedBenchmark
dependencyCount: 83
Package: ideal
Version: 1.16.1
Depends: topGO
Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges,
S4Vectors, ggplot2 (>= 2.0.0), heatmaply, plotly, pheatmap,
pcaExplorer, IHW, gplots, UpSetR, goseq, stringr, dplyr, limma,
GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0),
shinydashboard, shinyBS, DT, rentrez, rintrojs, ggrepel, knitr,
rmarkdown, shinyAce, BiocParallel, grDevices, base64enc,
methods
Suggests: testthat, BiocStyle, airway, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, edgeR
License: MIT + file LICENSE
MD5sum: 5b2484ffcdb7c05208e1ee20f17041a2
NeedsCompilation: no
Title: Interactive Differential Expression AnaLysis
Description: This package provides functions for an Interactive
Differential Expression AnaLysis of RNA-sequencing datasets, to
extract quickly and effectively information downstream the step
of differential expression. A Shiny application encapsulates
the whole package.
biocViews: ImmunoOncology, GeneExpression, DifferentialExpression,
RNASeq, Sequencing, Visualization, QualityControl, GUI,
GeneSetEnrichment, ReportWriting
Author: Federico Marini [aut, cre]
(<https://orcid.org/0000-0003-3252-7758>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
URL: https://github.com/federicomarini/ideal,
https://federicomarini.github.io/ideal/
VignetteBuilder: knitr
BugReports: https://github.com/federicomarini/ideal/issues
git_url: https://git.bioconductor.org/packages/ideal
git_branch: RELEASE_3_13
git_last_commit: e898da4
git_last_commit_date: 2021-10-07
Date/Publication: 2021-10-10
source.ver: src/contrib/ideal_1.16.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ideal_1.16.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ideal_1.16.1.tgz
vignettes: vignettes/ideal/inst/doc/ideal-usersguide.html
vignetteTitles: ideal User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ideal/inst/doc/ideal-usersguide.R
dependencyCount: 205
Package: IdeoViz
Version: 1.28.0
Depends: Biobase, IRanges, GenomicRanges, RColorBrewer,
rtracklayer,graphics,GenomeInfoDb
License: GPL-2
Archs: i386, x64
MD5sum: 90c320dcee2715a16a8f188623190672
NeedsCompilation: no
Title: Plots data (continuous/discrete) along chromosomal ideogram
Description: Plots data associated with arbitrary genomic intervals
along chromosomal ideogram.
biocViews: Visualization,Microarray
Author: Shraddha Pai <shraddha.pai@utoronto.ca>, Jingliang Ren
Maintainer: Shraddha Pai <shraddha.pai@utoronto.ca>
git_url: https://git.bioconductor.org/packages/IdeoViz
git_branch: RELEASE_3_13
git_last_commit: d115002
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IdeoViz_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IdeoViz_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IdeoViz_1.28.0.tgz
vignettes: vignettes/IdeoViz/inst/doc/Vignette.pdf
vignetteTitles: IdeoViz: a package for plotting simple data along
ideograms
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/IdeoViz/inst/doc/Vignette.R
dependencyCount: 45
Package: idiogram
Version: 1.68.0
Depends: R (>= 2.10), methods, Biobase, annotate, plotrix
Suggests: hu6800.db, hgu95av2.db, golubEsets
License: GPL-2
Archs: i386, x64
MD5sum: da871f853dc5a00dbe0af648d0b14d6a
NeedsCompilation: no
Title: idiogram
Description: A package for plotting genomic data by chromosomal
location
biocViews: Visualization
Author: Karl J. Dykema <karl.dykema@vai.org>
Maintainer: Karl J. Dykema <karl.dykema@vai.org>
git_url: https://git.bioconductor.org/packages/idiogram
git_branch: RELEASE_3_13
git_last_commit: 626777a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/idiogram_1.68.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/idiogram_1.68.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/idiogram_1.68.0.tgz
vignettes: vignettes/idiogram/inst/doc/idiogram.pdf
vignetteTitles: HOWTO: idiogram
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/idiogram/inst/doc/idiogram.R
dependencyCount: 50
Package: idpr
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: ggplot2 (>= 3.3.0), magrittr (>= 1.5), dplyr (>= 0.8.5), plyr
(>= 1.8.6), jsonlite (>= 1.6.1), rlang (>= 0.4.6), Biostrings
(>= 2.56.0), methods (>= 4.0.0)
Suggests: knitr, rmarkdown, msa, ape, testthat, seqinr
License: LGPL-3
MD5sum: 5bf6e55b7f9b6b84ff9c626ec1582626
NeedsCompilation: no
Title: Profiling and Analyzing Intrinsically Disordered Proteins in R
Description: ‘idpr’ aims to integrate tools for the computational
analysis of intrinsically disordered proteins (IDPs) within R.
This package is used to identify known characteristics of IDPs
for a sequence of interest with easily reported and dynamic
results. Additionally, this package includes tools for
IDP-based sequence analysis to be used in conjunction with
other R packages.
biocViews: StructuralPrediction, Proteomics, CellBiology
Author: William M. McFadden [cre, aut], Judith L. Yanowitz [aut, fnd],
Michael Buszczak [ctb, fnd]
Maintainer: William M. McFadden <wmm27@pitt.edu>
VignetteBuilder: knitr
BugReports: https://github.com/wmm27/idpr/issues
git_url: https://git.bioconductor.org/packages/idpr
git_branch: RELEASE_3_13
git_last_commit: 035b726
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/idpr_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/idpr_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/idpr_1.2.0.tgz
vignettes: vignettes/idpr/inst/doc/chargeHydropathy-vignette.html,
vignettes/idpr/inst/doc/disorderedMatrices-vignette.html,
vignettes/idpr/inst/doc/idpr-vignette.html,
vignettes/idpr/inst/doc/iupred-vignette.html,
vignettes/idpr/inst/doc/sequenceMAP-vignette.html,
vignettes/idpr/inst/doc/structuralTendency-vignette.html
vignetteTitles: Charge and Hydropathy Vignette, Disordered Matrices
Vignette, idpr Package Overview Vignette, IUPred Vignette,
Sequence Map Vignette, Structural Tendency Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/idpr/inst/doc/chargeHydropathy-vignette.R,
vignettes/idpr/inst/doc/disorderedMatrices-vignette.R,
vignettes/idpr/inst/doc/idpr-vignette.R,
vignettes/idpr/inst/doc/iupred-vignette.R,
vignettes/idpr/inst/doc/sequenceMAP-vignette.R,
vignettes/idpr/inst/doc/structuralTendency-vignette.R
dependencyCount: 58
Package: idr2d
Version: 1.6.0
Depends: R (>= 3.6)
Imports: dplyr (>= 0.7.6), futile.logger (>= 1.4.3), GenomeInfoDb (>=
1.14.0), GenomicRanges (>= 1.30), ggplot2 (>= 3.1.1),
grDevices, idr (>= 1.2), IRanges (>= 2.18.0), magrittr (>=
1.5), methods, reticulate (>= 1.13), scales (>= 1.0.0), stats,
stringr (>= 1.3.1), utils
Suggests: DT (>= 0.4), htmltools (>= 0.3.6), knitr (>= 1.20), rmarkdown
(>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: a59fd8d731f7787b1640112409a2cfa5
NeedsCompilation: no
Title: Irreproducible Discovery Rate for Genomic Interactions Data
Description: A tool to measure reproducibility between genomic
experiments that produce two-dimensional peaks (interactions
between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an
extension of the original idr package, which is intended for
(one-dimensional) ChIP-seq peaks.
biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics,
FunctionalGenomics, Classification, HiC
Author: Konstantin Krismer [aut, cre, cph]
(<https://orcid.org/0000-0001-8994-3416>), David Gifford [ths,
cph] (<https://orcid.org/0000-0003-1709-4034>)
Maintainer: Konstantin Krismer <krismer@mit.edu>
URL: https://idr2d.mit.edu
SystemRequirements: Python (>= 3.5.0), hic-straw
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/idr2d
git_branch: RELEASE_3_13
git_last_commit: df5b4c8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/idr2d_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/idr2d_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/idr2d_1.6.0.tgz
vignettes: vignettes/idr2d/inst/doc/idr1d.html,
vignettes/idr2d/inst/doc/idr2d.html
vignetteTitles: Identify reproducible genomic peaks from replicate
ChIP-seq experiments, Identify reproducible genomic
interactions from replicate ChIA-PET experiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/idr2d/inst/doc/idr1d.R,
vignettes/idr2d/inst/doc/idr2d.R
dependencyCount: 69
Package: iGC
Version: 1.22.0
Depends: R (>= 3.2.0)
Imports: plyr, data.table
Suggests: BiocStyle, knitr, rmarkdown
Enhances: doMC
License: GPL-2
MD5sum: 07525ed63816c79121c5fe4748a91e48
NeedsCompilation: no
Title: An integrated analysis package of Gene expression and Copy
number alteration
Description: This package is intended to identify differentially
expressed genes driven by Copy Number Alterations from samples
with both gene expression and CNA data.
biocViews: Software, Biological Question, DifferentialExpression,
GenomicVariation, AssayDomain, CopyNumberVariation,
GeneExpression, ResearchField, Genetics, Technology,
Microarray, Sequencing, WorkflowStep, MultipleComparison
Author: Yi-Pin Lai [aut], Liang-Bo Wang [aut, cre], Tzu-Pin Lu [aut],
Eric Y. Chuang [aut]
Maintainer: Liang-Bo Wang <r02945054@ntu.edu.tw>
URL: http://github.com/ccwang002/iGC
VignetteBuilder: knitr
BugReports: http://github.com/ccwang002/iGC/issues
git_url: https://git.bioconductor.org/packages/iGC
git_branch: RELEASE_3_13
git_last_commit: ea783bb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iGC_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iGC_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iGC_1.22.0.tgz
vignettes: vignettes/iGC/inst/doc/Introduction.html
vignetteTitles: Introduction to iGC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iGC/inst/doc/Introduction.R
dependencyCount: 5
Package: IgGeneUsage
Version: 1.6.0
Depends: methods, R (>= 3.6.0), Rcpp (>= 0.12.0), SummarizedExperiment,
StanHeaders (> 2.18.1)
Imports: rstan (>= 2.19.2), reshape2 (>= 1.4.3)
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), ggplot2,
ggforce, gridExtra, ggrepel
License: file LICENSE
MD5sum: 4409139db240543d55dc6be57094af19
NeedsCompilation: no
Title: Differential gene usage in immune repertoires
Description: Decoding the properties of immune repertoires is key in
understanding the response of adaptive immunity to challenges
such as viral infection. One important task in immune
repertoire profiling is the detection of biases in Ig gene
usage between biological conditions. IgGeneUsage is a
computational tool for the analysis of differential gene usage
in immune repertoires. It employs Bayesian hierarchical models
to fit complex gene usage data from immune repertoire
sequencing experiments and quantifies Ig gene usage biases as
probabilities.
biocViews: DifferentialExpression, Regression, Genetics, Bayesian
Author: Simo Kitanovski [aut, cre]
Maintainer: Simo Kitanovski <simo.kitanovski@uni-due.de>
VignetteBuilder: knitr
BugReports: https://github.com/snaketron/IgGeneUsage/issues
git_url: https://git.bioconductor.org/packages/IgGeneUsage
git_branch: RELEASE_3_13
git_last_commit: add6692
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IgGeneUsage_1.6.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/IgGeneUsage_1.6.0.tgz
vignettes: vignettes/IgGeneUsage/inst/doc/IgUsageCaseStudies.html
vignetteTitles: User Manual: IgGeneUsage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/IgGeneUsage/inst/doc/IgUsageCaseStudies.R
dependencyCount: 81
Package: igvR
Version: 1.12.0
Depends: R (>= 3.5.0), GenomicRanges, GenomicAlignments, BrowserViz (>=
2.9.1)
Imports: methods, BiocGenerics, httpuv, utils, MotifDb, seqLogo,
rtracklayer, VariantAnnotation, RColorBrewer
Suggests: RUnit, BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: f5e645df5e1933b5cdfce64a17b5c23f
NeedsCompilation: no
Title: igvR: integrative genomics viewer
Description: Access to igv.js, the Integrative Genomics Viewer running
in a web browser.
biocViews: Visualization, ThirdPartyClient, GenomeBrowsers
Author: Paul Shannon
Maintainer: Paul Shannon <paul.thurmond.shannon@gmail.com>
URL: https://paul-shannon.github.io/igvR/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/igvR
git_branch: RELEASE_3_13
git_last_commit: 947368f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/igvR_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/igvR_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/igvR_1.12.0.tgz
vignettes: vignettes/igvR/inst/doc/alzheimersVariantsNearMEF2C.html,
vignettes/igvR/inst/doc/basicIntro.html,
vignettes/igvR/inst/doc/chooseStockOrCustomGenome.html,
vignettes/igvR/inst/doc/ctcfChipSeq.html
vignetteTitles: "Explore VCF variants,, GWAS snps,, promoters and
histone marks around the MEF2C gene in Alzheimers Disease",
"Introduction: a simple demo", "Choose a Stock or Custom
Genome", "Explore ChIP-seq alignments from a bam file,, MACS2
narrowPeaks,, conservation,, H3K4me3 methylation and motif
matching"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/igvR/inst/doc/alzheimersVariantsNearMEF2C.R,
vignettes/igvR/inst/doc/basicIntro.R,
vignettes/igvR/inst/doc/chooseStockOrCustomGenome.R,
vignettes/igvR/inst/doc/ctcfChipSeq.R
dependencyCount: 107
Package: IHW
Version: 1.20.0
Depends: R (>= 3.3.0)
Imports: methods, slam, lpsymphony, fdrtool, BiocGenerics
Suggests: ggplot2, dplyr, gridExtra, scales, DESeq2, airway, testthat,
Matrix, BiocStyle, knitr, rmarkdown, devtools
License: Artistic-2.0
MD5sum: 18c60d897af0b91c6cb2bf8649e3a889
NeedsCompilation: no
Title: Independent Hypothesis Weighting
Description: Independent hypothesis weighting (IHW) is a multiple
testing procedure that increases power compared to the method
of Benjamini and Hochberg by assigning data-driven weights to
each hypothesis. The input to IHW is a two-column table of
p-values and covariates. The covariate can be any
continuous-valued or categorical variable that is thought to be
informative on the statistical properties of each hypothesis
test, while it is independent of the p-value under the null
hypothesis.
biocViews: ImmunoOncology, MultipleComparison, RNASeq
Author: Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut]
Maintainer: Nikos Ignatiadis <nikos.ignatiadis01@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IHW
git_branch: RELEASE_3_13
git_last_commit: 1d7e10d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IHW_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IHW_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IHW_1.20.0.tgz
vignettes: vignettes/IHW/inst/doc/introduction_to_ihw.html
vignetteTitles: "Introduction to IHW"
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R
dependsOnMe: IHWpaper
importsMe: ideal, DGEobj.utils
suggestsMe: DEWSeq, metagenomeSeq, SummarizedBenchmark,
BloodCancerMultiOmics2017, BisRNA
dependencyCount: 10
Package: illuminaio
Version: 0.34.0
Imports: base64
Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2),
BiocStyle
License: GPL-2
MD5sum: 4fcd941ac1dee531e8af3bc11394bd54
NeedsCompilation: yes
Title: Parsing Illumina Microarray Output Files
Description: Tools for parsing Illumina's microarray output files,
including IDAT.
biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms
Author: Keith Baggerly [aut], Henrik Bengtsson [aut], Kasper Daniel
Hansen [aut, cre], Matt Ritchie [aut], Mike L. Smith [aut], Tim
Triche Jr. [ctb]
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
URL: https://github.com/HenrikBengtsson/illuminaio
BugReports: https://github.com/HenrikBengtsson/illuminaio/issues
git_url: https://git.bioconductor.org/packages/illuminaio
git_branch: RELEASE_3_13
git_last_commit: a15b557
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/illuminaio_0.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/illuminaio_0.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/illuminaio_0.34.0.tgz
vignettes: vignettes/illuminaio/inst/doc/EncryptedFormat.pdf,
vignettes/illuminaio/inst/doc/illuminaio.pdf
vignetteTitles: Description of Encrypted IDAT Format, Introduction to
illuminaio
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/illuminaio/inst/doc/illuminaio.R
dependsOnMe: normalize450K, RnBeads, wateRmelon, EGSEA123
importsMe: beadarray, crlmm, methylumi, minfi, sesame
suggestsMe: limma
dependencyCount: 4
Package: ILoReg
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: Matrix, parallel, foreach, aricode, LiblineaR, SparseM,
ggplot2, cowplot, RSpectra, umap, Rtsne, fastcluster,
parallelDist, cluster, dendextend, DescTools, plyr, scales,
pheatmap, reshape2, dplyr, doRNG, SingleCellExperiment,
SummarizedExperiment, S4Vectors, methods, stats, doSNOW, utils
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: f7d90d39feb7be0c70d42175554229f2
NeedsCompilation: no
Title: ILoReg: a tool for high-resolution cell population
identification from scRNA-Seq data
Description: ILoReg is a tool for identification of cell populations
from scRNA-seq data. In particular, ILoReg is useful for
finding cell populations with subtle transcriptomic
differences. The method utilizes a self-supervised learning
method, called Iteratitive Clustering Projection (ICP), to find
cluster probabilities, which are used in noise reduction prior
to PCA and the subsequent hierarchical clustering and t-SNE
steps. Additionally, functions for differential expression
analysis to find gene markers for the populations and gene
expression visualization are provided.
biocViews: SingleCell, Software, Clustering, DimensionReduction,
RNASeq, Visualization, Transcriptomics, DataRepresentation,
DifferentialExpression, Transcription, GeneExpression
Author: Johannes Smolander [cre, aut], Sini Junttila [aut], Mikko S
Venäläinen [aut], Laura L Elo [aut]
Maintainer: Johannes Smolander <johannes.smolander@gmail.com>
URL: https://github.com/elolab/ILoReg
VignetteBuilder: knitr
BugReports: https://github.com/elolab/ILoReg/issues
git_url: https://git.bioconductor.org/packages/ILoReg
git_branch: RELEASE_3_13
git_last_commit: b18c823
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ILoReg_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ILoReg_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ILoReg_1.2.0.tgz
vignettes: vignettes/ILoReg/inst/doc/ILoReg.html
vignetteTitles: ILoReg package manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ILoReg/inst/doc/ILoReg.R
dependencyCount: 115
Package: imageHTS
Version: 1.42.0
Depends: R (>= 2.9.0), EBImage (>= 4.3.12), cellHTS2 (>= 2.10.0)
Imports: tools, Biobase, hwriter, methods, vsn, stats, utils, e1071
Suggests: BiocStyle, MASS
License: LGPL-2.1
MD5sum: f76394014dcbd8dfc8008fcee63eccfa
NeedsCompilation: no
Title: Analysis of high-throughput microscopy-based screens
Description: imageHTS is an R package dedicated to the analysis of
high-throughput microscopy-based screens. The package provides
a modular and extensible framework to segment cells, extract
quantitative cell features, predict cell types and browse
screen data through web interfaces. Designed to operate in
distributed environments, imageHTS provides a standardized
access to remote data and facilitates the dissemination of
high-throughput microscopy-based datasets.
biocViews: ImmunoOncology, Software, CellBasedAssays, Preprocessing,
Visualization
Author: Gregoire Pau, Xian Zhang, Michael Boutros, Wolfgang Huber
Maintainer: Joseph Barry <joseph.barry@embl.de>
git_url: https://git.bioconductor.org/packages/imageHTS
git_branch: RELEASE_3_13
git_last_commit: f7a9006
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-23
source.ver: src/contrib/imageHTS_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/imageHTS_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/imageHTS_1.42.0.tgz
vignettes: vignettes/imageHTS/inst/doc/imageHTS-introduction.pdf
vignetteTitles: Analysis of high-throughput microscopy-based screens
with imageHTS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/imageHTS/inst/doc/imageHTS-introduction.R
dependencyCount: 104
Package: IMAS
Version: 1.16.0
Depends: R (> 3.0.0),GenomicFeatures, ggplot2, IVAS
Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges,
foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, stats,
ggfortify, grDevices, methods, Matrix, utils, graphics,
gridExtra, grid, lattice, Rsamtools, survival, BiocParallel,
GenomicAlignments, parallel
Suggests: BiocStyle, RUnit
License: GPL-2
MD5sum: 79e7041bfa266f9169c53be74d34a880
NeedsCompilation: no
Title: Integrative analysis of Multi-omics data for Alternative
Splicing
Description: Integrative analysis of Multi-omics data for Alternative
splicing.
biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression,
DifferentialSplicing, GeneExpression, GeneRegulation,
Regression, RNASeq, Sequencing, SNP, Software, Transcription
Author: Seonggyun Han, Younghee Lee
Maintainer: Seonggyun Han <hangost@ssu.ac.kr>
git_url: https://git.bioconductor.org/packages/IMAS
git_branch: RELEASE_3_13
git_last_commit: b45a2cb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IMAS_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IMAS_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IMAS_1.16.0.tgz
vignettes: vignettes/IMAS/inst/doc/IMAS.pdf
vignetteTitles: IMAS : Integrative analysis of Multi-omics data for
Alternative Splicing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IMAS/inst/doc/IMAS.R
dependencyCount: 125
Package: IMMAN
Version: 1.12.0
Imports: STRINGdb, Biostrings, igraph, graphics, utils, seqinr
Suggests: knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: f2f51e3822a2c98a71d44e325a9b2847
NeedsCompilation: no
Title: Interlog protein network reconstruction by Mapping and Mining
ANalysis
Description: Reconstructing Interlog Protein Network (IPN) integrated
from several Protein protein Interaction Networks (PPINs).
Using this package, overlaying different PPINs to mine
conserved common networks between diverse species will be
applicable.
biocViews: SequenceMatching, Alignment, SystemsBiology,
GraphAndNetwork, Network, Proteomics
Author: Minoo Ashtiani, Payman Nickchi, Abdollah Safari, Mehdi Mirzaie,
Mohieddin Jafari
Maintainer: Minoo Ashtiani <ashtiani.minoo@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IMMAN
git_branch: RELEASE_3_13
git_last_commit: 9df3185
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IMMAN_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IMMAN_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IMMAN_1.12.0.tgz
vignettes: vignettes/IMMAN/inst/doc/IMMAN.html
vignetteTitles: IMMAN
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IMMAN/inst/doc/IMMAN.R
dependencyCount: 59
Package: ImmuneSpaceR
Version: 1.20.0
Depends: R (>= 3.5.0)
Imports: utils, R6, data.table, curl, httr, Rlabkey (>= 2.3.1),
Biobase, pheatmap, ggplot2 (>= 3.2.0), scales, stats, gplots,
plotly, heatmaply (>= 0.7.0), jsonlite, rmarkdown,
preprocessCore, flowCore, flowWorkspace, digest
Suggests: knitr, testthat
License: GPL-2
Archs: i386, x64
MD5sum: 7c572570cdeedce36d345ac21f9deccc
NeedsCompilation: no
Title: A Thin Wrapper around the ImmuneSpace Database
Description: Provides a convenient API for accessing data sets within
ImmuneSpace (www.immunespace.org), the data repository and
analysis platform of the Human Immunology Project Consortium
(HIPC).
biocViews: DataImport, DataRepresentation, ThirdPartyClient
Author: Greg Finak [aut], Renan Sauteraud [aut], Mike Jiang [aut], Gil
Guday [aut], Leo Dashevskiy [aut], Evan Henrich [aut], Ju Yeong
Kim [aut], Lauren Wolfe [aut], Helen Miller [aut], Raphael
Gottardo [aut], ImmuneSpace Package Maintainer [cre, cph]
Maintainer: ImmuneSpace Package Maintainer <immunespace@gmail.com>
URL: https://github.com/RGLab/ImmuneSpaceR
VignetteBuilder: knitr
BugReports: https://github.com/RGLab/ImmuneSpaceR/issues
git_url: https://git.bioconductor.org/packages/ImmuneSpaceR
git_branch: RELEASE_3_13
git_last_commit: e938b1b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ImmuneSpaceR_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ImmuneSpaceR_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ImmuneSpaceR_1.20.0.tgz
vignettes: vignettes/ImmuneSpaceR/inst/doc/getDataset.html,
vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.html,
vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.html,
vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.html,
vignettes/ImmuneSpaceR/inst/doc/report_SDY144.html,
vignettes/ImmuneSpaceR/inst/doc/report_SDY180.html,
vignettes/ImmuneSpaceR/inst/doc/report_SDY269.html
vignetteTitles: Downloading Datasets with getDataset, Handling
Expression Matrices with ImmuneSpaceR, interactive_netrc()
Function Walkthrough, An Introduction to the ImmuneSpaceR
Package, SDY144: Correlation of HAI/Virus Neutralizition Titer
and Cell Counts, SDY180: Abundance of Plasmablasts Measured by
Multiparameter Flow Cytometry, SDY269: Correlating HAI with
Flow Cytometry and ELISPOT Results
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ImmuneSpaceR/inst/doc/getDataset.R,
vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.R,
vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.R,
vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.R,
vignettes/ImmuneSpaceR/inst/doc/report_SDY144.R,
vignettes/ImmuneSpaceR/inst/doc/report_SDY180.R,
vignettes/ImmuneSpaceR/inst/doc/report_SDY269.R
dependencyCount: 132
Package: immunoClust
Version: 1.24.0
Depends: R(>= 3.6), flowCore
Imports: methods, stats, graphics, grid, lattice, grDevices
Suggests: BiocStyle, utils, testthat
License: Artistic-2.0
MD5sum: ba66b8863902b3626ee5c634b0d338bb
NeedsCompilation: yes
Title: immunoClust - Automated Pipeline for Population Detection in
Flow Cytometry
Description: immunoClust is a model based clustering approach for Flow
Cytometry samples. The cell-events of single Flow Cytometry
samples are modelled by a mixture of multinominal normal- or
t-distributions. The cell-event clusters of several samples are
modelled by a mixture of multinominal normal-distributions
aiming stable co-clusters across these samples.
biocViews: Clustering, FlowCytometry, SingleCell, CellBasedAssays,
ImmunoOncology
Author: Till Soerensen [aut, cre]
Maintainer: Till Soerensen <till.soerensen@bioretis.com>
git_url: https://git.bioconductor.org/packages/immunoClust
git_branch: RELEASE_3_13
git_last_commit: 87ad450
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/immunoClust_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/immunoClust_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/immunoClust_1.24.0.tgz
vignettes: vignettes/immunoClust/inst/doc/immunoClust.pdf
vignetteTitles: immunoClust package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/immunoClust/inst/doc/immunoClust.R
dependencyCount: 21
Package: immunotation
Version: 1.0.1
Depends: R (>= 4.1)
Imports: stringr, ontologyIndex, curl, ggplot2, readr, rvest, tidyr,
xml2, maps, rlang
Suggests: BiocGenerics, rmarkdown, BiocStyle, knitr, testthat, DT
License: GPL-3
MD5sum: 143d163652177fb8925a8b1d85497ab7
NeedsCompilation: no
Title: Tools for working with diverse immune genes
Description: MHC (major histocompatibility complex) molecules are cell
surface complexes that present antigens to T cells. The
repertoire of antigens presented in a given genetic background
largely depends on the sequence of the encoded MHC molecules,
and thus, in humans, on the highly variable HLA (human
leukocyte antigen) genes of the hyperpolymorphic HLA locus.
More than 28,000 different HLA alleles have been reported, with
significant differences in allele frequencies between human
populations worldwide. Reproducible and consistent annotation
of HLA alleles in large-scale bioinformatics workflows remains
challenging, because the available reference databases and
software tools often use different HLA naming schemes. The
package immunotation provides tools for consistent annotation
of HLA genes in typical immunoinformatics workflows such as for
example the prediction of MHC-presented peptides in different
human donors. Converter functions that provide mappings between
different HLA naming schemes are based on the MHC restriction
ontology (MRO). The package also provides automated access to
HLA alleles frequencies in worldwide human reference
populations stored in the Allele Frequency Net Database.
biocViews: Software, ImmunoOncology, BiomedicalInformatics, Genetics,
Annotation
Author: Katharina Imkeller [cre, aut]
Maintainer: Katharina Imkeller <k.imkeller@dkfz.de>
VignetteBuilder: knitr
BugReports: https://github.com/imkeller/immunotation/issues
git_url: https://git.bioconductor.org/packages/immunotation
git_branch: RELEASE_3_13
git_last_commit: 050aa59
git_last_commit_date: 2021-08-16
Date/Publication: 2021-08-17
source.ver: src/contrib/immunotation_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/immunotation_1.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/immunotation_1.0.1.tgz
vignettes: vignettes/immunotation/inst/doc/immunotation_vignette.html
vignetteTitles: User guide immunotation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/immunotation/inst/doc/immunotation_vignette.R
dependencyCount: 68
Package: IMPCdata
Version: 1.28.0
Depends: R (>= 2.3.0)
Imports: rjson
License: file LICENSE
MD5sum: 2cd85b3ca6990fde3054a5d9346fca15
NeedsCompilation: no
Title: Retrieves data from IMPC database
Description: Package contains methods for data retrieval from IMPC
Database.
biocViews: ExperimentData
Author: Natalja Kurbatova, Jeremy Mason
Maintainer: Jeremy Mason <jmason@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/IMPCdata
git_branch: RELEASE_3_13
git_last_commit: f7fdef4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IMPCdata_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IMPCdata_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IMPCdata_1.28.0.tgz
vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf
vignetteTitles: IMPCdata Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/IMPCdata/inst/doc/IMPCdata.R
dependencyCount: 1
Package: impute
Version: 1.66.0
Depends: R (>= 2.10)
License: GPL-2
MD5sum: cd268e8df29f5cff5ce3376b4cd63241
NeedsCompilation: yes
Title: impute: Imputation for microarray data
Description: Imputation for microarray data (currently KNN only)
biocViews: Microarray
Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan,
Gilbert Chu
Maintainer: Balasubramanian Narasimhan <naras@stat.Stanford.EDU>
git_url: https://git.bioconductor.org/packages/impute
git_branch: RELEASE_3_13
git_last_commit: 5a92999
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/impute_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/impute_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/impute_1.66.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: AMARETTO, CGHcall, TIN, curatedBreastData, MetaGxOvarian,
FAMT, iC10, imputeLCMD, moduleColor, snpReady, swamp
importsMe: biscuiteer, CancerSubtypes, cola, DExMA, doppelgangR, EGAD,
fastLiquidAssociation, genefu, genomation, MAGAR, MatrixQCvis,
MEAT, MethylMix, miRLAB, MSnbase, netboost, Pigengene, pmp,
POMA, REMP, RNAAgeCalc, Rnits, MetaGxBreast, MetaGxPancreas,
armada, DIscBIO, lilikoi, maGUI, mi4p, Rnmr1D, samr, speaq,
specmine, WGCNA
suggestsMe: BioNet, graphite, MethPed, MsCoreUtils, QFeatures, RnBeads,
scp, TBSignatureProfiler, TCGAutils, DDPNA, DGCA, GeoTcgaData,
GSA
dependencyCount: 0
Package: INDEED
Version: 2.6.0
Depends: glasso (>= 1.8), R (>= 3.5)
Imports: devtools (>= 1.13.0), graphics (>= 3.3.1), stats (>= 3.3.1),
utils (>= 3.3.1), igraph (>= 1.2.4), visNetwork(>= 2.0.6)
Suggests: knitr (>= 1.19), rmarkdown (>= 1.8), testthat (>= 2.0.0)
License: Artistic-2.0
MD5sum: 64eb10795cbf2b72114ca0747a1c82c0
NeedsCompilation: no
Title: Interactive Visualization of Integrated Differential Expression
and Differential Network Analysis for Biomarker Candidate
Selection Package
Description: An R package for integrated differential expression and
differential network analysis based on omic data for cancer
biomarker discovery. Both correlation and partial correlation
can be used to generate differential network to aid the
traditional differential expression analysis to identify
changes between biomolecules on both their expression and
pairwise association levels. A detailed description of the
methodology has been published in Methods journal (PMID:
27592383). An interactive visualization feature allows for the
exploration and selection of candidate biomarkers.
biocViews: ImmunoOncology, Software, ResearchField, BiologicalQuestion,
StatisticalMethod, DifferentialExpression, MassSpectrometry,
Metabolomics
Author: Yiming Zuo <yimingzuo@gmail.com>, Kian Ghaffari
<kg.ghaffari@gmail.com>, Zhenzhi Li <zzrickli@gmail.com>
Maintainer: Ressom group <hwr@georgetown.edu>, Yiming Zuo
<yimingzuo@gmail.com>
URL: http://github.com/ressomlab/INDEED
VignetteBuilder: knitr
BugReports: http://github.com/ressomlab/INDEED/issues
git_url: https://git.bioconductor.org/packages/INDEED
git_branch: RELEASE_3_13
git_last_commit: c8037cf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/INDEED_2.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/INDEED_2.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/INDEED_2.6.0.tgz
vignettes: vignettes/INDEED/inst/doc/Introduction_to_INDEED.pdf
vignetteTitles: INDEED R package for cancer biomarker discovery
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/INDEED/inst/doc/Introduction_to_INDEED.R
dependencyCount: 86
Package: infercnv
Version: 1.8.1
Depends: R(>= 4.0)
Imports: graphics, grDevices, RColorBrewer, gplots, futile.logger,
stats, utils, methods, ape, phyclust, Matrix, fastcluster,
dplyr, HiddenMarkov, ggplot2, edgeR, coin, caTools, digest,
RANN, leiden, reshape, rjags, fitdistrplus, future, foreach,
doParallel, BiocGenerics, SummarizedExperiment,
SingleCellExperiment, tidyr, parallel, coda, gridExtra,
argparse
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: BSD_3_clause + file LICENSE
Archs: i386, x64
MD5sum: a9ad6b34570b8acd1cee190d171e28ab
NeedsCompilation: no
Title: Infer Copy Number Variation from Single-Cell RNA-Seq Data
Description: Using single-cell RNA-Seq expression to visualize CNV in
cells.
biocViews: Software, CopyNumberVariation, VariantDetection,
StructuralVariation, GenomicVariation, Genetics,
Transcriptomics, StatisticalMethod, Bayesian,
HiddenMarkovModel, SingleCell
Author: Timothy Tickle [aut], Itay Tirosh [aut], Christophe Georgescu
[aut, cre], Maxwell Brown [aut], Brian Haas [aut]
Maintainer: Christophe Georgescu <cgeorges@broadinstitute.org>
URL: https://github.com/broadinstitute/inferCNV/wiki
SystemRequirements: JAGS 4.x.y
VignetteBuilder: knitr
BugReports: https://github.com/broadinstitute/inferCNV/issues
git_url: https://git.bioconductor.org/packages/infercnv
git_branch: RELEASE_3_13
git_last_commit: 18e7f52
git_last_commit_date: 2021-08-16
Date/Publication: 2021-08-17
source.ver: src/contrib/infercnv_1.8.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/infercnv_1.8.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/infercnv_1.8.1.tgz
vignettes: vignettes/infercnv/inst/doc/inferCNV.html
vignetteTitles: Visualizing Large-scale Copy Number Variation in
Single-Cell RNA-Seq Expression Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/infercnv/inst/doc/inferCNV.R
dependencyCount: 113
Package: infinityFlow
Version: 1.2.0
Depends: R (>= 4.0.0), flowCore
Imports: stats, grDevices, utils, graphics, pbapply, matlab, png,
raster, grid, uwot, gtools, Biobase, generics, parallel,
methods, xgboost
Suggests: knitr, rmarkdown, keras, tensorflow, glmnetUtils, e1071
License: GPL-3
MD5sum: d896c91fbf8680e306fab9d18c559094
NeedsCompilation: no
Title: Augmenting Massively Parallel Cytometry Experiments Using
Multivariate Non-Linear Regressions
Description: Pipeline to analyze and merge data files produced by
BioLegend's LEGENDScreen or BD Human Cell Surface Marker
Screening Panel (BD Lyoplates).
biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell,
Proteomics
Author: Etienne Becht [cre, aut]
Maintainer: Etienne Becht <etienne.becht@protonmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/infinityFlow
git_branch: RELEASE_3_13
git_last_commit: c7ea4d4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/infinityFlow_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/infinityFlow_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/infinityFlow_1.2.0.tgz
vignettes: vignettes/infinityFlow/inst/doc/basic_usage.html,
vignettes/infinityFlow/inst/doc/training_non_default_regression_models.html
vignetteTitles: Basic usage of the infinityFlow package, Training non
default regression models
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/infinityFlow/inst/doc/basic_usage.R,
vignettes/infinityFlow/inst/doc/training_non_default_regression_models.R
dependencyCount: 43
Package: Informeasure
Version: 1.2.0
Depends: R (>= 4.0)
Imports: entropy
Suggests: knitr, rmarkdown, testthat, SummarizedExperiment
License: GPL-3
MD5sum: 41433bc255f783a96c964786df3a0fa0
NeedsCompilation: no
Title: R implementation of Information measures
Description: This package compiles most of the information measures
currently available: mutual information, conditional mutual
information, interaction information, partial information
decomposition and part mutual information. All of these
estimators can be used to quantify nonlinear dependence between
variables in (biological regulatory) network inference. The
first estimator is used to infer bivariate networks while the
last four estimators are dedicated to analysis of trivariate
networks.
biocViews: GeneExpression, NetworkInference, Network, Software
Author: Chu Pan [aut, cre]
Maintainer: Chu Pan <chu.pan@hnu.edu.cn>
URL: https://github.com/chupan1218/Informeasure
VignetteBuilder: knitr
BugReports: https://github.com/chupan1218/Informeasure/issues
git_url: https://git.bioconductor.org/packages/Informeasure
git_branch: RELEASE_3_13
git_last_commit: bc8ba3f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Informeasure_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Informeasure_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Informeasure_1.2.0.tgz
vignettes: vignettes/Informeasure/inst/doc/Informeasure.html
vignetteTitles: Informeasure: a tool to quantify nonlinear dependence
between variables in biological regulatory networks from an
information theory perspective
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Informeasure/inst/doc/Informeasure.R
dependencyCount: 1
Package: InPAS
Version: 2.0.0
Depends: R (>= 3.1), methods, Biobase, GenomicRanges, S4Vectors
Imports: AnnotationDbi, BSgenome, cleanUpdTSeq, preprocessCore,
IRanges, GenomeInfoDb, depmixS4, limma, BiocParallel,
Biostrings, dplyr, magrittr, plyranges, readr, RSQLite, DBI,
purrr, GenomicFeatures, ggplot2, reshape2
Suggests: RUnit, BiocGenerics, BiocManager, rtracklayer, BiocStyle,
knitr, markdown, rmarkdown, EnsDb.Hsapiens.v86,
EnsDb.Mmusculus.v79, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm10,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 945c40f540f13491764085d67064c53d
NeedsCompilation: no
Title: A Bioconductor package for identifying novel Alternative
PolyAdenylation Sites (PAS) from RNA-seq data
Description: Alternative polyadenylation (APA) is one of the important
post- transcriptional regulation mechanisms which occurs in
most human genes. InPAS facilitates the discovery of novel APA
sites and the differential usage of APA sites from RNA-Seq
data. It leverages cleanUpdTSeq to fine tune identified APA
sites by removing false sites.
biocViews: RNASeq, Sequencing, AlternativeSplicing, Coverage,
DifferentialSplicing, GeneRegulation, Transcription,
ImmunoOncology
Author: Jianhong Ou [aut, cre], Haibo Liu [aut], Lihua Julie Zhu [aut],
Sungmi M. Park [aut], Michael R. Green [aut]
Maintainer: Jianhong Ou <jianhong.ou@duke.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/InPAS
git_branch: RELEASE_3_13
git_last_commit: d402f5c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/InPAS_2.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/InPAS_2.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/InPAS_2.0.0.tgz
vignettes: vignettes/InPAS/inst/doc/InPAS.html
vignetteTitles: InPAS Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/InPAS/inst/doc/InPAS.R
dependencyCount: 135
Package: INPower
Version: 1.28.0
Depends: R (>= 3.1.0), mvtnorm
Suggests: RUnit, BiocGenerics
License: GPL-2 + file LICENSE
MD5sum: b04e1f9e5b8ca7777f019d19c1e93645
NeedsCompilation: no
Title: An R package for computing the number of susceptibility SNPs
Description: An R package for computing the number of susceptibility
SNPs and power of future studies
biocViews: SNP
Author: Ju-Hyun Park
Maintainer: Bill Wheeler <wheelerb@imsweb.com>
git_url: https://git.bioconductor.org/packages/INPower
git_branch: RELEASE_3_13
git_last_commit: 5a4a810
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/INPower_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/INPower_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/INPower_1.28.0.tgz
vignettes: vignettes/INPower/inst/doc/vignette.pdf
vignetteTitles: INPower Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/INPower/inst/doc/vignette.R
dependencyCount: 3
Package: INSPEcT
Version: 1.22.0
Depends: R (>= 3.6), methods, Biobase, BiocParallel
Imports: pROC, deSolve, rootSolve, KernSmooth, gdata, GenomicFeatures,
GenomicRanges, IRanges, BiocGenerics, GenomicAlignments,
Rsamtools, S4Vectors, GenomeInfoDb, DESeq2, plgem, rtracklayer,
SummarizedExperiment, TxDb.Mmusculus.UCSC.mm9.knownGene, shiny
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-2
MD5sum: 799ade615501588cfd0af4112a45f611
NeedsCompilation: no
Title: Modeling RNA synthesis, processing and degradation with RNA-seq
data
Description: INSPEcT (INference of Synthesis, Processing and
dEgradation rates from Transcriptomic data) RNA-seq data in
time-course experiments or steady-state conditions, with or
without the support of nascent RNA data.
biocViews: Sequencing, RNASeq, GeneRegulation, TimeCourse,
SystemsBiology
Author: Stefano de Pretis
Maintainer: Stefano de Pretis <ste.depo@gmail.com>, Mattia Furlan
<Mattia.Furlan@iit.it>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/INSPEcT
git_branch: RELEASE_3_13
git_last_commit: b877791
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/INSPEcT_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/INSPEcT_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/INSPEcT_1.22.0.tgz
vignettes: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html,
vignettes/INSPEcT/inst/doc/INSPEcT.html
vignetteTitles: INSPEcT_GUI.html, INSPEcT - INference of Synthesis,,
Processing and dEgradation rates from Transcriptomic data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.R,
vignettes/INSPEcT/inst/doc/INSPEcT.R
dependencyCount: 140
Package: InTAD
Version: 1.12.0
Depends: R (>= 3.5), methods, S4Vectors, IRanges, GenomicRanges,
MultiAssayExperiment, SummarizedExperiment,stats
Imports:
BiocGenerics,Biobase,rtracklayer,parallel,graphics,mclust,qvalue,
ggplot2,utils,ggpubr
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: GPL (>=2)
MD5sum: 553387b73a7b4172bdf4f761d6794433
NeedsCompilation: no
Title: Search for correlation between epigenetic signals and gene
expression in TADs
Description: The package is focused on the detection of correlation
between expressed genes and selected epigenomic signals (i.e.
enhancers obtained from ChIP-seq data) either within
topologically associated domains (TADs) or between chromatin
contact loop anchors. Various parameters can be controlled to
investigate the influence of external factors and visualization
plots are available for each analysis step.
biocViews: Epigenetics, Sequencing, ChIPSeq, RNASeq, HiC,
GeneExpression,ImmunoOncology
Author: Konstantin Okonechnikov, Serap Erkek, Lukas Chavez
Maintainer: Konstantin Okonechnikov <k.okonechnikov@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/InTAD
git_branch: RELEASE_3_13
git_last_commit: 487822e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/InTAD_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/InTAD_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/InTAD_1.12.0.tgz
vignettes: vignettes/InTAD/inst/doc/InTAD.html
vignetteTitles: Correlation of epigenetic signals and genes in TADs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/InTAD/inst/doc/InTAD.R
dependencyCount: 140
Package: intansv
Version: 1.32.0
Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges
Imports: BiocGenerics, IRanges
License: MIT + file LICENSE
MD5sum: d65c8329e27646e0f6bda661a8253a4e
NeedsCompilation: no
Title: Integrative analysis of structural variations
Description: This package provides efficient tools to read and
integrate structural variations predicted by popular softwares.
Annotation and visulation of structural variations are also
implemented in the package.
biocViews: Genetics, Annotation, Sequencing, Software
Author: Wen Yao <ywhzau@gmail.com>
Maintainer: Wen Yao <ywhzau@gmail.com>
git_url: https://git.bioconductor.org/packages/intansv
git_branch: RELEASE_3_13
git_last_commit: b773954
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/intansv_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/intansv_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/intansv_1.32.0.tgz
vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf
vignetteTitles: An Introduction to intansv
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/intansv/inst/doc/intansvOverview.R
dependencyCount: 153
Package: interacCircos
Version: 1.2.0
Depends: R (>= 4.1)
Imports: RColorBrewer, htmlwidgets, plyr, methods
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: af6199447a655a5aafba36172477ead6
NeedsCompilation: no
Title: The Generation of Interactive Circos Plot
Description: Implement in an efficient approach to display the genomic
data, relationship, information in an interactive circular
genome(Circos) plot. 'interacCircos' are inspired by
'circosJS', 'BioCircos.js' and 'NG-Circos' and we integrate the
modules of 'circosJS', 'BioCircos.js' and 'NG-Circos' into this
R package, based on 'htmlwidgets' framework.
biocViews: Visualization
Author: Zhe Cui [aut, cre]
Maintainer: Zhe Cui <mrcuizhe@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/interacCircos
git_branch: RELEASE_3_13
git_last_commit: a4bd6d5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/interacCircos_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/interacCircos_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/interacCircos_1.2.0.tgz
vignettes: vignettes/interacCircos/inst/doc/interacCircos.html
vignetteTitles: interacCircos
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/interacCircos/inst/doc/interacCircos.R
dependencyCount: 14
Package: InteractionSet
Version: 1.20.0
Depends: GenomicRanges, SummarizedExperiment
Imports: methods, Matrix, Rcpp, BiocGenerics, S4Vectors (>= 0.27.12),
IRanges, GenomeInfoDb
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: ef961683238f0ebc39c2bce806b15b88
NeedsCompilation: yes
Title: Base Classes for Storing Genomic Interaction Data
Description: Provides the GInteractions, InteractionSet and
ContactMatrix objects and associated methods for storing and
manipulating genomic interaction data from Hi-C and ChIA-PET
experiments.
biocViews: Infrastructure, DataRepresentation, Software, HiC
Author: Aaron Lun [aut, cre], Malcolm Perry [aut], Elizabeth
Ing-Simmons [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/InteractionSet
git_branch: RELEASE_3_13
git_last_commit: 020b6c6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/InteractionSet_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/InteractionSet_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/InteractionSet_1.20.0.tgz
vignettes: vignettes/InteractionSet/inst/doc/interactions.html
vignetteTitles: Genomic interaction classes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/InteractionSet/inst/doc/interactions.R
dependsOnMe: diffHic, GenomicInteractions, MACPET, sevenC
importsMe: CAGEfightR, ChIPpeakAnno, HiCcompare, trackViewer
suggestsMe: CAGEWorkflow
dependencyCount: 27
Package: InteractiveComplexHeatmap
Version: 1.0.0
Depends: R (>= 4.0.0),
Imports: ComplexHeatmap (>= 2.7.10), grDevices, stats, shiny, grid,
GetoptLong, S4Vectors (>= 0.26.1), digest, IRanges, kableExtra
(>= 1.3.1), utils, svglite, htmltools, clisymbols, jsonlite,
RColorBrewer
Suggests: knitr, rmarkdown, testthat, EnrichedHeatmap, GenomicRanges,
data.table, circlize, GenomicFeatures, tidyverse, tidyHeatmap,
cluster, org.Hs.eg.db, simplifyEnrichment, GO.db, SC3,
GOexpress, SingleCellExperiment, scater, gplots, pheatmap,
airway, DESeq2, DT, cola, BiocManager, gridtext, HilbertCurve
(>= 1.21.1), shinydashboard, SummarizedExperiment, pkgndep, ks
License: MIT + file LICENSE
MD5sum: 0c2180046c2bddb66c8a479e573fc617
NeedsCompilation: no
Title: Make Interactive Complex Heatmaps
Description: This package can easily make heatmaps which are produced
by the ComplexHeatmap package into interactive applications. It
provides two types of interactivities: 1. on the interactive
graphics device, and 2. on a Shiny app. It also provides
functions for integrating the interactive heatmap widgets for
more complex Shiny app development.
biocViews: Software, Visualization, Sequencing
Author: Zuguang Gu [aut, cre] (<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/InteractiveComplexHeatmap
VignetteBuilder: knitr
BugReports:
https://github.com/jokergoo/InteractiveComplexHeatmap/issues
git_url:
https://git.bioconductor.org/packages/InteractiveComplexHeatmap
git_branch: RELEASE_3_13
git_last_commit: 2c4d5af
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/InteractiveComplexHeatmap_1.0.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/InteractiveComplexHeatmap_1.0.0.zip
mac.binary.ver:
bin/macosx/contrib/4.1/InteractiveComplexHeatmap_1.0.0.tgz
vignettes:
vignettes/InteractiveComplexHeatmap/inst/doc/decoration.html,
vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.html,
vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.html,
vignettes/InteractiveComplexHeatmap/inst/doc/implementation.html,
vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.html,
vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.html,
vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.html
vignetteTitles: 4. Decorations on heatmaps, 6. A Shiny app for
visualizing DESeq2 results, 7. Implement interactive heatmap
from scratch, 2. How interactive complex heatmap is
implemented, 5. Interactivate heatmaps indirectly generated by
pheatmap(),, heatmap.2() and heatmap(), 1. How to visualize
heatmaps interactively, 3. Functions for Shiny app development
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/InteractiveComplexHeatmap/inst/doc/decoration.R,
vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.R,
vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.R,
vignettes/InteractiveComplexHeatmap/inst/doc/implementation.R,
vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.R,
vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.R,
vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.R
suggestsMe: simplifyEnrichment
dependencyCount: 97
Package: interactiveDisplay
Version: 1.30.0
Depends: R (>= 2.10), methods, BiocGenerics, grid
Imports: interactiveDisplayBase (>= 1.7.3), shiny, RColorBrewer,
ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi
Suggests: RUnit, hgu95av2.db, knitr, GenomicRanges,
SummarizedExperiment, GOstats, ggbio, GO.db, Gviz, rtracklayer,
metagenomeSeq, gplots, vegan, Biobase
Enhances: rstudio
License: Artistic-2.0
MD5sum: 966ad1fbbc4d33fef8bbb59b1dbff395
NeedsCompilation: no
Title: Package for enabling powerful shiny web displays of Bioconductor
objects
Description: The interactiveDisplay package contains the methods needed
to generate interactive Shiny based display methods for
Bioconductor objects.
biocViews: GO, GeneExpression, Microarray, Sequencing, Classification,
Network, QualityControl, Visualization, Visualization,
Genetics, DataRepresentation, GUI, AnnotationData
Author: Shawn Balcome, Marc Carlson
Maintainer: Shawn Balcome <balc0022@umn.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/interactiveDisplay
git_branch: RELEASE_3_13
git_last_commit: 7c9aab4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/interactiveDisplay_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/interactiveDisplay_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/interactiveDisplay_1.30.0.tgz
vignettes: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.pdf
vignetteTitles: interactiveDisplay: A package for enabling interactive
visualization of Bioconductor objects
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.R
suggestsMe: metagenomeSeq
dependencyCount: 107
Package: interactiveDisplayBase
Version: 1.30.0
Depends: R (>= 2.10), methods, BiocGenerics
Imports: shiny, DT
Suggests: knitr
Enhances: rstudioapi
License: Artistic-2.0
MD5sum: df29aa49cbf9458f655569fb96f77918
NeedsCompilation: no
Title: Base package for enabling powerful shiny web displays of
Bioconductor objects
Description: The interactiveDisplayBase package contains the the basic
methods needed to generate interactive Shiny based display
methods for Bioconductor objects.
biocViews: GO, GeneExpression, Microarray, Sequencing, Classification,
Network, QualityControl, Visualization, Visualization,
Genetics, DataRepresentation, GUI, AnnotationData
Author: Shawn Balcome [aut, cre], Marc Carlson [ctb], Marcel Ramos
[ctb]
Maintainer: Shawn Balcome <balc0022@umn.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/interactiveDisplayBase
git_branch: RELEASE_3_13
git_last_commit: 84be4b4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/interactiveDisplayBase_1.30.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/interactiveDisplayBase_1.30.0.zip
mac.binary.ver:
bin/macosx/contrib/4.1/interactiveDisplayBase_1.30.0.tgz
vignettes:
vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html
vignetteTitles: Using interactiveDisplayBase for Bioconductor object
visualization and modification
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R
importsMe: AnnotationHub, interactiveDisplay
suggestsMe: recount3
dependencyCount: 42
Package: InterCellar
Version: 1.0.0
Depends: R (>= 4.1)
Imports: config, golem, shiny, DT, shinydashboard, shinyFiles,
shinycssloaders, data.table, fs, dplyr, tidyr, circlize,
colourpicker, dendextend, factoextra, ggplot2, plotly, plyr,
shinyFeedback, shinyalert, tibble, umap, visNetwork,
wordcloud2, readxl, htmlwidgets, colorspace, signal, scales,
htmltools, ComplexHeatmap, grDevices, stats, tools, utils,
biomaRt, rlang, fmsb
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, glue, graphite,
processx, attempt, BiocStyle, igraph
License: MIT + file LICENSE
MD5sum: 7b22b782ebb48333a44881ea0f703439
NeedsCompilation: no
Title: InterCellar: an R-Shiny app for interactive analysis and
exploration of cell-cell communication in single-cell
transcriptomics
Description: InterCellar is implemented as an R/Bioconductor Package
containing a Shiny app that allows users to interactively
analyze cell-cell communication from scRNA-seq data. Starting
from precomputed ligand-receptor interactions, InterCellar
provides filtering options, annotations and multiple
visualizations to explore clusters, genes and functions.
Finally, the user can define interaction-pairs modules and link
them to significant functional terms from Pathways or Gene
Ontology.
biocViews: Software, SingleCell, Visualization, GO, Transcriptomics
Author: Marta Interlandi [cre, aut]
(<https://orcid.org/0000-0002-6863-2552>)
Maintainer: Marta Interlandi <marta.interlandi01@gmail.com>
URL: https://github.com/martaint/InterCellar
VignetteBuilder: knitr
BugReports: https://github.com/martaint/InterCellar/issues
git_url: https://git.bioconductor.org/packages/InterCellar
git_branch: RELEASE_3_13
git_last_commit: 094f1da
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/InterCellar_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/InterCellar_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/InterCellar_1.0.0.tgz
vignettes: vignettes/InterCellar/inst/doc/user_guide.html
vignetteTitles: InterCellar User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/InterCellar/inst/doc/user_guide.R
dependencyCount: 228
Package: IntEREst
Version: 1.16.0
Depends: R (>= 3.4), GenomicRanges, Rsamtools, SummarizedExperiment,
edgeR, S4Vectors
Imports: seqLogo, Biostrings, GenomicFeatures (>= 1.39.4), IRanges,
seqinr, graphics, grDevices, stats, utils, grid, methods, DBI,
RMySQL, GenomicAlignments, BiocParallel, BiocGenerics, DEXSeq,
DESeq2
Suggests: clinfun, knitr, BSgenome.Hsapiens.UCSC.hg19
License: GPL-2
Archs: i386, x64
MD5sum: 49725658571c118ce7ca30b2405fac89
NeedsCompilation: no
Title: Intron-Exon Retention Estimator
Description: This package performs Intron-Exon Retention analysis on
RNA-seq data (.bam files).
biocViews: Software, AlternativeSplicing, Coverage,
DifferentialSplicing, Sequencing, RNASeq, Alignment,
Normalization, DifferentialExpression, ImmunoOncology
Author: Ali Oghabian <Ali.Oghabian@Helsinki.Fi>, Dario Greco
<dario.greco@helsinki.fi>, Mikko Frilander
<Mikko.Frilander@helsinki.fi>
Maintainer: Ali Oghabian <Ali.Oghabian@Helsinki.Fi>, Mikko Frilander
<Mikko.Frilander@helsinki.fi>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IntEREst
git_branch: RELEASE_3_13
git_last_commit: 4d75a7c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IntEREst_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IntEREst_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IntEREst_1.16.0.tgz
vignettes: vignettes/IntEREst/inst/doc/IntEREst.html
vignetteTitles: IntEREst,, Intron Exon Retention Estimator
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IntEREst/inst/doc/IntEREst.R
dependencyCount: 130
Package: InterMineR
Version: 1.14.1
Depends: R (>= 3.4.1)
Imports: Biostrings, RCurl, XML, xml2, RJSONIO, sqldf, igraph, httr,
S4Vectors, IRanges, GenomicRanges, SummarizedExperiment,
methods
Suggests: BiocStyle, Gviz, knitr, rmarkdown, GeneAnswers, GO.db,
org.Hs.eg.db
License: LGPL
MD5sum: 97052789bf1e59a46470865e3c8333db
NeedsCompilation: no
Title: R Interface with InterMine-Powered Databases
Description: Databases based on the InterMine platform such as FlyMine,
modMine (modENCODE), RatMine, YeastMine, HumanMine and
TargetMine are integrated databases of genomic, expression and
protein data for various organisms. Integrating data makes it
possible to run sophisticated data mining queries that span
domains of biological knowledge. This R package provides
interfaces with these databases through webservices. It makes
most from the correspondence of the data frame object in R and
the table object in databases, while hiding the details of data
exchange through XML or JSON.
biocViews: GeneExpression, SNP, GeneSetEnrichment,
DifferentialExpression, GeneRegulation, GenomeAnnotation,
GenomeWideAssociation, FunctionalPrediction,
AlternativeSplicing, ComparativeGenomics, FunctionalGenomics,
Proteomics, SystemsBiology, Microarray, MultipleComparison,
Pathways, GO, KEGG, Reactome, Visualization
Author: Bing Wang, Julie Sullivan, Rachel Lyne, Konstantinos Kyritsis,
Celia Sanchez
Maintainer: InterMine Team <r.lyne@gen.cam.ac.uk>
VignetteBuilder: knitr
BugReports: https://github.com/intermine/intermineR/issues
git_url: https://git.bioconductor.org/packages/InterMineR
git_branch: RELEASE_3_13
git_last_commit: 1a090a5
git_last_commit_date: 2021-05-27
Date/Publication: 2021-05-27
source.ver: src/contrib/InterMineR_1.14.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/InterMineR_1.14.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/InterMineR_1.14.1.tgz
vignettes: vignettes/InterMineR/inst/doc/InterMineR.html
vignetteTitles: InterMineR Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/InterMineR/inst/doc/InterMineR.R
dependencyCount: 60
Package: IntramiRExploreR
Version: 1.14.1
Depends: R (>= 3.4)
Imports: igraph (>= 1.0.1), FGNet (>= 3.0.7), knitr (>= 1.12.3), stats,
utils, grDevices, graphics
Suggests: RDAVIDWebService, gProfileR, topGO, org.Dm.eg.db, rmarkdown,
testthat
License: GPL-2
MD5sum: 0fc2029d8fa83f258ac36fa94d51a7bf
NeedsCompilation: no
Title: Predicting Targets for Drosophila Intragenic miRNAs
Description: Intra-miR-ExploreR, an integrative miRNA target prediction
bioinformatics tool, identifies targets combining expression
and biophysical interactions of a given microRNA (miR). Using
the tool, we have identified targets for 92 intragenic miRs in
D. melanogaster, using available microarray expression data,
from Affymetrix 1 and Affymetrix2 microarray array platforms,
providing a global perspective of intragenic miR targets in
Drosophila. Predicted targets are grouped according to
biological functions using the DAVID Gene Ontology tool and are
ranked based on a biologically relevant scoring system,
enabling the user to identify functionally relevant targets for
a given miR.
biocViews: Software, Microarray, GeneTarget, StatisticalMethod,
GeneExpression, GenePrediction
Author: Surajit Bhattacharya and Daniel Cox
Maintainer: Surajit Bhattacharya <sbhattacharya3@student.gsu.edu>
URL: https://github.com/sbhattacharya3/IntramiRExploreR/
VignetteBuilder: knitr
BugReports: https://github.com/sbhattacharya3/IntramiRExploreR/issues
git_url: https://git.bioconductor.org/packages/IntramiRExploreR
git_branch: RELEASE_3_13
git_last_commit: 7289e0f
git_last_commit_date: 2021-10-08
Date/Publication: 2021-10-10
source.ver: src/contrib/IntramiRExploreR_1.14.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IntramiRExploreR_1.14.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/IntramiRExploreR_1.14.1.tgz
vignettes:
vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.html
vignetteTitles: IntramiRExploreR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.R
dependencyCount: 32
Package: inveRsion
Version: 1.40.0
Depends: methods, haplo.stats
Imports: graphics, methods, utils
License: GPL (>= 2)
MD5sum: 1beb5f820da06557cb81cedb81108759
NeedsCompilation: yes
Title: Inversions in genotype data
Description: Package to find genetic inversions in genotype (SNP array)
data.
biocViews: Microarray, SNP
Author: Alejandro Caceres
Maintainer: Alejandro Caceres <acaceres@creal.cat>
git_url: https://git.bioconductor.org/packages/inveRsion
git_branch: RELEASE_3_13
git_last_commit: ac7553c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/inveRsion_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/inveRsion_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/inveRsion_1.40.0.tgz
vignettes: vignettes/inveRsion/inst/doc/inveRsion.pdf
vignetteTitles: Quick start guide for inveRsion package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/inveRsion/inst/doc/inveRsion.R
dependencyCount: 85
Package: IONiseR
Version: 2.16.0
Depends: R (>= 3.4)
Imports: rhdf5, dplyr, magrittr, tidyr, ShortRead, Biostrings, ggplot2,
methods, BiocGenerics, XVector, tibble, stats, BiocParallel,
bit64, stringr, utils
Suggests: BiocStyle, knitr, rmarkdown, gridExtra, testthat,
minionSummaryData
License: MIT + file LICENSE
MD5sum: 42760fdc666cd730ef7ee58cfcea79ab
NeedsCompilation: no
Title: Quality Assessment Tools for Oxford Nanopore MinION data
Description: IONiseR provides tools for the quality assessment of
Oxford Nanopore MinION data. It extracts summary statistics
from a set of fast5 files and can be used either before or
after base calling. In addition to standard summaries of the
read-types produced, it provides a number of plots for
visualising metrics relative to experiment run time or
spatially over the surface of a flowcell.
biocViews: QualityControl, DataImport, Sequencing
Author: Mike Smith [aut, cre]
Maintainer: Mike Smith <grimbough@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IONiseR
git_branch: RELEASE_3_13
git_last_commit: e9be4a9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IONiseR_2.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IONiseR_2.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IONiseR_2.16.0.tgz
vignettes: vignettes/IONiseR/inst/doc/IONiseR.html
vignetteTitles: Quality assessment tools for nanopore data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/IONiseR/inst/doc/IONiseR.R
dependencyCount: 85
Package: iPAC
Version: 1.36.0
Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest
License: GPL-2
MD5sum: 6d20ee4e6a081e7ab7f035556267d8f4
NeedsCompilation: no
Title: Identification of Protein Amino acid Clustering
Description: iPAC is a novel tool to identify somatic amino acid
mutation clustering within proteins while taking into account
protein structure.
biocViews: Clustering, Proteomics
Author: Gregory Ryslik, Hongyu Zhao
Maintainer: Gregory Ryslik <gregory.ryslik@yale.edu>
git_url: https://git.bioconductor.org/packages/iPAC
git_branch: RELEASE_3_13
git_last_commit: c449c77
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iPAC_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iPAC_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iPAC_1.36.0.tgz
vignettes: vignettes/iPAC/inst/doc/iPAC.pdf
vignetteTitles: iPAC: identification of Protein Amino acid Mutations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iPAC/inst/doc/iPAC.R
dependsOnMe: QuartPAC
dependencyCount: 30
Package: ipdDb
Version: 1.10.0
Depends: R (>= 3.5.0), methods, AnnotationDbi (>= 1.43.1),
AnnotationHub
Imports: Biostrings, GenomicRanges, RSQLite, DBI, IRanges, stats,
assertthat
Suggests: knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: c4ed30095735618a963d074a28308df5
NeedsCompilation: no
Title: IPD IMGT/HLA and IPD KIR database for Homo sapiens
Description: All alleles from the IPD IMGT/HLA
<https://www.ebi.ac.uk/ipd/imgt/hla/> and IPD KIR
<https://www.ebi.ac.uk/ipd/kir/> database for Homo sapiens.
Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis
J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA,
and Hammond JA KIR Nomenclature in non-human species
Immunogenetics (2018), in preparation.
biocViews: GenomicVariation, SequenceMatching, VariantAnnotation,
DataRepresentation,AnnotationHubSoftware
Author: Steffen Klasberg
Maintainer: Steffen Klasberg <klasberg@dkms-lab.de>
URL: https://github.com/DKMS-LSL/ipdDb
organism: Homo sapiens
VignetteBuilder: knitr
BugReports: https://github.com/DKMS-LSL/ipdDb/issues/new
git_url: https://git.bioconductor.org/packages/ipdDb
git_branch: RELEASE_3_13
git_last_commit: 926b8bd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ipdDb_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ipdDb_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ipdDb_1.10.0.tgz
vignettes: vignettes/ipdDb/inst/doc/Readme.html
vignetteTitles: ipdDb
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ipdDb/inst/doc/Readme.R
dependencyCount: 88
Package: IPO
Version: 1.18.0
Depends: xcms (>= 1.50.0), rsm, CAMERA, grDevices, graphics, stats,
utils
Imports: BiocParallel
Suggests: RUnit, BiocGenerics, msdata, mtbls2, faahKO, knitr
Enhances: parallel
License: GPL (>= 2) + file LICENSE
MD5sum: c7a4e8e6c02d081efd802628554071bc
NeedsCompilation: no
Title: Automated Optimization of XCMS Data Processing parameters
Description: The outcome of XCMS data processing strongly depends on
the parameter settings. IPO (`Isotopologue Parameter
Optimization`) is a parameter optimization tool that is
applicable for different kinds of samples and liquid
chromatography coupled to high resolution mass spectrometry
devices, fast and free of labeling steps. IPO uses natural,
stable 13C isotopes to calculate a peak picking score.
Retention time correction is optimized by minimizing the
relative retention time differences within features and
grouping parameters are optimized by maximizing the number of
features showing exactly one peak from each injection of a
pooled sample. The different parameter settings are achieved by
design of experiment. The resulting scores are evaluated using
response surface models.
biocViews: ImmunoOncology, Metabolomics, MassSpectrometry
Author: Gunnar Libiseller <Gunnar.Libiseller@joanneum.at>, Christoph
Magnes <christoph.magnes@joanneum.at>, Thomas Riebenbauer
<Thomas.Riebenbauer@joanneum.at>
Maintainer: Thomas Riebenbauer <Thomas.Riebenbauer@joanneum.at>
URL: https://github.com/rietho/IPO
VignetteBuilder: knitr
BugReports: https://github.com/rietho/IPO/issues/new
git_url: https://git.bioconductor.org/packages/IPO
git_branch: RELEASE_3_13
git_last_commit: f29adc9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IPO_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IPO_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IPO_1.18.0.tgz
vignettes: vignettes/IPO/inst/doc/IPO.html
vignetteTitles: XCMS Parameter Optimization with IPO
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/IPO/inst/doc/IPO.R
dependencyCount: 128
Package: IRanges
Version: 2.26.0
Depends: R (>= 4.0.0), methods, utils, stats, BiocGenerics (>= 0.37.0),
S4Vectors (>= 0.29.19)
Imports: stats4
LinkingTo: S4Vectors
Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments,
GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset,
RUnit, BiocStyle
License: Artistic-2.0
Archs: i386, x64
MD5sum: 43b060681e67be16c09afd9b25774fb9
NeedsCompilation: yes
Title: Foundation of integer range manipulation in Bioconductor
Description: Provides efficient low-level and highly reusable S4
classes for storing, manipulating and aggregating over
annotated ranges of integers. Implements an algebra of range
operations, including efficient algorithms for finding overlaps
and nearest neighbors. Defines efficient list-like classes for
storing, transforming and aggregating large grouped data, i.e.,
collections of atomic vectors and DataFrames.
biocViews: Infrastructure, DataRepresentation
Author: H. Pagès, P. Aboyoun and M. Lawrence
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/IRanges
BugReports: https://github.com/Bioconductor/IRanges/issues
git_url: https://git.bioconductor.org/packages/IRanges
git_branch: RELEASE_3_13
git_last_commit: 3195613
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IRanges_2.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IRanges_2.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IRanges_2.26.0.tgz
vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf
vignetteTitles: An Overview of the IRanges package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R
dependsOnMe: AnnotationDbi, AnnotationHubData, BaalChIP, bambu,
biomvRCNS, Biostrings, BiSeq, BSgenome, BubbleTree, bumphunter,
CAFE, casper, chimeraviz, ChIPpeakAnno, chipseq, CODEX,
consensusSeekeR, CSAR, CSSQ, customProDB, deepSNV,
DelayedArray, DESeq2, DEXSeq, DirichletMultinomial, DMCFB,
DMCHMM, DMRcaller, epigenomix, epihet, ExCluster, exomeCopy,
fCCAC, GenomeInfoDb, GenomicAlignments, GenomicDistributions,
GenomicFeatures, GenomicRanges, groHMM, gtrellis, Gviz,
HelloRanges, HiTC, IdeoViz, InTAD, methyAnalysis, MotifDb,
NADfinder, ORFik, OTUbase, pepStat, periodicDNA, plyranges,
proBAMr, PSICQUIC, RepViz, rfPred, rGADEM, rGREAT,
RJMCMCNucleosomes, RNAmodR, Scale4C, SCOPE, SGSeq, SICtools,
Structstrings, TEQC, triplex, VariantTools, VplotR, XVector,
pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501,
pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine,
pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine,
pd.canine.2, pd.celegans, pd.chicken, pd.chigene.1.0.st,
pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st,
pd.citrus, pd.clariom.d.human, pd.clariom.s.human,
pd.clariom.s.human.ht, pd.clariom.s.mouse,
pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht,
pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st,
pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array,
pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1,
pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2,
pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st,
pd.equgene.1.1.st, pd.felgene.1.0.st, pd.felgene.1.1.st,
pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5,
pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st,
pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a,
pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219,
pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d,
pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm,
pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800,
pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1,
pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize,
pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240,
pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st,
pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a,
pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c,
pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0,
pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1,
pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st,
pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0,
pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180,
pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st,
pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine,
pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st,
pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1,
pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st,
pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st,
pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c,
pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice,
pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0,
pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean,
pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato,
pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2,
pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98,
pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish,
SNPlocs.Hsapiens.dbSNP.20101109,
SNPlocs.Hsapiens.dbSNP.20120608,
SNPlocs.Hsapiens.dbSNP141.GRCh38,
SNPlocs.Hsapiens.dbSNP142.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, harbChIP,
LiebermanAidenHiC2009
importsMe: ALDEx2, AllelicImbalance, alpine, amplican, AneuFinder,
annmap, annotatr, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli,
AssessORF, ATACseqQC, ballgown, bamsignals, BBCAnalyzer,
beadarray, BiocOncoTK, biovizBase, BiSeq, BitSeq, bnbc,
BPRMeth, branchpointer, breakpointR, BRGenomics, BSgenome,
bsseq, BUMHMM, BumpyMatrix, BUSpaRse, CAGEfightR, CAGEr,
cBioPortalData, ChIC, ChIPanalyser, chipenrich, ChIPexoQual,
ChIPQC, ChIPseeker, chipseq, ChIPseqR, ChIPsim, ChromHeatMap,
ChromSCape, chromstaR, chromswitch, chromVAR, cicero, CINdex,
circRNAprofiler, cleanUpdTSeq, cleaver, cn.mops, CNEr,
CNVfilteR, CNVPanelizer, CNVRanger, CNVrd2, COCOA, coMET,
compEpiTools, ComplexHeatmap, contiBAIT, conumee, copynumber,
CopyNumberPlots, CopywriteR, CoverageView, CRISPRseek,
CrispRVariants, csaw, dada2, DAMEfinder, dasper, debrowser,
DECIPHER, DegNorm, DelayedMatrixStats, deltaCaptureC,
derfinder, derfinderHelper, derfinderPlot, DEScan2, DiffBind,
diffHic, diffloop, diffUTR, DMRcate, DMRScan, dmrseq,
DominoEffect, dpeak, DRIMSeq, easyRNASeq, EDASeq, eisaR, ELMER,
EnrichedHeatmap, enrichTF, ensembldb, epidecodeR, epigraHMM,
EpiTxDb, epivizr, epivizrData, erma, esATAC, EventPointer,
FastqCleaner, fastseg, fcScan, FilterFFPE, FindMyFriends,
FRASER, GA4GHclient, gcapc, genbankr, geneAttribution,
GeneGeneInteR, GENESIS, GenoGAM, genomation, genomeIntervals,
GenomicAlignments, GenomicDataCommons, GenomicFiles,
GenomicInteractions, GenomicOZone, GenomicScores,
GenomicTuples, genotypeeval, GenVisR, ggbio, girafe, gmapR,
gmoviz, GOfuncR, GOpro, GOTHiC, gpart, GSVA, GUIDEseq, gwascat,
h5vc, HDF5Array, heatmaps, HiCBricks, HiCcompare, HilbertCurve,
HTSeqGenie, hummingbird, icetea, ideal, idr2d, IMAS, InPAS,
INSPEcT, intansv, InteractionSet, InteractiveComplexHeatmap,
IntEREst, InterMineR, ipdDb, iSEEu, IsoformSwitchAnalyzeR,
isomiRs, IVAS, karyoploteR, LOLA, MACPET, MADSEQ, maser,
MatrixRider, mCSEA, MDTS, MEAL, MEDIPS, MesKit, metagene,
metagene2, metaseqR2, MethCP, methimpute, methInheritSim,
MethReg, methrix, methyAnalysis, methylCC, methylInheritance,
methylKit, methylPipe, MethylSeekR, methylSig, methylumi, mia,
minfi, MinimumDistance, MIRA, missMethyl, MMAPPR2, Modstrings,
mosaics, MOSim, motifbreakR, motifmatchr, msa,
MsBackendMassbank, MsBackendMgf, msgbsR, MSnbase,
MultiAssayExperiment, MultiDataSet, mumosa, musicatk,
MutationalPatterns, NanoStringNCTools, ncRNAtools, normr,
nucleoSim, nucleR, oligoClasses, OmaDB, OMICsPCA, openPrimeR,
Organism.dplyr, OrganismDbi, OUTRIDER, packFinder,
panelcn.mops, pcaExplorer, pdInfoBuilder, PhIPData, Pi, PICS,
PING, plethy, podkat, polyester, pqsfinder, pram, prebs,
preciseTAD, PrecisionTrialDrawer, primirTSS, proActiv,
profileplyr, PureCN, Pviz, QDNAseq, QFeatures, qpgraph,
qPLEXanalyzer, qsea, QuasR, R3CPET, r3Cseq, R453Plus1Toolbox,
RaggedExperiment, ramr, RareVariantVis, Rcade, RCAS, recount,
recoup, REDseq, regioneR, regutools, REMP, Repitools,
ReportingTools, rfaRm, RiboDiPA, RiboProfiling, riboSeqR,
ribosomeProfilingQC, RIPAT, rnaEditr, RNAmodR.AlkAnilineSeq,
RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, roar, Rqc, Rsamtools,
RSVSim, RTN, rtracklayer, sarks, SCAN.UPC, SCArray, scHOT,
segmentSeq, SeqArray, seqCAT, seqPattern, seqsetvis, SeqSQC,
SeqVarTools, sesame, sevenC, ShortRead, signeR, SimFFPE,
SingleMoleculeFootprinting, sitadela, SMITE, snapcount,
SNPhood, soGGi, SomaticSignatures, SparseSignatures, Spectra,
spicyR, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR,
StructuralVariantAnnotation, SummarizedExperiment, SynExtend,
systemPipeR, TAPseq, target, TarSeqQC, TCGAbiolinks, TCGAutils,
TCseq, TFBSTools, TFEA.ChIP, TFHAZ, TitanCNA, TnT, tracktables,
trackViewer, transcriptR, TransView, TreeSummarizedExperiment,
tricycle, tRNA, tRNAdbImport, tRNAscanImport, tscR, TSRchitect,
TVTB, tximeta, UMI4Cats, Uniquorn, universalmotif, VanillaICE,
VarCon, VariantAnnotation, VariantExperiment, VariantFiltering,
VaSP, wavClusteR, wiggleplotr, XCIR, xcms, XNAString, XVector,
yamss, fitCons.UCSC.hg19, GenomicState,
MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5,
MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5,
MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5,
MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5,
MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5,
MafDb.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38,
MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19,
MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38,
MafH5.gnomAD.v3.1.1.GRCh38, pd.081229.hg18.promoter.medip.hx1,
pd.2006.07.18.hg18.refseq.promoter,
pd.2006.07.18.mm8.refseq.promoter,
pd.2006.10.31.rn34.refseq.promoter, pd.charm.hg18.example,
pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.mirna.3.1,
phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38,
phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20101109,
SNPlocs.Hsapiens.dbSNP.20120608,
SNPlocs.Hsapiens.dbSNP141.GRCh38,
SNPlocs.Hsapiens.dbSNP142.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP151.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP141.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data,
leeBamViews, MethylSeqData, pd.atdschip.tiling,
SomaticCancerAlterations, spatialLIBD, systemPipeRdata,
ActiveDriverWGS, alakazam, BinQuasi, crispRdesignR, ExomeDepth,
geno2proteo, HiCfeat, hoardeR, ICAMS, intePareto, LoopRig,
MAAPER, noisyr, oncoPredict, PACVr, RapidoPGS, RTIGER, Signac,
simMP, STRMPS, tidygenomics, utr.annotation, VALERIE
suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, Chicago,
ClassifyR, epivizrChart, Glimma, gwascat, GWASTools,
HilbertVis, HilbertVisGUI, maftools, martini, MiRaGE,
multicrispr, MungeSumstats, regionReport, RTCGA, S4Vectors,
SigsPack, splatter, TFutils, yeastRNASeq, cancerTiming,
fuzzyjoin, gkmSVM, LDheatmap, pagoo, polyRAD, rliger,
seqmagick, Seurat, sigminer, valr
linksToMe: Biostrings, CNEr, DECIPHER, GenomicAlignments,
GenomicRanges, kebabs, MatrixRider, Rsamtools, rtracklayer,
ShortRead, Structstrings, triplex, VariantAnnotation,
VariantFiltering, XVector
dependencyCount: 8
Package: IRISFGM
Version: 1.0.0
Depends: R (>= 4.1)
Imports: Rcpp (>= 1.0.0), MCL, anocva, Polychrome, RColorBrewer,
colorspace, AnnotationDbi, ggplot2, org.Hs.eg.db, org.Mm.eg.db,
pheatmap, AdaptGauss, DEsingle,DrImpute, Matrix, Seurat,
SingleCellExperiment, clusterProfiler, ggpubr, ggraph, igraph,
mixtools, scater, scran, stats, methods, grDevices, graphics,
utils, knitr
LinkingTo: Rcpp
License: GPL-2
MD5sum: 97aded40520a5e991c3310b4366fb44f
NeedsCompilation: yes
Title: Comprehensive Analysis of Gene Interactivity Networks Based on
Single-Cell RNA-Seq
Description: Single-cell RNA-Seq data is useful in discovering cell
heterogeneity and signature genes in specific cell populations
in cancer and other complex diseases. Specifically, the
investigation of functional gene modules (FGM) can help to
understand gene interactive networks and complex biological
processes. QUBIC2 is recognized as one of the most efficient
and effective tools for FGM identification from scRNA-Seq data.
However, its availability is limited to a C implementation, and
its applicative power is affected by only a few downstream
analyses functionalities. We developed an R package named
IRIS-FGM (integrative scRNA-Seq interpretation system for
functional gene module analysis) to support the investigation
of FGMs and cell clustering using scRNA-Seq data. Empowered by
QUBIC2, IRIS-FGM can identify co-expressed and co-regulated
FGMs, predict types/clusters, identify differentially expressed
genes, and perform functional enrichment analysis. It is
noteworthy that IRIS-FGM also applies Seurat objects that can
be easily used in the Seurat vignettes.
biocViews: Software, GeneExpression, SingleCell, Clustering,
DifferentialExpression, Preprocessing, DimensionReduction,
Visualization, Normalization, DataImport
Author: Yuzhou Chang [aut, cre], Qin Ma [aut], Carter Allen [aut],
Dongjun Chung [aut]
Maintainer: Yuzhou Chang <yuzhou.chang@osumc.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IRISFGM
git_branch: RELEASE_3_13
git_last_commit: aacc387
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IRISFGM_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IRISFGM_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IRISFGM_1.0.0.tgz
vignettes: vignettes/IRISFGM/inst/doc/IRISFGM_Rpackage.html
vignetteTitles: IRIS-FGM vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IRISFGM/inst/doc/IRISFGM_Rpackage.R
dependencyCount: 290
Package: ISAnalytics
Version: 1.2.1
Depends: R (>= 4.1), magrittr
Imports: utils, reactable, htmltools, dplyr, readr, tidyr, purrr,
rlang, tibble, BiocParallel, stringr, fs, zip, lubridate,
lifecycle, ggplot2, ggrepel, stats, upsetjs, psych, grDevices,
data.table, readxl, tools, Rcapture, plotly
Suggests: testthat, covr, knitr, BiocStyle, knitcitations, sessioninfo,
rmarkdown, roxygen2, vegan, withr, extraDistr
License: CC BY 4.0
MD5sum: 1c40c7c03af8415956ecc1a48fc730dc
NeedsCompilation: no
Title: Analyze gene therapy vector insertion sites data identified from
genomics next generation sequencing reads for clonal tracking
studies
Description: In gene therapy, stem cells are modified using viral
vectors to deliver the therapeutic transgene and replace
functional properties since the genetic modification is stable
and inherited in all cell progeny. The retrieval and mapping of
the sequences flanking the virus-host DNA junctions allows the
identification of insertion sites (IS), essential for
monitoring the evolution of genetically modified cells in vivo.
A comprehensive toolkit for the analysis of IS is required to
foster clonal trackign studies and supporting the assessment of
safety and long term efficacy in vivo. This package is aimed at
(1) supporting automation of IS workflow, (2) performing base
and advance analysis for IS tracking (clonal abundance, clonal
expansions and statistics for insertional mutagenesis, etc.),
(3) providing basic biology insights of transduced stem cells
in vivo.
biocViews: BiomedicalInformatics, Sequencing, SingleCell
Author: Andrea Calabria [aut, cre], Giulio Spinozzi [aut], Giulia Pais
[aut]
Maintainer: Andrea Calabria <calabria.andrea@hsr.it>
URL: https://calabrialab.github.io/ISAnalytics,
https://github.com//calabrialab/isanalytics
VignetteBuilder: knitr
BugReports: https://github.com/calabrialab/ISAnalytics/issues
git_url: https://git.bioconductor.org/packages/ISAnalytics
git_branch: RELEASE_3_13
git_last_commit: ab38e96
git_last_commit_date: 2021-06-08
Date/Publication: 2021-06-10
source.ver: src/contrib/ISAnalytics_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ISAnalytics_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ISAnalytics_1.2.1.tgz
vignettes:
vignettes/ISAnalytics/inst/doc/aggregate_function_usage.html,
vignettes/ISAnalytics/inst/doc/collision_removal.html,
vignettes/ISAnalytics/inst/doc/how_to_import_functions.html,
vignettes/ISAnalytics/inst/doc/no_rstudio_usage.html
vignetteTitles: Working with aggregate functions, Collision removal
functionality, How to use import functions, Using ISAnalytics
without RStudio support
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ISAnalytics/inst/doc/aggregate_function_usage.R,
vignettes/ISAnalytics/inst/doc/collision_removal.R,
vignettes/ISAnalytics/inst/doc/how_to_import_functions.R,
vignettes/ISAnalytics/inst/doc/no_rstudio_usage.R
dependencyCount: 97
Package: iSEE
Version: 2.4.0
Depends: SummarizedExperiment, SingleCellExperiment
Imports: methods, BiocGenerics, S4Vectors, utils, stats, shiny,
shinydashboard, shinyAce, shinyjs, DT, rintrojs, ggplot2,
ggrepel, colourpicker, igraph, vipor, mgcv, graphics,
grDevices, viridisLite, shinyWidgets, ComplexHeatmap, circlize,
grid
Suggests: testthat, BiocStyle, knitr, rmarkdown, scRNAseq,
TENxPBMCData, scater, DelayedArray, HDF5Array, RColorBrewer,
viridis, htmltools
License: MIT + file LICENSE
MD5sum: 2ecd503198449fae34c67cb9878a6481
NeedsCompilation: no
Title: Interactive SummarizedExperiment Explorer
Description: Create an interactive Shiny-based graphical user interface
for exploring data stored in SummarizedExperiment objects,
including row- and column-level metadata. The interface
supports transmission of selections between plots and tables,
code tracking, interactive tours, interactive or programmatic
initialization, preservation of app state, and extensibility to
new panel types via S4 classes. Special attention is given to
single-cell data in a SingleCellExperiment object with
visualization of dimensionality reduction results.
biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction,
FeatureExtraction, Clustering, Transcription, GeneExpression,
Transcriptomics, SingleCell, CellBasedAssays
Author: Kevin Rue-Albrecht [aut, cre]
(<https://orcid.org/0000-0003-3899-3872>), Federico Marini
[aut] (<https://orcid.org/0000-0003-3252-7758>), Charlotte
Soneson [aut] (<https://orcid.org/0000-0003-3833-2169>), Aaron
Lun [aut] (<https://orcid.org/0000-0002-3564-4813>)
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/iSEE/iSEE
VignetteBuilder: knitr
BugReports: https://github.com/iSEE/iSEE/issues
git_url: https://git.bioconductor.org/packages/iSEE
git_branch: RELEASE_3_13
git_last_commit: 5c9f140
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iSEE_2.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iSEE_2.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iSEE_2.4.0.tgz
vignettes: vignettes/iSEE/inst/doc/basic.html,
vignettes/iSEE/inst/doc/bigdata.html,
vignettes/iSEE/inst/doc/configure.html,
vignettes/iSEE/inst/doc/custom.html,
vignettes/iSEE/inst/doc/ecm.html,
vignettes/iSEE/inst/doc/links.html,
vignettes/iSEE/inst/doc/voice.html
vignetteTitles: 1. The iSEE User's Guide, 6. Using iSEE with big data,
3. Configuring iSEE apps, 5. Deploying custom panels, 4. The
ExperimentColorMap Class, 2. Sharing information across panels,
7. Speech recognition
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/iSEE/inst/doc/basic.R,
vignettes/iSEE/inst/doc/bigdata.R,
vignettes/iSEE/inst/doc/configure.R,
vignettes/iSEE/inst/doc/custom.R,
vignettes/iSEE/inst/doc/ecm.R, vignettes/iSEE/inst/doc/links.R,
vignettes/iSEE/inst/doc/voice.R
dependsOnMe: iSEEu, OSCA.advanced
suggestsMe: schex, DuoClustering2018, HCAData, TabulaMurisData
dependencyCount: 106
Package: iSEEu
Version: 1.4.0
Depends: iSEE
Imports: methods, S4Vectors, IRanges, shiny, SummarizedExperiment,
SingleCellExperiment, ggplot2, DT, stats, colourpicker,
shinyAce
Suggests: scRNAseq, scater, scran, airway, edgeR, AnnotationDbi,
org.Hs.eg.db, GO.db, KEGGREST, knitr, igraph, rmarkdown,
BiocStyle, htmltools, Rtsne, uwot, testthat (>= 2.1.0), covr
License: MIT + file LICENSE
MD5sum: 50c2dd454d2c7d118557b7ac74a75a51
NeedsCompilation: no
Title: iSEE Universe
Description: iSEEu (the iSEE universe) contains diverse functionality
to extend the usage of the iSEE package, including additional
classes for the panels, or modes allowing easy configuration of
iSEE applications.
biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction,
FeatureExtraction, Clustering, Transcription, GeneExpression,
Transcriptomics, SingleCell, CellBasedAssays
Author: Kevin Rue-Albrecht [aut, cre]
(<https://orcid.org/0000-0003-3899-3872>), Charlotte Soneson
[aut] (<https://orcid.org/0000-0003-3833-2169>), Federico
Marini [aut] (<https://orcid.org/0000-0003-3252-7758>), Aaron
Lun [aut] (<https://orcid.org/0000-0002-3564-4813>), Michael
Stadler [ctb]
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/iSEE/iSEEu
VignetteBuilder: knitr
BugReports: https://github.com/iSEE/iSEEu/issues
git_url: https://git.bioconductor.org/packages/iSEEu
git_branch: RELEASE_3_13
git_last_commit: 51da58e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iSEEu_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iSEEu_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iSEEu_1.4.0.tgz
vignettes: vignettes/iSEEu/inst/doc/universe.html
vignetteTitles: Panel universe
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/iSEEu/inst/doc/universe.R
dependencyCount: 107
Package: iSeq
Version: 1.44.0
Depends: R (>= 2.10.0)
License: GPL (>= 2)
Archs: i386, x64
MD5sum: c64f212bd3f70ffff13abb6aa3362bde
NeedsCompilation: yes
Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden
Ising Models
Description: Bayesian hidden Ising models are implemented to identify
IP-enriched genomic regions from ChIP-seq data. They can be
used to analyze ChIP-seq data with and without controls and
replicates.
biocViews: ChIPSeq, Sequencing
Author: Qianxing Mo
Maintainer: Qianxing Mo <qianxing.mo@moffitt.org>
git_url: https://git.bioconductor.org/packages/iSeq
git_branch: RELEASE_3_13
git_last_commit: adb8311
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iSeq_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iSeq_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iSeq_1.44.0.tgz
vignettes: vignettes/iSeq/inst/doc/iSeq.pdf
vignetteTitles: iSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iSeq/inst/doc/iSeq.R
dependencyCount: 0
Package: isobar
Version: 1.38.0
Depends: R (>= 2.10.0), Biobase, stats, methods
Imports: distr, plyr, biomaRt, ggplot2
Suggests: MSnbase, OrgMassSpecR, XML, RJSONIO, Hmisc, gplots,
RColorBrewer, gridExtra, limma, boot, DBI, MASS
License: LGPL-2
Archs: i386, x64
MD5sum: f0a6f3ed9d983a2a43ec65d91212eb9c
NeedsCompilation: no
Title: Analysis and quantitation of isobarically tagged MSMS proteomics
data
Description: isobar provides methods for preprocessing, normalization,
and report generation for the analysis of quantitative mass
spectrometry proteomics data labeled with isobaric tags, such
as iTRAQ and TMT. Features modules for integrating and
validating PTM-centric datasets (isobar-PTM). More information
on http://www.ms-isobar.org.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry,
Bioinformatics, MultipleComparisons, QualityControl
Author: Florian P Breitwieser <florian.bw@gmail.com> and Jacques
Colinge <jacques.colinge@inserm.fr>, with contributions from
Alexey Stukalov <stukalov@biochem.mpg.de>, Xavier Robin
<xavier.robin@unige.ch> and Florent Gluck
<florent.gluck@unige.ch>
Maintainer: Florian P Breitwieser <florian.bw@gmail.com>
URL: https://github.com/fbreitwieser/isobar
BugReports: https://github.com/fbreitwieser/isobar/issues
git_url: https://git.bioconductor.org/packages/isobar
git_branch: RELEASE_3_13
git_last_commit: 444a501
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/isobar_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/isobar_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/isobar_1.38.0.tgz
vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf,
vignettes/isobar/inst/doc/isobar-ptm.pdf,
vignettes/isobar/inst/doc/isobar-usecases.pdf,
vignettes/isobar/inst/doc/isobar.pdf
vignetteTitles: isobar for developers, isobar for quantification of PTM
datasets, Usecases for isobar package, isobar package for iTRAQ
and TMT protein quantification
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/isobar/inst/doc/isobar-devel.R,
vignettes/isobar/inst/doc/isobar-ptm.R,
vignettes/isobar/inst/doc/isobar-usecases.R,
vignettes/isobar/inst/doc/isobar.R
suggestsMe: RforProteomics
dependencyCount: 93
Package: IsoCorrectoR
Version: 1.10.0
Depends: R (>= 3.5)
Imports: dplyr, magrittr, methods, quadprog, readr, readxl, stringr,
tibble, tools, utils, pracma, WriteXLS
Suggests: IsoCorrectoRGUI, knitr, rmarkdown, testthat
License: GPL-3
MD5sum: 317d21fb7f96b98421fc684d293b9d8a
NeedsCompilation: no
Title: Correction for natural isotope abundance and tracer purity in MS
and MS/MS data from stable isotope labeling experiments
Description: IsoCorrectoR performs the correction of mass spectrometry
data from stable isotope labeling/tracing metabolomics
experiments with regard to natural isotope abundance and tracer
impurity. Data from both MS and MS/MS measurements can be
corrected (with any tracer isotope: 13C, 15N, 18O...), as well
as ultra-high resolution MS data from multiple-tracer
experiments (e.g. 13C and 15N used simultaneously). See the
Bioconductor package IsoCorrectoRGUI for a graphical user
interface to IsoCorrectoR. NOTE: With R version 4.0.0, writing
correction results to Excel files may currently not work on
Windows. However, writing results to csv works as before.
biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing,
ImmunoOncology
Author: Christian Kohler [cre, aut], Paul Heinrich [aut]
Maintainer: Christian Kohler <christian.kohler@ur.de>
URL: https://genomics.ur.de/files/IsoCorrectoR/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IsoCorrectoR
git_branch: RELEASE_3_13
git_last_commit: a18f1fb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IsoCorrectoR_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IsoCorrectoR_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IsoCorrectoR_1.10.0.tgz
vignettes: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.html
vignetteTitles: IsoCorrectoR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.R
importsMe: IsoCorrectoRGUI
dependencyCount: 44
Package: IsoCorrectoRGUI
Version: 1.8.0
Depends: R (>= 3.6)
Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils
Suggests: knitr, rmarkdown, testthat
License: GPL-3
MD5sum: f971f25882ffd8d1275410f6fa321f37
NeedsCompilation: no
Title: Graphical User Interface for IsoCorrectoR
Description: IsoCorrectoRGUI is a Graphical User Interface for the
IsoCorrectoR package. IsoCorrectoR performs the correction of
mass spectrometry data from stable isotope labeling/tracing
metabolomics experiments with regard to natural isotope
abundance and tracer impurity. Data from both MS and MS/MS
measurements can be corrected (with any tracer isotope: 13C,
15N, 18O...), as well as high resolution MS data from
multiple-tracer experiments (e.g. 13C and 15N used
simultaneously).
biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing,
GUI, ImmunoOncology
Author: Christian Kohler [cre, aut], Paul Kuerner [aut], Paul Heinrich
[aut]
Maintainer: Christian Kohler <christian.kohler@ur.de>
URL: https://genomics.ur.de/files/IsoCorrectoRGUI
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IsoCorrectoRGUI
git_branch: RELEASE_3_13
git_last_commit: 7e73d7c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IsoCorrectoRGUI_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IsoCorrectoRGUI_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IsoCorrectoRGUI_1.8.0.tgz
vignettes: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.html
vignetteTitles: IsoCorrectoR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.R
suggestsMe: IsoCorrectoR
dependencyCount: 47
Package: IsoformSwitchAnalyzeR
Version: 1.14.1
Depends: R (>= 3.6), limma, DEXSeq, ggplot2
Imports: methods, BSgenome, plyr, reshape2, gridExtra, Biostrings (>=
2.50.0), IRanges, GenomicRanges, DRIMSeq, RColorBrewer,
rtracklayer, VennDiagram, DBI, grDevices, graphics, stats,
utils, GenomeInfoDb, grid, tximport (>= 1.7.1), tximeta (>=
1.7.12), edgeR, futile.logger, stringr, dplyr, magrittr, readr,
tibble, XVector, BiocGenerics, RCurl, Biobase
Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, rmarkdown
License: GPL (>= 2)
MD5sum: cc99e5e3f3d66e640e8151395901a94f
NeedsCompilation: yes
Title: Identify, Annotate and Visualize Alternative Splicing and
Isoform Switches with Functional Consequences from both short-
and long-read RNA-seq data.
Description: Analysis of alternative splicing and isoform switches with
predicted functional consequences (e.g. gain/loss of protein
domains etc.) from quantification of all types of RNASeq by
tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff
etc.
biocViews: GeneExpression, Transcription, AlternativeSplicing,
DifferentialExpression, DifferentialSplicing, Visualization,
StatisticalMethod, TranscriptomeVariant, BiomedicalInformatics,
FunctionalGenomics, SystemsBiology, Transcriptomics, RNASeq,
Annotation, FunctionalPrediction, GenePrediction, DataImport,
MultipleComparison, BatchEffect, ImmunoOncology
Author: Kristoffer Vitting-Seerup [cre, aut]
(<https://orcid.org/0000-0002-6450-0608>)
Maintainer: Kristoffer Vitting-Seerup <k.vitting.seerup@gmail.com>
URL: http://bioconductor.org/packages/IsoformSwitchAnalyzeR/
VignetteBuilder: knitr
BugReports:
https://github.com/kvittingseerup/IsoformSwitchAnalyzeR/issues
git_url: https://git.bioconductor.org/packages/IsoformSwitchAnalyzeR
git_branch: RELEASE_3_13
git_last_commit: 215dc6f
git_last_commit_date: 2021-10-01
Date/Publication: 2021-10-03
source.ver: src/contrib/IsoformSwitchAnalyzeR_1.14.1.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/IsoformSwitchAnalyzeR_1.14.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/IsoformSwitchAnalyzeR_1.14.1.tgz
vignettes:
vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html
vignetteTitles: IsoformSwitchAnalyzeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.R
dependencyCount: 160
Package: IsoGeneGUI
Version: 2.28.0
Depends: tcltk, xlsx
Imports: Rcpp, tkrplot, multtest, relimp, geneplotter, RColorBrewer,
Iso, IsoGene, ORCME, ORIClust, orQA, goric, ff, Biobase, jpeg
Suggests: RUnit
License: GPL-2
Archs: i386, x64
MD5sum: 801fe664ce2416425a857328adeb795d
NeedsCompilation: no
Title: A graphical user interface to conduct a dose-response analysis
of microarray data
Description: The IsoGene Graphical User Interface (IsoGene-GUI) is a
user friendly interface of the IsoGene package which is aimed
to identify for genes with a monotonic trend in the expression
levels with respect to the increasing doses. Additionally, GUI
extension of original package contains various tools to perform
clustering of dose-response profiles. Testing is addressed
through several test statistics: global likelihood ratio test
(E2), Bartholomew 1961, Barlow et al. 1972 and Robertson et al.
1988), Williams (1971, 1972), Marcus (1976), the M (Hu et al.
2005) and the modified M (Lin et al. 2007). The p-values of the
global likelihood ratio test (E2) are obtained using the exact
distribution and permutations. The other four test statistics
are obtained using permutations. Several p-values adjustment
are provided: Bonferroni, Holm (1979), Hochberg (1988), and
Sidak procedures for controlling the family-wise Type I error
rate (FWER), and BH (Benjamini and Hochberg 1995) and BY
(Benjamini and Yekutieli 2001) procedures are used for
controlling the FDR. The inference is based on resampling
methods, which control the False Discovery Rate (FDR), for both
permutations (Ge et al., 2003) and the Significance Analysis of
Microarrays (SAM, Tusher et al., 2001). Clustering methods are
outsourced from CRAN packages ORCME, ORIClust. The package
ORCME is based on delta-clustering method (Cheng and Church,
2000) and ORIClust on Order Restricted Information Criterion
(Liu et al., 2009), both perform same task but from different
perspective and their outputs are clusters of genes.
Additionally, profile selection for given gene based on
Generalized ORIC (Kuiper et al., 2014) from package goric and
permutation test for E2 based on package orQA are included in
IsoGene-GUI. None of these four packages has GUI.
biocViews: Microarray, DifferentialExpression, GUI
Author: Setia Pramana, Dan Lin, Philippe Haldermans, Tobias Verbeke,
Martin Otava
Maintainer: Setia Pramana <setia.pramana@ki.se>
URL:
http://ibiostat.be/online-resources/online-resources/isogenegui/isogenegui-package
git_url: https://git.bioconductor.org/packages/IsoGeneGUI
git_branch: RELEASE_3_13
git_last_commit: 3b72011
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IsoGeneGUI_2.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IsoGeneGUI_2.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IsoGeneGUI_2.28.0.tgz
vignettes: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.pdf
vignetteTitles: IsoGeneGUI Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.R
dependencyCount: 81
Package: ISoLDE
Version: 1.20.0
Depends: R (>= 3.3.0),graphics,grDevices,stats,utils
License: GPL (>= 2.0)
MD5sum: f58d87e22756378bc7f1d4b01b327b1c
NeedsCompilation: yes
Title: Integrative Statistics of alleLe Dependent Expression
Description: This package provides ISoLDE a new method for identifying
imprinted genes. This method is dedicated to data arising from
RNA sequencing technologies. The ISoLDE package implements
original statistical methodology described in the publication
below.
biocViews: ImmunoOncology, GeneExpression, Transcription,
GeneSetEnrichment, Genetics, Sequencing, RNASeq,
MultipleComparison, SNP, GeneticVariability, Epigenetics,
MathematicalBiology, GeneRegulation
Author: Christelle Reynès [aut, cre], Marine Rohmer [aut], Guilhem
Kister [aut]
Maintainer: Christelle Reynès <christelle.reynes@igf.cnrs.fr>
URL: www.r-project.org
git_url: https://git.bioconductor.org/packages/ISoLDE
git_branch: RELEASE_3_13
git_last_commit: d936509
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ISoLDE_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ISoLDE_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ISoLDE_1.20.0.tgz
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 4
Package: isomiRs
Version: 1.20.0
Depends: R (>= 3.5), DiscriMiner, SummarizedExperiment
Imports: AnnotationDbi, assertive.sets, BiocGenerics, Biobase, broom,
cluster, cowplot, DEGreport, DESeq2, IRanges, dplyr,
GenomicRanges, gplots, ggplot2, gtools, gridExtra, grid,
grDevices, graphics, GGally, limma, methods, RColorBrewer,
readr, reshape, rlang, stats, stringr, S4Vectors, tidyr, tibble
Suggests: knitr, org.Mm.eg.db, targetscan.Hs.eg.db, pheatmap,
BiocStyle, testthat
License: MIT + file LICENSE
MD5sum: 0d43608d670381601c169b985a776343
NeedsCompilation: no
Title: Analyze isomiRs and miRNAs from small RNA-seq
Description: Characterization of miRNAs and isomiRs, clustering and
differential expression.
biocViews: miRNA, RNASeq, DifferentialExpression, Clustering,
ImmunoOncology
Author: Lorena Pantano [aut, cre], Georgia Escaramis [aut] (CIBERESP -
CIBER Epidemiologia y Salud Publica)
Maintainer: Lorena Pantano <lorena.pantano@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/lpantano/isomiRs/issues
git_url: https://git.bioconductor.org/packages/isomiRs
git_branch: RELEASE_3_13
git_last_commit: f09067d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/isomiRs_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/isomiRs_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/isomiRs_1.20.0.tgz
vignettes: vignettes/isomiRs/inst/doc/isomiRs.html
vignetteTitles: miRNA and isomiR analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/isomiRs/inst/doc/isomiRs.R
dependencyCount: 154
Package: ITALICS
Version: 2.52.0
Depends: R (>= 2.0.0), GLAD, ITALICSData, oligo, affxparser,
pd.mapping50k.xba240
Imports: affxparser, DBI, GLAD, oligo, oligoClasses, stats
Suggests: pd.mapping50k.hind240, pd.mapping250k.sty, pd.mapping250k.nsp
License: GPL-2
MD5sum: 65a7dcef71307fa88012532aacde7366
NeedsCompilation: no
Title: ITALICS
Description: A Method to normalize of Affymetrix GeneChip Human Mapping
100K and 500K set
biocViews: Microarray, CopyNumberVariation
Author: Guillem Rigaill, Philippe Hupe
Maintainer: Guillem Rigaill <italics@curie.fr>
URL: http://bioinfo.curie.fr
git_url: https://git.bioconductor.org/packages/ITALICS
git_branch: RELEASE_3_13
git_last_commit: bb3b357
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ITALICS_2.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ITALICS_2.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ITALICS_2.52.0.tgz
vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf
vignetteTitles: ITALICS
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ITALICS/inst/doc/ITALICS.R
dependencyCount: 60
Package: iterativeBMA
Version: 1.50.0
Depends: BMA, leaps, Biobase (>= 2.5.5)
License: GPL (>= 2)
Archs: i386, x64
MD5sum: ec48084033cc956a1d4ec31cc512453f
NeedsCompilation: no
Title: The Iterative Bayesian Model Averaging (BMA) algorithm
Description: The iterative Bayesian Model Averaging (BMA) algorithm is
a variable selection and classification algorithm with an
application of classifying 2-class microarray samples, as
described in Yeung, Bumgarner and Raftery (Bioinformatics 2005,
21: 2394-2402).
biocViews: Microarray, Classification
Author: Ka Yee Yeung, University of Washington, Seattle, WA, with
contributions from Adrian Raftery and Ian Painter
Maintainer: Ka Yee Yeung <kayee@u.washington.edu>
URL: http://faculty.washington.edu/kayee/research.html
git_url: https://git.bioconductor.org/packages/iterativeBMA
git_branch: RELEASE_3_13
git_last_commit: 66815b3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iterativeBMA_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iterativeBMA_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iterativeBMA_1.50.0.tgz
vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf
vignetteTitles: The Iterative Bayesian Model Averaging Algorithm
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iterativeBMA/inst/doc/iterativeBMA.R
dependencyCount: 22
Package: iterativeBMAsurv
Version: 1.50.0
Depends: BMA, leaps, survival, splines
Imports: graphics, grDevices, stats, survival, utils
License: GPL (>= 2)
MD5sum: b65f06f38db46353488199a4ac9f79fd
NeedsCompilation: no
Title: The Iterative Bayesian Model Averaging (BMA) Algorithm For
Survival Analysis
Description: The iterative Bayesian Model Averaging (BMA) algorithm for
survival analysis is a variable selection method for applying
survival analysis to microarray data.
biocViews: Microarray
Author: Amalia Annest, University of Washington, Tacoma, WA Ka Yee
Yeung, University of Washington, Seattle, WA
Maintainer: Ka Yee Yeung <kayee@u.washington.edu>
URL: http://expression.washington.edu/ibmasurv/protected
git_url: https://git.bioconductor.org/packages/iterativeBMAsurv
git_branch: RELEASE_3_13
git_last_commit: 9637225
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iterativeBMAsurv_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iterativeBMAsurv_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iterativeBMAsurv_1.50.0.tgz
vignettes: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.pdf
vignetteTitles: The Iterative Bayesian Model Averaging Algorithm For
Survival Analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.R
dependencyCount: 19
Package: iterClust
Version: 1.14.0
Depends: R (>= 3.4.1)
Imports: Biobase, cluster, stats, methods
Suggests: tsne, bcellViper
License: file LICENSE
Archs: i386, x64
MD5sum: 2a67f3aec0b979d322dfb7154a68835e
NeedsCompilation: no
Title: Iterative Clustering
Description: A framework for performing clustering analysis
iteratively.
biocViews: StatisticalMethod, Clustering
Author: Hongxu Ding and Andrea Califano
Maintainer: Hongxu Ding <hd2326@columbia.edu>
URL: https://github.com/hd2326/iterClust
BugReports: https://github.com/hd2326/iterClust/issues
git_url: https://git.bioconductor.org/packages/iterClust
git_branch: RELEASE_3_13
git_last_commit: 8eb13f4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iterClust_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iterClust_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iterClust_1.14.0.tgz
vignettes: vignettes/iterClust/inst/doc/introduction.pdf
vignetteTitles: introduction.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/iterClust/inst/doc/introduction.R
dependencyCount: 9
Package: iteremoval
Version: 1.12.0
Depends: R (>= 3.5.0), ggplot2 (>= 2.2.1)
Imports: magrittr, graphics, utils, GenomicRanges, SummarizedExperiment
Suggests: testthat, knitr
License: GPL-2
MD5sum: 4979f4cee895cb1ccb27c1febb1d20f2
NeedsCompilation: no
Title: Iteration removal method for feature selection
Description: The package provides a flexible algorithm to screen
features of two distinct groups in consideration of overfitting
and overall performance. It was originally tailored for
methylation locus screening of NGS data, and it can also be
used as a generic method for feature selection. Each step of
the algorithm provides a default method for simple
implemention, and the method can be replaced by a user defined
function.
biocViews: StatisticalMethod
Author: Jiacheng Chuan [aut, cre]
Maintainer: Jiacheng Chuan <jiacheng_chuan@outlook.com>
URL: https://github.com/cihga39871/iteremoval
VignetteBuilder: knitr
BugReports: https://github.com/cihga39871/iteremoval/issues
git_url: https://git.bioconductor.org/packages/iteremoval
git_branch: RELEASE_3_13
git_last_commit: d955ef4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/iteremoval_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/iteremoval_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/iteremoval_1.12.0.tgz
vignettes: vignettes/iteremoval/inst/doc/iteremoval.html
vignetteTitles: An introduction to iteremoval
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iteremoval/inst/doc/iteremoval.R
dependencyCount: 56
Package: IVAS
Version: 2.12.0
Depends: R (> 3.0.0),GenomicFeatures, ggplot2, Biobase
Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges,
foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, ggfortify,
grDevices, methods, Matrix, BiocParallel,utils, stats
Suggests: BiocStyle
License: GPL-2
MD5sum: c1ba8b6bf7a5e6e3a0eb4e22db28c964
NeedsCompilation: no
Title: Identification of genetic Variants affecting Alternative
Splicing
Description: Identification of genetic variants affecting alternative
splicing.
biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression,
DifferentialSplicing, GeneExpression, GeneRegulation,
Regression, RNASeq, Sequencing, SNP, Software, Transcription
Author: Seonggyun Han, Sangsoo Kim
Maintainer: Seonggyun Han <hangost@ssu.ac.kr>
git_url: https://git.bioconductor.org/packages/IVAS
git_branch: RELEASE_3_13
git_last_commit: 72bbdb6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IVAS_2.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IVAS_2.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IVAS_2.12.0.tgz
vignettes: vignettes/IVAS/inst/doc/IVAS.pdf
vignetteTitles: IVAS : Identification of genetic Variants affecting
Alternative Splicing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IVAS/inst/doc/IVAS.R
dependsOnMe: IMAS
importsMe: ASpediaFI
dependencyCount: 123
Package: ivygapSE
Version: 1.14.1
Depends: R (>= 3.5.0), SummarizedExperiment
Imports: shiny, survival, survminer, hwriter, plotly, ggplot2,
S4Vectors, graphics, stats, utils, UpSetR
Suggests: knitr, png, limma, grid, DT, randomForest, digest, testthat,
rmarkdown
License: Artistic-2.0
MD5sum: a23be592156eabf634ab637800ee087e
NeedsCompilation: no
Title: A SummarizedExperiment for Ivy-GAP data
Description: Define a SummarizedExperiment and exploratory app for
Ivy-GAP glioblastoma image, expression, and clinical data.
biocViews: Transcription, Software, Visualization, Survival,
GeneExpression, Sequencing
Author: Vince Carey
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ivygapSE
git_branch: RELEASE_3_13
git_last_commit: 85da261
git_last_commit_date: 2021-07-28
Date/Publication: 2021-07-29
source.ver: src/contrib/ivygapSE_1.14.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ivygapSE_1.14.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ivygapSE_1.14.1.tgz
vignettes: vignettes/ivygapSE/inst/doc/ivygapSE.html
vignetteTitles: ivygapSE -- SummarizedExperiment for Ivy-GAP
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ivygapSE/inst/doc/ivygapSE.R
dependencyCount: 164
Package: IWTomics
Version: 1.16.0
Depends: GenomicRanges
Imports:
parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools
Suggests: knitr
License: GPL (>=2)
MD5sum: 7d0f3f00d74eb61725137d253e22daea
NeedsCompilation: no
Title: Interval-Wise Testing for Omics Data
Description: Implementation of the Interval-Wise Testing (IWT) for
omics data. This inferential procedure tests for differences in
"Omics" data between two groups of genomic regions (or between
a group of genomic regions and a reference center of symmetry),
and does not require fixing location and scale at the outset.
biocViews: StatisticalMethod, MultipleComparison,
DifferentialExpression, DifferentialMethylation,
DifferentialPeakCalling, GenomeAnnotation, DataImport
Author: Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone
Vantini
Maintainer: Marzia A Cremona <mac78@psu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IWTomics
git_branch: RELEASE_3_13
git_last_commit: 5299f73
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/IWTomics_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/IWTomics_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/IWTomics_1.16.0.tgz
vignettes: vignettes/IWTomics/inst/doc/IWTomics.pdf
vignetteTitles: Introduction to IWTomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IWTomics/inst/doc/IWTomics.R
dependencyCount: 72
Package: karyoploteR
Version: 1.18.0
Depends: R (>= 3.4), regioneR, GenomicRanges, methods
Imports: regioneR, GenomicRanges, IRanges, Rsamtools, stats, graphics,
memoise, rtracklayer, GenomeInfoDb, S4Vectors, biovizBase,
digest, bezier, GenomicFeatures, bamsignals, AnnotationDbi,
grDevices, VariantAnnotation
Suggests: BiocStyle, knitr, testthat, magrittr,
BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg19.masked,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene, org.Hs.eg.db, org.Mm.eg.db,
pasillaBamSubset
License: Artistic-2.0
MD5sum: a8d9bc14f4e46f2454e9fd8876961ec8
NeedsCompilation: no
Title: Plot customizable linear genomes displaying arbitrary data
Description: karyoploteR creates karyotype plots of arbitrary genomes
and offers a complete set of functions to plot arbitrary data
on them. It mimicks many R base graphics functions coupling
them with a coordinate change function automatically mapping
the chromosome and data coordinates into the plot coordinates.
In addition to the provided data plotting functions, it is easy
to add new ones.
biocViews: Visualization, CopyNumberVariation, Sequencing, Coverage,
DNASeq, ChIPSeq, MethylSeq, DataImport, OneChannel
Author: Bernat Gel <bgel@igtp.cat>
Maintainer: Bernat Gel <bgel@igtp.cat>
URL: https://github.com/bernatgel/karyoploteR
VignetteBuilder: knitr
BugReports: https://github.com/bernatgel/karyoploteR/issues
git_url: https://git.bioconductor.org/packages/karyoploteR
git_branch: RELEASE_3_13
git_last_commit: 7dcb80b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/karyoploteR_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/karyoploteR_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/karyoploteR_1.18.0.tgz
vignettes: vignettes/karyoploteR/inst/doc/karyoploteR.html
vignetteTitles: karyoploteR vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/karyoploteR/inst/doc/karyoploteR.R
dependsOnMe: CopyNumberPlots
importsMe: CNVfilteR, CNViz, multicrispr, RIPAT
suggestsMe: Category
dependencyCount: 144
Package: KBoost
Version: 1.0.0
Depends: R (>= 4.1), stats, utils
Suggests: knitr, rmarkdown, testthat
License: GPL-2 | GPL-3
MD5sum: 0bcd4000ffc2555f3dd8e2ad7e402a0c
NeedsCompilation: no
Title: Inference of gene regulatory networks from gene expression data
Description: Reconstructing gene regulatory networks and transcription
factor activity is crucial to understand biological processes
and holds potential for developing personalized treatment. Yet,
it is still an open problem as state-of-art algorithm are often
not able to handle large amounts of data. Furthermore, many of
the present methods predict numerous false positives and are
unable to integrate other sources of information such as
previously known interactions. Here we introduce KBoost, an
algorithm that uses kernel PCA regression, boosting and
Bayesian model averaging for fast and accurate reconstruction
of gene regulatory networks. KBoost can also use a prior
network built on previously known transcription factor targets.
We have benchmarked KBoost using three different datasets
against other high performing algorithms. The results show that
our method compares favourably to other methods across
datasets.
biocViews: Network, GraphAndNetwork, Bayesian, NetworkInference,
GeneRegulation, Transcriptomics, SystemsBiology, Transcription,
GeneExpression, Regression, PrincipalComponent
Author: Luis F. Iglesias-Martinez [aut, cre]
(<https://orcid.org/0000-0002-9110-2189>), Barbara de Kegel
[aut], Walter Kolch [aut]
Maintainer: Luis F. Iglesias-Martinez <luis.iglesiasmartinez@ucd.ie>
URL: https://github.com/Luisiglm/KBoost
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/KBoost
git_branch: RELEASE_3_13
git_last_commit: de290b9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/KBoost_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/KBoost_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/KBoost_1.0.0.tgz
vignettes: vignettes/KBoost/inst/doc/KBoost.html
vignetteTitles: KBoost
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KBoost/inst/doc/KBoost.R
dependencyCount: 2
Package: KCsmart
Version: 2.50.0
Depends: siggenes, multtest, KernSmooth
Imports: methods, BiocGenerics
Enhances: Biobase, CGHbase
License: GPL-3
MD5sum: cd0aac2b78c816ad5bd70cfd278d4f05
NeedsCompilation: no
Title: Multi sample aCGH analysis package using kernel convolution
Description: Multi sample aCGH analysis package using kernel
convolution
biocViews: CopyNumberVariation, Visualization, aCGH, Microarray
Author: Jorma de Ronde, Christiaan Klijn, Arno Velds
Maintainer: Jorma de Ronde <j.d.ronde@nki.nl>
git_url: https://git.bioconductor.org/packages/KCsmart
git_branch: RELEASE_3_13
git_last_commit: 130f4ca
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/KCsmart_2.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/KCsmart_2.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/KCsmart_2.50.0.tgz
vignettes: vignettes/KCsmart/inst/doc/KCS.pdf
vignetteTitles: KCsmart example session
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KCsmart/inst/doc/KCS.R
dependencyCount: 19
Package: kebabs
Version: 1.26.1
Depends: R (>= 3.2.0), Biostrings (>= 2.35.5), kernlab
Imports: methods, stats, Rcpp (>= 0.11.2), Matrix, XVector (>= 0.7.3),
S4Vectors (>= 0.27.3), e1071, LiblineaR, graphics, grDevices,
utils, apcluster
LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors
Suggests: SparseM, Biobase, BiocGenerics, knitr
License: GPL (>= 2.1)
MD5sum: ae6f170462ba5eec6ba572a90b65ec7c
NeedsCompilation: yes
Title: Kernel-Based Analysis Of Biological Sequences
Description: The package provides functionality for kernel-based
analysis of DNA, RNA, and amino acid sequences via SVM-based
methods. As core functionality, kebabs implements following
sequence kernels: spectrum kernel, mismatch kernel, gappy pair
kernel, and motif kernel. Apart from an efficient
implementation of standard position-independent functionality,
the kernels are extended in a novel way to take the position of
patterns into account for the similarity measure. Because of
the flexibility of the kernel formulation, other kernels like
the weighted degree kernel or the shifted weighted degree
kernel with constant weighting of positions are included as
special cases. An annotation-specific variant of the kernels
uses annotation information placed along the sequence together
with the patterns in the sequence. The package allows for the
generation of a kernel matrix or an explicit feature
representation in dense or sparse format for all available
kernels which can be used with methods implemented in other R
packages. With focus on SVM-based methods, kebabs provides a
framework which simplifies the usage of existing SVM
implementations in kernlab, e1071, and LiblineaR. Binary and
multi-class classification as well as regression tasks can be
used in a unified way without having to deal with the different
functions, parameters, and formats of the selected SVM. As
support for choosing hyperparameters, the package provides
cross validation - including grouped cross validation, grid
search and model selection functions. For easier biological
interpretation of the results, the package computes feature
weights for all SVMs and prediction profiles which show the
contribution of individual sequence positions to the prediction
result and indicate the relevance of sequence sections for the
learning result and the underlying biological functions.
biocViews: SupportVectorMachine, Classification, Clustering, Regression
Author: Johannes Palme
Maintainer: Ulrich Bodenhofer <bodenhofer@bioinf.jku.at>
URL: http://www.bioinf.jku.at/software/kebabs/
https://github.com/UBod/kebabs
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/kebabs
git_branch: RELEASE_3_13
git_last_commit: 6abd0f9
git_last_commit_date: 2021-06-18
Date/Publication: 2021-06-20
source.ver: src/contrib/kebabs_1.26.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/kebabs_1.26.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/kebabs_1.26.1.tgz
vignettes: vignettes/kebabs/inst/doc/kebabs.pdf
vignetteTitles: KeBABS - An R Package for Kernel Based Analysis of
Biological Sequences
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/kebabs/inst/doc/kebabs.R
dependsOnMe: procoil
importsMe: FindMyFriends, odseq
suggestsMe: apcluster
dependencyCount: 30
Package: KEGGgraph
Version: 1.52.0
Depends: R (>= 3.5.0)
Imports: methods, XML (>= 2.3-0), graph, utils, RCurl, Rgraphviz
Suggests: RBGL, testthat, RColorBrewer, org.Hs.eg.db, hgu133plus2.db,
SPIA
License: GPL (>= 2)
MD5sum: a751cf00e2bb53bc3ea81a7d8faf20d4
NeedsCompilation: no
Title: KEGGgraph: A graph approach to KEGG PATHWAY in R and
Bioconductor
Description: KEGGGraph is an interface between KEGG pathway and graph
object as well as a collection of tools to analyze, dissect and
visualize these graphs. It parses the regularly updated KGML
(KEGG XML) files into graph models maintaining all essential
pathway attributes. The package offers functionalities
including parsing, graph operation, visualization and etc.
biocViews: Pathways, GraphAndNetwork, Visualization, KEGG
Author: Jitao David Zhang, with inputs from Paul Shannon
Maintainer: Jitao David Zhang <jitao_david.zhang@roche.com>
URL: http://www.nextbiomotif.com
git_url: https://git.bioconductor.org/packages/KEGGgraph
git_branch: RELEASE_3_13
git_last_commit: 295f4c0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/KEGGgraph_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/KEGGgraph_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/KEGGgraph_1.52.0.tgz
vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf,
vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf
vignetteTitles: KEGGgraph: graph approach to KEGG PATHWAY, KEGGgraph:
Application Examples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraph.R,
vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R
dependsOnMe: ROntoTools, SPIA
importsMe: clipper, DEGraph, EnrichmentBrowser, MetaboSignal,
MWASTools, NCIgraph, pathview, PFP, iCARH, kangar00, NFP,
pathfindR
suggestsMe: DEGraph, GenomicRanges, maGUI, rags2ridges, specmine
dependencyCount: 14
Package: KEGGlincs
Version: 1.18.0
Depends: R (>= 3.3), KOdata, hgu133a.db, org.Hs.eg.db (>= 3.3.0)
Imports:
AnnotationDbi,KEGGgraph,igraph,plyr,gtools,httr,RJSONIO,KEGGREST,
methods,graphics,stats,utils, XML, grDevices
Suggests: BiocManager (>= 1.20.3), knitr, graph
License: GPL-3
MD5sum: 9234aa5486b7e705141dc6d72f8b646e
NeedsCompilation: no
Title: Visualize all edges within a KEGG pathway and overlay LINCS data
Description: See what is going on 'under the hood' of KEGG pathways by
explicitly re-creating the pathway maps from information
obtained from KGML files.
biocViews: NetworkInference, GeneExpression, DataRepresentation,
ThirdPartyClient,CellBiology,GraphAndNetwork,Pathways,KEGG,Network
Author: Shana White
Maintainer: Shana White <vandersm@mail.uc.edu>, Mario Medvedovic
<medvedm@ucmail.uc.edu>
SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/KEGGlincs
git_branch: RELEASE_3_13
git_last_commit: 899d372
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/KEGGlincs_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/KEGGlincs_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/KEGGlincs_1.18.0.tgz
vignettes: vignettes/KEGGlincs/inst/doc/Example-workflow.html
vignetteTitles: KEGGlincs Workflows
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KEGGlincs/inst/doc/Example-workflow.R
dependencyCount: 61
Package: keggorthology
Version: 2.44.0
Depends: R (>= 2.5.0),stats,graph,hgu95av2.db
Imports: AnnotationDbi,graph,DBI, graph, grDevices, methods, stats,
tools, utils
Suggests: RBGL,ALL
License: Artistic-2.0
MD5sum: e1fe3231fe411b359dd7fba5ef4901bf
NeedsCompilation: no
Title: graph support for KO, KEGG Orthology
Description: graphical representation of the Feb 2010 KEGG Orthology.
The KEGG orthology is a set of pathway IDs that are not to be
confused with the KEGG ortholog IDs.
biocViews: Pathways, GraphAndNetwork, Visualization, KEGG
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
git_url: https://git.bioconductor.org/packages/keggorthology
git_branch: RELEASE_3_13
git_last_commit: 479043c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/keggorthology_2.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/keggorthology_2.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/keggorthology_2.44.0.tgz
vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf
vignetteTitles: keggorthology overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/keggorthology/inst/doc/keggorth.R
suggestsMe: MLInterfaces
dependencyCount: 49
Package: KEGGREST
Version: 1.32.0
Depends: R (>= 3.5.0)
Imports: methods, httr, png, Biostrings
Suggests: RUnit, BiocGenerics, knitr, markdown
License: Artistic-2.0
MD5sum: b8ddb57f3e974ff5396ffbb9e8efd1eb
NeedsCompilation: no
Title: Client-side REST access to the Kyoto Encyclopedia of Genes and
Genomes (KEGG)
Description: A package that provides a client interface to the Kyoto
Encyclopedia of Genes and Genomes (KEGG) REST server. Based on
KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG
(python package) by Aurelien Mazurie.
biocViews: Annotation, Pathways, ThirdPartyClient, KEGG
Author: Dan Tenenbaum [aut], Jeremy Volkening [ctb], Bioconductor
Package Maintainer [aut, cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/KEGGREST
git_branch: RELEASE_3_13
git_last_commit: 25656cd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/KEGGREST_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/KEGGREST_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/KEGGREST_1.32.0.tgz
vignettes: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html
vignetteTitles: Accessing the KEGG REST API
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R
dependsOnMe: ROntoTools, Hiiragi2013
importsMe: ADAM, adSplit, AnnotationDbi, attract, BiocSet,
ChIPpeakAnno, CNEr, EnrichmentBrowser, famat, FELLA, gage,
MetaboSignal, MWASTools, PADOG, pathview, SBGNview, SMITE,
transomics2cytoscape, YAPSA, g2f, MetaDBparse, omu, pathfindR
suggestsMe: Category, categoryCompare, GenomicRanges, globaltest,
iSEEu, MLP, padma, RTopper, CALANGO, maGUI, ptm, scDiffCom,
specmine
dependencyCount: 28
Package: KinSwingR
Version: 1.10.0
Depends: R (>= 3.5)
Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices
Suggests: knitr, rmarkdown
License: GPL-3
Archs: i386, x64
MD5sum: 75c1bac2c7e45c059ed8a5d9591c8e31
NeedsCompilation: no
Title: KinSwingR: network-based kinase activity prediction
Description: KinSwingR integrates phosphosite data derived from
mass-spectrometry data and kinase-substrate predictions to
predict kinase activity. Several functions allow the user to
build PWM models of kinase-subtrates, statistically infer
PWM:substrate matches, and integrate these data to infer kinase
activity.
biocViews: Proteomics, SequenceMatching, Network
Author: Ashley J. Waardenberg [aut, cre]
Maintainer: Ashley J. Waardenberg <a.waardenberg@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/KinSwingR
git_branch: RELEASE_3_13
git_last_commit: d2364b7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/KinSwingR_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/KinSwingR_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/KinSwingR_1.10.0.tgz
vignettes: vignettes/KinSwingR/inst/doc/KinSwingR.html
vignetteTitles: KinSwingR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KinSwingR/inst/doc/KinSwingR.R
dependencyCount: 34
Package: kissDE
Version: 1.12.0
Imports: aod, Biobase, DESeq2, DSS, ggplot2, gplots, graphics,
grDevices, matrixStats, stats, utils, foreach, doParallel,
parallel
Suggests: BiocStyle, testthat
License: GPL (>= 2)
MD5sum: 5ae2dc377be3a07b80f3a9c7223b0eb0
NeedsCompilation: no
Title: Retrieves Condition-Specific Variants in RNA-Seq Data
Description: Retrieves condition-specific variants in RNA-seq data
(SNVs, alternative-splicings, indels). It has been developed as
a post-treatment of 'KisSplice' but can also be used with
user's own data.
biocViews: AlternativeSplicing, DifferentialSplicing,
ExperimentalDesign, GenomicVariation, RNASeq, Transcriptomics
Author: Clara Benoit-Pilven [aut], Camille Marchet [aut], Janice
Kielbassa [aut], Lilia Brinza [aut], Audric Cologne [aut],
Aurélie Siberchicot [aut, cre], Vincent Lacroix [aut], Frank
Picard [ctb], Laurent Jacob [ctb], Vincent Miele [ctb]
Maintainer: Aurélie Siberchicot <aurelie.siberchicot@univ-lyon1.fr>
git_url: https://git.bioconductor.org/packages/kissDE
git_branch: RELEASE_3_13
git_last_commit: c2a1f56
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/kissDE_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/kissDE_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/kissDE_1.12.0.tgz
vignettes: vignettes/kissDE/inst/doc/kissDE.pdf
vignetteTitles: kissDE.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/kissDE/inst/doc/kissDE.R
dependencyCount: 126
Package: KnowSeq
Version: 1.6.3
Depends: R (>= 4.0), cqn (>= 1.28.1)
Imports: stringr, methods, ggplot2 (>= 3.3.0), jsonlite, kernlab,
rlist, rmarkdown, reshape2, e1071, randomForest, caret, XML,
praznik, R.utils, httr, sva (>= 3.30.1), edgeR (>= 3.24.3),
limma (>= 3.38.3), grDevices, graphics, stats, utils, Hmisc (>=
4.4.0), gridExtra
Suggests: knitr
License: GPL (>=2)
MD5sum: cd1288b4e94c1744b8a9ca2b635de44a
NeedsCompilation: no
Title: KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline
Description: KnowSeq proposes a novel methodology that comprises the
most relevant steps in the Transcriptomic gene expression
analysis. KnowSeq expects to serve as an integrative tool that
allows to process and extract relevant biomarkers, as well as
to assess them through a Machine Learning approaches. Finally,
the last objective of KnowSeq is the biological knowledge
extraction from the biomarkers (Gene Ontology enrichment,
Pathway listing and Visualization and Evidences related to the
addressed disease). Although the package allows analyzing all
the data manually, the main strenght of KnowSeq is the
possibilty of carrying out an automatic and intelligent HTML
report that collect all the involved steps in one document. It
is important to highligh that the pipeline is totally modular
and flexible, hence it can be started from whichever of the
different steps. KnowSeq expects to serve as a novel tool to
help to the experts in the field to acquire robust knowledge
and conclusions for the data and diseases to study.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
DataImport, Classification, FeatureExtraction, Sequencing,
RNASeq, BatchEffect, Normalization, Preprocessing,
QualityControl, Genetics, Transcriptomics, Microarray,
Alignment, Pathways, SystemsBiology, GO, ImmunoOncology
Author: Daniel Castillo-Secilla [aut, cre], Juan Manuel Galvez [ctb],
Francisco Carrillo-Perez [ctb], Marta Verona-Almeida [ctb],
Daniel Redondo-Sanchez [ctb], Francisco Manuel Ortuno [ctb],
Luis Javier Herrera [ctb], Ignacio Rojas [ctb]
Maintainer: Daniel Castillo-Secilla <cased@ugr.es>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/KnowSeq
git_branch: RELEASE_3_13
git_last_commit: b744a16
git_last_commit_date: 2021-10-09
Date/Publication: 2021-10-10
source.ver: src/contrib/KnowSeq_1.6.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/KnowSeq_1.6.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/KnowSeq_1.6.3.tgz
vignettes: vignettes/KnowSeq/inst/doc/KnowSeq.html
vignetteTitles: The KnowSeq users guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KnowSeq/inst/doc/KnowSeq.R
dependencyCount: 166
Package: LACE
Version: 1.4.0
Depends: R (>= 4.0.0)
Imports: data.tree, graphics, grDevices, igraph, parallel,
RColorBrewer, Rfast, stats, SummarizedExperiment, utils
Suggests: BiocGenerics, BiocStyle, testthat, knitr
License: file LICENSE
MD5sum: 5bd61071399616a871878e3e5f08529d
NeedsCompilation: no
Title: Longitudinal Analysis of Cancer Evolution (LACE)
Description: LACE is an algorithmic framework that processes
single-cell somatic mutation profiles from cancer samples
collected at different time points and in distinct experimental
settings, to produce longitudinal models of cancer evolution.
The approach solves a Boolean Matrix Factorization problem with
phylogenetic constraints, by maximizing a weighed likelihood
function computed on multiple time points.
biocViews: BiomedicalInformatics, SingleCell, SomaticMutation
Author: Daniele Ramazzotti [aut]
(<https://orcid.org/0000-0002-6087-2666>), Fabrizio Angaroni
[aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De
Sano [ctb]
Maintainer: Davide Maspero <d.maspero@campus.unimib.it>
URL: https://github.com/BIMIB-DISCo/LACE
VignetteBuilder: knitr
BugReports: https://github.com/BIMIB-DISCo/LACE
git_url: https://git.bioconductor.org/packages/LACE
git_branch: RELEASE_3_13
git_last_commit: 9293f28
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LACE_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LACE_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LACE_1.4.0.tgz
vignettes: vignettes/LACE/inst/doc/vignette.pdf
vignetteTitles: LACE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/LACE/inst/doc/vignette.R
dependencyCount: 38
Package: lapmix
Version: 1.58.0
Depends: R (>= 2.6.0),stats
Imports: Biobase, graphics, grDevices, methods, stats, tools, utils
License: GPL (>= 2)
MD5sum: 5dd52a02688127d4797f3b382f2d26fc
NeedsCompilation: no
Title: Laplace Mixture Model in Microarray Experiments
Description: Laplace mixture modelling of microarray experiments. A
hierarchical Bayesian approach is used, and the hyperparameters
are estimated using empirical Bayes. The main purpose is to
identify differentially expressed genes.
biocViews: Microarray, OneChannel, DifferentialExpression
Author: Yann Ruffieux, contributions from Debjani Bhowmick, Anthony C.
Davison, and Darlene R. Goldstein
Maintainer: Yann Ruffieux <yann.ruffieux@epfl.ch>
URL: http://www.r-project.org, http://www.bioconductor.org,
http://stat.epfl.ch
git_url: https://git.bioconductor.org/packages/lapmix
git_branch: RELEASE_3_13
git_last_commit: c1178cd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/lapmix_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/lapmix_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/lapmix_1.58.0.tgz
vignettes: vignettes/lapmix/inst/doc/lapmix-example.pdf
vignetteTitles: lapmix example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lapmix/inst/doc/lapmix-example.R
dependencyCount: 9
Package: LBE
Version: 1.60.0
Depends: stats
Imports: graphics, grDevices, methods, stats, utils
Suggests: qvalue
License: GPL-2
MD5sum: 382dde2da359ba566f47587c03c82cbc
NeedsCompilation: no
Title: Estimation of the false discovery rate.
Description: LBE is an efficient procedure for estimating the
proportion of true null hypotheses, the false discovery rate
(and so the q-values) in the framework of estimating procedures
based on the marginal distribution of the p-values without
assumption for the alternative hypothesis.
biocViews: MultipleComparison
Author: Cyril Dalmasso
Maintainer: Cyril Dalmasso <dalmasso@vjf.inserm.fr>
git_url: https://git.bioconductor.org/packages/LBE
git_branch: RELEASE_3_13
git_last_commit: 7b28f58
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LBE_1.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LBE_1.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LBE_1.60.0.tgz
vignettes: vignettes/LBE/inst/doc/LBE.pdf
vignetteTitles: LBE Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LBE/inst/doc/LBE.R
dependsOnMe: PhViD
dependencyCount: 5
Package: ldblock
Version: 1.22.1
Depends: R (>= 3.5), methods
Imports: Matrix, snpStats, VariantAnnotation, GenomeInfoDb, httr,
ensembldb, EnsDb.Hsapiens.v75, Rsamtools, GenomicFiles (>=
1.13.6), BiocGenerics (>= 0.25.1)
Suggests: RUnit, knitr, BiocStyle, gwascat, rmarkdown
License: Artistic-2.0
MD5sum: 11dfd1b6e61eeb373dbc8803230cd2df
NeedsCompilation: no
Title: data structures for linkage disequilibrium measures in
populations
Description: Define data structures for linkage disequilibrium measures
in populations.
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ldblock
git_branch: RELEASE_3_13
git_last_commit: 6c98ba3
git_last_commit_date: 2021-07-28
Date/Publication: 2021-07-29
source.ver: src/contrib/ldblock_1.22.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ldblock_1.22.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ldblock_1.22.1.tgz
vignettes: vignettes/ldblock/inst/doc/ldblock.html
vignetteTitles: ldblock package: linkage disequilibrium data structures
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ldblock/inst/doc/ldblock.R
dependencyCount: 107
Package: LEA
Version: 3.4.0
Depends: R (>= 3.3.0), methods, stats, utils, graphics
Suggests: knitr
License: GPL-3
MD5sum: 7a1e3e8af0aad7c6461e6aff1cc9a784
NeedsCompilation: yes
Title: LEA: an R package for Landscape and Ecological Association
Studies
Description: LEA is an R package dedicated to population genomics,
landscape genomics and genotype-environment association tests.
LEA can run analyses of population structure and genome-wide
tests for local adaptation. The package includes statistical
methods for estimating ancestry coefficients from large
genotypic matrices and for evaluating the number of ancestral
populations (snmf, pca). It performs statistical tests using
latent factor mixed models for identifying genetic
polymorphisms that exhibit association with environmental
gradients or phenotypic traits (lfmm and lfmm2). {\tt LEA} also
performs imputation of missing genotypes, and computes
predictive values of genetic offsets based on new or future
environments. The package includes factor methods for
estimating ancestry coefficients from large genotypic matrices
and for evaluating the number of ancestral populations (snmf,
pca). It implements latent factor mixed models for identifying
LEA is mainly based on optimized programs that can scale with
the dimension of large data sets.
biocViews: Software, Statistical Method, Clustering, Regression
Author: Eric Frichot <eric.frichot@gmail.com>, Olivier Francois
<olivier.francois@grenoble-inp.fr>, Clement Gain
<clement.gain@univ-grenoble-alpes.fr>
Maintainer: Olivier Francois <olivier.francois@grenoble-inp.fr>, Eric
Frichot <eric.frichot@gmail.com>
URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/LEA
git_branch: RELEASE_3_13
git_last_commit: ed416f8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LEA_3.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LEA_3.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LEA_3.4.0.tgz
vignettes: vignettes/LEA/inst/doc/LEA.pdf
vignetteTitles: LEA: An R Package for Landscape and Ecological
Association Studies
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LEA/inst/doc/LEA.R
dependencyCount: 4
Package: LedPred
Version: 1.26.0
Depends: R (>= 3.2.0), e1071 (>= 1.6)
Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl,
ROCR, testthat
License: MIT | file LICENSE
MD5sum: db8ac0d22fdc049884d564686ae9cbf7
NeedsCompilation: no
Title: Learning from DNA to Predict Enhancers
Description: This package aims at creating a predictive model of
regulatory sequences used to score unknown sequences based on
the content of DNA motifs, next-generation sequencing (NGS)
peaks and signals and other numerical scores of the sequences
using supervised classification. The package contains a
workflow based on the support vector machine (SVM) algorithm
that maps features to sequences, optimize SVM parameters and
feature number and creates a model that can be stored and used
to score the regulatory potential of unknown sequences.
biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq,
Sequencing, Classification
Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez
Maintainer: Aitor Gonzalez <aitor.gonzalez@univ-amu.fr>
BugReports: https://github.com/aitgon/LedPred/issues
git_url: https://git.bioconductor.org/packages/LedPred
git_branch: RELEASE_3_13
git_last_commit: e2e060f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LedPred_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LedPred_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LedPred_1.26.0.tgz
vignettes: vignettes/LedPred/inst/doc/LedPred.pdf
vignetteTitles: LedPred Example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/LedPred/inst/doc/LedPred.R
dependencyCount: 74
Package: lefser
Version: 1.2.0
Depends: SummarizedExperiment, R (>= 4.0.0)
Imports: coin, MASS, ggplot2, stats, methods
Suggests: knitr, rmarkdown, curatedMetagenomicData, BiocStyle,
testthat, pkgdown, covr, withr
License: Artistic-2.0
MD5sum: 7f4d801797414c1058f919bf5991ab0d
NeedsCompilation: no
Title: R implementation of the LEfSE method for microbiome biomarker
discovery
Description: lefser is an implementation in R of the popular "LDA
Effect Size (LEfSe)" method for microbiome biomarker discovery.
It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and
Linear Discriminant Analysis to find biomarkers of groups and
sub-groups.
biocViews: Software, Sequencing, DifferentialExpression, Microbiome,
StatisticalMethod, Classification
Author: Asya Khleborodova [cre, aut], Ludwig Geistlinger [ctb], Marcel
Ramos [ctb] (<https://orcid.org/0000-0002-3242-0582>), Levi
Waldron [ctb]
Maintainer: Asya Khleborodova <asya.bioconductor@gmail.com>
URL: https://github.com/waldronlab/lefser
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/lefser/issues
git_url: https://git.bioconductor.org/packages/lefser
git_branch: RELEASE_3_13
git_last_commit: 6976537
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/lefser_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/lefser_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/lefser_1.2.0.tgz
vignettes: vignettes/lefser/inst/doc/lefser.html
vignetteTitles: Introduction to the lefser R implementation of the
popular LEfSE software for biomarker discovery in microbiome
analysis.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lefser/inst/doc/lefser.R
dependencyCount: 66
Package: les
Version: 1.42.0
Depends: R (>= 2.13.2), methods, graphics, fdrtool
Imports: boot, gplots, RColorBrewer
Suggests: Biobase, limma
Enhances: parallel
License: GPL-3
MD5sum: 755bee14e02554d838920e079435252a
NeedsCompilation: no
Title: Identifying Differential Effects in Tiling Microarray Data
Description: The 'les' package estimates Loci of Enhanced Significance
(LES) in tiling microarray data. These are regions of
regulation such as found in differential transcription,
CHiP-chip, or DNA modification analysis. The package provides a
universal framework suitable for identifying differential
effects in tiling microarray data sets, and is independent of
the underlying statistics at the level of single probes.
biocViews: Microarray, DifferentialExpression, ChIPchip,
DNAMethylation, Transcription
Author: Julian Gehring, Clemens Kreutz, Jens Timmer
Maintainer: Julian Gehring <jg-bioc@gmx.com>
git_url: https://git.bioconductor.org/packages/les
git_branch: RELEASE_3_13
git_last_commit: 9224538
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/les_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/les_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/les_1.42.0.tgz
vignettes: vignettes/les/inst/doc/les.pdf
vignetteTitles: Introduction to the les package: Identifying
Differential Effects in Tiling Microarray Data with the Loci of
Enhanced Significance Framework
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/les/inst/doc/les.R
importsMe: GSRI
dependencyCount: 13
Package: levi
Version: 1.10.0
Imports: DT(>= 0.4), RColorBrewer(>= 1.1-2), colorspace(>= 1.3-2),
dplyr(>= 0.7.4), ggplot2(>= 2.2.1), httr(>= 1.3.1), igraph(>=
1.2.1), reshape2(>= 1.4.3), shiny(>= 1.0.5), shinydashboard(>=
0.7.0), shinyjs(>= 1.0), xml2(>= 1.2.0), knitr, Rcpp (>=
0.12.18), grid, grDevices, stats, utils, testthat, methods
LinkingTo: Rcpp
License: GPL (>= 2)
MD5sum: 81d58fa0d1b4e3ae8b602a7653a49ad3
NeedsCompilation: yes
Title: Landscape Expression Visualization Interface
Description: The tool integrates data from biological networks with
transcriptomes, displaying a heatmap with surface curves to
evidence the altered regions.
biocViews: GeneExpression, Sequencing, Network, Software
Author: Jose Rafael Pilan <rafael.pilan@unesp.br>, Isabelle Mira da
Silva <isabelle.silva@unesp.br>, Agnes Alessandra Sekijima
Takeda <agnes.takeda@unesp.br>, Jose Luiz Rybarczyk Filho
<jose.luiz@unesp.br>
Maintainer: Jose Luiz Rybarczyk Filho <jose.luiz@unesp.br>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/levi
git_branch: RELEASE_3_13
git_last_commit: 55fd6fb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/levi_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/levi_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/levi_1.10.0.tgz
vignettes: vignettes/levi/inst/doc/levi.html
vignetteTitles: "Using levi"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/levi/inst/doc/levi.R
dependencyCount: 98
Package: lfa
Version: 1.22.0
Depends: R (>= 3.2)
Imports: corpcor
Suggests: knitr, ggplot2
License: GPL-3
Archs: i386, x64
MD5sum: 7e443aa5fcdf75ef29f6b7562381f9c3
NeedsCompilation: yes
Title: Logistic Factor Analysis for Categorical Data
Description: LFA is a method for a PCA analogue on Binomial data via
estimation of latent structure in the natural parameter.
biocViews: SNP, DimensionReduction, PrincipalComponent
Author: Wei Hao, Minsun Song, John D. Storey
Maintainer: Wei Hao <whao@princeton.edu>, John D. Storey
<jstorey@princeton.edu>
URL: https://github.com/StoreyLab/lfa
VignetteBuilder: knitr
BugReports: https://github.com/StoreyLab/lfa/issues
git_url: https://git.bioconductor.org/packages/lfa
git_branch: RELEASE_3_13
git_last_commit: 4bc83c6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/lfa_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/lfa_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/lfa_1.22.0.tgz
vignettes: vignettes/lfa/inst/doc/lfa.pdf
vignetteTitles: lfa Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lfa/inst/doc/lfa.R
importsMe: gcatest, jackstraw
suggestsMe: popkin
dependencyCount: 2
Package: limma
Version: 3.48.3
Depends: R (>= 3.6.0)
Imports: grDevices, graphics, stats, utils, methods
Suggests: affy, AnnotationDbi, BiasedUrn, Biobase, ellipse, GO.db,
gplots, illuminaio, locfit, MASS, org.Hs.eg.db, splines,
statmod (>= 1.2.2), vsn
License: GPL (>=2)
MD5sum: dc171a69a1cf4dcd1f9afaa3bc558ccd
NeedsCompilation: yes
Title: Linear Models for Microarray Data
Description: Data analysis, linear models and differential expression
for microarray data.
biocViews: ExonArray, GeneExpression, Transcription,
AlternativeSplicing, DifferentialExpression,
DifferentialSplicing, GeneSetEnrichment, DataImport, Bayesian,
Clustering, Regression, TimeCourse, Microarray, MicroRNAArray,
mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel,
Sequencing, RNASeq, BatchEffect, MultipleComparison,
Normalization, Preprocessing, QualityControl,
BiomedicalInformatics, CellBiology, Cheminformatics,
Epigenetics, FunctionalGenomics, Genetics, ImmunoOncology,
Metabolomics, Proteomics, SystemsBiology, Transcriptomics
Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb],
Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy
[ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron
Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn
de Graaf [ctb], Yunshun Chen [ctb], Mette Langaas [ctb], Egil
Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb],
Dongseok Choi [ctb]
Maintainer: Gordon Smyth <smyth@wehi.edu.au>
URL: http://bioinf.wehi.edu.au/limma
git_url: https://git.bioconductor.org/packages/limma
git_branch: RELEASE_3_13
git_last_commit: 80282e9
git_last_commit_date: 2021-08-09
Date/Publication: 2021-08-10
source.ver: src/contrib/limma_3.48.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/limma_3.48.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/limma_3.48.3.tgz
vignettes: vignettes/limma/inst/doc/intro.pdf,
vignettes/limma/inst/doc/usersguide.pdf
vignetteTitles: Limma One Page Introduction, usersguide.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: ASpli, BLMA, cghMCR, codelink, convert, Cormotif, deco,
DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, HTqPCR,
IsoformSwitchAnalyzeR, maigesPack, marray, metagenomeSeq,
metaseqR2, mpra, qpcrNorm, qusage, RBM, Ringo, RnBeads, Rnits,
splineTimeR, TOAST, tRanslatome, TurboNorm, variancePartition,
wateRmelon, CCl4, Fletcher2013a, HD2013SGI, ReactomeGSA.data,
EGSEA123, maEndToEnd, methylationArrayAnalysis, RNAseq123,
OSCA.advanced, OSCA.basic, OSCA.workflows, BALLI, BioInsight,
CORM, countTransformers, cp4p, DAAGbio, DRomics, PerfMeas
importsMe: a4Base, ABSSeq, affycoretools, affylmGUI, AMARETTO,
animalcules, ArrayExpress, arrayQuality, arrayQualityMetrics,
artMS, ASpediaFI, ATACseqQC, attract, autonomics, AWFisher,
ballgown, BatchQC, beadarray, biotmle, BloodGen3Module, bnem,
bsseq, BubbleTree, bumphunter, CancerSubtypes, casper, ChAMP,
clusterExperiment, CNVRanger, coexnet, combi, compcodeR,
consensusDE, consensusOV, CountClust, crlmm, crossmeta, csaw,
cTRAP, ctsGE, CytoTree, DAMEfinder, DaMiRseq, debrowser, DEP,
derfinderPlot, DEsubs, DExMA, DiffBind, diffcyt, diffHic,
diffloop, diffUTR, distinct, DMRcate, Doscheda, DRIMSeq, eegc,
EGAD, EGSEA, eisaR, EnrichmentBrowser, epigraHMM,
erccdashboard, escape, EventPointer, EWCE, ExploreModelMatrix,
flowBin, gCrisprTools, GDCRNATools, genefu, GeneSelectMMD,
GEOquery, Glimma, GOsummaries, hipathia, HTqPCR, icetea,
iCheck, iChip, iCOBRA, ideal, InPAS, isomiRs, KnowSeq,
limmaGUI, Linnorm, lipidr, lmdme, mAPKL, MatrixQCvis, MBQN,
mCSEA, MEAL, methylKit, MethylMix, microbiomeExplorer, MIGSA,
miloR, minfi, miRLAB, missMethyl, MLSeq, moanin, monocle,
MoonlightR, msImpute, msqrob2, MSstats, MSstatsTMT,
MultiDataSet, muscat, NADfinder, nethet, nondetects,
NormalyzerDE, OLIN, omicRexposome, oppti, OVESEG, PAA, PADOG,
PathoStat, pcaExplorer, PECA, pepStat, phantasus, phenoTest,
PhosR, polyester, POMA, POWSC, projectR, psichomics, pwrEWAS,
qPLEXanalyzer, qsea, RegEnrich, regsplice, Ringo, RNAinteract,
ROSeq, RTCGAToolbox, RTN, RTopper, satuRn, scClassify, scone,
scran, SEPIRA, seqsetvis, shinyepico, SimBindProfiles,
SingleCellSignalR, singleCellTK, snapCGH, SPsimSeq, STATegRa,
sva, systemPipeR, timecourse, TimeSeriesExperiment, ToxicoGx,
TPP, TPP2D, transcriptogramer, TVTB, tweeDEseq, vsn, weitrix,
Wrench, yamss, yarn, BeadArrayUseCases, DmelSGI,
signatureSearchData, ExpHunterSuite,
ExpressionNormalizationWorkflow, recountWorkflow,
aliases2entrez, BPM, Cascade, cinaR, DCGL, DGEobj.utils,
DiPALM, dsb, GWASbyCluster, immcp, INCATome, lilikoi,
lipidomeR, maGUI, metaMA, mi4p, MiDA, miRtest, MKmisc, MKomics,
nlcv, Patterns, plfMA, RANKS, RPPanalyzer, scBio, scRNAtools,
SQDA, ssizeRNA, statVisual, tinyarray, wrProteo
suggestsMe: ABarray, ADaCGH2, beadarraySNP, biobroom, BiocSet, BioNet,
BioQC, Category, categoryCompare, celaref, CellBench, CellMixS,
ChIPpeakAnno, ClassifyR, CMA, coGPS, CONSTANd, cydar, dearseq,
DEGreport, derfinder, DEScan2, dyebias, easyreporting, fgsea,
fishpond, gage, geva, glmGamPoi, GSRI, GSVA, Harman, Heatplus,
isobar, ivygapSE, les, lumi, MAST, methylumi, MLP, npGSEA,
oligo, oppar, piano, PREDA, proDA, puma, QFeatures,
randRotation, Rcade, recountmethylation, ribosomeProfilingQC,
rtracklayer, stageR, subSeq, SummarizedBenchmark, TCGAbiolinks,
tidybulk, topconfects, tximeta, tximport, ViSEAGO, zFPKM,
BloodCancerMultiOmics2017, GeuvadisTranscriptExpr,
mammaPrintData, msigdb, seventyGeneData, arrays, CAGEWorkflow,
fluentGenomics, simpleSingleCell, AnnoProbe, aroma.affymetrix,
canvasXpress, COCONUT, corncob, dnet, GeoTcgaData, hexbin, LPS,
NACHO, propr, protti, seqgendiff, Seurat, st, wrGraph, wrMisc,
wrTopDownFrag
dependencyCount: 5
Package: limmaGUI
Version: 1.68.0
Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot,
xtable, utils
License: GPL (>=2)
MD5sum: 68aa2226215f50f0561b2930f11acec9
NeedsCompilation: no
Title: GUI for limma Package With Two Color Microarrays
Description: A Graphical User Interface for differential expression
analysis of two-color microarray data using the limma package.
biocViews: GUI, GeneExpression, DifferentialExpression, DataImport,
Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray,
TwoChannel, BatchEffect, MultipleComparison, Normalization,
Preprocessing, QualityControl
Author: James Wettenhall [aut], Gordon Smyth [aut], Keith Satterley
[ctb]
Maintainer: Gordon Smyth <smyth@wehi.edu.au>
URL: http://bioinf.wehi.edu.au/limmaGUI/
git_url: https://git.bioconductor.org/packages/limmaGUI
git_branch: RELEASE_3_13
git_last_commit: 10cfb5f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/limmaGUI_1.68.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/limmaGUI_1.68.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/limmaGUI_1.68.0.tgz
vignettes: vignettes/limmaGUI/inst/doc/extract.pdf,
vignettes/limmaGUI/inst/doc/limmaGUI.pdf,
vignettes/limmaGUI/inst/doc/LinModIntro.pdf,
vignettes/limmaGUI/inst/doc/about.html,
vignettes/limmaGUI/inst/doc/CustMenu.html,
vignettes/limmaGUI/inst/doc/import.html,
vignettes/limmaGUI/inst/doc/index.html,
vignettes/limmaGUI/inst/doc/InputFiles.html,
vignettes/limmaGUI/inst/doc/lgDevel.html,
vignettes/limmaGUI/inst/doc/windowsFocus.html
vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI
Vignette, LinModIntro.pdf, about.html, CustMenu.html,
import.html, index.html, InputFiles.html, lgDevel.html,
windowsFocus.html
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R
dependencyCount: 10
Package: LineagePulse
Version: 1.12.0
Imports: BiocParallel, circlize, compiler, ComplexHeatmap, ggplot2,
gplots, grDevices, grid, knitr, Matrix, methods, RColorBrewer,
SingleCellExperiment, splines, stats, SummarizedExperiment,
utils
License: Artistic-2.0
MD5sum: ef562395cc25d73e646eded8edadeceb
NeedsCompilation: no
Title: Differential expression analysis and model fitting for
single-cell RNA-seq data
Description: LineagePulse is a differential expression and expression
model fitting package tailored to single-cell RNA-seq data
(scRNA-seq). LineagePulse accounts for batch effects, drop-out
and variable sequencing depth. One can use LineagePulse to
perform longitudinal differential expression analysis across
pseudotime as a continuous coordinate or between discrete
groups of cells (e.g. pre-defined clusters or experimental
conditions). Expression model fits can be directly extracted
from LineagePulse.
biocViews: ImmunoOncology, Software, StatisticalMethod, TimeCourse,
Sequencing, DifferentialExpression, GeneExpression,
CellBiology, CellBasedAssays, SingleCell
Author: David S Fischer [aut, cre], Fabian Theis [ctb], Nir Yosef [ctb]
Maintainer: David S Fischer <david.fischer@helmholtz-muenchen.de>
VignetteBuilder: knitr
BugReports: https://github.com/YosefLab/LineagePulse/issues
git_url: https://git.bioconductor.org/packages/LineagePulse
git_branch: RELEASE_3_13
git_last_commit: 6b86f9a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LineagePulse_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LineagePulse_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LineagePulse_1.12.0.tgz
vignettes: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.html
vignetteTitles: LineagePulse
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.R
dependencyCount: 90
Package: LinkHD
Version: 1.6.0
Depends: R(>= 3.6.0), methods, ggplot2, stats
Imports: scales, cluster, graphics, ggpubr, gridExtra, vegan, rio,
MultiAssayExperiment, emmeans, reshape2, data.table
Suggests: MASS (>= 7.3.0), knitr, rmarkdown, BiocStyle
License: GPL-3
Archs: i386, x64
MD5sum: 899a0da7bdeffc1cbd4eecee1378368f
NeedsCompilation: no
Title: LinkHD: a versatile framework to explore and integrate
heterogeneous data
Description: Here we present Link-HD, an approach to integrate
heterogeneous datasets, as a generalization of STATIS-ACT
(“Structuration des Tableaux A Trois Indices de la
Statistique–Analyse Conjointe de Tableauxâ€), a family of
methods to join and compare information from multiple
subspaces. However, STATIS-ACT has some drawbacks since it only
allows continuous data and it is unable to establish
relationships between samples and features. In order to tackle
these constraints, we incorporate multiple distance options and
a linear regression based Biplot model in order to stablish
relationships between observations and variable and perform
variable selection.
biocViews: Classification,MultipleComparison,Regression,Software
Author: Laura M. Zingaretti [aut, cre]
Maintainer: "Laura M Zingaretti" <m.lau.zingaretti@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/LinkHD
git_branch: RELEASE_3_13
git_last_commit: 6b9017a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LinkHD_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LinkHD_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LinkHD_1.6.0.tgz
vignettes: vignettes/LinkHD/inst/doc/LinkHD.html
vignetteTitles: Annotating Genomic Variants
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LinkHD/inst/doc/LinkHD.R
dependencyCount: 129
Package: Linnorm
Version: 2.16.0
Depends: R(>= 3.4)
Imports: Rcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan,
mclust, apcluster, ggplot2, ellipse, limma, utils, statmod,
MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo,
stats, amap, Rtsne, gmodels
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle, knitr, rmarkdown, gplots, RColorBrewer, moments,
testthat
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 4dbbd6b977485f16dba867aff643dfd2
NeedsCompilation: yes
Title: Linear model and normality based normalization and
transformation method (Linnorm)
Description: Linnorm is an algorithm for normalizing and transforming
RNA-seq, single cell RNA-seq, ChIP-seq count data or any large
scale count data. It has been independently reviewed by Tian et
al. on Nature Methods
(https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work
with raw count, CPM, RPKM, FPKM and TPM.
biocViews: ImmunoOncology, Sequencing, ChIPSeq, RNASeq,
DifferentialExpression, GeneExpression, Genetics,
Normalization, Software, Transcription, BatchEffect,
PeakDetection, Clustering, Network, SingleCell
Author: Shun Hang Yip <shunyip@bu.edu>, Panwen Wang
<pwwang@pwwang.com>, Jean-Pierre Kocher
<Kocher.JeanPierre@mayo.edu>, Pak Chung Sham <pcsham@hku.hk>,
Junwen Wang <junwen@uw.edu>
Maintainer: Ken Shun Hang Yip <shunyip@bu.edu>
URL: https://doi.org/10.1093/nar/gkx828
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Linnorm
git_branch: RELEASE_3_13
git_last_commit: 5c45f1e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Linnorm_2.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Linnorm_2.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Linnorm_2.16.0.tgz
vignettes: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.pdf
vignetteTitles: Linnorm User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.R
importsMe: mnem
dependencyCount: 69
Package: lionessR
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: stats, SummarizedExperiment, S4Vectors
Suggests: knitr, rmarkdown, igraph, reshape2, limma,
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 0d212fb66eb4f464498648ce15b07229
NeedsCompilation: no
Title: Modeling networks for individual samples using LIONESS
Description: LIONESS, or Linear Interpolation to Obtain Network
Estimates for Single Samples, can be used to reconstruct
single-sample networks (https://arxiv.org/abs/1505.06440). This
code implements the LIONESS equation in the lioness function in
R to reconstruct single-sample networks. The default network
reconstruction method we use is based on Pearson correlation.
However, lionessR can run on any network reconstruction
algorithms that returns a complete, weighted adjacency matrix.
lionessR works for both unipartite and bipartite networks.
biocViews: Network, NetworkInference, GeneExpression
Author: Marieke Lydia Kuijjer [aut]
(<https://orcid.org/0000-0001-6280-3130>), Ping-Han Hsieh [cre]
(<https://orcid.org/0000-0003-3054-1409>)
Maintainer: Ping-Han Hsieh <dn070017@gmail.com>
URL: https://github.com/mararie/lionessR
VignetteBuilder: knitr
BugReports: https://github.com/mararie/lionessR/issues
git_url: https://git.bioconductor.org/packages/lionessR
git_branch: RELEASE_3_13
git_last_commit: 8d66d42
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/lionessR_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/lionessR_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/lionessR_1.6.0.tgz
vignettes: vignettes/lionessR/inst/doc/lionessR.html
vignetteTitles: lionessR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/lionessR/inst/doc/lionessR.R
dependencyCount: 26
Package: lipidr
Version: 2.6.0
Depends: R (>= 3.6.0), SummarizedExperiment
Imports: methods, stats, utils, data.table, S4Vectors, rlang, dplyr,
tidyr, forcats, ggplot2, limma, fgsea, ropls, imputeLCMD,
magrittr
Suggests: knitr, rmarkdown, BiocStyle, ggrepel, plotly, iheatmapr,
spelling, testthat
License: MIT + file LICENSE
MD5sum: a367017c30d1517c43257e03405c9432
NeedsCompilation: no
Title: Data Mining and Analysis of Lipidomics Datasets
Description: lipidr an easy-to-use R package implementing a complete
workflow for downstream analysis of targeted and untargeted
lipidomics data. lipidomics results can be imported into lipidr
as a numerical matrix or a Skyline export, allowing integration
into current analysis frameworks. Data mining of lipidomics
datasets is enabled through integration with Metabolomics
Workbench API. lipidr allows data inspection, normalization,
univariate and multivariate analysis, displaying informative
visualizations. lipidr also implements a novel Lipid Set
Enrichment Analysis (LSEA), harnessing molecular information
such as lipid class, total chain length and unsaturation.
biocViews: Lipidomics, MassSpectrometry, Normalization, QualityControl,
Visualization
Author: Ahmed Mohamed [cre] (<https://orcid.org/0000-0001-6507-5300>),
Ahmed Mohamed [aut], Jeffrey Molendijk [aut]
Maintainer: Ahmed Mohamed <mohamed@kuicr.kyoto-u.ac.jp>
URL: https://github.com/ahmohamed/lipidr
VignetteBuilder: knitr
BugReports: https://github.com/ahmohamed/lipidr/issues/
git_url: https://git.bioconductor.org/packages/lipidr
git_branch: RELEASE_3_13
git_last_commit: 23f05f8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/lipidr_2.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/lipidr_2.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/lipidr_2.6.0.tgz
vignettes: vignettes/lipidr/inst/doc/workflow.html
vignetteTitles: lipidr_workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/lipidr/inst/doc/workflow.R
dependencyCount: 90
Package: LiquidAssociation
Version: 1.46.0
Depends: geepack, methods, yeastCC, org.Sc.sgd.db
Imports: Biobase, graphics, grDevices, methods, stats
License: GPL (>=3)
MD5sum: 48973f765092fb230ba0a8367019c4ac
NeedsCompilation: no
Title: LiquidAssociation
Description: The package contains functions for calculate direct and
model-based estimators for liquid association. It also provides
functions for testing the existence of liquid association given
a gene triplet data.
biocViews: Pathways, GeneExpression, CellBiology, Genetics, Network,
TimeCourse
Author: Yen-Yi Ho <yho@jhsph.edu>
Maintainer: Yen-Yi Ho <yho@jhsph.edu>
git_url: https://git.bioconductor.org/packages/LiquidAssociation
git_branch: RELEASE_3_13
git_last_commit: 25fba07
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LiquidAssociation_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LiquidAssociation_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LiquidAssociation_1.46.0.tgz
vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf
vignetteTitles: LiquidAssociation Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.R
dependsOnMe: fastLiquidAssociation
dependencyCount: 67
Package: lisaClust
Version: 1.0.0
Depends: R (>= 4.1)
Imports: ggplot2, class, concaveman, grid, BiocParallel, spatstat.core,
spatstat.geom, BiocGenerics, S4Vectors, methods, spicyR, purrr,
stats, data.table, dplyr, tidyr
Suggests: BiocStyle, knitr, rmarkdown
License: GPL (>=2)
MD5sum: ae57322ad7f6f1564526c86ad45b28ca
NeedsCompilation: no
Title: lisaClust: Clustering of Local Indicators of Spatial Association
Description: lisaClust provides a series of functions to identify and
visualise regions of tissue where spatial associations between
cell-types is similar. This package can be used to provide a
high-level summary of cell-type colocalization in multiplexed
imaging data that has been segmented at a single-cell
resolution.
biocViews: SingleCell, CellBasedAssays
Author: Ellis Patrick [aut, cre], Nicolas Canete [aut]
Maintainer: Ellis Patrick <ellis.patrick@sydney.edu.au>
VignetteBuilder: knitr
BugReports: https://github.com/ellispatrick/lisaClust/issues
git_url: https://git.bioconductor.org/packages/lisaClust
git_branch: RELEASE_3_13
git_last_commit: 1e46ec3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/lisaClust_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/lisaClust_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/lisaClust_1.0.0.tgz
vignettes: vignettes/lisaClust/inst/doc/lisaClust.html
vignetteTitles: "Inroduction to lisaClust"
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lisaClust/inst/doc/lisaClust.R
dependencyCount: 93
Package: lmdme
Version: 1.34.0
Depends: R (>= 2.14.1), pls, stemHypoxia
Imports: stats, methods, limma
Enhances: parallel
License: GPL (>=2)
Archs: i386, x64
MD5sum: 7bee40f44fbf72aa7e65204b5a2a71ab
NeedsCompilation: no
Title: Linear Model decomposition for Designed Multivariate Experiments
Description: linear ANOVA decomposition of Multivariate Designed
Experiments implementation based on limma lmFit. Features:
i)Flexible formula type interface, ii) Fast limma based
implementation, iii) p-values for each estimated coefficient
levels in each factor, iv) F values for factor effects and v)
plotting functions for PCA and PLS.
biocViews: Microarray, OneChannel, TwoChannel, Visualization,
DifferentialExpression, ExperimentData, Cancer
Author: Cristobal Fresno and Elmer A. Fernandez
Maintainer: Cristobal Fresno <cfresno@bdmg.com.ar>
URL: http://www.bdmg.com.ar/?page_id=38
git_url: https://git.bioconductor.org/packages/lmdme
git_branch: RELEASE_3_13
git_last_commit: d60d48d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/lmdme_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/lmdme_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/lmdme_1.34.0.tgz
vignettes: vignettes/lmdme/inst/doc/lmdme-vignette.pdf
vignetteTitles: lmdme: linear model framework for PCA/PLS analysis of
ANOVA decomposition on Designed Multivariate Experiments in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lmdme/inst/doc/lmdme-vignette.R
dependencyCount: 8
Package: LOBSTAHS
Version: 1.18.1
Depends: R (>= 3.4), xcms, CAMERA, methods
Imports: utils
Suggests: PtH2O2lipids, knitr, rmarkdown
License: GPL (>= 3) + file LICENSE
MD5sum: 24e6a6a5b2516c69064a279f9bc3e034
NeedsCompilation: no
Title: Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy
Sequences
Description: LOBSTAHS is a multifunction package for screening,
annotation, and putative identification of mass spectral
features in large, HPLC-MS lipid datasets. In silico data for a
wide range of lipids, oxidized lipids, and oxylipins can be
generated from user-supplied structural criteria with a
database generation function. LOBSTAHS then applies these
databases to assign putative compound identities to features in
any high-mass accuracy dataset that has been processed using
xcms and CAMERA. Users can then apply a series of orthogonal
screening criteria based on adduct ion formation patterns,
chromatographic retention time, and other properties, to
evaluate and assign confidence scores to this list of
preliminary assignments. During the screening routine, LOBSTAHS
rejects assignments that do not meet the specified criteria,
identifies potential isomers and isobars, and assigns a variety
of annotation codes to assist the user in evaluating the
accuracy of each assignment.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Lipidomics,
DataImport
Author: James Collins [aut, cre], Helen Fredricks [aut], Bethanie
Edwards [aut], Henry Holm [aut], Benjamin Van Mooy [aut],
Daniel Lowenstein [aut]
Maintainer: James Collins <james.r.collins@aya.yale.edu>, Henry Holm
<hholm@whoi.edu>, Daniel Lowenstein <dlowenstein@whoi.edu>
URL: http://bioconductor.org/packages/LOBSTAHS
VignetteBuilder: knitr
BugReports: https://github.com/vanmooylipidomics/LOBSTAHS/issues/new
git_url: https://git.bioconductor.org/packages/LOBSTAHS
git_branch: RELEASE_3_13
git_last_commit: fec93aa
git_last_commit_date: 2021-08-30
Date/Publication: 2021-08-31
source.ver: src/contrib/LOBSTAHS_1.18.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LOBSTAHS_1.18.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/LOBSTAHS_1.18.1.tgz
vignettes: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.html
vignetteTitles: Discovery,, Identification,, and Screening of Lipids
and Oxylipins in HPLC-MS Datasets Using LOBSTAHS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.R
dependsOnMe: PtH2O2lipids
dependencyCount: 126
Package: loci2path
Version: 1.12.0
Depends: R (>= 3.4)
Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods,
grDevices, stats, graphics, GenomicRanges, BiocParallel,
S4Vectors
Suggests: BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: b03605fb5d9ae697a1f03541ed414f82
NeedsCompilation: no
Title: Loci2path: regulatory annotation of genomic intervals based on
tissue-specific expression QTLs
Description: loci2path performs statistics-rigorous enrichment analysis
of eQTLs in genomic regions of interest. Using eQTL collections
provided by the Genotype-Tissue Expression (GTEx) project and
pathway collections from MSigDB.
biocViews: FunctionalGenomics, Genetics, GeneSetEnrichment, Software,
GeneExpression, Sequencing, Coverage, BioCarta
Author: Tianlei Xu
Maintainer: Tianlei Xu <tianlei.xu@emory.edu>
URL: https://github.com/StanleyXu/loci2path
VignetteBuilder: knitr
BugReports: https://github.com/StanleyXu/loci2path/issues
git_url: https://git.bioconductor.org/packages/loci2path
git_branch: RELEASE_3_13
git_last_commit: fd21ec3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/loci2path_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/loci2path_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/loci2path_1.12.0.tgz
vignettes: vignettes/loci2path/inst/doc/loci2path-vignette.html
vignetteTitles: loci2path
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/loci2path/inst/doc/loci2path-vignette.R
dependencyCount: 42
Package: logicFS
Version: 2.12.0
Depends: LogicReg, mcbiopi, survival
Imports: graphics, methods, stats
Suggests: genefilter, siggenes
License: LGPL (>= 2)
MD5sum: dc71a515f80972b58923cfc81ee25740
NeedsCompilation: no
Title: Identification of SNP Interactions
Description: Identification of interactions between binary variables
using Logic Regression. Can, e.g., be used to find interesting
SNP interactions. Contains also a bagging version of logic
regression for classification.
biocViews: SNP, Classification, Genetics
Author: Holger Schwender, Tobias Tietz
Maintainer: Holger Schwender <holger.schw@gmx.de>
git_url: https://git.bioconductor.org/packages/logicFS
git_branch: RELEASE_3_13
git_last_commit: fe75c2c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/logicFS_2.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/logicFS_2.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/logicFS_2.12.0.tgz
vignettes: vignettes/logicFS/inst/doc/logicFS.pdf
vignetteTitles: logicFS Manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/logicFS/inst/doc/logicFS.R
suggestsMe: trio
dependencyCount: 12
Package: logitT
Version: 1.50.0
Depends: affy
Suggests: SpikeInSubset
License: GPL (>= 2)
MD5sum: 6e5759e3267017d64e00aea28f336202
NeedsCompilation: yes
Title: logit-t Package
Description: The logitT library implements the Logit-t algorithm
introduced in --A high performance test of differential gene
expression for oligonucleotide arrays-- by William J Lemon,
Sandya Liyanarachchi and Ming You for use with Affymetrix data
stored in an AffyBatch object in R.
biocViews: Microarray, DifferentialExpression
Author: Tobias Guennel <tguennel@vcu.edu>
Maintainer: Tobias Guennel <tguennel@vcu.edu>
URL: http://www.bioconductor.org
git_url: https://git.bioconductor.org/packages/logitT
git_branch: RELEASE_3_13
git_last_commit: aab4a92
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/logitT_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/logitT_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/logitT_1.50.0.tgz
vignettes: vignettes/logitT/inst/doc/logitT.pdf
vignetteTitles: logitT primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/logitT/inst/doc/logitT.R
dependencyCount: 13
Package: LOLA
Version: 1.22.0
Depends: R (>= 2.10)
Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table,
reshape2, utils, stats, methods
Suggests: parallel, testthat, knitr, BiocStyle, rmarkdown
Enhances: simpleCache, qvalue, ggplot2
License: GPL-3
MD5sum: b8434ae6ceac7cab4a650d51c1560ef7
NeedsCompilation: no
Title: Locus overlap analysis for enrichment of genomic ranges
Description: Provides functions for testing overlap of sets of genomic
regions with public and custom region set (genomic ranges)
databases. This makes it possible to do automated enrichment
analysis for genomic region sets, thus facilitating
interpretation of functional genomics and epigenomics data.
biocViews: GeneSetEnrichment, GeneRegulation, GenomeAnnotation,
SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq,
Sequencing
Author: Nathan Sheffield <http://www.databio.org> [aut, cre], Christoph
Bock [ctb]
Maintainer: Nathan Sheffield <nathan@code.databio.org>
URL: http://code.databio.org/LOLA
VignetteBuilder: knitr
BugReports: http://github.com/nsheff/LOLA
git_url: https://git.bioconductor.org/packages/LOLA
git_branch: RELEASE_3_13
git_last_commit: 43df70e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LOLA_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LOLA_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LOLA_1.22.0.tgz
vignettes: vignettes/LOLA/inst/doc/choosingUniverse.html,
vignettes/LOLA/inst/doc/gettingStarted.html,
vignettes/LOLA/inst/doc/usingLOLACore.html
vignetteTitles: 3. Choosing a Universe, 1. Getting Started with LOLA,
2. Using LOLA Core
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LOLA/inst/doc/choosingUniverse.R,
vignettes/LOLA/inst/doc/gettingStarted.R,
vignettes/LOLA/inst/doc/usingLOLACore.R
suggestsMe: COCOA, DeepBlueR, MAGAR, MIRA, ramr
dependencyCount: 25
Package: LoomExperiment
Version: 1.10.1
Depends: R (>= 3.5.0), S4Vectors, SingleCellExperiment,
SummarizedExperiment, methods, rhdf5, BiocIO
Imports: DelayedArray, GenomicRanges, HDF5Array, Matrix, stats,
stringr, utils
Suggests: testthat, BiocStyle, knitr, rmarkdown, reticulate
License: Artistic-2.0
MD5sum: 2e44f91eb5563b841cf39bfab0fd7faf
NeedsCompilation: no
Title: LoomExperiment container
Description: The LoomExperiment package provide a means to easily
convert the Bioconductor "Experiment" classes to loom files and
vice versa.
biocViews: ImmunoOncology, DataRepresentation, DataImport,
Infrastructure, SingleCell
Author: Martin Morgan, Daniel Van Twisk
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/LoomExperiment
git_branch: RELEASE_3_13
git_last_commit: 347a524
git_last_commit_date: 2021-05-23
Date/Publication: 2021-05-23
source.ver: src/contrib/LoomExperiment_1.10.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LoomExperiment_1.10.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/LoomExperiment_1.10.1.tgz
vignettes: vignettes/LoomExperiment/inst/doc/LoomExperiment.html
vignetteTitles: An introduction to the LoomExperiment class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LoomExperiment/inst/doc/LoomExperiment.R
dependsOnMe: OSCA.intro
suggestsMe: hca
dependencyCount: 36
Package: LowMACA
Version: 1.22.0
Depends: R (>= 2.10)
Imports: cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer,
methods, LowMACAAnnotation, BiocParallel, motifStack,
Biostrings, httr, grid, gridBase
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 12d975a93155673c6dd7b71ce1b1e937
NeedsCompilation: no
Title: LowMACA - Low frequency Mutation Analysis via Consensus
Alignment
Description: The LowMACA package is a simple suite of tools to
investigate and analyze the mutation profile of several
proteins or pfam domains via consensus alignment. You can
conduct an hypothesis driven exploratory analysis using our
package simply providing a set of genes or pfam domains of your
interest.
biocViews: SomaticMutation, SequenceMatching, WholeGenome, Sequencing,
Alignment, DataImport, MultipleSequenceAlignment
Author: Stefano de Pretis , Giorgio Melloni
Maintainer: Stefano de Pretis <ste.depo@gmail.com>, Giorgio Melloni
<melloni.giorgio@gmail.com>
SystemRequirements: clustalo, gs, perl
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/LowMACA
git_branch: RELEASE_3_13
git_last_commit: b9b4a18
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LowMACA_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LowMACA_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LowMACA_1.22.0.tgz
vignettes: vignettes/LowMACA/inst/doc/LowMACA.html
vignetteTitles: Bioconductor style for HTML documents
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LowMACA/inst/doc/LowMACA.R
dependencyCount: 86
Package: LPE
Version: 1.66.0
Depends: R (>= 2.10)
Imports: stats
License: LGPL
MD5sum: f568b92837a583382797fcbfbfe90b87
NeedsCompilation: no
Title: Methods for analyzing microarray data using Local Pooled Error
(LPE) method
Description: This LPE library is used to do significance analysis of
microarray data with small number of replicates. It uses
resampling based FDR adjustment, and gives less conservative
results than traditional 'BH' or 'BY' procedures. Data accepted
is raw data in txt format from MAS4, MAS5 or dChip. Data can
also be supplied after normalization. LPE library is primarily
used for analyzing data between two conditions. To use it for
paired data, see LPEP library. For using LPE in multiple
conditions, use HEM library.
biocViews: Microarray, DifferentialExpression
Author: Nitin Jain <emailnitinjain@gmail.com>, Michael O'Connell
<michaelo@warath.com>, Jae K. Lee <jaeklee@virginia.edu>.
Includes R source code contributed by HyungJun Cho
<hcho@virginia.edu>
Maintainer: Nitin Jain <emailnitinjain@gmail.com>
URL: http://www.r-project.org,
http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/,
http://sourceforge.net/projects/r-lpe/
git_url: https://git.bioconductor.org/packages/LPE
git_branch: RELEASE_3_13
git_last_commit: a178516
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LPE_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LPE_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LPE_1.66.0.tgz
vignettes: vignettes/LPE/inst/doc/LPE.pdf
vignetteTitles: LPE test for microarray data with small number of
replicates
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LPE/inst/doc/LPE.R
dependsOnMe: LPEadj, PLPE
importsMe: LPEadj
suggestsMe: ABarray
dependencyCount: 1
Package: LPEadj
Version: 1.52.0
Depends: LPE
Imports: LPE, stats
License: LGPL
Archs: i386, x64
MD5sum: 7528e3f8ac3ca551bd08a5c1e6cbe555
NeedsCompilation: no
Title: A correction of the local pooled error (LPE) method to replace
the asymptotic variance adjustment with an unbiased adjustment
based on sample size.
Description: Two options are added to the LPE algorithm. The original
LPE method sets all variances below the max variance in the
ordered distribution of variances to the maximum variance. in
LPEadj this option is turned off by default. The second option
is to use a variance adjustment based on sample size rather
than pi/2. By default the LPEadj uses the sample size based
variance adjustment.
biocViews: Microarray, Proteomics
Author: Carl Murie <carl.murie@mcgill.ca>, Robert Nadon
<robert.nadon@mcgill.ca>
Maintainer: Carl Murie <carl.murie@mcgill.ca>
git_url: https://git.bioconductor.org/packages/LPEadj
git_branch: RELEASE_3_13
git_last_commit: 40e7947
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LPEadj_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LPEadj_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LPEadj_1.52.0.tgz
vignettes: vignettes/LPEadj/inst/doc/LPEadj.pdf
vignetteTitles: LPEadj test for microarray data with small number of
replicates
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LPEadj/inst/doc/LPEadj.R
dependencyCount: 2
Package: lpNet
Version: 2.24.0
Depends: lpSolve
License: Artistic License 2.0
MD5sum: 91b78e5825100a7730ec40f5d9f8adeb
NeedsCompilation: no
Title: Linear Programming Model for Network Inference
Description: lpNet aims at infering biological networks, in particular
signaling and gene networks. For that it takes perturbation
data, either steady-state or time-series, as input and
generates an LP model which allows the inference of signaling
networks. For parameter identification either leave-one-out
cross-validation or stratified n-fold cross-validation can be
used.
biocViews: NetworkInference
Author: Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali
Maintainer: Lars Kaderali <lars.kaderali@uni-greifswald.de>
git_url: https://git.bioconductor.org/packages/lpNet
git_branch: RELEASE_3_13
git_last_commit: 3348f9c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/lpNet_2.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/lpNet_2.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/lpNet_2.24.0.tgz
vignettes: vignettes/lpNet/inst/doc/vignette_lpNet.pdf
vignetteTitles: lpNet,, network inference with a linear optimization
program.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lpNet/inst/doc/vignette_lpNet.R
dependencyCount: 1
Package: lpsymphony
Version: 1.20.0
Depends: R (>= 3.0.0)
Suggests: BiocStyle, knitr, testthat
Enhances: slam
License: EPL
MD5sum: 7cedbc58491619948f68295bf0839a64
NeedsCompilation: yes
Title: Symphony integer linear programming solver in R
Description: This package was derived from Rsymphony_0.1-17 from CRAN.
These packages provide an R interface to SYMPHONY, an
open-source linear programming solver written in C++. The main
difference between this package and Rsymphony is that it
includes the solver source code (SYMPHONY version 5.6), while
Rsymphony expects to find header and library files on the
users' system. Thus the intention of lpsymphony is to provide
an easy to install interface to SYMPHONY. For Windows,
precompiled DLLs are included in this package.
biocViews: Infrastructure, ThirdPartyClient
Author: Vladislav Kim [aut, cre], Ted Ralphs [ctb], Menal Guzelsoy
[ctb], Ashutosh Mahajan [ctb], Reinhard Harter [ctb], Kurt
Hornik [ctb], Cyrille Szymanski [ctb], Stefan Theussl [ctb]
Maintainer: Vladislav Kim <Vladislav.Kim@embl.de>
URL: http://R-Forge.R-project.org/projects/rsymphony,
https://projects.coin-or.org/SYMPHONY,
http://www.coin-or.org/download/source/SYMPHONY/
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/lpsymphony
git_branch: RELEASE_3_13
git_last_commit: 5bb6274
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/lpsymphony_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/lpsymphony_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/lpsymphony_1.20.0.tgz
vignettes: vignettes/lpsymphony/inst/doc/lpsymphony.pdf
vignetteTitles: Introduction to lpsymphony
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lpsymphony/inst/doc/lpsymphony.R
importsMe: IHW, Maaslin2
suggestsMe: oppr, TestDesign
dependencyCount: 0
Package: LRBaseDbi
Version: 2.2.0
Depends: R (>= 3.5.0)
Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase
Suggests: RUnit, BiocGenerics, BiocStyle
License: Artistic-2.0
MD5sum: a18c4e42401e05e062944f522c6cfd9e
NeedsCompilation: no
Title: DBI to construct LRBase-related package
Description: Interface to construct LRBase package (LRBase.XXX.eg.db).
biocViews: Infrastructure
Author: Koki Tsuyuzaki
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
VignetteBuilder: utils
git_url: https://git.bioconductor.org/packages/LRBaseDbi
git_branch: RELEASE_3_13
git_last_commit: 4c1b2fd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LRBaseDbi_2.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LRBaseDbi_2.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LRBaseDbi_2.2.0.tgz
vignettes: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.pdf
vignetteTitles: LRBaseDbi
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.R
dependsOnMe: LRBase.Ath.eg.db, LRBase.Bta.eg.db, LRBase.Cel.eg.db,
LRBase.Dme.eg.db, LRBase.Dre.eg.db, LRBase.Gga.eg.db,
LRBase.Hsa.eg.db, LRBase.Mmu.eg.db, LRBase.Pab.eg.db,
LRBase.Rno.eg.db, LRBase.Ssc.eg.db, LRBase.Xtr.eg.db
suggestsMe: scTensor
dependencyCount: 46
Package: LRcell
Version: 1.0.0
Depends: R (>= 4.1), ExperimentHub, AnnotationHub
Imports: BiocParallel, dplyr, ggplot2, ggrepel, magrittr, stats, utils
Suggests: LRcellTypeMarkers, BiocStyle, knitr, rmarkdown, roxygen2,
testthat
License: MIT + file LICENSE
MD5sum: f6bcada9d194b822c40df5734259181f
NeedsCompilation: no
Title: Differential cell type change analysis using Logistic/linear
Regression
Description: The goal of LRcell is to identify specific sub-cell types
that drives the changes observed in a bulk RNA-seq differential
gene expression experiment. To achieve this, LRcell utilizes
sets of cell marker genes acquired from single-cell
RNA-sequencing (scRNA-seq) as indicators for various cell types
in the tissue of interest. Next, for each cell type, using its
marker genes as indicators, we apply Logistic Regression on the
complete set of genes with differential expression p-values to
calculate a cell-type significance p-value. Finally, these
p-values are compared to predict which one(s) are likely to be
responsible for the differential gene expression pattern
observed in the bulk RNA-seq experiments. LRcell is inspired by
the LRpath[@sartor2009lrpath] algorithm developed by Sartor et
al., originally designed for pathway/gene set enrichment
analysis. LRcell contains three major components: LRcell
analysis, plot generation and marker gene selection. All
modules in this package are written in R. This package also
provides marker genes in the Prefrontal Cortex (pFC) human
brain region, human PBMC and nine mouse brain regions (Frontal
Cortex, Cerebellum, Globus Pallidus, Hippocampus,
Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra
and Thalamus).
biocViews: SingleCell, GeneSetEnrichment, Sequencing, Regression,
GeneExpression, DifferentialExpression
Author: Wenjing Ma [cre, aut] (<https://orcid.org/0000-0001-8757-651X>)
Maintainer: Wenjing Ma <wenjing.ma@emory.edu>
VignetteBuilder: knitr
BugReports: https://github.com/marvinquiet/LRcell/issues
git_url: https://git.bioconductor.org/packages/LRcell
git_branch: RELEASE_3_13
git_last_commit: 85370d4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LRcell_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LRcell_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LRcell_1.0.0.tgz
vignettes: vignettes/LRcell/inst/doc/LRcell-vignette.html
vignetteTitles: LRcell Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/LRcell/inst/doc/LRcell-vignette.R
suggestsMe: LRcellTypeMarkers
dependencyCount: 113
Package: lumi
Version: 2.44.0
Depends: R (>= 2.10), Biobase (>= 2.5.5)
Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures,
GenomicRanges, annotate, lattice, mgcv (>= 1.4-0), nleqslv,
KernSmooth, preprocessCore, RSQLite, DBI, AnnotationDbi, MASS,
graphics, stats, stats4, methods
Suggests: beadarray, limma, vsn, lumiBarnes, lumiHumanAll.db,
lumiHumanIDMapping, genefilter, RColorBrewer
License: LGPL (>= 2)
MD5sum: 5d68a531ac7ec09e7fea4af5ff7aad0b
NeedsCompilation: no
Title: BeadArray Specific Methods for Illumina Methylation and
Expression Microarrays
Description: The lumi package provides an integrated solution for the
Illumina microarray data analysis. It includes functions of
Illumina BeadStudio (GenomeStudio) data input, quality control,
BeadArray-specific variance stabilization, normalization and
gene annotation at the probe level. It also includes the
functions of processing Illumina methylation microarrays,
especially Illumina Infinium methylation microarrays.
biocViews: Microarray, OneChannel, Preprocessing, DNAMethylation,
QualityControl, TwoChannel
Author: Pan Du, Richard Bourgon, Gang Feng, Simon Lin
Maintainer: Lei Huang <lhuang1998@gmail.com>
git_url: https://git.bioconductor.org/packages/lumi
git_branch: RELEASE_3_13
git_last_commit: 9783629
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/lumi_2.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/lumi_2.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/lumi_2.44.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: iCheck, wateRmelon, lumiHumanIDMapping,
lumiMouseIDMapping, lumiRatIDMapping, ffpeExampleData,
lumiBarnes, MAQCsubset, MAQCsubsetILM, mvoutData
importsMe: arrayMvout, ffpe, methyAnalysis, MineICA
suggestsMe: beadarray, blima, Harman, methylumi, tigre,
beadarrayFilter, maGUI
dependencyCount: 159
Package: LymphoSeq
Version: 1.20.0
Depends: R (>= 3.3), LymphoSeqDB
Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq,
RColorBrewer, circlize, grid, utils, stats, ggtree, msa,
Biostrings, phangorn, stringdist, UpSetR
Suggests: knitr, pheatmap, wordcloud, rmarkdown
License: Artistic-2.0
MD5sum: 6af4ed60853166e1fa6974d58730a5e8
NeedsCompilation: no
Title: Analyze high-throughput sequencing of T and B cell receptors
Description: This R package analyzes high-throughput sequencing of T
and B cell receptor complementarity determining region 3 (CDR3)
sequences generated by Adaptive Biotechnologies' ImmunoSEQ
assay. Its input comes from tab-separated value (.tsv) files
exported from the ImmunoSEQ analyzer.
biocViews: Software, Technology, Sequencing, TargetedResequencing,
Alignment, MultipleSequenceAlignment
Author: David Coffey <dcoffey@fredhutch.org>
Maintainer: David Coffey <dcoffey@fredhutch.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/LymphoSeq
git_branch: RELEASE_3_13
git_last_commit: 672762d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/LymphoSeq_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/LymphoSeq_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/LymphoSeq_1.20.0.tgz
vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.html
vignetteTitles: Analysis of high-throughput sequencing of T and B cell
receptors with LymphoSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LymphoSeq/inst/doc/LymphoSeq.R
dependencyCount: 91
Package: M3C
Version: 1.14.0
Depends: R (>= 3.5.0)
Imports: ggplot2, Matrix, doSNOW, cluster, parallel, foreach,
doParallel, matrixcalc, Rtsne, corpcor, umap
Suggests: knitr, rmarkdown
License: AGPL-3
Archs: i386, x64
MD5sum: ed459b5f5bf0ba90ffa3a72ad9da84d6
NeedsCompilation: no
Title: Monte Carlo Reference-based Consensus Clustering
Description: M3C is a consensus clustering algorithm that uses a Monte
Carlo simulation to eliminate overestimation of K and can
reject the null hypothesis K=1.
biocViews: Clustering, GeneExpression, Transcription, RNASeq,
Sequencing, ImmunoOncology
Author: Christopher John, David Watson
Maintainer: Christopher John <chris.r.john86@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/M3C
git_branch: RELEASE_3_13
git_last_commit: 9875790
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/M3C_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/M3C_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/M3C_1.14.0.tgz
vignettes: vignettes/M3C/inst/doc/M3Cvignette.pdf
vignetteTitles: M3C
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/M3C/inst/doc/M3Cvignette.R
importsMe: lilikoi
suggestsMe: parameters
dependencyCount: 62
Package: M3Drop
Version: 1.18.0
Depends: R (>= 3.4), numDeriv
Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics,
stats, matrixStats, Matrix, irlba, reldist, Hmisc, methods
Suggests: ROCR, knitr, M3DExampleData, scater, SingleCellExperiment,
monocle, Seurat, Biobase
License: GPL (>=2)
MD5sum: 9646f045cdaf547a7dbbbc1e6cab2f09
NeedsCompilation: no
Title: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq
Description: This package fits a Michaelis-Menten model to the pattern
of dropouts in single-cell RNASeq data. This model is used as a
null to identify significantly variable (i.e. differentially
expressed) genes for use in downstream analysis, such as
clustering cells.
biocViews: RNASeq, Sequencing, Transcriptomics, GeneExpression,
Software, DifferentialExpression, DimensionReduction,
FeatureExtraction
Author: Tallulah Andrews <tallulandrews@gmail.com>
Maintainer: Tallulah Andrews <tallulandrews@gmail.com>
URL: https://github.com/tallulandrews/M3Drop
VignetteBuilder: knitr
BugReports: https://github.com/tallulandrews/M3Drop/issues
git_url: https://git.bioconductor.org/packages/M3Drop
git_branch: RELEASE_3_13
git_last_commit: e393b5a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/M3Drop_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/M3Drop_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/M3Drop_1.18.0.tgz
vignettes: vignettes/M3Drop/inst/doc/M3Drop_Vignette.pdf
vignetteTitles: Introduction to M3Drop
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/M3Drop/inst/doc/M3Drop_Vignette.R
importsMe: scMerge
dependencyCount: 82
Package: maanova
Version: 1.62.0
Depends: R (>= 2.10)
Imports: Biobase, graphics, grDevices, methods, stats, utils
Suggests: qvalue, snow
Enhances: Rmpi
License: GPL (>= 2)
MD5sum: 9acfea7c57464f8f81eecb26be47f9b1
NeedsCompilation: yes
Title: Tools for analyzing Micro Array experiments
Description: Analysis of N-dye Micro Array experiment using mixed model
effect. Containing analysis of variance, permutation and
bootstrap, cluster and consensus tree.
biocViews: Microarray, DifferentialExpression, Clustering
Author: Hao Wu, modified by Hyuna Yang and Keith Sheppard with ideas
from Gary Churchill, Katie Kerr and Xiangqin Cui.
Maintainer: Keith Sheppard <keith.sheppard@jax.org>
URL: http://research.jax.org/faculty/churchill
git_url: https://git.bioconductor.org/packages/maanova
git_branch: RELEASE_3_13
git_last_commit: 72e45cb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/maanova_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/maanova_1.62.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/maanova_1.62.0.tgz
vignettes: vignettes/maanova/inst/doc/maanova.pdf
vignetteTitles: R/maanova HowTo
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 8
Package: Maaslin2
Version: 1.6.0
Depends: R (>= 3.6)
Imports: robustbase, biglm, pcaPP, edgeR, metagenomeSeq, lpsymphony,
pbapply, car, dplyr, vegan, chemometrics, ggplot2, pheatmap,
logging, data.table, lmerTest, hash, optparse, MuMIn,
grDevices, stats, utils, glmmTMB, MASS, cplm, pscl
Suggests: knitr, testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: df4204c6932adc994e8f43875c327af7
NeedsCompilation: no
Title: "Multivariable Association Discovery in Population-scale
Meta-omics Studies"
Description: MaAsLin2 is comprehensive R package for efficiently
determining multivariable association between clinical metadata
and microbial meta'omic features. MaAsLin2 relies on general
linear models to accommodate most modern epidemiological study
designs, including cross-sectional and longitudinal, and offers
a variety of data exploration, normalization, and
transformation methods. MaAsLin2 is the next generation of
MaAsLin.
biocViews: Metagenomics, Software, Microbiome, Normalization
Author: Himel Mallick [aut], Ali Rahnavard [aut], Lauren McIver [aut,
cre]
Maintainer: Lauren McIver <lauren.j.mciver@gmail.com>
URL: http://huttenhower.sph.harvard.edu/maaslin2
VignetteBuilder: knitr
BugReports: https://github.com/biobakery/maaslin2/issues
git_url: https://git.bioconductor.org/packages/Maaslin2
git_branch: RELEASE_3_13
git_last_commit: 9daea4b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Maaslin2_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Maaslin2_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Maaslin2_1.6.0.tgz
vignettes: vignettes/Maaslin2/inst/doc/maaslin2.html
vignetteTitles: MaAsLin2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Maaslin2/inst/doc/maaslin2.R
importsMe: MMUPHin
dependencyCount: 151
Package: macat
Version: 1.66.0
Depends: Biobase, annotate
Suggests: hgu95av2.db, stjudem
License: Artistic-2.0
MD5sum: 76d558b28ab96ebcf510de2188f80071
NeedsCompilation: no
Title: MicroArray Chromosome Analysis Tool
Description: This library contains functions to investigate links
between differential gene expression and the chromosomal
localization of the genes. MACAT is motivated by the common
observation of phenomena involving large chromosomal regions in
tumor cells. MACAT is the implementation of a statistical
approach for identifying significantly differentially expressed
chromosome regions. The functions have been tested on a
publicly available data set about acute lymphoblastic leukemia
(Yeoh et al.Cancer Cell 2002), which is provided in the library
'stjudem'.
biocViews: Microarray, DifferentialExpression, Visualization
Author: Benjamin Georgi, Matthias Heinig, Stefan Roepcke, Sebastian
Schmeier, Joern Toedling
Maintainer: Joern Toedling <jtoedling@yahoo.de>
git_url: https://git.bioconductor.org/packages/macat
git_branch: RELEASE_3_13
git_last_commit: 0b67287
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/macat_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/macat_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/macat_1.66.0.tgz
vignettes: vignettes/macat/inst/doc/macat.pdf
vignetteTitles: MicroArray Chromosome Analysis Tool
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/macat/inst/doc/macat.R
dependencyCount: 49
Package: maCorrPlot
Version: 1.62.0
Depends: lattice
Imports: graphics, grDevices, lattice, stats
License: GPL (>= 2)
MD5sum: e87058aad19a4e9e294fa4aaf1d3e709
NeedsCompilation: no
Title: Visualize artificial correlation in microarray data
Description: Graphically displays correlation in microarray data that
is due to insufficient normalization
biocViews: Microarray, Preprocessing, Visualization
Author: Alexander Ploner <Alexander.Ploner@ki.se>
Maintainer: Alexander Ploner <Alexander.Ploner@ki.se>
URL:
http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=15799785
git_url: https://git.bioconductor.org/packages/maCorrPlot
git_branch: RELEASE_3_13
git_last_commit: eca52bb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/maCorrPlot_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/maCorrPlot_1.62.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/maCorrPlot_1.62.0.tgz
vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf
vignetteTitles: maCorrPlot Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/maCorrPlot/inst/doc/maCorrPlot.R
dependencyCount: 6
Package: MACPET
Version: 1.12.0
Depends: R (>= 3.6.1), InteractionSet (>= 1.13.0), bigmemory (>=
4.5.33), BH (>= 1.66.0.1), Rcpp (>= 1.0.1)
Imports: intervals (>= 0.15.1), plyr (>= 1.8.4), Rsamtools (>= 2.1.3),
stats (>= 3.6.1), utils (>= 3.6.1), methods (>= 3.6.1),
GenomicRanges (>= 1.37.14), S4Vectors (>= 0.23.17), IRanges (>=
2.19.10), GenomeInfoDb (>= 1.21.1), gtools (>= 3.8.1),
GenomicAlignments (>= 1.21.4), knitr (>= 1.23), rtracklayer (>=
1.45.1), BiocParallel (>= 1.19.0), Rbowtie (>= 1.25.0),
GEOquery (>= 2.53.0), Biostrings (>= 2.53.2), ShortRead (>=
1.43.0), futile.logger (>= 1.4.3)
LinkingTo: Rcpp, bigmemory, BH
Suggests: ggplot2 (>= 3.2.0), igraph (>= 1.2.4.1), rmarkdown (>= 1.14),
reshape2 (>= 1.4.3), BiocStyle (>= 2.13.2)
License: GPL-3
MD5sum: 39ca5cf6b0bde68b14dd1cfb7515c8ff
NeedsCompilation: yes
Title: Model based analysis for paired-end data
Description: The MACPET package can be used for complete interaction
analysis for ChIA-PET data. MACPET reads ChIA-PET data in BAM
or SAM format and separates the data into Self-ligated, Intra-
and Inter-chromosomal PETs. Furthermore, MACPET breaks the
genome into regions and applies 2D mixture models for
identifying candidate peaks/binding sites using skewed
generalized students-t distributions (SGT). It then uses a
local poisson model for finding significant binding sites.
Finally it runs an additive interaction-analysis model for
calling for significant interactions between those peaks.
MACPET is mainly written in C++, and it also supports the
BiocParallel package.
biocViews: Software, DNA3DStructure, PeakDetection, StatisticalMethod,
Clustering, Classification, HiC
Author: Ioannis Vardaxis
Maintainer: Ioannis Vardaxis <iova89@hotmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MACPET
git_branch: RELEASE_3_13
git_last_commit: 2e34842
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MACPET_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MACPET_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MACPET_1.12.0.tgz
vignettes: vignettes/MACPET/inst/doc/MACPET.pdf
vignetteTitles: MACPET
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MACPET/inst/doc/MACPET.R
dependencyCount: 103
Package: MACSQuantifyR
Version: 1.6.0
Imports: readxl, graphics, tools, utils, grDevices, ggplot2, ggrepel,
methods, stats, latticeExtra, lattice, rmarkdown, png, grid,
gridExtra, prettydoc, rvest, xml2
Suggests: knitr, testthat, R.utils, spelling
License: Artistic-2.0
Archs: i386, x64
MD5sum: 22609fe4e7a8f2804bd8751d50348005
NeedsCompilation: no
Title: Fast treatment of MACSQuantify FACS data
Description: Automatically process the metadata of MACSQuantify FACS
sorter. It runs multiple modules: i) imports of raw file and
graphical selection of duplicates in well plate, ii) computes
statistics on data and iii) can compute combination index.
biocViews: DataImport, Preprocessing, Normalization, FlowCytometry,
DataRepresentation, GUI
Author: Raphaël Bonnet [aut, cre], Marielle Nebout [dtc],Giulia
Biondani [dtc], Jean-François Peyron[aut,ths], Inserm [fnd]
Maintainer: Raphaël Bonnet <raphael.bonnet@univ-cotedazur.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MACSQuantifyR
git_branch: RELEASE_3_13
git_last_commit: 2958ed8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MACSQuantifyR_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MACSQuantifyR_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MACSQuantifyR_1.6.0.tgz
vignettes: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.html,
vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.html,
vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.html
vignetteTitles: MACSQuantifyR_step_by_step_analysis,
MACSQuantifyR_simple_pipeline, MACSQuantifyR_quick_introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.R,
vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.R,
vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.R
dependencyCount: 75
Package: MACSr
Version: 1.0.0
Depends: R (>= 4.1.0)
Imports: utils, reticulate, S4Vectors, methods, basilisk,
ExperimentHub, AnnotationHub
Suggests: testthat, knitr, rmarkdown, BiocStyle, MACSdata
License: BSD_3_clause + file LICENSE
MD5sum: da75aa1e76767e5a2f4f42065ac78993
NeedsCompilation: no
Title: MACS: Model-based Analysis for ChIP-Seq
Description: The Model-based Analysis of ChIP-Seq (MACS) is a widely
used toolkit for identifying transcript factor binding sites.
This package is an R wrapper of the lastest MACS3.
biocViews: Software, ChIPSeq, ATACSeq, ImmunoOncology
Author: Qiang Hu [aut, cre]
Maintainer: Qiang Hu <Qiang.Hu@roswellpark.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MACSr
git_branch: RELEASE_3_13
git_last_commit: 783b2b1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MACSr_1.0.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/MACSr_1.0.0.tgz
vignettes: vignettes/MACSr/inst/doc/MACSr.html
vignetteTitles: MACSr
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MACSr/inst/doc/MACSr.R
dependencyCount: 97
Package: made4
Version: 1.66.0
Depends: RColorBrewer,gplots,scatterplot3d, Biobase,
SummarizedExperiment
Imports: ade4
Suggests: affy, BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: b6e98bb2cabe2f7f2879d4c131661219
NeedsCompilation: no
Title: Multivariate analysis of microarray data using ADE4
Description: Multivariate data analysis and graphical display of
microarray data. Functions include for supervised dimension
reduction (between group analysis) and joint dimension
reduction of 2 datasets (coinertia analysis). It contains
functions that require R package ade4.
biocViews: Clustering, Classification, DimensionReduction,
PrincipalComponent,Transcriptomics, MultipleComparison,
GeneExpression, Sequencing, Microarray
Author: Aedin Culhane
Maintainer: Aedin Culhane <Aedin@jimmy.harvard.edu>
URL: http://www.hsph.harvard.edu/aedin-culhane/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/made4
git_branch: RELEASE_3_13
git_last_commit: d444c18
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/made4_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/made4_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/made4_1.66.0.tgz
vignettes: vignettes/made4/inst/doc/introduction.html
vignetteTitles: Authoring R Markdown vignettes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/made4/inst/doc/introduction.R
importsMe: deco, omicade4
dependencyCount: 36
Package: MADSEQ
Version: 1.18.0
Depends: R(>= 3.4), rjags(>= 4-6),
Imports: VGAM, coda, BSgenome, BSgenome.Hsapiens.UCSC.hg19, S4Vectors,
methods, preprocessCore, GenomicAlignments, Rsamtools,
Biostrings, GenomicRanges, IRanges, VariantAnnotation,
SummarizedExperiment, GenomeInfoDb, rtracklayer, graphics,
stats, grDevices, utils, zlibbioc, vcfR
Suggests: knitr
License: GPL(>=2)
MD5sum: 984872bcc704fd998cbd77ae07202009
NeedsCompilation: no
Title: Mosaic Aneuploidy Detection and Quantification using Massive
Parallel Sequencing Data
Description: The MADSEQ package provides a group of hierarchical
Bayeisan models for the detection of mosaic aneuploidy, the
inference of the type of aneuploidy and also for the
quantification of the fraction of aneuploid cells in the
sample.
biocViews: GenomicVariation, SomaticMutation, VariantDetection,
Bayesian, CopyNumberVariation, Sequencing, Coverage
Author: Yu Kong, Adam Auton, John Murray Greally
Maintainer: Yu Kong <yu.kong@phd.einstein.yu.edu>
URL: https://github.com/ykong2/MADSEQ
VignetteBuilder: knitr
BugReports: https://github.com/ykong2/MADSEQ/issues
git_url: https://git.bioconductor.org/packages/MADSEQ
git_branch: RELEASE_3_13
git_last_commit: 64cfc9c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MADSEQ_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MADSEQ_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MADSEQ_1.18.0.tgz
vignettes: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.html
vignetteTitles: R Package MADSEQ
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.R
dependencyCount: 115
Package: maftools
Version: 2.8.05
Depends: R (>= 3.3)
Imports: data.table, grDevices, methods, RColorBrewer, Rhtslib,
survival
LinkingTo: Rhtslib, zlibbioc
Suggests: berryFunctions, Biostrings, BSgenome,
BSgenome.Hsapiens.UCSC.hg19, GenomicRanges, IRanges, knitr,
mclust, MultiAssayExperiment, NMF, R.utils, RaggedExperiment,
rmarkdown, S4Vectors, pheatmap, curl
License: MIT + file LICENSE
MD5sum: f17ee5e9acf7af22ea25b07ac4d69e67
NeedsCompilation: yes
Title: Summarize, Analyze and Visualize MAF Files
Description: Analyze and visualize Mutation Annotation Format (MAF)
files from large scale sequencing studies. This package
provides various functions to perform most commonly used
analyses in cancer genomics and to create feature rich
customizable visualzations with minimal effort.
biocViews: DataRepresentation, DNASeq, Visualization, DriverMutation,
VariantAnnotation, FeatureExtraction, Classification,
SomaticMutation, Sequencing, FunctionalGenomics, Survival
Author: Anand Mayakonda [aut, cre]
(<https://orcid.org/0000-0003-1162-687X>)
Maintainer: Anand Mayakonda <anand_mt@hotmail.com>
URL: https://github.com/PoisonAlien/maftools
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/PoisonAlien/maftools/issues
git_url: https://git.bioconductor.org/packages/maftools
git_branch: RELEASE_3_13
git_last_commit: 0c14f2a
git_last_commit_date: 2021-09-07
Date/Publication: 2021-09-09
source.ver: src/contrib/maftools_2.8.05.tar.gz
win.binary.ver: bin/windows/contrib/4.1/maftools_2.8.05.zip
mac.binary.ver: bin/macosx/contrib/4.1/maftools_2.8.05.tgz
vignettes: vignettes/maftools/inst/doc/cancer_hotspots.html,
vignettes/maftools/inst/doc/maftools.html,
vignettes/maftools/inst/doc/oncoplots.html
vignetteTitles: 03: Somatic status of cancer hotspots, 01: Summarize,,
Analyze,, and Visualize MAF Files, 02: Customizing oncoplots
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/maftools/inst/doc/cancer_hotspots.R,
vignettes/maftools/inst/doc/maftools.R,
vignettes/maftools/inst/doc/oncoplots.R
importsMe: CIMICE, musicatk, TCGAbiolinksGUI, TCGAWorkflow,
oncoPredict, pathwayTMB, Rediscover, sigminer, SMDIC
suggestsMe: MultiAssayExperiment, survtype, TCGAbiolinks
dependencyCount: 14
Package: MAGAR
Version: 1.0.1
Depends: R (>= 4.1), HDF5Array, RnBeads, snpStats, crlmm
Imports: doParallel, igraph, bigstatsr, rjson, plyr, data.table,
UpSetR, reshape2, jsonlite, methods, ff, argparse, impute,
RnBeads.hg19, utils, stats
Suggests: gridExtra, VennDiagram, qqman, LOLA, RUnit, rmutil,
rmarkdown, JASPAR2018, TFBSTools, seqLogo, knitr, devtools,
BiocGenerics, BiocManager
License: GPL-3
Archs: i386, x64
MD5sum: a1af2d54c54afdfb40f8230a2d4b50d4
NeedsCompilation: no
Title: MAGAR: R-package to compute methylation Quantitative Trait Loci
(methQTL) from DNA methylation and genotyping data
Description: "Methylation-Aware Genotype Association in R" (MAGAR)
computes methQTL from DNA methylation and genotyping data from
matched samples. MAGAR uses a linear modeling stragety to call
CpGs/SNPs that are methQTLs. MAGAR accounts for the local
correlation structure of CpGs.
biocViews: Regression, Epigenetics, DNAMethylation, SNP,
GeneticVariability, MethylationArray, Microarray, CpGIsland,
MethylSeq, Sequencing, mRNAMicroarray, Preprocessing,
CopyNumberVariation, TwoChannel, ImmunoOncology,
DifferentialMethylation, BatchEffect, QualityControl,
DataImport, Network, Clustering, GraphAndNetwork
Author: Michael Scherer [cre, aut]
(<https://orcid.org/0000-0001-7990-6179>)
Maintainer: Michael Scherer <mscherer@mpi-inf.mpg.de>
URL: https://github.com/MPIIComputationalEpigenetics/MAGAR
VignetteBuilder: knitr
BugReports:
https://github.com/MPIIComputationalEpigenetics/MAGAR/issues
git_url: https://git.bioconductor.org/packages/MAGAR
git_branch: RELEASE_3_13
git_last_commit: 7901096
git_last_commit_date: 2021-07-06
Date/Publication: 2021-07-08
source.ver: src/contrib/MAGAR_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MAGAR_1.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/MAGAR_1.0.1.tgz
vignettes: vignettes/MAGAR/inst/doc/MAGAR.html
vignetteTitles: MAGAR: Methylation-Aware Genotype Association in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MAGAR/inst/doc/MAGAR.R
dependencyCount: 194
Package: MAGeCKFlute
Version: 1.12.0
Depends: R (>= 3.5)
Imports: Biobase, clusterProfiler (>= 3.16.1), enrichplot, gridExtra,
ggplot2, ggrepel, grDevices, grid, reshape2, stats, utils
Suggests: biomaRt, BiocStyle, DOSE, dendextend, graphics, knitr,
msigdbr, pheatmap, png, pathview, scales, sva, testthat,
License: GPL (>=3)
MD5sum: 128ad63923b5677171da652db9ac30f6
NeedsCompilation: no
Title: Integrative Analysis Pipeline for Pooled CRISPR Functional
Genetic Screens
Description: CRISPR (clustered regularly interspaced short palindrome
repeats) coupled with nuclease Cas9 (CRISPR/Cas9) screens
represent a promising technology to systematically evaluate
gene functions. Data analysis for CRISPR/Cas9 screens is a
critical process that includes identifying screen hits and
exploring biological functions for these hits in downstream
analysis. We have previously developed two algorithms, MAGeCK
and MAGeCK-VISPR, to analyze CRISPR/Cas9 screen data in various
scenarios. These two algorithms allow users to perform quality
control, read count generation and normalization, and calculate
beta score to evaluate gene selection performance. In
downstream analysis, the biological functional analysis is
required for understanding biological functions of these
identified genes with different screening purposes. Here, We
developed MAGeCKFlute for supporting downstream analysis.
MAGeCKFlute provides several strategies to remove potential
biases within sgRNA-level read counts and gene-level beta
scores. The downstream analysis with the package includes
identifying essential, non-essential, and target-associated
genes, and performing biological functional category analysis,
pathway enrichment analysis and protein complex enrichment
analysis of these genes. The package also visualizes genes in
multiple ways to benefit users exploring screening data.
Collectively, MAGeCKFlute enables accurate identification of
essential, non-essential, and targeted genes, as well as their
related biological functions. This vignette explains the use of
the package and demonstrates typical workflows.
biocViews: FunctionalGenomics, CRISPR, PooledScreens, QualityControl,
Normalization, GeneSetEnrichment, Pathways, Visualization,
GeneTarget, KEGG
Author: Binbin Wang, Wubing Zhang, Feizhen Wu, Wei Li & X. Shirley Liu
Maintainer: Wubing Zhang<Watson5bZhang@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MAGeCKFlute
git_branch: RELEASE_3_13
git_last_commit: 119a2de
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MAGeCKFlute_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MAGeCKFlute_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MAGeCKFlute_1.12.0.tgz
vignettes: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.html,
vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.html
vignetteTitles: MAGeCKFlute_enrichment.Rmd, MAGeCKFlute.Rmd
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.R,
vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.R
dependencyCount: 126
Package: maigesPack
Version: 1.56.0
Depends: R (>= 2.10), convert, graph, limma, marray, methods
Suggests: amap, annotate, class, e1071, MASS, multtest, OLIN, R2HTML,
rgl, som
License: GPL (>= 2)
MD5sum: 72437ecde6546b31e1da8386c2ce5707
NeedsCompilation: yes
Title: Functions to handle cDNA microarray data, including several
methods of data analysis
Description: This package uses functions of various other packages
together with other functions in a coordinated way to handle
and analyse cDNA microarray data
biocViews: Microarray, TwoChannel, Preprocessing, ThirdPartyClient,
DifferentialExpression, Clustering, Classification,
GraphAndNetwork
Author: Gustavo H. Esteves <gesteves@gmail.com>, with contributions
from Roberto Hirata Jr <hirata@ime.usp.br>, E. Jordao Neves
<neves@ime.usp.br>, Elier B. Cristo <elier@ime.usp.br>, Ana C.
Simoes <anakqui@ime.usp.br> and Lucas Fahham
<fahham@linux.ime.usp.br>
Maintainer: Gustavo H. Esteves <gesteves@gmail.com>
URL: http://www.maiges.org/en/software/
git_url: https://git.bioconductor.org/packages/maigesPack
git_branch: RELEASE_3_13
git_last_commit: 3f54ee8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/maigesPack_1.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/maigesPack_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/maigesPack_1.56.0.tgz
vignettes: vignettes/maigesPack/inst/doc/maigesPack_tutorial.pdf
vignetteTitles: maigesPack Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/maigesPack/inst/doc/maigesPack_tutorial.R
dependencyCount: 13
Package: MAIT
Version: 1.26.0
Depends: R (>= 2.10), CAMERA, Rcpp, pls
Imports:
gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret
Suggests: faahKO
Enhances: rgl
License: GPL-2
MD5sum: 2961ba04377fd69a09fcc46ba2849a22
NeedsCompilation: no
Title: Statistical Analysis of Metabolomic Data
Description: The MAIT package contains functions to perform end-to-end
statistical analysis of LC/MS Metabolomic Data. Special
emphasis is put on peak annotation and in modular function
design of the functions.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Software
Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina
Andres-LaCueva, Alexandre Perera
Maintainer: Pol Sola-Santos <pol.soladelossantos@gmail.com>
git_url: https://git.bioconductor.org/packages/MAIT
git_branch: RELEASE_3_13
git_last_commit: 5d0e003
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MAIT_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MAIT_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MAIT_1.26.0.tgz
vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf
vignetteTitles: \maketitleMAIT Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MAIT/inst/doc/MAIT_Vignette.R
suggestsMe: specmine
dependencyCount: 207
Package: makecdfenv
Version: 1.68.0
Depends: R (>= 2.6.0), affyio
Imports: Biobase, affy, methods, stats, utils, zlibbioc
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 114de15f559c9c107789e521b9b6b1d2
NeedsCompilation: yes
Title: CDF Environment Maker
Description: This package has two functions. One reads a Affymetrix
chip description file (CDF) and creates a hash table
environment containing the location/probe set membership
mapping. The other creates a package that automatically loads
that environment.
biocViews: OneChannel, DataImport, Preprocessing
Author: Rafael A. Irizarry <rafa@jhu.edu>, Laurent Gautier
<laurent@cbs.dtu.dk>, Wolfgang Huber
<w.huber@dkfz-heidelberg.de>, Ben Bolstad <bmb@bmbolstad.com>
Maintainer: James W. MacDonald <jmacdon@u.washington.edu>
git_url: https://git.bioconductor.org/packages/makecdfenv
git_branch: RELEASE_3_13
git_last_commit: f277e17
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/makecdfenv_1.68.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/makecdfenv_1.68.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/makecdfenv_1.68.0.tgz
vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf
vignetteTitles: makecdfenv primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/makecdfenv/inst/doc/makecdfenv.R
dependsOnMe: altcdfenvs
dependencyCount: 13
Package: MANOR
Version: 1.64.0
Depends: R (>= 2.10)
Imports: GLAD, graphics, grDevices, stats, utils
Suggests: knitr, rmarkdown, bookdown
License: GPL-2
MD5sum: bed31cae3d4d16c04b3d40278598e89c
NeedsCompilation: yes
Title: CGH Micro-Array NORmalization
Description: Importation, normalization, visualization, and quality
control functions to correct identified sources of variability
in array-CGH experiments.
biocViews: Microarray, TwoChannel, DataImport, QualityControl,
Preprocessing, CopyNumberVariation, Normalization
Author: Pierre Neuvial <pierre.neuvial@math.cnrs.fr>, Philippe Hupé
<philippe.hupe@curie.fr>
Maintainer: Pierre Neuvial <pierre.neuvial@math.cnrs.fr>
URL: http://bioinfo.curie.fr/projects/manor/index.html
VignetteBuilder: knitr
BugReports: https://github.com/pneuvial/MANOR/issues
git_url: https://git.bioconductor.org/packages/MANOR
git_branch: RELEASE_3_13
git_last_commit: 6c2e394
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MANOR_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MANOR_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MANOR_1.64.0.tgz
vignettes: vignettes/MANOR/inst/doc/MANOR.html
vignetteTitles: Overview of the MANOR package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MANOR/inst/doc/MANOR.R
dependencyCount: 9
Package: MantelCorr
Version: 1.62.0
Depends: R (>= 2.10)
Imports: stats
License: GPL (>= 2)
MD5sum: 432d7b1f70b7a99f5687676eab6d00ad
NeedsCompilation: no
Title: Compute Mantel Cluster Correlations
Description: Computes Mantel cluster correlations from a (p x n)
numeric data matrix (e.g. microarray gene-expression data).
biocViews: Clustering
Author: Brian Steinmeyer and William Shannon
Maintainer: Brian Steinmeyer <steinmeb@ilya.wustl.edu>
git_url: https://git.bioconductor.org/packages/MantelCorr
git_branch: RELEASE_3_13
git_last_commit: f1b59c5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MantelCorr_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MantelCorr_1.62.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MantelCorr_1.62.0.tgz
vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf
vignetteTitles: MantelCorrVignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MantelCorr/inst/doc/MantelCorrVignette.R
dependencyCount: 1
Package: mAPKL
Version: 1.22.0
Depends: R (>= 3.6.0), Biobase
Imports: multtest, clusterSim, apcluster, limma, e1071, AnnotationDbi,
methods, parmigene,igraph,reactome.db
Suggests: BiocStyle, knitr, mAPKLData, hgu133plus2.db, RUnit,
BiocGenerics
License: GPL (>= 2)
Archs: i386, x64
MD5sum: d9505d305358682b5b538c0e6f2a95c5
NeedsCompilation: no
Title: A Hybrid Feature Selection method for gene expression data
Description: We propose a hybrid FS method (mAP-KL), which combines
multiple hypothesis testing and affinity propagation
(AP)-clustering algorithm along with the Krzanowski & Lai
cluster quality index, to select a small yet informative subset
of genes.
biocViews: FeatureExtraction, DifferentialExpression, Microarray,
GeneExpression
Author: Argiris Sakellariou
Maintainer: Argiris Sakellariou <argisake@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mAPKL
git_branch: RELEASE_3_13
git_last_commit: 3f87c86
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mAPKL_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mAPKL_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mAPKL_1.22.0.tgz
vignettes: vignettes/mAPKL/inst/doc/mAPKL.pdf
vignetteTitles: mAPKL Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mAPKL/inst/doc/mAPKL.R
dependencyCount: 82
Package: maPredictDSC
Version: 1.30.0
Depends: R (>= 2.15.0),
MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO
Suggests: parallel
License: GPL-2
MD5sum: 2425de6deb8669ac1cd4acff6c436037
NeedsCompilation: no
Title: Phenotype prediction using microarray data: approach of the best
overall team in the IMPROVER Diagnostic Signature Challenge
Description: This package implements the classification pipeline of the
best overall team (Team221) in the IMPROVER Diagnostic
Signature Challenge. Additional functionality is added to
compare 27 combinations of data preprocessing, feature
selection and classifier types.
biocViews: Microarray, Classification
Author: Adi Laurentiu Tarca <atarca@med.wayne.edu>
Maintainer: Adi Laurentiu Tarca <atarca@med.wayne.edu>
URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC
git_url: https://git.bioconductor.org/packages/maPredictDSC
git_branch: RELEASE_3_13
git_last_commit: 69aac22
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/maPredictDSC_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/maPredictDSC_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/maPredictDSC_1.30.0.tgz
vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf
vignetteTitles: maPredictDSC
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/maPredictDSC/inst/doc/maPredictDSC.R
dependencyCount: 130
Package: mapscape
Version: 1.16.0
Depends: R (>= 3.3)
Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), base64enc (>=
0.1-3), stringr (>= 1.0.0)
Suggests: knitr, rmarkdown
License: GPL-3
Archs: i386, x64
MD5sum: 21335fd0652b858c5fee3ba5b119e3f9
NeedsCompilation: no
Title: mapscape
Description: MapScape integrates clonal prevalence, clonal hierarchy,
anatomic and mutational information to provide interactive
visualization of spatial clonal evolution. There are four
inputs to MapScape: (i) the clonal phylogeny, (ii) clonal
prevalences, (iii) an image reference, which may be a medical
image or drawing and (iv) pixel locations for each sample on
the referenced image. Optionally, MapScape can accept a data
table of mutations for each clone and their variant allele
frequencies in each sample. The output of MapScape consists of
a cropped anatomical image surrounded by two representations of
each tumour sample. The first, a cellular aggregate, visually
displays the prevalence of each clone. The second shows a
skeleton of the clonal phylogeny while highlighting only those
clones present in the sample. Together, these representations
enable the analyst to visualize the distribution of clones
throughout anatomic space.
biocViews: Visualization
Author: Maia Smith [aut, cre]
Maintainer: Maia Smith <maiaannesmith@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mapscape
git_branch: RELEASE_3_13
git_last_commit: dfb96d2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mapscape_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mapscape_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mapscape_1.16.0.tgz
vignettes: vignettes/mapscape/inst/doc/mapscape_vignette.html
vignetteTitles: MapScape vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mapscape/inst/doc/mapscape_vignette.R
dependencyCount: 17
Package: marr
Version: 1.2.0
Depends: R (>= 4.0)
Imports: Rcpp, SummarizedExperiment, utils, methods, ggplot2, dplyr,
magrittr, rlang, S4Vectors
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, testthat, covr
License: GPL (>= 3)
Archs: i386, x64
MD5sum: d2da18374fbfe5cc462deabd5acd28e2
NeedsCompilation: yes
Title: Maximum rank reproducibility
Description: marr (Maximum Rank Reproducibility) is a nonparametric
approach that detects reproducible signals using a maximal rank
statistic for high-dimensional biological data. In this R
package, we implement functions that measures the
reproducibility of features per sample pair and sample pairs
per feature in high-dimensional biological replicate
experiments. The user-friendly plot functions in this package
also plot histograms of the reproducibility of features per
sample pair and sample pairs per feature. Furthermore, our
approach also allows the users to select optimal filtering
threshold values for the identification of reproducible
features and sample pairs based on output visualization checks
(histograms). This package also provides the subset of data
filtered by reproducible features and/or sample pairs.
biocViews: QualityControl, Metabolomics, MassSpectrometry, RNASeq,
ChIPSeq
Author: Tusharkanti Ghosh [aut, cre], Max McGrath [aut], Daisy Philtron
[aut], Katerina Kechris [aut], Debashis Ghosh [aut, cph]
Maintainer: Tusharkanti Ghosh <tusharkantighosh30@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/Ghoshlab/marr/issues
git_url: https://git.bioconductor.org/packages/marr
git_branch: RELEASE_3_13
git_last_commit: f158650
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/marr_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/marr_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/marr_1.2.0.tgz
vignettes: vignettes/marr/inst/doc/MarrVignette.html
vignetteTitles: The marr user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/marr/inst/doc/MarrVignette.R
dependencyCount: 61
Package: marray
Version: 1.70.0
Depends: R (>= 2.10.0), limma, methods
Suggests: tkWidgets
License: LGPL
MD5sum: 0c4b48276c25dcb69e2328d24563b30b
NeedsCompilation: no
Title: Exploratory analysis for two-color spotted microarray data
Description: Class definitions for two-color spotted microarray data.
Fuctions for data input, diagnostic plots, normalization and
quality checking.
biocViews: Microarray, TwoChannel, Preprocessing
Author: Yee Hwa (Jean) Yang <jeany@maths.usyd.edu.au> with
contributions from Agnes Paquet and Sandrine Dudoit.
Maintainer: Yee Hwa (Jean) Yang <jean@biostat.ucsf.edu>
URL: http://www.maths.usyd.edu.au/u/jeany/
git_url: https://git.bioconductor.org/packages/marray
git_branch: RELEASE_3_13
git_last_commit: 9f63605
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/marray_1.70.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/marray_1.70.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/marray_1.70.0.tgz
vignettes: vignettes/marray/inst/doc/marray.pdf,
vignettes/marray/inst/doc/marrayClasses.pdf,
vignettes/marray/inst/doc/marrayClassesShort.pdf,
vignettes/marray/inst/doc/marrayInput.pdf,
vignettes/marray/inst/doc/marrayNorm.pdf,
vignettes/marray/inst/doc/marrayPlots.pdf
vignetteTitles: marray Overview, marrayClasses Overview, marrayClasses
Tutorial (short), marrayInput Introduction, marray
Normalization, marrayPlots Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/marray/inst/doc/marray.R,
vignettes/marray/inst/doc/marrayClasses.R,
vignettes/marray/inst/doc/marrayClassesShort.R,
vignettes/marray/inst/doc/marrayInput.R,
vignettes/marray/inst/doc/marrayNorm.R,
vignettes/marray/inst/doc/marrayPlots.R
dependsOnMe: CGHbase, convert, dyebias, maigesPack, MineICA, nnNorm,
OLIN, RBM, stepNorm, TurboNorm, beta7, dyebiasexamples
importsMe: arrayQuality, ChAMP, methylPipe, MSstats, nnNorm, OLIN,
OLINgui, piano, stepNorm, timecourse
suggestsMe: DEGraph, Mfuzz, hexbin, maGUI
dependencyCount: 6
Package: martini
Version: 1.12.0
Depends: R (>= 4.0)
Imports: igraph (>= 1.0.1), Matrix, methods (>= 3.3.2), Rcpp (>=
0.12.8), snpStats (>= 1.20.0), stats, utils,
LinkingTo: Rcpp, RcppEigen (>= 0.3.3.5.0)
Suggests: biomaRt (>= 2.34.1), circlize (>= 0.4.11), STRINGdb (>=
2.2.0), httr (>= 1.2.1), IRanges (>= 2.8.2), S4Vectors (>=
0.12.2), memoise (>= 2.0.0), knitr, testthat, readr, rmarkdown
License: GPL-3
MD5sum: 0bc0b4388c0629ed0aec99676e219161
NeedsCompilation: yes
Title: GWAS Incorporating Networks
Description: martini deals with the low power inherent to GWAS studies
by using prior knowledge represented as a network. SNPs are the
vertices of the network, and the edges represent biological
relationships between them (genomic adjacency, belonging to the
same gene, physical interaction between protein products). The
network is scanned using SConES, which looks for groups of SNPs
maximally associated with the phenotype, that form a close
subnetwork.
biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability,
Genetics, FeatureExtraction, GraphAndNetwork, Network
Author: Hector Climente-Gonzalez [aut, cre]
(<https://orcid.org/0000-0002-3030-7471>), Chloe-Agathe
Azencott [aut] (<https://orcid.org/0000-0003-1003-301X>)
Maintainer: Hector Climente-Gonzalez <hector.climente@riken.jp>
URL: https://github.com/hclimente/martini
VignetteBuilder: knitr
BugReports: https://github.com/hclimente/martini/issues
git_url: https://git.bioconductor.org/packages/martini
git_branch: RELEASE_3_13
git_last_commit: 70d4c22
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/martini_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/martini_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/martini_1.12.0.tgz
vignettes: vignettes/martini/inst/doc/scones_usage.html,
vignettes/martini/inst/doc/simulate_phenotype.html
vignetteTitles: Running SConES, Simulating SConES-based phenotypes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/martini/inst/doc/scones_usage.R,
vignettes/martini/inst/doc/simulate_phenotype.R
dependencyCount: 19
Package: maser
Version: 1.10.0
Depends: R (>= 3.5.0), ggplot2, GenomicRanges
Imports: dplyr, rtracklayer, reshape2, Gviz, DT, GenomeInfoDb, stats,
utils, IRanges, methods, BiocGenerics, parallel, data.table
Suggests: testthat, knitr, rmarkdown, BiocStyle, AnnotationHub
License: MIT + file LICENSE
MD5sum: 2761f54c38eb5150a8706966b73ecec6
NeedsCompilation: no
Title: Mapping Alternative Splicing Events to pRoteins
Description: This package provides functionalities for downstream
analysis, annotation and visualizaton of alternative splicing
events generated by rMATS.
biocViews: AlternativeSplicing, Transcriptomics, Visualization
Author: Diogo F.T. Veiga [aut, cre]
Maintainer: Diogo F.T. Veiga <diogof.veiga@gmail.com>
URL: https://github.com/DiogoVeiga/maser
VignetteBuilder: knitr
BugReports: https://github.com/DiogoVeiga/maser/issues
git_url: https://git.bioconductor.org/packages/maser
git_branch: RELEASE_3_13
git_last_commit: c37ef16
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/maser_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/maser_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/maser_1.10.0.tgz
vignettes: vignettes/maser/inst/doc/Introduction.html,
vignettes/maser/inst/doc/Protein_mapping.html
vignetteTitles: Introduction, Mapping protein features to splicing
events
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/maser/inst/doc/Introduction.R,
vignettes/maser/inst/doc/Protein_mapping.R
dependencyCount: 149
Package: maSigPro
Version: 1.64.0
Depends: R (>= 2.3.1)
Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS
License: GPL (>= 2)
MD5sum: 4c097da1ab99a3bb70ca02a42864551e
NeedsCompilation: no
Title: Significant Gene Expression Profile Differences in Time Course
Gene Expression Data
Description: maSigPro is a regression based approach to find genes for
which there are significant gene expression profile differences
between experimental groups in time course microarray and
RNA-Seq experiments.
biocViews: Microarray, RNA-Seq, Differential Expression, TimeCourse
Author: Ana Conesa and Maria Jose Nueda
Maintainer: Maria Jose Nueda <mj.nueda@ua.es>
git_url: https://git.bioconductor.org/packages/maSigPro
git_branch: RELEASE_3_13
git_last_commit: 2e4cf8e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/maSigPro_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/maSigPro_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/maSigPro_1.64.0.tgz
vignettes: vignettes/maSigPro/inst/doc/maSigPro.pdf,
vignettes/maSigPro/inst/doc/maSigProUsersGuide.pdf
vignetteTitles: maSigPro Vignette, maSigProUsersGuide.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 12
Package: maskBAD
Version: 1.36.0
Depends: R (>= 2.10), gcrma (>= 2.27.1), affy
Suggests: hgu95av2probe, hgu95av2cdf
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 0cd6e532bb03e8fcc9bd4ca68730126b
NeedsCompilation: no
Title: Masking probes with binding affinity differences
Description: Package includes functions to analyze and mask microarray
expression data.
biocViews: Microarray
Author: Michael Dannemann <michael_dannemann@eva.mpg.de>
Maintainer: Michael Dannemann <michael_dannemann@eva.mpg.de>
git_url: https://git.bioconductor.org/packages/maskBAD
git_branch: RELEASE_3_13
git_last_commit: b1e1a3a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/maskBAD_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/maskBAD_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/maskBAD_1.36.0.tgz
vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf
vignetteTitles: Package maskBAD
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/maskBAD/inst/doc/maskBAD.R
dependencyCount: 26
Package: MassArray
Version: 1.44.0
Depends: R (>= 2.10.0), methods
Imports: graphics, grDevices, stats, utils
License: GPL (>=2)
MD5sum: 666fe7f37dde5593bd9e8031dc40d1fe
NeedsCompilation: no
Title: Analytical Tools for MassArray Data
Description: This package is designed for the import, quality control,
analysis, and visualization of methylation data generated using
Sequenom's MassArray platform. The tools herein contain a
highly detailed amplicon prediction for optimal assay design.
Also included are quality control measures of data, such as
primer dimer and bisulfite conversion efficiency estimation.
Methylation data are calculated using the same algorithms
contained in the EpiTyper software package. Additionally,
automatic SNP-detection can be used to flag potentially
confounded data from specific CG sites. Visualization includes
barplots of methylation data as well as UCSC Genome
Browser-compatible BED tracks. Multiple assays can be
positionally combined for integrated analysis.
biocViews: ImmunoOncology, DNAMethylation, SNP, MassSpectrometry,
Genetics, DataImport, Visualization
Author: Reid F. Thompson <reid.thompson@gmail.com>, John M. Greally
<john.greally@einstein.yu.edu>
Maintainer: Reid F. Thompson <reid.thompson@gmail.com>
git_url: https://git.bioconductor.org/packages/MassArray
git_branch: RELEASE_3_13
git_last_commit: fdc3f88
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MassArray_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MassArray_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MassArray_1.44.0.tgz
vignettes: vignettes/MassArray/inst/doc/MassArray.pdf
vignetteTitles: 1. Primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MassArray/inst/doc/MassArray.R
dependencyCount: 5
Package: massiR
Version: 1.28.0
Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2)
Suggests: biomaRt, RUnit, BiocGenerics
License: GPL-3
Archs: i386, x64
MD5sum: 941c3d08548db509507cd8749ab1967f
NeedsCompilation: no
Title: massiR: MicroArray Sample Sex Identifier
Description: Predicts the sex of samples in gene expression microarray
datasets
biocViews: Software, Microarray, GeneExpression, Clustering,
Classification, QualityControl
Author: Sam Buckberry
Maintainer: Sam Buckberry <sam.buckberry@adelaide.edu.au>
git_url: https://git.bioconductor.org/packages/massiR
git_branch: RELEASE_3_13
git_last_commit: 303c8d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/massiR_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/massiR_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/massiR_1.28.0.tgz
vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf
vignetteTitles: massiR_Example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/massiR/inst/doc/massiR_Vignette.R
dependencyCount: 15
Package: MassSpecWavelet
Version: 1.58.0
Depends: waveslim
Suggests: xcms, caTools
License: LGPL (>= 2)
MD5sum: b9d7ebdf64eca536f2e2e3130da1dbc4
NeedsCompilation: yes
Title: Mass spectrum processing by wavelet-based algorithms
Description: Processing Mass Spectrometry spectrum by using wavelet
based algorithm
biocViews: ImmunoOncology, MassSpectrometry, Proteomics
Author: Pan Du, Warren Kibbe, Simon Lin
Maintainer: Pan Du <dupan.mail@gmail.com>
git_url: https://git.bioconductor.org/packages/MassSpecWavelet
git_branch: RELEASE_3_13
git_last_commit: cbc5406
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MassSpecWavelet_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MassSpecWavelet_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MassSpecWavelet_1.58.0.tgz
vignettes: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.pdf
vignetteTitles: MassSpecWavelet
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R
importsMe: cosmiq, xcms, Rnmr1D, speaq
dependencyCount: 5
Package: MAST
Version: 1.18.0
Depends: SingleCellExperiment (>= 1.2.0), R(>= 3.5)
Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr,
stringr, abind, methods, parallel, reshape2, stats, stats4,
graphics, utils, SummarizedExperiment(>= 1.5.3), progress
Suggests: knitr, rmarkdown, testthat, lme4(>= 1.0), blme, roxygen2(>
6.0.0), numDeriv, car, gdata, lattice, GGally, GSEABase, NMF,
TxDb.Hsapiens.UCSC.hg19.knownGene, rsvd, limma, RColorBrewer,
BiocStyle, scater, DelayedArray, Matrix, HDF5Array, zinbwave,
dplyr
License: GPL(>= 2)
MD5sum: b92a9603a42bffe8a959df0ddbd3d7c0
NeedsCompilation: no
Title: Model-based Analysis of Single Cell Transcriptomics
Description: Methods and models for handling zero-inflated single cell
assay data.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
RNASeq, Transcriptomics, SingleCell
Author: Andrew McDavid [aut, cre], Greg Finak [aut], Masanao Yajima
[aut]
Maintainer: Andrew McDavid <Andrew_McDavid@urmc.rochester.edu>
URL: https://github.com/RGLab/MAST/
VignetteBuilder: knitr
BugReports: https://github.com/RGLab/MAST/issues
git_url: https://git.bioconductor.org/packages/MAST
git_branch: RELEASE_3_13
git_last_commit: 27cc634
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MAST_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MAST_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MAST_1.18.0.tgz
vignettes: vignettes/MAST/inst/doc/MAITAnalysis.html,
vignettes/MAST/inst/doc/MAST-interoperability.html,
vignettes/MAST/inst/doc/MAST-Intro.html
vignetteTitles: Using MAST for filtering,, differential expression and
gene set enrichment in MAIT cells, Interoptability between MAST
and SingleCellExperiment-derived packages, An Introduction to
MAST
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MAST/inst/doc/MAITAnalysis.R,
vignettes/MAST/inst/doc/MAST-interoperability.R,
vignettes/MAST/inst/doc/MAST-Intro.R
dependsOnMe: POWSC
importsMe: celaref, singleCellTK
suggestsMe: clusterExperiment, Seurat
dependencyCount: 67
Package: matchBox
Version: 1.34.0
Depends: R (>= 2.8.0)
License: Artistic-2.0
MD5sum: 4f08ea9e1fd836a092f569b2ed09d518
NeedsCompilation: no
Title: Utilities to compute, compare, and plot the agreement between
ordered vectors of features (ie. distinct genomic experiments).
The package includes Correspondence-At-the-TOP (CAT) analysis.
Description: The matchBox package enables comparing ranked vectors of
features, merging multiple datasets, removing redundant
features, using CAT-plots and Venn diagrams, and computing
statistical significance.
biocViews: Software, Annotation, Microarray, MultipleComparison,
Visualization
Author: Luigi Marchionni <marchion@jhu.edu>, Anuj Gupta
<agupta52@jhu.edu>
Maintainer: Luigi Marchionni <marchion@jhu.edu>, Anuj Gupta
<agupta52@jhu.edu>
git_url: https://git.bioconductor.org/packages/matchBox
git_branch: RELEASE_3_13
git_last_commit: 6bd6775
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/matchBox_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/matchBox_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/matchBox_1.34.0.tgz
vignettes: vignettes/matchBox/inst/doc/matchBox.pdf
vignetteTitles: Working with the matchBox package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/matchBox/inst/doc/matchBox.R
dependencyCount: 0
Package: MatrixGenerics
Version: 1.4.3
Depends: matrixStats (>= 0.60.1)
Imports: methods
Suggests: sparseMatrixStats, DelayedMatrixStats, SummarizedExperiment,
testthat (>= 2.1.0)
License: Artistic-2.0
MD5sum: 11515a0894edb3b98967e59324b0bc8f
NeedsCompilation: no
Title: S4 Generic Summary Statistic Functions that Operate on
Matrix-Like Objects
Description: S4 generic functions modeled after the 'matrixStats' API
for alternative matrix implementations. Packages with
alternative matrix implementation can depend on this package
and implement the generic functions that are defined here for a
useful set of row and column summary statistics. Other package
developers can import this package and handle a different
matrix implementations without worrying about
incompatibilities.
biocViews: Infrastructure, Software
Author: Constantin Ahlmann-Eltze [aut]
(<https://orcid.org/0000-0002-3762-068X>), Peter Hickey [aut,
cre] (<https://orcid.org/0000-0002-8153-6258>), Hervé Pagès
[aut]
Maintainer: Peter Hickey <peter.hickey@gmail.com>
URL: https://bioconductor.org/packages/MatrixGenerics
BugReports: https://github.com/Bioconductor/MatrixGenerics/issues
git_url: https://git.bioconductor.org/packages/MatrixGenerics
git_branch: RELEASE_3_13
git_last_commit: a651a24
git_last_commit_date: 2021-08-25
Date/Publication: 2021-08-26
source.ver: src/contrib/MatrixGenerics_1.4.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MatrixGenerics_1.4.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/MatrixGenerics_1.4.3.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: DelayedArray, DelayedMatrixStats, GenomicFiles,
sparseMatrixStats, SummarizedExperiment, VariantAnnotation
importsMe: CoreGx, MinimumDistance, PDATK, RaggedExperiment, scone,
scPCA, tLOH, VanillaICE
dependencyCount: 2
Package: MatrixQCvis
Version: 1.0.0
Depends: SummarizedExperiment (>= 1.20.0), plotly (>= 4.9.3), shiny (>=
1.6.0)
Imports: ComplexHeatmap (>= 2.7.9), dplyr (>= 1.0.5), ggplot2 (>=
3.3.3), grDevices (>= 4.1.0), Hmisc (>= 4.5-0), htmlwidgets (>=
1.5.3), impute (>= 1.65.0), imputeLCMD (>= 2.0), limma (>=
3.47.12), methods (>= 4.1.0), openxlsx (>= 4.2.3), pcaMethods
(>= 1.83.0), proDA (>= 1.5.0), UpSetR (>= 1.4.0), rlang (>=
0.4.10), rmarkdown (>= 2.7), Rtsne (>= 0.15), S4Vectors (>=
0.29.15), shinydashboard (>= 0.7.1), shinyhelper (>= 0.3.2),
shinyjs (>= 2.0.0), stats (>= 4.1.0), tibble (>= 3.1.1), tidyr
(>= 1.1.3), umap (>= 0.2.7.0), vegan (>= 2.5-7), vsn (>=
3.59.1)
Suggests: BiocGenerics (>= 0.37.4), BiocStyle (>= 2.19.2), hexbin (>=
1.28.2), knitr (>= 1.33), testthat (>= 3.0.2)
License: GPL (>= 3)
MD5sum: a1976b174074a36208b6d7bf1f46038f
NeedsCompilation: no
Title: Shiny-based interactive data-quality exploration for omics data
Description: Data quality assessment is an integral part of preparatory
data analysis to ensure sound biological information retrieval.
We present here the MatrixQCvis package, which provides
shiny-based interactive visualization of data quality metrics
at the per-sample and per-feature level. It is broadly
applicable to quantitative omics data types that come in
matrix-like format (features x samples). It enables the
detection of low-quality samples, drifts, outliers and batch
effects in data sets. Visualizations include amongst others
bar- and violin plots of the (count/intensity) values, mean vs
standard deviation plots, MA plots, empirical cumulative
distribution function (ECDF) plots, visualizations of the
distances between samples, and multiple types of dimension
reduction plots. Furthermore, MatrixQCvis allows for
differential expression analysis based on the limma (moderated
t-tests) and proDA (Wald tests) packages. MatrixQCvis builds
upon the popular Bioconductor SummarizedExperiment S4 class and
enables thus the facile integration into existing workflows.
The package is especially tailored towards metabolomics and
proteomics mass spectrometry data, but also allows to assess
the data quality of other data types that can be represented in
a SummarizedExperiment object.
biocViews: Visualization, GUI, DimensionReduction, Metabolomics,
Proteomics
Author: Thomas Naake [aut, cre], Wolfgang Huber [aut]
Maintainer: Thomas Naake <thomasnaake@googlemail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MatrixQCvis
git_branch: RELEASE_3_13
git_last_commit: fb7fe47
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MatrixQCvis_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MatrixQCvis_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MatrixQCvis_1.0.0.tgz
vignettes: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.html
vignetteTitles: QC for metabolomics and proteomics data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.R
dependencyCount: 163
Package: MatrixRider
Version: 1.24.0
Depends: R (>= 3.1.2)
Imports: methods, TFBSTools, IRanges, XVector, Biostrings
LinkingTo: IRanges, XVector, Biostrings, S4Vectors
Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014
License: GPL-3
Archs: i386, x64
MD5sum: e8ef659ce4e65e63616550c97da1a6bf
NeedsCompilation: yes
Title: Obtain total affinity and occupancies for binding site matrices
on a given sequence
Description: Calculates a single number for a whole sequence that
reflects the propensity of a DNA binding protein to interact
with it. The DNA binding protein has to be described with a PFM
matrix, for example gotten from Jaspar.
biocViews: GeneRegulation, Genetics, MotifAnnotation
Author: Elena Grassi
Maintainer: Elena Grassi <grassi.e@gmail.com>
git_url: https://git.bioconductor.org/packages/MatrixRider
git_branch: RELEASE_3_13
git_last_commit: 9eb06fd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MatrixRider_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MatrixRider_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MatrixRider_1.24.0.tgz
vignettes: vignettes/MatrixRider/inst/doc/MatrixRider.pdf
vignetteTitles: Total affinity and occupancies
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MatrixRider/inst/doc/MatrixRider.R
dependencyCount: 123
Package: matter
Version: 1.18.0
Depends: R (>= 3.5), BiocParallel, Matrix, methods, stats, biglm
Imports: BiocGenerics, ProtGenerics, digest, irlba, utils
Suggests: BiocStyle, testthat
License: Artistic-2.0
MD5sum: 026dc5e55870e50ee7981b6d78afccb4
NeedsCompilation: yes
Title: A framework for rapid prototyping with file-based data
structures
Description: Memory-efficient reading, writing, and manipulation of
structured binary data as file-based vectors, matrices, arrays,
lists, and data frames.
biocViews: Infrastructure, DataRepresentation
Author: Kylie A. Bemis <k.bemis@northeastern.edu>
Maintainer: Kylie A. Bemis <k.bemis@northeastern.edu>
URL: https://github.com/kuwisdelu/matter
git_url: https://git.bioconductor.org/packages/matter
git_branch: RELEASE_3_13
git_last_commit: dbca756
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/matter_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/matter_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/matter_1.18.0.tgz
vignettes: vignettes/matter/inst/doc/matter-supp1.pdf,
vignettes/matter/inst/doc/matter-supp2.pdf,
vignettes/matter/inst/doc/matter.pdf
vignetteTitles: matter: Supplementary 1 - Simulations and comparative
benchmarks, matter: Supplementary 2 - 3D mass spectrometry
imaging case study, matter: Rapid prototyping with data on disk
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/matter/inst/doc/matter-supp1.R,
vignettes/matter/inst/doc/matter-supp2.R,
vignettes/matter/inst/doc/matter.R
importsMe: Cardinal
dependencyCount: 22
Package: MBAmethyl
Version: 1.26.0
Depends: R (>= 2.15)
License: Artistic-2.0
MD5sum: 2cdce15b5580569bbd43a16ed401ebb4
NeedsCompilation: no
Title: Model-based analysis of DNA methylation data
Description: This package provides a function for reconstructing DNA
methylation values from raw measurements. It iteratively
implements the group fused lars to smooth related-by-location
methylation values and the constrained least squares to remove
probe affinity effect across multiple sequences.
biocViews: DNAMethylation, MethylationArray
Author: Tao Wang, Mengjie Chen
Maintainer: Tao Wang <tao.wang.tw376@yale.edu>
git_url: https://git.bioconductor.org/packages/MBAmethyl
git_branch: RELEASE_3_13
git_last_commit: 69e4737
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MBAmethyl_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MBAmethyl_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MBAmethyl_1.26.0.tgz
vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf
vignetteTitles: MBAmethyl Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MBAmethyl/inst/doc/MBAmethyl.R
dependencyCount: 0
Package: MBASED
Version: 1.26.0
Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges,
SummarizedExperiment
Suggests: BiocStyle
License: Artistic-2.0
MD5sum: 862d3395994a1fd882f5d10020a86f95
NeedsCompilation: no
Title: Package containing functions for ASE analysis using
Meta-analysis Based Allele-Specific Expression Detection
Description: The package implements MBASED algorithm for detecting
allele-specific gene expression from RNA count data, where
allele counts at individual loci (SNVs) are integrated into a
gene-specific measure of ASE, and utilizes simulations to
appropriately assess the statistical significance of observed
ASE.
biocViews: Sequencing, GeneExpression, Transcription
Author: Oleg Mayba, Houston Gilbert
Maintainer: Oleg Mayba <mayba.oleg@gene.com>
git_url: https://git.bioconductor.org/packages/MBASED
git_branch: RELEASE_3_13
git_last_commit: 8b51d18
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MBASED_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MBASED_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MBASED_1.26.0.tgz
vignettes: vignettes/MBASED/inst/doc/MBASED.pdf
vignetteTitles: MBASED
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MBASED/inst/doc/MBASED.R
dependencyCount: 34
Package: MBCB
Version: 1.46.0
Depends: R (>= 2.9.0), tcltk, tcltk2
Imports: preprocessCore, stats, utils
License: GPL (>= 2)
MD5sum: a869fa5a2fd02f659af0b38c213196bb
NeedsCompilation: no
Title: MBCB (Model-based Background Correction for Beadarray)
Description: This package provides a model-based background correction
method, which incorporates the negative control beads to
pre-process Illumina BeadArray data.
biocViews: Microarray, Preprocessing
Author: Yang Xie <Yang.Xie@UTSouthwestern.edu>
Maintainer: Jeff Allen <Jeffrey.Allen@UTSouthwestern.edu>
URL: http://www.utsouthwestern.edu
git_url: https://git.bioconductor.org/packages/MBCB
git_branch: RELEASE_3_13
git_last_commit: 151a5fe
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MBCB_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MBCB_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MBCB_1.46.0.tgz
vignettes: vignettes/MBCB/inst/doc/MBCB.pdf
vignetteTitles: MBCB
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MBCB/inst/doc/MBCB.R
dependencyCount: 5
Package: mbkmeans
Version: 1.8.0
Depends: R (>= 3.6)
Imports: methods, DelayedArray, Rcpp, S4Vectors, SingleCellExperiment,
SummarizedExperiment, ClusterR, benchmarkme, Matrix,
BiocParallel
LinkingTo: Rcpp, RcppArmadillo (>= 0.7.2), Rhdf5lib, beachmat, ClusterR
Suggests: beachmat, HDF5Array, Rhdf5lib, BiocStyle, TENxPBMCData,
scater, DelayedMatrixStats, bluster, knitr, testthat, rmarkdown
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 13b7b96b83cf27ca3edc14b755d4751e
NeedsCompilation: yes
Title: Mini-batch K-means Clustering for Single-Cell RNA-seq
Description: Implements the mini-batch k-means algorithm for large
datasets, including support for on-disk data representation.
biocViews: Clustering, GeneExpression, RNASeq, Software,
Transcriptomics, Sequencing, SingleCell
Author: Yuwei Ni [aut, cph], Davide Risso [aut, cre, cph], Stephanie
Hicks [aut, cph], Elizabeth Purdom [aut, cph]
Maintainer: Davide Risso <risso.davide@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/drisso/mbkmeans/issues
git_url: https://git.bioconductor.org/packages/mbkmeans
git_branch: RELEASE_3_13
git_last_commit: 2d2b03a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mbkmeans_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mbkmeans_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mbkmeans_1.8.0.tgz
vignettes: vignettes/mbkmeans/inst/doc/Vignette.html
vignetteTitles: mbkmeans vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mbkmeans/inst/doc/Vignette.R
importsMe: clusterExperiment, scDblFinder
suggestsMe: bluster
dependencyCount: 89
Package: mBPCR
Version: 1.46.0
Depends: oligoClasses, GWASTools
Imports: Biobase, graphics, methods, utils, grDevices
Suggests: xtable
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 490bd8ce140e4d8840f02d330fd6f2b6
NeedsCompilation: no
Title: Bayesian Piecewise Constant Regression for DNA copy number
estimation
Description: It contains functions for estimating the DNA copy number
profile using mBPCR with the aim of detecting regions with copy
number changes.
biocViews: aCGH, SNP, Microarray, CopyNumberVariation
Author: P.M.V. Rancoita <rancoita.paola@gmail.com>, with contributions
from M. Hutter <marcus.hutter@anu.edu.au>
Maintainer: P.M.V. Rancoita <rancoita.paola@gmail.com>
URL: http://www.idsia.ch/~paola/mBPCR
git_url: https://git.bioconductor.org/packages/mBPCR
git_branch: RELEASE_3_13
git_last_commit: 9e76c33
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mBPCR_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mBPCR_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mBPCR_1.46.0.tgz
vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf
vignetteTitles: mBPCR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mBPCR/inst/doc/mBPCR.R
dependencyCount: 90
Package: MBQN
Version: 2.4.0
Depends: R (>= 4.0)
Imports: stats, graphics, utils, limma (>= 3.30.13),
SummarizedExperiment (>= 1.10.0), preprocessCore (>= 1.36.0),
BiocFileCache, rappdirs, rpx, xml2, RCurl, ggplot2, PairedData
Suggests: knitr
License: GPL-3 + file LICENSE
MD5sum: f1ada0a58a3e14b5dde58669f38bc6d2
NeedsCompilation: no
Title: Mean/Median-balanced quantile normalization
Description: Modified quantile normalization for omics or other
matrix-like data distorted in location and scale.
biocViews: Normalization, Preprocessing, Proteomics, Software
Author: Ariane Schad [aut, cre]
(<https://orcid.org/0000-0002-1921-8902>), Clemens Kreutz [aut,
ctb] (<https://orcid.org/0000-0002-8796-5766>), Eva Brombacher
[aut, ctb] (<https://orcid.org/0000-0002-5488-0985>)
Maintainer: Ariane Schad <ariane.schad@fdm.uni-freiburg.de>
URL: https://github.com/arianeschad/mbqn
VignetteBuilder: knitr
BugReports: https://github.com/arianeschad/MBQN/issues
git_url: https://git.bioconductor.org/packages/MBQN
git_branch: RELEASE_3_13
git_last_commit: 5d61122
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MBQN_2.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MBQN_2.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MBQN_2.4.0.tgz
vignettes: vignettes/MBQN/inst/doc/MBQNpackage.html
vignetteTitles: MBQN Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MBQN/inst/doc/MBQNpackage.R
dependencyCount: 93
Package: MBttest
Version: 1.20.0
Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base,
utils,grDevices
Suggests: BiocStyle, BiocGenerics
License: GPL-3
MD5sum: 4960d958318f0d0a3002001a7cfd78b0
NeedsCompilation: no
Title: Multiple Beta t-Tests
Description: MBttest method was developed from beta t-test method of
Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly
2010), DESeq (Anders and Huber 2010) and exact test (Robinson
and Smyth 2007, 2008) and the GLM of McCarthy et al(2012),
MBttest is of high work efficiency,that is, it has high power,
high conservativeness of FDR estimation and high stability.
MBttest is suit- able to transcriptomic data, tag data, SAGE
data (count data) from small samples or a few replicate
libraries. It can be used to identify genes, mRNA isoforms or
tags differentially expressed between two conditions.
biocViews: Sequencing, DifferentialExpression, MultipleComparison,
SAGE, GeneExpression, Transcription,
AlternativeSplicing,Coverage, DifferentialSplicing
Author: Yuan-De Tan
Maintainer: Yuan-De Tan <tanyuande@gmail.com>
git_url: https://git.bioconductor.org/packages/MBttest
git_branch: RELEASE_3_13
git_last_commit: cd8e6d5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MBttest_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MBttest_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MBttest_1.20.0.tgz
vignettes: vignettes/MBttest/inst/doc/MBttest-manual.pdf,
vignettes/MBttest/inst/doc/MBttest.pdf
vignetteTitles: MBttest-manual.pdf, Analysing RNA-Seq count data with
the "MBttest" package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MBttest/inst/doc/MBttest.R
dependencyCount: 11
Package: MCbiclust
Version: 1.16.0
Depends: R (>= 3.4)
Imports: BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db,
org.Hs.eg.db, GGally, ggplot2, scales, cluster, WGCNA
Suggests: gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr,
pander, devtools, testthat, GSVA
License: GPL-2
MD5sum: ca84d781db8aaec79fddcc2d78dfe340
NeedsCompilation: no
Title: Massive correlating biclusters for gene expression data and
associated methods
Description: Custom made algorithm and associated methods for finding,
visualising and analysing biclusters in large gene expression
data sets. Algorithm is based on with a supplied gene set of
size n, finding the maximum strength correlation matrix
containing m samples from the data set.
biocViews: ImmunoOncology, Clustering, Microarray, StatisticalMethod,
Software, RNASeq, GeneExpression
Author: Robert Bentham
Maintainer: Robert Bentham <robert.bentham.11@ucl.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MCbiclust
git_branch: RELEASE_3_13
git_last_commit: b0ffc3e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MCbiclust_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MCbiclust_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MCbiclust_1.16.0.tgz
vignettes: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.html
vignetteTitles: Introduction to MCbiclust
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.R
dependencyCount: 130
Package: mCSEA
Version: 1.12.0
Depends: R (>= 3.5), mCSEAdata, Homo.sapiens
Imports: biomaRt, fgsea, GenomicFeatures, GenomicRanges, ggplot2,
graphics, grDevices, Gviz, IRanges, limma, methods, parallel,
S4Vectors, stats, SummarizedExperiment, utils
Suggests: Biobase, BiocGenerics, BiocStyle, FlowSorted.Blood.450k,
knitr, leukemiasEset, minfi, minfiData, rmarkdown, RUnit
License: GPL-2
Archs: i386, x64
MD5sum: 268a272e886adc3f1f4f01e1c1dc78c4
NeedsCompilation: no
Title: Methylated CpGs Set Enrichment Analysis
Description: Identification of diferentially methylated regions (DMRs)
in predefined regions (promoters, CpG islands...) from the
human genome using Illumina's 450K or EPIC microarray data.
Provides methods to rank CpG probes based on linear models and
includes plotting functions.
biocViews: ImmunoOncology, DifferentialMethylation, DNAMethylation,
Epigenetics, Genetics, GenomeAnnotation, MethylationArray,
Microarray, MultipleComparison, TwoChannel
Author: Jordi Martorell-Marugán and Pedro Carmona-Sáez
Maintainer: Jordi Martorell-Marugán <jmartorellm@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mCSEA
git_branch: RELEASE_3_13
git_last_commit: 63a7b6c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mCSEA_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mCSEA_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mCSEA_1.12.0.tgz
vignettes: vignettes/mCSEA/inst/doc/mCSEA.pdf
vignetteTitles: Predefined DMRs identification with mCSEA package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mCSEA/inst/doc/mCSEA.R
suggestsMe: shinyepico
dependencyCount: 154
Package: mdp
Version: 1.12.0
Depends: R (>= 4.0)
Imports: ggplot2, gridExtra, grid, stats, utils
Suggests: testthat, knitr, rmarkdown, fgsea, BiocManager
License: GPL-3
MD5sum: b9049a4fb6b6088235222122de78f182
NeedsCompilation: no
Title: Molecular Degree of Perturbation calculates scores for
transcriptome data samples based on their perturbation from
controls
Description: The Molecular Degree of Perturbation webtool quantifies
the heterogeneity of samples. It takes a data.frame of omic
data that contains at least two classes (control and test) and
assigns a score to all samples based on how perturbed they are
compared to the controls. It is based on the Molecular Distance
to Health (Pankla et al. 2009), and expands on this algorithm
by adding the options to calculate the z-score using the
modified z-score (using median absolute deviation), change the
z-score zeroing threshold, and look at genes that are most
perturbed in the test versus control classes.
biocViews: BiomedicalInformatics, QualityControl, Transcriptomics,
SystemsBiology, Microarray, QualityControl
Author: Melissa Lever [aut], Pedro Russo [aut], Helder Nakaya [aut,
cre]
Maintainer: Helder Nakaya <hnakaya@usp.br>
URL: https://mdp.sysbio.tools/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mdp
git_branch: RELEASE_3_13
git_last_commit: 51bbb72
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mdp_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mdp_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mdp_1.12.0.tgz
vignettes: vignettes/mdp/inst/doc/my-vignette.html
vignetteTitles: Running the mdp package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mdp/inst/doc/my-vignette.R
dependencyCount: 39
Package: mdqc
Version: 1.54.0
Depends: R (>= 2.2.1), cluster, MASS
License: LGPL (>= 2)
MD5sum: 626d011c14bcf9905c7e370f61ddef88
NeedsCompilation: no
Title: Mahalanobis Distance Quality Control for microarrays
Description: MDQC is a multivariate quality assessment method for
microarrays based on quality control (QC) reports. The
Mahalanobis distance of an array's quality attributes is used
to measure the similarity of the quality of that array against
the quality of the other arrays. Then, arrays with unusually
high distances can be flagged as potentially low-quality.
biocViews: Microarray, QualityControl
Author: Justin Harrington
Maintainer: Gabriela Cohen-Freue <gcohen@mrl.ubc.ca>
git_url: https://git.bioconductor.org/packages/mdqc
git_branch: RELEASE_3_13
git_last_commit: 8301ba4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mdqc_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mdqc_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mdqc_1.54.0.tgz
vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf
vignetteTitles: Introduction to MDQC
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mdqc/inst/doc/mdqcvignette.R
importsMe: arrayMvout
dependencyCount: 7
Package: MDTS
Version: 1.12.0
Depends: R (>= 3.5.0)
Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings,
DNAcopy, Rsamtools, parallel, stringr
Suggests: testthat, knitr
License: Artistic-2.0
MD5sum: b1c62de68ff87f3605f2c1639dcd9a85
NeedsCompilation: no
Title: Detection of de novo deletion in targeted sequencing trios
Description: A package for the detection of de novo copy number
deletions in targeted sequencing of trios with high sensitivity
and positive predictive value.
biocViews: StatisticalMethod, Technology, Sequencing,
TargetedResequencing, Coverage, DataImport
Author: Jack M.. Fu [aut, cre]
Maintainer: Jack M.. Fu <jmfu@jhsph.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MDTS
git_branch: RELEASE_3_13
git_last_commit: e0ad157
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MDTS_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MDTS_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MDTS_1.12.0.tgz
vignettes: vignettes/MDTS/inst/doc/mdts.html
vignetteTitles: Title of your vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MDTS/inst/doc/mdts.R
dependencyCount: 43
Package: MEAL
Version: 1.22.0
Depends: R (>= 3.6.0), Biobase, MultiDataSet
Imports: GenomicRanges, limma, vegan, BiocGenerics, minfi, IRanges,
S4Vectors, methods, parallel, ggplot2 (>= 2.0.0), permute,
Gviz, missMethyl, isva, SummarizedExperiment, SmartSVA,
graphics, stats, utils, matrixStats
Suggests: testthat, IlluminaHumanMethylationEPICanno.ilm10b2.hg19,
IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData,
BiocStyle, rmarkdown, brgedata
License: Artistic-2.0
MD5sum: 49b0b71a557ec77f5de559ea8cdadc07
NeedsCompilation: no
Title: Perform methylation analysis
Description: Package to integrate methylation and expression data. It
can also perform methylation or expression analysis alone.
Several plotting functionalities are included as well as a new
region analysis based on redundancy analysis. Effect of SNPs on
a region can also be estimated.
biocViews: DNAMethylation, Microarray, Software, WholeGenome
Author: Carlos Ruiz-Arenas [aut, cre], Juan R. Gonzalez [aut]
Maintainer: Xavier Escribà Montagut <xavier.escriba@isglobal.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MEAL
git_branch: RELEASE_3_13
git_last_commit: bd59164
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MEAL_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MEAL_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MEAL_1.22.0.tgz
vignettes: vignettes/MEAL/inst/doc/caseExample.html,
vignettes/MEAL/inst/doc/MEAL.html
vignetteTitles: Expression and Methylation Analysis with MEAL,
Methylation Analysis with MEAL
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MEAL/inst/doc/caseExample.R,
vignettes/MEAL/inst/doc/MEAL.R
dependencyCount: 209
Package: MeasurementError.cor
Version: 1.64.0
License: LGPL
MD5sum: bca5ce5ddd4e5b8aa467d12044e3fdf8
NeedsCompilation: no
Title: Measurement Error model estimate for correlation coefficient
Description: Two-stage measurement error model for correlation
estimation with smaller bias than the usual sample correlation
biocViews: StatisticalMethod
Author: Beiying Ding
Maintainer: Beiying Ding <bding@amgen.com>
git_url: https://git.bioconductor.org/packages/MeasurementError.cor
git_branch: RELEASE_3_13
git_last_commit: dbc2540
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MeasurementError.cor_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MeasurementError.cor_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MeasurementError.cor_1.64.0.tgz
vignettes:
vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf
vignetteTitles: MeasurementError.cor Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.R
dependencyCount: 0
Package: MEAT
Version: 1.4.0
Depends: R (>= 4.0)
Imports: impute (>= 1.58), dynamicTreeCut (>= 1.63), glmnet (>= 2.0),
grDevices, graphics, stats, utils, stringr, tibble, RPMM (>=
1.25), minfi (>= 1.30), dplyr, SummarizedExperiment, wateRmelon
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: ea350a0885d97af483623949216dfd73
NeedsCompilation: no
Title: Muscle Epigenetic Age Test
Description: This package estimates epigenetic age in skeletal muscle,
using DNA methylation data generated with the Illumina Infinium
technology (HM27, HM450 and HMEPIC).
biocViews: Epigenetics, DNAMethylation, Microarray, Normalization,
BiomedicalInformatics, MethylationArray, Preprocessing
Author: Sarah Voisin [aut, cre]
(<https://orcid.org/0000-0002-4074-7083>), Steve Horvath [ctb]
(<https://orcid.org/0000-0002-4110-3589>)
Maintainer: Sarah Voisin <sarah.voisin.aeris@gmail.com>
URL: https://github.com/sarah-voisin/MEAT
VignetteBuilder: knitr
BugReports: https://github.com/sarah-voisin/MEAT/issues
git_url: https://git.bioconductor.org/packages/MEAT
git_branch: RELEASE_3_13
git_last_commit: 57d07d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MEAT_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MEAT_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MEAT_1.4.0.tgz
vignettes: vignettes/MEAT/inst/doc/MEAT.html
vignetteTitles: MEAT
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MEAT/inst/doc/MEAT.R
dependencyCount: 173
Package: MEB
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: e1071, SummarizedExperiment
Suggests: knitr,rmarkdown,BiocStyle
License: GPL-2
MD5sum: e1f9eaa359701c40f02283d583b376cb
NeedsCompilation: no
Title: A normalization-invariant minimum enclosing ball method to
detect differentially expressed genes for RNA-seq data
Description: Identifying differentially expressed genes between the
same or different species is an urgent demand for biological
and medical research. For RNA-seq data, systematic technical
effects and different sequencing depths are usually encountered
when conducting experiments. Normalization is regarded as an
essential step in the discovery of biologically important
changes in expression. The present methods usually involve
normalization of the data with a scaling factor, followed by
detection of significant genes. However, more than one scaling
factor may exist because of the complexity of real data.
Consequently, methods that normalize data by a single scaling
factor may deliver suboptimal performance or may not even work.
The development of modern machine learning techniques has
provided a new perspective regarding discrimination between
differentially expressed (DE) and non-DE genes. However, in
reality, the non-DE genes comprise only a small set and may
contain housekeeping genes (in same species) or conserved
orthologous genes (in different species). Therefore, the
process of detecting DE genes can be formulated as a one-class
classification problem, where only non-DE genes are observed,
while DE genes are completely absent from the training data. We
transform the problem to an outlier detection problem by
treating DE genes as outliers, and we propose a
normalization-invariant minimum enclosing ball (NIMEB) method
to construct a smallest possible ball to contain the known
non-DE genes in a feature space. The genes outside the minimum
enclosing ball can then be naturally considered to be DE genes.
Compared with the existing methods, the proposed NIMEB method
does not require data normalization, which is particularly
attractive when the RNA-seq data include more than one scaling
factor. Furthermore, the NIMEB method could be easily extended
to different species without normalization.
biocViews: DifferentialExpression, GeneExpression, Normalization,
Classification, Sequencing
Author: Yan Zhou, Jiadi Zhu
Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>, Yan Zhou
<zhouy1016@szu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MEB
git_branch: RELEASE_3_13
git_last_commit: 818e7d9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MEB_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MEB_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MEB_1.6.0.tgz
vignettes: vignettes/MEB/inst/doc/NIMEB.html
vignetteTitles: MEB Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MEB/inst/doc/NIMEB.R
dependencyCount: 30
Package: MEDIPS
Version: 1.44.0
Depends: R (>= 3.0), BSgenome, Rsamtools
Imports: GenomicRanges, Biostrings, graphics, gtools, IRanges, methods,
stats, utils, edgeR, DNAcopy, biomaRt, rtracklayer,
preprocessCore
Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, BiocStyle
License: GPL (>=2)
MD5sum: 767f3c62f5da99910902ca81688e3ef3
NeedsCompilation: no
Title: DNA IP-seq data analysis
Description: MEDIPS was developed for analyzing data derived from
methylated DNA immunoprecipitation (MeDIP) experiments followed
by sequencing (MeDIP-seq). However, MEDIPS provides
functionalities for the analysis of any kind of quantitative
sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others)
including calculation of differential coverage between groups
of samples and saturation and correlation analysis.
biocViews: DNAMethylation, CpGIsland, DifferentialExpression,
Sequencing, ChIPSeq, Preprocessing, QualityControl,
Visualization, Microarray, Genetics, Coverage,
GenomeAnnotation, CopyNumberVariation, SequenceMatching
Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich, Isaac Lopez
Moyado
Maintainer: Lukas Chavez <lukaschavez@ucsd.edu>
git_url: https://git.bioconductor.org/packages/MEDIPS
git_branch: RELEASE_3_13
git_last_commit: 4df0aee
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MEDIPS_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MEDIPS_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MEDIPS_1.44.0.tgz
vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf
vignetteTitles: MEDIPS
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MEDIPS/inst/doc/MEDIPS.R
dependencyCount: 102
Package: MEDME
Version: 1.52.0
Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils
Imports: Biostrings, MASS, drc
Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9
License: GPL (>= 2)
MD5sum: 33396ab0832822d1f7a85efcbd4493e1
NeedsCompilation: yes
Title: Modelling Experimental Data from MeDIP Enrichment
Description: MEDME allows the prediction of absolute and relative
methylation levels based on measures obtained by
MeDIP-microarray experiments
biocViews: Microarray, CpGIsland, DNAMethylation
Author: Mattia Pelizzola and Annette Molinaro
Maintainer: Mattia Pelizzola <mattia.pelizzola@gmail.com>
git_url: https://git.bioconductor.org/packages/MEDME
git_branch: RELEASE_3_13
git_last_commit: c435a94
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MEDME_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MEDME_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MEDME_1.52.0.tgz
vignettes: vignettes/MEDME/inst/doc/MEDME.pdf
vignetteTitles: MEDME.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MEDME/inst/doc/MEDME.R
dependencyCount: 112
Package: megadepth
Version: 1.2.3
Imports: xfun, utils, fs, GenomicRanges, readr, cmdfun, dplyr, magrittr
Suggests: covr, knitr, BiocStyle, sessioninfo, rmarkdown, rtracklayer,
derfinder, GenomeInfoDb, tools, RefManageR, testthat
License: Artistic-2.0
MD5sum: 523a3a6347b3f807353e80e4d0322576
NeedsCompilation: no
Title: megadepth: BigWig and BAM related utilities
Description: This package provides an R interface to Megadepth by
Christopher Wilks available at
https://github.com/ChristopherWilks/megadepth. It is
particularly useful for computing the coverage of a set of
genomic regions across bigWig or BAM files. With this package,
you can build base-pair coverage matrices for regions or
annotations of your choice from BigWig files. Megadepth was
used to create the raw files provided by
https://bioconductor.org/packages/recount3.
biocViews: Software, Coverage, DataImport, Transcriptomics, RNASeq,
Preprocessing
Author: Leonardo Collado-Torres [aut]
(<https://orcid.org/0000-0003-2140-308X>), David Zhang [aut,
cre] (<https://orcid.org/0000-0003-2382-8460>)
Maintainer: David Zhang <david.zhang.12@ucl.ac.uk>
URL: https://github.com/LieberInstitute/megadepth
SystemRequirements: megadepth
(<https://github.com/ChristopherWilks/megadepth>)
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/megadepth
git_url: https://git.bioconductor.org/packages/megadepth
git_branch: RELEASE_3_13
git_last_commit: 844d5f2
git_last_commit_date: 2021-08-23
Date/Publication: 2021-08-24
source.ver: src/contrib/megadepth_1.2.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/megadepth_1.2.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/megadepth_1.2.3.tgz
vignettes: vignettes/megadepth/inst/doc/megadepth.html
vignetteTitles: megadepth quick start guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/megadepth/inst/doc/megadepth.R
importsMe: dasper
dependencyCount: 85
Package: MEIGOR
Version: 1.26.0
Depends: Rsolnp, snowfall, CNORode, deSolve
Suggests: CellNOptR, knitr
License: GPL-3
MD5sum: de02f83bc2ec6db1ca1e6677e76e5c6f
NeedsCompilation: no
Title: MEIGO - MEtaheuristics for bIoinformatics Global Optimization
Description: Global Optimization
biocViews: SystemsBiology
Author: Jose A. Egea, David Henriques, Alexandre Fdez. Villaverde,
Thomas Cokelaer
Maintainer: Jose A. Egea <josea.egea@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MEIGOR
git_branch: RELEASE_3_13
git_last_commit: c728ace
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MEIGOR_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MEIGOR_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MEIGOR_1.26.0.tgz
vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.pdf
vignetteTitles: Main vignette:Global Optimization for Bioinformatics
and Systems Biology
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R
importsMe: bioOED
dependencyCount: 61
Package: Melissa
Version: 1.8.0
Depends: R (>= 3.5.0), BPRMeth, GenomicRanges
Imports: data.table, parallel, ROCR, matrixcalc, mclust, ggplot2,
doParallel, foreach, MCMCpack, cowplot, magrittr, mvtnorm,
truncnorm, assertthat, BiocStyle, stats, utils
Suggests: testthat, knitr, rmarkdown
License: GPL-3 | file LICENSE
MD5sum: 2564b4540b83c01461a61eaaf073118d
NeedsCompilation: no
Title: Bayesian clustering and imputationa of single cell methylomes
Description: Melissa is a Baysian probabilistic model for jointly
clustering and imputing single cell methylomes. This is done by
taking into account local correlations via a Generalised Linear
Model approach and global similarities using a mixture
modelling approach.
biocViews: ImmunoOncology, DNAMethylation, GeneExpression,
GeneRegulation, Epigenetics, Genetics, Clustering,
FeatureExtraction, Regression, RNASeq, Bayesian, KEGG,
Sequencing, Coverage, SingleCell
Author: C. A. Kapourani [aut, cre]
Maintainer: C. A. Kapourani <kapouranis.andreas@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Melissa
git_branch: RELEASE_3_13
git_last_commit: 4652c05
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Melissa_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Melissa_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Melissa_1.8.0.tgz
vignettes: vignettes/Melissa/inst/doc/process_files.html,
vignettes/Melissa/inst/doc/run_melissa.html
vignetteTitles: 1: Process and filter scBS-seq data, 2: Cluster and
impute scBS-seq data using Melissa
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Melissa/inst/doc/process_files.R,
vignettes/Melissa/inst/doc/run_melissa.R
dependencyCount: 105
Package: memes
Version: 1.0.4
Depends: R (>= 4.1)
Imports: Biostrings, dplyr, cmdfun (>= 1.0.2), GenomicRanges, ggplot2,
ggseqlogo, magrittr, matrixStats, methods, patchwork, processx,
purrr, rlang, readr, stats, tools, tibble, tidyr, utils,
usethis, universalmotif (>= 1.9.3), xml2
Suggests: cowplot, BSgenome.Dmelanogaster.UCSC.dm3,
BSgenome.Dmelanogaster.UCSC.dm6, forcats, testthat (>= 2.1.0),
knitr, MotifDb, pheatmap, PMCMRplus, plyranges (>= 1.9.1),
rmarkdown, covr
License: MIT + file LICENSE
MD5sum: dcf034f2fe9e4a252d1a2d612eca8cf2
NeedsCompilation: no
Title: motif matching, comparison, and de novo discovery using the MEME
Suite
Description: A seamless interface to the MEME Suite family of tools for
motif analysis. 'memes' provides data aware utilities for using
GRanges objects as entrypoints to motif analysis, data
structures for examining & editing motif lists, and novel data
visualizations. 'memes' functions and data structures are
amenable to both base R and tidyverse workflows.
biocViews: DataImport, FunctionalGenomics, GeneRegulation,
MotifAnnotation, MotifDiscovery, SequenceMatching, Software
Author: Spencer Nystrom [aut, cre, cph]
(<https://orcid.org/0000-0003-1000-1579>)
Maintainer: Spencer Nystrom <nystromdev@gmail.com>
URL: https://snystrom.github.io/memes/,
https://github.com/snystrom/memes
SystemRequirements: Meme Suite (v5.3.3 or above)
<http://meme-suite.org/doc/download.html>
VignetteBuilder: knitr
BugReports: https://github.com/snystrom/memes/issues
git_url: https://git.bioconductor.org/packages/memes
git_branch: RELEASE_3_13
git_last_commit: 7a80e64
git_last_commit_date: 2021-08-06
Date/Publication: 2021-08-08
source.ver: src/contrib/memes_1.0.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/memes_1.0.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/memes_1.0.4.tgz
vignettes: vignettes/memes/inst/doc/core_ame.html,
vignettes/memes/inst/doc/core_dreme.html,
vignettes/memes/inst/doc/core_fimo.html,
vignettes/memes/inst/doc/core_tomtom.html,
vignettes/memes/inst/doc/install_guide.html,
vignettes/memes/inst/doc/integrative_analysis.html,
vignettes/memes/inst/doc/tidy_motifs.html
vignetteTitles: Motif Enrichment Testing using AME, Denovo Motif
Discovery Using DREME, Motif Scanning using FIMO, Motif
Comparison using TomTom, Install MEME, ChIP-seq Analysis,
Tidying Motif Metadata
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/memes/inst/doc/core_ame.R,
vignettes/memes/inst/doc/core_dreme.R,
vignettes/memes/inst/doc/core_fimo.R,
vignettes/memes/inst/doc/core_tomtom.R,
vignettes/memes/inst/doc/install_guide.R,
vignettes/memes/inst/doc/integrative_analysis.R,
vignettes/memes/inst/doc/tidy_motifs.R
dependencyCount: 110
Package: Mergeomics
Version: 1.20.0
Depends: R (>= 3.0.1)
Suggests: RUnit, BiocGenerics
License: GPL (>= 2)
Archs: i386, x64
MD5sum: cebef1da0431978871ccbdb2ed422482
NeedsCompilation: no
Title: Integrative network analysis of omics data
Description: The Mergeomics pipeline serves as a flexible framework for
integrating multidimensional omics-disease associations,
functional genomics, canonical pathways and gene-gene
interaction networks to generate mechanistic hypotheses. It
includes two main parts, 1) Marker set enrichment analysis
(MSEA); 2) Weighted Key Driver Analysis (wKDA).
biocViews: Software
Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin
Zhang, Xia Yang
Maintainer: Zeyneb Kurt <zeynebkurt@gmail.com>
git_url: https://git.bioconductor.org/packages/Mergeomics
git_branch: RELEASE_3_13
git_last_commit: 30fe263
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Mergeomics_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Mergeomics_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Mergeomics_1.20.0.tgz
vignettes: vignettes/Mergeomics/inst/doc/Mergeomics.pdf
vignetteTitles: Mergeomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Mergeomics/inst/doc/Mergeomics.R
dependencyCount: 0
Package: MeSHDbi
Version: 1.28.0
Depends: R (>= 3.0.1), BiocGenerics (>= 0.15.10)
Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase
Suggests: RUnit
License: Artistic-2.0
MD5sum: 328148f76fbf26facb12b5d2d66226f1
NeedsCompilation: no
Title: DBI to construct MeSH-related package from sqlite file
Description: The package is unified implementation of MeSH.db,
MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct
Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import
sqlite file and generate MeSH.XXX.eg.db.
biocViews: Annotation, AnnotationData, Infrastructure
Author: Koki Tsuyuzaki
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
git_url: https://git.bioconductor.org/packages/MeSHDbi
git_branch: RELEASE_3_13
git_last_commit: 5e396b6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MeSHDbi_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MeSHDbi_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MeSHDbi_1.28.0.tgz
vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf
vignetteTitles: MeSH.db
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: MeSH.Aca.eg.db, MeSH.Aga.PEST.eg.db, MeSH.Ame.eg.db,
MeSH.Aml.eg.db, MeSH.Ana.eg.db, MeSH.Ani.FGSC.eg.db,
MeSH.AOR.db, MeSH.Ath.eg.db, MeSH.Bfl.eg.db,
MeSH.Bsu.168.eg.db, MeSH.Bta.eg.db, MeSH.Cal.SC5314.eg.db,
MeSH.Cbr.eg.db, MeSH.Cel.eg.db, MeSH.Cfa.eg.db, MeSH.Cin.eg.db,
MeSH.Cja.eg.db, MeSH.Cpo.eg.db, MeSH.Cre.eg.db, MeSH.Dan.eg.db,
MeSH.db, MeSH.Dda.3937.eg.db, MeSH.Ddi.AX4.eg.db,
MeSH.Der.eg.db, MeSH.Dgr.eg.db, MeSH.Dme.eg.db, MeSH.Dmo.eg.db,
MeSH.Dpe.eg.db, MeSH.Dre.eg.db, MeSH.Dse.eg.db, MeSH.Dsi.eg.db,
MeSH.Dvi.eg.db, MeSH.Dya.eg.db, MeSH.Eca.eg.db,
MeSH.Eco.K12.MG1655.eg.db, MeSH.Eco.O157.H7.Sakai.eg.db,
MeSH.Gga.eg.db, MeSH.Gma.eg.db, MeSH.Hsa.eg.db, MeSH.Laf.eg.db,
MeSH.Lma.eg.db, MeSH.Mdo.eg.db, MeSH.Mes.eg.db, MeSH.Mga.eg.db,
MeSH.Miy.eg.db, MeSH.Mml.eg.db, MeSH.Mmu.eg.db, MeSH.Mtr.eg.db,
MeSH.Nle.eg.db, MeSH.Oan.eg.db, MeSH.Ocu.eg.db, MeSH.Oni.eg.db,
MeSH.Osa.eg.db, MeSH.Pab.eg.db, MeSH.Pae.PAO1.eg.db,
MeSH.PCR.db, MeSH.Pfa.3D7.eg.db, MeSH.Pto.eg.db,
MeSH.Ptr.eg.db, MeSH.Rno.eg.db, MeSH.Sce.S288c.eg.db,
MeSH.Sco.A32.eg.db, MeSH.Sil.eg.db, MeSH.Spu.eg.db,
MeSH.Ssc.eg.db, MeSH.Syn.eg.db, MeSH.Tbr.9274.eg.db,
MeSH.Tgo.ME49.eg.db, MeSH.Tgu.eg.db, MeSH.Vvi.eg.db,
MeSH.Xla.eg.db, MeSH.Xtr.eg.db, MeSH.Zma.eg.db
importsMe: meshr, scTensor
dependencyCount: 46
Package: meshes
Version: 1.18.1
Depends: R (>= 3.6.0)
Imports: AnnotationDbi, DOSE, enrichplot, GOSemSim, MeSH.db, methods,
utils, yulab.utils
Suggests: knitr, rmarkdown, MeSH.Cel.eg.db, MeSH.Hsa.eg.db, prettydoc
License: Artistic-2.0
MD5sum: a1616b978f042cbafb0be0c7eeaedbc6
NeedsCompilation: no
Title: MeSH Enrichment and Semantic analyses
Description: MeSH (Medical Subject Headings) is the NLM controlled
vocabulary used to manually index articles for MEDLINE/PubMed.
MeSH terms were associated by Entrez Gene ID by three methods,
gendoo, gene2pubmed and RBBH. This association is fundamental
for enrichment and semantic analyses. meshes supports
enrichment analysis (over-representation and gene set
enrichment analysis) of gene list or whole expression profile.
The semantic comparisons of MeSH terms provide quantitative
ways to compute similarities between genes and gene groups.
meshes implemented five methods proposed by Resnik, Schlicker,
Jiang, Lin and Wang respectively and supports more than 70
species.
biocViews: Annotation, Clustering, MultipleComparison, Software
Author: Guangchuang Yu [aut, cre]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/biomedical-knowledge-mining-book/
VignetteBuilder: knitr
BugReports: https://github.com/GuangchuangYu/meshes/issues
git_url: https://git.bioconductor.org/packages/meshes
git_branch: RELEASE_3_13
git_last_commit: 0a45118
git_last_commit_date: 2021-08-20
Date/Publication: 2021-08-22
source.ver: src/contrib/meshes_1.18.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/meshes_1.18.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/meshes_1.18.1.tgz
vignettes: vignettes/meshes/inst/doc/meshes.html
vignetteTitles: meshes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/meshes/inst/doc/meshes.R
dependencyCount: 126
Package: meshr
Version: 1.28.0
Depends: R (>= 3.0.1)
Imports: methods, stats, utils, fdrtool, MeSH.db, MeSH.AOR.db,
MeSH.PCR.db, MeSHDbi, MeSH.Hsa.eg.db, MeSH.Aca.eg.db,
MeSH.Bsu.168.eg.db, MeSH.Syn.eg.db, cummeRbund, org.Hs.eg.db,
Category, S4Vectors, BiocGenerics, RSQLite
License: Artistic-2.0
MD5sum: 28d5ff00dd6f376ae54828e4805928b1
NeedsCompilation: no
Title: Tools for conducting enrichment analysis of MeSH
Description: A set of annotation maps describing the entire MeSH
assembled using data from MeSH.
biocViews: AnnotationData, FunctionalAnnotation, Bioinformatics,
Statistics, Annotation, MultipleComparisons, MeSHDb
Author: Koki Tsuyuzaki, Itoshi Nikaido, Gota Morota
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
git_url: https://git.bioconductor.org/packages/meshr
git_branch: RELEASE_3_13
git_last_commit: f8913a9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/meshr_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/meshr_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/meshr_1.28.0.tgz
vignettes: vignettes/meshr/inst/doc/MeSH.pdf
vignetteTitles: MeSH.db
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/meshr/inst/doc/MeSH.R
importsMe: scTensor
dependencyCount: 163
Package: MesKit
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: methods, data.table, Biostrings, dplyr, tidyr (>= 1.0.0), ape
(>= 5.4.1), ggrepel, pracma, ggridges, AnnotationDbi, IRanges,
circlize, cowplot, mclust, phangorn, ComplexHeatmap (>= 1.9.3),
ggplot2, RColorBrewer, grDevices, stats, utils, S4Vectors
Suggests: shiny, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 (>=
1.4.0), org.Hs.eg.db, clusterProfiler,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL-3
MD5sum: a2548fe44491b861c7713ddec1effeed
NeedsCompilation: no
Title: A tool kit for dissecting cancer evolution from multi-region
derived tumor biopsies via somatic alterations
Description: MesKit provides commonly used analysis and visualization
modules based on mutational data generated by multi-region
sequencing (MRS). This package allows to depict mutational
profiles, measure heterogeneity within or between tumors from
the same patient, track evolutionary dynamics, as well as
characterize mutational patterns on different levels. Shiny
application was also developed for a need of GUI-based
analysis. As a handy tool, MesKit can facilitate the
interpretation of tumor heterogeneity and the understanding of
evolutionary relationship between regions in MRS study.
Author: Mengni Liu [aut, cre]
(<https://orcid.org/0000-0001-9938-9973>), Jianyu Chen [aut,
ctb] (<https://orcid.org/0000-0003-4491-9265>), Xin Wang [aut,
ctb] (<https://orcid.org/0000-0002-6072-599X>)
Maintainer: Mengni Liu <niinleslie@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MesKit
git_branch: RELEASE_3_13
git_last_commit: faa3f2a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MesKit_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MesKit_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MesKit_1.2.0.tgz
vignettes: vignettes/MesKit/inst/doc/MesKit.html
vignetteTitles: Analyze and Visualize Multi-region Whole-exome
Sequencing Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MesKit/inst/doc/MesKit.R
dependencyCount: 104
Package: messina
Version: 1.28.0
Depends: R (>= 3.1.0), survival (>= 2.37-4), methods
Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid
(>= 3.1.0), foreach (>= 1.4.1), graphics
LinkingTo: Rcpp
Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>=
2.22.0), BiocStyle
Enhances: doMC (>= 1.3.3)
License: EPL (>= 1.0)
MD5sum: d2f9749d47d12daf45ff2821c1bed4a9
NeedsCompilation: yes
Title: Single-gene classifiers and outlier-resistant detection of
differential expression for two-group and survival problems
Description: Messina is a collection of algorithms for constructing
optimally robust single-gene classifiers, and for identifying
differential expression in the presence of outliers or unknown
sample subgroups. The methods have application in identifying
lead features to develop into clinical tests (both diagnostic
and prognostic), and in identifying differential expression
when a fraction of samples show unusual patterns of expression.
biocViews: GeneExpression, DifferentialExpression,
BiomedicalInformatics, Classification, Survival
Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph]
Maintainer: Mark Pinese <mpinese@ccia.org.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/messina
git_branch: RELEASE_3_13
git_last_commit: 16fcd78
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/messina_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/messina_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/messina_1.28.0.tgz
vignettes: vignettes/messina/inst/doc/messina.pdf
vignetteTitles: Using Messina
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/messina/inst/doc/messina.R
dependencyCount: 44
Package: Metab
Version: 1.26.0
Depends: xcms, R (>= 3.0.1), svDialogs
Imports: pander
Suggests: RUnit, BiocGenerics
License: GPL (>=2)
MD5sum: 962ee3ec889c8f304fc0a7daebdda817
NeedsCompilation: no
Title: Metab: An R Package for a High-Throughput Analysis of
Metabolomics Data Generated by GC-MS.
Description: Metab is an R package for high-throughput processing of
metabolomics data analysed by the Automated Mass Spectral
Deconvolution and Identification System (AMDIS)
(http://chemdata.nist.gov/mass-spc/amdis/downloads/). In
addition, it performs statistical hypothesis test (t-test) and
analysis of variance (ANOVA). Doing so, Metab considerably
speed up the data mining process in metabolomics and produces
better quality results. Metab was developed using interactive
features, allowing users with lack of R knowledge to appreciate
its functionalities.
biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, AMDIS, GCMS
Author: Raphael Aggio <ragg005@aucklanduni.ac.nz>
Maintainer: Raphael Aggio <ragg005@aucklanduni.ac.nz>
git_url: https://git.bioconductor.org/packages/Metab
git_branch: RELEASE_3_13
git_last_commit: dfe078a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Metab_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Metab_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Metab_1.26.0.tgz
vignettes: vignettes/Metab/inst/doc/MetabPackage.pdf
vignetteTitles: Applying Metab
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Metab/inst/doc/MetabPackage.R
dependencyCount: 98
Package: metabCombiner
Version: 1.2.2
Depends: R (>= 4.0), dplyr (>= 1.0)
Imports: methods, mgcv, caret, S4Vectors, stats, utils, rlang,
graphics, matrixStats, tidyr
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: GPL-3
MD5sum: 989f99ab5890848c08c1271e0aa41d05
NeedsCompilation: yes
Title: Method for Combining LC-MS Metabolomics Feature Measurements
Description: This package aligns LC-HRMS metabolomics datasets acquired
from biologically similar specimens analyzed under similar, but
not necessarily identical, conditions. Peak-picked and simply
aligned metabolomics feature tables (consisting of m/z, rt, and
per-sample abundance measurements, plus optional identifiers &
adduct annotations) are accepted as input. The package outputs
a combined table of feature pair alignments, organized into
groups of similar m/z, and ranked by a similarity score. Input
tables are assumed to be acquired using similar (but not
necessarily identical) analytical methods.
biocViews: Software, MassSpectrometry, Metabolomics
Author: Hani Habra [aut, cre], Alla Karnovsky [ths]
Maintainer: Hani Habra <hhabra1@gmail.com>
VignetteBuilder: knitr
BugReports: https://www.github.com/hhabra/metabCombiner/issues
git_url: https://git.bioconductor.org/packages/metabCombiner
git_branch: RELEASE_3_13
git_last_commit: ef53e69
git_last_commit_date: 2021-08-23
Date/Publication: 2021-08-24
source.ver: src/contrib/metabCombiner_1.2.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metabCombiner_1.2.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/metabCombiner_1.2.2.tgz
vignettes: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.html
vignetteTitles: Combine LC-MS Metabolomics Datasets with metabCombiner
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.R
dependencyCount: 84
Package: MetaboCoreUtils
Version: 1.0.0
Depends: R (>= 4.1)
Imports: stringr, utils
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 6f4166d4d515930185ca9c414317ed05
NeedsCompilation: no
Title: Core Utils for Metabolomics Data
Description: MetaboCoreUtils defines metabolomics-related core
functionality provided as low-level functions to allow a data
structure-independent usage across various R packages. This
includes functions to calculate between ion (adduct) and
compound mass-to-charge ratios and masses or functions to work
with chemical formulas. The package provides also a set of
adduct definitions and information on some commercially
available internal standard mixes commonly used in MS
experiments.
biocViews: Infrastructure, Metabolomics, MassSpectrometry
Author: Johannes Rainer [aut, cre]
(<https://orcid.org/0000-0002-6977-7147>), Michael Witting
[aut] (<https://orcid.org/0000-0002-1462-4426>)
Maintainer: Johannes Rainer <Johannes.Rainer@eurac.edu>
URL: https://github.com/RforMassSpectrometry/MetaboCoreUtils
VignetteBuilder: knitr
BugReports:
https://github.com/RforMassSpectrometry/MetaboCoreUtils/issues
git_url: https://git.bioconductor.org/packages/MetaboCoreUtils
git_branch: RELEASE_3_13
git_last_commit: 0c30b89
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MetaboCoreUtils_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MetaboCoreUtils_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MetaboCoreUtils_1.0.0.tgz
vignettes: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.html
vignetteTitles: Core Utils for Metabolomics Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.R
dependencyCount: 8
Package: metabolomicsWorkbenchR
Version: 1.2.0
Depends: R (>= 4.0)
Imports: data.table, httr, jsonlite, methods, MultiAssayExperiment,
struct, SummarizedExperiment, utils
Suggests: BiocStyle, covr, knitr, HDF5Array, rmarkdown, structToolbox,
testthat, pmp, grid, png
License: GPL-3
MD5sum: bfb8249e80c8e5c6642c912726067141
NeedsCompilation: no
Title: Metabolomics Workbench in R
Description: This package provides functions for interfacing with the
Metabolomics Workbench RESTful API. Study, compound, protein
and gene information can be searched for using the API. Methods
to obtain study data in common Bioconductor formats such as
SummarizedExperiment and MultiAssayExperiment are also
included.
biocViews: Software, Metabolomics
Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut]
Maintainer: Gavin Rhys Lloyd <g.r.lloyd@bham.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/metabolomicsWorkbenchR
git_branch: RELEASE_3_13
git_last_commit: 2e1df4c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/metabolomicsWorkbenchR_1.2.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/metabolomicsWorkbenchR_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/metabolomicsWorkbenchR_1.2.0.tgz
vignettes:
vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.html,
vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.html
vignetteTitles: Example using structToolbox,
Introduction_to_metabolomicsWorkbenchR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.R,
vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.R
suggestsMe: fobitools
dependencyCount: 65
Package: metabomxtr
Version: 1.26.0
Depends: methods,Biobase
Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2
Suggests: xtable, reshape2
License: GPL-2
Archs: i386, x64
MD5sum: a69d2b59acade4ea9e53c267032aec20
NeedsCompilation: no
Title: A package to run mixture models for truncated metabolomics data
with normal or lognormal distributions
Description: The functions in this package return optimized parameter
estimates and log likelihoods for mixture models of truncated
data with normal or lognormal distributions.
biocViews: ImmunoOncology, Metabolomics, MassSpectrometry
Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens
Maintainer: Michael Nodzenski <michael.nodzenski@northwestern.edu>
git_url: https://git.bioconductor.org/packages/metabomxtr
git_branch: RELEASE_3_13
git_last_commit: 3cc8a48
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/metabomxtr_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metabomxtr_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/metabomxtr_1.26.0.tgz
vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf,
vignettes/metabomxtr/inst/doc/mixnorm_Vignette.pdf
vignetteTitles: metabomxtr, mixnorm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R,
vignettes/metabomxtr/inst/doc/mixnorm_Vignette.R
dependencyCount: 56
Package: MetaboSignal
Version: 1.22.0
Depends: R(>= 3.3)
Imports: KEGGgraph, hpar, igraph, RCurl, KEGGREST, EnsDb.Hsapiens.v75,
stats, graphics, utils, org.Hs.eg.db, biomaRt, AnnotationDbi,
MWASTools, mygene
Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown
License: GPL-3
MD5sum: 909e504dc4efb1bd629b860e20e1bd61
NeedsCompilation: no
Title: MetaboSignal: a network-based approach to overlay and explore
metabolic and signaling KEGG pathways
Description: MetaboSignal is an R package that allows merging,
analyzing and customizing metabolic and signaling KEGG
pathways. It is a network-based approach designed to explore
the topological relationship between genes (signaling- or
enzymatic-genes) and metabolites, representing a powerful tool
to investigate the genetic landscape and regulatory networks of
metabolic phenotypes.
biocViews: GraphAndNetwork, GeneSignaling, GeneTarget, Network,
Pathways, KEGG, Reactome, Software
Author: Andrea Rodriguez-Martinez, Rafael Ayala, Joram M. Posma, Ana L.
Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas
Maintainer: Andrea Rodriguez-Martinez
<andrea.rodriguez-martinez13@imperial.ac.uk>, Rafael Ayala
<rafael.ayala@oist.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetaboSignal
git_branch: RELEASE_3_13
git_last_commit: b02adbe
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MetaboSignal_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MetaboSignal_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MetaboSignal_1.22.0.tgz
vignettes: vignettes/MetaboSignal/inst/doc/MetaboSignal.html,
vignettes/MetaboSignal/inst/doc/MetaboSignal2.html
vignetteTitles: MetaboSignal, MetaboSignal 2: merging KEGG with
additional interaction resources
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetaboSignal/inst/doc/MetaboSignal.R,
vignettes/MetaboSignal/inst/doc/MetaboSignal2.R
dependencyCount: 198
Package: metaCCA
Version: 1.20.0
Suggests: knitr
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: c1c4f25051aeed604258b43988991a91
NeedsCompilation: no
Title: Summary Statistics-Based Multivariate Meta-Analysis of
Genome-Wide Association Studies Using Canonical Correlation
Analysis
Description: metaCCA performs multivariate analysis of a single or
multiple GWAS based on univariate regression coefficients. It
allows multivariate representation of both phenotype and
genotype. metaCCA extends the statistical technique of
canonical correlation analysis to the setting where original
individual-level records are not available, and employs a
covariance shrinkage algorithm to achieve robustness.
biocViews: GenomeWideAssociation, SNP, Genetics, Regression,
StatisticalMethod, Software
Author: Anna Cichonska <anna.cichonska@gmail.com>
Maintainer: Anna Cichonska <anna.cichonska@gmail.com>
URL: https://doi.org/10.1093/bioinformatics/btw052
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/metaCCA
git_branch: RELEASE_3_13
git_last_commit: d52a6f6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/metaCCA_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metaCCA_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/metaCCA_1.20.0.tgz
vignettes: vignettes/metaCCA/inst/doc/metaCCA.pdf
vignetteTitles: metaCCA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/metaCCA/inst/doc/metaCCA.R
dependencyCount: 0
Package: MetaCyto
Version: 1.14.0
Depends: R (>= 3.4)
Imports: flowCore (>= 1.4),tidyr (>=
0.7),fastcluster,ggplot2,metafor,cluster,FlowSOM, grDevices,
graphics, stats, utils
Suggests: knitr, dplyr
License: GPL (>= 2)
MD5sum: 1c7b84f799a89355c5bfdde0ffed9582
NeedsCompilation: no
Title: MetaCyto: A package for meta-analysis of cytometry data
Description: This package provides functions for preprocessing,
automated gating and meta-analysis of cytometry data. It also
provides functions that facilitate the collection of cytometry
data from the ImmPort database.
biocViews: ImmunoOncology, CellBiology, FlowCytometry, Clustering,
StatisticalMethod, Software, CellBasedAssays, Preprocessing
Author: Zicheng Hu, Chethan Jujjavarapu, Sanchita Bhattacharya, Atul J.
Butte
Maintainer: Zicheng Hu <zicheng.hu@ucsf.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetaCyto
git_branch: RELEASE_3_13
git_last_commit: 39b62a4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MetaCyto_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MetaCyto_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MetaCyto_1.14.0.tgz
vignettes: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.R
dependencyCount: 198
Package: metagene
Version: 2.24.0
Depends: R (>= 3.5.0), R6 (>= 2.0), GenomicRanges, BiocParallel
Imports: rtracklayer, gplots, tools, GenomicAlignments, GenomeInfoDb,
GenomicFeatures, IRanges, ggplot2, muStat, Rsamtools,
matrixStats, purrr, data.table, magrittr, methods, utils,
ensembldb, EnsDb.Hsapiens.v86, stringr
Suggests: BiocGenerics, similaRpeak, RUnit, knitr, BiocStyle,
rmarkdown, similaRpeak
License: Artistic-2.0 | file LICENSE
MD5sum: 5cf76ef46b8fd7e40f1f9f6e20e494e1
NeedsCompilation: no
Title: A package to produce metagene plots
Description: This package produces metagene plots to compare the
behavior of DNA-interacting proteins at selected groups of
genes/features. Bam files are used to increase the resolution.
Multiple combination of group of bam files and/or group of
genomic regions can be compared in a single analysis.
Bootstraping analysis is used to compare the groups and locate
regions with statistically different enrichment profiles.
biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment,
Sequencing
Author: Charles Joly Beauparlant
<charles.joly-beauparlant@crchul.ulaval.ca>, Fabien Claude
Lamaze <fabien.lamaze.1@ulaval.ca>, Rawane Samb
<rawane.samb.1@ulaval.ca>, Cedric Lippens
<lippens.cedric@protonmail>, Astrid Louise Deschenes
<Astrid-Louise.Deschenes@crchudequebec.ulaval.ca> and Arnaud
Droit <arnaud.droit@crchuq.ulaval.ca>.
Maintainer: Charles Joly Beauparlant
<charles.joly-beauparlant@crchul.ulaval.ca>
VignetteBuilder: knitr
BugReports: https://github.com/CharlesJB/metagene/issues
git_url: https://git.bioconductor.org/packages/metagene
git_branch: RELEASE_3_13
git_last_commit: f85b3d4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/metagene_2.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metagene_2.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/metagene_2.24.0.tgz
vignettes: vignettes/metagene/inst/doc/metagene_rnaseq.html,
vignettes/metagene/inst/doc/metagene.html
vignetteTitles: RNA-seq exp ext, Introduction to metagene
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/metagene/inst/doc/metagene_rnaseq.R,
vignettes/metagene/inst/doc/metagene.R
dependencyCount: 121
Package: metagene2
Version: 1.8.1
Depends: R (>= 4.0), R6 (>= 2.0), GenomicRanges, BiocParallel
Imports: rtracklayer, tools, GenomicAlignments, GenomeInfoDb, IRanges,
ggplot2, Rsamtools, purrr, data.table, methods, dplyr,
magrittr, reshape2
Suggests: BiocGenerics, RUnit, knitr, BiocStyle, rmarkdown
License: Artistic-2.0
MD5sum: e1042fdfbc5fc9327b3167bbe65ac0b0
NeedsCompilation: no
Title: A package to produce metagene plots
Description: This package produces metagene plots to compare coverages
of sequencing experiments at selected groups of genomic
regions. It can be used for such analyses as assessing the
binding of DNA-interacting proteins at promoter regions or
surveying antisense transcription over the length of a gene.
The metagene2 package can manage all aspects of the analysis,
from normalization of coverages to plot facetting according to
experimental metadata. Bootstraping analysis is used to provide
confidence intervals of per-sample mean coverages.
biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment,
Sequencing
Author: Eric Fournier [cre, aut], Charles Joly Beauparlant [aut],
Cedric Lippens [aut], Arnaud Droit [aut]
Maintainer: Eric Fournier <ericfournier2@yahoo.ca>
URL: https://github.com/ArnaudDroitLab/metagene2
VignetteBuilder: knitr
BugReports: https://github.com/ArnaudDroitLab/metagene2/issues
git_url: https://git.bioconductor.org/packages/metagene2
git_branch: RELEASE_3_13
git_last_commit: d1121a9
git_last_commit_date: 2021-07-13
Date/Publication: 2021-07-15
source.ver: src/contrib/metagene2_1.8.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metagene2_1.8.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/metagene2_1.8.1.tgz
vignettes: vignettes/metagene2/inst/doc/metagene2.html
vignetteTitles: Introduction to metagene2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metagene2/inst/doc/metagene2.R
dependencyCount: 83
Package: metagenomeSeq
Version: 1.34.0
Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer
Imports: parallel, matrixStats, foreach, Matrix, gplots, graphics,
grDevices, stats, utils, Wrench
Suggests: annotate, BiocGenerics, biomformat, knitr, gss, testthat (>=
0.8), vegan, interactiveDisplay, IHW
License: Artistic-2.0
MD5sum: 7ee78687f6244d6ab489fec0e4c2c628
NeedsCompilation: no
Title: Statistical analysis for sparse high-throughput sequencing
Description: metagenomeSeq is designed to determine features (be it
Operational Taxanomic Unit (OTU), species, etc.) that are
differentially abundant between two or more groups of multiple
samples. metagenomeSeq is designed to address the effects of
both normalization and under-sampling of microbial communities
on disease association detection and the testing of feature
correlations.
biocViews: ImmunoOncology, Classification, Clustering,
GeneticVariability, DifferentialExpression, Microbiome,
Metagenomics, Normalization, Visualization, MultipleComparison,
Sequencing, Software
Author: Joseph Nathaniel Paulson, Nathan D. Olson, Domenick J. Braccia,
Justin Wagner, Hisham Talukder, Mihai Pop, Hector Corrada Bravo
Maintainer: Joseph N. Paulson <jpaulson@jimmy.harvard.edu>
URL: https://github.com/nosson/metagenomeSeq/
VignetteBuilder: knitr
BugReports: https://github.com/nosson/metagenomeSeq/issues
git_url: https://git.bioconductor.org/packages/metagenomeSeq
git_branch: RELEASE_3_13
git_last_commit: ff2f710
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/metagenomeSeq_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metagenomeSeq_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/metagenomeSeq_1.34.0.tgz
vignettes: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.pdf,
vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf
vignetteTitles: fitTimeSeries: differential abundance analysis through
time or location, metagenomeSeq: statistical analysis for
sparse high-throughput sequencing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.R,
vignettes/metagenomeSeq/inst/doc/metagenomeSeq.R
dependsOnMe: metavizr, microbiomeExplorer, etec16s
importsMe: Maaslin2, microbiomeDASim, MetaLonDA
suggestsMe: interactiveDisplay, phyloseq, Wrench
dependencyCount: 28
Package: metahdep
Version: 1.50.0
Depends: R (>= 2.10), methods
Suggests: affyPLM
License: GPL-3
MD5sum: 98e1d287424c8531276b729bfb45b6b2
NeedsCompilation: yes
Title: Hierarchical Dependence in Meta-Analysis
Description: Tools for meta-analysis in the presence of hierarchical
(and/or sampling) dependence, including with gene expression
studies
biocViews: Microarray, DifferentialExpression
Author: John R. Stevens, Gabriel Nicholas
Maintainer: John R. Stevens <john.r.stevens@usu.edu>
git_url: https://git.bioconductor.org/packages/metahdep
git_branch: RELEASE_3_13
git_last_commit: 49da854
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/metahdep_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metahdep_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/metahdep_1.50.0.tgz
vignettes: vignettes/metahdep/inst/doc/metahdep.pdf
vignetteTitles: metahdep Primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metahdep/inst/doc/metahdep.R
dependencyCount: 1
Package: metaMS
Version: 1.28.0
Depends: R (>= 4.0), methods, CAMERA, xcms (>= 1.35)
Imports: Matrix, tools, robustbase, BiocGenerics, graphics, stats,
utils
Suggests: metaMSdata, RUnit
License: GPL (>= 2)
MD5sum: fde2f197dba9d1f92de0cc52511cf619
NeedsCompilation: no
Title: MS-based metabolomics annotation pipeline
Description: MS-based metabolomics data processing and compound
annotation pipeline.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics
Author: Ron Wehrens [aut] (author of GC-MS part, Initial Maintainer),
Pietro Franceschi [aut] (author of LC-MS part), Nir Shahaf
[ctb], Matthias Scholz [ctb], Georg Weingart [ctb] (development
of GC-MS approach), Elisabete Carvalho [ctb] (testing and
feedback of GC-MS pipeline), Yann Guitton [ctb, cre]
(<https://orcid.org/0000-0002-4479-0636>), Julien Saint-Vanne
[ctb]
Maintainer: Yann Guitton <yann.guitton@gmail.com>
URL: https://github.com/yguitton/metaMS
git_url: https://git.bioconductor.org/packages/metaMS
git_branch: RELEASE_3_13
git_last_commit: 1f3d024
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/metaMS_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metaMS_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/metaMS_1.28.0.tgz
vignettes: vignettes/metaMS/inst/doc/runGC.pdf,
vignettes/metaMS/inst/doc/runLC.pdf
vignetteTitles: runGC, runLC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metaMS/inst/doc/runGC.R,
vignettes/metaMS/inst/doc/runLC.R
suggestsMe: CluMSID
dependencyCount: 126
Package: MetaNeighbor
Version: 1.12.0
Depends: R(>= 3.5)
Imports: grDevices, graphics, methods, stats (>= 3.4), utils (>= 3.4),
Matrix (>= 1.2), matrixStats (>= 0.54), beanplot (>= 1.2),
gplots (>= 3.0.1), RColorBrewer (>= 1.1.2),
SummarizedExperiment (>= 1.12), SingleCellExperiment, igraph,
dplyr, tidyr, tibble, ggplot2
Suggests: knitr (>= 1.17), rmarkdown (>= 1.6), testthat (>= 1.0.2),
UpSetR
License: MIT + file LICENSE
MD5sum: d6b6c4e7fdd050281e1458630154fd2c
NeedsCompilation: no
Title: Single cell replicability analysis
Description: MetaNeighbor allows users to quantify cell type
replicability across datasets using neighbor voting.
biocViews: ImmunoOncology, GeneExpression, GO, MultipleComparison,
SingleCell, Transcriptomics
Author: Megan Crow [aut, cre], Sara Ballouz [ctb], Manthan Shah [ctb],
Stephan Fischer [ctb], Jesse Gillis [aut]
Maintainer: Stephan Fischer <fischer@cshl.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetaNeighbor
git_branch: RELEASE_3_13
git_last_commit: c11a943
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MetaNeighbor_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MetaNeighbor_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MetaNeighbor_1.12.0.tgz
vignettes: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.pdf
vignetteTitles: MetaNeighbor user guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.R
dependencyCount: 69
Package: metapod
Version: 1.0.0
Imports: Rcpp
LinkingTo: Rcpp
Suggests: testthat, knitr, BiocStyle, rmarkdown
License: GPL-3
MD5sum: d6a964e8bf5d602cd50859cf4646d070
NeedsCompilation: yes
Title: Meta-Analyses on P-Values of Differential Analyses
Description: Implements a variety of methods for combining p-values in
differential analyses of genome-scale datasets. Functions can
combine p-values across different tests in the same analysis
(e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for
corresponding tests across separate analyses (e.g., replicated
comparisons, effect of different treatment conditions). Support
is provided for handling log-transformed input p-values,
missing values and weighting where appropriate.
biocViews: MultipleComparison, DifferentialPeakCalling
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/metapod
git_branch: RELEASE_3_13
git_last_commit: 704fa80
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/metapod_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metapod_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/metapod_1.0.0.tgz
vignettes: vignettes/metapod/inst/doc/overview.html
vignetteTitles: Meta-analysis strategies
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metapod/inst/doc/overview.R
importsMe: csaw, mumosa, scran
suggestsMe: TSCAN
dependencyCount: 3
Package: metaSeq
Version: 1.32.0
Depends: R (>= 2.13.0), NOISeq, snow, Rcpp
License: Artistic-2.0
MD5sum: 91b699f2c21c4db2c887d39531dbd63a
NeedsCompilation: no
Title: Meta-analysis of RNA-Seq count data in multiple studies
Description: The probabilities by one-sided NOISeq are combined by
Fisher's method or Stouffer's method
biocViews: RNASeq, DifferentialExpression, Sequencing, ImmunoOncology
Author: Koki Tsuyuzaki, Itoshi Nikaido
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
git_url: https://git.bioconductor.org/packages/metaSeq
git_branch: RELEASE_3_13
git_last_commit: 29543d0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/metaSeq_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metaSeq_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/metaSeq_1.32.0.tgz
vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf
vignetteTitles: metaSeq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metaSeq/inst/doc/metaSeq.R
dependencyCount: 15
Package: metaseqR2
Version: 1.4.0
Depends: R (>= 4.0.0), DESeq2, limma, locfit, splines
Imports: ABSSeq, baySeq, Biobase, BiocGenerics, BiocParallel, biomaRt,
Biostrings, corrplot, DSS, DT, EDASeq, edgeR, genefilter,
GenomeInfoDb, GenomicAlignments, GenomicFeatures,
GenomicRanges, gplots, graphics, grDevices, harmonicmeanp,
heatmaply, htmltools, httr, IRanges, jsonlite, lattice, log4r,
magrittr, MASS, Matrix, methods, NBPSeq, pander, parallel,
qvalue, rmarkdown, rmdformats, Rsamtools, RSQLite, rtracklayer,
S4Vectors, stats, stringr, SummarizedExperiment, survcomp,
utils, VennDiagram, vsn, yaml, zoo
Suggests: BiocManager, BSgenome, knitr, RMySQL, RUnit
Enhances: TCC
License: GPL (>= 3)
Archs: i386, x64
MD5sum: 87450eca79e00221db44b721c9e9bf26
NeedsCompilation: yes
Title: An R package for the analysis and result reporting of RNA-Seq
data by combining multiple statistical algorithms
Description: Provides an interface to several normalization and
statistical testing packages for RNA-Seq gene expression data.
Additionally, it creates several diagnostic plots, performs
meta-analysis by combinining the results of several statistical
tests and reports the results in an interactive way.
biocViews: Software, GeneExpression, DifferentialExpression,
WorkflowStep, Preprocessing, QualityControl, Normalization,
ReportWriting, RNASeq, Transcription, Sequencing,
Transcriptomics, Bayesian, Clustering, CellBiology,
BiomedicalInformatics, FunctionalGenomics, SystemsBiology,
ImmunoOncology, AlternativeSplicing, DifferentialSplicing,
MultipleComparison, TimeCourse, DataImport, ATACSeq,
Epigenetics, Regression, ProprietaryPlatforms,
GeneSetEnrichment, BatchEffect, ChIPSeq
Author: Panagiotis Moulos [aut, cre]
Maintainer: Panagiotis Moulos <moulos@fleming.gr>
URL: http://www.fleming.gr
VignetteBuilder: knitr
BugReports: https://github.com/pmoulos/metaseqR2/issues
git_url: https://git.bioconductor.org/packages/metaseqR2
git_branch: RELEASE_3_13
git_last_commit: c9bb1dc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/metaseqR2_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metaseqR2_1.4.14.zip
mac.binary.ver: bin/macosx/contrib/4.1/metaseqR2_1.4.0.tgz
vignettes: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.html,
vignettes/metaseqR2/inst/doc/metaseqr2-statistics.html
vignetteTitles: Building an annotation database for metaseqR2, RNA-Seq
data analysis with metaseqR2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.R,
vignettes/metaseqR2/inst/doc/metaseqr2-statistics.R
dependencyCount: 222
Package: metavizr
Version: 1.15.0
Depends: R (>= 3.4), metagenomeSeq (>= 1.17.1), methods, data.table,
Biobase, digest
Imports: epivizr, epivizrData, epivizrServer, epivizrStandalone, vegan,
GenomeInfoDb, phyloseq, httr
Suggests: knitr, BiocStyle, matrixStats, msd16s (>= 0.109.1), etec16s,
testthat, gss, curatedMetagenomicData
License: MIT + file LICENSE
MD5sum: 8bf146bccf5a2b376b2aad9f2dd656e9
NeedsCompilation: no
Title: R Interface to the metaviz web app for interactive metagenomics
data analysis and visualization
Description: This package provides Websocket communication to the
metaviz web app (http://metaviz.cbcb.umd.edu) for interactive
visualization of metagenomics data. Objects in R/bioc
interactive sessions can be displayed in plots and data can be
explored using a facetzoom visualization. Fundamental
Bioconductor data structures are supported (e.g., MRexperiment
objects), while providing an easy mechanism to support other
data structures. Visualizations (using d3.js) can be easily
added to the web app as well.
biocViews: Visualization, Infrastructure, GUI, Metagenomics,
ImmunoOncology
Author: Hector Corrada Bravo [cre, aut], Florin Chelaru [aut], Justin
Wagner [aut], Jayaram Kancherla [aut], Joseph Paulson [aut]
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/metavizr
git_branch: master
git_last_commit: 24e40f9
git_last_commit_date: 2020-10-27
Date/Publication: 2021-03-19
source.ver: src/contrib/metavizr_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/metavizr_1.15.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/metavizr_1.15.0.tgz
vignettes: vignettes/metavizr/inst/doc/IntroToMetavizr.html
vignetteTitles: Introduction to metavizr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/metavizr/inst/doc/IntroToMetavizr.R
dependencyCount: 160
Package: MetaVolcanoR
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: methods, data.table, dplyr, tidyr, plotly, ggplot2, cowplot,
parallel, metafor, metap, rlang, topconfects, grDevices,
graphics, stats, htmlwidgets
Suggests: knitr, testthat
License: GPL-3
MD5sum: 0e43d6a1377d9f2d6378db1e64d062af
NeedsCompilation: no
Title: Gene Expression Meta-analysis Visualization Tool
Description: MetaVolcanoR combines differential gene expression
results. It implements three strategies to summarize
differential gene expression from different studies. i) Random
Effects Model (REM) approach, ii) a p-value combining-approach,
and iii) a vote-counting approach. In all cases, MetaVolcano
exploits the Volcano plot reasoning to visualize the gene
expression meta-analysis results.
biocViews: GeneExpression, DifferentialExpression, Transcriptomics,
mRNAMicroarray, RNASeq
Author: Cesar Prada [aut, cre], Diogenes Lima [aut], Helder Nakaya
[aut, ths]
Maintainer: Cesar Prada <cesar.prada@usp.br>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetaVolcanoR
git_branch: RELEASE_3_13
git_last_commit: cc412b9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MetaVolcanoR_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MetaVolcanoR_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MetaVolcanoR_1.6.0.tgz
vignettes: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.html,
vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.html
vignetteTitles: MetaVolcanoR: Differential expression meta-analysis
tool, MetaVolcanoR inputs: differential expression examples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.R,
vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.R
dependencyCount: 98
Package: MetCirc
Version: 1.22.0
Depends: R (>= 3.5), amap (>= 0.8), circlize (>= 0.3.9), scales (>=
0.3.0), shiny (>= 1.0.0), MSnbase (>= 2.15.3),
Imports: ggplot2 (>= 3.2.1), S4Vectors (>= 0.22.0)
Suggests: BiocGenerics, graphics (>= 3.5), grDevices (>= 3.5), knitr
(>= 1.11), methods (>= 3.5), stats (>= 3.5), testthat (>=
2.2.1)
License: GPL (>= 3)
Archs: i386, x64
MD5sum: 9fbab0d5dd3985ac2830bfc00106eeb3
NeedsCompilation: no
Title: Navigating mass spectral similarity in high-resolution MS/MS
metabolomics data
Description: MetCirc comprises a workflow to interactively explore
high-resolution MS/MS metabolomics data. MetCirc uses the
Spectrum2 and MSpectra infrastructure defined in the package
MSnbase that stores MS/MS spectra. MetCirc offers functionality
to calculate similarity between precursors based on the
normalised dot product, neutral losses or user-defined
functions and visualise similarities in a circular layout.
Within the interactive framework the user can annotate MS/MS
features based on their similarity to (known) related MS/MS
features.
biocViews: ImmunoOncology, Metabolomics, MassSpectrometry,
Visualization
Author: Thomas Naake <thomasnaake@googlemail.com>, Johannes Rainer
<johannes.rainer@eurac.edu> and Emmanuel Gaquerel
<emmanuel.gaquerel@ibmp-cnrs.unistra.fr>
Maintainer: Thomas Naake <thomasnaake@googlemail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetCirc
git_branch: RELEASE_3_13
git_last_commit: ff94e6d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MetCirc_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MetCirc_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MetCirc_1.22.0.tgz
vignettes: vignettes/MetCirc/inst/doc/MetCirc.pdf
vignetteTitles: Workflow for Metabolomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetCirc/inst/doc/MetCirc.R
dependencyCount: 101
Package: MethCP
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: methods, utils, stats, S4Vectors, bsseq, DSS, methylKit,
DNAcopy, GenomicRanges, IRanges, GenomeInfoDb, BiocParallel
Suggests: testthat, knitr, rmarkdown
License: Artistic-2.0
MD5sum: ed0dbbb306c4a8ca05f3aa9bb9aa08a0
NeedsCompilation: no
Title: Differential methylation anlsysis for bisulfite sequencing data
Description: MethCP is a differentially methylated region (DMR)
detecting method for whole-genome bisulfite sequencing (WGBS)
data, which is applicable for a wide range of experimental
designs beyond the two-group comparisons, such as time-course
data. MethCP identifies DMRs based on change point detection,
which naturally segments the genome and provides region-level
differential analysis.
biocViews: DifferentialMethylation, Sequencing, WholeGenome, TimeCourse
Author: Boying Gong [aut, cre]
Maintainer: Boying Gong <jorothy_gong@berkeley.edu>
VignetteBuilder: knitr
BugReports: https://github.com/boyinggong/methcp/issues
git_url: https://git.bioconductor.org/packages/MethCP
git_branch: RELEASE_3_13
git_last_commit: 4b722d1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MethCP_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MethCP_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MethCP_1.6.0.tgz
vignettes: vignettes/MethCP/inst/doc/methcp.html
vignetteTitles: methcp: User’s Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethCP/inst/doc/methcp.R
dependencyCount: 108
Package: methimpute
Version: 1.14.0
Depends: R (>= 3.4.0), GenomicRanges, ggplot2
Imports: Rcpp (>= 0.12.4.5), methods, utils, grDevices, stats,
GenomeInfoDb, IRanges, Biostrings, reshape2, minpack.lm,
data.table
LinkingTo: Rcpp
Suggests: knitr, BiocStyle
License: Artistic-2.0
Archs: i386, x64
MD5sum: 92316356a326d492ce4d0681a0845743
NeedsCompilation: yes
Title: Imputation-guided re-construction of complete methylomes from
WGBS data
Description: This package implements functions for calling methylation
for all cytosines in the genome.
biocViews: ImmunoOncology, Software, DNAMethylation, Epigenetics,
HiddenMarkovModel, Sequencing, Coverage
Author: Aaron Taudt
Maintainer: Aaron Taudt <aaron.taudt@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/methimpute
git_branch: RELEASE_3_13
git_last_commit: b24384b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methimpute_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/methimpute_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/methimpute_1.14.0.tgz
vignettes: vignettes/methimpute/inst/doc/methimpute.pdf
vignetteTitles: Methylation status calling with METHimpute
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methimpute/inst/doc/methimpute.R
dependencyCount: 59
Package: methInheritSim
Version: 1.14.0
Depends: R (>= 3.4)
Imports: methylKit, GenomicRanges, GenomeInfoDb, parallel,
BiocGenerics, S4Vectors, methods, stats, IRanges, msm
Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance
License: Artistic-2.0
MD5sum: 07910a57fa73426093efc517b052caed
NeedsCompilation: no
Title: Simulating Whole-Genome Inherited Bisulphite Sequencing Data
Description: Simulate a multigeneration methylation case versus control
experiment with inheritance relation using a real control
dataset.
biocViews: BiologicalQuestion, Epigenetics, DNAMethylation,
DifferentialMethylation, MethylSeq, Software, ImmunoOncology,
StatisticalMethod, WholeGenome, Sequencing
Author: Pascal Belleau, Astrid Deschênes and Arnaud Droit
Maintainer: Pascal Belleau <pascal_belleau@hotmail.com>
URL: https://github.com/belleau/methInheritSim
VignetteBuilder: knitr
BugReports: https://github.com/belleau/methInheritSim/issues
git_url: https://git.bioconductor.org/packages/methInheritSim
git_branch: RELEASE_3_13
git_last_commit: b70d290
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methInheritSim_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/methInheritSim_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/methInheritSim_1.14.0.tgz
vignettes: vignettes/methInheritSim/inst/doc/methInheritSim.html
vignetteTitles: Simulating Whole-Genome Inherited Bisulphite Sequencing
Data
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methInheritSim/inst/doc/methInheritSim.R
suggestsMe: methylInheritance
dependencyCount: 98
Package: MethPed
Version: 1.20.0
Depends: R (>= 3.0.0), Biobase
Imports: randomForest, grDevices, graphics, stats
Suggests: BiocStyle, knitr, markdown, impute
License: GPL-2
MD5sum: 2ea83bb1afe66e604ab6a81eb7136a8f
NeedsCompilation: no
Title: A DNA methylation classifier tool for the identification of
pediatric brain tumor subtypes
Description: Classification of pediatric tumors into biologically
defined subtypes is challenging and multifaceted approaches are
needed. For this aim, we developed a diagnostic classifier
based on DNA methylation profiles. We offer MethPed as an
easy-to-use toolbox that allows researchers and clinical
diagnosticians to test single samples as well as large cohorts
for subclass prediction of pediatric brain tumors. The current
version of MethPed can classify the following tumor
diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG),
Ependymoma, Embryonal tumors with multilayered rosettes (ETMR),
Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3),
Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and
Pilocytic Astrocytoma (PiloAstro).
biocViews: ImmunoOncology, DNAMethylation, Classification, Epigenetics
Author: Mohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut],
Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph]
Maintainer: Helena Carén <helenacarenlab@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MethPed
git_branch: RELEASE_3_13
git_last_commit: 8b346a2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MethPed_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MethPed_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MethPed_1.20.0.tgz
vignettes: vignettes/MethPed/inst/doc/MethPed-vignette.html
vignetteTitles: MethPed User Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R
dependencyCount: 9
Package: MethReg
Version: 1.2.1
Depends: R (>= 4.0)
Imports: dplyr, plyr, GenomicRanges, SummarizedExperiment,
DelayedArray, ggplot2, ggpubr, tibble, tidyr, S4Vectors,
sesameData, stringr, readr, methods, stats, Matrix, MASS,
rlang, pscl, IRanges, sfsmisc, progress, utils
Suggests: rmarkdown, BiocStyle, testthat (>= 2.1.0), parallel,
downloader, R.utils, doParallel, reshape2, JASPAR2020,
TFBSTools, motifmatchr, matrixStats, biomaRt, dorothea, viper,
stageR, BiocFileCache, png, htmltools, knitr, jpeg, sesame,
BSgenome.Hsapiens.UCSC.hg38
License: GPL-3
MD5sum: e2ef11a1eb3c509519c10e4589727fd3
NeedsCompilation: no
Title: Assessing the regulatory potential of DNA methylation regions or
sites on gene transcription
Description: Epigenome-wide association studies (EWAS) detects a large
number of DNA methylation differences, often hundreds of
differentially methylated regions and thousands of CpGs, that
are significantly associated with a disease, many are located
in non-coding regions. Therefore, there is a critical need to
better understand the functional impact of these CpG
methylations and to further prioritize the significant changes.
MethReg is an R package for integrative modeling of DNA
methylation, target gene expression and transcription factor
binding sites data, to systematically identify and rank
functional CpG methylations. MethReg evaluates, prioritizes and
annotates CpG sites with high regulatory potential using
matched methylation and gene expression data, along with
external TF-target interaction databases based on manually
curation, ChIP-seq experiments or gene regulatory network
analysis.
biocViews: MethylationArray, Regression, GeneExpression, Epigenetics,
GeneTarget, Transcription
Author: Tiago Silva [aut, cre]
(<https://orcid.org/0000-0003-1343-6850>), Lily Wang [aut]
Maintainer: Tiago Silva <tiagochst@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/TransBioInfoLab/MethReg/issues/
git_url: https://git.bioconductor.org/packages/MethReg
git_branch: RELEASE_3_13
git_last_commit: 1a84a50
git_last_commit_date: 2021-05-27
Date/Publication: 2021-05-30
source.ver: src/contrib/MethReg_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MethReg_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/MethReg_1.2.1.tgz
vignettes: vignettes/MethReg/inst/doc/MethReg.html
vignetteTitles: MethReg: estimating regulatory potential of DNA
methylation in gene transcription
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethReg/inst/doc/MethReg.R
dependencyCount: 171
Package: methrix
Version: 1.6.0
Depends: R (>= 3.6), data.table (>= 1.12.4), SummarizedExperiment
Imports: rtracklayer, DelayedArray, HDF5Array, BSgenome,
DelayedMatrixStats, parallel, methods, ggplot2, matrixStats,
graphics, stats, utils, GenomicRanges, IRanges
Suggests: knitr, rmarkdown, DSS, bsseq, plotly,
BSgenome.Mmusculus.UCSC.mm9, MafDb.1Kgenomes.phase3.GRCh38,
MafDb.1Kgenomes.phase3.hs37d5, GenomicScores, Biostrings,
RColorBrewer, GenomeInfoDb, testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: fe3845bddb1814ecc3d4775db9a00ad4
NeedsCompilation: no
Title: Fast and efficient summarization of generic bedGraph files from
Bisufite sequencing
Description: Bedgraph files generated by Bisulfite pipelines often come
in various flavors. Critical downstream step requires
summarization of these files into methylation/coverage
matrices. This step of data aggregation is done by Methrix,
including many other useful downstream functions.
biocViews: DNAMethylation, Sequencing, Coverage
Author: Anand Mayakonda [aut, cre]
(<https://orcid.org/0000-0003-1162-687X>), Reka Toth [aut]
(<https://orcid.org/0000-0002-6096-1052>), Rajbir Batra [ctb],
Clarissa Feuerstein-Akgöz [ctb], Joschka Hey [ctb], Maximilian
Schönung [ctb], Pavlo Lutsik [ctb]
Maintainer: Anand Mayakonda <anand_mt@hotmail.com>
URL: https://github.com/CompEpigen/methrix
VignetteBuilder: knitr
BugReports: https://github.com/CompEpigen/methrix/issues
git_url: https://git.bioconductor.org/packages/methrix
git_branch: RELEASE_3_13
git_last_commit: 74b8250
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methrix_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/methrix_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/methrix_1.6.0.tgz
vignettes: vignettes/methrix/inst/doc/methrix.html
vignetteTitles: Methrix tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/methrix/inst/doc/methrix.R
dependencyCount: 82
Package: MethTargetedNGS
Version: 1.24.0
Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings
License: Artistic-2.0
Archs: i386, x64
MD5sum: e45c0a7f1f301c0c360e38baa3b549ea
NeedsCompilation: no
Title: Perform Methylation Analysis on Next Generation Sequencing Data
Description: Perform step by step methylation analysis of Next
Generation Sequencing data.
biocViews: ResearchField, Genetics, Sequencing, Alignment,
SequenceMatching, DataImport
Author: Muhammad Ahmer Jamil with Contribution of Prof. Holger Frohlich
and Priv.-Doz. Dr. Osman El-Maarri
Maintainer: Muhammad Ahmer Jamil <engr.ahmerjamil@gmail.com>
SystemRequirements: HMMER3
git_url: https://git.bioconductor.org/packages/MethTargetedNGS
git_branch: RELEASE_3_13
git_last_commit: ca02db6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MethTargetedNGS_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MethTargetedNGS_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MethTargetedNGS_1.24.0.tgz
vignettes: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.pdf
vignetteTitles: Introduction to MethTargetedNGS
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.R
dependencyCount: 35
Package: methyAnalysis
Version: 1.34.0
Depends: R (>= 2.10), grid, BiocGenerics, IRanges, GenomeInfoDb (>=
1.22.0), GenomicRanges, Biobase (>= 2.34.0), org.Hs.eg.db
Imports: grDevices, stats, utils, lumi, methylumi, Gviz, genoset,
SummarizedExperiment, IRanges, GenomicRanges,
VariantAnnotation, rtracklayer,
bigmemoryExtras,GenomicFeatures, annotate, Biobase (>= 2.5.5),
AnnotationDbi, genefilter, biomaRt, methods, parallel
Suggests: FDb.InfiniumMethylation.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: Artistic-2.0
MD5sum: ce23e9a79d813f1d58be83edca0a7b64
NeedsCompilation: no
Title: DNA methylation data analysis and visualization
Description: The methyAnalysis package aims for the DNA methylation
data analysis and visualization. A MethyGenoSet class is
defined to keep the chromosome location information together
with the data. The package also includes functions of
estimating the methylation levels from Methy-Seq data.
biocViews: Microarray, DNAMethylation, Visualization
Author: Pan Du, Richard Bourgon
Maintainer: Lei Huang <lhuang1998@gmail.com>
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/methyAnalysis
git_branch: RELEASE_3_13
git_last_commit: 96aa183
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methyAnalysis_1.34.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/methyAnalysis_1.34.0.tgz
vignettes: vignettes/methyAnalysis/inst/doc/methyAnalysis.pdf
vignetteTitles: An Introduction to the methyAnalysis package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methyAnalysis/inst/doc/methyAnalysis.R
suggestsMe: methylumi
dependencyCount: 192
Package: MethylAid
Version: 1.26.0
Depends: R (>= 3.4)
Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase,
grDevices, graphics, hexbin, matrixStats, minfi (>= 1.22.0),
methods, RColorBrewer, shiny, stats, SummarizedExperiment,
utils
Suggests: BiocStyle, knitr, MethylAidData, minfiData, minfiDataEPIC,
RUnit
License: GPL (>= 2)
MD5sum: e80b4f434c1c4d8e9b0e469aa2e2a1d6
NeedsCompilation: no
Title: Visual and interactive quality control of large Illumina DNA
Methylation array data sets
Description: A visual and interactive web application using RStudio's
shiny package. Bad quality samples are detected using
sample-dependent and sample-independent controls present on the
array and user adjustable thresholds. In depth exploration of
bad quality samples can be performed using several interactive
diagnostic plots of the quality control probes present on the
array. Furthermore, the impact of any batch effect provided by
the user can be explored.
biocViews: DNAMethylation, MethylationArray, Microarray, TwoChannel,
QualityControl, BatchEffect, Visualization, GUI
Author: Maarten van Iterson [aut, cre], Elmar Tobi[ctb], Roderick
Slieker[ctb], Wouter den Hollander[ctb], Rene Luijk[ctb] and
Bas Heijmans[ctb]
Maintainer: L.J.Sinke <L.J.Sinke@lumc.nl>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MethylAid
git_branch: RELEASE_3_13
git_last_commit: 877d41a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MethylAid_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MethylAid_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MethylAid_1.26.0.tgz
vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf
vignetteTitles: MethylAid: Visual and Interactive quality control of
Illumina Human DNA Methylation array data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R
dependsOnMe: MethylAidData
dependencyCount: 164
Package: methylCC
Version: 1.6.0
Depends: R (>= 3.6), FlowSorted.Blood.450k
Imports: Biobase, GenomicRanges, IRanges, S4Vectors, dplyr, magrittr,
minfi, bsseq, quadprog, plyranges, stats, utils, bumphunter,
genefilter, methods, IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylation450kanno.ilmn12.hg19
Suggests: knitr, testthat (>= 2.1.0), BiocGenerics, BiocStyle, tidyr,
ggplot2
License: CC BY 4.0
MD5sum: 38b105da59d89428535ed1f1ae9e1240
NeedsCompilation: no
Title: Estimate the cell composition of whole blood in DNA methylation
samples
Description: A tool to estimate the cell composition of DNA methylation
whole blood sample measured on any platform technology
(microarray and sequencing).
biocViews: Microarray, Sequencing, DNAMethylation, MethylationArray,
MethylSeq, WholeGenome
Author: Stephanie C. Hicks [aut, cre]
(<https://orcid.org/0000-0002-7858-0231>), Rafael Irizarry
[aut] (<https://orcid.org/0000-0002-3944-4309>)
Maintainer: Stephanie C. Hicks <shicks19@jhu.edu>
URL: https://github.com/stephaniehicks/methylCC/
VignetteBuilder: knitr
BugReports: https://github.com/stephaniehicks/methylCC/
git_url: https://git.bioconductor.org/packages/methylCC
git_branch: RELEASE_3_13
git_last_commit: 560219f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methylCC_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/methylCC_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/methylCC_1.6.0.tgz
vignettes: vignettes/methylCC/inst/doc/methylCC.html
vignetteTitles: The methylCC user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylCC/inst/doc/methylCC.R
dependencyCount: 157
Package: methylGSA
Version: 1.10.0
Depends: R (>= 3.5)
Imports: RobustRankAggreg, ggplot2, stringr, stats, clusterProfiler,
missMethyl, org.Hs.eg.db, reactome.db, BiocParallel, GO.db,
AnnotationDbi, shiny,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19
Suggests: knitr, rmarkdown, testthat, enrichplot
License: GPL-2
MD5sum: d737ec8552755c04780709a69ac9dc8a
NeedsCompilation: no
Title: Gene Set Analysis Using the Outcome of Differential Methylation
Description: The main functions for methylGSA are methylglm and
methylRRA. methylGSA implements logistic regression adjusting
number of probes as a covariate. methylRRA adjusts multiple
p-values of each gene by Robust Rank Aggregation. For more
detailed help information, please see the vignette.
biocViews:
DNAMethylation,DifferentialMethylation,GeneSetEnrichment,Regression,
GeneRegulation,Pathways
Author: Xu Ren [aut, cre], Pei Fen Kuan [aut]
Maintainer: Xu Ren <xuren2120@gmail.com>
URL: https://github.com/reese3928/methylGSA
VignetteBuilder: knitr
BugReports: https://github.com/reese3928/methylGSA/issues
git_url: https://git.bioconductor.org/packages/methylGSA
git_branch: RELEASE_3_13
git_last_commit: 935072a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methylGSA_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/methylGSA_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/methylGSA_1.10.0.tgz
vignettes: vignettes/methylGSA/inst/doc/methylGSA-vignette.html
vignetteTitles: methylGSA: Gene Set Analysis for DNA Methylation
Datasets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylGSA/inst/doc/methylGSA-vignette.R
dependencyCount: 213
Package: methylInheritance
Version: 1.16.0
Depends: R (>= 3.5)
Imports: methylKit, BiocParallel, GenomicRanges, IRanges, S4Vectors,
methods, parallel, ggplot2, gridExtra, rebus
Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit,
methInheritSim
License: Artistic-2.0
MD5sum: a3b00c49542bfbff118446b76dbde5ea
NeedsCompilation: no
Title: Permutation-Based Analysis associating Conserved Differentially
Methylated Elements Across Multiple Generations to a Treatment
Effect
Description: Permutation analysis, based on Monte Carlo sampling, for
testing the hypothesis that the number of conserved
differentially methylated elements, between several
generations, is associated to an effect inherited from a
treatment and that stochastic effect can be dismissed.
biocViews: BiologicalQuestion, Epigenetics, DNAMethylation,
DifferentialMethylation, MethylSeq, Software, ImmunoOncology,
StatisticalMethod, WholeGenome, Sequencing
Author: Astrid Deschênes [cre, aut]
(<https://orcid.org/0000-0001-7846-6749>), Pascal Belleau [aut]
(<https://orcid.org/0000-0002-0802-1071>), Arnaud Droit [aut]
Maintainer: Astrid Deschênes <adeschen@hotmail.com>
URL: https://github.com/adeschen/methylInheritance
VignetteBuilder: knitr
BugReports: https://github.com/adeschen/methylInheritance/issues
git_url: https://git.bioconductor.org/packages/methylInheritance
git_branch: RELEASE_3_13
git_last_commit: 0c4894a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methylInheritance_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/methylInheritance_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/methylInheritance_1.16.0.tgz
vignettes: vignettes/methylInheritance/inst/doc/methylInheritance.html
vignetteTitles: Permutation-Based Analysis associating Conserved
Differentially Methylated Elements Across Multiple Generations
to a Treatment Effect
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylInheritance/inst/doc/methylInheritance.R
suggestsMe: methInheritSim
dependencyCount: 101
Package: methylKit
Version: 1.18.0
Depends: R (>= 3.5.0), GenomicRanges (>= 1.18.1), methods
Imports: IRanges, data.table (>= 1.9.6), parallel, S4Vectors (>=
0.13.13), GenomeInfoDb, KernSmooth, qvalue, emdbook, Rsamtools,
gtools, fastseg, rtracklayer, mclust, mgcv, Rcpp, R.utils,
limma, grDevices, graphics, stats, utils
LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, genomation,
BiocManager
License: Artistic-2.0
MD5sum: 18d2329199dd6e51af592930c8ae59dd
NeedsCompilation: yes
Title: DNA methylation analysis from high-throughput bisulfite
sequencing results
Description: methylKit is an R package for DNA methylation analysis and
annotation from high-throughput bisulfite sequencing. The
package is designed to deal with sequencing data from RRBS and
its variants, but also target-capture methods and whole genome
bisulfite sequencing. It also has functions to analyze
base-pair resolution 5hmC data from experimental protocols such
as oxBS-Seq and TAB-Seq. Methylation calling can be performed
directly from Bismark aligned BAM files.
biocViews: DNAMethylation, Sequencing, MethylSeq
Author: Altuna Akalin [aut, cre], Matthias Kormaksson [aut], Sheng Li
[aut], Arsene Wabo [ctb], Adrian Bierling [aut], Alexander
Gosdschan [aut]
Maintainer: Altuna Akalin <aakalin@gmail.com>, Alexander Gosdschan
<alex.gos90@gmail.com>
URL: http://code.google.com/p/methylkit/
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/methylKit
git_branch: RELEASE_3_13
git_last_commit: 8fef778
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methylKit_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/methylKit_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/methylKit_1.18.0.tgz
vignettes: vignettes/methylKit/inst/doc/methylKit.html
vignetteTitles: methylKit: User Guide v`r packageVersion('methylKit')`
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylKit/inst/doc/methylKit.R
importsMe: MethCP, methInheritSim, methylInheritance
dependencyCount: 94
Package: MethylMix
Version: 2.22.0
Depends: R (>= 3.2.0)
Imports: foreach, RPMM, RColorBrewer, ggplot2, RCurl, impute,
data.table, limma, R.matlab, digest
Suggests: BiocStyle, doParallel, testthat, knitr, rmarkdown
License: GPL-2
MD5sum: 2db0a87d1ca9b89e683624042a1c8713
NeedsCompilation: no
Title: MethylMix: Identifying methylation driven cancer genes
Description: MethylMix is an algorithm implemented to identify hyper
and hypomethylated genes for a disease. MethylMix is based on a
beta mixture model to identify methylation states and compares
them with the normal DNA methylation state. MethylMix uses a
novel statistic, the Differential Methylation value or DM-value
defined as the difference of a methylation state with the
normal methylation state. Finally, matched gene expression data
is used to identify, besides differential, functional
methylation states by focusing on methylation changes that
effect gene expression. References: Gevaert 0. MethylMix: an R
package for identifying DNA methylation-driven genes.
Bioinformatics (Oxford, England). 2015;31(11):1839-41.
doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R,
Plevritis SK. Pancancer analysis of DNA methylation-driven
genes using MethylMix. Genome Biology. 2015;16(1):17.
doi:10.1186/s13059-014-0579-8.
biocViews:
DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,DifferentialExpression,Pathways,Network
Author: Olivier Gevaert
Maintainer: Olivier Gevaert <olivier.gevaert@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MethylMix
git_branch: RELEASE_3_13
git_last_commit: 612ce15
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MethylMix_2.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MethylMix_2.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MethylMix_2.22.0.tgz
vignettes: vignettes/MethylMix/inst/doc/vignettes.html
vignetteTitles: MethylMix
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethylMix/inst/doc/vignettes.R
dependencyCount: 53
Package: methylMnM
Version: 1.30.0
Depends: R (>= 2.12.1), edgeR, statmod
License: GPL-3
MD5sum: c27bb054aa2f8e0c1e63be01423c2c63
NeedsCompilation: yes
Title: detect different methylation level (DMR)
Description: To give the exactly p-value and q-value of MeDIP-seq and
MRE-seq data for different samples comparation.
biocViews: Software, DNAMethylation, Sequencing
Author: Yan Zhou, Bo Zhang, Nan Lin, BaoXue Zhang and Ting Wang
Maintainer: Yan Zhou<zhouy1016@163.com>
git_url: https://git.bioconductor.org/packages/methylMnM
git_branch: RELEASE_3_13
git_last_commit: bb6759d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methylMnM_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/methylMnM_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/methylMnM_1.30.0.tgz
vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf
vignetteTitles: methylMnM
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylMnM/inst/doc/methylMnM.R
importsMe: SIMD
dependencyCount: 12
Package: methylPipe
Version: 1.26.0
Depends: R (>= 3.2.0), methods, grDevices, graphics, stats, utils,
GenomicRanges, SummarizedExperiment (>= 0.2.0), Rsamtools
Imports: marray, gplots, IRanges, BiocGenerics, Gviz,
GenomicAlignments, Biostrings, parallel, data.table,
GenomeInfoDb, S4Vectors
Suggests: BSgenome.Hsapiens.UCSC.hg18,
TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR
License: GPL(>=2)
MD5sum: 5752a0296587863bf07777795b0f64e2
NeedsCompilation: yes
Title: Base resolution DNA methylation data analysis
Description: Memory efficient analysis of base resolution DNA
methylation data in both the CpG and non-CpG sequence context.
Integration of DNA methylation data derived from any
methodology providing base- or low-resolution data.
biocViews: MethylSeq, DNAMethylation, Coverage, Sequencing
Author: Kamal Kishore
Maintainer: Kamal Kishore <kamal.fartiyal84@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/methylPipe
git_branch: RELEASE_3_13
git_last_commit: ca8dade
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methylPipe_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/methylPipe_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/methylPipe_1.26.0.tgz
vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf
vignetteTitles: methylPipe.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylPipe/inst/doc/methylPipe.R
dependsOnMe: ListerEtAlBSseq
importsMe: compEpiTools
dependencyCount: 148
Package: methylscaper
Version: 1.0.1
Depends: R (>= 4.1.0)
Imports: shiny, shinyjs, seriation, BiocParallel, seqinr, Biostrings,
Rfast, grDevices, graphics, stats, utils, tools, methods,
shinyFiles, data.table, SummarizedExperiment
Suggests: knitr, rmarkdown, devtools
License: GPL-2
MD5sum: 3bea4e2fd7e6e2cda71eaedd50c3c058
NeedsCompilation: no
Title: Visualization of Methylation Data
Description: methylscaper is an R package for processing and
visualizing data jointly profiling methylation and chromatin
accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The
package supports both single-cell and single-molecule data, and
a common interface for jointly visualizing both data types
through the generation of ordered representational
methylation-state matrices. The Shiny app allows for an
interactive seriation process of refinement and re-weighting
that optimally orders the cells or DNA molecules to discover
methylation patterns and nucleosome positioning.
biocViews: DNAMethylation, Epigenetics, PrincipalComponent,
Visualization, SingleCell, NucleosomePositioning
Author: Bacher Rhonda [aut, cre], Parker Knight [aut]
Maintainer: Bacher Rhonda <rbacher@ufl.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/methylscaper
git_branch: RELEASE_3_13
git_last_commit: e9c558f
git_last_commit_date: 2021-09-30
Date/Publication: 2021-10-03
source.ver: src/contrib/methylscaper_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/methylscaper_1.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/methylscaper_1.0.1.tgz
vignettes: vignettes/methylscaper/inst/doc/methylScaper.html
vignetteTitles: Using methylscaper to visualize joint methylation and
nucleosome occupancy data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylscaper/inst/doc/methylScaper.R
dependencyCount: 93
Package: MethylSeekR
Version: 1.32.0
Depends: rtracklayer (>= 1.16.3), parallel (>= 2.15.1), mhsmm (>=
0.4.4)
Imports: IRanges (>= 1.16.3), BSgenome (>= 1.26.1), GenomicRanges (>=
1.10.5), geneplotter (>= 1.34.0), graphics (>= 2.15.2),
grDevices (>= 2.15.2), parallel (>= 2.15.2), stats (>= 2.15.2),
utils (>= 2.15.2)
Suggests: BSgenome.Hsapiens.UCSC.hg18
License: GPL (>=2)
Archs: i386, x64
MD5sum: 416e9aca89ff66e0e70b56e49d03414e
NeedsCompilation: no
Title: Segmentation of Bis-seq data
Description: This is a package for the discovery of regulatory regions
from Bis-seq data
biocViews: Sequencing, MethylSeq, DNAMethylation
Author: Lukas Burger, Dimos Gaidatzis, Dirk Schubeler and Michael
Stadler
Maintainer: Lukas Burger <Lukas.Burger@fmi.ch>
git_url: https://git.bioconductor.org/packages/MethylSeekR
git_branch: RELEASE_3_13
git_last_commit: a68c342
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MethylSeekR_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MethylSeekR_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MethylSeekR_1.32.0.tgz
vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf
vignetteTitles: MethylSeekR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethylSeekR/inst/doc/MethylSeekR.R
suggestsMe: methylPipe, RnBeads
dependencyCount: 77
Package: methylSig
Version: 1.4.0
Depends: R (>= 3.6)
Imports: bsseq, DelayedArray, DelayedMatrixStats, DSS, IRanges,
GenomeInfoDb, GenomicRanges, methods, parallel, stats,
S4Vectors
Suggests: BiocStyle, bsseqData, knitr, rmarkdown, testthat (>= 2.1.0),
covr
License: GPL-3
MD5sum: 672d1cde07bfaec6bd7d2257bc14743e
NeedsCompilation: no
Title: MethylSig: Differential Methylation Testing for WGBS and RRBS
Data
Description: MethylSig is a package for testing for differentially
methylated cytosines (DMCs) or regions (DMRs) in whole-genome
bisulfite sequencing (WGBS) or reduced representation bisulfite
sequencing (RRBS) experiments. MethylSig uses a beta binomial
model to test for significant differences between groups of
samples. Several options exist for either site-specific or
sliding window tests, and variance estimation.
biocViews: DNAMethylation, DifferentialMethylation, Epigenetics,
Regression, MethylSeq
Author: Yongseok Park [aut], Raymond G. Cavalcante [aut, cre]
Maintainer: Raymond G. Cavalcante <rcavalca@umich.edu>
VignetteBuilder: knitr
BugReports: https://github.com/sartorlab/methylSig/issues
git_url: https://git.bioconductor.org/packages/methylSig
git_branch: RELEASE_3_13
git_last_commit: a8c68a6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methylSig_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/methylSig_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/methylSig_1.4.0.tgz
vignettes: vignettes/methylSig/inst/doc/updating-methylSig-code.html,
vignettes/methylSig/inst/doc/using-methylSig.html
vignetteTitles: Updating methylSig code, Using methylSig
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylSig/inst/doc/updating-methylSig-code.R,
vignettes/methylSig/inst/doc/using-methylSig.R
dependencyCount: 75
Package: methylumi
Version: 2.38.0
Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2,
matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi
Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges,
SummarizedExperiment, Biobase, graphics, lattice, annotate,
genefilter, AnnotationDbi, minfi, stats4, illuminaio
Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats,
parallel, rtracklayer, Biostrings, methyAnalysis,
TCGAMethylation450k,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr
License: GPL-2
MD5sum: 414501b1e8fa6c6386723328d5c664c5
NeedsCompilation: no
Title: Handle Illumina methylation data
Description: This package provides classes for holding and manipulating
Illumina methylation data. Based on eSet, it can contain MIAME
information, sample information, feature information, and
multiple matrices of data. An "intelligent" import function,
methylumiR can read the Illumina text files and create a
MethyLumiSet. methylumIDAT can directly read raw IDAT files
from HumanMethylation27 and HumanMethylation450 microarrays.
Normalization, background correction, and quality control
features for GoldenGate, Infinium, and Infinium HD arrays are
also included.
biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl,
CpGIsland
Author: Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr., Moiz Bootwalla
Maintainer: Sean Davis <sdavis2@mail.nih.gov>
VignetteBuilder: knitr
BugReports: https://github.com/seandavi/methylumi/issues/new
git_url: https://git.bioconductor.org/packages/methylumi
git_branch: RELEASE_3_13
git_last_commit: 13b2e06
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/methylumi_2.38.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/methylumi_2.38.0.tgz
vignettes: vignettes/methylumi/inst/doc/methylumi.pdf,
vignettes/methylumi/inst/doc/methylumi450k.pdf
vignetteTitles: An Introduction to the methylumi package, Working with
Illumina 450k Arrays using methylumi
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylumi/inst/doc/methylumi.R,
vignettes/methylumi/inst/doc/methylumi450k.R
dependsOnMe: bigmelon, RnBeads, skewr, wateRmelon
importsMe: ffpe, lumi, methyAnalysis, missMethyl
dependencyCount: 153
Package: MetID
Version: 1.10.0
Depends: R (>= 3.5)
Imports: utils (>= 3.3.1), stats (>= 3.4.2), devtools (>= 1.13.0),
stringr (>= 1.3.0), Matrix (>= 1.2-12), igraph (>= 1.2.1),
ChemmineR (>= 2.30.2)
Suggests: knitr (>= 1.19), rmarkdown (>= 1.8)
License: Artistic-2.0
Archs: i386, x64
MD5sum: 869d4e6f598bae15429654e00e3b2ffa
NeedsCompilation: no
Title: Network-based prioritization of putative metabolite IDs
Description: This package uses an innovative network-based approach
that will enhance our ability to determine the identities of
significant ions detected by LC-MS.
biocViews: AssayDomain, BiologicalQuestion, Infrastructure,
ResearchField, StatisticalMethod, Technology, WorkflowStep,
Network, KEGG
Author: Zhenzhi Li <zzrickli@gmail.com>
Maintainer: Zhenzhi Li <zzrickli@gmail.com>
URL: https://github.com/ressomlab/MetID
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetID
git_branch: RELEASE_3_13
git_last_commit: 3378aab
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MetID_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MetID_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MetID_1.10.0.tgz
vignettes: vignettes/MetID/inst/doc/Introduction_to_MetID.html
vignetteTitles: Introduction to MetID
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetID/inst/doc/Introduction_to_MetID.R
dependencyCount: 116
Package: MetNet
Version: 1.10.0
Depends: R (>= 4.0), S4Vectors (>= 0.28.1), SummarizedExperiment (>=
1.20.0)
Imports: bnlearn (>= 4.3), BiocParallel (>= 1.12.0), dplyr (>= 1.0.3),
ggplot2 (>= 3.3.3), Hmisc (>= 4.4-2), GENIE3 (>= 1.7.0),
methods (>= 3.5), mpmi (>= 0.42), parmigene (>= 1.0.2), ppcor
(>= 1.1), rlang (>= 0.4.10), sna (>= 2.4), stabs (>= 0.6),
stats (>= 3.6), tibble (>= 3.0.5), tidyr (>= 1.1.2)
Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), glmnet (>=
2.0-18), igraph (>= 1.1.2), knitr (>= 1.11), rmarkdown (>=
1.15), testthat (>= 2.2.1)
License: GPL (>= 3)
Archs: i386, x64
MD5sum: 8fb8e05b86249e778fcdca72df2b56eb
NeedsCompilation: no
Title: Inferring metabolic networks from untargeted high-resolution
mass spectrometry data
Description: MetNet contains functionality to infer metabolic network
topologies from quantitative data and high-resolution
mass/charge information. Using statistical models (including
correlation, mutual information, regression and Bayes
statistics) and quantitative data (intensity values of
features) adjacency matrices are inferred that can be combined
to a consensus matrix. Mass differences calculated between
mass/charge values of features will be matched against a data
frame of supplied mass/charge differences referring to
transformations of enzymatic activities. In a third step, the
two levels of information are combined to form a adjacency
matrix inferred from both quantitative and structure
information.
biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Network,
Regression
Author: Thomas Naake [aut, cre], Liesa Salzer [ctb]
Maintainer: Thomas Naake <thomasnaake@googlemail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetNet
git_branch: RELEASE_3_13
git_last_commit: 7e3eee4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MetNet_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MetNet_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MetNet_1.10.0.tgz
vignettes: vignettes/MetNet/inst/doc/MetNet.html
vignetteTitles: Workflow for high-resolution metabolomics data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetNet/inst/doc/MetNet.R
dependencyCount: 112
Package: mfa
Version: 1.14.0
Depends: R (>= 3.4.0)
Imports: methods, stats, ggplot2, Rcpp, dplyr, ggmcmc, MCMCpack,
MCMCglmm, coda, magrittr, tibble, Biobase
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: GPL (>= 2)
MD5sum: 7398a1cfd4495d087309555dc8b24761
NeedsCompilation: yes
Title: Bayesian hierarchical mixture of factor analyzers for modelling
genomic bifurcations
Description: MFA models genomic bifurcations using a Bayesian
hierarchical mixture of factor analysers.
biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell
Author: Kieran Campbell [aut, cre]
Maintainer: Kieran Campbell <kieranrcampbell@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mfa
git_branch: RELEASE_3_13
git_last_commit: b2d11d1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mfa_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mfa_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mfa_1.14.0.tgz
vignettes: vignettes/mfa/inst/doc/introduction_to_mfa.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mfa/inst/doc/introduction_to_mfa.R
suggestsMe: splatter
dependencyCount: 71
Package: Mfuzz
Version: 2.52.0
Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071
Imports: tcltk, tkWidgets
Suggests: marray
License: GPL-2
MD5sum: f77dd8af3a2e27011f3952d3c855b8cf
NeedsCompilation: no
Title: Soft clustering of time series gene expression data
Description: Package for noise-robust soft clustering of gene
expression time-series data (including a graphical user
interface)
biocViews: Microarray, Clustering, TimeCourse, Preprocessing,
Visualization
Author: Matthias Futschik <matthias.futschik@sysbiolab.eu>
Maintainer: Matthias Futschik <matthias.futschik@sysbiolab.eu>
URL: http://mfuzz.sysbiolab.eu/
git_url: https://git.bioconductor.org/packages/Mfuzz
git_branch: RELEASE_3_13
git_last_commit: 91496e8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Mfuzz_2.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Mfuzz_2.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Mfuzz_2.52.0.tgz
vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf
vignetteTitles: Introduction to Mfuzz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Mfuzz/inst/doc/Mfuzz.R
dependsOnMe: cycle, TimiRGeN
importsMe: DAPAR, Patterns
suggestsMe: pwOmics
dependencyCount: 17
Package: MGFM
Version: 1.26.0
Depends: AnnotationDbi,annotate
Suggests: hgu133a.db
License: GPL-3
MD5sum: 27447a5d51d607e8b1daa9011b453120
NeedsCompilation: no
Title: Marker Gene Finder in Microarray gene expression data
Description: The package is designed to detect marker genes from
Microarray gene expression data sets
biocViews: Genetics, GeneExpression, Microarray
Author: Khadija El Amrani
Maintainer: Khadija El Amrani <khadija.el-amrani@charite.de>
git_url: https://git.bioconductor.org/packages/MGFM
git_branch: RELEASE_3_13
git_last_commit: c2c218c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MGFM_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MGFM_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MGFM_1.26.0.tgz
vignettes: vignettes/MGFM/inst/doc/MGFM.pdf
vignetteTitles: Using MGFM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MGFM/inst/doc/MGFM.R
dependsOnMe: sampleClassifier
dependencyCount: 49
Package: MGFR
Version: 1.18.0
Depends: R (>= 3.5)
Imports: biomaRt, annotate
License: GPL-3
MD5sum: 9fa0cdd66b9e9083ef718bf3a40b8e37
NeedsCompilation: no
Title: Marker Gene Finder in RNA-seq data
Description: The package is designed to detect marker genes from
RNA-seq data.
biocViews: ImmunoOncology, Genetics, GeneExpression, RNASeq
Author: Khadija El Amrani
Maintainer: Khadija El Amrani <a.khadija@gmx.de>
git_url: https://git.bioconductor.org/packages/MGFR
git_branch: RELEASE_3_13
git_last_commit: 9cc8ca1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MGFR_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MGFR_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MGFR_1.18.0.tgz
vignettes: vignettes/MGFR/inst/doc/MGFR.pdf
vignetteTitles: Using MGFR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MGFR/inst/doc/MGFR.R
dependsOnMe: sampleClassifier
dependencyCount: 74
Package: mgsa
Version: 1.40.0
Depends: R (>= 2.14.0), methods, gplots
Imports: graphics, stats, utils
Suggests: DBI, RSQLite, GO.db, testthat
License: Artistic-2.0
MD5sum: 5b12edb853e1765fcc8f516aff935a8b
NeedsCompilation: yes
Title: Model-based gene set analysis
Description: Model-based Gene Set Analysis (MGSA) is a Bayesian
modeling approach for gene set enrichment. The package mgsa
implements MGSA and tools to use MGSA together with the Gene
Ontology.
biocViews: Pathways, GO, GeneSetEnrichment
Author: Sebastian Bauer <mail@sebastianbauer.info>, Julien Gagneur
<gagneur@genzentrum.lmu.de>
Maintainer: Sebastian Bauer <mail@sebastianbauer.info>
URL: https://github.com/sba1/mgsa-bioc
git_url: https://git.bioconductor.org/packages/mgsa
git_branch: RELEASE_3_13
git_last_commit: 77f80bf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mgsa_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mgsa_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mgsa_1.40.0.tgz
vignettes: vignettes/mgsa/inst/doc/mgsa.pdf
vignetteTitles: Overview of the mgsa package.
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mgsa/inst/doc/mgsa.R
dependencyCount: 9
Package: mia
Version: 1.0.8
Depends: R (>= 4.1), SummarizedExperiment, SingleCellExperiment,
TreeSummarizedExperiment (>= 1.99.3)
Imports: methods, stats, utils, MASS, ape, decontam, vegan,
BiocGenerics, S4Vectors, IRanges, Biostrings, DECIPHER,
BiocParallel, DelayedArray, DelayedMatrixStats, scuttle,
scater, DirichletMultinomial, rlang, dplyr, tibble, tidyr
Suggests: testthat, knitr, patchwork, BiocStyle, yaml, phyloseq, dada2,
stringr, biomformat, reldist, ade4, microbiomeDataSets,
rmarkdown
License: Artistic-2.0 | file LICENSE
Archs: i386, x64
MD5sum: 7c9226a22fcceb91ba691c4af036bc4c
NeedsCompilation: no
Title: Microbiome analysis
Description: mia implements tools for microbiome analysis based on the
SummarizedExperiment, SingleCellExperiment and
TreeSummarizedExperiment infrastructure. Data wrangling and
analysis in the context of taxonomic data is the main scope.
Additional functions for common task are implemented such as
community indices calculation and summarization.
biocViews: Microbiome, Software, DataImport
Author: Felix G.M. Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>), Sudarshan A. Shetty
[aut] (<https://orcid.org/0000-0001-7280-9915>), Tuomas Borman
[aut] (<https://orcid.org/0000-0002-8563-8884>), Leo Lahti
[aut] (<https://orcid.org/0000-0001-5537-637X>), Yang Cao
[ctb], Nathan D. Olson [ctb], Levi Waldron [ctb], Marcel Ramos
[ctb], Héctor Corrada Bravo [ctb], Jayaram Kancherla [ctb],
Domenick Braccia [ctb]
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/microbiome/mia
VignetteBuilder: knitr
BugReports: https://github.com/microbiome/mia/issues
git_url: https://git.bioconductor.org/packages/mia
git_branch: RELEASE_3_13
git_last_commit: 95eeac0
git_last_commit_date: 2021-07-30
Date/Publication: 2021-08-01
source.ver: src/contrib/mia_1.0.8.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mia_1.0.8.zip
mac.binary.ver: bin/macosx/contrib/4.1/mia_1.0.8.tgz
vignettes: vignettes/mia/inst/doc/mia.html
vignetteTitles: mia
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mia/inst/doc/mia.R
dependsOnMe: miaViz
suggestsMe: curatedMetagenomicData
dependencyCount: 114
Package: miaViz
Version: 1.0.1
Depends: R (>= 4.1), SummarizedExperiment, TreeSummarizedExperiment,
mia (>= 0.99), ggplot2, ggraph (>= 2.0)
Imports: methods, stats, S4Vectors, BiocGenerics, BiocParallel,
DelayedArray, scater, ggtree, ggnewscale, viridis, tibble,
tidytree, tidygraph, rlang, purrr, tidyr, dplyr, ape,
DirichletMultinomial
Suggests: knitr, rmarkdown, BiocStyle, testthat, patchwork,
microbiomeDataSets
License: Artistic-2.0 | file LICENSE
MD5sum: 5ed31ae14843adc8f4eb365ab5c09e6b
NeedsCompilation: no
Title: Microbiome Analysis Plotting and Visualization
Description: miaViz implements plotting function to work with
TreeSummarizedExperiment and related objects in a context of
microbiome analysis. Among others this includes plotting tree,
graph and microbiome series data.
biocViews: Microbiome, Software, Visualization
Author: Felix G.M. Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>), Tuomas Borman [aut]
(<https://orcid.org/0000-0002-8563-8884>)
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/miaViz
git_branch: RELEASE_3_13
git_last_commit: 345f172
git_last_commit_date: 2021-06-25
Date/Publication: 2021-06-27
source.ver: src/contrib/miaViz_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miaViz_1.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/miaViz_1.0.1.tgz
vignettes: vignettes/miaViz/inst/doc/miaViz.html
vignetteTitles: miaViz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/miaViz/inst/doc/miaViz.R
dependencyCount: 133
Package: MiChip
Version: 1.46.0
Depends: R (>= 2.3.0), Biobase
Imports: Biobase
License: GPL (>= 2)
MD5sum: 31ded3a49c080a0e7a76fa62258402c6
NeedsCompilation: no
Title: MiChip Parsing and Summarizing Functions
Description: This package takes the MiChip miRNA microarray .grp
scanner output files and parses these out, providing summary
and plotting functions to analyse MiChip hybridizations. A set
of hybridizations is packaged into an ExpressionSet allowing it
to be used by other BioConductor packages.
biocViews: Microarray, Preprocessing
Author: Jonathon Blake <blake@embl.de>
Maintainer: Jonathon Blake <blake@embl.de>
git_url: https://git.bioconductor.org/packages/MiChip
git_branch: RELEASE_3_13
git_last_commit: f25dafd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MiChip_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MiChip_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MiChip_1.46.0.tgz
vignettes: vignettes/MiChip/inst/doc/MiChip.pdf
vignetteTitles: MiChip miRNA Microarray Processing
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MiChip/inst/doc/MiChip.R
dependencyCount: 7
Package: microbiome
Version: 1.14.0
Depends: R (>= 3.6.0), phyloseq, ggplot2
Imports: dplyr, reshape2, Rtsne, scales, stats, tibble, tidyr, utils,
vegan
Suggests: BiocGenerics, BiocStyle, Cairo, knitr, rmarkdown, testthat
License: BSD_2_clause + file LICENSE
MD5sum: 228d381c68d58a8ead919e2dc7b21f16
NeedsCompilation: no
Title: Microbiome Analytics
Description: Utilities for microbiome analysis.
biocViews: Metagenomics,Microbiome,Sequencing,SystemsBiology
Author: Leo Lahti [aut, cre], Sudarshan Shetty [aut]
Maintainer: Leo Lahti <leo.lahti@iki.fi>
URL: http://microbiome.github.io/microbiome
VignetteBuilder: knitr
BugReports: https://github.com/microbiome/microbiome/issues
git_url: https://git.bioconductor.org/packages/microbiome
git_branch: RELEASE_3_13
git_last_commit: bd8313a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/microbiome_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/microbiome_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/microbiome_1.14.0.tgz
vignettes: vignettes/microbiome/inst/doc/vignette.html
vignetteTitles: microbiome R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/microbiome/inst/doc/vignette.R
importsMe: ANCOMBC
dependencyCount: 84
Package: microbiomeDASim
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: graphics, ggplot2, MASS, tmvtnorm, Matrix, mvtnorm, pbapply,
stats, phyloseq, metagenomeSeq, Biobase
Suggests: testthat (>= 2.1.0), knitr, devtools
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: a507cf92a6f6af523fd3053aa502d804
NeedsCompilation: no
Title: Microbiome Differential Abundance Simulation
Description: A toolkit for simulating differential microbiome data
designed for longitudinal analyses. Several functional forms
may be specified for the mean trend. Observations are drawn
from a multivariate normal model. The objective of this package
is to be able to simulate data in order to accurately compare
different longitudinal methods for differential abundance.
biocViews: Microbiome, Visualization, Software
Author: Justin Williams, Hector Corrada Bravo, Jennifer Tom, Joseph
Nathaniel Paulson
Maintainer: Justin Williams <williazo@ucla.edu>
URL: https://github.com/williazo/microbiomeDASim
VignetteBuilder: knitr
BugReports: https://github.com/williazo/microbiomeDASim/issues
git_url: https://git.bioconductor.org/packages/microbiomeDASim
git_branch: RELEASE_3_13
git_last_commit: 880241f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/microbiomeDASim_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/microbiomeDASim_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/microbiomeDASim_1.6.0.tgz
vignettes: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.pdf
vignetteTitles: microbiomeDASim
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.R
dependencyCount: 94
Package: microbiomeExplorer
Version: 1.2.0
Depends: shiny, magrittr, metagenomeSeq, Biobase
Imports: shinyjs (>= 2.0.0), shinydashboard, shinycssloaders,
shinyWidgets, rmarkdown (>= 1.9.0), DESeq2, RColorBrewer,
dplyr, tidyr, purrr, rlang, knitr, readr, DT (>= 0.12.0),
biomformat, tools, stringr, vegan, matrixStats, heatmaply, car,
broom, limma, reshape2, tibble, forcats, lubridate, methods,
plotly (>= 4.9.1)
Suggests: V8, testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: f516d38629730d783d88adc40751ee84
NeedsCompilation: no
Title: Microbiome Exploration App
Description: The MicrobiomeExplorer R package is designed to facilitate
the analysis and visualization of marker-gene survey feature
data. It allows a user to perform and visualize typical
microbiome analytical workflows either through the command line
or an interactive Shiny application included with the package.
In addition to applying common analytical workflows the
application enables automated analysis report generation.
biocViews: Classification, Clustering, GeneticVariability,
DifferentialExpression, Microbiome, Metagenomics,
Normalization, Visualization, MultipleComparison, Sequencing,
Software, ImmunoOncology
Author: Joseph Paulson [aut], Janina Reeder [aut, cre], Mo Huang [aut],
Genentech [cph, fnd]
Maintainer: Janina Reeder <reederj1@gene.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/microbiomeExplorer
git_branch: RELEASE_3_13
git_last_commit: f1b7bd5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/microbiomeExplorer_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/microbiomeExplorer_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/microbiomeExplorer_1.2.0.tgz
vignettes: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.html
vignetteTitles: microbiomeExplorer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.R
dependencyCount: 205
Package: MicrobiotaProcess
Version: 1.4.4
Depends: R (>= 4.0.0)
Imports: ape, tidyr, ggplot2, magrittr, dplyr, Biostrings, ggrepel,
vegan, zoo, ggtree, tidytree (>= 0.3.5), MASS, methods, rlang,
tibble, grDevices, stats, utils, coin, ggsignif, patchwork,
ggstar, tidyselect, SummarizedExperiment, foreach, treeio
Suggests: rmarkdown, prettydoc, testthat, knitr, nlme, phangorn,
picante, plyr, DECIPHER, randomForest, biomformat, scales,
yaml, withr, S4Vectors, purrr, seqmagick, glue, corrr, ggupset,
ggVennDiagram, gghalves, ggalluvial, forcats, pillar, cli,
phyloseq, aplot, ggnewscale, ggside, ggtreeExtra
License: GPL (>= 3.0)
MD5sum: 0cbf3a641ffc595f5247fead7381a18d
NeedsCompilation: no
Title: an R package for analysis, visualization and biomarker discovery
of microbiome
Description: MicrobiotaProcess is an R package for analysis,
visualization and biomarker discovery of microbial datasets. It
introduces MPSE class, this make it more interoperable with the
existing computing ecosystem. Moreover, it introduces a tidy
microbiome data structure paradigm and analysis grammar. It
provides a wide variety of microbiome analsys procedures under
the unified and common framework (tidy-like framework).
biocViews: Visualization, Microbiome, Software, MultipleComparison,
FeatureExtraction
Author: Shuangbin Xu [aut, cre]
(<https://orcid.org/0000-0003-3513-5362>), Guangchuang Yu [aut,
ctb] (<https://orcid.org/0000-0002-6485-8781>)
Maintainer: Shuangbin Xu <xshuangbin@163.com>
URL: https://github.com/YuLab-SMU/MicrobiotaProcess/
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/MicrobiotaProcess/issues
git_url: https://git.bioconductor.org/packages/MicrobiotaProcess
git_branch: RELEASE_3_13
git_last_commit: 3305c9c
git_last_commit_date: 2021-09-30
Date/Publication: 2021-10-03
source.ver: src/contrib/MicrobiotaProcess_1.4.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MicrobiotaProcess_1.4.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/MicrobiotaProcess_1.4.4.tgz
vignettes: vignettes/MicrobiotaProcess/inst/doc/Introduction.html
vignetteTitles: Introduction to MicrobiotaProcess
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MicrobiotaProcess/inst/doc/Introduction.R
dependencyCount: 95
Package: microRNA
Version: 1.50.0
Depends: R (>= 2.10)
Imports: Biostrings (>= 2.11.32)
License: Artistic-2.0
MD5sum: 199c6fdbd3065917df2645fe70242905
NeedsCompilation: yes
Title: Data and functions for dealing with microRNAs
Description: Different data resources for microRNAs and some functions
for manipulating them.
biocViews: Infrastructure, GenomeAnnotation, SequenceMatching
Author: R. Gentleman, S. Falcon
Maintainer: "James F. Reid" <james.reid@ifom-ieo-campus.it>
git_url: https://git.bioconductor.org/packages/microRNA
git_branch: RELEASE_3_13
git_last_commit: a72a3a3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/microRNA_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/microRNA_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/microRNA_1.50.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
suggestsMe: rtracklayer
dependencyCount: 19
Package: midasHLA
Version: 1.0.0
Depends: R (>= 4.1), MultiAssayExperiment (>= 1.8.3)
Imports: assertthat (>= 0.2.0), broom (>= 0.5.1), dplyr (>= 0.8.0.1),
formattable (>= 0.2.0.1), HardyWeinberg (>= 1.6.3), kableExtra
(>= 1.1.0), knitr (>= 1.21), magrittr (>= 1.5), methods,
stringi (>= 1.2.4), rlang (>= 0.3.1), S4Vectors (>= 0.20.1),
stats, SummarizedExperiment (>= 1.12.0), tibble (>= 2.0.1),
utils, qdapTools (>= 1.3.3)
Suggests: broom.mixed (>= 0.2.4), cowplot (>= 1.0.0), devtools (>=
2.0.1), ggplot2 (>= 3.1.0), ggpubr (>= 0.2.5), rmarkdown,
seqinr (>= 3.4-5), survival (>= 2.43-3), testthat (>= 2.0.1),
tidyr (>= 1.1.2)
License: MIT + file LICENCE
MD5sum: b6bf40d69c1dde3c8afd3292e164ff35
NeedsCompilation: no
Title: R package for immunogenomics data handling and association
analysis
Description: MiDAS is a R package for immunogenetics data
transformation and statistical analysis. MiDAS accepts input
data in the form of HLA alleles and KIR types, and can
transform it into biologically meaningful variables, enabling
HLA amino acid fine mapping, analyses of HLA evolutionary
divergence, KIR gene presence, as well as validated HLA-KIR
interactions. Further, it allows comprehensive statistical
association analysis workflows with phenotypes of diverse
measurement scales. MiDAS closes a gap between the inference of
immunogenetic variation and its efficient utilization to make
relevant discoveries related to T cell, Natural Killer cell,
and disease biology.
biocViews: CellBiology, Genetics, StatisticalMethod
Author: Christian Hammer [aut], Maciej Migdał [aut, cre]
Maintainer: Maciej Migdał <mcjmigdal@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/midasHLA
git_branch: RELEASE_3_13
git_last_commit: 9e6b7ff
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/midasHLA_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/midasHLA_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/midasHLA_1.0.0.tgz
vignettes: vignettes/midasHLA/inst/doc/MiDAS_tutorial.html,
vignettes/midasHLA/inst/doc/MiDAS_vignette.html
vignetteTitles: MiDAS tutorial, MiDAS quick start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/midasHLA/inst/doc/MiDAS_tutorial.R,
vignettes/midasHLA/inst/doc/MiDAS_vignette.R
dependencyCount: 100
Package: MIGSA
Version: 1.16.0
Depends: R (>= 3.4), methods, BiocGenerics
Imports: AnnotationDbi, Biobase, BiocParallel, compiler, data.table,
edgeR, futile.logger, ggdendro, ggplot2, GO.db, GOstats, graph,
graphics, grDevices, grid, GSEABase, ismev, jsonlite, limma,
matrixStats, org.Hs.eg.db, RBGL, reshape2, Rgraphviz, stats,
utils, vegan
Suggests: BiocStyle, breastCancerMAINZ, breastCancerNKI,
breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP,
breastCancerVDX, knitr, mGSZ, MIGSAdata, RUnit
License: GPL (>= 2)
MD5sum: 16b8ebbdde22ef9b2f3eebdae9882347
NeedsCompilation: no
Title: Massive and Integrative Gene Set Analysis
Description: Massive and Integrative Gene Set Analysis. The MIGSA
package allows to perform a massive and integrative gene set
analysis over several expression and gene sets simultaneously.
It provides a common gene expression analytic framework that
grants a comprehensive and coherent analysis. Only a minimal
user parameter setting is required to perform both singular and
gene set enrichment analyses in an integrative manner by means
of the best available methods, i.e. dEnricher and mGSZ
respectively. The greatest strengths of this big omics data
tool are the availability of several functions to explore,
analyze and visualize its results in order to facilitate the
data mining task over huge information sources. MIGSA package
also provides several functions that allow to easily load the
most updated gene sets from several repositories.
biocViews: Software, GeneSetEnrichment, Visualization, GeneExpression,
Microarray, RNASeq, KEGG
Author: Juan C. Rodriguez, Cristobal Fresno, Andrea S. Llera and Elmer
A. Fernandez
Maintainer: Juan C. Rodriguez <jcrodriguez@unc.edu.ar>
URL: https://github.com/jcrodriguez1989/MIGSA/
BugReports: https://github.com/jcrodriguez1989/MIGSA/issues
git_url: https://git.bioconductor.org/packages/MIGSA
git_branch: RELEASE_3_13
git_last_commit: 021bffb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MIGSA_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MIGSA_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MIGSA_1.16.0.tgz
vignettes: vignettes/MIGSA/inst/doc/gettingPbcmcData.pdf,
vignettes/MIGSA/inst/doc/gettingTcgaData.pdf,
vignettes/MIGSA/inst/doc/MIGSA.pdf
vignetteTitles: Getting pbcmc datasets, Getting TCGA datasets, Massive
and Integrative Gene Set Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MIGSA/inst/doc/gettingPbcmcData.R,
vignettes/MIGSA/inst/doc/gettingTcgaData.R,
vignettes/MIGSA/inst/doc/MIGSA.R
dependencyCount: 108
Package: miloR
Version: 1.0.0
Depends: R (>= 4.0.0), edgeR
Imports: BiocNeighbors, SingleCellExperiment, Matrix (>= 1.3-0),
S4Vectors, stats, stringr, methods, igraph, irlba, cowplot,
BiocParallel, BiocSingular, limma, ggplot2, tibble,
matrixStats, ggraph, gtools, SummarizedExperiment, patchwork,
tidyr, dplyr, ggrepel, ggbeeswarm, RColorBrewer, grDevices
Suggests: testthat, MASS, mvtnorm, scater, scran, covr, knitr,
rmarkdown, uwot, BiocStyle, MouseGastrulationData, magick,
RCurl, curl, graphics
License: GPL-3 + file LICENSE
MD5sum: dcf68be268133fb88791cb95df62eec1
NeedsCompilation: no
Title: Differential neighbourhood abundance testing on a graph
Description: This package performs single-cell differential abundance
testing. Cell states are modelled as representative
neighbourhoods on a nearest neighbour graph. Hypothesis testing
is performed using a negative bionomial generalized linear
model.
biocViews: SingleCell, MultipleComparison, FunctionalGenomics, Software
Author: Mike Morgan [aut, cre], Emma Dann [aut, ctb]
Maintainer: Mike Morgan <michael.morgan@cruk.cam.ac.uk>
URL: https://marionilab.github.io/miloR
VignetteBuilder: knitr
BugReports: https://github.com/MarioniLab/miloR/issues
git_url: https://git.bioconductor.org/packages/miloR
git_branch: RELEASE_3_13
git_last_commit: 6e2750a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/miloR_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miloR_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/miloR_1.0.0.tgz
vignettes: vignettes/miloR/inst/doc/milo_demo.html,
vignettes/miloR/inst/doc/milo_gastrulation.html
vignetteTitles: Differential abundance testing with Milo, Differential
abundance testing with Milo - Mouse gastrulation example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/miloR/inst/doc/milo_demo.R,
vignettes/miloR/inst/doc/milo_gastrulation.R
dependencyCount: 101
Package: mimager
Version: 1.16.0
Depends: Biobase
Imports: BiocGenerics, S4Vectors, preprocessCore, grDevices, methods,
grid, gtable, scales, DBI, affy, affyPLM, oligo, oligoClasses
Suggests: knitr, rmarkdown, BiocStyle, testthat, lintr, Matrix, abind,
affydata, hgu95av2cdf, oligoData, pd.hugene.1.0.st.v1
License: MIT + file LICENSE
MD5sum: eb58c7979fa3e766b7584aa4267aebf0
NeedsCompilation: no
Title: mimager: The Microarray Imager
Description: Easily visualize and inspect microarrays for spatial
artifacts.
biocViews: Infrastructure, Visualization, Microarray
Author: Aaron Wolen [aut, cre, cph]
Maintainer: Aaron Wolen <aaron@wolen.com>
URL: https://github.com/aaronwolen/mimager
VignetteBuilder: knitr
BugReports: https://github.com/aaronwolen/mimager/issues
git_url: https://git.bioconductor.org/packages/mimager
git_branch: RELEASE_3_13
git_last_commit: e793400
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mimager_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mimager_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mimager_1.16.0.tgz
vignettes: vignettes/mimager/inst/doc/introduction.html
vignetteTitles: mimager overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mimager/inst/doc/introduction.R
dependencyCount: 67
Package: MIMOSA
Version: 1.30.0
Depends: R (>= 3.0.2), MASS, plyr, reshape, Biobase, ggplot2
Imports: methods, Formula, data.table, pracma, MCMCpack, coda, modeest,
testthat, Rcpp, scales, dplyr, tidyr, rlang
LinkingTo: Rcpp, RcppArmadillo
Suggests: parallel, knitr
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: cfc8965ed26241b73e5e9b37e0d60e0e
NeedsCompilation: yes
Title: Mixture Models for Single-Cell Assays
Description: Modeling count data using Dirichlet-multinomial and
beta-binomial mixtures with applications to single-cell assays.
biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays
Author: Greg Finak <gfinak@fhcrc.org>
Maintainer: Greg Finak <gfinak@fhcrc.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MIMOSA
git_branch: RELEASE_3_13
git_last_commit: 8910727
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MIMOSA_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MIMOSA_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MIMOSA_1.30.0.tgz
vignettes: vignettes/MIMOSA/inst/doc/MIMOSA.pdf
vignetteTitles: MIMOSA: Mixture Models For Single Cell Assays
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MIMOSA/inst/doc/MIMOSA.R
dependencyCount: 91
Package: mina
Version: 1.0.0
Depends: R (>= 4.0.0)
Imports: methods, stats, Rcpp, MCL, RSpectra, apcluster, bigmemory,
foreach, ggplot2, parallel, parallelDist, reshape2, plyr,
biganalytics, stringr, Hmisc, utils
LinkingTo: Rcpp, RcppParallel, RcppArmadillo
Suggests: knitr, rmarkdown
Enhances: doMC
License: GPL
MD5sum: 52eaea105165b8ee48155bf34aa5476e
NeedsCompilation: yes
Title: Microbial community dIversity and Network Analysis
Description: An increasing number of microbiome datasets have been
generated and analyzed with the help of rapidly developing
sequencing technologies. At present, analysis of taxonomic
profiling data is mainly conducted using composition-based
methods, which ignores interactions between community members.
Besides this, a lack of efficient ways to compare microbial
interaction networks limited the study of community dynamics.
To better understand how community diversity is affected by
complex interactions between its members, we developed a
framework (Microbial community dIversity and Network Analysis,
mina), a comprehensive framework for microbial community
diversity analysis and network comparison. By defining and
integrating network-derived community features, we greatly
reduce noise-to-signal ratio for diversity analyses. A
bootstrap and permutation-based method was implemented to
assess community network dissimilarities and extract
discriminative features in a statistically principled way.
biocViews: Software, WorkflowStep
Author: Rui Guan [aut, cre], Ruben Garrido-Oter [ctb]
Maintainer: Rui Guan <guan@mpipz.mpg.de>
VignetteBuilder: knitr
BugReports: https://github.com/Guan06/mina
git_url: https://git.bioconductor.org/packages/mina
git_branch: RELEASE_3_13
git_last_commit: 7bf29d4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mina_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mina_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mina_1.0.0.tgz
vignettes: vignettes/mina/inst/doc/mina.html
vignetteTitles: Microbial dIversity and Network Analysis with MINA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mina/inst/doc/mina.R
dependencyCount: 89
Package: MineICA
Version: 1.32.0
Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.8), Biobase, plyr,
ggplot2, scales, foreach, xtable, biomaRt, gtools, GOstats,
cluster, marray, mclust, RColorBrewer, colorspace, igraph,
Rgraphviz, graph, annotate, Hmisc, fastICA, JADE
Imports: AnnotationDbi, lumi, fpc, lumiHumanAll.db
Suggests: biomaRt, GOstats, cluster, hgu133a.db, mclust, igraph,
breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP,
breastCancerVDX, future, future.apply
Enhances: doMC
License: GPL-2
MD5sum: f2b3217ea58b0a057ba6d6b0261dd4fc
NeedsCompilation: no
Title: Analysis of an ICA decomposition obtained on genomics data
Description: The goal of MineICA is to perform Independent Component
Analysis (ICA) on multiple transcriptome datasets, integrating
additional data (e.g molecular, clinical and pathological).
This Integrative ICA helps the biological interpretation of the
components by studying their association with variables (e.g
sample annotations) and gene sets, and enables the comparison
of components from different datasets using correlation-based
graph.
biocViews: Visualization, MultipleComparison
Author: Anne Biton
Maintainer: Anne Biton <anne.biton@gmail.com>
git_url: https://git.bioconductor.org/packages/MineICA
git_branch: RELEASE_3_13
git_last_commit: def84c7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MineICA_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MineICA_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MineICA_1.32.0.tgz
vignettes: vignettes/MineICA/inst/doc/MineICA.pdf
vignetteTitles: MineICA: Independent component analysis of genomic data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MineICA/inst/doc/MineICA.R
dependencyCount: 205
Package: minet
Version: 3.50.0
Imports: infotheo
License: Artistic-2.0
MD5sum: 38515d4651b292fa38e5cd97f305ffec
NeedsCompilation: yes
Title: Mutual Information NETworks
Description: This package implements various algorithms for inferring
mutual information networks from data.
biocViews: Microarray, GraphAndNetwork, Network, NetworkInference
Author: Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi
Maintainer: Patrick E. Meyer <software@meyerp.com>
URL: http://minet.meyerp.com
git_url: https://git.bioconductor.org/packages/minet
git_branch: RELEASE_3_13
git_last_commit: 78c0c53
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/minet_3.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/minet_3.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/minet_3.50.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: BUS, geNetClassifier, netresponse
importsMe: BioNERO, coexnet, epiNEM, RTN, TCGAWorkflow, TGS
suggestsMe: CNORfeeder, predictionet, TCGAbiolinks, WGCNA
dependencyCount: 1
Package: minfi
Version: 1.38.0
Depends: methods, BiocGenerics (>= 0.15.3), GenomicRanges,
SummarizedExperiment (>= 1.1.6), Biostrings, bumphunter (>=
1.1.9)
Imports: S4Vectors, GenomeInfoDb, Biobase (>= 2.33.2), IRanges,
beanplot, RColorBrewer, lattice, nor1mix, siggenes, limma,
preprocessCore, illuminaio (>= 0.23.2), DelayedMatrixStats (>=
1.3.4), mclust, genefilter, nlme, reshape, MASS, quadprog,
data.table, GEOquery, stats, grDevices, graphics, utils,
DelayedArray (>= 0.15.16), HDF5Array, BiocParallel
Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0),
IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1),
minfiData (>= 0.18.0), minfiDataEPIC, FlowSorted.Blood.450k (>=
1.0.1), RUnit, digest, BiocStyle, knitr, rmarkdown, tools
License: Artistic-2.0
MD5sum: beebc9cef9252424c977624c4d8b5832
NeedsCompilation: no
Title: Analyze Illumina Infinium DNA methylation arrays
Description: Tools to analyze & visualize Illumina Infinium methylation
arrays.
biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation,
Epigenetics, Microarray, MethylationArray, MultiChannel,
TwoChannel, DataImport, Normalization, Preprocessing,
QualityControl
Author: Kasper Daniel Hansen [cre, aut], Martin Aryee [aut], Rafael A.
Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb],
E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim
Triche [ctb], Shan V. Andrews [ctb], Peter F. Hickey [ctb]
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
URL: https://github.com/hansenlab/minfi
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/minfi/issues
git_url: https://git.bioconductor.org/packages/minfi
git_branch: RELEASE_3_13
git_last_commit: ff3bf4a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/minfi_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/minfi_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/minfi_1.38.0.tgz
vignettes: vignettes/minfi/inst/doc/minfi.html
vignetteTitles: minfi User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/minfi/inst/doc/minfi.R
dependsOnMe: bigmelon, ChAMP, conumee, DMRcate, methylumi, REMP,
shinyMethyl, IlluminaHumanMethylation27kanno.ilmn12.hg19,
IlluminaHumanMethylation27kmanifest,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylationEPICanno.ilm10b2.hg19,
IlluminaHumanMethylationEPICanno.ilm10b3.hg19,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19,
IlluminaHumanMethylationEPICmanifest, BeadSorted.Saliva.EPIC,
FlowSorted.Blood.450k, FlowSorted.Blood.EPIC,
FlowSorted.CordBlood.450k, FlowSorted.CordBloodCombined.450k,
FlowSorted.CordBloodNorway.450k, FlowSorted.DLPFC.450k,
minfiData, minfiDataEPIC, methylationArrayAnalysis
importsMe: EnMCB, funtooNorm, MEAL, MEAT, MethylAid, methylCC,
methylumi, missMethyl, quantro, recountmethylation, shinyepico,
skewr
suggestsMe: epivizr, epivizrChart, Harman, mCSEA, MultiDataSet, planet,
RnBeads, sesame, brgedata, MLML2R
dependencyCount: 138
Package: MinimumDistance
Version: 1.36.0
Depends: R (>= 3.5.0), VanillaICE (>= 1.47.1)
Imports: methods, BiocGenerics, MatrixGenerics, Biobase, S4Vectors (>=
0.23.18), IRanges, GenomeInfoDb, GenomicRanges (>= 1.17.16),
SummarizedExperiment (>= 1.15.4), oligoClasses, DNAcopy, ff,
foreach, matrixStats, lattice, data.table, grid, stats, utils
Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18,
BSgenome.Hsapiens.UCSC.hg19, RUnit
Enhances: snow, doSNOW
License: Artistic-2.0
Archs: i386, x64
MD5sum: 4df5b69e64ce9eeff955d56ff58e87d8
NeedsCompilation: no
Title: A Package for De Novo CNV Detection in Case-Parent Trios
Description: Analysis of de novo copy number variants in trios from
high-dimensional genotyping platforms.
biocViews: Microarray, SNP, CopyNumberVariation
Author: Robert B Scharpf and Ingo Ruczinski
Maintainer: Robert Scharpf <rscharpf@jhu.edu>
git_url: https://git.bioconductor.org/packages/MinimumDistance
git_branch: RELEASE_3_13
git_last_commit: 9e0f7b9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MinimumDistance_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MinimumDistance_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MinimumDistance_1.36.0.tgz
vignettes: vignettes/MinimumDistance/inst/doc/MinimumDistance.pdf
vignetteTitles: Detection of de novo copy number alterations in
case-parent trios
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MinimumDistance/inst/doc/MinimumDistance.R
dependencyCount: 85
Package: MiPP
Version: 1.64.0
Depends: R (>= 2.4)
Imports: Biobase, e1071, MASS, stats
License: GPL (>= 2)
Archs: i386, x64
MD5sum: bd1019ae653ba57f6d098b0263d11c09
NeedsCompilation: no
Title: Misclassification Penalized Posterior Classification
Description: This package finds optimal sets of genes that seperate
samples into two or more classes.
biocViews: Microarray, Classification
Author: HyungJun Cho <hj4cho@korea.ac.kr>, Sukwoo Kim
<s4kim@korea.ac.kr>, Mat Soukup <soukup@fda.gov>, and Jae K.
Lee <jaeklee@virginia.edu>
Maintainer: Sukwoo Kim <s4kim@korea.ac.kr>
URL:
http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/
git_url: https://git.bioconductor.org/packages/MiPP
git_branch: RELEASE_3_13
git_last_commit: 765dab9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MiPP_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MiPP_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MiPP_1.64.0.tgz
vignettes: vignettes/MiPP/inst/doc/MiPP.pdf
vignetteTitles: MiPP Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 12
Package: miQC
Version: 1.0.0
Imports: SingleCellExperiment, flexmix, ggplot2, splines, BiocParallel
Suggests: scRNAseq, scater, biomaRt, BiocStyle, knitr, rmarkdown
License: BSD_3_clause + file LICENSE
MD5sum: 440940be524dc67bced4a6d2f971120f
NeedsCompilation: no
Title: Flexible, probabilistic metrics for quality control of scRNA-seq
data
Description: Single-cell RNA-sequencing (scRNA-seq) has made it
possible to profile gene expression in tissues at high
resolution. An important preprocessing step prior to
performing downstream analyses is to identify and remove cells
with poor or degraded sample quality using quality control (QC)
metrics. Two widely used QC metrics to identify a
‘low-quality’ cell are (i) if the cell includes a high
proportion of reads that map to mitochondrial DNA encoded genes
(mtDNA) and (ii) if a small number of genes are detected. miQC
is data-driven QC metric that jointly models both the
proportion of reads mapping to mtDNA and the number of detected
genes with mixture models in a probabilistic framework to
predict the low-quality cells in a given dataset.
biocViews: SingleCell, QualityControl, GeneExpression, Preprocessing,
Sequencing
Author: Ariel Hippen [aut, cre], Stephanie Hicks [aut]
Maintainer: Ariel Hippen <ariel.hippen@gmail.com>
URL: https://github.com/greenelab/miQC
VignetteBuilder: knitr
BugReports: https://github.com/greenelab/miQC/issues
git_url: https://git.bioconductor.org/packages/miQC
git_branch: RELEASE_3_13
git_last_commit: d69caf1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-20
source.ver: src/contrib/miQC_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miQC_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/miQC_1.0.0.tgz
vignettes: vignettes/miQC/inst/doc/miQC.html
vignetteTitles: miQC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/miQC/inst/doc/miQC.R
dependencyCount: 67
Package: MIRA
Version: 1.14.0
Depends: R (>= 3.5)
Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table,
ggplot2, Biobase, stats, bsseq, methods
Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown,
AnnotationHub, LOLA
License: GPL-3
Archs: i386, x64
MD5sum: d8b080725da11febe5f7e2cf0f11f761
NeedsCompilation: no
Title: Methylation-Based Inference of Regulatory Activity
Description: DNA methylation contains information about the regulatory
state of the cell. MIRA aggregates genome-scale DNA methylation
data into a DNA methylation profile for a given region set with
shared biological annotation. Using this profile, MIRA infers
and scores the collective regulatory activity for the region
set. MIRA facilitates regulatory analysis in situations where
classical regulatory assays would be difficult and allows
public sources of region sets to be leveraged for novel insight
into the regulatory state of DNA methylation datasets.
biocViews: ImmunoOncology, DNAMethylation, GeneRegulation,
GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq,
MethylSeq, Sequencing, Epigenetics, Coverage
Author: Nathan Sheffield <http://www.databio.org> [aut], Christoph Bock
[ctb], John Lawson [aut, cre]
Maintainer: John Lawson <jtl2hk@virginia.edu>
URL: http://databio.org/mira
VignetteBuilder: knitr
BugReports: https://github.com/databio/MIRA
git_url: https://git.bioconductor.org/packages/MIRA
git_branch: RELEASE_3_13
git_last_commit: ac36885
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MIRA_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MIRA_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MIRA_1.14.0.tgz
vignettes: vignettes/MIRA/inst/doc/BiologicalApplication.html,
vignettes/MIRA/inst/doc/GettingStarted.html
vignetteTitles: Applying MIRA to a Biological Question, Getting Started
with Methylation-based Inference of Regulatory Activity
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MIRA/inst/doc/BiologicalApplication.R,
vignettes/MIRA/inst/doc/GettingStarted.R
importsMe: COCOA
dependencyCount: 91
Package: MiRaGE
Version: 1.34.0
Depends: R (>= 3.1.0), Biobase(>= 2.23.3)
Imports: BiocGenerics, S4Vectors, AnnotationDbi, BiocManager
Suggests: seqinr (>= 3.0.7), biomaRt (>= 2.19.1), GenomicFeatures (>=
1.15.4), Biostrings (>= 2.31.3), BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm10, miRNATarget, humanStemCell,
IRanges, GenomicRanges (>= 1.8.3), BSgenome,
beadarrayExampleData
License: GPL
MD5sum: 4a65a1a6c22b5d6f157d2bb8ec02e635
NeedsCompilation: no
Title: MiRNA Ranking by Gene Expression
Description: The package contains functions for inferece of target gene
regulation by miRNA, based on only target gene expression
profile.
biocViews: ImmunoOncology, Microarray, GeneExpression, RNASeq,
Sequencing, SAGE
Author: Y-h. Taguchi <tag@granular.com>
Maintainer: Y-h. Taguchi <tag@granular.com>
git_url: https://git.bioconductor.org/packages/MiRaGE
git_branch: RELEASE_3_13
git_last_commit: 726e7d4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MiRaGE_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MiRaGE_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MiRaGE_1.34.0.tgz
vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf
vignetteTitles: How to use MiRaGE Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R
dependencyCount: 47
Package: miRBaseConverter
Version: 1.16.0
Depends: R (>= 3.4)
Imports: stats
Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils, rmarkdown
License: GPL (>= 2)
MD5sum: 3319b1e9a22ab14ce3e6f1c837b385bf
NeedsCompilation: no
Title: A comprehensive and high-efficiency tool for converting and
retrieving the information of miRNAs in different miRBase
versions
Description: A comprehensive tool for converting and retrieving the
miRNA Name, Accession, Sequence, Version, History and Family
information in different miRBase versions. It can process a
huge number of miRNAs in a short time without other depends.
biocViews: Software, miRNA
Author: Taosheng Xu<taosheng.x@gmail.com>, Thuc
Le<Thuc.Le@unisa.edu.au>
Maintainer: Taosheng Xu<taosheng.x@gmail.com>
URL: https://github.com/taoshengxu/miRBaseConverter
VignetteBuilder: knitr
BugReports: https://github.com/taoshengxu/miRBaseConverter/issues
git_url: https://git.bioconductor.org/packages/miRBaseConverter
git_branch: RELEASE_3_13
git_last_commit: 59eb323
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/miRBaseConverter_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miRBaseConverter_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/miRBaseConverter_1.16.0.tgz
vignettes:
vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.html
vignetteTitles: "miRBaseConverter: A comprehensive and high-efficiency
tool for converting and retrieving the information of miRNAs in
different miRBase versions"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.R
importsMe: ExpHunterSuite
dependencyCount: 1
Package: miRcomp
Version: 1.22.0
Depends: R (>= 3.2), Biobase (>= 2.22.0), miRcompData
Imports: utils, methods, graphics, KernSmooth, stats
Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, shiny
License: GPL-3 | file LICENSE
MD5sum: 41a699cb008ca0e6214349041cc4bac2
NeedsCompilation: no
Title: Tools to assess and compare miRNA expression estimatation
methods
Description: Based on a large miRNA dilution study, this package
provides tools to read in the raw amplification data and use
these data to assess the performance of methods that estimate
expression from the amplification curves.
biocViews: Software, qPCR, Preprocessing, QualityControl
Author: Matthew N. McCall <mccallm@gmail.com>, Lauren Kemperman
<lkemperm@u.rochester.edu>
Maintainer: Matthew N. McCall <mccallm@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/miRcomp
git_branch: RELEASE_3_13
git_last_commit: 47b8874
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/miRcomp_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miRcomp_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/miRcomp_1.22.0.tgz
vignettes: vignettes/miRcomp/inst/doc/miRcomp.html
vignetteTitles: Assessment and comparison of miRNA expression
estimation methods (miRcomp)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R
dependencyCount: 9
Package: mirIntegrator
Version: 1.22.0
Depends: R (>= 3.3)
Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi,
Rgraphviz
Suggests: RUnit, BiocGenerics
License: GPL (>=3)
MD5sum: c0d309ad4809a13ef7ebab3a2163921b
NeedsCompilation: no
Title: Integrating microRNA expression into signaling pathways for
pathway analysis
Description: Tools for augmenting signaling pathways to perform pathway
analysis of microRNA and mRNA expression levels.
biocViews: Network, Microarray, GraphAndNetwork, Pathways, KEGG
Author: Diana Diaz <dmd at wayne dot edu>
Maintainer: Diana Diaz <dmd@wayne.edu>
URL: http://datad.github.io/mirIntegrator/
git_url: https://git.bioconductor.org/packages/mirIntegrator
git_branch: RELEASE_3_13
git_last_commit: 7ce3bbd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mirIntegrator_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mirIntegrator_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mirIntegrator_1.22.0.tgz
vignettes: vignettes/mirIntegrator/inst/doc/mirIntegrator.pdf
vignetteTitles: mirIntegrator Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mirIntegrator/inst/doc/mirIntegrator.R
dependencyCount: 79
Package: miRLAB
Version: 1.22.0
Imports: methods, stats, utils, RCurl, httr, stringr, Hmisc, energy,
entropy, gplots, glmnet, impute, limma,
pcalg,TCGAbiolinks,dplyr,SummarizedExperiment, ctc,
InvariantCausalPrediction, Category, GOstats, org.Hs.eg.db
Suggests: knitr,BiocGenerics, AnnotationDbi,RUnit
License: GPL (>=2)
MD5sum: 21dfcfafa26c7da6c014ac528d435348
NeedsCompilation: no
Title: Dry lab for exploring miRNA-mRNA relationships
Description: Provide tools exploring miRNA-mRNA relationships,
including popular miRNA target prediction methods, ensemble
methods that integrate individual methods, functions to get
data from online resources, functions to validate the results,
and functions to conduct enrichment analyses.
biocViews: miRNA, GeneExpression, NetworkInference, Network
Author: Thuc Duy Le, Junpeng Zhang, Mo Chen, Vu Viet Hoang Pham
Maintainer: Vu Viet Hoang Pham <VuVietHoang.Pham@unisa.edu.au>
URL: https://github.com/pvvhoang/miRLAB
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/miRLAB
git_branch: RELEASE_3_13
git_last_commit: 829c088
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/miRLAB_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miRLAB_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/miRLAB_1.22.0.tgz
vignettes: vignettes/miRLAB/inst/doc/miRLAB-vignette.html
vignetteTitles: miRLAB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRLAB/inst/doc/miRLAB-vignette.R
dependencyCount: 187
Package: miRmine
Version: 1.14.0
Depends: R (>= 3.4), SummarizedExperiment
Suggests: BiocStyle, knitr, rmarkdown, DESeq2
License: GPL (>= 3)
MD5sum: 0b4195d0ea727fd89e6b25adef35f91f
NeedsCompilation: no
Title: Data package with miRNA-seq datasets from miRmine database as
RangedSummarizedExperiment
Description: miRmine database is a collection of expression profiles
from different publicly available miRNA-seq datasets, Panwar et
al (2017) miRmine: A Database of Human miRNA Expression,
prepared with this data package as RangedSummarizedExperiment.
biocViews: Homo_sapiens_Data, RNASeqData, SequencingData,
ExpressionData
Author: Dusan Randjelovic [aut, cre]
Maintainer: Dusan Randjelovic <dusan.randjelovic@sbgenomics.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/miRmine
git_branch: RELEASE_3_13
git_last_commit: 467a1fd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/miRmine_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miRmine_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/miRmine_1.14.0.tgz
vignettes: vignettes/miRmine/inst/doc/miRmine.html
vignetteTitles: miRmine
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRmine/inst/doc/miRmine.R
dependencyCount: 26
Package: miRNAmeConverter
Version: 1.20.0
Depends: miRBaseVersions.db
Imports: DBI, AnnotationDbi, reshape2
Suggests: methods, testthat, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 97bb3fde020eca4ba85c30248fcc966f
NeedsCompilation: no
Title: Convert miRNA Names to Different miRBase Versions
Description: Translating mature miRNA names to different miRBase
versions, sequence retrieval, checking names for validity and
detecting miRBase version of a given set of names (data from
http://www.mirbase.org/).
biocViews: Preprocessing, miRNA
Author: Stefan Haunsberger [aut, cre]
Maintainer: Stefan J. Haunsberger <stefan.haunsberger@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/miRNAmeConverter
git_branch: RELEASE_3_13
git_last_commit: bd0599b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/miRNAmeConverter_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miRNAmeConverter_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/miRNAmeConverter_1.20.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 53
Package: miRNApath
Version: 1.52.0
Depends: methods, R(>= 2.7.0)
License: LGPL-2.1
MD5sum: 23491ea6416c4d732e3a00e34856a3c8
NeedsCompilation: no
Title: miRNApath: Pathway Enrichment for miRNA Expression Data
Description: This package provides pathway enrichment techniques for
miRNA expression data. Specifically, the set of methods handles
the many-to-many relationship between miRNAs and the multiple
genes they are predicted to target (and thus affect.) It also
handles the gene-to-pathway relationships separately. Both
steps are designed to preserve the additive effects of miRNAs
on genes, many miRNAs affecting one gene, one miRNA affecting
multiple genes, or many miRNAs affecting many genes.
biocViews: Annotation, Pathways, DifferentialExpression,
NetworkEnrichment, miRNA
Author: James M. Ward <jmw86069@gmail.com> with contributions from
Yunling Shi, Cindy Richards, John P. Cogswell
Maintainer: James M. Ward <jmw86069@gmail.com>
git_url: https://git.bioconductor.org/packages/miRNApath
git_branch: RELEASE_3_13
git_last_commit: 640c48d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/miRNApath_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miRNApath_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/miRNApath_1.52.0.tgz
vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf
vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRNApath/inst/doc/miRNApath.R
dependencyCount: 1
Package: miRNAtap
Version: 1.26.0
Depends: R (>= 3.3.0), AnnotationDbi
Imports: DBI, RSQLite, stringr, sqldf, plyr, methods
Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat
License: GPL-2
MD5sum: 947167586a49787cf1103e5b95f4c1b8
NeedsCompilation: no
Title: miRNAtap: microRNA Targets - Aggregated Predictions
Description: The package facilitates implementation of workflows
requiring miRNA predictions, it allows to integrate ranked
miRNA target predictions from multiple sources available online
and aggregate them with various methods which improves quality
of predictions above any of the single sources. Currently
predictions are available for Homo sapiens, Mus musculus and
Rattus norvegicus (the last one through homology translation).
biocViews: Software, Classification, Microarray, Sequencing, miRNA
Author: Maciej Pajak, T. Ian Simpson
Maintainer: T. Ian Simpson <ian.simpson@ed.ac.uk>
git_url: https://git.bioconductor.org/packages/miRNAtap
git_branch: RELEASE_3_13
git_last_commit: 22a41a2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/miRNAtap_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miRNAtap_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/miRNAtap_1.26.0.tgz
vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf
vignetteTitles: miRNAtap
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R
dependsOnMe: miRNAtap.db
importsMe: SpidermiR, miRNAtap.db
dependencyCount: 54
Package: miRSM
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: WGCNA, flashClust, dynamicTreeCut, GFA, igraph, linkcomm, MCL,
NMF, biclust, iBBiG, fabia, BicARE, isa2, s4vd, BiBitR, rqubic,
Biobase, PMA, stats, dbscan, subspace, mclust, SOMbrero,
ppclust, miRspongeR, Rcpp, utils, SummarizedExperiment,
GSEABase, org.Hs.eg.db, MatrixCorrelation, energy
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
MD5sum: 31df173c7577afee88bc6e43d4a028f8
NeedsCompilation: yes
Title: Inferring miRNA sponge modules in heterogeneous data
Description: The package aims to identify miRNA sponge modules in
heterogeneous data. It provides several functions to study
miRNA sponge modules, including popular methods for inferring
gene modules (candidate miRNA sponge modules), and a function
to identify miRNA sponge modules, as well as several functions
to conduct modular analysis of miRNA sponge modules.
biocViews: GeneExpression, BiomedicalInformatics, Clustering,
GeneSetEnrichment, Microarray, Software, GeneRegulation,
GeneTarget
Author: Junpeng Zhang [aut, cre]
Maintainer: Junpeng Zhang <zhangjunpeng_411@yahoo.com>
URL: https://github.com/zhangjunpeng411/miRSM
VignetteBuilder: knitr
BugReports: https://github.com/zhangjunpeng411/miRSM/issues
git_url: https://git.bioconductor.org/packages/miRSM
git_branch: RELEASE_3_13
git_last_commit: 7d6b0e5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/miRSM_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miRSM_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/miRSM_1.10.0.tgz
vignettes: vignettes/miRSM/inst/doc/miRSM.html
vignetteTitles: miRSM: inferring miRNA sponge modules in heterogeneous
data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRSM/inst/doc/miRSM.R
dependencyCount: 253
Package: miRspongeR
Version: 1.18.0
Depends: R (>= 3.5.0)
Imports: corpcor, parallel, igraph, MCL, clusterProfiler, ReactomePA,
DOSE, survival, grDevices, graphics, stats, varhandle,
linkcomm, utils, Rcpp, org.Hs.eg.db
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
MD5sum: 6abf4d0343deede79ba0f9ab5f515522
NeedsCompilation: yes
Title: Identification and analysis of miRNA sponge interaction networks
and modules
Description: This package provides several functions to study miRNA
sponge (also called ceRNA or miRNA decoy), including popular
methods for identifying miRNA sponge interactions, and the
integrative method to integrate miRNA sponge interactions from
different methods, as well as the functions to validate miRNA
sponge interactions, and infer miRNA sponge modules, conduct
enrichment analysis of modules, and conduct survival analysis
of modules.
biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment,
Survival, Microarray, Software
Author: Junpeng Zhang
Maintainer: Junpeng Zhang <zhangjunpeng_411@yahoo.com>
URL: <https://github.com/zhangjunpeng411/miRspongeR>
VignetteBuilder: knitr
BugReports: https://github.com/zhangjunpeng411/miRspongeR/issues
git_url: https://git.bioconductor.org/packages/miRspongeR
git_branch: RELEASE_3_13
git_last_commit: d01440c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/miRspongeR_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/miRspongeR_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/miRspongeR_1.18.0.tgz
vignettes: vignettes/miRspongeR/inst/doc/miRspongeR.html
vignetteTitles: miRspongeR: identification and analysis of miRNA sponge
interaction networks and modules
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRspongeR/inst/doc/miRspongeR.R
importsMe: miRSM
dependencyCount: 141
Package: mirTarRnaSeq
Version: 1.0.0
Depends: R (>= 4.1.0)
Imports: purrr, MASS, pscl, assertthat, caTools, dplyr, pheatmap,
reshape2, corrplot, grDevices, graphics, stats, utils,
data.table, R.utils
Suggests: BiocStyle, knitr, rmarkdown, R.cache
License: MIT
MD5sum: e65fcab6f3da1f406e7539b0987a25b1
NeedsCompilation: no
Title: mirTarRnaSeq
Description: mirTarRnaSeq R package can be used for interactive mRNA
miRNA sequencing statistical analysis. This package utilizes
expression or differential expression mRNA and miRNA sequencing
results and performs interactive correlation and various GLMs
(Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis
between mRNA and miRNA expriments. These experiments can be
time point experiments, and or condition expriments.
biocViews: miRNA, Regression, Software, Sequencing, SmallRNA,
TimeCourse, DifferentialExpression
Author: Mercedeh Movassagh [aut, cre]
(<https://orcid.org/0000-0001-7690-0230>), Sarah Morton [aut],
Rafael Irizarry [aut], Jeffrey Bailey [aut], Joseph N Paulson
[aut]
Maintainer: Mercedeh Movassagh <mercedeh@ds.dfci.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mirTarRnaSeq
git_branch: RELEASE_3_13
git_last_commit: 97072ad
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mirTarRnaSeq_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mirTarRnaSeq_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mirTarRnaSeq_1.0.0.tgz
vignettes: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.pdf
vignetteTitles: mirTarRnaSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.R
dependencyCount: 50
Package: missMethyl
Version: 1.26.1
Depends: R (>= 3.6.0), IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19
Imports: AnnotationDbi, BiasedUrn, Biobase, BiocGenerics,
GenomicRanges, GO.db, IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylationEPICmanifest, IRanges, limma, methods,
methylumi, minfi, org.Hs.eg.db, ruv, S4Vectors, statmod,
stringr, SummarizedExperiment
Suggests: BiocStyle, edgeR, knitr, minfiData, rmarkdown,
tweeDEseqCountData, DMRcate, ExperimentHub
License: GPL-2
MD5sum: 6eb081a08b381bcaa1058228bc7cfde0
NeedsCompilation: no
Title: Analysing Illumina HumanMethylation BeadChip Data
Description: Normalisation, testing for differential variability and
differential methylation and gene set testing for data from
Illumina's Infinium HumanMethylation arrays. The normalisation
procedure is subset-quantile within-array normalisation (SWAN),
which allows Infinium I and II type probes on a single array to
be normalised together. The test for differential variability
is based on an empirical Bayes version of Levene's test.
Differential methylation testing is performed using RUV, which
can adjust for systematic errors of unknown origin in
high-dimensional data by using negative control probes. Gene
ontology analysis is performed by taking into account the
number of probes per gene on the array, as well as taking into
account multi-gene associated probes.
biocViews: Normalization, DNAMethylation, MethylationArray,
GenomicVariation, GeneticVariability, DifferentialMethylation,
GeneSetEnrichment
Author: Belinda Phipson and Jovana Maksimovic
Maintainer: Belinda Phipson <belinda.phipson@petermac.org>, Jovana
Maksimovic <jovana.maksimovic@petermac.org>, Andrew Lonsdale
<andrew.lonsdale@petermac.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/missMethyl
git_branch: RELEASE_3_13
git_last_commit: 3a43a5e
git_last_commit_date: 2021-06-18
Date/Publication: 2021-06-20
source.ver: src/contrib/missMethyl_1.26.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/missMethyl_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/missMethyl_1.26.1.tgz
vignettes: vignettes/missMethyl/inst/doc/missMethyl.html
vignetteTitles: missMethyl: Analysing Illumina HumanMethylation
BeadChip Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R
dependsOnMe: methylationArrayAnalysis
importsMe: DMRcate, MEAL, methylGSA
suggestsMe: RnBeads
dependencyCount: 163
Package: missRows
Version: 1.12.0
Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment
Imports: plyr, stats, gtools, S4Vectors
Suggests: BiocStyle, knitr, testthat
License: Artistic-2.0
MD5sum: 7a354fc639d0bb1ef0ff93c44d91a9c4
NeedsCompilation: no
Title: Handling Missing Individuals in Multi-Omics Data Integration
Description: The missRows package implements the MI-MFA method to deal
with missing individuals ('biological units') in multi-omics
data integration. The MI-MFA method generates multiple imputed
datasets from a Multiple Factor Analysis model, then the yield
results are combined in a single consensus solution. The
package provides functions for estimating coordinates of
individuals and variables, imputing missing individuals, and
various diagnostic plots to inspect the pattern of missingness
and visualize the uncertainty due to missing values.
biocViews: Software, StatisticalMethod, DimensionReduction,
PrincipalComponent, MathematicalBiology, Visualization
Author: Ignacio Gonzalez and Valentin Voillet
Maintainer: Gonzalez Ignacio <ignacio.gonzalez@bbox.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/missRows
git_branch: RELEASE_3_13
git_last_commit: 810c54f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/missRows_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/missRows_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/missRows_1.12.0.tgz
vignettes: vignettes/missRows/inst/doc/missRows.pdf
vignetteTitles: missRows
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/missRows/inst/doc/missRows.R
dependencyCount: 66
Package: mistyR
Version: 1.0.3
Depends: R (>= 4.0)
Imports: assertthat, caret, deldir, digest, distances, dplyr, filelock,
furrr (>= 0.2.0), ggplot2, MASS, purrr, ranger, readr, rlang,
rlist, R.utils, stats, stringr, tibble, tidyr, withr
Suggests: BiocStyle, covr, future, igraph, knitr, Matrix, progeny,
rmarkdown, sctransform, SingleCellExperiment,
SpatialExperiment, SummarizedExperiment, testthat (>= 3.0.0)
License: GPL-3
Archs: x64
MD5sum: 16d0f54d77360e07d755aae686ad1d86
NeedsCompilation: no
Title: Multiview Intercellular SpaTial modeling framework
Description: mistyR is an implementation of the Multiview Intercellular
SpaTialmodeling framework (MISTy). MISTy is an explainable
machine learning framework for knowledge extraction and
analysis of single-cell, highly multiplexed, spatially resolved
data. MISTy facilitates an in-depth understanding of marker
interactions by profiling the intra- and intercellular
relationships. MISTy is a flexible framework able to process a
custom number of views. Each of these views can describe a
different spatial context, i.e., define a relationship among
the observed expressions of the markers, such as intracellular
regulation or paracrine regulation, but also, the views can
also capture cell-type specific relationships, capture
relations between functional footprints or focus on relations
between different anatomical regions. Each MISTy view is
considered as a potential source of variability in the measured
marker expressions. Each MISTy view is then analyzed for its
contribution to the total expression of each marker and is
explained in terms of the interactions with other measurements
that led to the observed contribution.
biocViews: Software, BiomedicalInformatics, CellBiology,
SystemsBiology, Regression, DecisionTree, SingleCell
Author: Jovan Tanevski [cre, aut]
(<https://orcid.org/0000-0001-7177-1003>), Ricardo Omar Ramirez
Flores [ctb] (<https://orcid.org/0000-0003-0087-371X>)
Maintainer: Jovan Tanevski <jovan.tanevski@uni-heidelberg.de>
URL: https://github.com/saezlab/mistyR
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/mistyR/issues
git_url: https://git.bioconductor.org/packages/mistyR
git_branch: RELEASE_3_13
git_last_commit: cf6b099
git_last_commit_date: 2021-07-22
Date/Publication: 2021-07-22
source.ver: src/contrib/mistyR_1.0.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mistyR_1.0.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/mistyR_1.0.3.tgz
vignettes: vignettes/mistyR/inst/doc/mistySpatialExperiment.pdf,
vignettes/mistyR/inst/doc/mistyR.html
vignetteTitles: mistyR and SpatialExperiment, Getting started
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mistyR/inst/doc/mistyR.R,
vignettes/mistyR/inst/doc/mistySpatialExperiment.R
dependencyCount: 104
Package: mitch
Version: 1.4.1
Depends: R (>= 4.0)
Imports: stats, grDevices, graphics, utils, MASS, plyr, reshape2,
parallel, GGally, grid, gridExtra, knitr, rmarkdown, ggplot2,
gplots, beeswarm, echarts4r
Suggests: stringi, testthat (>= 2.1.0)
License: CC BY-SA 4.0 + file LICENSE
Archs: i386, x64
MD5sum: 77d17638d116ceab6e425047da8734d8
NeedsCompilation: no
Title: Multi-Contrast Gene Set Enrichment Analysis
Description: mitch is an R package for multi-contrast enrichment
analysis. At it’s heart, it uses a rank-MANOVA based
statistical approach to detect sets of genes that exhibit
enrichment in the multidimensional space as compared to the
background. The rank-MANOVA concept dates to work by Cox and
Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is
useful for pathway analysis of profiling studies with one, two
or more contrasts, or in studies with multiple omics profiling,
for example proteomic, transcriptomic, epigenomic analysis of
the same samples. mitch is perfectly suited for pathway level
differential analysis of scRNA-seq data. The main strengths of
mitch are that it can import datasets easily from many upstream
tools and has advanced plotting features to visualise these
enrichments.
biocViews: GeneExpression, GeneSetEnrichment, SingleCell,
Transcriptomics, Epigenetics, Proteomics,
DifferentialExpression, Reactome
Author: Mark Ziemann [aut, cre, cph], Antony Kaspi [aut, cph]
Maintainer: Mark Ziemann <mark.ziemann@gmail.com>
URL: https://github.com/markziemann/mitch
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mitch
git_branch: RELEASE_3_13
git_last_commit: 7a8ffff
git_last_commit_date: 2021-09-09
Date/Publication: 2021-09-12
source.ver: src/contrib/mitch_1.4.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mitch_1.4.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/mitch_1.4.1.tgz
vignettes: vignettes/mitch/inst/doc/mitchWorkflow.html
vignetteTitles: mitch Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mitch/inst/doc/mitchWorkflow.R
dependencyCount: 97
Package: mixOmics
Version: 6.16.3
Depends: R (>= 3.5.0), MASS, lattice, ggplot2
Imports: igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr,
tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra,
grDevices, graphics, stats, ggrepel, BiocParallel, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, rgl
License: GPL (>= 2)
MD5sum: 77b6508053a92a8da802621ce44093a0
NeedsCompilation: no
Title: Omics Data Integration Project
Description: Multivariate methods are well suited to large omics data
sets where the number of variables (e.g. genes, proteins,
metabolites) is much larger than the number of samples
(patients, cells, mice). They have the appealing properties of
reducing the dimension of the data by using instrumental
variables (components), which are defined as combinations of
all variables. Those components are then used to produce useful
graphical outputs that enable better understanding of the
relationships and correlation structures between the different
data sets that are integrated. mixOmics offers a wide range of
multivariate methods for the exploration and integration of
biological datasets with a particular focus on variable
selection. The package proposes several sparse multivariate
models we have developed to identify the key variables that are
highly correlated, and/or explain the biological outcome of
interest. The data that can be analysed with mixOmics may come
from high throughput sequencing technologies, such as omics
data (transcriptomics, metabolomics, proteomics, metagenomics
etc) but also beyond the realm of omics (e.g. spectral
imaging). The methods implemented in mixOmics can also handle
missing values without having to delete entire rows with
missing data. A non exhaustive list of methods include variants
of generalised Canonical Correlation Analysis, sparse Partial
Least Squares and sparse Discriminant Analysis. Recently we
implemented integrative methods to combine multiple data sets:
N-integration with variants of Generalised Canonical
Correlation Analysis and P-integration with variants of
multi-group Partial Least Squares.
biocViews: ImmunoOncology, Microarray, Sequencing, Metabolomics,
Metagenomics, Proteomics, GenePrediction, MultipleComparison,
Classification, Regression
Author: Kim-Anh Le Cao [aut], Florian Rohart [aut], Ignacio Gonzalez
[aut], Sebastien Dejean [aut], Al J Abadi [ctb, cre], Benoit
Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb],
Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb]
Maintainer: Al J Abadi <al.jal.abadi@gmail.com>
URL: http://www.mixOmics.org
VignetteBuilder: knitr
BugReports: https://github.com/mixOmicsTeam/mixOmics/issues/
git_url: https://git.bioconductor.org/packages/mixOmics
git_branch: RELEASE_3_13
git_last_commit: 759d581
git_last_commit_date: 2021-07-27
Date/Publication: 2021-07-29
source.ver: src/contrib/mixOmics_6.16.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mixOmics_6.16.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/mixOmics_6.16.3.tgz
vignettes: vignettes/mixOmics/inst/doc/vignette.html
vignetteTitles: mixOmics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mixOmics/inst/doc/vignette.R
dependsOnMe: timeOmics, bootsPLS, mixKernel, RGCxGC, sgPLS
importsMe: AlpsNMR, DepecheR, multiSight, POMA, MetabolomicsBasics,
plsmod, plsRcox, RVAideMemoire
suggestsMe: autonomics, ChemoSpec, SelectBoost
dependencyCount: 67
Package: MLInterfaces
Version: 1.72.0
Depends: R (>= 3.5), Rcpp, methods, BiocGenerics (>= 0.13.11), Biobase,
annotate, cluster
Imports: gdata, pls, sfsmisc, MASS, rpart, genefilter, fpc, ggvis,
shiny, gbm, RColorBrewer, hwriter, threejs (>= 0.2.2), mlbench,
stats4, tools, grDevices, graphics, stats, magrittr
Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL,
hgu95av2.db, som, hu6800.db, lattice, caret (>= 5.07),
golubEsets, ada, keggorthology, kernlab, mboost, party, klaR,
testthat
Enhances: parallel
License: LGPL
MD5sum: f6d9772bd98587ee4b0be3b30b22cbf9
NeedsCompilation: no
Title: Uniform interfaces to R machine learning procedures for data in
Bioconductor containers
Description: This package provides uniform interfaces to machine
learning code for data in R and Bioconductor containers.
biocViews: Classification, Clustering
Author: Vince Carey <stvjc@channing.harvard.edu>, Robert Gentleman,
Jess Mar, and contributions from Jason Vertrees
<jv@cs.dartmouth.edu> and Laurent Gatto <lg390@cam.ac.uk>
Maintainer: V. Carey <stvjc@channing.harvard.edu>
git_url: https://git.bioconductor.org/packages/MLInterfaces
git_branch: RELEASE_3_13
git_last_commit: a48dad8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MLInterfaces_1.72.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MLInterfaces_1.72.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MLInterfaces_1.72.0.tgz
vignettes: vignettes/MLInterfaces/inst/doc/MLint_devel.pdf,
vignettes/MLInterfaces/inst/doc/MLInterfaces.pdf,
vignettes/MLInterfaces/inst/doc/MLprac2_2.pdf,
vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf
vignetteTitles: MLInterfaces devel for schema-based MLearn,
MLInterfaces Primer, A machine learning tutorial: applications
of the Bioconductor MLInterfaces package to expression and
ChIP-Seq data, MLInterfaces Computer Cluster
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R,
vignettes/MLInterfaces/inst/doc/MLInterfaces.R,
vignettes/MLInterfaces/inst/doc/MLprac2_2.R,
vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R
dependsOnMe: pRoloc, SigCheck, proteomics, dGAselID, nlcv
dependencyCount: 112
Package: MLP
Version: 1.40.0
Imports: AnnotationDbi, gplots, graphics, stats, utils
Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db,
org.Cf.eg.db, KEGGREST, annotate, Rgraphviz, GOstats, graph,
limma, mouse4302.db, reactome.db
License: GPL-3
MD5sum: 8cb92f04e70d8089df592b1849f2db85
NeedsCompilation: no
Title: Mean Log P Analysis
Description: Pathway analysis based on p-values associated to genes
from a genes expression analysis of interest. Utility functions
enable to extract pathways from the Gene Ontology Biological
Process (GOBP), Molecular Function (GOMF) and Cellular
Component (GOCC), Kyoto Encyclopedia of Genes of Genomes (KEGG)
and Reactome databases. Methodology, and helper functions to
display the results as a table, barplot of pathway
significance, Gene Ontology graph and pathway significance are
available.
biocViews: Genetics, GeneExpression, Pathways, Reactome, KEGG, GO
Author: Nandini Raghavan [aut], Tobias Verbeke [aut], An De Bondt
[aut], Javier Cabrera [ctb], Dhammika Amaratunga [ctb], Tine
Casneuf [ctb], Willem Ligtenberg [ctb], Laure Cougnaud [cre]
Maintainer: Tobias Verbeke <tobias.verbeke@openanalytics.eu>
git_url: https://git.bioconductor.org/packages/MLP
git_branch: RELEASE_3_13
git_last_commit: 40d425d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MLP_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MLP_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MLP_1.40.0.tgz
vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf
vignetteTitles: UsingMLP
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MLP/inst/doc/UsingMLP.R
importsMe: esetVis
suggestsMe: a4
dependencyCount: 50
Package: MLSeq
Version: 2.10.0
Depends: caret, ggplot2
Imports: methods, DESeq2, edgeR, limma, Biobase, SummarizedExperiment,
plyr, foreach, utils, sSeq, xtable
Suggests: knitr, testthat, BiocStyle, VennDiagram, pamr
License: GPL(>=2)
MD5sum: 71a990824fa52bb3f6968f621b4defab
NeedsCompilation: no
Title: Machine Learning Interface for RNA-Seq Data
Description: This package applies several machine learning methods,
including SVM, bagSVM, Random Forest and CART to RNA-Seq data.
biocViews: ImmunoOncology, Sequencing, RNASeq, Classification,
Clustering
Author: Gokmen Zararsiz [aut, cre], Dincer Goksuluk [aut], Selcuk
Korkmaz [aut], Vahap Eldem [aut], Izzet Parug Duru [ctb], Ahmet
Ozturk [aut], Ahmet Ergun Karaagaoglu [aut, ths]
Maintainer: Gokmen Zararsiz <gokmenzararsiz@hotmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MLSeq
git_branch: RELEASE_3_13
git_last_commit: 2d28c92
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MLSeq_2.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MLSeq_2.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MLSeq_2.10.0.tgz
vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf
vignetteTitles: Beginner's guide to the "MLSeq" package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R
importsMe: GARS
dependencyCount: 135
Package: MMAPPR2
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: ensemblVEP (>= 1.20.0), gmapR, Rsamtools, VariantAnnotation,
BiocParallel, Biobase, BiocGenerics, dplyr, GenomeInfoDb,
GenomicRanges, IRanges, S4Vectors, tidyr, VariantTools,
magrittr, methods, grDevices, graphics, stats, utils, stringr,
data.table
Suggests: testthat, mockery, roxygen2, knitr, rmarkdown, BiocStyle,
MMAPPR2data
License: GPL-3
OS_type: unix
MD5sum: 4e7811bdab26aef7553eda91de37d931
NeedsCompilation: no
Title: Mutation Mapping Analysis Pipeline for Pooled RNA-Seq
Description: MMAPPR2 maps mutations resulting from pooled RNA-seq data
from the F2 cross of forward genetic screens. Its predecessor
is described in a paper published in Genome Research (Hill et
al. 2013). MMAPPR2 accepts aligned BAM files as well as a
reference genome as input, identifies loci of high sequence
disparity between the control and mutant RNA sequences,
predicts variant effects using Ensembl's Variant Effect
Predictor, and outputs a ranked list of candidate mutations.
biocViews: RNASeq, PooledScreens, DNASeq, VariantDetection
Author: Kyle Johnsen [aut], Nathaniel Jenkins [aut], Jonathon Hill
[cre]
Maintainer: Jonathon Hill <jhill@byu.edu>
URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3613585/,
https://github.com/kjohnsen/MMAPPR2
SystemRequirements: Ensembl VEP, Samtools
VignetteBuilder: knitr
BugReports: https://github.com/kjohnsen/MMAPPR2/issues
git_url: https://git.bioconductor.org/packages/MMAPPR2
git_branch: RELEASE_3_13
git_last_commit: 7c16b5e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MMAPPR2_1.6.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/MMAPPR2_1.6.0.tgz
vignettes: vignettes/MMAPPR2/inst/doc/MMAPPR2.html
vignetteTitles: An Introduction to MMAPPR2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MMAPPR2/inst/doc/MMAPPR2.R
dependencyCount: 104
Package: MMDiff2
Version: 1.20.0
Depends: R (>= 3.3), Rsamtools, Biobase,
Imports: GenomicRanges, locfit, BSgenome, Biostrings, shiny, ggplot2,
RColorBrewer, graphics, grDevices, parallel, S4Vectors, methods
Suggests: MMDiffBamSubset, MotifDb, knitr, BiocStyle,
BSgenome.Mmusculus.UCSC.mm9
License: Artistic-2.0
MD5sum: c2a5883fb9ed8982c4739bfd752dee56
NeedsCompilation: no
Title: Statistical Testing for ChIP-Seq data sets
Description: This package detects statistically significant differences
between read enrichment profiles in different ChIP-Seq samples.
To take advantage of shape differences it uses Kernel methods
(Maximum Mean Discrepancy, MMD).
biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Software
Author: Gabriele Schweikert [cre, aut], David Kuo [aut]
Maintainer: Gabriele Schweikert <gschweik@staffmail.ed.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MMDiff2
git_branch: RELEASE_3_13
git_last_commit: d1abce8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MMDiff2_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MMDiff2_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MMDiff2_1.20.0.tgz
vignettes: vignettes/MMDiff2/inst/doc/MMDiff2.pdf
vignetteTitles: An Introduction to the MMDiff2 method
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MMDiff2/inst/doc/MMDiff2.R
suggestsMe: MMDiffBamSubset
dependencyCount: 95
Package: MMUPHin
Version: 1.6.2
Depends: R (>= 3.6)
Imports: Maaslin2, metafor, fpc, igraph, ggplot2, dplyr, tidyr,
cowplot, utils, stats, grDevices
Suggests: testthat, BiocStyle, knitr, rmarkdown, magrittr, vegan,
phyloseq, curatedMetagenomicData, genefilter
License: MIT + file LICENSE
MD5sum: 4fc674258bcd52e0fbafaa893cefb858
NeedsCompilation: no
Title: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in
Microbiome Studies
Description: MMUPHin is an R package for meta-analysis tasks of
microbiome cohorts. It has function interfaces for: a)
covariate-controlled batch- and cohort effect adjustment, b)
meta-analysis differential abundance testing, c) meta-analysis
unsupervised discrete structure (clustering) discovery, and d)
meta-analysis unsupervised continuous structure discovery.
biocViews: Metagenomics, Microbiome, BatchEffect
Author: Siyuan Ma
Maintainer: Siyuan MA <syma.research@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MMUPHin
git_branch: RELEASE_3_13
git_last_commit: 5119619
git_last_commit_date: 2021-10-03
Date/Publication: 2021-10-07
source.ver: src/contrib/MMUPHin_1.6.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MMUPHin_1.6.2.zip
vignettes: vignettes/MMUPHin/inst/doc/MMUPHin.html
vignetteTitles: MMUPHin
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MMUPHin/inst/doc/MMUPHin.R
dependencyCount: 162
Package: mnem
Version: 1.8.0
Depends: R (>= 4.1)
Imports: cluster, graph, Rgraphviz, flexclust, lattice, naturalsort,
snowfall, stats4, tsne, methods, graphics, stats, utils,
Linnorm, data.table, Rcpp, RcppEigen, matrixStats, grDevices,
e1071, ggplot2, wesanderson
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, devtools, rmarkdown, BiocGenerics, RUnit, epiNEM
License: GPL-3
MD5sum: 14081b31cfc48955ab48c3a5f975bb2f
NeedsCompilation: yes
Title: Mixture Nested Effects Models
Description: Mixture Nested Effects Models (mnem) is an extension of
Nested Effects Models and allows for the analysis of single
cell perturbation data provided by methods like Perturb-Seq
(Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In
those experiments each of many cells is perturbed by a
knock-down of a specific gene, i.e. several cells are perturbed
by a knock-down of gene A, several by a knock-down of gene B,
... and so forth. The observed read-out has to be multi-trait
and in the case of the Perturb-/Crop-Seq gene are expression
profiles for each cell. mnem uses a mixture model to
simultaneously cluster the cell population into k clusters and
and infer k networks causally linking the perturbed genes for
each cluster. The mixture components are inferred via an
expectation maximization algorithm.
biocViews: Pathways, SystemsBiology, NetworkInference, Network, RNASeq,
PooledScreens, SingleCell, CRISPR, ATACSeq, DNASeq,
GeneExpression
Author: Martin Pirkl [aut, cre]
Maintainer: Martin Pirkl <martinpirkl@yahoo.de>
URL: https://github.com/cbg-ethz/mnem/
VignetteBuilder: knitr
BugReports: https://github.com/cbg-ethz/mnem/issues
git_url: https://git.bioconductor.org/packages/mnem
git_branch: RELEASE_3_13
git_last_commit: b3e3d91
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mnem_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mnem_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mnem_1.8.0.tgz
vignettes: vignettes/mnem/inst/doc/mnem.html
vignetteTitles: mnem
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mnem/inst/doc/mnem.R
dependsOnMe: nempi
importsMe: bnem, dce, epiNEM
dependencyCount: 84
Package: moanin
Version: 1.0.0
Depends: R (>= 4.0), SummarizedExperiment, topGO, stats
Imports: S4Vectors, MASS (>= 1.0.0), limma, viridis, edgeR, graphics,
methods, grDevices, reshape2, NMI, zoo, ClusterR, splines,
matrixStats
Suggests: testthat (>= 1.0.0), timecoursedata, knitr, rmarkdown, covr,
BiocStyle
License: BSD 3-clause License + file LICENSE
MD5sum: 0c06cb0e3c8c4328f8f9ed29cf3f7a85
NeedsCompilation: no
Title: An R Package for Time Course RNASeq Data Analysis
Description: Simple and efficient workflow for time-course gene
expression data, built on publictly available open-source
projects hosted on CRAN and bioconductor. moanin provides
helper functions for all the steps required for analysing
time-course data using functional data analysis: (1) functional
modeling of the timecourse data; (2) differential expression
analysis; (3) clustering; (4) downstream analysis.
biocViews: TimeCourse, GeneExpression, RNASeq, Microarray,
DifferentialExpression, Clustering
Author: Elizabeth Purdom [aut]
(<https://orcid.org/0000-0001-9455-7990>), Nelle Varoquaux
[aut, cre] (<https://orcid.org/0000-0002-8748-6546>)
Maintainer: Nelle Varoquaux <nelle.varoquaux@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/moanin
git_branch: RELEASE_3_13
git_last_commit: 9fbe399
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/moanin_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/moanin_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/moanin_1.0.0.tgz
vignettes: vignettes/moanin/inst/doc/documentation.html
vignetteTitles: Installation
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/moanin/inst/doc/documentation.R
dependencyCount: 96
Package: MODA
Version: 1.18.0
Depends: R (>= 3.3)
Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut,
igraph, cluster, AMOUNTAIN, RColorBrewer
Suggests: BiocStyle, knitr, rmarkdown
License: GPL (>= 2)
MD5sum: 9cc83fa44eac1f21a4dfbcc097c62939
NeedsCompilation: no
Title: MODA: MOdule Differential Analysis for weighted gene
co-expression network
Description: MODA can be used to estimate and construct
condition-specific gene co-expression networks, and identify
differentially expressed subnetworks as conserved or condition
specific modules which are potentially associated with relevant
biological processes.
biocViews: GeneExpression, Microarray, DifferentialExpression, Network
Author: Dong Li, James B. Brown, Luisa Orsini, Zhisong Pan, Guyu Hu and
Shan He
Maintainer: Dong Li <dxl466@cs.bham.ac.uk>
git_url: https://git.bioconductor.org/packages/MODA
git_branch: RELEASE_3_13
git_last_commit: ee5a413
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MODA_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MODA_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MODA_1.18.0.tgz
vignettes: vignettes/MODA/inst/doc/MODA.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 111
Package: ModCon
Version: 1.0.0
Depends: data.table, parallel, utils, stats, R (>= 4.1)
Suggests: testthat, knitr, rmarkdown, dplyr, shinycssloaders, shiny,
shinyFiles, shinydashboard, shinyjs
License: GPL-3 + file LICENSE
MD5sum: a9f2ace3215894fbae49a77298da59b5
NeedsCompilation: no
Title: Modifying splice site usage by changing the mRNP code, while
maintaining the genetic code
Description: Collection of functions to calculate a nucleotide sequence
surrounding for splice donors sites to either activate or
repress donor usage. The proposed alternative nucleotide
sequence encodes the same amino acid and could be applied e.g.
in reporter systems to silence or activate cryptic splice donor
sites.
biocViews: FunctionalGenomics, AlternativeSplicing
Author: Johannes Ptok [aut, cre]
(<https://orcid.org/0000-0002-0322-5649>)
Maintainer: Johannes Ptok <Johannes.Ptok@posteo.de>
URL: https://github.com/caggtaagtat/ModCon
SystemRequirements: Perl
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ModCon
git_branch: RELEASE_3_13
git_last_commit: c25e352
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ModCon_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ModCon_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ModCon_1.0.0.tgz
vignettes: vignettes/ModCon/inst/doc/ModCon.html
vignetteTitles: Designing SD context with ModCon
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ModCon/inst/doc/ModCon.R
dependencyCount: 5
Package: Modstrings
Version: 1.8.0
Depends: R (>= 3.6), Biostrings (>= 2.51.5)
Imports: methods, BiocGenerics, GenomicRanges, S4Vectors, IRanges,
XVector, stringi, stringr, crayon, grDevices
Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis
License: Artistic-2.0
MD5sum: 3553b5d321c787750a57543b8b5c5695
NeedsCompilation: no
Title: Working with modified nucleotide sequences
Description: Representing nucleotide modifications in a nucleotide
sequence is usually done via special characters from a number
of sources. This represents a challenge to work with in R and
the Biostrings package. The Modstrings package implements this
functionallity for RNA and DNA sequences containing modified
nucleotides by translating the character internally in order to
work with the infrastructure of the Biostrings package. For
this the ModRNAString and ModDNAString classes and derivates
and functions to construct and modify these objects despite the
encoding issues are implemenented. In addition the conversion
from sequences to list like location information (and the
reverse operation) is implemented as well.
biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing,
Software
Author: Felix G.M. Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>), Denis L.J.
Lafontaine [ctb, fnd]
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/Modstrings/issues
git_url: https://git.bioconductor.org/packages/Modstrings
git_branch: RELEASE_3_13
git_last_commit: 8d52df1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Modstrings_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Modstrings_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Modstrings_1.8.0.tgz
vignettes: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.html,
vignettes/Modstrings/inst/doc/ModRNAString-alphabet.html,
vignettes/Modstrings/inst/doc/Modstrings.html
vignetteTitles: Modstrings-DNA-alphabet, Modstrings-RNA-alphabet,
Modstrings
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.R,
vignettes/Modstrings/inst/doc/ModRNAString-alphabet.R,
vignettes/Modstrings/inst/doc/Modstrings.R
dependsOnMe: EpiTxDb, RNAmodR, tRNAdbImport
importsMe: tRNA
suggestsMe: EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3
dependencyCount: 24
Package: MOFA2
Version: 1.2.2
Depends: R (>= 4.0)
Imports: rhdf5, dplyr, tidyr, reshape2, pheatmap, ggplot2, methods,
RColorBrewer, cowplot, ggrepel, reticulate, HDF5Array,
grDevices, stats, magrittr, forcats, utils, corrplot,
DelayedArray, Rtsne, uwot, basilisk, stringi
Suggests: knitr, testthat, Seurat, ggpubr, foreach, psych,
MultiAssayExperiment, SummarizedExperiment,
SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown,
data.table, tidyverse, BiocStyle, Matrix
License: GPL (>= 2) + file LICENSE
Archs: i386, x64
MD5sum: c179e9c976d2633e5f3d8210102df1a0
NeedsCompilation: yes
Title: Multi-Omics Factor Analysis v2
Description: The MOFA2 package contains a collection of tools for
training and analysing multi-omic factor analysis (MOFA). MOFA
is a probabilistic factor model that aims to identify principal
axes of variation from data sets that can comprise multiple
omic layers and/or groups of samples. Additional time or space
information on the samples can be incorporated using the
MEFISTO framework, which is part of MOFA2. Downstream analysis
functions to inspect molecular features underlying each factor,
vizualisation, imputation etc are available.
biocViews: DimensionReduction, Bayesian, Visualization
Author: Ricard Argelaguet [aut]
(<https://orcid.org/0000-0003-3199-3722>), Damien Arnol [aut]
(<https://orcid.org/0000-0003-2462-534X>), Danila Bredikhin
[aut] (<https://orcid.org/0000-0001-8089-6983>), Britta Velten
[aut, cre] (<https://orcid.org/0000-0002-8397-3515>)
Maintainer: Britta Velten <britta.velten@gmail.com>
URL: https://biofam.github.io/MOFA2/index.html
SystemRequirements: Python (>=3), numpy, pandas, h5py, scipy, argparse,
sklearn, mofapy2
VignetteBuilder: knitr
BugReports: https://github.com/bioFAM/MOFA2
git_url: https://git.bioconductor.org/packages/MOFA2
git_branch: RELEASE_3_13
git_last_commit: 98a2d44
git_last_commit_date: 2021-08-23
Date/Publication: 2021-08-24
source.ver: src/contrib/MOFA2_1.2.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MOFA2_1.2.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/MOFA2_1.2.2.tgz
vignettes: vignettes/MOFA2/inst/doc/downstream_analysis.html,
vignettes/MOFA2/inst/doc/getting_started_R.html,
vignettes/MOFA2/inst/doc/MEFISTO_temporal.html
vignetteTitles: Downstream analysis: Overview, MOFA2: How to train a
model in R, MEFISTO on simulated data (temporal)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MOFA2/inst/doc/downstream_analysis.R,
vignettes/MOFA2/inst/doc/getting_started_R.R,
vignettes/MOFA2/inst/doc/MEFISTO_temporal.R
dependencyCount: 88
Package: MOGAMUN
Version: 1.2.1
Imports: stats, utils, RCy3, stringr, graphics, grDevices, RUnit,
BiocParallel, igraph
Suggests: BiocStyle, knitr, rmarkdown, markdown
License: GPL-3 + file LICENSE
MD5sum: bca30f71429e10202f229f113c45136e
NeedsCompilation: no
Title: MOGAMUN: A Multi-Objective Genetic Algorithm to Find Active
Modules in Multiplex Biological Networks
Description: MOGAMUN is a multi-objective genetic algorithm that
identifies active modules in a multiplex biological network.
This allows analyzing different biological networks at the same
time. MOGAMUN is based on NSGA-II (Non-Dominated Sorting
Genetic Algorithm, version II), which we adapted to work on
networks.
biocViews: SystemsBiology, GraphAndNetwork, DifferentialExpression,
BiomedicalInformatics, Transcriptomics, Clustering, Network
Author: Elva-MarÃa Novoa-del-Toro [aut, cre]
(<https://orcid.org/0000-0002-6135-5839>)
Maintainer: Elva-MarÃa Novoa-del-Toro <elvanov@hotmail.com>
URL: https://github.com/elvanov/MOGAMUN
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MOGAMUN
git_branch: RELEASE_3_13
git_last_commit: d512ee8
git_last_commit_date: 2021-06-23
Date/Publication: 2021-06-24
source.ver: src/contrib/MOGAMUN_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MOGAMUN_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/MOGAMUN_1.2.1.tgz
vignettes: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.html
vignetteTitles: Finding active modules with MOGAMUN
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.R
dependencyCount: 73
Package: mogsa
Version: 1.26.0
Depends: R (>= 3.4.0)
Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase,
Biobase, parallel, corpcor, svd, cluster, grDevices, graphics,
stats, utils
Suggests: BiocStyle, knitr, org.Hs.eg.db
License: GPL-2
MD5sum: 14016f6e6de2f3d6e121a894c9d3fb69
NeedsCompilation: no
Title: Multiple omics data integrative clustering and gene set analysis
Description: This package provide a method for doing gene set analysis
based on multiple omics data.
biocViews: GeneExpression, PrincipalComponent, StatisticalMethod,
Clustering, Software
Author: Chen Meng
Maintainer: Chen Meng <mengchen18@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mogsa
git_branch: RELEASE_3_13
git_last_commit: 8fbbfc7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mogsa_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mogsa_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mogsa_1.26.0.tgz
vignettes: vignettes/mogsa/inst/doc/moCluster-knitr.pdf,
vignettes/mogsa/inst/doc/mogsa-knitr.pdf
vignetteTitles: moCluster: Integrative clustering using multiple omics
data, mogsa: gene set analysis on multiple omics data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mogsa/inst/doc/moCluster-knitr.R,
vignettes/mogsa/inst/doc/mogsa-knitr.R
dependencyCount: 68
Package: MOMA
Version: 1.4.0
Depends: R (>= 4.0)
Imports: circlize, cluster, ComplexHeatmap, dplyr, ggplot2, graphics,
grid, grDevices, magrittr, methods, MKmisc,
MultiAssayExperiment, parallel, qvalue, RColorBrewer, readr,
reshape2, rlang, stats, stringr, tibble, tidyr, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, viper
License: GPL-3
MD5sum: 42212ca6c0b76e1b79f0f8f35770e432
NeedsCompilation: no
Title: Multi Omic Master Regulator Analysis
Description: This package implements the inference of candidate master
regulator proteins from multi-omics' data (MOMA) algorithm, as
well as ancillary analysis and visualization functions.
biocViews: Software, NetworkEnrichment, NetworkInference, Network,
FeatureExtraction, Clustering, FunctionalGenomics,
Transcriptomics, SystemsBiology
Author: Evan Paull [aut], Sunny Jones [aut, cre], Mariano Alvarez [aut]
Maintainer: Sunny Jones <sunnyjjones@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/califano-lab/MOMA/issues
git_url: https://git.bioconductor.org/packages/MOMA
git_branch: RELEASE_3_13
git_last_commit: 8a75832
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MOMA_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MOMA_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MOMA_1.4.0.tgz
vignettes: vignettes/MOMA/inst/doc/moma.html
vignetteTitles: MOMA - Multi Omic Master Regulator Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MOMA/inst/doc/moma.R
dependencyCount: 96
Package: monocle
Version: 2.20.0
Depends: R (>= 2.10.0), methods, Matrix (>= 1.2-6), Biobase, ggplot2
(>= 1.0.0), VGAM (>= 1.0-6), DDRTree (>= 0.1.4),
Imports: parallel, igraph (>= 1.0.1), BiocGenerics, HSMMSingleCell (>=
0.101.5), plyr, cluster, combinat, fastICA, grid, irlba (>=
2.0.0), matrixStats, densityClust (>= 0.3), Rtsne, MASS,
reshape2, limma, tibble, dplyr, qlcMatrix, pheatmap, stringr,
proxy, slam, viridis, stats, biocViews, RANN(>= 2.5), Rcpp (>=
0.12.0)
LinkingTo: Rcpp
Suggests: destiny, Hmisc, knitr, Seurat, scater, testthat
License: Artistic-2.0
MD5sum: 17b041f3f6828d3eb8d2ab3c6f58c893
NeedsCompilation: yes
Title: Clustering, differential expression, and trajectory analysis for
single- cell RNA-Seq
Description: Monocle performs differential expression and time-series
analysis for single-cell expression experiments. It orders
individual cells according to progress through a biological
process, without knowing ahead of time which genes define
progress through that process. Monocle also performs
differential expression analysis, clustering, visualization,
and other useful tasks on single cell expression data. It is
designed to work with RNA-Seq and qPCR data, but could be used
with other types as well.
biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression,
DifferentialExpression, Infrastructure, DataImport,
DataRepresentation, Visualization, Clustering,
MultipleComparison, QualityControl
Author: Cole Trapnell
Maintainer: Cole Trapnell <coletrap@uw.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/monocle
git_branch: RELEASE_3_13
git_last_commit: dd8f8f2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/monocle_2.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/monocle_2.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/monocle_2.20.0.tgz
vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf
vignetteTitles: Monocle: Cell counting,, differential expression,, and
trajectory analysis for single-cell RNA-Seq experiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R
dependsOnMe: cicero, ctgGEM, phemd
importsMe: tradeSeq, uSORT
suggestsMe: M3Drop, scran, sincell, Seurat
dependencyCount: 85
Package: MoonlightR
Version: 1.18.0
Depends: R (>= 3.5), doParallel, foreach
Imports: parmigene, randomForest, SummarizedExperiment, gplots,
circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase,
limma, grDevices, graphics, TCGAbiolinks, GEOquery, stats,
RISmed, grid, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2,
png
License: GPL (>= 3)
MD5sum: 70615486888da4c4d4c7623b0679119e
NeedsCompilation: no
Title: Identify oncogenes and tumor suppressor genes from omics data
Description: Motivation: The understanding of cancer mechanism requires
the identification of genes playing a role in the development
of the pathology and the characterization of their role
(notably oncogenes and tumor suppressors). Results: We present
an R/bioconductor package called MoonlightR which returns a
list of candidate driver genes for specific cancer types on the
basis of TCGA expression data. The method first infers gene
regulatory networks and then carries out a functional
enrichment analysis (FEA) (implementing an upstream regulator
analysis, URA) to score the importance of well-known biological
processes with respect to the studied cancer type. Eventually,
by means of random forests, MoonlightR predicts two specific
roles for the candidate driver genes: i) tumor suppressor genes
(TSGs) and ii) oncogenes (OCGs). As a consequence, this
methodology does not only identify genes playing a dual role
(e.g. TSG in one cancer type and OCG in another) but also helps
in elucidating the biological processes underlying their
specific roles. In particular, MoonlightR can be used to
discover OCGs and TSGs in the same cancer type. This may help
in answering the question whether some genes change role
between early stages (I, II) and late stages (III, IV) in
breast cancer. In the future, this analysis could be useful to
determine the causes of different resistances to
chemotherapeutic treatments.
biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation,
GeneExpression, MethylationArray, DifferentialExpression,
Pathways, Network, Survival, GeneSetEnrichment,
NetworkEnrichment
Author: Antonio Colaprico*, Catharina Olsen*, Claudia Cava, Thilde
Terkelsen, Laura Cantini, Andre Olsen, Gloria Bertoli, Andrei
Zinovyev, Emmanuel Barillot, Isabella Castiglioni, Elena
Papaleo, Gianluca Bontempi
Maintainer: Antonio Colaprico <axc1833@med.miami.edu>, Catharina Olsen
<colsen@ulb.ac.be>
URL: https://github.com/ibsquare/MoonlightR
VignetteBuilder: knitr
BugReports: https://github.com/ibsquare/MoonlightR/issues
git_url: https://git.bioconductor.org/packages/MoonlightR
git_branch: RELEASE_3_13
git_last_commit: d01eece
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MoonlightR_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MoonlightR_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MoonlightR_1.18.0.tgz
vignettes: vignettes/MoonlightR/inst/doc/Moonlight.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MoonlightR/inst/doc/Moonlight.R
dependencyCount: 182
Package: mosaics
Version: 2.30.0
Depends: R (>= 3.0.0), methods, graphics, Rcpp
Imports: MASS, splines, lattice, IRanges, GenomicRanges,
GenomicAlignments, Rsamtools, GenomeInfoDb, S4Vectors
LinkingTo: Rcpp
Suggests: mosaicsExample
Enhances: parallel
License: GPL (>= 2)
MD5sum: d0c48eccc3bd7614462a2b0638dd08d7
NeedsCompilation: yes
Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference
for ChIP-Seq)
Description: This package provides functions for fitting MOSAiCS and
MOSAiCS-HMM, a statistical framework to analyze one-sample or
two-sample ChIP-seq data of transcription factor binding and
histone modification.
biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics
Author: Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles
Maintainer: Dongjun Chung <dongjun.chung@gmail.com>
URL: http://groups.google.com/group/mosaics_user_group
SystemRequirements: Perl
git_url: https://git.bioconductor.org/packages/mosaics
git_branch: RELEASE_3_13
git_last_commit: d9cceee
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mosaics_2.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mosaics_2.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mosaics_2.30.0.tgz
vignettes: vignettes/mosaics/inst/doc/mosaics-example.pdf
vignetteTitles: MOSAiCS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mosaics/inst/doc/mosaics-example.R
dependencyCount: 41
Package: MOSim
Version: 1.6.0
Depends: R (>= 3.6)
Imports: HiddenMarkov, zoo, methods, matrixStats, dplyr, stringi,
lazyeval, rlang, stats, utils, purrr, scales, stringr, tibble,
tidyr, ggplot2, Biobase, IRanges, S4Vectors
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 8836e141bd921fd13131f6035581a12f
NeedsCompilation: no
Title: Multi-Omics Simulation (MOSim)
Description: MOSim package simulates multi-omic experiments that mimic
regulatory mechanisms within the cell, allowing flexible
experimental design including time course and multiple groups.
biocViews: Software, TimeCourse, ExperimentalDesign, RNASeq
Author: Carlos MartÃnez [cre, aut], Sonia Tarazona [aut]
Maintainer: Carlos MartÃnez <cmarmir@gmail.com>
URL: https://github.com/Neurergus/MOSim
VignetteBuilder: knitr
BugReports: https://github.com/Neurergus/MOSim/issues
git_url: https://git.bioconductor.org/packages/MOSim
git_branch: RELEASE_3_13
git_last_commit: c168670
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MOSim_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MOSim_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MOSim_1.6.0.tgz
vignettes: vignettes/MOSim/inst/doc/MOSim.pdf
vignetteTitles: MOSim
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MOSim/inst/doc/MOSim.R
dependencyCount: 57
Package: motifbreakR
Version: 2.6.1
Depends: R (>= 3.5.0), grid, MotifDb
Imports: methods, compiler, grDevices, grImport, stringr, BiocGenerics,
S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges,
Biostrings, BSgenome, rtracklayer, VariantAnnotation,
BiocParallel, motifStack, Gviz, matrixStats, TFMPvalue,
SummarizedExperiment
Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP.20120608,
SNPlocs.Hsapiens.dbSNP142.GRCh37, knitr, rmarkdown,
BSgenome.Drerio.UCSC.danRer7, BiocStyle
License: GPL-2
MD5sum: ca03f49f74c265b34831e0b26f7bbc69
NeedsCompilation: no
Title: A Package For Predicting The Disruptiveness Of Single Nucleotide
Polymorphisms On Transcription Factor Binding Sites
Description: We introduce motifbreakR, which allows the biologist to
judge in the first place whether the sequence surrounding the
polymorphism is a good match, and in the second place how much
information is gained or lost in one allele of the polymorphism
relative to another. MotifbreakR is both flexible and
extensible over previous offerings; giving a choice of
algorithms for interrogation of genomes with motifs from public
sources that users can choose from; these are 1) a weighted-sum
probability matrix, 2) log-probabilities, and 3) weighted by
relative entropy. MotifbreakR can predict effects for novel or
previously described variants in public databases, making it
suitable for tasks beyond the scope of its original design.
Lastly, it can be used to interrogate any genome curated within
Bioconductor (currently there are 22).
biocViews: ChIPSeq, Visualization, MotifAnnotation
Author: Simon Gert Coetzee [aut, cre], Dennis J. Hazelett [aut]
Maintainer: Simon Gert Coetzee <simon.coetzee@cshs.org>
VignetteBuilder: knitr
BugReports: https://github.com/Simon-Coetzee/motifbreakR/issues
git_url: https://git.bioconductor.org/packages/motifbreakR
git_branch: RELEASE_3_13
git_last_commit: ed220a9
git_last_commit_date: 2021-07-20
Date/Publication: 2021-07-22
source.ver: src/contrib/motifbreakR_2.6.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/motifbreakR_2.6.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/motifbreakR_2.6.1.tgz
vignettes: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html
vignetteTitles: motifbreakR: an Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.R
dependencyCount: 151
Package: motifcounter
Version: 1.16.0
Depends: R(>= 3.0)
Imports: Biostrings, methods
Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc
License: GPL-2
Archs: i386, x64
MD5sum: e35bb7bf1ad3585b1d9a25912e1e8821
NeedsCompilation: yes
Title: R package for analysing TFBSs in DNA sequences
Description: 'motifcounter' provides motif matching, motif counting and
motif enrichment functionality based on position frequency
matrices. The main features of the packages include the
utilization of higher-order background models and accounting
for self-overlapping motif matches when determining motif
enrichment. The background model allows to capture dinucleotide
(or higher-order nucleotide) composition adequately which may
reduced model biases and misleading results compared to using
simple GC background models. When conducting a motif enrichment
analysis based on the motif match count, the package relies on
a compound Poisson distribution or alternatively a
combinatorial model. These distribution account for
self-overlapping motif structures as exemplified by repeat-like
or palindromic motifs, and allow to determine the p-value and
fold-enrichment for a set of observed motif matches.
biocViews: Transcription,MotifAnnotation,SequenceMatching,Software
Author: Wolfgang Kopp [aut, cre]
Maintainer: Wolfgang Kopp <wolfgang.kopp@mdc-berlin.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/motifcounter
git_branch: RELEASE_3_13
git_last_commit: 2e4bd42
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/motifcounter_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/motifcounter_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/motifcounter_1.16.0.tgz
vignettes: vignettes/motifcounter/inst/doc/motifcounter.html
vignetteTitles: Introduction to the `motifcounter` package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/motifcounter/inst/doc/motifcounter.R
dependencyCount: 19
Package: MotifDb
Version: 1.34.0
Depends: R (>= 3.5.0), methods, BiocGenerics, S4Vectors, IRanges,
GenomicRanges, Biostrings
Imports: rtracklayer, splitstackshape
Suggests: RUnit, seqLogo, BiocStyle, knitr, rmarkdown
License: Artistic-2.0 | file LICENSE
License_is_FOSS: no
License_restricts_use: yes
MD5sum: 2fc4a4b61f051f120eb7c70f195764ae
NeedsCompilation: no
Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs
Description: More than 9900 annotated position frequency matrices from
14 public sources, for multiple organisms.
biocViews: MotifAnnotation
Author: Paul Shannon, Matt Richards
Maintainer: Paul Shannon <pshannon@systemsbiology.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MotifDb
git_branch: RELEASE_3_13
git_last_commit: ed81ab9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MotifDb_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MotifDb_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MotifDb_1.34.0.tgz
vignettes: vignettes/MotifDb/inst/doc/MotifDb.html
vignetteTitles: "A collection of PWMs"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R
dependsOnMe: motifbreakR, trena, generegulation
importsMe: igvR, rTRMui
suggestsMe: ATACseqQC, DiffLogo, memes, MMDiff2, motifcounter,
motifStack, profileScoreDist, PWMEnrich, rTRM, TFutils,
universalmotif, vtpnet
dependencyCount: 46
Package: motifmatchr
Version: 1.14.0
Depends: R (>= 3.3)
Imports: Matrix, Rcpp, methods, TFBSTools, Biostrings, BSgenome,
S4Vectors, SummarizedExperiment, GenomicRanges, IRanges,
Rsamtools, GenomeInfoDb
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19
License: GPL-3 + file LICENSE
Archs: i386, x64
MD5sum: 1742f7d6535e2601f8ab9d1dd72814b2
NeedsCompilation: yes
Title: Fast Motif Matching in R
Description: Quickly find motif matches for many motifs and many
sequences. Wraps C++ code from the MOODS motif calling library,
which was developed by Pasi Rastas, Janne Korhonen, and Petri
Martinmäki.
biocViews: MotifAnnotation
Author: Alicia Schep [aut, cre], Stanford University [cph]
Maintainer: Alicia Schep <aschep@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/motifmatchr
git_branch: RELEASE_3_13
git_last_commit: b395fda
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/motifmatchr_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/motifmatchr_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/motifmatchr_1.14.0.tgz
vignettes: vignettes/motifmatchr/inst/doc/motifmatchr.html
vignetteTitles: motifmatchr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/motifmatchr/inst/doc/motifmatchr.R
importsMe: enrichTF, esATAC, pageRank
suggestsMe: chromVAR, MethReg, CAGEWorkflow, Signac
dependencyCount: 124
Package: motifStack
Version: 1.36.1
Depends: R (>= 2.15.1), methods, grid
Imports: ade4, Biostrings, ggplot2, grDevices, graphics, htmlwidgets,
stats, stats4, utils, XML
Suggests: grImport, grImport2, BiocGenerics, MotifDb, RColorBrewer,
BiocStyle, knitr, RUnit, rmarkdown
License: GPL (>= 2)
MD5sum: d103c179665482717541ff49704e4759
NeedsCompilation: no
Title: Plot stacked logos for single or multiple DNA, RNA and amino
acid sequence
Description: The motifStack package is designed for graphic
representation of multiple motifs with different similarity
scores. It works with both DNA/RNA sequence motif and amino
acid sequence motif. In addition, it provides the flexibility
for users to customize the graphic parameters such as the font
type and symbol colors.
biocViews: SequenceMatching, Visualization, Sequencing, Microarray,
Alignment, ChIPchip, ChIPSeq, MotifAnnotation, DataImport
Author: Jianhong Ou, Michael Brodsky, Scot Wolfe and Lihua Julie Zhu
Maintainer: Jianhong Ou <jianhong.ou@duke.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/motifStack
git_branch: RELEASE_3_13
git_last_commit: c7c163b
git_last_commit_date: 2021-09-30
Date/Publication: 2021-10-03
source.ver: src/contrib/motifStack_1.36.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/motifStack_1.36.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/motifStack_1.36.1.tgz
vignettes: vignettes/motifStack/inst/doc/motifStack_HTML.html
vignetteTitles: motifStack Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R
dependsOnMe: generegulation
importsMe: ATACseqQC, atSNP, dagLogo, LowMACA, motifbreakR,
ribosomeProfilingQC, TCGAWorkflow
suggestsMe: ChIPpeakAnno, TFutils, universalmotif
dependencyCount: 61
Package: MPFE
Version: 1.28.0
License: GPL (>= 3)
MD5sum: 552dc29276f2a15ac40eb3887deb0edb
NeedsCompilation: no
Title: Estimation of the amplicon methylation pattern distribution from
bisulphite sequencing data
Description: Estimate distribution of methylation patterns from a table
of counts from a bisulphite sequencing experiment given a
non-conversion rate and read error rate.
biocViews: HighThroughputSequencingData, DNAMethylation, MethylSeq
Author: Peijie Lin, Sylvain Foret, Conrad Burden
Maintainer: Conrad Burden <conrad.burden@anu.edu.au>
git_url: https://git.bioconductor.org/packages/MPFE
git_branch: RELEASE_3_13
git_last_commit: 25a44b8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MPFE_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MPFE_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MPFE_1.28.0.tgz
vignettes: vignettes/MPFE/inst/doc/MPFE.pdf
vignetteTitles: MPFE
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MPFE/inst/doc/MPFE.R
dependencyCount: 0
Package: mpra
Version: 1.14.0
Depends: R (>= 3.4.0), methods, BiocGenerics, SummarizedExperiment,
limma
Imports: S4Vectors, scales, stats, graphics, statmod
Suggests: BiocStyle, knitr, rmarkdown, RUnit
License: Artistic-2.0
Archs: i386, x64
MD5sum: 9f51d2be6428e63b71befaec9dfd4eef
NeedsCompilation: no
Title: Analyze massively parallel reporter assays
Description: Tools for data management, count preprocessing, and
differential analysis in massively parallel report assays
(MPRA).
biocViews: Software, GeneRegulation, Sequencing, FunctionalGenomics
Author: Leslie Myint [cre, aut], Kasper D. Hansen [aut]
Maintainer: Leslie Myint <leslie.myint@gmail.com>
URL: https://github.com/hansenlab/mpra
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/mpra/issues
git_url: https://git.bioconductor.org/packages/mpra
git_branch: RELEASE_3_13
git_last_commit: 83bfddd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mpra_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mpra_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mpra_1.14.0.tgz
vignettes: vignettes/mpra/inst/doc/mpra.html
vignetteTitles: mpra User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mpra/inst/doc/mpra.R
dependencyCount: 39
Package: MPRAnalyze
Version: 1.10.0
Imports: BiocParallel, methods, progress, stats, SummarizedExperiment
Suggests: knitr
License: GPL-3
MD5sum: 8357dbadae10b49af6cb4c87773911dc
NeedsCompilation: no
Title: Statistical Analysis of MPRA data
Description: MPRAnalyze provides statistical framework for the analysis
of data generated by Massively Parallel Reporter Assays
(MPRAs), used to directly measure enhancer activity. MPRAnalyze
can be used for quantification of enhancer activity,
classification of active enhancers and comparative analyses of
enhancer activity between conditions. MPRAnalyze construct a
nested pair of generalized linear models (GLMs) to relate the
DNA and RNA observations, easily adjustable to various
experimental designs and conditions, and provides a set of
rigorous statistical testig schemes.
biocViews: ImmunoOncology, Software, StatisticalMethod, Sequencing,
GeneExpression, CellBiology, CellBasedAssays,
DifferentialExpression, ExperimentalDesign, Classification
Author: Tal Ashuach [aut, cre], David S Fischer [aut], Anat Kriemer
[ctb], Fabian J Theis [ctb], Nir Yosef [ctb],
Maintainer: Tal Ashuach <tal_ashuach@berkeley.edu>
URL: https://github.com/YosefLab/MPRAnalyze
VignetteBuilder: knitr
BugReports: https://github.com/YosefLab/MPRAnalyze
git_url: https://git.bioconductor.org/packages/MPRAnalyze
git_branch: RELEASE_3_13
git_last_commit: a4175d2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MPRAnalyze_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MPRAnalyze_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MPRAnalyze_1.10.0.tgz
vignettes: vignettes/MPRAnalyze/inst/doc/vignette.html
vignetteTitles: Analyzing MPRA data with MPRAnalyze
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MPRAnalyze/inst/doc/vignette.R
dependencyCount: 44
Package: MQmetrics
Version: 1.0.0
Imports: ggplot2, readr, magrittr, dplyr, purrr, reshape2, gridExtra,
utils, stringr, chron, ggpubr, stats, cowplot, RColorBrewer,
ggridges, tidyr, scales, grid, rlang, ggforce, grDevices,
gtable, plyr, knitr, rmarkdown
Suggests: testthat (>= 3.0.0)
License: GPL-3
Archs: i386, x64
MD5sum: 4c38bee76d00e91e0e6ca6c984e98905
NeedsCompilation: no
Title: Quality Control of Protemics Data
Description: The package MQmetrics (MaxQuant metrics) provides a
workflow to analyze the quality and reproducibility of your
proteomics mass spectrometry analysis from MaxQuant.Input data
are extracted from several MaxQuant output tables, and produces
a pdf report. It includes several visualization tools to check
numerous parameters regarding the quality of the runs.It also
includes two functions to visualize the iRT peptides from
Biognosysin case they were spiked in the samples.
biocViews: Infrastructure, Proteomics, MassSpectrometry,
QualityControl, DataImport
Author: Alvaro Sanchez-Villalba [aut, cre], Thomas Stehrer [aut], Marek
Vrbacky [aut]
Maintainer: Alvaro Sanchez-Villalba <sanchezvillalba.alvaro@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MQmetrics
git_branch: RELEASE_3_13
git_last_commit: f3e2d25
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MQmetrics_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MQmetrics_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MQmetrics_1.0.0.tgz
vignettes: vignettes/MQmetrics/inst/doc/MQmetrics.html
vignetteTitles: MQmetrics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MQmetrics/inst/doc/MQmetrics.R
dependencyCount: 118
Package: msa
Version: 1.24.0
Depends: R (>= 3.1.0), methods, Biostrings (>= 2.40.0)
Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0),
S4Vectors, tools
LinkingTo: Rcpp
Suggests: Biobase, knitr, seqinr, ape, phangorn
License: GPL (>= 2)
MD5sum: 2e9063398d365dd92a77c9eed3b0ed26
NeedsCompilation: yes
Title: Multiple Sequence Alignment
Description: The 'msa' package provides a unified R/Bioconductor
interface to the multiple sequence alignment algorithms
ClustalW, ClustalOmega, and Muscle. All three algorithms are
integrated in the package, therefore, they do not depend on any
external software tools and are available for all major
platforms. The multiple sequence alignment algorithms are
complemented by a function for pretty-printing multiple
sequence alignments using the LaTeX package TeXshade.
biocViews: MultipleSequenceAlignment, Alignment, MultipleComparison,
Sequencing
Author: Enrico Bonatesta, Christoph Horejs-Kainrath, Ulrich Bodenhofer
Maintainer: Ulrich Bodenhofer <bodenhofer@bioinf.jku.at>
URL: http://www.bioinf.jku.at/software/msa/
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/msa
git_branch: RELEASE_3_13
git_last_commit: d939cac
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/msa_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/msa_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/msa_1.24.0.tgz
vignettes: vignettes/msa/inst/doc/msa.pdf
vignetteTitles: msa - An R Package for Multiple Sequence Alignment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msa/inst/doc/msa.R
importsMe: LymphoSeq, odseq
suggestsMe: idpr, bio3d
dependencyCount: 20
Package: MsBackendMassbank
Version: 1.0.0
Depends: R (>= 4.0), Spectra (>= 1.0)
Imports: BiocParallel, S4Vectors, IRanges, methods, ProtGenerics,
MsCoreUtils, DBI, utils
Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19),
RSQLite, rmarkdown
License: Artistic-2.0
MD5sum: f3192e26c7b30fc9b7042f01431a566b
NeedsCompilation: no
Title: Mass Spectrometry Data Backend for MassBank record Files
Description: Mass spectrometry (MS) data backend supporting import and
export of MS/MS library spectra from MassBank record files.
Different backends are available that allow handling of data in
plain MassBank text file format or allow also to interact
directly with MassBank SQL databases. Objects from this package
are supposed to be used with the Spectra Bioconductor package.
This package thus adds MassBank support to the Spectra package.
biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport
Author: RforMassSpectrometry Package Maintainer [cre], Michael Witting
[aut] (<https://orcid.org/0000-0002-1462-4426>), Johannes
Rainer [aut] (<https://orcid.org/0000-0002-6977-7147>)
Maintainer: RforMassSpectrometry Package Maintainer
<maintainer@rformassspectrometry.org>
URL: https://github.com/RforMassSpectrometry/MsBackendMassbank
VignetteBuilder: knitr
BugReports:
https://github.com/RforMassSpectrometry/MsBackendMassbank/issues
git_url: https://git.bioconductor.org/packages/MsBackendMassbank
git_branch: RELEASE_3_13
git_last_commit: 4d24e2c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MsBackendMassbank_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MsBackendMassbank_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MsBackendMassbank_1.0.0.tgz
vignettes: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.html
vignetteTitles: Description and usage of MsBackendMassbank
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.R
dependencyCount: 27
Package: MsBackendMgf
Version: 1.0.0
Depends: R (>= 4.1), Spectra (>= 1.0)
Imports: BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats
Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19),
rmarkdown
License: Artistic-2.0
MD5sum: b32c1d5f9f126fbca0873ae06a1796de
NeedsCompilation: no
Title: Mass Spectrometry Data Backend for Mascot Generic Format (mgf)
Files
Description: Mass spectrometry (MS) data backend supporting import and
export of MS/MS spectra data from Mascot Generic Format (mgf)
files. Objects defined in this package are supposed to be used
with the Spectra Bioconductor package. This package thus adds
mgf file support to the Spectra package.
biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics,
DataImport
Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto
[aut] (<https://orcid.org/0000-0002-1520-2268>), Johannes
Rainer [aut] (<https://orcid.org/0000-0002-6977-7147>),
Sebastian Gibb [aut] (<https://orcid.org/0000-0001-7406-4443>)
Maintainer: RforMassSpectrometry Package Maintainer
<maintainer@rformassspectrometry.org>
URL: https://github.com/RforMassSpectrometry/MsBackendMgf
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/MsBackendMgf/issues
git_url: https://git.bioconductor.org/packages/MsBackendMgf
git_branch: RELEASE_3_13
git_last_commit: 291d8e9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MsBackendMgf_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MsBackendMgf_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MsBackendMgf_1.0.0.tgz
vignettes: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.html
vignetteTitles: Description and usage of MsBackendMgf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.R
suggestsMe: xcms
dependencyCount: 26
Package: MsCoreUtils
Version: 1.4.0
Depends: R (>= 3.6.0)
Imports: methods, S4Vectors, MASS, stats, clue
LinkingTo: Rcpp
Suggests: testthat, knitr, BiocStyle, rmarkdown, roxygen2, imputeLCMD,
impute, norm, pcaMethods, vsn, preprocessCore
License: Artistic-2.0
MD5sum: 97ceb21933212c6e9add483c694cd88c
NeedsCompilation: yes
Title: Core Utils for Mass Spectrometry Data
Description: MsCoreUtils defines low-level functions for mass
spectrometry data and is independent of any high-level data
structures. These functions include mass spectra processing
functions (noise estimation, smoothing, binning), quantitative
aggregation functions (median polish, robust summarisation,
...), missing data imputation, data normalisation (quantiles,
vsn, ...) as well as misc helper functions, that are used
across high-level data structure within the R for Mass
Spectrometry packages.
biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics
Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto
[aut] (<https://orcid.org/0000-0002-1520-2268>), Johannes
Rainer [aut] (<https://orcid.org/0000-0002-6977-7147>),
Sebastian Gibb [aut] (<https://orcid.org/0000-0001-7406-4443>),
Adriaan Sticker [ctb], Sigurdur Smarason [ctb], Thomas Naake
[ctb], Josep Maria Badia Aparicio [ctb]
(<https://orcid.org/0000-0002-5704-1124>), Michael Witting
[ctb] (<https://orcid.org/0000-0002-1462-4426>)
Maintainer: RforMassSpectrometry Package Maintainer
<maintainer@rformassspectrometry.org>
URL: https://github.com/RforMassSpectrometry/MsCoreUtils
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/MsCoreUtils/issues
git_url: https://git.bioconductor.org/packages/MsCoreUtils
git_branch: RELEASE_3_13
git_last_commit: edaca2a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MsCoreUtils_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MsCoreUtils_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MsCoreUtils_1.4.0.tgz
vignettes: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.html
vignetteTitles: Core Utils for Mass Spectrometry Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.R
importsMe: MsBackendMassbank, MsBackendMgf, MsFeatures, MSnbase,
QFeatures, scp, Spectra, xcms
suggestsMe: msqrob2
dependencyCount: 13
Package: MSEADbi
Version: 1.2.0
Depends: R (>= 4.0)
Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase
Suggests: RUnit, BiocGenerics, BiocStyle, knitr, testthat (>= 2.1.0)
License: Artistic-2.0
MD5sum: c3662ca711bf531c6dc76798f6671d80
NeedsCompilation: no
Title: DBI to construct MSEA-related package
Description: Interface to construct annotation package for MSEA
(MSEA.XXX.pb.db). The program design is same as Bioconductor
LRBaseDbi or MeSHDbi pacakge, and the usage is also the same as
these packages.
biocViews: Infrastructure
Author: Kozo Nishida [aut, cre]
(<https://orcid.org/0000-0001-8501-7319>), Koki Tsuyuzaki [aut]
(<https://orcid.org/0000-0003-3797-2148>), Atsushi Fukushima
[aut] (<https://orcid.org/0000-0001-9015-1694>)
Maintainer: Kozo Nishida <kozo.nishida@gmail.com>
VignetteBuilder: knitr
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/MSEADbi
git_branch: RELEASE_3_13
git_last_commit: 79634ad
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MSEADbi_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSEADbi_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSEADbi_1.2.0.tgz
vignettes: vignettes/MSEADbi/inst/doc/MSEADbi.html
vignetteTitles: MSEADbi
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSEADbi/inst/doc/MSEADbi.R
dependencyCount: 46
Package: MsFeatures
Version: 1.0.0
Depends: R (>= 4.1)
Imports: methods, ProtGenerics (>= 1.23.5), MsCoreUtils,
SummarizedExperiment, stats
Suggests: testthat, roxygen2, BiocStyle, pheatmap, knitr, rmarkdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: 07b27172c81ecdc841186f9d6bcdb205
NeedsCompilation: no
Title: Functionality for Mass Spectrometry Features
Description: The MsFeature package defines functionality for Mass
Spectrometry features. This includes functions to group (LC-MS)
features based on some of their properties, such as retention
time (coeluting features), or correlation of signals across
samples. This packge hence allows to group features, and its
results can be used as an input for the `QFeatures` package
which allows to aggregate abundance levels of features within
each group. This package defines concepts and functions for
base and common data types, implementations for more specific
data types are expected to be implemented in the respective
packages (such as e.g. `xcms`). All functionality of this
package is implemented in a modular way which allows
combination of different grouping approaches and enables its
re-use in other R packages.
biocViews: Infrastructure, MassSpectrometry, Metabolomics
Author: Johannes Rainer [aut, cre]
(<https://orcid.org/0000-0002-6977-7147>)
Maintainer: Johannes Rainer <Johannes.Rainer@eurac.edu>
URL: https://github.com/RforMassSpectrometry/MsFeatures
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/MsFeatures/issues
git_url: https://git.bioconductor.org/packages/MsFeatures
git_branch: RELEASE_3_13
git_last_commit: 7c06b89
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MsFeatures_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MsFeatures_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MsFeatures_1.0.0.tgz
vignettes: vignettes/MsFeatures/inst/doc/MsFeatures.html
vignetteTitles: Grouping Mass Spectrometry Features
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsFeatures/inst/doc/MsFeatures.R
dependencyCount: 32
Package: msgbsR
Version: 1.16.0
Depends: R (>= 3.4), GenomicRanges, methods
Imports: BSgenome, easyRNASeq, edgeR, GenomicAlignments,
GenomicFeatures, GenomeInfoDb, ggbio, ggplot2, IRanges,
parallel, plyr, Rsamtools, R.utils, stats,
SummarizedExperiment, S4Vectors, utils
Suggests: roxygen2, BSgenome.Rnorvegicus.UCSC.rn6
License: GPL-2
MD5sum: a3fe1bef56db48fcca23159d9983ffa0
NeedsCompilation: no
Title: msgbsR: methylation sensitive genotyping by sequencing (MS-GBS)
R functions
Description: Pipeline for the anaysis of a MS-GBS experiment.
biocViews: ImmunoOncology, DifferentialMethylation, DataImport,
Epigenetics, MethylSeq
Author: Benjamin Mayne
Maintainer: Benjamin Mayne <benjamin.mayne@adelaide.edu.au>
git_url: https://git.bioconductor.org/packages/msgbsR
git_branch: RELEASE_3_13
git_last_commit: eee8300
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/msgbsR_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/msgbsR_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/msgbsR_1.16.0.tgz
vignettes: vignettes/msgbsR/inst/doc/msgbsR_Vignette.pdf
vignetteTitles: msgbsR_Example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msgbsR/inst/doc/msgbsR_Vignette.R
dependencyCount: 165
Package: MSGFgui
Version: 1.26.0
Depends: mzR, xlsx
Imports: shiny, mzID (>= 1.2), MSGFplus, shinyFiles (>= 0.4.0), tools
Suggests: knitr, testthat
License: GPL (>= 2)
MD5sum: ce14fc65dedff041bea3ff2e6aa4aaf9
NeedsCompilation: no
Title: A shiny GUI for MSGFplus
Description: This package makes it possible to perform analyses using
the MSGFplus package in a GUI environment. Furthermore it
enables the user to investigate the results using interactive
plots, summary statistics and filtering. Lastly it exposes the
current results to another R session so the user can seamlessly
integrate the gui into other workflows.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, GUI,
Visualization
Author: Thomas Lin Pedersen
Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MSGFgui
git_branch: RELEASE_3_13
git_last_commit: b956389
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MSGFgui_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSGFgui_1.26.0.zip
vignettes: vignettes/MSGFgui/inst/doc/Using_MSGFgui.html
vignetteTitles: Using MSGFgui
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSGFgui/inst/doc/Using_MSGFgui.R
dependencyCount: 62
Package: MSGFplus
Version: 1.26.0
Depends: methods
Imports: mzID, ProtGenerics
Suggests: knitr, testthat
License: GPL (>= 2)
Archs: i386, x64
MD5sum: d48727c320cfb09393f8ad712b353622
NeedsCompilation: no
Title: An interface between R and MS-GF+
Description: This package contains function to perform peptide
identification using the MS-GF+ algorithm. The package contains
functionality for building up a parameter set both in code and
through a simple GUI, as well as running the algorithm in
batches, potentially asynchronously.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics
Author: Thomas Lin Pedersen
Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com>
SystemRequirements: Java (>= 1.7)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MSGFplus
git_branch: RELEASE_3_13
git_last_commit: c0c3ae3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MSGFplus_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSGFplus_1.26.0.zip
vignettes: vignettes/MSGFplus/inst/doc/Using_MSGFplus.html
vignetteTitles: Using MSGFgui
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSGFplus/inst/doc/Using_MSGFplus.R
dependsOnMe: proteomics
importsMe: MSGFgui
dependencyCount: 12
Package: msImpute
Version: 1.2.0
Depends: R (>= 4.0)
Imports: softImpute, methods, stats, graphics, pdist, reticulate,
scran, data.table, FNN, matrixStats, rdetools, limma, mvtnorm
Suggests: BiocStyle, knitr, rmarkdown, ComplexHeatmap, imputeLCMD
License: GPL (>=2)
MD5sum: dcf9a07a1347bae55fdec282158e9a18
NeedsCompilation: no
Title: Imputation of label-free mass spectrometry peptides
Description: MsImpute is a package for imputation of peptide intensity
in proteomics experiments. It additionally contains tools for
MAR/MNAR diagnosis and assessment of distortions to the
probability distribution of the data post imputation.
Currently, msImpute completes missing values by low-rank
approximation of the underlying data matrix.
biocViews: MassSpectrometry, Proteomics, Software
Author: Soroor Hediyeh-zadeh [aut, cre]
(<https://orcid.org/0000-0001-7513-6779>)
Maintainer: Soroor Hediyeh-zadeh <hediyehzadeh.s@wehi.edu.au>
SystemRequirements: python
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/msImpute/issues
git_url: https://git.bioconductor.org/packages/msImpute
git_branch: RELEASE_3_13
git_last_commit: 60d2a5e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/msImpute_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/msImpute_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/msImpute_1.2.0.tgz
vignettes: vignettes/msImpute/inst/doc/msImpute-vignette.html
vignetteTitles: msImpute: proteomics missing values imputation and
diagnosis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msImpute/inst/doc/msImpute-vignette.R
dependencyCount: 71
Package: msmsEDA
Version: 1.30.0
Depends: R (>= 3.0.1), MSnbase
Imports: MASS, gplots, RColorBrewer
License: GPL-2
MD5sum: d8c767febcc1c6eb615c4b0a7baa1b09
NeedsCompilation: no
Title: Exploratory Data Analysis of LC-MS/MS data by spectral counts
Description: Exploratory data analysis to assess the quality of a set
of LC-MS/MS experiments, and visualize de influence of the
involved factors.
biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics
Author: Josep Gregori, Alex Sanchez, and Josep Villanueva
Maintainer: Josep Gregori <josep.gregori@gmail.com>
git_url: https://git.bioconductor.org/packages/msmsEDA
git_branch: RELEASE_3_13
git_last_commit: e9ca537
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/msmsEDA_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/msmsEDA_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/msmsEDA_1.30.0.tgz
vignettes: vignettes/msmsEDA/inst/doc/msmsData-Vignette.pdf
vignetteTitles: msmsEDA: Batch effects detection in LC-MSMS experiments
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msmsEDA/inst/doc/msmsData-Vignette.R
dependsOnMe: msmsTests
suggestsMe: Harman, RforProteomics
dependencyCount: 82
Package: msmsTests
Version: 1.30.0
Depends: R (>= 3.0.1), MSnbase, msmsEDA
Imports: edgeR, qvalue
License: GPL-2
MD5sum: 2cb8ad91c9d9cde7e52fd1295be8e573
NeedsCompilation: no
Title: LC-MS/MS Differential Expression Tests
Description: Statistical tests for label-free LC-MS/MS data by spectral
counts, to discover differentially expressed proteins between
two biological conditions. Three tests are available: Poisson
GLM regression, quasi-likelihood GLM regression, and the
negative binomial of the edgeR package.The three models admit
blocking factors to control for nuissance variables.To assure a
good level of reproducibility a post-test filter is available,
where we may set the minimum effect size considered biologicaly
relevant, and the minimum expression of the most abundant
condition.
biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics
Author: Josep Gregori, Alex Sanchez, and Josep Villanueva
Maintainer: Josep Gregori i Font <josep.gregori@gmail.com>
git_url: https://git.bioconductor.org/packages/msmsTests
git_branch: RELEASE_3_13
git_last_commit: aed00b6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/msmsTests_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/msmsTests_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/msmsTests_1.30.0.tgz
vignettes: vignettes/msmsTests/inst/doc/msmsTests-Vignette.pdf,
vignettes/msmsTests/inst/doc/msmsTests-Vignette2.pdf
vignetteTitles: msmsTests: post test filters to improve
reproducibility, msmsTests: controlling batch effects by
blocking
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msmsTests/inst/doc/msmsTests-Vignette.R,
vignettes/msmsTests/inst/doc/msmsTests-Vignette2.R
importsMe: MSnID
suggestsMe: RforProteomics
dependencyCount: 90
Package: MSnbase
Version: 2.18.0
Depends: R (>= 3.5), methods, BiocGenerics (>= 0.7.1), Biobase (>=
2.15.2), mzR (>= 2.19.6), S4Vectors, ProtGenerics (>= 1.23.7)
Imports: MsCoreUtils, BiocParallel, IRanges (>= 2.13.28), plyr, vsn,
grid, stats4, affy, impute, pcaMethods, MALDIquant (>= 1.16),
mzID (>= 1.5.2), digest, lattice, ggplot2, XML, scales, MASS,
Rcpp
LinkingTo: Rcpp
Suggests: testthat, pryr, gridExtra, microbenchmark, zoo, knitr (>=
1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.7.1), msdata (>=
0.19.3), roxygen2, rgl, rpx, AnnotationHub, BiocStyle (>=
2.5.19), rmarkdown, imputeLCMD, norm, gplots, shiny, magrittr,
SummarizedExperiment
License: Artistic-2.0
MD5sum: cd7e9be5e4fa53657e418c25271d9fd5
NeedsCompilation: yes
Title: Base Functions and Classes for Mass Spectrometry and Proteomics
Description: MSnbase provides infrastructure for manipulation,
processing and visualisation of mass spectrometry and
proteomics data, ranging from raw to quantitative and annotated
data.
biocViews: ImmunoOncology, Infrastructure, Proteomics,
MassSpectrometry, QualityControl, DataImport
Author: Laurent Gatto, Johannes Rainer and Sebastian Gibb with
contributions from Guangchuang Yu, Samuel Wieczorek,
Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie
Cotton, Arne Smits, Martina Fisher, Ludger Goeminne, Adriaan
Sticker and Lieven Clement.
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://lgatto.github.io/MSnbase
VignetteBuilder: knitr
BugReports: https://github.com/lgatto/MSnbase/issues
git_url: https://git.bioconductor.org/packages/MSnbase
git_branch: RELEASE_3_13
git_last_commit: 8653c08
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MSnbase_2.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSnbase_2.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSnbase_2.18.0.tgz
vignettes: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.html,
vignettes/MSnbase/inst/doc/v02-MSnbase-io.html,
vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.html,
vignettes/MSnbase/inst/doc/v04-benchmarking.html,
vignettes/MSnbase/inst/doc/v05-MSnbase-development.html
vignetteTitles: Base Functions and Classes for MS-based Proteomics,
MSnbase IO capabilities, MSnbase: centroiding of profile-mode
MS data, MSnbase benchmarking, A short introduction to
`MSnbase` development
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.R,
vignettes/MSnbase/inst/doc/v02-MSnbase-io.R,
vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.R,
vignettes/MSnbase/inst/doc/v04-benchmarking.R,
vignettes/MSnbase/inst/doc/v05-MSnbase-development.R
dependsOnMe: Autotuner, MetCirc, msmsEDA, msmsTests, pRoloc, pRolocGUI,
qPLEXanalyzer, xcms, pRolocdata, RforProteomics, proteomics
importsMe: cliqueMS, CluMSID, DAPAR, DEP, MSnID, MSstatsQC,
peakPantheR, POMA, PrInCE, ProteomicsAnnotationHubData,
ptairMS, topdownr, DAPARdata, qPLEXdata, RAMClustR
suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, msqrob2,
proDA, qcmetrics, wpm, msdata, enviGCMS, pmd
dependencyCount: 76
Package: MSnID
Version: 1.26.0
Depends: R (>= 2.10), Rcpp
Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach,
doParallel, parallel, methods, iterators, data.table, Biobase,
ProtGenerics, reshape2, dplyr, mzR, BiocStyle, msmsTests,
ggplot2, RUnit, BiocGenerics, Biostrings, purrr, rlang,
stringr, tibble, AnnotationHub, AnnotationDbi, xtable
License: Artistic-2.0
MD5sum: 48e1af3bb36cca32e6a75ee317b025a2
NeedsCompilation: no
Title: Utilities for Exploration and Assessment of Confidence of LC-MSn
Proteomics Identifications
Description: Extracts MS/MS ID data from mzIdentML (leveraging mzID
package) or text files. After collating the search results from
multiple datasets it assesses their identification quality and
optimize filtering criteria to achieve the maximum number of
identifications while not exceeding a specified false discovery
rate. Also contains a number of utilities to explore the MS/MS
results and assess missed and irregular enzymatic cleavages,
mass measurement accuracy, etc.
biocViews: Proteomics, MassSpectrometry, ImmunoOncology
Author: Vlad Petyuk with contributions from Laurent Gatto
Maintainer: Vlad Petyuk <petyuk@gmail.com>
git_url: https://git.bioconductor.org/packages/MSnID
git_branch: RELEASE_3_13
git_last_commit: 542b32c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MSnID_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSnID_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSnID_1.26.0.tgz
vignettes: vignettes/MSnID/inst/doc/handling_mods.pdf,
vignettes/MSnID/inst/doc/msnid_vignette.pdf
vignetteTitles: Handling Modifications with MSnID, MSnID Package for
Handling MS/MS Identifications
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSnID/inst/doc/handling_mods.R,
vignettes/MSnID/inst/doc/msnid_vignette.R
dependsOnMe: proteomics
suggestsMe: RforProteomics
dependencyCount: 160
Package: MSPrep
Version: 1.2.0
Depends: R (>= 4.0)
Imports: SummarizedExperiment, S4Vectors, pcaMethods (>= 1.24.0), VIM,
crmn, preprocessCore, sva, dplyr (>= 0.7), tidyr, tibble (>=
1.2), magrittr, rlang, stats, stringr, methods, ddpcr,
missForest
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 1.0.2)
License: GPL-3
MD5sum: 190a17cbcd4341cdf19084523aaf384e
NeedsCompilation: no
Title: Package for Summarizing, Filtering, Imputing, and Normalizing
Metabolomics Data
Description: Package performs summarization of replicates, filtering by
frequency, several different options for imputing missing data,
and a variety of options for transforming, batch correcting,
and normalizing data.
biocViews: Metabolomics, MassSpectrometry, Preprocessing
Author: Max McGrath [aut, cre], Matt Mulvahill [aut], Grant Hughes
[aut], Sean Jacobson [aut], Harrison Pielke-lombardo [aut],
Katerina Kechris [aut, cph, ths]
Maintainer: Max McGrath <max.mcgrath@ucdenver.edu>
URL: https://github.com/KechrisLab/MSPrep
VignetteBuilder: knitr
BugReports: https://github.com/KechrisLab/MSPrep/issues
git_url: https://git.bioconductor.org/packages/MSPrep
git_branch: RELEASE_3_13
git_last_commit: a135a71
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MSPrep_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSPrep_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSPrep_1.2.0.tgz
vignettes: vignettes/MSPrep/inst/doc/using_MSPrep.html
vignetteTitles: Using MSPrep
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSPrep/inst/doc/using_MSPrep.R
dependencyCount: 187
Package: msPurity
Version: 1.18.0
Depends: Rcpp
Imports: plyr, dplyr, dbplyr, magrittr, foreach, parallel, doSNOW,
stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite,
uuid, jsonlite
Suggests: testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData,
CAMERA, RPostgres, RMySQL
License: GPL-3 + file LICENSE
MD5sum: 94f0f8b7c583e99e971f20cf35f7de6a
NeedsCompilation: no
Title: Automated Evaluation of Precursor Ion Purity for Mass
Spectrometry Based Fragmentation in Metabolomics
Description: msPurity R package was developed to: 1) Assess the
spectral quality of fragmentation spectra by evaluating the
"precursor ion purity". 2) Process fragmentation spectra. 3)
Perform spectral matching. What is precursor ion purity? -What
we call "Precursor ion purity" is a measure of the contribution
of a selected precursor peak in an isolation window used for
fragmentation. The simple calculation involves dividing the
intensity of the selected precursor peak by the total intensity
of the isolation window. When assessing MS/MS spectra this
calculation is done before and after the MS/MS scan of interest
and the purity is interpolated at the recorded time of the
MS/MS acquisition. Additionally, isotopic peaks can be removed,
low abundance peaks are removed that are thought to have
limited contribution to the resulting MS/MS spectra and the
isolation efficiency of the mass spectrometer can be used to
normalise the intensities used for the calculation.
biocViews: MassSpectrometry, Metabolomics, Software
Author: Thomas N. Lawson [aut, cre]
(<https://orcid.org/0000-0002-5915-7980>), Ralf Weber [ctb],
Martin Jones [ctb], Julien Saint-Vanne [ctb], Andris Jankevics
[ctb]
Maintainer: Thomas N. Lawson <thomas.nigel.lawson@gmail.com>
URL: https://github.com/computational-metabolomics/msPurity/
VignetteBuilder: knitr
BugReports:
https://github.com/computational-metabolomics/msPurity/issues/new
git_url: https://git.bioconductor.org/packages/msPurity
git_branch: RELEASE_3_13
git_last_commit: d9cf561
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/msPurity_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/msPurity_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/msPurity_1.18.0.tgz
vignettes:
vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.html,
vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.html,
vignettes/msPurity/inst/doc/msPurity-vignette.html
vignetteTitles: msPurity spectral matching, msPurity spectral database
schema, msPurity
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.R,
vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.R,
vignettes/msPurity/inst/doc/msPurity-vignette.R
dependencyCount: 75
Package: msqrob2
Version: 1.0.0
Depends: R (>= 4.1), QFeatures (>= 1.1.2)
Imports: stats, methods, lme4, purrr, BiocParallel, Matrix, MASS,
limma, SummarizedExperiment, codetools
Suggests: multcomp, gridExtra, knitr, BiocStyle, RefManageR,
sessioninfo, rmarkdown, testthat, tidyverse, plotly, msdata,
MSnbase, matrixStats, MsCoreUtils
License: Artistic-2.0
MD5sum: 120e1d0f66e3561a6640d6e26e672351
NeedsCompilation: no
Title: Robust statistical inference for quantitative LC-MS proteomics
Description: msqrob2 provides a robust linear mixed model framework for
assessing differential abundance in MS-based Quantitative
proteomics experiments. Our workflows can start from raw
peptide intensities or summarised protein expression values.
The model parameter estimates can be stabilized by ridge
regression, empirical Bayes variance estimation and robust
M-estimation. msqrob2's hurde workflow can handle missing data
without having to rely on hard-to-verify imputation
assumptions, and, outcompetes state-of-the-art methods with and
without imputation for both high and low missingness. It builds
on QFeature infrastructure for quantitative mass spectrometry
data to store the model results together with the raw data and
preprocessed data.
biocViews: Proteomics, MassSpectrometry, DifferentialExpression,
MultipleComparison, Regression, ExperimentalDesign, Software,
ImmunoOncology, Normalization, TimeCourse, Preprocessing
Author: Lieven Clement [aut, cre]
(<https://orcid.org/0000-0002-9050-4370>), Laurent Gatto [aut]
(<https://orcid.org/0000-0002-1520-2268>), Oliver M. Crook
[aut] (<https://orcid.org/0000-0001-5669-8506>), Adriaan
Sticker [ctb], Ludger Goeminne [ctb], Milan Malfait [ctb]
(<https://orcid.org/0000-0001-9144-3701>), Stijn Vandenbulcke
[aut]
Maintainer: Lieven Clement <lieven.clement@ugent.be>
URL: https://github.com/statOmics/msqrob2
VignetteBuilder: knitr
BugReports: https://github.com/statOmics/msqrob2/issues
git_url: https://git.bioconductor.org/packages/msqrob2
git_branch: RELEASE_3_13
git_last_commit: 7f5bee4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/msqrob2_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/msqrob2_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/msqrob2_1.0.0.tgz
vignettes: vignettes/msqrob2/inst/doc/cptac.html
vignetteTitles: A. label-free workflow with two group design
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msqrob2/inst/doc/cptac.R
dependencyCount: 72
Package: MSstats
Version: 4.0.1
Depends: R (>= 4.0)
Imports: MSstatsConvert, data.table, checkmate, MASS, limma, lme4,
preprocessCore, survival, utils, Rcpp, ggplot2, ggrepel,
gplots, marray, stats, grDevices, graphics, methods
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle, knitr, rmarkdown, MSstatsBioData, tinytest, covr,
markdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: 4a8db1098019f4195d19dd75580c7b66
NeedsCompilation: yes
Title: Protein Significance Analysis in DDA, SRM and DIA for Label-free
or Label-based Proteomics Experiments
Description: A set of tools for statistical relative protein
significance analysis in DDA, SRM and DIA experiments.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software,
Normalization, QualityControl, TimeCourse
Author: Meena Choi [aut, cre], Mateusz Staniak [aut], Tsung-Heng Tsai
[aut], Ting Huang [aut], Olga Vitek [aut]
Maintainer: Meena Choi <mnchoi67@gmail.com>
URL: http://msstats.org
VignetteBuilder: knitr
BugReports: https://groups.google.com/forum/#!forum/msstats
git_url: https://git.bioconductor.org/packages/MSstats
git_branch: RELEASE_3_13
git_last_commit: afa9e58
git_last_commit_date: 2021-05-31
Date/Publication: 2021-06-01
source.ver: src/contrib/MSstats_4.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSstats_4.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSstats_4.0.1.tgz
vignettes: vignettes/MSstats/inst/doc/MSstats.html
vignetteTitles: MSstats: Protein/Peptide significance analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstats/inst/doc/MSstats.R
importsMe: artMS, MSstatsPTM, MSstatsSampleSize, MSstatsTMT
suggestsMe: MSstatsTMTPTM, MSstatsBioData
dependencyCount: 63
Package: MSstatsConvert
Version: 1.2.2
Depends: R (>= 4.0)
Imports: data.table, log4r, methods, checkmate, utils, stringi
Suggests: tinytest, covr, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 7aa2ecb0ad3622a92e6037e6443de6aa
NeedsCompilation: no
Title: Import Data from Various Mass Spectrometry Signal Processing
Tools to MSstats Format
Description: MSstatsConvert provides tools for importing reports of
Mass Spectrometry data processing tools into R format suitable
for statistical analysis using the MSstats and MSstatsTMT
packages.
biocViews: MassSpectrometry, Proteomics, Software, DataImport,
QualityControl
Author: Mateusz Staniak [aut, cre], Meena Choi [aut], Ting Huang [aut],
Olga Vitek [aut]
Maintainer: Mateusz Staniak <mtst@mstaniak.pl>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MSstatsConvert
git_branch: RELEASE_3_13
git_last_commit: bcc4339
git_last_commit_date: 2021-06-15
Date/Publication: 2021-06-17
source.ver: src/contrib/MSstatsConvert_1.2.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSstatsConvert_1.2.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSstatsConvert_1.2.2.tgz
vignettes: vignettes/MSstatsConvert/inst/doc/msstats_data_format.html
vignetteTitles: Working with MSstatsConvert
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsConvert/inst/doc/msstats_data_format.R
importsMe: MSstats, MSstatsPTM, MSstatsTMT
dependencyCount: 9
Package: MSstatsLOBD
Version: 1.0.0
Depends: R (>= 4.0)
Imports: minpack.lm, ggplot2, utils, stats, grDevices
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, dplyr
License: Artistic-2.0
MD5sum: 82a7e9ec14f26b7fe9959796595881ab
NeedsCompilation: no
Title: Assay characterization: estimation of limit of blanc(LoB) and
limit of detection(LOD)
Description: The MSstatsLOBD package allows calculation and
visualization of limit of blac (LOB) and limit of detection
(LOD). We define the LOB as the highest apparent concentration
of a peptide expected when replicates of a blank sample
containing no peptides are measured. The LOD is defined as the
measured concentration value for which the probability of
falsely claiming the absence of a peptide in the sample is
0.05, given a probability 0.05 of falsely claiming its
presence. These functionalities were previously a part of the
MSstats package. The methodology is described in Galitzine
(2018) <doi:10.1074/mcp.RA117.000322>.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software,
DifferentialExpression, OneChannel, TwoChannel, Normalization,
QualityControl
Author: Devon Kohler [aut, cre], Mateusz Staniak [aut], Cyril Galitzine
[aut], Meena Choi [aut], Olga Vitek [aut]
Maintainer: Devon Kohler <kohler.d@northeastern.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Vitek-Lab/MSstatsLODQ/issues
git_url: https://git.bioconductor.org/packages/MSstatsLOBD
git_branch: RELEASE_3_13
git_last_commit: b490f04
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MSstatsLOBD_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSstatsLOBD_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSstatsLOBD_1.0.0.tgz
vignettes: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.html
vignetteTitles: LOB/LOD Estimation Workflow
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.R
dependencyCount: 40
Package: MSstatsPTM
Version: 1.2.4
Depends: R (>= 4.0)
Imports: dplyr, gridExtra, stringr, stats, ggplot2, grDevices,
MSstatsTMT, MSstatsConvert, MSstats, data.table, Rcpp,
Biostrings, checkmate, ggrepel
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, tinytest, covr
License: Artistic-2.0
MD5sum: ffa6cc81d7e3fb35b738167a8d778be3
NeedsCompilation: yes
Title: Statistical Characterization of Post-translational Modifications
Description: MSstatsPTM provides general statistical methods for
quantitative characterization of post-translational
modifications (PTMs). Supports DDA, DIA, and tandem mass tag
(TMT) labeling. Typically, the analysis involves the
quantification of PTM sites (i.e., modified residues) and their
corresponding proteins, as well as the integration of the
quantification results. MSstatsPTM provides functions for
summarization, estimation of PTM site abundance, and detection
of changes in PTMs across experimental conditions.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software,
DifferentialExpression, OneChannel, TwoChannel, Normalization,
QualityControl
Author: Devon Kohler [aut, cre], Tsung-Heng Tsai [aut], Ting Huang
[aut], Mateusz Staniak [aut], Meena Choi [aut], Olga Vitek
[aut]
Maintainer: Devon Kohler <kohler.d@northeastern.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Vitek-Lab/MSstatsPTM/issues
git_url: https://git.bioconductor.org/packages/MSstatsPTM
git_branch: RELEASE_3_13
git_last_commit: a5c9e20
git_last_commit_date: 2021-10-06
Date/Publication: 2021-10-07
source.ver: src/contrib/MSstatsPTM_1.2.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSstatsPTM_1.2.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSstatsPTM_1.2.4.tgz
vignettes:
vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.html,
vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.html
vignetteTitles: MSstatsPTM LabelFree Workflow, MSstatsPTM TMT Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.R,
vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.R
dependencyCount: 83
Package: MSstatsQC
Version: 2.10.0
Depends: R (>= 3.5.0)
Imports: dplyr,plotly,ggplot2,ggExtra, stats,grid, MSnbase, qcmetrics
Suggests: knitr,rmarkdown, testthat, RforProteomics
License: Artistic License 2.0
MD5sum: a52fb8e075ac03f3c5fe904d5e81bb73
NeedsCompilation: no
Title: Longitudinal system suitability monitoring and quality control
for proteomic experiments
Description: MSstatsQC is an R package which provides longitudinal
system suitability monitoring and quality control tools for
proteomic experiments.
biocViews: Software, QualityControl, Proteomics, MassSpectrometry
Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut]
Maintainer: Eralp Dogu <eralp.dogu@gmail.com>
URL: http://msstats.org/msstatsqc
VignetteBuilder: knitr
BugReports: https://groups.google.com/forum/#!forum/msstatsqc
git_url: https://git.bioconductor.org/packages/MSstatsQC
git_branch: RELEASE_3_13
git_last_commit: 724c584
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MSstatsQC_2.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSstatsQC_2.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSstatsQC_2.10.0.tgz
vignettes: vignettes/MSstatsQC/inst/doc/MSstatsQC.html
vignetteTitles: MSstatsQC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsQC/inst/doc/MSstatsQC.R
importsMe: MSstatsQCgui
dependencyCount: 126
Package: MSstatsQCgui
Version: 1.12.0
Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, grid
Suggests: knitr
License: Artistic License 2.0
Archs: i386, x64
MD5sum: e7b201324c1e3fb248ea82c1db4c7774
NeedsCompilation: no
Title: A graphical user interface for MSstatsQC package
Description: MSstatsQCgui is a Shiny app which provides longitudinal
system suitability monitoring and quality control tools for
proteomic experiments.
biocViews: Software, QualityControl, Proteomics, MassSpectrometry, GUI
Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut]
Maintainer: Eralp Dogu <eralp.dogu@gmail.com>
URL: http://msstats.org/msstatsqc
VignetteBuilder: knitr
BugReports: https://groups.google.com/forum/#!forum/msstatsqc
git_url: https://git.bioconductor.org/packages/MSstatsQCgui
git_branch: RELEASE_3_13
git_last_commit: c4b77db
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MSstatsQCgui_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSstatsQCgui_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSstatsQCgui_1.12.0.tgz
vignettes: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.html
vignetteTitles: MSstatsQCgui
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.R
dependencyCount: 128
Package: MSstatsSampleSize
Version: 1.6.0
Depends: R (>= 3.6)
Imports: ggplot2, BiocParallel, caret, gridExtra, reshape2, stats,
utils, grDevices, graphics, MSstats
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: ee7aebbb949ecf3c342966a022cd59f4
NeedsCompilation: no
Title: Simulation tool for optimal design of high-dimensional MS-based
proteomics experiment
Description: The packages estimates the variance in the input protein
abundance data and simulates data with predefined number of
biological replicates based on the variance estimation. It
reports the mean predictive accuracy of the classifier and mean
protein importance over multiple iterations of the simulation.
biocViews: MassSpectrometry, Proteomics, Software,
DifferentialExpression, Classification, PrincipalComponent,
ExperimentalDesign, Visualization
Author: Ting Huang [aut, cre], Meena Choi [aut], Olga Vitek [aut]
Maintainer: Ting Huang <thuang0703@gmail.com>
URL: http://msstats.org
VignetteBuilder: knitr
BugReports: https://groups.google.com/forum/#!forum/msstats
git_url: https://git.bioconductor.org/packages/MSstatsSampleSize
git_branch: RELEASE_3_13
git_last_commit: 0e335a0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MSstatsSampleSize_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSstatsSampleSize_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSstatsSampleSize_1.6.0.tgz
vignettes: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.html
vignetteTitles: MSstatsSampleSize User Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.R
dependencyCount: 108
Package: MSstatsTMT
Version: 2.0.1
Depends: R (>= 4.0)
Imports: limma, lme4, lmerTest, methods, data.table, stats, utils,
ggplot2, grDevices, graphics, MSstats, MSstatsConvert,
checkmate
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: fed248cc945797df7b7473eefaa061cc
NeedsCompilation: no
Title: Protein Significance Analysis in shotgun mass spectrometry-based
proteomic experiments with tandem mass tag (TMT) labeling
Description: The package provides statistical tools for detecting
differentially abundant proteins in shotgun mass
spectrometry-based proteomic experiments with tandem mass tag
(TMT) labeling. It provides multiple functionalities, including
aata visualization, protein quantification and normalization,
and statistical modeling and inference. Furthermore, it is
inter-operable with other data processing tools, such as
Proteome Discoverer, MaxQuant, OpenMS and SpectroMine.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software
Author: Ting Huang [aut, cre], Meena Choi [aut], Mateusz Staniak [aut],
Sicheng Hao [aut], Olga Vitek [aut]
Maintainer: Ting Huang <thuang0703@gmail.com>
URL: http://msstats.org/msstatstmt/
VignetteBuilder: knitr
BugReports: https://groups.google.com/forum/#!forum/msstats
git_url: https://git.bioconductor.org/packages/MSstatsTMT
git_branch: RELEASE_3_13
git_last_commit: 8d487f0
git_last_commit_date: 2021-06-14
Date/Publication: 2021-06-15
source.ver: src/contrib/MSstatsTMT_2.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSstatsTMT_2.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSstatsTMT_2.0.1.tgz
vignettes: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.html
vignetteTitles: MSstatsTMT User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.R
importsMe: MSstatsPTM, MSstatsTMTPTM
dependencyCount: 66
Package: MSstatsTMTPTM
Version: 1.1.2
Depends: R (>= 4.0)
Imports: dplyr, gridExtra, stringr, reshape2, stats, utils, ggplot2,
grDevices, graphics, MSstatsTMT, Rcpp
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, testthat, MSstats, covr
License: Artistic-2.0
Archs: i386, x64
MD5sum: 85291fa2ab550bcb46fca3d8387b5d6f
NeedsCompilation: yes
Title: Post Translational Modification (PTM) Significance Analysis in
shotgun mass spectrometry-based proteomic experiments with
tandem mass tag (TMT) labeling
Description: Tools for Post Translational Modification (PTM) and
protein significance analysis in shotgun mass
spectrometry-based proteomic experiments with tandem mass tag
(TMT) labeling. The functions in this package should be used
after PTM/protein summarization. They can be used to both plot
the summarized results and model the summarized datasets.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software,
DifferentialExpression, OneChannel, TwoChannel, Normalization,
QualityControl
Author: Devon Kohler [aut, cre], Ting Huang [aut], Mateusz Staniak
[aut], Meena Choi [aut], Tsung-Heng Tsai [aut], Olga Vitek
[aut]
Maintainer: Devon Kohler <kohler.d@northeastern.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Vitek-Lab/MSstatsTMTPTM/issues
git_url: https://git.bioconductor.org/packages/MSstatsTMTPTM
git_branch: master
git_last_commit: d938f9b
git_last_commit_date: 2021-02-15
Date/Publication: 2021-03-19
source.ver: src/contrib/MSstatsTMTPTM_1.1.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MSstatsTMTPTM_1.1.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/MSstatsTMTPTM_1.1.2.tgz
vignettes: vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.html,
vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.Workflow.html
vignetteTitles: MSstatsTMTPTM : A package for post translational
modification (PTM) significance analysis in shotgun mass
spectrometry-based proteomic experiments with tandem mass tag
(TMT) labeling", MSstatsTMTPTM.Workflow.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.R,
vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.Workflow.R
dependencyCount: 75
Package: Mulcom
Version: 1.42.0
Depends: R (>= 2.10), Biobase
Imports: graphics, grDevices, stats, methods, fields
License: GPL-2
MD5sum: 763041df2e7bd75334840c33425b0cc5
NeedsCompilation: yes
Title: Calculates Mulcom test
Description: Identification of differentially expressed genes and false
discovery rate (FDR) calculation by Multiple Comparison test.
biocViews: StatisticalMethod, MultipleComparison, Microarray,
DifferentialExpression, GeneExpression
Author: Claudio Isella
Maintainer: Claudio Isella <claudio.isella@ircc.it>
git_url: https://git.bioconductor.org/packages/Mulcom
git_branch: RELEASE_3_13
git_last_commit: d21dc82
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Mulcom_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Mulcom_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Mulcom_1.42.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 47
Package: MultiAssayExperiment
Version: 1.18.0
Depends: R (>= 4.0.0), SummarizedExperiment (>= 1.3.81)
Imports: methods, GenomicRanges (>= 1.25.93), BiocGenerics, S4Vectors
(>= 0.23.19), IRanges, Biobase, stats, tidyr, utils
Suggests: BiocStyle, HDF5Array (>= 1.19.17), knitr, maftools (>=
2.7.10), rmarkdown, R.rsp, RaggedExperiment, UpSetR, survival,
survminer, testthat
License: Artistic-2.0
MD5sum: cf09a85c83a09eec33c7f6bd11937f90
NeedsCompilation: no
Title: Software for the integration of multi-omics experiments in
Bioconductor
Description: MultiAssayExperiment harmonizes data management of
multiple experimental assays performed on an overlapping set of
specimens. It provides a familiar Bioconductor user experience
by extending concepts from SummarizedExperiment, supporting an
open-ended mix of standard data classes for individual assays,
and allowing subsetting by genomic ranges or rownames.
Facilities are provided for reshaping data into wide and long
formats for adaptability to graphing and downstream analysis.
biocViews: Infrastructure, DataRepresentation
Author: Marcel Ramos [aut, cre], Levi Waldron [aut], MultiAssay SIG
[ctb]
Maintainer: Marcel Ramos <marcel.ramos@roswellpark.org>
URL: http://waldronlab.io/MultiAssayExperiment/
VignetteBuilder: knitr, R.rsp
Video: https://youtu.be/w6HWAHaDpyk, https://youtu.be/Vh0hVVUKKFM
BugReports: https://github.com/waldronlab/MultiAssayExperiment/issues
git_url: https://git.bioconductor.org/packages/MultiAssayExperiment
git_branch: RELEASE_3_13
git_last_commit: b1fa42c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MultiAssayExperiment_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MultiAssayExperiment_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MultiAssayExperiment_1.18.0.tgz
vignettes:
vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment_cheatsheet.pdf,
vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html,
vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html,
vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.html
vignetteTitles: MultiAssayExperiment_cheatsheet.pdf, Coordinating
Analysis of Multi-Assay Experiments, Quick-start Guide,
HDF5Array and MultiAssayExperiment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.R,
vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.R,
vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.R
dependsOnMe: CAGEr, cBioPortalData, ClassifyR, evaluomeR, glmSparseNet,
hipathia, InTAD, midasHLA, missRows, QFeatures, TimiRGeN,
curatedTCGAData, microbiomeDataSets, OMICsPCAdata,
SingleCellMultiModal
importsMe: AffiXcan, AMARETTO, animalcules, autonomics, CoreGx, corral,
ELMER, GOpro, LinkHD, metabolomicsWorkbenchR, MOMA, MultiBaC,
OMICsPCA, omicsPrint, padma, PDATK, PharmacoGx, scp, TCGAutils,
HMP2Data
suggestsMe: BiocOncoTK, CNVRanger, deco, maftools, MOFA2, MultiDataSet,
RaggedExperiment, brgedata, MOFAdata
dependencyCount: 46
Package: MultiBaC
Version: 1.2.0
Imports: Matrix, ggplot2, MultiAssayExperiment, ropls, graphics,
methods
Suggests: knitr, rmarkdown, BiocStyle, devtools
License: GPL-3
MD5sum: 9636ac1e92336cd0e5c1cffc6681ce5d
NeedsCompilation: no
Title: Multiomic Batch effect Correction
Description: MultiBaC is a strategy to correct batch effects from
multiomic datasets distributed across different labs or data
acquisition events. MultiBaC is the first Batch effect
correction algorithm that dealing with batch effect correction
in multiomics datasets. MultiBaC is able to remove batch
effects across different omics generated within separate
batches provided that at least one common omic data type is
included in all the batches considered.
biocViews: Software, StatisticalMethod, PrincipalComponent,
DataRepresentation, GeneExpression, Transcription, BatchEffect
Author: person("Manuel", "Ugidos", email = "manuelugidos@gmail.com"),
person("Sonia", "Tarazona", email = "sotacam@gmail.com"),
person("MarÃa José", "Nueda", email = "mjnueda@ua.es")
Maintainer: The package maintainer <manuelugidos@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MultiBaC
git_branch: RELEASE_3_13
git_last_commit: 7f3cc72
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MultiBaC_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MultiBaC_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MultiBaC_1.2.0.tgz
vignettes: vignettes/MultiBaC/inst/doc/MultiBaC.html
vignetteTitles: MultiBaC
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MultiBaC/inst/doc/MultiBaC.R
dependencyCount: 70
Package: multiClust
Version: 1.22.0
Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics,
grDevices
Suggests: knitr, gplots, RUnit, BiocGenerics, preprocessCore, Biobase,
GEOquery
License: GPL (>= 2)
MD5sum: 28e3f180c1465c3fb8549cf5b56c4632
NeedsCompilation: no
Title: multiClust: An R-package for Identifying Biologically Relevant
Clusters in Cancer Transcriptome Profiles
Description: Clustering is carried out to identify patterns in
transcriptomics profiles to determine clinically relevant
subgroups of patients. Feature (gene) selection is a critical
and an integral part of the process. Currently, there are many
feature selection and clustering methods to identify the
relevant genes and perform clustering of samples. However,
choosing an appropriate methodology is difficult. In addition,
extensive feature selection methods have not been supported by
the available packages. Hence, we developed an integrative
R-package called multiClust that allows researchers to
experiment with the choice of combination of methods for gene
selection and clustering with ease. Using multiClust, we
identified the best performing clustering methodology in the
context of clinical outcome. Our observations demonstrate that
simple methods such as variance-based ranking perform well on
the majority of data sets, provided that the appropriate number
of genes is selected. However, different gene ranking and
selection methods remain relevant as no methodology works for
all studies.
biocViews: FeatureExtraction, Clustering, GeneExpression, Survival
Author: Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri
[aut], Krish Karuturi [aut], Joshy George [aut]
Maintainer: Nathan Lawlor <nathan.lawlor03@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/multiClust
git_branch: RELEASE_3_13
git_last_commit: 055e880
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/multiClust_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/multiClust_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/multiClust_1.22.0.tgz
vignettes: vignettes/multiClust/inst/doc/multiClust.html
vignetteTitles: "A Guide to multiClust"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/multiClust/inst/doc/multiClust.R
dependencyCount: 47
Package: multicrispr
Version: 1.2.0
Depends: R (>= 4.0)
Imports: assertive, BiocGenerics, Biostrings, BSgenome, CRISPRseek,
data.table, GenomeInfoDb, GenomicFeatures, GenomicRanges,
ggplot2, grid, karyoploteR, magrittr, methods, parallel,
plyranges, Rbowtie, reticulate, rtracklayer, stats, stringi,
tidyr, tidyselect, utils
Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Mmusculus.UCSC.mm10,
BSgenome.Scerevisiae.UCSC.sacCer1, ensembldb, IRanges, knitr,
magick, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene
License: GPL-2
Archs: i386, x64
MD5sum: aa8e1de803e595dadec4ce56d103e89f
NeedsCompilation: no
Title: Multi-locus multi-purpose Crispr/Cas design
Description: This package is for designing Crispr/Cas9 and Prime
Editing experiments. It contains functions to (1) define and
transform genomic targets, (2) find spacers (4) count offtarget
(mis)matches, and (5) compute Doench2016/2014 targeting
efficiency. Care has been taken for multicrispr to scale well
towards large target sets, enabling the design of large
Crispr/Cas9 libraries.
biocViews: CRISPR, Software
Author: Aditya Bhagwat [aut, cre], Johannes Graumann [sad, ctb], Mette
Bentsen [ctb], Jens Preussner [ctb], Michael Lawrence [ctb],
Hervé Pagès [ctb], Mario Looso [sad, rth]
Maintainer: Aditya Bhagwat <aditya.bhagwat@mpi-bn.mpg.de>
URL: https://loosolab.pages.gwdg.de/software/multicrispr/
VignetteBuilder: knitr
BugReports:
https://gitlab.gwdg.de/loosolab/software/multicrispr/-/issues
git_url: https://git.bioconductor.org/packages/multicrispr
git_branch: RELEASE_3_13
git_last_commit: 88c1a4f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/multicrispr_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/multicrispr_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/multicrispr_1.2.0.tgz
vignettes: vignettes/multicrispr/inst/doc/crispr_grna_design.html,
vignettes/multicrispr/inst/doc/genome_arithmetics.html,
vignettes/multicrispr/inst/doc/prime_editing.html
vignetteTitles: grna_design, genome_arithmetics, prime_editing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/multicrispr/inst/doc/crispr_grna_design.R,
vignettes/multicrispr/inst/doc/genome_arithmetics.R,
vignettes/multicrispr/inst/doc/prime_editing.R
dependencyCount: 179
Package: MultiDataSet
Version: 1.20.2
Depends: R (>= 3.3), Biobase
Imports: BiocGenerics, GenomicRanges, IRanges, S4Vectors,
SummarizedExperiment, methods, utils, ggplot2, ggrepel, qqman,
limma
Suggests: brgedata, minfi, minfiData, knitr, rmarkdown, testthat,
omicade4, iClusterPlus, GEOquery, MultiAssayExperiment,
BiocStyle, RaggedExperiment
License: file LICENSE
Archs: i386, x64
MD5sum: b9746709458f271e8775016e0d15f1e0
NeedsCompilation: no
Title: Implementation of MultiDataSet and ResultSet
Description: Implementation of the BRGE's (Bioinformatic Research Group
in Epidemiology from Center for Research in Environmental
Epidemiology) MultiDataSet and ResultSet. MultiDataSet is
designed for integrating multi omics data sets and ResultSet is
a container for omics results. This package contains base
classes for MEAL and rexposome packages.
biocViews: Software, DataRepresentation
Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut],
Juan R. Gonzalez [aut]
Maintainer: Xavier Escrib<e0> Montagut <xavier.escriba@isglobal.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MultiDataSet
git_branch: RELEASE_3_13
git_last_commit: 3ef346c
git_last_commit_date: 2021-10-07
Date/Publication: 2021-10-10
source.ver: src/contrib/MultiDataSet_1.20.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MultiDataSet_1.20.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/MultiDataSet_1.20.2.tgz
vignettes:
vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.html,
vignettes/MultiDataSet/inst/doc/MultiDataSet.html
vignetteTitles: Adding a new type of data to MultiDataSet objects,
Introduction to MultiDataSet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.R,
vignettes/MultiDataSet/inst/doc/MultiDataSet.R
dependsOnMe: MEAL
importsMe: biosigner, omicRexposome, ropls
dependencyCount: 61
Package: multiGSEA
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: magrittr, graphite, AnnotationDbi, dplyr, fgsea, metap,
rappdirs, rlang, methods
Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Ss.eg.db,
org.Bt.eg.db, org.Ce.eg.db, org.Dm.eg.db, org.Dr.eg.db,
org.Gg.eg.db, org.Xl.eg.db, org.Cf.eg.db, metaboliteIDmapping,
knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0)
License: GPL-3
MD5sum: f2de73b3ba4ff7fe26eea3c67fab048e
NeedsCompilation: no
Title: Combining GSEA-based pathway enrichment with multi omics data
integration
Description: Extracted features from pathways derived from 8 different
databases (KEGG, Reactome, Biocarta, etc.) can be used on
transcriptomic, proteomic, and/or metabolomic level to
calculate a combined GSEA-based enrichment score.
biocViews: GeneSetEnrichment, Pathways, Reactome, BioCarta
Author: Sebastian Canzler [aut, cre]
(<https://orcid.org/0000-0001-7935-9582>), Jörg Hackermüller
[aut] (<https://orcid.org/0000-0003-4920-7072>)
Maintainer: Sebastian Canzler <sebastian.canzler@ufz.de>
URL: https://github.com/yigbt/multiGSEA
VignetteBuilder: knitr
BugReports: https://github.com/yigbt/multiGSEA/issues
git_url: https://git.bioconductor.org/packages/multiGSEA
git_branch: RELEASE_3_13
git_last_commit: db2c0e1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/multiGSEA_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/multiGSEA_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/multiGSEA_1.2.0.tgz
vignettes: vignettes/multiGSEA/inst/doc/multiGSEA.html
vignetteTitles: multiGSEA.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/multiGSEA/inst/doc/multiGSEA.R
dependencyCount: 117
Package: multiHiCcompare
Version: 1.10.0
Depends: R (>= 4.0.0)
Imports: data.table, dplyr, HiCcompare, edgeR, BiocParallel, qqman,
pheatmap, methods, GenomicRanges, graphics, stats, utils,
pbapply, GenomeInfoDbData, GenomeInfoDb, aggregation
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: MIT + file LICENSE
MD5sum: 3b43dc6a642dbfcbaea2086b23e5b1aa
NeedsCompilation: no
Title: Normalize and detect differences between Hi-C datasets when
replicates of each experimental condition are available
Description: multiHiCcompare provides functions for joint normalization
and difference detection in multiple Hi-C datasets. This
extension of the original HiCcompare package now allows for
Hi-C experiments with more than 2 groups and multiple samples
per group. multiHiCcompare operates on processed Hi-C data in
the form of sparse upper triangular matrices. It accepts four
column (chromosome, region1, region2, IF) tab-separated text
files storing chromatin interaction matrices. multiHiCcompare
provides cyclic loess and fast loess (fastlo) methods adapted
to jointly normalizing Hi-C data. Additionally, it provides a
general linear model (GLM) framework adapting the edgeR package
to detect differences in Hi-C data in a distance dependent
manner.
biocViews: Software, HiC, Sequencing, Normalization
Author: John Stansfield <stansfieldjc@vcu.edu>, Mikhail Dozmorov
<mikhail.dozmorov@vcuhealth.org>
Maintainer: John Stansfield <stansfieldjc@vcu.edu>, Mikhail Dozmorov
<mikhail.dozmorov@vcuhealth.org>
URL: https://github.com/dozmorovlab/multiHiCcompare
VignetteBuilder: knitr
BugReports: https://github.com/dozmorovlab/multiHiCcompare/issues
git_url: https://git.bioconductor.org/packages/multiHiCcompare
git_branch: RELEASE_3_13
git_last_commit: 7780e80
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/multiHiCcompare_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/multiHiCcompare_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/multiHiCcompare_1.10.0.tgz
vignettes:
vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.html,
vignettes/multiHiCcompare/inst/doc/multiHiCcompare.html
vignetteTitles: juiceboxVisualization, multiHiCcompare
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.R,
vignettes/multiHiCcompare/inst/doc/multiHiCcompare.R
suggestsMe: HiCcompare
dependencyCount: 104
Package: MultiMed
Version: 2.14.0
Depends: R (>= 3.1.0)
Suggests: RUnit, BiocGenerics
License: GPL (>= 2) + file LICENSE
MD5sum: 52ad7abdfcfe9ef02e928c48c0eed0c4
NeedsCompilation: no
Title: Testing multiple biological mediators simultaneously
Description: Implements methods for testing multiple mediators
biocViews: MultipleComparison, StatisticalMethod, Software
Author: Simina M. Boca, Ruth Heller, Joshua N. Sampson
Maintainer: Simina M. Boca <smb310@georgetown.edu>
git_url: https://git.bioconductor.org/packages/MultiMed
git_branch: RELEASE_3_13
git_last_commit: d86037d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MultiMed_2.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MultiMed_2.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MultiMed_2.14.0.tgz
vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf
vignetteTitles: MultiMedTutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MultiMed/inst/doc/MultiMed.R
dependencyCount: 0
Package: multiMiR
Version: 1.14.0
Depends: R (>= 3.4)
Imports: stats, XML, RCurl, purrr (>= 0.2.2), tibble (>= 1.2), methods,
BiocGenerics, AnnotationDbi, dplyr,
Suggests: BiocStyle, edgeR, knitr, rmarkdown, testthat (>= 1.0.2)
License: MIT + file LICENSE
MD5sum: d44c483d7a56e6a6f24497b6126ea1e6
NeedsCompilation: no
Title: Integration of multiple microRNA-target databases with their
disease and drug associations
Description: A collection of microRNAs/targets from external resources,
including validated microRNA-target databases (miRecords,
miRTarBase and TarBase), predicted microRNA-target databases
(DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA
and TargetScan) and microRNA-disease/drug databases
(miR2Disease, Pharmaco-miR VerSe and PhenomiR).
biocViews: miRNAData, Homo_sapiens_Data, Mus_musculus_Data,
Rattus_norvegicus_Data, OrganismData
Author: Yuanbin Ru [aut], Matt Mulvahill [cre, aut], Spencer Mahaffey
[aut], Katerina Kechris [aut, cph, ths]
Maintainer: Matt Mulvahill <matt.mulvahill@gmail.com>
URL: https://github.com/KechrisLab/multiMiR
VignetteBuilder: knitr
BugReports: https://github.com/KechrisLab/multiMiR/issues
git_url: https://git.bioconductor.org/packages/multiMiR
git_branch: RELEASE_3_13
git_last_commit: 2166ba3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/multiMiR_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/multiMiR_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/multiMiR_1.14.0.tgz
vignettes: vignettes/multiMiR/inst/doc/multiMiR.html
vignetteTitles: The multiMiR user's guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/multiMiR/inst/doc/multiMiR.R
dependencyCount: 58
Package: multiOmicsViz
Version: 1.16.0
Depends: R (>= 3.3.2)
Imports: methods, parallel, doParallel, foreach, grDevices, graphics,
utils, SummarizedExperiment, stats
Suggests: BiocGenerics
License: LGPL
MD5sum: c1ed278c6ea46a1fb572b15bcc818423
NeedsCompilation: no
Title: Plot the effect of one omics data on other omics data along the
chromosome
Description: Calculate the spearman correlation between the source
omics data and other target omics data, identify the
significant correlations and plot the significant correlations
on the heat map in which the x-axis and y-axis are ordered by
the chromosomal location.
biocViews: Software, Visualization, SystemsBiology
Author: Jing Wang <jingwang.uestc@gmail.com>
Maintainer: Jing Wang <jingwang.uestc@gmail.com>
git_url: https://git.bioconductor.org/packages/multiOmicsViz
git_branch: RELEASE_3_13
git_last_commit: 30ea852
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/multiOmicsViz_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/multiOmicsViz_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/multiOmicsViz_1.16.0.tgz
vignettes: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.pdf
vignetteTitles: multiOmicsViz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.R
dependencyCount: 30
Package: multiscan
Version: 1.52.0
Depends: R (>= 2.3.0)
Imports: Biobase, utils
License: GPL (>= 2)
MD5sum: 8ad053d5e4758334aef82fa5bcd55a62
NeedsCompilation: yes
Title: R package for combining multiple scans
Description: Estimates gene expressions from several laser scans of the
same microarray
biocViews: Microarray, Preprocessing
Author: Mizanur Khondoker <mizanur.khondoker@ed.ac.uk>, Chris Glasbey,
Bruce Worton.
Maintainer: Mizanur Khondoker <mizanur.khondoker@ed.ac.uk>
git_url: https://git.bioconductor.org/packages/multiscan
git_branch: RELEASE_3_13
git_last_commit: c76196c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/multiscan_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/multiscan_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/multiscan_1.52.0.tgz
vignettes: vignettes/multiscan/inst/doc/multiscan.pdf
vignetteTitles: An R Package for Estimating Gene Expressions using
Multiple Scans
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/multiscan/inst/doc/multiscan.R
dependencyCount: 7
Package: multiSight
Version: 1.0.0
Depends: R (>= 4.1)
Imports: golem, config, R6, shiny, shinydashboard, DT, dplyr, stringr,
anyLib, caret, biosigner, mixOmics, stats, DESeq2,
clusterProfiler, rWikiPathways, ReactomePA, enrichplot, ppcor,
metap, infotheo, igraph, networkD3, easyPubMed, utils,
htmltools, rmarkdown
Suggests: org.Mm.eg.db, rlang, markdown, attempt, processx, testthat,
knitr, BiocStyle
License: CeCILL + file LICENSE
MD5sum: 903c0a71470ee6108097a70ec4722543
NeedsCompilation: no
Title: Multi-omics Classification, Functional Enrichment and Network
Inference analysis
Description: multiSight is an R package providing an user-friendly
graphical interface to analyze your omic datasets in a
multi-omics manner based on Stouffer's p-value pooling and
multi-block statistical methods. For each omic dataset you
furnish, multiSight provides classification models with feature
selection you can use as biosignature: (i) To forecast
phenotypes (e.g. to diagnostic tasks, histological subtyping),
(ii) To design Pathways and gene ontology enrichments (Over
Representation Analysis), (iii) To build Network inference
linked to PubMed querying to make assumptions easier and
data-driven.
biocViews: Software, RNASeq, miRNA, Network, NetworkInference,
DifferentialExpression, Classification, Pathways,
GeneSetEnrichment
Author: Florian Jeanneret [cre, aut]
(<https://orcid.org/0000-0002-9301-4019>), Stephane Gazut [aut]
Maintainer: Florian Jeanneret <florian.jeanneret@cea.fr>
VignetteBuilder: knitr
BugReports: https://github.com/Fjeanneret/multiSight/issues
git_url: https://git.bioconductor.org/packages/multiSight
git_branch: RELEASE_3_13
git_last_commit: c94fb29
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/multiSight_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/multiSight_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/multiSight_1.0.0.tgz
vignettes: vignettes/multiSight/inst/doc/multiSight.html
vignetteTitles: multiSight quick start guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/multiSight/inst/doc/multiSight.R
dependencyCount: 275
Package: multtest
Version: 2.48.0
Depends: R (>= 2.10), methods, BiocGenerics, Biobase
Imports: survival, MASS, stats4
Suggests: snow
License: LGPL
MD5sum: d4f80c2a5f634474baabf6a2949d0cee
NeedsCompilation: yes
Title: Resampling-based multiple hypothesis testing
Description: Non-parametric bootstrap and permutation resampling-based
multiple testing procedures (including empirical Bayes methods)
for controlling the family-wise error rate (FWER), generalized
family-wise error rate (gFWER), tail probability of the
proportion of false positives (TPPFP), and false discovery rate
(FDR). Several choices of bootstrap-based null distribution
are implemented (centered, centered and scaled,
quantile-transformed). Single-step and step-wise methods are
available. Tests based on a variety of t- and F-statistics
(including t-statistics based on regression parameters from
linear and survival models as well as those based on
correlation parameters) are included. When probing hypotheses
with t-statistics, users may also select a potentially faster
null distribution which is multivariate normal with mean zero
and variance covariance matrix derived from the vector
influence function. Results are reported in terms of adjusted
p-values, confidence regions and test statistic cutoffs. The
procedures are directly applicable to identifying
differentially expressed genes in DNA microarray experiments.
biocViews: Microarray, DifferentialExpression, MultipleComparison
Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra
Taylor, Sandrine Dudoit
Maintainer: Katherine S. Pollard <katherine.pollard@gladstone.ucsf.edu>
git_url: https://git.bioconductor.org/packages/multtest
git_branch: RELEASE_3_13
git_last_commit: 5da1a87
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/multtest_2.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/multtest_2.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/multtest_2.48.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: aCGH, BicARE, iPAC, KCsmart, PREDA, rain, REDseq,
siggenes, webbioc, cp4p, DiffCorr, GExMap, PCS
importsMe: a4Base, ABarray, adSplit, ALDEx2, anota, ChIPpeakAnno,
IsoGeneGUI, mAPKL, metabomxtr, nethet, OCplus, phyloseq,
RTopper, SingleCellSignalR, singleCellTK, webbioc, hddplot,
INCATome, MetaIntegrator, mutoss, nlcv, pRF, TcGSA
suggestsMe: annaffy, ecolitk, factDesign, GOstats, GSEAlm, maigesPack,
ropls, topGO, xcms, cherry, metagam, POSTm
dependencyCount: 15
Package: mumosa
Version: 1.0.0
Depends: SingleCellExperiment
Imports: stats, utils, methods, igraph, Matrix, BiocGenerics,
BiocParallel, IRanges, S4Vectors, DelayedArray,
DelayedMatrixStats, SummarizedExperiment, BiocNeighbors,
BiocSingular, ScaledMatrix, beachmat, scuttle, metapod, scran,
batchelor, uwot
Suggests: testthat, knitr, BiocStyle, rmarkdown, scater, bluster,
DropletUtils, scRNAseq
License: GPL-3
MD5sum: f0634894f977edc4c6c25ad0d805244f
NeedsCompilation: no
Title: Multi-Modal Single-Cell Analysis Methods
Description: Assorted utilities for multi-modal analyses of single-cell
datasets. Includes functions to combine multiple modalities for
downstream analysis, perform MNN-based batch correction across
multiple modalities, and to compute correlations between assay
values for different modalities.
biocViews: ImmunoOncology, SingleCell, RNASeq
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: http://bioconductor.org/packages/mumosa
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/mumosa
git_branch: RELEASE_3_13
git_last_commit: bd0c58c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mumosa_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mumosa_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mumosa_1.0.0.tgz
vignettes: vignettes/mumosa/inst/doc/overview.html
vignetteTitles: Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mumosa/inst/doc/overview.R
dependsOnMe: OSCA.advanced
dependencyCount: 66
Package: MungeSumstats
Version: 1.0.1
Depends: R(>= 4.0)
Imports: data.table, utils, stats, GenomicRanges, BSgenome, Biostrings
Suggests: SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
BSgenome.Hsapiens.1000genomes.hs37d5,
BSgenome.Hsapiens.NCBI.GRCh38, methods, BiocGenerics, IRanges,
GenomeInfoDb, S4Vectors, rmarkdown, markdown, knitr, testthat
(>= 3.0.0), UpSetR, BiocStyle, covr
License: Artistic-2.0
MD5sum: a2193d5bce710d51f1b37564c4f408b4
NeedsCompilation: no
Title: Standardise summary statistics from GWAS
Description: The *MungeSumstats* package is designed to facilitate the
standardisation of GWAS summary statistics. It reformats
inputted summary statisitics to include SNP, CHR, BP and can
look up these values if any are missing. It also removes
duplicates across SNPs.
biocViews: SNP, WholeGenome, Genetics, ComparativeGenomics,
GenomeWideAssociation, GenomicVariation, Preprocessing
Author: Alan Murphy [cre] (<https://orcid.org/0000-0002-2487-8753>),
Nathan Skene [aut] (<https://orcid.org/0000-0002-6807-3180>)
Maintainer: Alan Murphy <alanmurph94@hotmail.com>
URL: https://github.com/neurogenomics/MungeSumstats
VignetteBuilder: knitr
BugReports: https://github.com/neurogenomics/MungeSumstats/issues
git_url: https://git.bioconductor.org/packages/MungeSumstats
git_branch: RELEASE_3_13
git_last_commit: c1fe775
git_last_commit_date: 2021-06-22
Date/Publication: 2021-06-24
source.ver: src/contrib/MungeSumstats_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MungeSumstats_1.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/MungeSumstats_1.0.1.tgz
vignettes: vignettes/MungeSumstats/inst/doc/MungeSumstats.html
vignetteTitles: Standardise the format of summary statistics from GWAS
with MungeSumstats
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MungeSumstats/inst/doc/MungeSumstats.R
dependencyCount: 46
Package: muscat
Version: 1.6.0
Depends: R (>= 4.1)
Imports: BiocParallel, blme, ComplexHeatmap, data.table, DESeq2, dplyr,
edgeR, ggplot2, glmmTMB, grDevices, grid, limma, lmerTest,
lme4, Matrix, matrixStats, methods, progress, purrr, S4Vectors,
scales, scater, scuttle, sctransform, stats,
SingleCellExperiment, SummarizedExperiment, variancePartition,
viridis
Suggests: BiocStyle, countsimQC, cowplot, ExperimentHub, iCOBRA, knitr,
phylogram, RColorBrewer, reshape2, rmarkdown, testthat, UpSetR
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 5aa2fde7deab2108da8be2fd68309f5c
NeedsCompilation: no
Title: Multi-sample multi-group scRNA-seq data analysis tools
Description: `muscat` provides various methods and visualization tools
for DS analysis in multi-sample, multi-group,
multi-(cell-)subpopulation scRNA-seq data, including cell-level
mixed models and methods based on aggregated “pseudobulk†data,
as well as a flexible simulation platform that mimics both
single and multi-sample scRNA-seq data.
biocViews: ImmunoOncology, DifferentialExpression, Sequencing,
SingleCell, Software, StatisticalMethod, Visualization
Author: Helena L. Crowell [aut, cre], Pierre-Luc Germain [aut],
Charlotte Soneson [aut], Anthony Sonrel [aut], Mark D. Robinson
[aut, fnd]
Maintainer: Helena L. Crowell <helena.crowell@uzh.ch>
URL: https://github.com/HelenaLC/muscat
VignetteBuilder: knitr
BugReports: https://github.com/HelenaLC/muscat/issues
git_url: https://git.bioconductor.org/packages/muscat
git_branch: RELEASE_3_13
git_last_commit: d05998b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/muscat_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/muscat_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/muscat_1.6.0.tgz
vignettes: vignettes/muscat/inst/doc/analysis.html,
vignettes/muscat/inst/doc/simulation.html
vignetteTitles: "1. DS analysis", "2. Data simulation"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/muscat/inst/doc/analysis.R,
vignettes/muscat/inst/doc/simulation.R
suggestsMe: muscData
dependencyCount: 170
Package: muscle
Version: 3.34.0
Depends: Biostrings
License: Unlimited
MD5sum: 80e33f3375e463548c9b454bc8fae64a
NeedsCompilation: yes
Title: Multiple Sequence Alignment with MUSCLE
Description: MUSCLE performs multiple sequence alignments of nucleotide
or amino acid sequences.
biocViews: MultipleSequenceAlignment, Alignment, Sequencing, Genetics,
SequenceMatching, DataImport
Author: Algorithm by Robert C. Edgar. R port by Alex T. Kalinka.
Maintainer: Alex T. Kalinka <alex.t.kalinka@gmail.com>
URL: http://www.drive5.com/muscle/
git_url: https://git.bioconductor.org/packages/muscle
git_branch: RELEASE_3_13
git_last_commit: 49f305f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/muscle_3.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/muscle_3.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/muscle_3.34.0.tgz
vignettes: vignettes/muscle/inst/doc/muscle-vignette.pdf
vignetteTitles: A guide to using muscle
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/muscle/inst/doc/muscle-vignette.R
importsMe: ptm
suggestsMe: seqmagick
dependencyCount: 19
Package: musicatk
Version: 1.2.0
Depends: R (>= 4.0.0), NMF
Imports: SummarizedExperiment, VariantAnnotation, cowplot, Biostrings,
base, methods, magrittr, tibble, tidyr, gtools, gridExtra,
maftools, MCMCprecision, MASS, matrixTests, data.table, dplyr,
rlang, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicRanges,
IRanges, S4Vectors, uwot, ggplot2, stringr,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9,
BSgenome.Mmusculus.UCSC.mm10, deconstructSigs, decompTumor2Sig,
topicmodels, ggrepel, withr, plotly, utils, factoextra,
cluster, ComplexHeatmap, stringi, philentropy
Suggests: testthat, BiocStyle, knitr, rmarkdown, survival, XVector,
qpdf, covr
License: LGPL-3
Archs: i386, x64
MD5sum: 116f9420b6fa593e8a181eea3fed7514
NeedsCompilation: no
Title: Mutational Signature Comprehensive Analysis Toolkit
Description: Mutational signatures are carcinogenic exposures or
aberrant cellular processes that can cause alterations to the
genome. We created musicatk (MUtational SIgnature Comprehensive
Analysis ToolKit) to address shortcomings in versatility and
ease of use in other pre-existing computational tools. Although
many different types of mutational data have been generated,
current software packages do not have a flexible framework to
allow users to mix and match different types of mutations in
the mutational signature inference process. Musicatk enables
users to count and combine multiple mutation types, including
SBS, DBS, and indels. Musicatk calculates replication strand,
transcription strand and combinations of these features along
with discovery from unique and proprietary genomic feature
associated with any mutation type. Musicatk also implements
several methods for discovery of new signatures as well as
methods to infer exposure given an existing set of signatures.
Musicatk provides functions for visualization and downstream
exploratory analysis including the ability to compare
signatures between cohorts and find matching signatures in
COSMIC V2 or COSMIC V3.
biocViews: Software, BiologicalQuestion, SomaticMutation,
VariantAnnotation
Author: Aaron Chevalier [cre] (0000-0002-3968-9250), Joshua D. Campbell
[aut] (<https://orcid.org/0000-0003-0780-8662>)
Maintainer: Aaron Chevalier <atgc@bu.edu>
VignetteBuilder: knitr
BugReports: https://github.com/campbio/musicatk/issues
git_url: https://git.bioconductor.org/packages/musicatk
git_branch: RELEASE_3_13
git_last_commit: c207280
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/musicatk_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/musicatk_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/musicatk_1.2.0.tgz
vignettes: vignettes/musicatk/inst/doc/musicatk.html
vignetteTitles: Mutational Signature Comprehensive Analysis Toolkit
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/musicatk/inst/doc/musicatk.R
dependencyCount: 233
Package: MutationalPatterns
Version: 3.2.0
Depends: R (>= 4.1.0), GenomicRanges (>= 1.24.0), NMF (>= 0.20.6)
Imports: stats, S4Vectors, BiocGenerics (>= 0.18.0), BSgenome (>=
1.40.0), VariantAnnotation (>= 1.18.1), dplyr (>= 0.8.3),
tibble(>= 2.1.3), purrr (>= 0.3.2), tidyr (>= 1.0.0), stringr
(>= 1.4.0), magrittr (>= 1.5), ggplot2 (>= 2.1.0), pracma (>=
1.8.8), IRanges (>= 2.6.0), GenomeInfoDb (>= 1.12.0),
Biostrings (>= 2.40.0), ggdendro (>= 0.1-20), cowplot (>=
0.9.2), ggalluvial (>= 0.12.2)
Suggests: BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), BiocStyle (>= 2.0.3),
TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), biomaRt (>=
2.28.0), gridExtra (>= 2.2.1), rtracklayer (>= 1.32.2), ccfindR
(>= 1.6.0), GenomicFeatures, AnnotationDbi, testthat, knitr,
rmarkdown
License: MIT + file LICENSE
MD5sum: 1e3d6f53804bdad25800261c6dce7548
NeedsCompilation: no
Title: Comprehensive genome-wide analysis of mutational processes
Description: Mutational processes leave characteristic footprints in
genomic DNA. This package provides a comprehensive set of
flexible functions that allows researchers to easily evaluate
and visualize a multitude of mutational patterns in base
substitution catalogues of e.g. healthy samples, tumour
samples, or DNA-repair deficient cells. The package covers a
wide range of patterns including: mutational signatures,
transcriptional and replicative strand bias, lesion
segregation, genomic distribution and association with genomic
features, which are collectively meaningful for studying the
activity of mutational processes. The package works with single
nucleotide variants (SNVs), insertions and deletions (Indels),
double base substitutions (DBSs) and larger multi base
substitutions (MBSs). The package provides functionalities for
both extracting mutational signatures de novo and determining
the contribution of previously identified mutational signatures
on a single sample level. MutationalPatterns integrates with
common R genomic analysis workflows and allows easy association
with (publicly available) annotation data.
biocViews: Genetics, SomaticMutation
Author: Freek Manders [aut] (<https://orcid.org/0000-0001-6197-347X>),
Francis Blokzijl [aut]
(<https://orcid.org/0000-0002-8084-8444>), Roel Janssen [aut]
(<https://orcid.org/0000-0003-4324-5350>), Jurrian de Kanter
[ctb] (<https://orcid.org/0000-0001-5665-3711>), Rurika Oka
[cre] (<https://orcid.org/0000-0003-4107-7250>), Ruben van
Boxtel [aut, cph] (<https://orcid.org/0000-0003-1285-2836>),
Edwin Cuppen [aut] (<https://orcid.org/0000-0002-0400-9542>)
Maintainer: Rurika Oka <R.Oka@prinsesmaximacentrum.nl>
URL: https://doi.org/10.1186/s13073-018-0539-0
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MutationalPatterns
git_branch: RELEASE_3_13
git_last_commit: 80fd57a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MutationalPatterns_3.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MutationalPatterns_3.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MutationalPatterns_3.2.0.tgz
vignettes:
vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.html
vignetteTitles: Introduction to MutationalPatterns
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.R
dependencyCount: 133
Package: MVCClass
Version: 1.66.0
Depends: R (>= 2.1.0), methods
License: LGPL
MD5sum: 49943f12166513c85b75710b932d967e
NeedsCompilation: no
Title: Model-View-Controller (MVC) Classes
Description: Creates classes used in model-view-controller (MVC) design
biocViews: Visualization, Infrastructure, GraphAndNetwork
Author: Elizabeth Whalen
Maintainer: Elizabeth Whalen <ewhalen@hsph.harvard.edu>
git_url: https://git.bioconductor.org/packages/MVCClass
git_branch: RELEASE_3_13
git_last_commit: 895bfdc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MVCClass_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MVCClass_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MVCClass_1.66.0.tgz
vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf
vignetteTitles: MVCClass
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: BioMVCClass
dependencyCount: 1
Package: MWASTools
Version: 1.16.0
Depends: R(>= 3.4)
Imports: glm2, ppcor, qvalue, car, boot, grid, ggplot2, gridExtra,
igraph, SummarizedExperiment, KEGGgraph, RCurl, KEGGREST,
ComplexHeatmap, stats, utils
Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown
License: CC BY-NC-ND 4.0
MD5sum: 98658cd7a0800ee2191f830049a86127
NeedsCompilation: no
Title: MWASTools: an integrated pipeline to perform metabolome-wide
association studies
Description: MWASTools provides a complete pipeline to perform
metabolome-wide association studies. Key functionalities of the
package include: quality control analysis of metabonomic data;
MWAS using different association models (partial correlations;
generalized linear models); model validation using
non-parametric bootstrapping; visualization of MWAS results;
NMR metabolite identification using STOCSY; and biological
interpretation of MWAS results.
biocViews: Metabolomics, Lipidomics, Cheminformatics, SystemsBiology,
QualityControl
Author: Andrea Rodriguez-Martinez, Joram M. Posma, Rafael Ayala, Ana L.
Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas
Maintainer: Andrea Rodriguez-Martinez
<andrea.rodriguez-martinez13@imperial.ac.uk>, Rafael Ayala
<rafael.ayala@oist.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MWASTools
git_branch: RELEASE_3_13
git_last_commit: 4c4de42
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/MWASTools_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/MWASTools_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/MWASTools_1.16.0.tgz
vignettes: vignettes/MWASTools/inst/doc/MWASTools.html
vignetteTitles: MWASTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MWASTools/inst/doc/MWASTools.R
importsMe: MetaboSignal
dependencyCount: 143
Package: mygene
Version: 1.28.0
Depends: R (>= 3.2.1), GenomicFeatures,
Imports: httr (>= 0.3), jsonlite (>= 0.9.7), S4Vectors, Hmisc, sqldf,
plyr
Suggests: BiocStyle
License: Artistic-2.0
Archs: i386, x64
MD5sum: 77e918071eb64f7c39be68263114e16b
NeedsCompilation: no
Title: Access MyGene.Info_ services
Description: MyGene.Info_ provides simple-to-use REST web services to
query/retrieve gene annotation data. It's designed with
simplicity and performance emphasized. *mygene*, is an
easy-to-use R wrapper to access MyGene.Info_ services.
biocViews: Annotation
Author: Adam Mark, Ryan Thompson, Cyrus Afrasiabi, Chunlei Wu
Maintainer: Adam Mark, Cyrus Afrasiabi, Chunlei Wu <cwu@scripps.edu>
git_url: https://git.bioconductor.org/packages/mygene
git_branch: RELEASE_3_13
git_last_commit: 876884a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mygene_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mygene_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mygene_1.28.0.tgz
vignettes: vignettes/mygene/inst/doc/mygene.pdf
vignetteTitles: Using mygene.R
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mygene/inst/doc/mygene.R
importsMe: MetaboSignal
dependencyCount: 138
Package: myvariant
Version: 1.22.0
Depends: R (>= 3.2.1), VariantAnnotation
Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb
Suggests: BiocStyle
License: Artistic-2.0
MD5sum: 9207d50915788a45250de445cef1eff8
NeedsCompilation: no
Title: Accesses MyVariant.info variant query and annotation services
Description: MyVariant.info is a comprehensive aggregation of variant
annotation resources. myvariant is a wrapper for querying
MyVariant.info services
biocViews: VariantAnnotation, Annotation, GenomicVariation
Author: Adam Mark
Maintainer: Adam Mark, Chunlei Wu <cwu@scripps.edu>
git_url: https://git.bioconductor.org/packages/myvariant
git_branch: RELEASE_3_13
git_last_commit: 926053d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/myvariant_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/myvariant_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/myvariant_1.22.0.tgz
vignettes: vignettes/myvariant/inst/doc/myvariant.pdf
vignetteTitles: Using MyVariant.R
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/myvariant/inst/doc/myvariant.R
dependencyCount: 136
Package: mzID
Version: 1.30.0
Depends: methods
Imports: XML, plyr, parallel, doParallel, foreach, iterators,
ProtGenerics
Suggests: knitr, testthat
License: GPL (>= 2)
MD5sum: a0320028ef68c5af16fb96445d1691ed
NeedsCompilation: no
Title: An mzIdentML parser for R
Description: A parser for mzIdentML files implemented using the XML
package. The parser tries to be general and able to handle all
types of mzIdentML files with the drawback of having less
'pretty' output than a vendor specific parser. Please contact
the maintainer with any problems and supply an mzIdentML file
so the problems can be fixed quickly.
biocViews: ImmunoOncology, DataImport, MassSpectrometry, Proteomics
Author: Laurent Gatto [ctb, cre]
(<https://orcid.org/0000-0002-1520-2268>), Thomas Pedersen
[aut] (<https://orcid.org/0000-0002-6977-7147>), Vladislav
Petyuk [ctb]
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mzID
git_branch: RELEASE_3_13
git_last_commit: 455f98b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/mzID_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mzID_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/mzID_1.30.0.tgz
vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf
vignetteTitles: Using mzID
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mzID/inst/doc/HOWTO_mzID.R
dependsOnMe: proteomics
importsMe: MSGFgui, MSGFplus, MSnbase, MSnID
suggestsMe: mzR, RforProteomics
dependencyCount: 11
Package: mzR
Version: 2.26.1
Depends: Rcpp (>= 0.10.1), methods, utils
Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.17.3),
ncdf4
LinkingTo: Rcpp, zlibbioc, Rhdf5lib (>= 1.1.4)
Suggests: msdata (>= 0.15.1), RUnit, mzID, BiocStyle (>= 2.5.19),
knitr, XML, rmarkdown
License: Artistic-2.0
MD5sum: 12beea3e36daffe05ec37c3ee9ee4073
NeedsCompilation: yes
Title: parser for netCDF, mzXML, mzData and mzML and mzIdentML files
(mass spectrometry data)
Description: mzR provides a unified API to the common file formats and
parsers available for mass spectrometry data. It comes with a
wrapper for the ISB random access parser for mass spectrometry
mzXML, mzData and mzML files. The package contains the original
code written by the ISB, and a subset of the proteowizard
library for mzML and mzIdentML. The netCDF reading code has
previously been used in XCMS.
biocViews: ImmunoOncology, Infrastructure, DataImport, Proteomics,
Metabolomics, MassSpectrometry
Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou,
Johannes Rainer
Maintainer: Steffen Neumann <sneumann@ipb-halle.de>, Laurent Gatto
<laurent.gatto@uclouvain.be>, Qiakng Kou <qkou@umail.iu.edu>
URL: https://github.com/sneumann/mzR/
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
BugReports: https://github.com/sneumann/mzR/issues/
git_url: https://git.bioconductor.org/packages/mzR
git_branch: RELEASE_3_13
git_last_commit: 5a5c15c5
git_last_commit_date: 2021-06-19
Date/Publication: 2021-06-20
source.ver: src/contrib/mzR_2.26.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/mzR_2.26.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/mzR_2.26.1.tgz
vignettes: vignettes/mzR/inst/doc/mzR.html
vignetteTitles: Accessin raw mass spectrometry and identification data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mzR/inst/doc/mzR.R
dependsOnMe: MSGFgui, MSnbase, proteomics
importsMe: adductomicsR, Autotuner, CluMSID, DIAlignR, MSnID, msPurity,
peakPantheR, ProteomicsAnnotationHubData, SIMAT, topdownr,
xcms, yamss, IDSL.IPA
suggestsMe: AnnotationHub, qcmetrics, Spectra, msdata, RforProteomics,
erah
dependencyCount: 12
Package: NADfinder
Version: 1.16.0
Depends: R (>= 3.4), BiocGenerics, IRanges, GenomicRanges, S4Vectors,
SummarizedExperiment
Imports: graphics, methods, baseline, signal, GenomicAlignments,
GenomeInfoDb, rtracklayer, limma, trackViewer, stats, utils,
Rsamtools, metap, EmpiricalBrownsMethod,ATACseqQC, corrplot,
csaw
Suggests: RUnit, BiocStyle, knitr, BSgenome.Mmusculus.UCSC.mm10,
testthat, BiocManager, rmarkdown
License: GPL (>= 2)
MD5sum: 3beed7e9c063dcc93a00d30a4cf5e08f
NeedsCompilation: no
Title: Call wide peaks for sequencing data
Description: Nucleolus is an important structure inside the nucleus in
eukaryotic cells. It is the site for transcribing rDNA into
rRNA and for assembling ribosomes, aka ribosome biogenesis. In
addition, nucleoli are dynamic hubs through which numerous
proteins shuttle and contact specific non-rDNA genomic loci.
Deep sequencing analyses of DNA associated with isolated
nucleoli (NAD- seq) have shown that specific loci, termed
nucleolus- associated domains (NADs) form frequent three-
dimensional associations with nucleoli. NAD-seq has been used
to study the biological functions of NAD and the dynamics of
NAD distribution during embryonic stem cell (ESC)
differentiation. Here, we developed a Bioconductor package
NADfinder for bioinformatic analysis of the NAD-seq data,
including baseline correction, smoothing, normalization, peak
calling, and annotation.
biocViews: Sequencing, DNASeq, GeneRegulation, PeakDetection
Author: Jianhong Ou, Haibo Liu, Jun Yu, Hervé Pagès, Paul Kaufman,
Lihua Julie Zhu
Maintainer: Jianhong Ou <jianhong.ou@duke.edu>, Lihua Julie Zhu
<julie.zhu@umassmed.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NADfinder
git_branch: RELEASE_3_13
git_last_commit: e592a6b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NADfinder_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NADfinder_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NADfinder_1.16.0.tgz
vignettes: vignettes/NADfinder/inst/doc/NADfinder.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R
dependencyCount: 218
Package: NanoMethViz
Version: 1.2.0
Depends: R (>= 4.0.0), methods, ggplot2
Imports: cpp11 (>= 0.2.5), readr, S4Vectors, SummarizedExperiment,
bsseq, forcats, assertthat, AnnotationDbi, Rcpp, dplyr,
data.table, e1071, fs, GenomicRanges, ggthemes, glue,
patchwork, purrr, rlang, RSQLite, Rsamtools, scales, stats,
stringr, tibble, tidyr, utils, withr, zlibbioc
LinkingTo: Rcpp
Suggests: DSS, Mus.musculus, Homo.sapiens, knitr, rmarkdown, testthat
(>= 3.0.0), covr
License: Apache License (>= 2.0)
MD5sum: 107e640b3875c91a43a6d9e8d0d0fd08
NeedsCompilation: yes
Title: Visualise methlation data from Oxford Nanopore sequencing
Description: NanoMethViz is a toolkit for visualising methylation data
from Oxford Nanopore sequencing. It can be used to explore
methylation patterns from reads derived from Oxford Nanopore
direct DNA sequencing with methylation called by callers
including nanopolish, f5c and megalodon. The plots in this
package allow the visualisation of methylation profiles
aggregated over experimental groups and across classes of
genomic features.
biocViews: Software, Visualization, DifferentialMethylation
Author: Shian Su [cre, aut]
Maintainer: Shian Su <su.s@wehi.edu.au>
URL: https://github.com/shians/NanoMethViz
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/Shians/NanoMethViz/issues
git_url: https://git.bioconductor.org/packages/NanoMethViz
git_branch: RELEASE_3_13
git_last_commit: 15c307c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NanoMethViz_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NanoMethViz_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NanoMethViz_1.2.0.tgz
vignettes: vignettes/NanoMethViz/inst/doc/ImportingData.html,
vignettes/NanoMethViz/inst/doc/Introduction.html
vignetteTitles: Importing Data, Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NanoMethViz/inst/doc/ImportingData.R,
vignettes/NanoMethViz/inst/doc/Introduction.R
dependencyCount: 133
Package: NanoStringDiff
Version: 1.22.0
Depends: Biobase
Imports: matrixStats, methods, Rcpp
LinkingTo: Rcpp
Suggests: testthat, BiocStyle
License: GPL
MD5sum: 5cf7ce325dc2e10bbf34eb625ba3d72c
NeedsCompilation: yes
Title: Differential Expression Analysis of NanoString nCounter Data
Description: This Package utilizes a generalized linear model(GLM) of
the negative binomial family to characterize count data and
allows for multi-factor design. NanoStrongDiff incorporate size
factors, calculated from positive controls and housekeeping
controls, and background level, obtained from negative
controls, in the model framework so that all the normalization
information provided by NanoString nCounter Analyzer is fully
utilized.
biocViews: DifferentialExpression, Normalization
Author: hong wang <hong.wang@uky.edu>, tingting zhai
<tingting.zhai@uky.edu>, chi wang <chi.wang@uky.edu>
Maintainer: tingting zhai <tingting.zhai@uky.edu>,hong wang
<hong.wang@uky.edu>
git_url: https://git.bioconductor.org/packages/NanoStringDiff
git_branch: RELEASE_3_13
git_last_commit: 82f8b18
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NanoStringDiff_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NanoStringDiff_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NanoStringDiff_1.22.0.tgz
vignettes: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf
vignetteTitles: NanoStringDiff Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.R
dependencyCount: 9
Package: NanoStringNCTools
Version: 1.0.0
Depends: R (>= 3.6), Biobase, S4Vectors, ggplot2
Imports: BiocGenerics, Biostrings, ggbeeswarm, ggiraph, ggthemes,
grDevices, IRanges, methods, pheatmap, RColorBrewer, stats,
utils
Suggests: biovizBase, ggbio, RUnit, rmarkdown, knitr, qpdf
License: Artistic-2.0
MD5sum: 2c0626ed05ee3b61291276aeb7bea2ee
NeedsCompilation: no
Title: NanoString nCounter Tools
Description: Tools for NanoString Technologies nCounter Technology.
Provides support for reading RCC files into an ExpressionSet
derived object. Also includes methods for QC and
normalizaztion of NanoString data.
biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport,
Transcriptomics, Proteomics, mRNAMicroarray,
ProprietaryPlatforms, RNASeq
Author: Patrick Aboyoun [aut], Nicole Ortogero [cre], Zhi Yang [ctb]
Maintainer: Nicole Ortogero <nortogero@nanostring.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NanoStringNCTools
git_branch: RELEASE_3_13
git_last_commit: 962adc0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NanoStringNCTools_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NanoStringNCTools_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NanoStringNCTools_1.0.0.tgz
vignettes: vignettes/NanoStringNCTools/inst/doc/Introduction.html
vignetteTitles: Introduction to the NanoStringRCCSet Class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NanoStringNCTools/inst/doc/Introduction.R
dependsOnMe: GeomxTools
dependencyCount: 71
Package: NanoStringQCPro
Version: 1.24.0
Depends: R (>= 3.2), methods
Imports: AnnotationDbi (>= 1.26.0), org.Hs.eg.db (>= 2.14.0), Biobase
(>= 2.24.0), knitr (>= 1.12), NMF (>= 0.20.5), RColorBrewer (>=
1.0-5), png (>= 0.1-7)
Suggests: roxygen2 (>= 4.0.1), testthat, BiocStyle, knitr, rmarkdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: d47a7b24fad4a1c19f80408bcf1c29e5
NeedsCompilation: no
Title: Quality metrics and data processing methods for NanoString mRNA
gene expression data
Description: NanoStringQCPro provides a set of quality metrics that can
be used to assess the quality of NanoString mRNA gene
expression data -- i.e. to identify outlier probes and outlier
samples. It also provides different background subtraction and
normalization approaches for this data. It outputs suggestions
for flagging samples/probes and an easily sharable html quality
control output.
biocViews: Microarray, mRNAMicroarray, Preprocessing, Normalization,
QualityControl, ReportWriting
Author: Dorothee Nickles <nickles.dorothee@gene.com>, Thomas Sandmann
<sandmann.thomas@gene.com>, Robert Ziman
<ziman.robert@gene.com>, Richard Bourgon
<bourgon.richard@gene.com>
Maintainer: Robert Ziman <ziman.robert@gene.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NanoStringQCPro
git_branch: RELEASE_3_13
git_last_commit: 1366855
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NanoStringQCPro_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NanoStringQCPro_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NanoStringQCPro_1.24.0.tgz
vignettes:
vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf
vignetteTitles: vignetteNanoStringQCPro.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 95
Package: nanotatoR
Version: 1.8.0
Depends: R (>= 3.6)
Imports: hash(>= 2.2.6), openxlsx(>= 4.0.17), rentrez(>= 1.1.0), stats,
grDevices, graphics, stringr, knitr, testthat, utils,
AnnotationDbi, httr, org.Hs.eg.db, rtracklayer
Suggests: rmarkdown, yaml
License: file LICENSE
MD5sum: 82bdaae75c9d0bfff6291cea48da4ad7
NeedsCompilation: no
Title: nanotatoR: next generation structural variant annotation and
classification
Description: Whole genome sequencing (WGS) has successfully been used
to identify single-nucleotide variants (SNV), small insertions
and deletions and, more recently, small copy number variants.
However, due to utilization of short reads, it is not well
suited for identification of structural variants (SV) and the
majority of SV calling tools having high false positive and
negative rates.Optical next-generation mapping (NGM) utilizes
long fluorescently labeled native-state DNA molecules for de
novo genome assembly to overcome the limitations of WGS. NGM
allows for a significant increase in SV detection capability.
However, accuracy of SV annotation is highly important for
variant classification and filtration to determine
pathogenicity.Here we create a new tool in R, for SV
annotation, including population frequency and gene function
and expression, using publicly available datasets. We use DGV
(Database of Genomic Variants), to calculate the population
frequency of the SVs identified by the Bionano SVCaller in the
NGM dataset of a cohort of 50 undiagnosed patients with a
variety of phenotypes. The new annotation tool, nanotatoR, also
calculates the internal frequency of SVs, which could be
beneficial in identification of potential false positive or
common calls. The software creates a primary gene list (PG)
from NCBI databases based on patient phenotype and compares the
list to the set of genes overlapping the patient’s SVs
generated by SVCaller, providing analysts with an easy way to
identify variants affecting genes in the PG. The output is
given in an Excel file format, which is subdivided into
multiple sheets based on SV type. Users then have a choice to
filter SVs using the provided annotation for identification of
inherited, de novo or rare variants. nanotatoR provides
integrated annotation and the expression patterns to enable
users to identify potential pathogenic SVs with greater
precision and faster turnaround times.
biocViews: Software, WorkflowStep, GenomeAssembly, VariantAnnotation
Author: Surajit Bhattacharya,Hayk Barsheghyan, Emmanuele C Delot and
Eric Vilain
Maintainer: Surajit Bhattacharya <sbhattach2@childrensnational.org>
URL: https://github.com/VilainLab/Nanotator
VignetteBuilder: knitr
BugReports: https://github.com/VilainLab/Nanotator/issues
git_url: https://git.bioconductor.org/packages/nanotatoR
git_branch: RELEASE_3_13
git_last_commit: 40f3c9e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/nanotatoR_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/nanotatoR_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/nanotatoR_1.8.0.tgz
vignettes: vignettes/nanotatoR/inst/doc/nanotatoR.html
vignetteTitles: nanotatoR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/nanotatoR/inst/doc/nanotatoR.R
dependencyCount: 103
Package: NBAMSeq
Version: 1.8.0
Depends: R (>= 3.6), SummarizedExperiment, S4Vectors
Imports: DESeq2, mgcv(>= 1.8-24), BiocParallel, genefilter, methods,
stats,
Suggests: knitr, rmarkdown, testthat, ggplot2
License: GPL-2
Archs: i386, x64
MD5sum: c66dbd7bc5bca07067fa69ecb97523bf
NeedsCompilation: no
Title: Negative Binomial Additive Model for RNA-Seq Data
Description: High-throughput sequencing experiments followed by
differential expression analysis is a widely used approach to
detect genomic biomarkers. A fundamental step in differential
expression analysis is to model the association between gene
counts and covariates of interest. NBAMSeq a flexible
statistical model based on the generalized additive model and
allows for information sharing across genes in variance
estimation.
biocViews: RNASeq, DifferentialExpression, GeneExpression, Sequencing,
Coverage
Author: Xu Ren [aut, cre], Pei Fen Kuan [aut]
Maintainer: Xu Ren <xuren2120@gmail.com>
URL: https://github.com/reese3928/NBAMSeq
VignetteBuilder: knitr
BugReports: https://github.com/reese3928/NBAMSeq/issues
git_url: https://git.bioconductor.org/packages/NBAMSeq
git_branch: RELEASE_3_13
git_last_commit: c51aa12
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NBAMSeq_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NBAMSeq_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NBAMSeq_1.8.0.tgz
vignettes: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.html
vignetteTitles: Negative Binomial Additive Model for RNA-Seq Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.R
dependencyCount: 93
Package: NBSplice
Version: 1.10.0
Depends: R (>= 3.5), methods
Imports: edgeR, stats, MASS, car, mppa, BiocParallel, ggplot2, reshape2
Suggests: knitr, RUnit, BiocGenerics, BiocStyle
License: GPL (>=2)
Archs: i386, x64
MD5sum: e02f8cb256c0dd8f893d9c5a10686238
NeedsCompilation: no
Title: Negative Binomial Models to detect Differential Splicing
Description: The package proposes a differential splicing evaluation
method based on isoform quantification. It applies generalized
linear models with negative binomial distribution to infer
changes in isoform relative expression.
biocViews: Software, StatisticalMethod, AlternativeSplicing,
Regression, DifferentialExpression, DifferentialSplicing,
RNASeq, ImmunoOncology
Author: Gabriela A. Merino and Elmer A. Fernandez
Maintainer: Gabriela Merino <merino.gabriela33@gmail.com>
URL: http://www.bdmg.com.ar
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NBSplice
git_branch: RELEASE_3_13
git_last_commit: 58b6cb9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NBSplice_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NBSplice_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NBSplice_1.10.0.tgz
vignettes: vignettes/NBSplice/inst/doc/NBSplice-vignette.html
vignetteTitles: NBSplice-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NBSplice/inst/doc/NBSplice-vignette.R
dependencyCount: 108
Package: ncdfFlow
Version: 2.38.0
Depends: R (>= 2.14.0), flowCore(>= 1.51.7), RcppArmadillo, methods, BH
Imports: Biobase,BiocGenerics,flowCore,zlibbioc
LinkingTo: Rcpp,RcppArmadillo,BH, Rhdf5lib
Suggests: testthat,parallel,flowStats,knitr
License: Artistic-2.0
MD5sum: 9a6b591efc4d5df04656994ae72dfcb8
NeedsCompilation: yes
Title: ncdfFlow: A package that provides HDF5 based storage for flow
cytometry data.
Description: Provides HDF5 storage based methods and functions for
manipulation of flow cytometry data.
biocViews: ImmunoOncology, FlowCytometry
Author: Mike Jiang,Greg Finak,N. Gopalakrishnan
Maintainer: Mike Jiang <wjiang2@fhcrc.org>, Jake Wagner
<jpwagner@fhcrc.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ncdfFlow
git_branch: RELEASE_3_13
git_last_commit: 7902e69
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ncdfFlow_2.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ncdfFlow_2.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ncdfFlow_2.38.0.tgz
vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf
vignetteTitles: Basic Functions for Flow Cytometry Data
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R
dependsOnMe: ggcyto
importsMe: flowStats, flowWorkspace
suggestsMe: COMPASS, cydar
dependencyCount: 20
Package: ncGTW
Version: 1.6.0
Depends: methods, BiocParallel, xcms
Imports: Rcpp, grDevices, graphics, stats
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, testthat, rmarkdown
License: GPL-2
MD5sum: 39728241bdf679ccc5e57e82b4a2e85c
NeedsCompilation: yes
Title: Alignment of LC-MS Profiles by Neighbor-wise Compound-specific
Graphical Time Warping with Misalignment Detection
Description: The purpose of ncGTW is to help XCMS for LC-MS data
alignment. Currently, ncGTW can detect the misaligned feature
groups by XCMS, and the user can choose to realign these
feature groups by ncGTW or not.
biocViews: Software, MassSpectrometry, Metabolomics, Alignment
Author: Chiung-Ting Wu <ctwu@vt.edu>
Maintainer: Chiung-Ting Wu <ctwu@vt.edu>
VignetteBuilder: knitr
BugReports: https://github.com/ChiungTingWu/ncGTW/issues
git_url: https://git.bioconductor.org/packages/ncGTW
git_branch: RELEASE_3_13
git_last_commit: cf40068
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ncGTW_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ncGTW_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ncGTW_1.6.0.tgz
vignettes: vignettes/ncGTW/inst/doc/ncGTW.html
vignetteTitles: ncGTW User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ncGTW/inst/doc/ncGTW.R
dependencyCount: 94
Package: NCIgraph
Version: 1.40.0
Depends: R (>= 2.10.0)
Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.methodsS3
Suggests: Rgraphviz
Enhances: DEGraph
License: GPL-3
MD5sum: 05f222b4f722893b010a56a860cf15d0
NeedsCompilation: no
Title: Pathways from the NCI Pathways Database
Description: Provides various methods to load the pathways from the NCI
Pathways Database in R graph objects and to re-format them.
biocViews: Pathways, GraphAndNetwork
Author: Laurent Jacob
Maintainer: Laurent Jacob <laurent.jacob@gmail.com>
git_url: https://git.bioconductor.org/packages/NCIgraph
git_branch: RELEASE_3_13
git_last_commit: b75cc38
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NCIgraph_1.40.0.tar.gz
vignettes: vignettes/NCIgraph/inst/doc/NCIgraph.pdf
vignetteTitles: NCIgraph: networks from the NCI pathway integrated
database as graphNEL objects.
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NCIgraph/inst/doc/NCIgraph.R
importsMe: DEGraph
suggestsMe: DEGraph
dependencyCount: 69
Package: ncRNAtools
Version: 1.2.1
Imports: httr, xml2, utils, methods, grDevices, ggplot2, IRanges,
GenomicRanges, S4Vectors
Suggests: knitr, BiocStyle, rmarkdown, RUnit, BiocGenerics
License: GPL-3
MD5sum: f15e076360be9c81db4513afd862ced5
NeedsCompilation: no
Title: An R toolkit for non-coding RNA
Description: ncRNAtools provides a set of basic tools for handling and
analyzing non-coding RNAs. These include tools to access the
RNAcentral database and to predict and visualize the secondary
structure of non-coding RNAs. The package also provides tools
to read, write and interconvert the file formats most commonly
used for representing such secondary structures.
biocViews: FunctionalGenomics, DataImport, ThirdPartyClient,
Visualization, StructuralPrediction
Author: Lara Selles Vidal [cre, aut]
(<https://orcid.org/0000-0003-2537-6824>), Rafael Ayala [aut]
(<https://orcid.org/0000-0002-9332-4623>), Guy-Bart Stan [aut]
(<https://orcid.org/0000-0002-5560-902X>), Rodrigo
Ledesma-Amaro [aut] (<https://orcid.org/0000-0003-2631-5898>)
Maintainer: Lara Selles Vidal <lara.selles@oist.jp>
VignetteBuilder: knitr
BugReports: https://github.com/LaraSellesVidal/ncRNAtools/issues
git_url: https://git.bioconductor.org/packages/ncRNAtools
git_branch: RELEASE_3_13
git_last_commit: cd5ad29
git_last_commit_date: 2021-06-27
Date/Publication: 2021-06-29
source.ver: src/contrib/ncRNAtools_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ncRNAtools_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ncRNAtools_1.2.1.tgz
vignettes: vignettes/ncRNAtools/inst/doc/ncRNAtools.html
vignetteTitles: rfaRm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ncRNAtools/inst/doc/ncRNAtools.R
dependencyCount: 59
Package: ndexr
Version: 1.14.1
Depends: igraph
Imports: httr, jsonlite, plyr, tidyr
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: BSD
MD5sum: e83b7eac09b2ea5a6828048e735169c7
NeedsCompilation: no
Title: NDEx R client library
Description: This package offers an interface to NDEx servers, e.g. the
public server at http://ndexbio.org/. It can retrieve and save
networks via the API. Networks are offered as RCX object and as
igraph representation.
biocViews: Pathways, DataImport, Network
Author: Florian Auer <florian.auer@informatik.uni-augsburg.de>, Frank
Kramer <frank.kramer@informatik.uni-augsburg.de>, Alex Ishkin
<aleksandr.ishkin@thomsonreuters.com>, Dexter Pratt
<depratt@ucsc.edu>
Maintainer: Florian Auer <florian.auer@informatik.uni-augsburg.de>
URL: https://github.com/frankkramer-lab/ndexr
VignetteBuilder: knitr
BugReports: https://github.com/frankkramer-lab/ndexr/issues
git_url: https://git.bioconductor.org/packages/ndexr
git_branch: RELEASE_3_13
git_last_commit: 57dc82e
git_last_commit_date: 2021-07-27
Date/Publication: 2021-07-27
source.ver: src/contrib/ndexr_1.14.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ndexr_1.14.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ndexr_1.14.1.tgz
vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html
vignetteTitles: NDExR Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R
importsMe: netgsa
dependencyCount: 39
Package: nearBynding
Version: 1.2.0
Depends: R (>= 4.0)
Imports: R.utils, matrixStats, plyranges, transport, Rsamtools,
S4Vectors, grDevices, graphics, rtracklayer, dplyr,
GenomeInfoDb, methods, GenomicRanges, utils, stats, magrittr,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene, ggplot2, gplots,
BiocGenerics, rlang
Suggests: knitr
License: Artistic-2.0
MD5sum: 5b49c799169ba4ae164e4c714773a6ae
NeedsCompilation: no
Title: Discern RNA structure proximal to protein binding
Description: Provides a pipeline to discern RNA structure at and
proximal to the site of protein binding within regions of the
transcriptome defined by the user. CLIP protein-binding data
can be input as either aligned BAM or peak-called bedGraph
files. RNA structure can either be predicted internally from
sequence or users have the option to input their own RNA
structure data. RNA structure binding profiles can be visually
and quantitatively compared across multiple formats.
biocViews: Visualization, MotifDiscovery, DataRepresentation,
StructuralPrediction, Clustering, MultipleComparison
Author: Veronica Busa [cre]
Maintainer: Veronica Busa <vbusa1@jhmi.edu>
SystemRequirements: bedtools (>= 2.28.0), Stereogene (>= v2.20), CapR
(>= 1.1.1)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/nearBynding
git_branch: RELEASE_3_13
git_last_commit: cb38368
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/nearBynding_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/nearBynding_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/nearBynding_1.2.0.tgz
vignettes: vignettes/nearBynding/inst/doc/nearBynding.pdf
vignetteTitles: nearBynding Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nearBynding/inst/doc/nearBynding.R
dependencyCount: 123
Package: Nebulosa
Version: 1.2.0
Depends: R (>= 4.0), ggplot2, patchwork
Imports: Seurat, SingleCellExperiment, SummarizedExperiment, ks,
Matrix, stats, methods
Suggests: testthat, BiocStyle, knitr, rmarkdown, covr, scater, scran,
DropletUtils, igraph, BiocFileCache, SeuratObject
License: GPL-3
Archs: i386, x64
MD5sum: df1aa001de5b9b5c140dd94ca77fa703
NeedsCompilation: no
Title: Single-Cell Data Visualisation Using Kernel Gene-Weighted
Density Estimation
Description: This package provides a enhanced visualization of
single-cell data based on gene-weighted density estimation.
Nebulosa recovers the signal from dropped-out features and
allows the inspection of the joint expression from multiple
features (e.g. genes). Seurat and SingleCellExperiment objects
can be used within Nebulosa.
biocViews: Software, GeneExpression, SingleCell, Visualization,
DimensionReduction
Author: Jose Alquicira-Hernandez [aut, cre]
(<https://orcid.org/0000-0002-9049-7780>)
Maintainer: Jose Alquicira-Hernandez <alquicirajose@gmail.com>
URL: https://github.com/powellgenomicslab/Nebulosa
VignetteBuilder: knitr
BugReports: https://github.com/powellgenomicslab/Nebulosa/issues
git_url: https://git.bioconductor.org/packages/Nebulosa
git_branch: RELEASE_3_13
git_last_commit: d253c3c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Nebulosa_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Nebulosa_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Nebulosa_1.2.0.tgz
vignettes: vignettes/Nebulosa/inst/doc/introduction.html,
vignettes/Nebulosa/inst/doc/nebulosa_seurat.html
vignetteTitles: Visualization of gene expression with Nebulosa,
Visualization of gene expression with Nebulosa (in Seurat)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Nebulosa/inst/doc/introduction.R,
vignettes/Nebulosa/inst/doc/nebulosa_seurat.R
dependencyCount: 165
Package: NeighborNet
Version: 1.10.0
Depends: methods
Imports: graph, stats
License: CC BY-NC-ND 4.0
Archs: i386, x64
MD5sum: 2cbe1ad13043aa93c05e71e0427f0706
NeedsCompilation: no
Title: Neighbor_net analysis
Description: Identify the putative mechanism explaining the active
interactions between genes in the investigated phenotype.
biocViews: Software, GeneExpression, StatisticalMethod, GraphAndNetwork
Author: Sahar Ansari <saharansari@wayne.edu> and Sorin Draghici
<sorin@wayne.edu>
Maintainer: Sahar Ansari <saharansari@wayne.edu>
git_url: https://git.bioconductor.org/packages/NeighborNet
git_branch: RELEASE_3_13
git_last_commit: 9ca22cb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NeighborNet_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NeighborNet_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NeighborNet_1.10.0.tgz
vignettes: vignettes/NeighborNet/inst/doc/neighborNet.pdf
vignetteTitles: NeighborNet
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NeighborNet/inst/doc/neighborNet.R
dependencyCount: 8
Package: nempi
Version: 1.0.0
Depends: R (>= 4.1), mnem
Imports: e1071, nnet, randomForest, naturalsort, graphics, stats,
utils, matrixStats, epiNEM
Suggests: knitr, BiocGenerics, rmarkdown
License: GPL-3
MD5sum: 490b7a8b77514556f2315133bbd13a74
NeedsCompilation: no
Title: Inferring unobserved perturbations from gene expression data
Description: Takes as input an incomplete perturbation profile and
differential gene expression in log odds and infers unobserved
perturbations and augments observed ones. The inference is done
by iteratively inferring a network from the perturbations and
inferring perturbations from the network. The network inference
is done by Nested Effects Models.
biocViews: Software, GeneExpression, DifferentialExpression,
DifferentialMethylation, GeneSignaling, Pathways, Network,
Classification, NeuralNetwork, NetworkInference, ATACSeq,
DNASeq, RNASeq, PooledScreens, CRISPR, SingleCell,
SystemsBiology
Author: Martin Pirkl [aut, cre]
Maintainer: Martin Pirkl <martinpirkl@yahoo.de>
URL: https://github.com/cbg-ethz/nempi/
VignetteBuilder: knitr
BugReports: https://github.com/cbg-ethz/nempi/issues
git_url: https://git.bioconductor.org/packages/nempi
git_branch: RELEASE_3_13
git_last_commit: 86f9104
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/nempi_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/nempi_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/nempi_1.0.0.tgz
vignettes: vignettes/nempi/inst/doc/nempi.html
vignetteTitles: nempi
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nempi/inst/doc/nempi.R
dependencyCount: 110
Package: netbiov
Version: 1.26.0
Depends: R (>= 3.1.0), igraph (>= 0.7.1)
Suggests: BiocStyle,RUnit,BiocGenerics,Matrix
License: GPL (>= 2)
MD5sum: 5fbc6f872d76ca9df0001e9b1f52bf10
NeedsCompilation: no
Title: A package for visualizing complex biological network
Description: A package that provides an effective visualization of
large biological networks
biocViews: GraphAndNetwork, Network, Software, Visualization
Author: Shailesh tripathi and Frank Emmert-Streib
Maintainer: Shailesh tripathi <shailesh.tripathy@gmail.com>
URL: http://www.bio-complexity.com
git_url: https://git.bioconductor.org/packages/netbiov
git_branch: RELEASE_3_13
git_last_commit: 41822fb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/netbiov_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/netbiov_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/netbiov_1.26.0.tgz
vignettes: vignettes/netbiov/inst/doc/netbiov-intro.pdf
vignetteTitles: netbiov: An R package for visualizing biological
networks
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/netbiov/inst/doc/netbiov-intro.R
dependencyCount: 11
Package: netboost
Version: 2.0.0
Depends: R (>= 4.0.0)
Imports: Rcpp, RcppParallel, parallel, grDevices, graphics, stats,
utils, dynamicTreeCut, WGCNA, impute, colorspace, methods,
R.utils
LinkingTo: Rcpp, RcppParallel
Suggests: knitr, rmarkdown
License: GPL-3
OS_type: unix
MD5sum: 9a55d628d66cb919e831613c2b73abc6
NeedsCompilation: yes
Title: Network Analysis Supported by Boosting
Description: Boosting supported network analysis for high-dimensional
omics applications. This package comes bundled with the
MC-UPGMA clustering package by Yaniv Loewenstein.
biocViews: Software, StatisticalMethod, GraphAndNetwork, Network,
Clustering, DimensionReduction, BiomedicalInformatics,
Epigenetics, Metabolomics, Transcriptomics
Author: Pascal Schlosser [aut, cre], Jochen Knaus [aut, ctb], Yaniv
Loewenstein [aut]
Maintainer: Pascal Schlosser <pascal.schlosser@uniklinik-freiburg.de>
URL: https://bioconductor.org/packages/release/bioc/html/netboost.html
SystemRequirements: GNU make, Bash, Perl, Gzip
VignetteBuilder: knitr
BugReports: https://github.com/PascalSchlosser/netboost/issues
git_url: https://git.bioconductor.org/packages/netboost
git_branch: RELEASE_3_13
git_last_commit: daccaa3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/netboost_2.0.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/netboost_2.0.0.tgz
vignettes: vignettes/netboost/inst/doc/netboost.html
vignetteTitles: The Netboost users guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/netboost/inst/doc/netboost.R
dependencyCount: 113
Package: netboxr
Version: 1.4.0
Depends: R (>= 4.0.0), igraph (>= 1.2.4.1), parallel
Imports: RColorBrewer, DT, stats, clusterProfiler, data.table, gplots,
jsonlite, plyr
Suggests: paxtoolsr, BiocStyle, org.Hs.eg.db, knitr, rmarkdown,
testthat, cgdsr
License: LGPL-3 + file LICENSE
MD5sum: a8f6d785cfc8baaac48e2f8b59698752
NeedsCompilation: no
Title: netboxr
Description: NetBox is a network-based approach that combines prior
knowledge with a network clustering algorithm. The algorithm
allows for the identification of functional modules and allows
for combining multiple data types, such as mutations and copy
number alterations. NetBox performs network analysis on human
interaction networks, and comes pre-loaded with a Human
Interaction Network (HIN) derived from four literature curated
data sources, including the Human Protein Reference Database
(HPRD), Reactome, NCI-Nature Pathway Interaction (PID)
Database, and the MSKCC Cancer Cell Map.
biocViews: Software,Network,Pathways,GraphAndNetwork,Reactome,
SystemsBiology, GeneSetEnrichment, NetworkEnrichment, KEGG
Author: Eric Minwei Liu [aut,cre], Augustin Luna [aut], Ethan Cerami
[aut]
Maintainer: Eirc Minwei Liu <emliu.research@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/netboxr
git_branch: RELEASE_3_13
git_last_commit: 8b61aeb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/netboxr_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/netboxr_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/netboxr_1.4.0.tgz
vignettes: vignettes/netboxr/inst/doc/netboxrTutorial.html
vignetteTitles: NetBoxR Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/netboxr/inst/doc/netboxrTutorial.R
dependencyCount: 139
Package: netDx
Version: 1.4.3
Depends: R (>= 3.6)
Imports:
ROCR,pracma,ggplot2,RCy3,glmnet,igraph,reshape2,parallel,stats,utils,MultiAssayExperiment,graphics,grDevices,methods,BiocFileCache,GenomicRanges,bigmemory,doParallel,foreach,combinat,rappdirs,GenomeInfoDb,S4Vectors,IRanges,RColorBrewer,
scater, netSmooth, clusterExperiment,Rtsne,httr
Suggests: curatedTCGAData, TCGAutils, rmarkdown, testthat, knitr,
BiocStyle
License: MIT + file LICENSE
MD5sum: e59b857f6f8b34b2bf7440dae57c8f3d
NeedsCompilation: no
Title: Network-based patient classifier
Description: netDx is a general-purpose algorithm to build a patient
classifier from heterogenous patient data. The method converts
data into patient similarity networks at the level of features.
Feature selection identifies features of predictive value to
each class. Methods are provided for versatile predictor design
and performance evaluation using standard measures. netDx
natively groups molecular data into pathway-level features and
connects with Cytoscape for network visualization of pathway
themes. For method details see: Pai et al. (2019). netDx:
interpretable patient classification using integrated patient
similarity networks. Molecular Systems Biology. 15, e8497
biocViews: Classification, BiomedicalInformatics, Network,
SystemsBiology
Author: Shraddha Pai [aut, cre]
(<https://orcid.org/0000-0002-1048-581X>), Philipp Weber [aut],
Ahmad Shah [aut], Luca Giudice [aut], Shirley Hui [aut], Ruth
Isserlin [aut], Hussam Kaka [aut], Gary Bader [aut]
Maintainer: Shraddha Pai <shraddha.pai@utoronto.ca>
URL: http://netdx.org
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/netDx
git_branch: RELEASE_3_13
git_last_commit: 4f053f2
git_last_commit_date: 2021-08-18
Date/Publication: 2021-08-19
source.ver: src/contrib/netDx_1.4.3.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/netDx_1.4.3.tgz
vignettes: vignettes/netDx/inst/doc/BuildPredictor.html,
vignettes/netDx/inst/doc/ThreeWayClassifier.html,
vignettes/netDx/inst/doc/ValidateNew.html
vignetteTitles: 01. Build binary predictor and view performance,, top
features and integrated Patient Similarity Network, 02. Build
three-way classifier (N-way; N>2) from multi-omic data, 04.
Validate model with selected features on an independent dataset
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/netDx/inst/doc/BuildPredictor.R,
vignettes/netDx/inst/doc/ThreeWayClassifier.R,
vignettes/netDx/inst/doc/ValidateNew.R
dependencyCount: 200
Package: nethet
Version: 1.24.0
Imports: glasso, mvtnorm, GeneNet, huge, CompQuadForm, ggm, mclust,
parallel, GSA, limma, multtest, ICSNP, glmnet, network,
ggplot2, grDevices, graphics, stats, utils
Suggests: knitr, xtable, BiocStyle, testthat
License: GPL-2
MD5sum: f412a65991835eb5317733faa07b79e5
NeedsCompilation: yes
Title: A bioconductor package for high-dimensional exploration of
biological network heterogeneity
Description: Package nethet is an implementation of statistical solid
methodology enabling the analysis of network heterogeneity from
high-dimensional data. It combines several implementations of
recent statistical innovations useful for estimation and
comparison of networks in a heterogeneous, high-dimensional
setting. In particular, we provide code for formal two-sample
testing in Gaussian graphical models (differential network and
GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel
network-based clustering algorithm available (mixed graphical
lasso, Stadler and Mukherjee, 2013).
biocViews: Clustering, GraphAndNetwork
Author: Nicolas Staedler, Frank Dondelinger
Maintainer: Nicolas Staedler <staedler.n@gmail.com>, Frank Dondelinger
<fdondelinger.work@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/nethet
git_branch: RELEASE_3_13
git_last_commit: 18df2f1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/nethet_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/nethet_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/nethet_1.24.0.tgz
vignettes: vignettes/nethet/inst/doc/nethet.pdf
vignetteTitles: nethet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nethet/inst/doc/nethet.R
dependencyCount: 76
Package: NetPathMiner
Version: 1.28.0
Depends: R (>= 3.0.2), igraph (>= 1.0)
Suggests: rBiopaxParser (>= 2.1), RCurl, graph, knitr, rmarkdown,
BiocStyle
License: GPL (>= 2)
MD5sum: f761f7131a3cae1e81661db91c619aac
NeedsCompilation: yes
Title: NetPathMiner for Biological Network Construction, Path Mining
and Visualization
Description: NetPathMiner is a general framework for network path
mining using genome-scale networks. It constructs networks from
KGML, SBML and BioPAX files, providing three network
representations, metabolic, reaction and gene representations.
NetPathMiner finds active paths and applies machine learning
methods to summarize found paths for easy interpretation. It
also provides static and interactive visualizations of networks
and paths to aid manual investigation.
biocViews: GraphAndNetwork, Pathways, Network, Clustering,
Classification
Author: Ahmed Mohamed <mohamed@kuicr.kyoto-u.ac.jp>, Tim Hancock
<timothy.hancock@kuicr.kyoto-u.ac.jp>, Ichigaku Takigawa
<takigawa@kuicr.kyoto-u.ac.jp>, Nicolas Wicker
<nicolas.wicker@unistra.fr>
Maintainer: Ahmed Mohamed <mohamed@kuicr.kyoto-u.ac.jp>
URL: https://github.com/ahmohamed/NetPathMiner
SystemRequirements: libxml2, libSBML (>= 5.5)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NetPathMiner
git_branch: RELEASE_3_13
git_last_commit: a0182f7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NetPathMiner_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NetPathMiner_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NetPathMiner_1.28.0.tgz
vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.html
vignetteTitles: NetPathMiner Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R
dependencyCount: 11
Package: netprioR
Version: 1.18.0
Depends: methods, graphics, R(>= 3.3)
Imports: stats, Matrix, dplyr, doParallel, foreach, parallel,
sparseMVN, ggplot2, gridExtra, pROC
Suggests: knitr, BiocStyle, pander
License: GPL-3
MD5sum: 3ec9aa855ae9c857a604e68606e42da3
NeedsCompilation: no
Title: A model for network-based prioritisation of genes
Description: A model for semi-supervised prioritisation of genes
integrating network data, phenotypes and additional prior
knowledge about TP and TN gene labels from the literature or
experts.
biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Network
Author: Fabian Schmich
Maintainer: Fabian Schmich <fabian.schmich@bsse.ethz.ch>
URL: http://bioconductor.org/packages/netprioR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/netprioR
git_branch: RELEASE_3_13
git_last_commit: a54190f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/netprioR_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/netprioR_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/netprioR_1.18.0.tgz
vignettes: vignettes/netprioR/inst/doc/netprioR.html
vignetteTitles: netprioR Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/netprioR/inst/doc/netprioR.R
dependencyCount: 52
Package: netresponse
Version: 1.52.0
Depends: R (>= 2.15.1), Rgraphviz, methods, minet, mclust, reshape2
Imports: dmt, ggplot2, graph, igraph, parallel, plyr, qvalue,
RColorBrewer
Suggests: knitr
License: GPL (>=2)
MD5sum: 0904d4e993349ecb60c955f1bbe17ac6
NeedsCompilation: yes
Title: Functional Network Analysis
Description: Algorithms for functional network analysis. Includes an
implementation of a variational Dirichlet process Gaussian
mixture model for nonparametric mixture modeling.
biocViews: CellBiology, Clustering, GeneExpression, Genetics, Network,
GraphAndNetwork, DifferentialExpression, Microarray,
NetworkInference, Transcription
Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso
Parkkinen
Maintainer: Leo Lahti <leo.lahti@iki.fi>
URL: https://github.com/antagomir/netresponse
VignetteBuilder: knitr
BugReports: https://github.com/antagomir/netresponse/issues
git_url: https://git.bioconductor.org/packages/netresponse
git_branch: RELEASE_3_13
git_last_commit: 1ecd688
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/netresponse_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/netresponse_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/netresponse_1.52.0.tgz
vignettes: vignettes/netresponse/inst/doc/NetResponse.html
vignetteTitles: microbiome R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/netresponse/inst/doc/NetResponse.R
dependencyCount: 56
Package: NetSAM
Version: 1.32.0
Depends: R (>= 3.0.0), seriation (>= 1.0-6), igraph (>= 0.6-1), tools
(>= 3.0.0), WGCNA (>= 1.34.0), biomaRt (>= 2.18.0)
Imports: methods, AnnotationDbi (>= 1.28.0), doParallel (>= 1.0.10),
foreach (>= 1.4.0), survival (>= 2.37-7), GO.db (>= 2.10.0),
R2HTML (>= 2.2.0), DBI (>= 0.5-1)
Suggests: RUnit, BiocGenerics, org.Sc.sgd.db, org.Hs.eg.db,
org.Mm.eg.db, org.Rn.eg.db, org.Dr.eg.db, org.Ce.eg.db,
org.Cf.eg.db, org.Dm.eg.db, org.At.tair.db, rmarkdown, knitr,
markdown
License: LGPL
MD5sum: 988570c4be4b1a74c06d1e639451bcd7
NeedsCompilation: no
Title: Network Seriation And Modularization
Description: The NetSAM (Network Seriation and Modularization) package
takes an edge-list representation of a weighted or unweighted
network as an input, performs network seriation and
modularization analysis, and generates as files that can be
used as an input for the one-dimensional network visualization
tool NetGestalt (http://www.netgestalt.org) or other network
analysis. The NetSAM package can also generate correlation
network (e.g. co-expression network) based on the input matrix
data, perform seriation and modularization analysis for the
correlation network and calculate the associations between the
sample features and modules or identify the associated GO terms
for the modules.
Author: Jing Wang <jing.wang@bcm.edu>
Maintainer: Zhiao Shi <zhiao.shi@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NetSAM
git_branch: RELEASE_3_13
git_last_commit: f04aab5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NetSAM_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NetSAM_1.31.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NetSAM_1.32.0.tgz
vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf
vignetteTitles: NetSAM User Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R
dependencyCount: 131
Package: netSmooth
Version: 1.12.0
Depends: R (>= 3.5), scater (>= 1.15.11), clusterExperiment (>= 2.1.6)
Imports: entropy, SummarizedExperiment, SingleCellExperiment, Matrix,
cluster, data.table, stats, methods, DelayedArray, HDF5Array
(>= 1.15.13)
Suggests: knitr, testthat, Rtsne, biomaRt, igraph, STRINGdb, NMI,
pheatmap, ggplot2, BiocStyle, rmarkdown, BiocParallel, uwot
License: GPL-3
Archs: i386, x64
MD5sum: 881e6a90b66059f9349afc0d544c3191
NeedsCompilation: no
Title: Network smoothing for scRNAseq
Description: netSmooth is an R package for network smoothing of single
cell RNA sequencing data. Using bio networks such as
protein-protein interactions as priors for gene co-expression,
netsmooth improves cell type identification from noisy, sparse
scRNAseq data.
biocViews: Network, GraphAndNetwork, SingleCell, RNASeq,
GeneExpression, Sequencing, Transcriptomics, Normalization,
Preprocessing, Clustering, DimensionReduction
Author: Jonathan Ronen [aut, cre], Altuna Akalin [aut]
Maintainer: Jonathan Ronen <yablee@gmail.com>
URL: https://github.com/BIMSBbioinfo/netSmooth
VignetteBuilder: knitr
BugReports: https://github.com/BIMSBbioinfo/netSmooth/issues
git_url: https://git.bioconductor.org/packages/netSmooth
git_branch: RELEASE_3_13
git_last_commit: a9a910f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/netSmooth_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/netSmooth_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/netSmooth_1.12.0.tgz
vignettes: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.html,
vignettes/netSmooth/inst/doc/netSmoothIntro.html
vignetteTitles: Generation of PPI graph, netSmooth example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.R,
vignettes/netSmooth/inst/doc/netSmoothIntro.R
importsMe: netDx
dependencyCount: 166
Package: networkBMA
Version: 2.32.0
Depends: R (>= 2.15.0), stats, utils, BMA, Rcpp (>= 0.10.3),
RcppArmadillo (>= 0.3.810.2), RcppEigen (>= 0.3.1.2.1), leaps
LinkingTo: Rcpp, RcppArmadillo, RcppEigen, BH
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 45586773dea2ffd4bea066444605b042
NeedsCompilation: yes
Title: Regression-based network inference using Bayesian Model
Averaging
Description: An extension of Bayesian Model Averaging (BMA) for network
construction using time series gene expression data. Includes
assessment functions and sample test data.
biocViews: GraphsAndNetwork, NetworkInference, GeneExpression,
GeneTarget, Network, Bayesian
Author: Chris Fraley, Wm. Chad Young, Ling-Hong Hung, Kaiyuan Shi, Ka
Yee Yeung, Adrian Raftery (with contributions from Kenneth Lo)
Maintainer: Ka Yee Yeung <kayee@u.washington.edu>
SystemRequirements: liblapack-dev
git_url: https://git.bioconductor.org/packages/networkBMA
git_branch: RELEASE_3_13
git_last_commit: ed81202
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/networkBMA_2.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/networkBMA_2.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/networkBMA_2.32.0.tgz
vignettes: vignettes/networkBMA/inst/doc/networkBMA.pdf
vignetteTitles: networkBMA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/networkBMA/inst/doc/networkBMA.R
suggestsMe: DREAM4
dependencyCount: 23
Package: NewWave
Version: 1.2.0
Depends: R (>= 4.0), SummarizedExperiment
Imports: methods, SingleCellExperiment, parallel, irlba, Matrix,
DelayedArray, BiocSingular, SharedObject, stats
Suggests: testthat, rmarkdown, splatter, mclust, Rtsne, ggplot2, Rcpp,
BiocStyle, knitr
License: GPL-3
MD5sum: dbb57a947ee35a639fcb64eca15d23b7
NeedsCompilation: no
Title: Negative binomial model for scRNA-seq
Description: A model designed for dimensionality reduction and batch
effect removal for scRNA-seq data. It is designed to be
massively parallelizable using shared objects that prevent
memory duplication, and it can be used with different
mini-batch approaches in order to reduce time consumption. It
assumes a negative binomial distribution for the data with a
dispersion parameter that can be both commonwise across gene
both genewise.
biocViews: Software, GeneExpression, Transcriptomics, SingleCell,
BatchEffect, Sequencing, Coverage, Regression
Author: Federico Agostinis [aut, cre], Chiara Romualdi [aut], Gabriele
Sales [aut], Davide Risso [aut]
Maintainer: Federico Agostinis <federico.agostinis@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/fedeago/NewWave/issues
git_url: https://git.bioconductor.org/packages/NewWave
git_branch: RELEASE_3_13
git_last_commit: 43d1601
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NewWave_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NewWave_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NewWave_1.2.0.tgz
vignettes: vignettes/NewWave/inst/doc/vignette.html
vignetteTitles: vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NewWave/inst/doc/vignette.R
dependencyCount: 41
Package: ngsReports
Version: 1.8.1
Depends: R (>= 4.0.0), BiocGenerics, ggplot2, tibble (>= 1.3.1)
Imports: Biostrings, checkmate, dplyr (>= 1.0.0), DT, forcats,
ggdendro, grDevices (>= 3.6.0), grid, lifecycle, lubridate,
methods, pander, plotly (>= 4.9.4), readr, reshape2, rmarkdown,
scales, stats, stringr, tidyr, tidyselect (>= 0.2.3), utils,
zoo
Suggests: BiocStyle, Cairo, knitr, testthat, truncnorm
License: file LICENSE
Archs: i386, x64
MD5sum: acafec1041462ec57d4b9db4a22b5c7b
NeedsCompilation: no
Title: Load FastqQC reports and other NGS related files
Description: This package provides methods and object classes for
parsing FastQC reports and output summaries from other NGS
tools into R. As well as parsing files, multiple plotting
methods have been implemented for visualising the parsed data.
Plots can be generated as static ggplot objects or interactive
plotly objects.
biocViews: QualityControl, ReportWriting
Author: Steve Pederson [aut, cre], Christopher Ward [aut], Thu-Hien To
[aut]
Maintainer: Steve Pederson <stephen.pederson.au@gmail.com>
URL: https://github.com/steveped/ngsReports
VignetteBuilder: knitr
BugReports: https://github.com/steveped/ngsReports/issues
git_url: https://git.bioconductor.org/packages/ngsReports
git_branch: RELEASE_3_13
git_last_commit: 03a044d
git_last_commit_date: 2021-06-14
Date/Publication: 2021-06-15
source.ver: src/contrib/ngsReports_1.8.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ngsReports_1.8.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ngsReports_1.8.1.tgz
vignettes: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.html
vignetteTitles: An Introduction To ngsReports
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.R
dependencyCount: 104
Package: nnNorm
Version: 2.56.0
Depends: R(>= 2.2.0), marray
Imports: graphics, grDevices, marray, methods, nnet, stats
License: LGPL
MD5sum: 3cb9b83f65d37f7efe6fc667bdb61c1b
NeedsCompilation: no
Title: Spatial and intensity based normalization of cDNA microarray
data based on robust neural nets
Description: This package allows to detect and correct for spatial and
intensity biases with two-channel microarray data. The
normalization method implemented in this package is based on
robust neural networks fitting.
biocViews: Microarray, TwoChannel, Preprocessing
Author: Adi Laurentiu Tarca <atarca@med.wayne.edu>
Maintainer: Adi Laurentiu Tarca <atarca@med.wayne.edu>
URL: http://bioinformaticsprb.med.wayne.edu/tarca/
git_url: https://git.bioconductor.org/packages/nnNorm
git_branch: RELEASE_3_13
git_last_commit: 04838e4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/nnNorm_2.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/nnNorm_2.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/nnNorm_2.56.0.tgz
vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf
vignetteTitles: nnNorm Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nnNorm/inst/doc/nnNorm.R
dependencyCount: 8
Package: NOISeq
Version: 2.36.0
Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>=
3.0.1), Matrix (>= 1.2)
License: Artistic-2.0
MD5sum: 3ae8bd1557ccae72b5d191432a1cc8f4
NeedsCompilation: no
Title: Exploratory analysis and differential expression for RNA-seq
data
Description: Analysis of RNA-seq expression data or other similar kind
of data. Exploratory plots to evualuate saturation, count
distribution, expression per chromosome, type of detected
features, features length, etc. Differential expression between
two experimental conditions with no parametric assumptions.
biocViews: ImmunoOncology, RNASeq, DifferentialExpression,
Visualization, Sequencing
Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto
Ferrer and Ana Conesa
Maintainer: Sonia Tarazona <sotacam@eio.upv.es>
git_url: https://git.bioconductor.org/packages/NOISeq
git_branch: RELEASE_3_13
git_last_commit: c6b5b95
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NOISeq_2.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NOISeq_2.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NOISeq_2.36.0.tgz
vignettes: vignettes/NOISeq/inst/doc/NOISeq.pdf,
vignettes/NOISeq/inst/doc/QCreport.pdf
vignetteTitles: NOISeq User's Guide, QCreport.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NOISeq/inst/doc/NOISeq.R
dependsOnMe: metaSeq
importsMe: CNVPanelizer, ExpHunterSuite
suggestsMe: compcodeR
dependencyCount: 12
Package: nondetects
Version: 2.22.0
Depends: R (>= 3.2), Biobase (>= 2.22.0)
Imports: limma, mvtnorm, utils, methods, arm, HTqPCR (>= 1.16.0)
Suggests: knitr, rmarkdown, BiocStyle (>= 1.0.0), RUnit, BiocGenerics
(>= 0.8.0)
License: GPL-3
Archs: i386, x64
MD5sum: b1956e8a3b22b481bc9fcb01e69dc9d9
NeedsCompilation: no
Title: Non-detects in qPCR data
Description: Methods to model and impute non-detects in the results of
qPCR experiments.
biocViews: Software, AssayDomain, GeneExpression, Technology, qPCR,
WorkflowStep, Preprocessing
Author: Matthew N. McCall <mccallm@gmail.com>, Valeriia Sherina
<valery.sherina@gmail.com>
Maintainer: Valeriia Sherina <valery.sherina@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/nondetects
git_branch: RELEASE_3_13
git_last_commit: c5d66a7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/nondetects_2.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/nondetects_2.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/nondetects_2.22.0.tgz
vignettes: vignettes/nondetects/inst/doc/nondetects.html
vignetteTitles: Title of your vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nondetects/inst/doc/nondetects.R
dependencyCount: 38
Package: NoRCE
Version: 1.4.0
Depends: R (>= 4.0)
Imports: KEGGREST,png,dplyr,graphics,RSQLite,DBI,tidyr,grDevices,
S4Vectors,SummarizedExperiment,reactome.db,rWikiPathways,RCurl,
dbplyr,utils,ggplot2,igraph,stats,reshape2,readr,
GO.db,zlibbioc,
biomaRt,rtracklayer,IRanges,GenomicRanges,GenomicFeatures,AnnotationDbi
Suggests: knitr,
TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Drerio.UCSC.danRer10.refGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,TxDb.Dmelanogaster.UCSC.dm6.ensGene,
testthat,TxDb.Celegans.UCSC.ce11.refGene,rmarkdown,
TxDb.Rnorvegicus.UCSC.rn6.refGene,TxDb.Hsapiens.UCSC.hg19.knownGene,
org.Mm.eg.db,
org.Rn.eg.db,org.Hs.eg.db,org.Dr.eg.db,BiocGenerics,
org.Sc.sgd.db, org.Ce.eg.db,org.Dm.eg.db, methods,
License: MIT + file LICENSE
MD5sum: b07977581a6126fe92188375444e1967
NeedsCompilation: no
Title: NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment
Description: While some non-coding RNAs (ncRNAs) have been found to
play critical regulatory roles in biological processes, most
remain functionally uncharacterized. This presents a challenge
whenever an interesting set of ncRNAs set needs to be analyzed
in a functional context. Transcripts located close-by on the
genome are often regulated together, and this spatial proximity
hints at a functional association. Based on this idea, we
present an R package, NoRCE, that performs cis enrichment
analysis for a given set of ncRNAs. Enrichment is carried out
by using the functional annotations of the coding genes located
proximally to the input ncRNAs. NoRCE allows incorporating
other biological information such as the topologically
associating domain (TAD) regions, co-expression patterns, and
miRNA target information. NoRCE repository includes several
data files, such as cell line specific TAD regions, functional
gene sets, and cancer expression data. Additionally, users can
input custom data files. Results can be retrieved in a tabular
format or viewed as graphs. NoRCE is currently available for
the following species: human, mouse, rat, zebrafish, fruit fly,
worm and yeast.
biocViews: BiologicalQuestion, DifferentialExpression,
GenomeAnnotation, GeneSetEnrichment, GeneTarget,
GenomeAssembly, GO
Author: Gulden Olgun [aut, cre]
Maintainer: Gulden Olgun <gulden@cs.bilkent.edu.tr>
VignetteBuilder: knitr
BugReports: https://github.com/guldenolgun/NoRCE/issues
git_url: https://git.bioconductor.org/packages/NoRCE
git_branch: RELEASE_3_13
git_last_commit: 9cb604c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NoRCE_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NoRCE_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NoRCE_1.4.0.tgz
vignettes: vignettes/NoRCE/inst/doc/NoRCE.html
vignetteTitles: Noncoding RNA Set Cis Annotation and Enrichment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/NoRCE/inst/doc/NoRCE.R
dependencyCount: 123
Package: normalize450K
Version: 1.20.0
Depends: R (>= 3.3), Biobase, illuminaio, quadprog
Imports: utils
License: BSD_2_clause + file LICENSE
MD5sum: d30aa9f580e5d4882fef887ba69dadb3
NeedsCompilation: no
Title: Preprocessing of Illumina Infinium 450K data
Description: Precise measurements are important for epigenome-wide
studies investigating DNA methylation in whole blood samples,
where effect sizes are expected to be small in magnitude. The
450K platform is often affected by batch effects and proper
preprocessing is recommended. This package provides functions
to read and normalize 450K '.idat' files. The normalization
corrects for dye bias and biases related to signal intensity
and methylation of probes using local regression. No adjustment
for probe type bias is performed to avoid the trade-off of
precision for accuracy of beta-values.
biocViews: Normalization, DNAMethylation, Microarray, TwoChannel,
Preprocessing, MethylationArray
Author: Jonathan Alexander Heiss
Maintainer: Jonathan Alexander Heiss <jonathan.heiss@posteo.de>
git_url: https://git.bioconductor.org/packages/normalize450K
git_branch: RELEASE_3_13
git_last_commit: 58b8a2c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/normalize450K_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/normalize450K_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/normalize450K_1.20.0.tgz
vignettes: vignettes/normalize450K/inst/doc/read_and_normalize450K.pdf
vignetteTitles: Normalization of 450K data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/normalize450K/inst/doc/read_and_normalize450K.R
dependencyCount: 13
Package: NormalyzerDE
Version: 1.10.0
Depends: R (>= 3.6)
Imports: vsn, preprocessCore, limma, MASS, ape, car, ggplot2, methods,
Biobase, RcmdrMisc, raster, utils, stats, SummarizedExperiment,
matrixStats, ggforce
Suggests: knitr, testthat, rmarkdown, roxygen2, hexbin, BiocStyle
License: Artistic-2.0
MD5sum: 86155e63d6599b6bdf949635765c4aeb
NeedsCompilation: no
Title: Evaluation of normalization methods and calculation of
differential expression analysis statistics
Description: NormalyzerDE provides screening of normalization methods
for LC-MS based expression data. It calculates a range of
normalized matrices using both existing approaches and a novel
time-segmented approach, calculates performance measures and
generates an evaluation report. Furthermore, it provides an
easy utility for Limma- or ANOVA- based differential expression
analysis.
biocViews: Normalization, MultipleComparison, Visualization, Bayesian,
Proteomics, Metabolomics, DifferentialExpression
Author: Jakob Willforss
Maintainer: Jakob Willforss <jakob.willforss@hotmail.com>
URL: https://github.com/ComputationalProteomics/NormalyzerDE
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NormalyzerDE
git_branch: RELEASE_3_13
git_last_commit: 94eb4bc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NormalyzerDE_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NormalyzerDE_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NormalyzerDE_1.10.0.tgz
vignettes: vignettes/NormalyzerDE/inst/doc/vignette.pdf
vignetteTitles: Differential expression and countering technical biases
using NormalyzerDE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NormalyzerDE/inst/doc/vignette.R
dependencyCount: 148
Package: NormqPCR
Version: 1.38.0
Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR,
qpcR
License: LGPL-3
MD5sum: d7f4c619221c55677729e0fbd5030717
NeedsCompilation: no
Title: Functions for normalisation of RT-qPCR data
Description: Functions for the selection of optimal reference genes and
the normalisation of real-time quantitative PCR data.
biocViews: MicrotitrePlateAssay, GeneExpression, qPCR
Author: Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman
Maintainer: James Perkins <jimrperkins@gmail.com>
URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html
git_url: https://git.bioconductor.org/packages/NormqPCR
git_branch: RELEASE_3_13
git_last_commit: e4655ed
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NormqPCR_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NormqPCR_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NormqPCR_1.38.0.tgz
vignettes: vignettes/NormqPCR/inst/doc/NormqPCR.pdf
vignetteTitles: NormqPCR: Functions for normalisation of RT-qPCR data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NormqPCR/inst/doc/NormqPCR.R
dependencyCount: 39
Package: normr
Version: 1.18.1
Depends: R (>= 3.3.0)
Imports: methods, stats, utils, grDevices, parallel, GenomeInfoDb,
GenomicRanges, IRanges, Rcpp (>= 0.11), qvalue (>= 2.2),
bamsignals (>= 1.4), rtracklayer (>= 1.32)
LinkingTo: Rcpp
Suggests: BiocStyle, testthat (>= 1.0), knitr, rmarkdown
Enhances: BiocParallel
License: GPL-2
MD5sum: 75c3e1fab71e24d0e616688e11daeba0
NeedsCompilation: yes
Title: Normalization and difference calling in ChIP-seq data
Description: Robust normalization and difference calling procedures for
ChIP-seq and alike data. Read counts are modeled jointly as a
binomial mixture model with a user-specified number of
components. A fitted background estimate accounts for the
effect of enrichment in certain regions and, therefore,
represents an appropriate null hypothesis. This robust
background is used to identify significantly enriched or
depleted regions.
biocViews: Bayesian, DifferentialPeakCalling, Classification,
DataImport, ChIPSeq, RIPSeq, FunctionalGenomics, Genetics,
MultipleComparison, Normalization, PeakDetection,
Preprocessing, Alignment
Author: Johannes Helmuth [aut, cre], Ho-Ryun Chung [aut]
Maintainer: Johannes Helmuth <johannes.helmuth@laborberlin.com>
URL: https://github.com/your-highness/normR
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/your-highness/normR/issues
git_url: https://git.bioconductor.org/packages/normr
git_branch: RELEASE_3_13
git_last_commit: f1d3f26
git_last_commit_date: 2021-09-20
Date/Publication: 2021-09-21
source.ver: src/contrib/normr_1.18.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/normr_1.18.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/normr_1.18.1.tgz
vignettes: vignettes/normr/inst/doc/normr.html
vignetteTitles: Introduction to the normR package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/normr/inst/doc/normr.R
dependencyCount: 80
Package: NPARC
Version: 1.4.0
Depends: R (>= 4.0.0)
Imports: dplyr, tidyr, BiocParallel, broom, MASS, rlang, magrittr,
stats, methods
Suggests: testthat, devtools, knitr, rprojroot, rmarkdown, ggplot2,
BiocStyle
License: GPL-3
MD5sum: 1251c81906d35fd5e27b976279d6b915
NeedsCompilation: no
Title: Non-parametric analysis of response curves for thermal proteome
profiling experiments
Description: Perform non-parametric analysis of response curves as
described by Childs, Bach, Franken et al. (2019):
Non-parametric analysis of thermal proteome profiles reveals
novel drug-binding proteins.
biocViews: Software, Proteomics
Author: Dorothee Childs, Nils Kurzawa
Maintainer: Nils Kurzawa <nils.kurzawa@embl.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NPARC
git_branch: RELEASE_3_13
git_last_commit: 12d4ac2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NPARC_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NPARC_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NPARC_1.4.0.tgz
vignettes: vignettes/NPARC/inst/doc/NPARC.html
vignetteTitles: Analysing thermal proteome profiling data with the
NPARC package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NPARC/inst/doc/NPARC.R
dependencyCount: 39
Package: npGSEA
Version: 1.28.0
Depends: GSEABase (>= 1.24.0)
Imports: Biobase, methods, BiocGenerics, graphics, stats
Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools,
BiocStyle
License: Artistic-2.0
MD5sum: ffc04cb096f2b707552eee439ec1fc36
NeedsCompilation: no
Title: Permutation approximation methods for gene set enrichment
analysis (non-permutation GSEA)
Description: Current gene set enrichment methods rely upon permutations
for inference. These approaches are computationally expensive
and have minimum achievable p-values based on the number of
permutations, not on the actual observed statistics. We have
derived three parametric approximations to the permutation
distributions of two gene set enrichment test statistics. We
are able to reduce the computational burden and granularity
issues of permutation testing with our method, which is
implemented in this package. npGSEA calculates gene set
enrichment statistics and p-values without the computational
cost of permutations. It is applicable in settings where one
or many gene sets are of interest. There are also built-in
plotting functions to help users visualize results.
biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways
Author: Jessica Larson and Art Owen
Maintainer: Jessica Larson <larson.jess@gmail.com>
git_url: https://git.bioconductor.org/packages/npGSEA
git_branch: RELEASE_3_13
git_last_commit: 7a10483
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/npGSEA_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/npGSEA_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/npGSEA_1.28.0.tgz
vignettes: vignettes/npGSEA/inst/doc/npGSEA.pdf
vignetteTitles: Running gene set enrichment analysis with the "npGSEA"
package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/npGSEA/inst/doc/npGSEA.R
dependencyCount: 51
Package: NTW
Version: 1.42.0
Depends: R (>= 2.3.0)
Imports: mvtnorm, stats, utils
License: GPL-2
MD5sum: 23f308495dfec2ef214b0e6bec7687de
NeedsCompilation: no
Title: Predict gene network using an Ordinary Differential Equation
(ODE) based method
Description: This package predicts the gene-gene interaction network
and identifies the direct transcriptional targets of the
perturbation using an ODE (Ordinary Differential Equation)
based method.
biocViews: Preprocessing
Author: Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang, Yuanhua Liu,
Christine Nardini
Maintainer: Yuanhua Liu <liuyuanhua@picb.ac.cn>
git_url: https://git.bioconductor.org/packages/NTW
git_branch: RELEASE_3_13
git_last_commit: 9529f61
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NTW_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NTW_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NTW_1.42.0.tgz
vignettes: vignettes/NTW/inst/doc/NTW.pdf
vignetteTitles: NTW vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NTW/inst/doc/NTW.R
dependencyCount: 4
Package: nucleoSim
Version: 1.20.0
Imports: stats, IRanges, S4Vectors, graphics, methods
Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit
License: Artistic-2.0
MD5sum: af8c4691d5ae27929c27c8a92b8732f3
NeedsCompilation: no
Title: Generate synthetic nucleosome maps
Description: This package can generate a synthetic map with reads
covering the nucleosome regions as well as a synthetic map with
forward and reverse reads emulating next-generation sequencing.
The user has choice between three different distributions for
the read positioning: Normal, Student and Uniform.
biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment
Author: Rawane Samb [aut], Astrid Deschênes [cre, aut], Pascal Belleau
[aut], Arnaud Droit [aut]
Maintainer: Astrid Deschenes <adeschen@hotmail.com>
URL: https://github.com/arnauddroitlab/nucleoSim
VignetteBuilder: knitr
BugReports: https://github.com/arnauddroitlab/nucleoSim/issues
git_url: https://git.bioconductor.org/packages/nucleoSim
git_branch: RELEASE_3_13
git_last_commit: c1bfc38
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/nucleoSim_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/nucleoSim_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/nucleoSim_1.20.0.tgz
vignettes: vignettes/nucleoSim/inst/doc/nucleoSim.html
vignetteTitles: Generate synthetic nucleosome maps
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R
suggestsMe: RJMCMCNucleosomes
dependencyCount: 9
Package: nucleR
Version: 2.24.0
Depends: methods
Imports: Biobase, BiocGenerics, Biostrings, GenomeInfoDb,
GenomicRanges, IRanges, Rsamtools, S4Vectors, ShortRead, dplyr,
ggplot2, magrittr, parallel, stats, utils, grDevices
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: LGPL (>= 3)
Archs: i386, x64
MD5sum: 29b5cf44d2ca4dce33769126292bad12
NeedsCompilation: no
Title: Nucleosome positioning package for R
Description: Nucleosome positioning for Tiling Arrays and NGS
experiments.
biocViews: NucleosomePositioning, Coverage, ChIPSeq, Microarray,
Sequencing, Genetics, QualityControl, DataImport
Author: Oscar Flores, Ricard Illa
Maintainer: Alba Sala <alba.sala@irbbarcelona.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/nucleR
git_branch: RELEASE_3_13
git_last_commit: b7e30e8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/nucleR_2.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/nucleR_2.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/nucleR_2.24.0.tgz
vignettes: vignettes/nucleR/inst/doc/nucleR.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nucleR/inst/doc/nucleR.R
dependencyCount: 76
Package: nuCpos
Version: 1.10.0
Depends: R (>= 3.6)
Imports: graphics, methods
Suggests: NuPoP, Biostrings, testthat
License: file LICENSE
MD5sum: a72a0d1b3f5388b1035671f7cba282f6
NeedsCompilation: yes
Title: An R package for prediction of nucleosome positions
Description: nuCpos, a derivative of NuPoP, is an R package for
prediction of nucleosome positions. In nuCpos, a duration
hidden Markov model is trained with a chemical map of
nucleosomes either from budding yeast, fission yeast, or mouse
embryonic stem cells. nuCpos outputs the Viterbi (most
probable) path of nucleosome-linker states, predicted
nucleosome occupancy scores and histone binding affinity (HBA)
scores as NuPoP does. nuCpos can also calculate local and whole
nucleosomal HBA scores for a given 147-bp sequence.
Furthermore, effect of genetic alterations on nucleosome
occupancy can be predicted with this package. The parental
package NuPoP, which is based on an MNase-seq-based map of
budding yeast nucleosomes, was developed by Ji-Ping Wang and
Liqun Xi, licensed under GPL-2.
biocViews: Genetics, Epigenetics, NucleosomePositioning,
HiddenMarkovModel, ImmunoOncology
Author: Hiroaki Kato, Takeshi Urano
Maintainer: Hiroaki Kato <hkato@med.shimane-u.ac.jp>
git_url: https://git.bioconductor.org/packages/nuCpos
git_branch: RELEASE_3_13
git_last_commit: 64a4e71
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/nuCpos_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/nuCpos_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/nuCpos_1.10.0.tgz
vignettes: vignettes/nuCpos/inst/doc/nuCpos-intro.pdf
vignetteTitles: An R package for prediction of nucleosome positioning
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/nuCpos/inst/doc/nuCpos-intro.R
dependencyCount: 2
Package: NuPoP
Version: 2.0.0
Depends: R (>= 4.0)
Imports: graphics, utils
Suggests: knitr, rmarkdown
License: GPL-2
MD5sum: 68f53196074dd446359a7747b2fc3fd4
NeedsCompilation: yes
Title: An R package for nucleosome positioning prediction
Description: NuPoP is an R package for Nucleosome Positioning
Prediction.This package is built upon a duration hidden Markov
model proposed in Xi et al, 2010; Wang et al, 2008. The core of
the package was written in Fotran. In addition to the R
package, a stand-alone Fortran software tool is also available
at http://nucleosome.stats.northwestern.edu.
biocViews: Genetics,Visualization,Classification,NucleosomePositioning,
HiddenMarkovModel
Author: Ji-Ping Wang <jzwang@northwestern.edu>; Liqun Xi
<liqunxi02@gmail.com>; Oscar Zarate <zarate@u.northwestern.edu>
Maintainer: Ji-Ping Wang<jzwang@northwestern.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NuPoP
git_branch: RELEASE_3_13
git_last_commit: 57cc4b6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/NuPoP_2.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/NuPoP_2.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/NuPoP_2.0.0.tgz
vignettes: vignettes/NuPoP/inst/doc/NuPoP.html
vignetteTitles: NuPoP
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NuPoP/inst/doc/NuPoP.R
suggestsMe: nuCpos
dependencyCount: 2
Package: occugene
Version: 1.52.0
Depends: R (>= 2.0.0)
License: GPL (>= 2)
MD5sum: 63e44f79bc6add1108561b0aa33c9416
NeedsCompilation: no
Title: Functions for Multinomial Occupancy Distribution
Description: Statistical tools for building random mutagenesis
libraries for prokaryotes. The package has functions for
handling the occupancy distribution for a multinomial and for
estimating the number of essential genes in random transposon
mutagenesis libraries.
biocViews: Annotation, Pathways
Author: Oliver Will <oliverrreader@gmail.com>
Maintainer: Oliver Will <oliverrreader@gmail.com>
git_url: https://git.bioconductor.org/packages/occugene
git_branch: RELEASE_3_13
git_last_commit: 9b6ea7f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/occugene_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/occugene_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/occugene_1.52.0.tgz
vignettes: vignettes/occugene/inst/doc/occugene.pdf
vignetteTitles: occugene
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/occugene/inst/doc/occugene.R
dependencyCount: 0
Package: OCplus
Version: 1.66.0
Depends: R (>= 2.1.0)
Imports: multtest (>= 1.7.3), graphics, grDevices, stats, akima
License: LGPL
MD5sum: a38495ffd1e4794bf97e42836c568c6b
NeedsCompilation: no
Title: Operating characteristics plus sample size and local fdr for
microarray experiments
Description: This package allows to characterize the operating
characteristics of a microarray experiment, i.e. the trade-off
between false discovery rate and the power to detect truly
regulated genes. The package includes tools both for planned
experiments (for sample size assessment) and for already
collected data (identification of differentially expressed
genes).
biocViews: Microarray, DifferentialExpression, MultipleComparison
Author: Yudi Pawitan <Yudi.Pawitan@ki.se> and Alexander Ploner
<Alexander.Ploner@ki.se>
Maintainer: Alexander Ploner <Alexander.Ploner@ki.se>
git_url: https://git.bioconductor.org/packages/OCplus
git_branch: RELEASE_3_13
git_last_commit: 9beeb38
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OCplus_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OCplus_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OCplus_1.66.0.tgz
vignettes: vignettes/OCplus/inst/doc/OCplus.pdf
vignetteTitles: OCplus Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OCplus/inst/doc/OCplus.R
dependencyCount: 18
Package: odseq
Version: 1.20.0
Depends: R (>= 3.2.3)
Imports: msa (>= 1.2.1), kebabs (>= 1.4.1), mclust (>= 5.1)
Suggests: knitr(>= 1.11)
License: MIT + file LICENSE
MD5sum: f5a3291473a0fe15ef9c8a0089042877
NeedsCompilation: no
Title: Outlier detection in multiple sequence alignments
Description: Performs outlier detection of sequences in a multiple
sequence alignment using bootstrap of predefined distance
metrics. Outlier sequences can make downstream analyses
unreliable or make the alignments less accurate while they are
being constructed. This package implements the OD-seq algorithm
proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for
aligned sequences and a variant using string kernels for
unaligned sequences.
biocViews: Alignment, MultipleSequenceAlignment
Author: José Jiménez
Maintainer: José Jiménez <jose@jimenezluna.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/odseq
git_branch: RELEASE_3_13
git_last_commit: a873deb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/odseq_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/odseq_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/odseq_1.20.0.tgz
vignettes: vignettes/odseq/inst/doc/vignette.pdf
vignetteTitles: A quick tutorial to outlier detection in MSAs
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/odseq/inst/doc/vignette.R
dependencyCount: 33
Package: oligo
Version: 1.56.0
Depends: R (>= 3.2.0), BiocGenerics (>= 0.13.11), oligoClasses (>=
1.29.6), Biobase (>= 2.27.3), Biostrings (>= 2.35.12)
Imports: affyio (>= 1.35.0), affxparser (>= 1.39.4), DBI (>= 0.3.1),
ff, graphics, methods, preprocessCore (>= 1.29.0), RSQLite (>=
1.0.0), splines, stats, stats4, utils, zlibbioc
LinkingTo: preprocessCore
Suggests: BSgenome.Hsapiens.UCSC.hg18, hapmap100kxba, pd.hg.u95av2,
pd.mapping50k.xba240, pd.huex.1.0.st.v2, pd.hg18.60mer.expr,
pd.hugene.1.0.st.v1, maqcExpression4plex, genefilter, limma,
RColorBrewer, oligoData, BiocStyle, knitr, RUnit, biomaRt,
AnnotationDbi, ACME, RCurl
Enhances: doMC, doMPI
License: LGPL (>= 2)
Archs: i386, x64
MD5sum: 369448b2fc10bad63fa63e145c4a7100
NeedsCompilation: yes
Title: Preprocessing tools for oligonucleotide arrays
Description: A package to analyze oligonucleotide arrays
(expression/SNP/tiling/exon) at probe-level. It currently
supports Affymetrix (CEL files) and NimbleGen arrays (XYS
files).
biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, SNP,
DifferentialExpression, ExonArray, GeneExpression, DataImport
Author: Benilton Carvalho and Rafael Irizarry
Maintainer: Benilton Carvalho <benilton@unicamp.br>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/oligo
git_branch: RELEASE_3_13
git_last_commit: b3b6d7e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/oligo_1.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/oligo_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/oligo_1.56.0.tgz
vignettes: vignettes/oligo/inst/doc/oug.pdf
vignetteTitles: oligo User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: ITALICS, pdInfoBuilder, puma, SCAN.UPC, oligoData,
pd.081229.hg18.promoter.medip.hx1,
pd.2006.07.18.hg18.refseq.promoter,
pd.2006.07.18.mm8.refseq.promoter,
pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st,
pd.aragene.1.1.st, pd.ath1.121501, pd.barley1,
pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis,
pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2,
pd.celegans, pd.charm.hg18.example, pd.chicken,
pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st,
pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human,
pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse,
pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht,
pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st,
pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array,
pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1,
pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2,
pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st,
pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1,
pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st,
pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5,
pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st,
pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a,
pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219,
pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d,
pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm,
pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800,
pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1,
pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize,
pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240,
pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st,
pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a,
pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c,
pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0,
pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b,
pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1,
pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2,
pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb,
pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st,
pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles,
pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st,
pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b,
pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1,
pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2,
pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b,
pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus,
pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34,
pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus,
pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st,
pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera,
pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis,
pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st,
pd.zebrafish, pd.atdschip.tiling, pumadata, maEndToEnd
importsMe: ArrayExpress, cn.farms, crossmeta, frma, ITALICS, mimager
suggestsMe: fastseg, frmaTools, hapmap100khind, hapmap100kxba,
hapmap500knsp, hapmap500ksty, hapmapsnp5, hapmapsnp6,
maqcExpression4plex, aroma.affymetrix, maGUI
dependencyCount: 53
Package: oligoClasses
Version: 1.54.0
Depends: R (>= 2.14)
Imports: BiocGenerics (>= 0.27.1), Biobase (>= 2.17.8), methods,
graphics, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7),
SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>=
1.23.2), foreach, BiocManager, utils, S4Vectors (>= 0.9.25),
RSQLite, DBI, ff
Suggests: hapmapsnp5, hapmapsnp6, pd.genomewidesnp.6,
pd.genomewidesnp.5, pd.mapping50k.hind240,
pd.mapping50k.xba240, pd.mapping250k.sty, pd.mapping250k.nsp,
genomewidesnp6Crlmm (>= 1.0.7), genomewidesnp5Crlmm (>= 1.0.6),
RUnit, human370v1cCrlmm, VanillaICE, crlmm
Enhances: doMC, doMPI, doSNOW, doParallel, doRedis
License: GPL (>= 2)
Archs: i386, x64
MD5sum: bb1bdbf80c8122a35ddd3ef1bc837ddd
NeedsCompilation: no
Title: Classes for high-throughput arrays supported by oligo and crlmm
Description: This package contains class definitions, validity checks,
and initialization methods for classes used by the oligo and
crlmm packages.
biocViews: Infrastructure
Author: Benilton Carvalho and Robert Scharpf
Maintainer: Benilton Carvalho <beniltoncarvalho@gmail.com> and Robert
Scharpf <rscharpf@jhsph.edu>
git_url: https://git.bioconductor.org/packages/oligoClasses
git_branch: RELEASE_3_13
git_last_commit: 1b919e6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/oligoClasses_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/oligoClasses_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/oligoClasses_1.54.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma,
pd.081229.hg18.promoter.medip.hx1,
pd.2006.07.18.hg18.refseq.promoter,
pd.2006.07.18.mm8.refseq.promoter,
pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st,
pd.aragene.1.1.st, pd.ath1.121501, pd.barley1,
pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis,
pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2,
pd.celegans, pd.charm.hg18.example, pd.chicken,
pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st,
pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human,
pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse,
pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht,
pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st,
pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array,
pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1,
pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2,
pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st,
pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1,
pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st,
pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5,
pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st,
pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a,
pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219,
pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d,
pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm,
pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800,
pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1,
pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize,
pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240,
pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st,
pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a,
pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c,
pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0,
pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b,
pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1,
pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2,
pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb,
pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st,
pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles,
pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st,
pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b,
pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1,
pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2,
pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b,
pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus,
pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34,
pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus,
pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st,
pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera,
pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis,
pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st,
pd.zebrafish, pd.atdschip.tiling, maEndToEnd
importsMe: affycoretools, frma, ITALICS, mimager, MinimumDistance,
pdInfoBuilder, puma, VanillaICE
suggestsMe: hapmapsnp6, aroma.affymetrix, scrime
dependencyCount: 49
Package: OLIN
Version: 1.70.0
Depends: R (>= 2.10), methods, locfit, marray
Imports: graphics, grDevices, limma, marray, methods, stats
Suggests: convert
License: GPL-2
MD5sum: b1737ab8a410af8d8c5a25a0d269df71
NeedsCompilation: no
Title: Optimized local intensity-dependent normalisation of two-color
microarrays
Description: Functions for normalisation of two-color microarrays by
optimised local regression and for detection of artefacts in
microarray data
biocViews: Microarray, TwoChannel, QualityControl, Preprocessing,
Visualization
Author: Matthias Futschik <mfutschik@ualg.pt>
Maintainer: Matthias Futschik <mfutschik@ualg.pt>
URL: http://olin.sysbiolab.eu
git_url: https://git.bioconductor.org/packages/OLIN
git_branch: RELEASE_3_13
git_last_commit: 2be671b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OLIN_1.70.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OLIN_1.70.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OLIN_1.70.0.tgz
vignettes: vignettes/OLIN/inst/doc/OLIN.pdf
vignetteTitles: Introduction to OLIN
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OLIN/inst/doc/OLIN.R
dependsOnMe: OLINgui
importsMe: OLINgui
suggestsMe: maigesPack
dependencyCount: 10
Package: OLINgui
Version: 1.66.0
Depends: R (>= 2.0.0), OLIN (>= 1.4.0)
Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools
License: GPL-2
MD5sum: d1b9fc8bd3e40f0b4b28841c8489b57d
NeedsCompilation: no
Title: Graphical user interface for OLIN
Description: Graphical user interface for the OLIN package
biocViews: Microarray, TwoChannel, QualityControl, Preprocessing,
Visualization
Author: Matthias Futschik <mfutschik@ualg.pt>
Maintainer: Matthias Futschik <mfutschik@ualg.pt>
URL: http://olin.sysbiolab.eu
git_url: https://git.bioconductor.org/packages/OLINgui
git_branch: RELEASE_3_13
git_last_commit: 62bd0d2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OLINgui_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OLINgui_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OLINgui_1.66.0.tgz
vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf
vignetteTitles: Introduction to OLINgui
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OLINgui/inst/doc/OLINgui.R
dependencyCount: 16
Package: OmaDB
Version: 2.8.0
Depends: R (>= 3.5), httr (>= 1.2.1), plyr(>= 1.8.4)
Imports: utils, ape, Biostrings, GenomicRanges, IRanges, methods,
topGO, jsonlite
Suggests: knitr, rmarkdown, testthat
License: GPL-3
MD5sum: 3dcad35db8aace83e86a341a26c4b157
NeedsCompilation: no
Title: R wrapper for the OMA REST API
Description: A package for the orthology prediction data download from
OMA database.
biocViews: Software, ComparativeGenomics, FunctionalGenomics, Genetics,
Annotation, GO, FunctionalPrediction
Author: Klara Kaleb
Maintainer: Klara Kaleb <klara.kaleb18@ic.ac.uk>, Adrian Altenhoff
<adrian.altenhoff@inf.ethz.ch>
URL: https://github.com/DessimozLab/OmaDB
VignetteBuilder: knitr
BugReports: https://github.com/DessimozLab/OmaDB/issues
git_url: https://git.bioconductor.org/packages/OmaDB
git_branch: RELEASE_3_13
git_last_commit: 73c1e16
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OmaDB_2.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OmaDB_2.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OmaDB_2.8.0.tgz
vignettes: vignettes/OmaDB/inst/doc/exploring_hogs.html,
vignettes/OmaDB/inst/doc/OmaDB.html,
vignettes/OmaDB/inst/doc/sequence_mapping.html,
vignettes/OmaDB/inst/doc/tree_visualisation.html
vignetteTitles: Exploring Hierarchical orthologous groups with OmaDB,
Get started with OmaDB, Sequence Mapping with OmaDB, Exploring
Taxonomic trees with OmaDB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OmaDB/inst/doc/exploring_hogs.R,
vignettes/OmaDB/inst/doc/OmaDB.R,
vignettes/OmaDB/inst/doc/sequence_mapping.R,
vignettes/OmaDB/inst/doc/tree_visualisation.R
importsMe: PhyloProfile
dependencyCount: 57
Package: omicade4
Version: 1.32.0
Depends: R (>= 3.0.0), ade4
Imports: made4, Biobase
Suggests: BiocStyle
License: GPL-2
Archs: i386, x64
MD5sum: 393a77bf40a5e589c5e508c3c88c4e52
NeedsCompilation: no
Title: Multiple co-inertia analysis of omics datasets
Description: This package performes multiple co-inertia analysis of
omics datasets.
biocViews: Software, Clustering, Classification, MultipleComparison
Author: Chen Meng, Aedin Culhane, Amin M. Gholami.
Maintainer: Chen Meng <mengchen18@gmail.com>
git_url: https://git.bioconductor.org/packages/omicade4
git_branch: RELEASE_3_13
git_last_commit: d2d06c2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/omicade4_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/omicade4_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/omicade4_1.32.0.tgz
vignettes: vignettes/omicade4/inst/doc/omicade4.pdf
vignetteTitles: Using omicade4
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/omicade4/inst/doc/omicade4.R
importsMe: omicRexposome
suggestsMe: biosigner, MultiDataSet, ropls
dependencyCount: 37
Package: OmicCircos
Version: 1.30.0
Depends: R (>= 2.14.0), methods,GenomicRanges
License: GPL-2
MD5sum: 87844527cfdccc109532c8060f2b8d74
NeedsCompilation: no
Title: High-quality circular visualization of omics data
Description: OmicCircos is an R application and package for generating
high-quality circular plots for omics data.
biocViews: Visualization,Statistics,Annotation
Author: Ying Hu <yhu@mail.nih.gov> Chunhua Yan <yanch@mail.nih.gov>
Maintainer: Ying Hu <yhu@mail.nih.gov>
git_url: https://git.bioconductor.org/packages/OmicCircos
git_branch: RELEASE_3_13
git_last_commit: 656dd6a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OmicCircos_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OmicCircos_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OmicCircos_1.30.0.tgz
vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf
vignetteTitles: OmicCircos vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.R
dependencyCount: 17
Package: omicplotR
Version: 1.12.0
Depends: R (>= 3.6), ALDEx2 (>= 1.18.0)
Imports: compositions, DT, grDevices, knitr, jsonlite, matrixStats,
rmarkdown, shiny, stats, vegan, zCompositions
License: MIT + file LICENSE
MD5sum: 633357be9087fdcaca7953b7b555cb36
NeedsCompilation: no
Title: Visual Exploration of Omic Datasets Using a Shiny App
Description: A Shiny app for visual exploration of omic datasets as
compositions, and differential abundance analysis using ALDEx2.
Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene
sequencing with visualizations such as principal component
analysis biplots (coloured using metadata for visualizing each
variable), dendrograms and stacked bar plots, and effect plots
(ALDEx2). Input is a table of counts and metadata file (if
metadata exists), with options to filter data by count or by
metadata to remove low counts, or to visualize select samples
according to selected metadata.
biocViews: Software, DifferentialExpression, GeneExpression, GUI,
RNASeq, DNASeq, Metagenomics, Transcriptomics, Bayesian,
Microbiome, Visualization, Sequencing, ImmunoOncology
Author: Daniel Giguere [aut, cre], Jean Macklaim [aut], Greg Gloor
[aut]
Maintainer: Daniel Giguere <dgiguer@uwo.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/omicplotR
git_branch: RELEASE_3_13
git_last_commit: b2013f0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/omicplotR_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/omicplotR_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/omicplotR_1.12.0.tgz
vignettes: vignettes/omicplotR/inst/doc/omicplotR.html
vignetteTitles: omicplotR: A tool for visualization of omic datasets as
compositions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/omicplotR/inst/doc/omicplotR.R
dependencyCount: 97
Package: omicRexposome
Version: 1.14.0
Depends: R (>= 3.4), Biobase
Imports: stats, utils, grDevices, graphics, methods, rexposome, limma,
sva, ggplot2, ggrepel, PMA, omicade4, gridExtra, MultiDataSet,
SmartSVA, isva, parallel, SummarizedExperiment, stringr
Suggests: BiocStyle, knitr, rmarkdown, snpStats, brgedata
License: MIT + file LICENSE
MD5sum: d187d9f9ec02094559eff5f464ae9671
NeedsCompilation: no
Title: Exposome and omic data associatin and integration analysis
Description: omicRexposome systematizes the association evaluation
between exposures and omic data, taking advantage of
MultiDataSet for coordinated data management, rexposome for
exposome data definition and limma for association testing.
Also to perform data integration mixing exposome and omic data
using multi co-inherent analysis (omicade4) and multi-canonical
correlation analysis (PMA).
biocViews: ImmunoOncology, WorkflowStep, MultipleComparison,
Visualization, GeneExpression, DifferentialExpression,
DifferentialMethylation, GeneRegulation, Epigenetics,
Proteomics, Transcriptomics, StatisticalMethod, Regression
Author: Carles Hernandez-Ferrer [aut, cre], Juan R. González [aut]
Maintainer: Xavier Escribà Montagut <xavier.escriba@isglobal.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/omicRexposome
git_branch: RELEASE_3_13
git_last_commit: f490520
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/omicRexposome_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/omicRexposome_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/omicRexposome_1.14.0.tgz
vignettes:
vignettes/omicRexposome/inst/doc/exposome_omic_integration.html
vignetteTitles: Exposome Data Integration with Omic Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/omicRexposome/inst/doc/exposome_omic_integration.R
dependencyCount: 212
Package: OmicsLonDA
Version: 1.8.0
Depends: R(>= 3.6)
Imports: SummarizedExperiment, gss, plyr, zoo, pracma, ggplot2,
BiocParallel, parallel, grDevices, graphics, stats, utils,
methods, BiocGenerics
Suggests: knitr, rmarkdown, testthat, devtools, BiocManager
License: MIT + file LICENSE
MD5sum: e1e5654ade862908e3ae6175bbf41691
NeedsCompilation: no
Title: Omics Longitudinal Differential Analysis
Description: Statistical method that provides robust identification of
time intervals where omics features (such as proteomics,
lipidomics, metabolomics, transcriptomics, microbiome, as well
as physiological parameters captured by wearable sensors such
as heart rhythm, body temperature, and activity level) are
significantly different between groups.
biocViews: TimeCourse, Survival, Microbiome, Metabolomics, Proteomics,
Lipidomics, Transcriptomics, Regression
Author: Ahmed A. Metwally, Tom Zhang, Michael Snyder
Maintainer: Ahmed A. Metwally <ametwall@stanford.edu>
URL: https://github.com/aametwally/OmicsLonDA
VignetteBuilder: knitr
BugReports: https://github.com/aametwally/OmicsLonDA/issues
git_url: https://git.bioconductor.org/packages/OmicsLonDA
git_branch: RELEASE_3_13
git_last_commit: edcb4b5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OmicsLonDA_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OmicsLonDA_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OmicsLonDA_1.8.0.tgz
vignettes: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.html
vignetteTitles: OmicsLonDA Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.R
dependencyCount: 68
Package: OMICsPCA
Version: 1.10.0
Depends: R (>= 3.5.0), OMICsPCAdata
Imports: HelloRanges, fpc, stats, MultiAssayExperiment, pdftools,
methods, grDevices, utils,clValid, NbClust, cowplot, rmarkdown,
kableExtra, rtracklayer, IRanges, GenomeInfoDb, reshape2,
ggplot2, factoextra, rgl, corrplot, MASS, graphics, FactoMineR,
PerformanceAnalytics, tidyr, data.table, cluster, magick
Suggests: knitr, RUnit, BiocGenerics
License: GPL-3
MD5sum: 3173cded111f916cd1596a4960e34d2f
NeedsCompilation: no
Title: An R package for quantitative integration and analysis of
multiple omics assays from heterogeneous samples
Description: OMICsPCA is an analysis pipeline designed to integrate
multi OMICs experiments done on various subjects (e.g. Cell
lines, individuals), treatments (e.g. disease/control) or time
points and to analyse such integrated data from various various
angles and perspectives. In it's core OMICsPCA uses Principal
Component Analysis (PCA) to integrate multiomics experiments
from various sources and thus has ability to over data
insufficiency issues by using the ingegrated data as
representatives. OMICsPCA can be used in various application
including analysis of overall distribution of OMICs assays
across various samples /individuals /time points; grouping
assays by user-defined conditions; identification of source of
variation, similarity/dissimilarity between assays, variables
or individuals.
biocViews: ImmunoOncology, MultipleComparison, PrincipalComponent,
DataRepresentation, Workflow, Visualization,
DimensionReduction, Clustering, BiologicalQuestion,
EpigeneticsWorkflow, Transcription, GeneticVariability, GUI,
BiomedicalInformatics, Epigenetics, FunctionalGenomics,
SingleCell
Author: Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb]
Maintainer: Subhadeep Das <subhadeep1024@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/OMICsPCA
git_branch: RELEASE_3_13
git_last_commit: d08002d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OMICsPCA_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OMICsPCA_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OMICsPCA_1.10.0.tgz
vignettes: vignettes/OMICsPCA/inst/doc/vignettes.html
vignetteTitles: OMICsPCA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OMICsPCA/inst/doc/vignettes.R
dependencyCount: 218
Package: omicsPrint
Version: 1.12.0
Depends: R (>= 3.5), MASS
Imports: methods, matrixStats, graphics, stats, SummarizedExperiment,
MultiAssayExperiment, RaggedExperiment
Suggests: BiocStyle, knitr, rmarkdown, testthat, GEOquery,
VariantAnnotation, Rsamtools, BiocParallel, GenomicRanges,
FDb.InfiniumMethylation.hg19, snpStats
License: GPL (>= 2)
MD5sum: 60374ce79589f751533c9019287bf872
NeedsCompilation: no
Title: Cross omic genetic fingerprinting
Description: omicsPrint provides functionality for cross omic genetic
fingerprinting, for example, to verify sample relationships
between multiple omics data types, i.e. genomic, transcriptomic
and epigenetic (DNA methylation).
biocViews: QualityControl, Genetics, Epigenetics, Transcriptomics,
DNAMethylation, Transcription, GeneticVariability,
ImmunoOncology
Author: Maarten van Iterson [aut], Davy Cats [cre]
Maintainer: Davy Cats <davycats.dc@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/omicsPrint
git_branch: RELEASE_3_13
git_last_commit: ad214c8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/omicsPrint_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/omicsPrint_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/omicsPrint_1.12.0.tgz
vignettes: vignettes/omicsPrint/inst/doc/omicsPrint.html
vignetteTitles: omicsPrint: detection of data linkage errors in
multiple omics studies
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/omicsPrint/inst/doc/omicsPrint.R
dependencyCount: 49
Package: Omixer
Version: 1.2.4
Depends: R (>= 4.0)
Imports: dplyr, ggplot2, forcats, tibble, gridExtra, magrittr, readr,
tidyselect, grid, stats, stringr, RColorBrewer
Suggests: knitr, rmarkdown, BiocStyle, magick, testthat
License: MIT + file LICENSE
MD5sum: c1d38d8db6ba47125ab77c355c8b7cae
NeedsCompilation: no
Title: Randomize Samples for -omics Profiling
Description: Omixer - an R package for multivariate and reproducible
randomization with lab-friendly sample layouts. Omixer ensures
optimal sample distribution across batches with well-documented
methods, and can output lab-friendly sample sheets for the wet
lab if needed.
biocViews: DataRepresentation, ExperimentalDesign, QualityControl,
Software, Visualization
Author: Lucy Sinke [cre, aut]
Maintainer: Lucy Sinke <l.j.sinke@lumc.nl>
VignetteBuilder: knitr
BugReports: <l.j.sinke@lumc.nl>
git_url: https://git.bioconductor.org/packages/Omixer
git_branch: RELEASE_3_13
git_last_commit: 341f989
git_last_commit_date: 2021-10-06
Date/Publication: 2021-10-07
source.ver: src/contrib/Omixer_1.2.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Omixer_1.2.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/Omixer_1.2.4.tgz
vignettes: vignettes/Omixer/inst/doc/omixer-vignette.html
vignetteTitles: my-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Omixer/inst/doc/omixer-vignette.R
dependencyCount: 57
Package: OmnipathR
Version: 3.0.4
Depends: R(>= 4.0)
Imports: checkmate, curl, digest, dplyr, httr, igraph, jsonlite, later,
logger, magrittr, progress, purrr, rappdirs, readr(>= 2.0.0),
readxl, rlang, stats, stringr, tibble, tidyr, tidyselect,
tools, utils, xml2, yaml
Suggests: BiocStyle, dnet, ggplot2, ggraph, gprofiler2, knitr, mlrMBO,
parallelMap, ParamHelpers, rmarkdown, smoof, testthat
License: MIT + file LICENSE
MD5sum: 4e5b472c1b825ff6f63afbaad32bcb03
NeedsCompilation: no
Title: OmniPath web service client and more
Description: A client for the OmniPath web service
(https://www.omnipathdb.org) and many other resources. It also
includes functions to transform and pretty print some of the
downloaded data, functions to access a number of other
resources such as BioPlex, ConsensusPathDB, EVEX, Gene
Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome,
HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway,
Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF
census, TRRUST and Vinayagam et al. 2011. Furthermore,
OmnipathR features a close integration with the NicheNet method
for ligand activity prediction from transcriptomics data, and
its R implementation `nichenetr` (available only on github).
biocViews: GraphAndNetwork, Network, Pathways, Software,
ThirdPartyClient, DataImport, DataRepresentation,
GeneSignaling, GeneRegulation, SystemsBiology, Transcriptomics,
SingleCell, Annotation, KEGG
Author: Alberto Valdeolivas [aut]
(<https://orcid.org/0000-0001-5482-9023>), Denes Turei [cre,
aut] (<https://orcid.org/0000-0002-7249-9379>), Attila Gabor
[aut] (<https://orcid.org/0000-0002-0776-1182>)
Maintainer: Denes Turei <turei.denes@gmail.com>
URL: https://saezlab.github.io/OmnipathR/
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/OmnipathR/issues
git_url: https://git.bioconductor.org/packages/OmnipathR
git_branch: RELEASE_3_13
git_last_commit: 91e2894
git_last_commit_date: 2021-08-20
Date/Publication: 2021-08-22
source.ver: src/contrib/OmnipathR_3.0.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OmnipathR_3.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OmnipathR_3.0.4.tgz
vignettes: vignettes/OmnipathR/inst/doc/bioc_workshop.html,
vignettes/OmnipathR/inst/doc/drug_targets.html,
vignettes/OmnipathR/inst/doc/nichenet.html,
vignettes/OmnipathR/inst/doc/omnipath_intro.html
vignetteTitles: OmniPath Bioconductor workshop, Building networks
around drug-targets using OmnipathR, Using NicheNet with
OmnipathR, OmnipathR: an R client for the OmniPath web service
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/OmnipathR/inst/doc/bioc_workshop.R,
vignettes/OmnipathR/inst/doc/drug_targets.R,
vignettes/OmnipathR/inst/doc/nichenet.R,
vignettes/OmnipathR/inst/doc/omnipath_intro.R
importsMe: wppi
dependencyCount: 61
Package: oncomix
Version: 1.14.0
Depends: R (>= 3.4.0)
Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats,
SummarizedExperiment
Suggests: knitr, rmarkdown, testthat, RMySQL
License: GPL-3
MD5sum: e2b85ab88d3da2b37a7ffa4f9bc2a794
NeedsCompilation: no
Title: Identifying Genes Overexpressed in Subsets of Tumors from
Tumor-Normal mRNA Expression Data
Description: This package helps identify mRNAs that are overexpressed
in subsets of tumors relative to normal tissue. Ideal inputs
would be paired tumor-normal data from the same tissue from
many patients (>15 pairs). This unsupervised approach relies on
the observation that oncogenes are characteristically
overexpressed in only a subset of tumors in the population, and
may help identify oncogene candidates purely based on
differences in mRNA expression between previously unknown
subtypes.
biocViews: GeneExpression, Sequencing
Author: Daniel Pique, John Greally, Jessica Mar
Maintainer: Daniel Pique <daniel.pique@med.einstein.yu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/oncomix
git_branch: RELEASE_3_13
git_last_commit: 4c21cd9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/oncomix_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/oncomix_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/oncomix_1.14.0.tgz
vignettes: vignettes/oncomix/inst/doc/oncomix.html
vignetteTitles: OncoMix Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/oncomix/inst/doc/oncomix.R
dependencyCount: 59
Package: OncoScore
Version: 1.20.0
Depends: R (>= 4.0.0),
Imports: biomaRt, grDevices, graphics, utils, methods,
Suggests: BiocGenerics, BiocStyle, knitr, testthat,
License: file LICENSE
MD5sum: cd82348e51d24682b418a704b035f455
NeedsCompilation: no
Title: A tool to identify potentially oncogenic genes
Description: OncoScore is a tool to measure the association of genes to
cancer based on citation frequencies in biomedical literature.
The score is evaluated from PubMed literature by dynamically
updatable web queries.
biocViews: BiomedicalInformatics
Author: Luca De Sano [aut] (<https://orcid.org/0000-0002-9618-3774>),
Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb], Daniele
Ramazzotti [cre, aut]
(<https://orcid.org/0000-0002-6087-2666>), Roberta Spinelli
[ctb]
Maintainer: Luca De Sano <luca.desano@gmail.com>
URL: https://github.com/danro9685/OncoScore
VignetteBuilder: knitr
BugReports: https://github.com/danro9685/OncoScore
git_url: https://git.bioconductor.org/packages/OncoScore
git_branch: RELEASE_3_13
git_last_commit: f0fc479
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OncoScore_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OncoScore_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OncoScore_1.20.0.tgz
vignettes: vignettes/OncoScore/inst/doc/vignette.pdf
vignetteTitles: OncoScore
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/OncoScore/inst/doc/vignette.R
dependencyCount: 72
Package: OncoSimulR
Version: 3.0.0
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz,
gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr,
smatr, ggplot2, ggrepel, stringr
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown,
bookdown, pander
License: GPL (>= 3)
MD5sum: 5148f6f48f32dbd7cc00dc1337c38590
NeedsCompilation: yes
Title: Forward Genetic Simulation of Cancer Progression with Epistasis
Description: Functions for forward population genetic simulation in
asexual populations, with special focus on cancer progression.
Fitness can be an arbitrary function of genetic interactions
between multiple genes or modules of genes, including
epistasis, order restrictions in mutation accumulation, and
order effects. Fitness can also be a function of the relative
and absolute frequencies of other genotypes (i.e.,
frequency-dependent fitness). Mutation rates can differ between
genes, and we can include mutator/antimutator genes (to model
mutator phenotypes). Simulating multi-species scenarios and
therapeutic interventions is also possible. Simulations use
continuous-time models and can include driver and passenger
genes and modules. Also included are functions for: simulating
random DAGs of the type found in Oncogenetic Trees, Conjunctive
Bayesian Networks, and other cancer progression models;
plotting and sampling from single or multiple realizations of
the simulations, including single-cell sampling; plotting the
parent-child relationships of the clones; generating random
fitness landscapes (Rough Mount Fuji, House of Cards, additive,
NK, Ising, and Eggbox models) and plotting them.
biocViews: BiologicalQuestion, SomaticMutation
Author: Ramon Diaz-Uriarte [aut, cre], Sergio Sanchez-Carrillo [aut],
Juan Antonio Miguel Gonzalez [aut], Mark Taylor [ctb], Arash
Partow [ctb], Sophie Brouillet [ctb], Sebastian Matuszewski
[ctb], Harry Annoni [ctb], Luca Ferretti [ctb], Guillaume Achaz
[ctb], Guillermo Gorines Cordero [ctb], Ivan Lorca Alonso
[ctb], Francisco Mu\~noz Lopez [ctb], David Roncero Moro\~no
[ctb], Alvaro Quevedo [ctb], Pablo Perez [ctb], Cristina Devesa
[ctb], Alejandro Herrador [ctb], Holger Froehlich [ctb],
Florian Markowetz [ctb], Achim Tresch [ctb], Theresa
Niederberger [ctb], Christian Bender [ctb], Matthias Maneck
[ctb], Claudio Lottaz [ctb], Tim Beissbarth [ctb], Sara Dorado
Alfaro [ctb], Miguel Hernandez del Valle [ctb], Alvaro Huertas
Garcia [ctb], Diego Ma\~nanes Cayero [ctb], Alejandro Martin
Mu\~noz [ctb], Marta Couce Iglesias [ctb], Silvia Garcia Cobos
[ctb], Carlos Madariaga Aramendi [ctb], Ana Rodriguez Ronchel
[ctb], Lucia Sanchez Garcia [ctb], Yolanda Benitez Quesada
[ctb], Asier Fernandez Pato [ctb], Esperanza Lopez Lopez [ctb],
Alberto Manuel Parra Perez [ctb], Jorge Garcia Calleja [ctb],
Ana del Ramo Galian [ctb], Alejandro de los Reyes Benitez
[ctb], Guillermo Garcia Hoyos [ctb], Rosalia Palomino Cabrera
[ctb], Rafael Barrero Rodriguez [ctb], Silvia Talavera Marcos
[ctb], Niklas Endres [ctb]
Maintainer: Ramon Diaz-Uriarte <rdiaz02@gmail.com>
URL: https://github.com/rdiaz02/OncoSimul,
https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/
VignetteBuilder: knitr
BugReports: https://github.com/rdiaz02/OncoSimul/issues
git_url: https://git.bioconductor.org/packages/OncoSimulR
git_branch: RELEASE_3_13
git_last_commit: 3add541
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OncoSimulR_3.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OncoSimulR_3.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OncoSimulR_3.0.0.tgz
vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.html
vignetteTitles: OncoSimulR: forward genetic simulation in asexual
populations with arbitrary epistatic interactions and a focus
on modeling tumor progression.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R
dependencyCount: 103
Package: oneSENSE
Version: 1.14.0
Depends: R (>= 3.4), webshot, shiny, shinyFiles, scatterplot3d
Imports: Rtsne, plotly, gplots, grDevices, graphics, stats, utils,
methods, flowCore
Suggests: knitr, rmarkdown
License: GPL (>=3)
MD5sum: a918644b877af9447843f70277880dc5
NeedsCompilation: no
Title: One-Dimensional Soli-Expression by Nonlinear Stochastic
Embedding (OneSENSE)
Description: A graphical user interface that facilitates the
dimensional reduction method based on the t-distributed
stochastic neighbor embedding (t-SNE) algorithm, for
categorical analysis of mass cytometry data. With One-SENSE,
measured parameters are grouped into predefined categories, and
cells are projected onto a space composed of one dimension for
each category. Each dimension is informative and can be
annotated through the use of heatplots aligned in parallel to
each axis, allowing for simultaneous visualization of two
catergories across a two-dimensional plot. The cellular
occupancy of the resulting plots alllows for direct assessment
of the relationships between the categories.
biocViews: ImmunoOncology, Software, FlowCytometry, GUI,
DimensionReduction
Author: Cheng Yang, Evan Newell, Yong Kee Tan
Maintainer: Yong Kee Tan <yongkee.t@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/oneSENSE
git_branch: RELEASE_3_13
git_last_commit: 732f387
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/oneSENSE_1.14.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/oneSENSE_1.14.0.tgz
vignettes: vignettes/oneSENSE/inst/doc/quickstart.html
vignetteTitles: Introduction to oneSENSE GUI
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/oneSENSE/inst/doc/quickstart.R
dependencyCount: 101
Package: onlineFDR
Version: 2.0.0
Imports: stats, Rcpp, RcppProgress, dplyr, tidyr, ggplot2, progress
LinkingTo: Rcpp, RcppProgress
Suggests: knitr, rmarkdown, testthat, covr
License: GPL-3
MD5sum: 8743562f4e45794a903a474a0cec14de
NeedsCompilation: yes
Title: Online error control
Description: This package allows users to control the false discovery
rate (FDR) or familywise error rate (FWER) for online
hypothesis testing, where hypotheses arrive sequentially in a
stream. In this framework, a null hypothesis is rejected based
only on the previous decisions, as the future p-values and the
number of hypotheses to be tested are unknown.
biocViews: MultipleComparison, Software, StatisticalMethod
Author: David S. Robertson [aut, cre], Lathan Liou [aut], Aaditya
Ramdas [aut], Adel Javanmard [ctb], Andrea Montanari [ctb],
Jinjin Tian [ctb], Tijana Zrnic [ctb], Natasha A. Karp [aut]
Maintainer: David S. Robertson <david.robertson@mrc-bsu.cam.ac.uk>
URL: https://dsrobertson.github.io/onlineFDR/index.html
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/onlineFDR
git_branch: RELEASE_3_13
git_last_commit: 94f9a83
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/onlineFDR_2.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/onlineFDR_2.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/onlineFDR_2.0.0.tgz
vignettes: vignettes/onlineFDR/inst/doc/advanced-usage.html,
vignettes/onlineFDR/inst/doc/onlineFDR.html,
vignettes/onlineFDR/inst/doc/theory.html
vignetteTitles: Advanced usage of onlineFDR, Using the onlineFDR
package, The theory behind onlineFDR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/onlineFDR/inst/doc/advanced-usage.R,
vignettes/onlineFDR/inst/doc/onlineFDR.R,
vignettes/onlineFDR/inst/doc/theory.R
dependencyCount: 49
Package: ontoProc
Version: 1.14.0
Depends: R (>= 3.5), ontologyIndex
Imports: Biobase, S4Vectors, methods, AnnotationDbi, stats, utils,
BiocFileCache, shiny, graph, Rgraphviz, ontologyPlot, dplyr,
magrittr, DT, igraph
Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle,
SingleCellExperiment, celldex, rmarkdown
License: Artistic-2.0
MD5sum: 2eaff23b5e46079dcc8556218248b024
NeedsCompilation: no
Title: processing of ontologies of anatomy, cell lines, and so on
Description: Support harvesting of diverse bioinformatic ontologies,
making particular use of the ontologyIndex package on CRAN. We
provide snapshots of key ontologies for terms about cells, cell
lines, chemical compounds, and anatomy, to help analyze
genome-scale experiments, particularly cell x compound screens.
Another purpose is to strengthen development of compelling use
cases for richer interfaces to emerging ontologies.
biocViews: Infrastructure, GO
Author: Vince Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ontoProc
git_branch: RELEASE_3_13
git_last_commit: 2ba1033
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ontoProc_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ontoProc_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ontoProc_1.14.0.tgz
vignettes: vignettes/ontoProc/inst/doc/ontoProc.html
vignetteTitles: ontoProc: some ontology-oriented utilites with
single-cell focus for Bioconductor
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ontoProc/inst/doc/ontoProc.R
importsMe: pogos, tenXplore
suggestsMe: BiocOncoTK, SingleRBook, scDiffCom
dependencyCount: 92
Package: openCyto
Version: 2.4.0
Depends: R (>= 3.5.0)
Imports: methods,Biobase,BiocGenerics,gtools,flowCore(>=
1.99.17),flowViz,ncdfFlow(>= 2.11.34),flowWorkspace(>=
3.99.1),flowStats(>= 3.99.1),flowClust(>=
3.11.4),MASS,clue,plyr,RBGL,graph,data.table,ks,RColorBrewer,lattice,rrcov,R.utils
LinkingTo: Rcpp
Suggests: flowWorkspaceData, knitr, testthat, utils, tools, parallel,
ggcyto, CytoML
License: Artistic-2.0
MD5sum: 282fbbe81bc93e51e015daed41339f94
NeedsCompilation: yes
Title: Hierarchical Gating Pipeline for flow cytometry data
Description: This package is designed to facilitate the automated
gating methods in sequential way to mimic the manual gating
strategy.
biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing,
DataRepresentation
Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo
Maintainer: Mike Jiang <wjiang2@fhcrc.org>,Jake Wagner
<jpwagner@fhcrc.org>
VignetteBuilder: knitr
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Date/Publication: 2021-05-19
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vignetteTitles: How to use different auto gating functions, How to
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importsMe: CytoML
suggestsMe: CATALYST, flowClust, flowCore, flowStats, flowTime,
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Package: openPrimeR
Version: 1.14.0
Depends: R (>= 4.0.0)
Imports: Biostrings (>= 2.38.4), XML (>= 3.98-1.4), scales (>= 0.4.0),
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License: GPL-2
MD5sum: f0edb69fcc48c03e90c3beacb2c5bdf1
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Title: Multiplex PCR Primer Design and Analysis
Description: An implementation of methods for designing, evaluating,
and comparing primer sets for multiplex PCR. Primers are
designed by solving a set cover problem such that the number of
covered template sequences is maximized with the smallest
possible set of primers. To guarantee that high-quality primers
are generated, only primers fulfilling constraints on their
physicochemical properties are selected. A Shiny app providing
a user interface for the functionalities of this package is
provided by the 'openPrimeRui' package.
biocViews: Software, Technology, Coverage, MultipleComparison
Author: Matthias Döring [aut, cre], Nico Pfeifer [aut]
Maintainer: Matthias Döring <matthias-doering@gmx.de>
SystemRequirements: MAFFT (>= 7.305), OligoArrayAux (>= 3.8), ViennaRNA
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git_url: https://git.bioconductor.org/packages/openPrimeR
git_branch: RELEASE_3_13
git_last_commit: cc9c24b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
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vignettes: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.html
vignetteTitles: openPrimeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.R
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dependencyCount: 117
Package: openPrimeRui
Version: 1.14.0
Depends: R (>= 4.0.0), openPrimeR (>= 0.99.0)
Imports: shiny (>= 1.0.2), shinyjs (>= 0.9), shinyBS (>= 0.61), DT (>=
0.2), rmarkdown (>= 1.0)
Suggests: knitr (>= 1.13)
License: GPL-2
MD5sum: 9f0ee90d606d74fd9fe124710dae776a
NeedsCompilation: no
Title: Shiny Application for Multiplex PCR Primer Design and Analysis
Description: A Shiny application providing methods for designing,
evaluating, and comparing primer sets for multiplex polymerase
chain reaction. Primers are designed by solving a set cover
problem such that the number of covered template sequences is
maximized with the smallest possible set of primers. To
guarantee that high-quality primers are generated, only primers
fulfilling constraints on their physicochemical properties are
selected.
biocViews: Software, Technology
Author: Matthias Döring [aut, cre], Nico Pfeifer [aut]
Maintainer: Matthias Döring <matthias-doering@gmx.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/openPrimeRui
git_branch: RELEASE_3_13
git_last_commit: 3c0ba8d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/openPrimeRui_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/openPrimeRui_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/openPrimeRui_1.14.0.tgz
vignettes: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.html
vignetteTitles: openPrimeRui
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.R
dependencyCount: 139
Package: OpenStats
Version: 1.4.0
Depends: nlme
Imports: MASS, jsonlite, Hmisc, methods, knitr, AICcmodavg, car, rlist,
summarytools, graphics, stats, utils
Suggests: rmarkdown
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 82ebe92e3a3ff7ddd4ec18d6d997b78e
NeedsCompilation: no
Title: A Robust and Scalable Software Package for Reproducible Analysis
of High-Throughput genotype-phenotype association
Description: Package contains several methods for statistical analysis
of genotype to phenotype association in high-throughput
screening pipelines.
biocViews: StatisticalMethod, BatchEffect, Bayesian
Author: Hamed Haseli Mashhadi
Maintainer: Hamed Haseli Mashhadi <hamedhm@ebi.ac.uk>
URL: https://git.io/Jv5w0
VignetteBuilder: knitr
BugReports: https://git.io/Jv5wg
git_url: https://git.bioconductor.org/packages/OpenStats
git_branch: RELEASE_3_13
git_last_commit: b63d9ac
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OpenStats_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OpenStats_1.4.0.zip
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vignettes: vignettes/OpenStats/inst/doc/OpenStats.html
vignetteTitles: OpenStats
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OpenStats/inst/doc/OpenStats.R
dependencyCount: 134
Package: oposSOM
Version: 2.10.0
Depends: R (>= 4.0.0), igraph (>= 1.0.0)
Imports: fastICA, tsne, scatterplot3d, pixmap, fdrtool, ape, biomaRt,
Biobase, RcppParallel, Rcpp, methods, graph, XML, png, RCurl
LinkingTo: RcppParallel, Rcpp
License: GPL (>=2)
Archs: i386, x64
MD5sum: 74e2df039bc1488e80de39e9054ac298
NeedsCompilation: yes
Title: Comprehensive analysis of transcriptome data
Description: This package translates microarray expression data into
metadata of reduced dimension. It provides various
sample-centered and group-centered visualizations, sample
similarity analyses and functional enrichment analyses. The
underlying SOM algorithm combines feature clustering,
multidimensional scaling and dimension reduction, along with
strong visualization capabilities. It enables extraction and
description of functional expression modules inherent in the
data.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
DataRepresentation, Visualization
Author: Henry Loeffler-Wirth <wirth@izbi.uni-leipzig.de>, Hoang Thanh
Le <le@izbi.uni-leipzig.de> and Martin Kalcher
<mkalcher@porkbox.net>
Maintainer: Henry Loeffler-Wirth <wirth@izbi.uni-leipzig.de>
URL: http://som.izbi.uni-leipzig.de
git_url: https://git.bioconductor.org/packages/oposSOM
git_branch: RELEASE_3_13
git_last_commit: 8a2b436
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/oposSOM_2.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/oposSOM_2.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/oposSOM_2.10.0.tgz
vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf
vignetteTitles: The oposSOM users guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/oposSOM/inst/doc/Vignette.R
dependencyCount: 85
Package: oppar
Version: 1.20.0
Depends: R (>= 3.3)
Imports: Biobase, methods, GSEABase, GSVA
Suggests: knitr, rmarkdown, limma, org.Hs.eg.db, GO.db, snow, parallel
License: GPL-2
MD5sum: a8ca8e369a68a3a250d7f62a050841fc
NeedsCompilation: yes
Title: Outlier profile and pathway analysis in R
Description: The R implementation of mCOPA package published by Wang et
al. (2012). Oppar provides methods for Cancer Outlier profile
Analysis. Although initially developed to detect outlier genes
in cancer studies, methods presented in oppar can be used for
outlier profile analysis in general. In addition, tools are
provided for gene set enrichment and pathway analysis.
biocViews: Pathways, GeneSetEnrichment, SystemsBiology, GeneExpression,
Software
Author: Chenwei Wang [aut], Alperen Taciroglu [aut], Stefan R Maetschke
[aut], Colleen C Nelson [aut], Mark Ragan [aut], Melissa Davis
[aut], Soroor Hediyeh zadeh [cre], Momeneh Foroutan [ctr]
Maintainer: Soroor Hediyeh zadeh <hediyehzadeh.s@wehi.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/oppar
git_branch: RELEASE_3_13
git_last_commit: 152bfed
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/oppar_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/oppar_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/oppar_1.20.0.tgz
vignettes: vignettes/oppar/inst/doc/oppar.html
vignetteTitles: OPPAR: Outlier Profile and Pathway Analysis in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/oppar/inst/doc/oppar.R
dependencyCount: 79
Package: oppti
Version: 1.6.0
Depends: R (>= 3.6)
Imports: limma, stats, reshape, ggplot2, grDevices, RColorBrewer,
pheatmap, knitr, methods, devtools
License: MIT
Archs: i386, x64
MD5sum: e18167dc449a0de2329cc5976c2f1a4b
NeedsCompilation: no
Title: Outlier Protein and Phosphosite Target Identifier
Description: The aim of oppti is to analyze protein (and phosphosite)
expressions to find outlying markers for each sample in the
given cohort(s) for the discovery of personalized actionable
targets.
biocViews: Proteomics, Regression, DifferentialExpression,
BiomedicalInformatics, GeneTarget, GeneExpression, Network
Author: Abdulkadir Elmas
Maintainer: Abdulkadir Elmas <abdulkadir.elmas@mssm.edu>
URL: https://github.com/Huang-lab/oppti
VignetteBuilder: knitr
BugReports: https://github.com/Huang-lab/oppti/issues
git_url: https://git.bioconductor.org/packages/oppti
git_branch: RELEASE_3_13
git_last_commit: db1f27a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/oppti_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/oppti_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/oppti_1.6.0.tgz
vignettes: vignettes/oppti/inst/doc/analysis.html
vignetteTitles: Outlier Protein and Phosphosite Target Identifier
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/oppti/inst/doc/analysis.R
dependencyCount: 99
Package: optimalFlow
Version: 1.4.0
Depends: dplyr, optimalFlowData, rlang (>= 0.4.0)
Imports: transport, parallel, Rfast, robustbase, dbscan, randomForest,
foreach, graphics, doParallel, stats, flowMeans, rgl, ellipse
Suggests: knitr, BiocStyle, rmarkdown, magick
License: Artistic-2.0
MD5sum: 475cf1b1aecd720ed01c80ab556ec8b4
NeedsCompilation: no
Title: optimalFlow
Description: Optimal-transport techniques applied to supervised flow
cytometry gating.
biocViews: Software, FlowCytometry, Technology
Author: Hristo Inouzhe <hristo.inouzhe@gmail.com>
Maintainer: Hristo Inouzhe <hristo.inouzhe@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/optimalFlow
git_branch: RELEASE_3_13
git_last_commit: 2b1d6ed
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/optimalFlow_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/optimalFlow_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/optimalFlow_1.4.0.tgz
vignettes: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.html
vignetteTitles: optimalFlow: optimal-transport approach to Flow
Cytometry analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.R
dependencyCount: 91
Package: OPWeight
Version: 1.14.0
Depends: R (>= 3.4.0),
Imports: graphics, qvalue, MASS, tibble, stats,
Suggests: airway, BiocStyle, cowplot, DESeq2, devtools, ggplot2,
gridExtra, knitr, Matrix, rmarkdown, scales, testthat
License: Artistic-2.0
MD5sum: 7d3703c2c8bb899fa4ab0b32438a6a8a
NeedsCompilation: no
Title: Optimal p-value weighting with independent information
Description: This package perform weighted-pvalue based multiple
hypothesis test and provides corresponding information such as
ranking probability, weight, significant tests, etc . To
conduct this testing procedure, the testing method apply a
probabilistic relationship between the test rank and the
corresponding test effect size.
biocViews: ImmunoOncology, BiomedicalInformatics, MultipleComparison,
Regression, RNASeq, SNP
Author: Mohamad Hasan [aut, cre], Paul Schliekelman [aut]
Maintainer: Mohamad Hasan <shakilmohamad7@gmail.com>
URL: https://github.com/mshasan/OPWeight
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/OPWeight
git_branch: RELEASE_3_13
git_last_commit: be45b17
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OPWeight_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OPWeight_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OPWeight_1.14.0.tgz
vignettes: vignettes/OPWeight/inst/doc/OPWeight.html
vignetteTitles: "Introduction to OPWeight"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OPWeight/inst/doc/OPWeight.R
dependencyCount: 45
Package: OrderedList
Version: 1.64.0
Depends: R (>= 3.6.1), Biobase, twilight
Imports: methods
License: GPL (>= 2)
MD5sum: 3eb5dec3e409d7e30d5bca4d273b311b
NeedsCompilation: no
Title: Similarities of Ordered Gene Lists
Description: Detection of similarities between ordered lists of genes.
Thereby, either simple lists can be compared or gene expression
data can be used to deduce the lists. Significance of
similarities is evaluated by shuffling lists or by resampling
in microarray data, respectively.
biocViews: Microarray, DifferentialExpression, MultipleComparison
Author: Xinan Yang, Stefanie Scheid, Claudio Lottaz
Maintainer: Claudio Lottaz <Claudio.Lottaz@klinik.uni-regensburg.de>
URL: http://compdiag.molgen.mpg.de/software/OrderedList.shtml
git_url: https://git.bioconductor.org/packages/OrderedList
git_branch: RELEASE_3_13
git_last_commit: 3c2cc23
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OrderedList_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OrderedList_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OrderedList_1.64.0.tgz
vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf
vignetteTitles: Similarities of Ordered Gene Lists
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OrderedList/inst/doc/tr_2006_01.R
dependencyCount: 10
Package: ORFhunteR
Version: 1.0.0
Depends: Biostrings, rtracklayer, Peptides
Imports: Rcpp (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg38, data.table,
stringr, randomForest, xfun, stats, utils, parallel, graphics
LinkingTo: Rcpp
Suggests: knitr, BiocStyle, rmarkdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: 436c80ee65506c8b4dea9889a5a1a973
NeedsCompilation: yes
Title: Predict open reading frames in nucleotide sequences
Description: The ORFhunteR package is a R and C++ library for an
automatic determination and annotation of open reading frames
(ORF) in a large set of RNA molecules. It efficiently
implements the machine learning model based on vectorization of
nucleotide sequences and the random forest classification
algorithm. The ORFhunteR package consists of a set of functions
written in the R language in conjunction with C++. The
efficiency of the package was confirmed by the examples of the
analysis of RNA molecules from the NCBI RefSeq and Ensembl
databases. The package can be used in basic and applied
biomedical research related to the study of the transcriptome
of normal as well as altered (for example, cancer) human cells.
biocViews: Technology, StatisticalMethod, Sequencing, RNASeq,
Classification, FeatureExtraction
Author: Vasily V. Grinev [aut, cre]
(<https://orcid.org/0000-0001-9981-7333>), Mikalai M. Yatskou
[aut], Victor V. Skakun [aut], Maryna Chepeleva [aut], Petr V.
Nazarov [aut] (<https://orcid.org/0000-0003-3443-0298>)
Maintainer: Vasily V. Grinev <grinev_vv@bsu.by>
VignetteBuilder: knitr
BugReports: https://github.com/rfctbio-bsu/ORFhunteR/issues
git_url: https://git.bioconductor.org/packages/ORFhunteR
git_branch: RELEASE_3_13
git_last_commit: aacc377
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ORFhunteR_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ORFhunteR_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ORFhunteR_1.0.0.tgz
vignettes: vignettes/ORFhunteR/inst/doc/ORFhunteR.html
vignetteTitles: The ORFhunteR package: User’s manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ORFhunteR/inst/doc/ORFhunteR.R
dependencyCount: 55
Package: ORFik
Version: 1.12.13
Depends: R (>= 3.6.0), IRanges (>= 2.17.1), GenomicRanges (>= 1.35.1),
GenomicAlignments (>= 1.19.0)
Imports: AnnotationDbi (>= 1.45.0), Biostrings (>= 2.51.1), biomartr,
BiocGenerics (>= 0.29.1), BiocParallel (>= 1.19.0), BSgenome,
cowplot (>= 1.0.0), data.table (>= 1.11.8), DESeq2 (>= 1.24.0),
fst (>= 0.9.2), GenomeInfoDb (>= 1.15.5), GenomicFeatures (>=
1.31.10), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), GGally (>=
1.4.0), httr (>= 1.3.0), methods (>= 3.6.0), R.utils, Rcpp (>=
1.0.0), Rsamtools (>= 1.35.0), rtracklayer (>= 1.43.0), stats,
SummarizedExperiment (>= 1.14.0), S4Vectors (>= 0.21.3), tools,
utils, xml2 (>= 1.2.0)
LinkingTo: Rcpp
Suggests: testthat, rmarkdown, knitr, BiocStyle,
BSgenome.Hsapiens.UCSC.hg19
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: f789694a425293bc33571ed824759892
NeedsCompilation: yes
Title: Open Reading Frames in Genomics
Description: R package for analysis of transcript and translation
features through manipulation of sequence data and NGS data
like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in
the sense that any transcript region can be analysed, as the
name hints to it was made with investigation of ribosomal
patterns over Open Reading Frames (ORFs) as it's primary use
case. ORFik is extremely fast through use of C++, data.table
and GenomicRanges. Package allows to reassign starts of the
transcripts with the use of CAGE-Seq data, automatic shifting
of RiboSeq reads, finding of Open Reading Frames for whole
genomes and much more.
biocViews: ImmunoOncology, Software, Sequencing, RiboSeq, RNASeq,
FunctionalGenomics, Coverage, Alignment, DataImport
Author: Haakon Tjeldnes [aut, cre, dtc], Kornel Labun [aut, cph],
Michal Swirski [ctb], Katarzyna Chyzynska [ctb, dtc], Yamila
Torres Cleuren [ctb, ths], Evind Valen [ths, fnd]
Maintainer: Haakon Tjeldnes <hauken_heyken@hotmail.com>
URL: https://github.com/Roleren/ORFik
VignetteBuilder: knitr
BugReports: https://github.com/Roleren/ORFik/issues
git_url: https://git.bioconductor.org/packages/ORFik
git_branch: RELEASE_3_13
git_last_commit: b86b7a7
git_last_commit_date: 2021-09-29
Date/Publication: 2021-09-30
source.ver: src/contrib/ORFik_1.12.13.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ORFik_1.12.13.zip
mac.binary.ver: bin/macosx/contrib/4.1/ORFik_1.12.13.tgz
vignettes: vignettes/ORFik/inst/doc/Annotation_Alignment.html,
vignettes/ORFik/inst/doc/ORFikExperiment.html,
vignettes/ORFik/inst/doc/ORFikOverview.html,
vignettes/ORFik/inst/doc/Ribo-seq_pipeline.html
vignetteTitles: Annotation_Alignment.html, ORFikExperiment.html, ORFik
Overview, Ribo-seq_pipeline.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ORFik/inst/doc/Annotation_Alignment.R,
vignettes/ORFik/inst/doc/ORFikExperiment.R,
vignettes/ORFik/inst/doc/ORFikOverview.R,
vignettes/ORFik/inst/doc/Ribo-seq_pipeline.R
dependencyCount: 141
Package: Organism.dplyr
Version: 1.20.0
Depends: R (>= 3.4), dplyr (>= 0.7.0), AnnotationFilter (>= 1.1.3)
Imports: RSQLite, S4Vectors, GenomeInfoDb, IRanges, GenomicRanges,
GenomicFeatures, AnnotationDbi, rlang, methods, tools, utils,
BiocFileCache, DBI, dbplyr, tibble
Suggests: org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene,
org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.ensGene, testthat,
knitr, rmarkdown, BiocStyle, ggplot2
License: Artistic-2.0
MD5sum: d9a7c3f8d62287487956d14a03b618ba
NeedsCompilation: no
Title: dplyr-based Access to Bioconductor Annotation Resources
Description: This package provides an alternative interface to
Bioconductor 'annotation' resources, in particular the gene
identifier mapping functionality of the 'org' packages (e.g.,
org.Hs.eg.db) and the genome coordinate functionality of the
'TxDb' packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene).
biocViews: Annotation, Sequencing, GenomeAnnotation
Author: Martin Morgan [aut, cre], Daniel van Twisk [ctb], Yubo Cheng
[aut]
Maintainer: Martin Morgan <martin.morgan@roswellpark.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Organism.dplyr
git_branch: RELEASE_3_13
git_last_commit: 1806248
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Organism.dplyr_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Organism.dplyr_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Organism.dplyr_1.20.0.tgz
vignettes: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.html
vignetteTitles: Organism.dplyr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.R
dependsOnMe: annotation
importsMe: Ularcirc
dependencyCount: 98
Package: OrganismDbi
Version: 1.34.0
Depends: R (>= 2.14.0), methods, BiocGenerics (>= 0.15.10),
AnnotationDbi (>= 1.33.15), GenomicFeatures (>= 1.39.4)
Imports: Biobase, BiocManager, GenomicRanges (>= 1.31.13), graph,
IRanges, RBGL, DBI, S4Vectors (>= 0.9.25), stats
Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19,
AnnotationHub, FDb.UCSC.tRNAs, mirbase.db, rtracklayer,
biomaRt, RUnit, RMariaDB
License: Artistic-2.0
MD5sum: ca8131a15bb425762a7d616d8930735a
NeedsCompilation: no
Title: Software to enable the smooth interfacing of different database
packages
Description: The package enables a simple unified interface to several
annotation packages each of which has its own schema by taking
advantage of the fact that each of these packages implements a
select methods.
biocViews: Annotation, Infrastructure
Author: Marc Carlson, Hervé Pagès, Martin Morgan, Valerie Obenchain
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/OrganismDbi
git_branch: RELEASE_3_13
git_last_commit: 253c4ae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OrganismDbi_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OrganismDbi_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OrganismDbi_1.34.0.tgz
vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.pdf
vignetteTitles: OrganismDbi: A meta framework for Annotation Packages
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R
dependsOnMe: Homo.sapiens, Mus.musculus, Rattus.norvegicus
importsMe: AnnotationHubData, epivizrData, ggbio, gpart, uncoverappLib
suggestsMe: ChIPpeakAnno, epivizrStandalone
dependencyCount: 99
Package: OSAT
Version: 1.40.0
Depends: methods,stats
Suggests: xtable, Biobase
License: Artistic-2.0
MD5sum: e94e281d75d84abf0d150453dcba150d
NeedsCompilation: no
Title: OSAT: Optimal Sample Assignment Tool
Description: A sizable genomics study such as microarray often involves
the use of multiple batches (groups) of experiment due to
practical complication. To minimize batch effects, a careful
experiment design should ensure the even distribution of
biological groups and confounding factors across batches. OSAT
(Optimal Sample Assignment Tool) is developed to facilitate the
allocation of collected samples to different batches. With
minimum steps, it produces setup that optimizes the even
distribution of samples in groups of biological interest into
different batches, reducing the confounding or correlation
between batches and the biological variables of interest. It
can also optimize the even distribution of confounding factors
across batches. Our tool can handle challenging instances where
incomplete and unbalanced sample collections are involved as
well as ideal balanced RCBD. OSAT provides a number of
predefined layout for some of the most commonly used genomics
platform. Related paper can be find at
http://www.biomedcentral.com/1471-2164/13/689 .
biocViews: DataRepresentation, Visualization, ExperimentalDesign,
QualityControl
Author: Li Yan
Maintainer: Li Yan <li.yan@roswellpark.org>
URL: http://www.biomedcentral.com/1471-2164/13/689
git_url: https://git.bioconductor.org/packages/OSAT
git_branch: RELEASE_3_13
git_last_commit: 722835e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OSAT_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OSAT_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OSAT_1.40.0.tgz
vignettes: vignettes/OSAT/inst/doc/OSAT.pdf
vignetteTitles: An introduction to OSAT
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OSAT/inst/doc/OSAT.R
dependencyCount: 2
Package: Oscope
Version: 1.22.0
Depends: EBSeq, cluster, testthat, BiocParallel
Suggests: BiocStyle
License: Artistic-2.0
MD5sum: 71842c132082eddf777d421f4167756c
NeedsCompilation: no
Title: Oscope - A statistical pipeline for identifying oscillatory
genes in unsynchronized single cell RNA-seq
Description: Oscope is a statistical pipeline developed to identifying
and recovering the base cycle profiles of oscillating genes in
an unsynchronized single cell RNA-seq experiment. The Oscope
pipeline includes three modules: a sine model module to search
for candidate oscillator pairs; a K-medoids clustering module
to cluster candidate oscillators into groups; and an extended
nearest insertion module to recover the base cycle order for
each oscillator group.
biocViews: ImmunoOncology, StatisticalMethod,RNASeq, Sequencing,
GeneExpression
Author: Ning Leng
Maintainer: Ning Leng <lengning1@gmail.com>
git_url: https://git.bioconductor.org/packages/Oscope
git_branch: RELEASE_3_13
git_last_commit: 6dc2f86
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Oscope_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Oscope_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Oscope_1.22.0.tgz
vignettes: vignettes/Oscope/inst/doc/Oscope_vignette.pdf
vignetteTitles: Oscope_vigette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Oscope/inst/doc/Oscope_vignette.R
dependencyCount: 56
Package: OTUbase
Version: 1.42.0
Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>=
1.23.15), Biobase, vegan
Imports: Biostrings
License: Artistic-2.0
Archs: i386, x64
MD5sum: 9a8bd816b62eefb83617f61cf39195f4
NeedsCompilation: no
Title: Provides structure and functions for the analysis of OTU data
Description: Provides a platform for Operational Taxonomic Unit based
analysis
biocViews: Sequencing, DataImport
Author: Daniel Beck, Matt Settles, and James A. Foster
Maintainer: Daniel Beck <danlbek@gmail.com>
git_url: https://git.bioconductor.org/packages/OTUbase
git_branch: RELEASE_3_13
git_last_commit: d423dfc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OTUbase_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OTUbase_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OTUbase_1.42.0.tgz
vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf
vignetteTitles: An introduction to OTUbase
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.R
dependencyCount: 51
Package: OUTRIDER
Version: 1.10.0
Depends: R (>= 3.6), BiocParallel, GenomicFeatures,
SummarizedExperiment, data.table, methods
Imports: BBmisc, BiocGenerics, DESeq2 (>= 1.16.1), generics,
GenomicRanges, ggplot2, grDevices, heatmaply, pheatmap,
graphics, IRanges, matrixStats, plotly, plyr, pcaMethods,
PRROC, RColorBrewer, Rcpp, reshape2, S4Vectors, scales,
splines, stats, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown, BiocStyle,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, RMariaDB,
AnnotationDbi, beeswarm, covr
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 0b5e8fd042687740dc2f65a2b3bfb0b0
NeedsCompilation: yes
Title: OUTRIDER - OUTlier in RNA-Seq fInDER
Description: Identification of aberrant gene expression in RNA-seq
data. Read count expectations are modeled by an autoencoder to
control for confounders in the data. Given these expectations,
the RNA-seq read counts are assumed to follow a negative
binomial distribution with a gene-specific dispersion. Outliers
are then identified as read counts that significantly deviate
from this distribution. Furthermore, OUTRIDER provides useful
plotting functions to analyze and visualize the results.
biocViews: ImmunoOncology, RNASeq, Transcriptomics, Alignment,
Sequencing, GeneExpression, Genetics
Author: Felix Brechtmann [aut], Christian Mertes [aut, cre], Agne
Matuseviciute [aut], Michaela Fee Müller [ctb], Vicente Yepez
[aut], Julien Gagneur [aut]
Maintainer: Christian Mertes <mertes@in.tum.de>
URL: https://github.com/gagneurlab/OUTRIDER
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/OUTRIDER
git_branch: RELEASE_3_13
git_last_commit: c9c57ae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OUTRIDER_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OUTRIDER_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OUTRIDER_1.10.0.tgz
vignettes: vignettes/OUTRIDER/inst/doc/OUTRIDER.pdf
vignetteTitles: OUTRIDER: OUTlier in RNA-seq fInDER
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/OUTRIDER/inst/doc/OUTRIDER.R
importsMe: FRASER
dependencyCount: 156
Package: OVESEG
Version: 1.8.0
Depends: R (>= 3.6)
Imports: stats, utils, methods, BiocParallel, SummarizedExperiment,
limma, fdrtool, Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, testthat, ggplot2, gridExtra,
grid, reshape2, scales
License: GPL-2
MD5sum: d2f7ad4b460a34ccb224669ed23a56bf
NeedsCompilation: yes
Title: OVESEG-test to detect tissue/cell-specific markers
Description: An R package for multiple-group comparison to detect
tissue/cell-specific marker genes among subtypes. It provides
functions to compute OVESEG-test statistics, derive component
weights in the mixture null distribution model and estimate
p-values from weightedly aggregated permutations. Obtained
posterior probabilities of component null hypotheses can also
portrait all kinds of upregulation patterns among subtypes.
biocViews: Software, MultipleComparison, CellBiology, GeneExpression
Author: Lulu Chen <luluchen@vt.edu>
Maintainer: Lulu Chen <luluchen@vt.edu>
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/Lululuella/OVESEG
git_url: https://git.bioconductor.org/packages/OVESEG
git_branch: RELEASE_3_13
git_last_commit: b69abfe
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/OVESEG_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/OVESEG_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/OVESEG_1.8.0.tgz
vignettes: vignettes/OVESEG/inst/doc/OVESEG.html
vignetteTitles: OVESEG User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OVESEG/inst/doc/OVESEG.R
dependencyCount: 36
Package: PAA
Version: 1.26.0
Depends: R (>= 3.2.0), Rcpp (>= 0.11.6)
Imports: e1071, gplots, gtools, limma, MASS, mRMRe, randomForest, ROCR,
sva
LinkingTo: Rcpp
Suggests: BiocStyle, RUnit, BiocGenerics, vsn
License: BSD_3_clause + file LICENSE
MD5sum: 69512b90d3428d9ff591506d007af5f8
NeedsCompilation: yes
Title: PAA (Protein Array Analyzer)
Description: PAA imports single color (protein) microarray data that
has been saved in gpr file format - esp. ProtoArray data. After
preprocessing (background correction, batch filtering,
normalization) univariate feature preselection is performed
(e.g., using the "minimum M statistic" approach - hereinafter
referred to as "mMs"). Subsequently, a multivariate feature
selection is conducted to discover biomarker candidates.
Therefore, either a frequency-based backwards elimination
aproach or ensemble feature selection can be used. PAA provides
a complete toolbox of analysis tools including several
different plots for results examination and evaluation.
biocViews: Classification, Microarray, OneChannel, Proteomics
Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre]
Maintainer: Michael Turewicz <michael.turewicz@rub.de>, Martin
Eisenacher <martin.eisenacher@rub.de>
URL: http://www.ruhr-uni-bochum.de/mpc/software/PAA/
SystemRequirements: C++ software package Random Jungle
git_url: https://git.bioconductor.org/packages/PAA
git_branch: RELEASE_3_13
git_last_commit: 39a0f50
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-27
source.ver: src/contrib/PAA_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PAA_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PAA_1.26.0.tgz
vignettes: vignettes/PAA/inst/doc/PAA_1.7.1.pdf,
vignettes/PAA/inst/doc/PAA_vignette.pdf
vignetteTitles: PAA_1.7.1.pdf, PAA tutorial
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R
dependencyCount: 82
Package: packFinder
Version: 1.4.0
Depends: R (>= 4.0.0)
Imports: Biostrings, GenomicRanges, kmer, ape, methods, IRanges,
S4Vectors
Suggests: biomartr, knitr, rmarkdown, testthat, dendextend, biocViews,
BiocCheck, BiocStyle
License: GPL-2
MD5sum: c0bcca213c3e91b78e1afbe88fd85f38
NeedsCompilation: no
Title: de novo Annotation of Pack-TYPE Transposable Elements
Description: Algorithm and tools for in silico pack-TYPE transposon
discovery. Filters a given genome for properties unique to DNA
transposons and provides tools for the investigation of
returned matches. Sequences are input in DNAString format, and
ranges are returned as a dataframe (in the format returned by
as.dataframe(GRanges)).
biocViews: Genetics, SequenceMatching, Annotation
Author: Jack Gisby [aut, cre], Marco Catoni [aut]
Maintainer: Jack Gisby <jackgisby@gmail.com>
URL: https://github.com/jackgisby/packFinder
VignetteBuilder: knitr
BugReports: https://github.com/jackgisby/packFinder/issues
git_url: https://git.bioconductor.org/packages/packFinder
git_branch: RELEASE_3_13
git_last_commit: a750a91
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/packFinder_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/packFinder_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/packFinder_1.4.0.tgz
vignettes: vignettes/packFinder/inst/doc/packFinder.html
vignetteTitles: packFinder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/packFinder/inst/doc/packFinder.R
dependencyCount: 30
Package: padma
Version: 1.2.0
Depends: R (>= 4.0.0), SummarizedExperiment, S4Vectors
Imports: FactoMineR, MultiAssayExperiment, methods, graphics, stats,
utils
Suggests: testthat, BiocStyle, knitr, rmarkdown, KEGGREST, missMDA,
ggplot2, ggrepel, car, cowplot
License: GPL (>=3)
MD5sum: e3c6b5ab036f81566916012d3e210fe4
NeedsCompilation: no
Title: Individualized Multi-Omic Pathway Deviation Scores Using
Multiple Factor Analysis
Description: Use multiple factor analysis to calculate individualized
pathway-centric scores of deviation with respect to the sampled
population based on multi-omic assays (e.g., RNA-seq, copy
number alterations, methylation, etc). Graphical and numerical
outputs are provided to identify highly aberrant individuals
for a particular pathway of interest, as well as the gene and
omics drivers of aberrant multi-omic profiles.
biocViews: Software, StatisticalMethod, PrincipalComponent,
GeneExpression, Pathways, RNASeq, BioCarta, MethylSeq
Author: Andrea Rau [cre, aut]
(<https://orcid.org/0000-0001-6469-488X>), Regina Manansala
[aut], Florence Jaffrézic [ctb], Denis Laloë [aut], Paul Auer
[aut]
Maintainer: Andrea Rau <andrea.rau@inrae.fr>
URL: https://github.com/andreamrau/padma
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/padma
git_branch: RELEASE_3_13
git_last_commit: 7bac95f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/padma_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/padma_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/padma_1.2.0.tgz
vignettes: vignettes/padma/inst/doc/padma.html
vignetteTitles: padma package:Quick-start guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/padma/inst/doc/padma.R
dependencyCount: 129
Package: PADOG
Version: 1.34.0
Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase
Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db,
hgu133a.db, KEGGREST, nlme
Suggests: doParallel, parallel
License: GPL (>= 2)
MD5sum: dde8f740fcd7c77cb72c02012794959a
NeedsCompilation: no
Title: Pathway Analysis with Down-weighting of Overlapping Genes
(PADOG)
Description: This package implements a general purpose gene set
analysis method called PADOG that downplays the importance of
genes that apear often accross the sets of genes to be
analyzed. The package provides also a benchmark for gene set
analysis methods in terms of sensitivity and ranking using 24
public datasets from KEGGdzPathwaysGEO package.
biocViews: Microarray, OneChannel, TwoChannel
Author: Adi Laurentiu Tarca <atarca@med.wayne.edu>; Zhonghui Xu
<zhonghui.xu@gmail.com>
Maintainer: Adi L. Tarca <atarca@med.wayne.edu>
git_url: https://git.bioconductor.org/packages/PADOG
git_branch: RELEASE_3_13
git_last_commit: 66c89e4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PADOG_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PADOG_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PADOG_1.34.0.tgz
vignettes: vignettes/PADOG/inst/doc/PADOG.pdf
vignetteTitles: PADOG
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PADOG/inst/doc/PADOG.R
dependsOnMe: BLMA
importsMe: EGSEA
dependencyCount: 61
Package: pageRank
Version: 1.2.0
Depends: R (>= 4.0)
Imports: GenomicRanges, igraph, motifmatchr, stats, utils, grDevices,
graphics
Suggests: bcellViper, BSgenome.Hsapiens.UCSC.hg19, JASPAR2018,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, TFBSTools,
GenomicFeatures, annotate
License: GPL-2
Archs: i386, x64
MD5sum: 9659c74fd408d7ac85a7befb7cddf60f
NeedsCompilation: no
Title: Temporal and Multiplex PageRank for Gene Regulatory Network
Analysis
Description: Implemented temporal PageRank analysis as defined by
Rozenshtein and Gionis. Implemented multiplex PageRank as
defined by Halu et al. Applied temporal and multiplex PageRank
in gene regulatory network analysis.
biocViews: StatisticalMethod, GeneTarget, Network
Author: Hongxu Ding [aut, cre, ctb, cph]
Maintainer: Hongxu Ding <hd2326@columbia.edu>
URL: https://github.com/hd2326/pageRank
BugReports: https://github.com/hd2326/pageRank/issues
git_url: https://git.bioconductor.org/packages/pageRank
git_branch: RELEASE_3_13
git_last_commit: a54ad76
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pageRank_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pageRank_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pageRank_1.2.0.tgz
vignettes: vignettes/pageRank/inst/doc/introduction.pdf
vignetteTitles: introduction.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pageRank/inst/doc/introduction.R
dependencyCount: 126
Package: PAIRADISE
Version: 1.8.0
Depends: R (>= 3.6), nloptr
Imports: SummarizedExperiment, S4Vectors, stats, methods, abind,
BiocParallel
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: ea773c2dc96e801dd5c804e53d95e743
NeedsCompilation: no
Title: PAIRADISE: Paired analysis of differential isoform expression
Description: This package implements the PAIRADISE procedure for
detecting differential isoform expression between matched
replicates in paired RNA-Seq data.
biocViews: RNASeq, DifferentialExpression, AlternativeSplicing,
StatisticalMethod, ImmunoOncology
Author: Levon Demirdjian, Ying Nian Wu, Yi Xing
Maintainer: Qiang Hu <Qiang.Hu@roswellpark.org>, Levon Demirdjian
<levondem@ucla.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PAIRADISE
git_branch: RELEASE_3_13
git_last_commit: 90a8c7b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PAIRADISE_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PAIRADISE_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PAIRADISE_1.8.0.tgz
vignettes: vignettes/PAIRADISE/inst/doc/pairadise.html
vignetteTitles: PAIRADISE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PAIRADISE/inst/doc/pairadise.R
dependencyCount: 35
Package: paircompviz
Version: 1.30.0
Depends: R (>= 2.10), Rgraphviz
Imports: Rgraphviz
Suggests: multcomp, reshape, rpart, plyr, xtable
License: GPL (>=3.0)
Archs: i386, x64
MD5sum: b1e2aa280841b1a48becacae4d5b12e7
NeedsCompilation: no
Title: Multiple comparison test visualization
Description: This package provides visualization of the results from
the multiple (i.e. pairwise) comparison tests such as
pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test.
The groups being compared are visualized as nodes in Hasse
diagram. Such approach enables very clear and vivid depiction
of which group is significantly greater than which others,
especially if comparing a large number of groups.
biocViews: GraphAndNetwork
Author: Michal Burda
Maintainer: Michal Burda <michal.burda@osu.cz>
git_url: https://git.bioconductor.org/packages/paircompviz
git_branch: RELEASE_3_13
git_last_commit: c2347e8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/paircompviz_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/paircompviz_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/paircompviz_1.30.0.tgz
vignettes: vignettes/paircompviz/inst/doc/vignette.pdf
vignetteTitles: Using paircompviz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/paircompviz/inst/doc/vignette.R
dependencyCount: 11
Package: pandaR
Version: 1.24.0
Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics,
Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit,
hexbin
Suggests: knitr
License: GPL-2
MD5sum: 3bf52a4e8475ffaeda9bb6ca3923c973
NeedsCompilation: no
Title: PANDA Algorithm
Description: Runs PANDA, an algorithm for discovering novel network
structure by combining information from multiple complementary
data sources.
biocViews: StatisticalMethod, GraphAndNetwork, Microarray,
GeneRegulation, NetworkInference, GeneExpression,
Transcription, Network
Author: Dan Schlauch, Joseph N. Paulson, Albert Young, John
Quackenbush, Kimberly Glass
Maintainer: Joseph N. Paulson <paulson.joseph@gene.com>, Dan Schlauch
<dschlauch@genospace.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pandaR
git_branch: RELEASE_3_13
git_last_commit: 930685b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pandaR_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pandaR_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pandaR_1.24.0.tgz
vignettes: vignettes/pandaR/inst/doc/pandaR.html
vignetteTitles: pandaR Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pandaR/inst/doc/pandaR.R
dependencyCount: 48
Package: panelcn.mops
Version: 1.14.0
Depends: R (>= 3.4), cn.mops, methods, utils, stats, graphics
Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, GenomeInfoDb,
grDevices
Suggests: knitr, rmarkdown, RUnit, BiocGenerics
License: LGPL (>= 2.0)
MD5sum: b5af1c981fa0920ed0dadd81a89f8b84
NeedsCompilation: no
Title: CNV detection tool for targeted NGS panel data
Description: CNV detection tool for targeted NGS panel data. Extension
of the cn.mops package.
biocViews: Sequencing, CopyNumberVariation, CellBiology,
GenomicVariation, VariantDetection, Genetics
Author: Verena Haunschmid, Gundula Povysil
Maintainer: Gundula Povysil <povysil@bioinf.jku.at>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/panelcn.mops
git_branch: RELEASE_3_13
git_last_commit: 88ac1dc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/panelcn.mops_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/panelcn.mops_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/panelcn.mops_1.14.0.tgz
vignettes: vignettes/panelcn.mops/inst/doc/panelcn.mops.pdf
vignetteTitles: panelcn.mops: Manual for the R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/panelcn.mops/inst/doc/panelcn.mops.R
suggestsMe: CopyNumberPlots
dependencyCount: 32
Package: panp
Version: 1.62.0
Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5)
Imports: Biobase, methods, stats, utils
Suggests: gcrma
License: GPL (>= 2)
MD5sum: 9768fafa2fe9d91664516a24c7221b13
NeedsCompilation: no
Title: Presence-Absence Calls from Negative Strand Matching Probesets
Description: A function to make gene presence/absence calls based on
distance from negative strand matching probesets (NSMP) which
are derived from Affymetrix annotation. PANP is applied after
gene expression values are created, and therefore can be used
after any preprocessing method such as MAS5 or GCRMA, or
PM-only methods like RMA. NSMP sets have been established for
the HGU133A and HGU133-Plus-2.0 chipsets to date.
biocViews: Infrastructure
Author: Peter Warren
Maintainer: Peter Warren <peter.warren@verizon.net>
git_url: https://git.bioconductor.org/packages/panp
git_branch: RELEASE_3_13
git_last_commit: 77c31f2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/panp_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/panp_1.62.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/panp_1.62.0.tgz
vignettes: vignettes/panp/inst/doc/panp.pdf
vignetteTitles: gene presence/absence calls
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/panp/inst/doc/panp.R
dependencyCount: 13
Package: PANR
Version: 1.38.0
Depends: R (>= 2.14), igraph
Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils,
RedeR
Suggests: snow
License: Artistic-2.0
MD5sum: 5684df1dbf5871ed5f674e75e319ec33
NeedsCompilation: no
Title: Posterior association networks and functional modules inferred
from rich phenotypes of gene perturbations
Description: This package provides S4 classes and methods for inferring
functional gene networks with edges encoding posterior beliefs
of gene association types and nodes encoding perturbation
effects.
biocViews: ImmunoOncology, NetworkInference, Visualization,
GraphAndNetwork, Clustering, CellBasedAssays
Author: Xin Wang <xin_wang@hms.harvard.edu>
Maintainer: Xin Wang <xin_wang@hms.harvard.edu>
git_url: https://git.bioconductor.org/packages/PANR
git_branch: RELEASE_3_13
git_last_commit: e07690c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PANR_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PANR_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PANR_1.38.0.tgz
vignettes: vignettes/PANR/inst/doc/PANR-Vignette.pdf
vignetteTitles: Main vignette:Posterior association network and
enriched functional gene modules inferred from rich phenotypes
of gene perturbations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PANR/inst/doc/PANR-Vignette.R
dependencyCount: 14
Package: PanVizGenerator
Version: 1.20.0
Depends: methods
Imports: shiny, tools, jsonlite, pcaMethods, FindMyFriends, igraph,
stats, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, digest
License: GPL (>= 2)
MD5sum: eb81da19ab7da5bc00ebdc3a76404f4e
NeedsCompilation: no
Title: Generate PanViz visualisations from your pangenome
Description: PanViz is a JavaScript based visualisation tool for
functionaly annotated pangenomes. PanVizGenerator is a
companion for PanViz that facilitates the necessary data
preprocessing step necessary to create a working PanViz
visualization. The output is fully self-contained so the
recipient of the visualization does not need R or
PanVizGenerator installed.
biocViews: ComparativeGenomics, GUI, Visualization
Author: Thomas Lin Pedersen
Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com>
URL: https://github.com/thomasp85/PanVizGenerator
VignetteBuilder: knitr
BugReports: https://github.com/thomasp85/PanVizGenerator/issues
git_url: https://git.bioconductor.org/packages/PanVizGenerator
git_branch: RELEASE_3_13
git_last_commit: 8eb2920
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PanVizGenerator_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PanVizGenerator_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PanVizGenerator_1.20.0.tgz
vignettes: vignettes/PanVizGenerator/inst/doc/panviz_howto.html
vignetteTitles: Creating PanViz visualizations with PanVizGenerator
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PanVizGenerator/inst/doc/panviz_howto.R
dependencyCount: 99
Package: parglms
Version: 1.24.1
Depends: methods
Imports: BiocGenerics, BatchJobs, foreach, doParallel
Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges,
gwascat, BiocStyle, rmarkdown
License: Artistic-2.0
MD5sum: 052e4d8f797ec1e0f8eac2cf645ad9af
NeedsCompilation: no
Title: support for parallelized estimation of GLMs/GEEs
Description: This package provides support for parallelized estimation
of GLMs/GEEs, catering for dispersed data.
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/parglms
git_branch: RELEASE_3_13
git_last_commit: 0ffcdf6
git_last_commit_date: 2021-07-28
Date/Publication: 2021-07-29
source.ver: src/contrib/parglms_1.24.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/parglms_1.24.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/parglms_1.24.1.tgz
vignettes: vignettes/parglms/inst/doc/parglms.pdf
vignetteTitles: parglms: parallelized GLM
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/parglms/inst/doc/parglms.R
dependencyCount: 36
Package: parody
Version: 1.50.0
Depends: R (>= 3.5.0), tools, utils
Suggests: knitr, BiocStyle, testthat, rmarkdown
License: Artistic-2.0
MD5sum: 70cc02348ffd2f04fa353de017aa1429
NeedsCompilation: no
Title: Parametric And Resistant Outlier DYtection
Description: Provide routines for univariate and multivariate outlier
detection with a focus on parametric methods, but support for
some methods based on resistant statistics.
biocViews: MultipleComparison
Author: Vince Carey [aut, cre]
(<https://orcid.org/0000-0003-4046-0063>)
Maintainer: Vince Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/parody
git_branch: RELEASE_3_13
git_last_commit: 7982ddb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/parody_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/parody_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/parody_1.50.0.tgz
vignettes: vignettes/parody/inst/doc/parody.html
vignetteTitles: parody: parametric and resistant outlier dytection
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/parody/inst/doc/parody.R
dependsOnMe: arrayMvout
dependencyCount: 2
Package: PAST
Version: 1.8.0
Depends: R (>= 4.0)
Imports: stats, utils, dplyr, rlang, iterators, parallel, foreach,
doParallel, qvalue, rtracklayer, ggplot2, GenomicRanges,
S4Vectors
Suggests: knitr, rmarkdown
License: GPL (>=3) + file LICENSE
MD5sum: 70bbe7fd8b3754acc64383e766988c68
NeedsCompilation: no
Title: Pathway Association Study Tool (PAST)
Description: PAST takes GWAS output and assigns SNPs to genes, uses
those genes to find pathways associated with the genes, and
plots pathways based on significance. Implements methods for
reading GWAS input data, finding genes associated with SNPs,
calculating enrichment score and significance of pathways, and
plotting pathways.
biocViews: Pathways, GeneSetEnrichment
Author: Thrash Adam [cre, aut], DeOrnellis Mason [aut]
Maintainer: Thrash Adam <thrash@igbb.msstate.edu>
URL: https://github.com/IGBB/past
VignetteBuilder: knitr
BugReports: https://github.com/IGBB/past/issues
git_url: https://git.bioconductor.org/packages/PAST
git_branch: RELEASE_3_13
git_last_commit: f595850
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PAST_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PAST_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PAST_1.8.0.tgz
vignettes: vignettes/PAST/inst/doc/past.html
vignetteTitles: PAST
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PAST/inst/doc/past.R
dependencyCount: 87
Package: Path2PPI
Version: 1.22.0
Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods
Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle
License: GPL (>= 2)
MD5sum: 4b0027a53f688777462f57044cd44af5
NeedsCompilation: no
Title: Prediction of pathway-related protein-protein interaction
networks
Description: Package to predict protein-protein interaction (PPI)
networks in target organisms for which only a view information
about PPIs is available. Path2PPI predicts PPI networks based
on sets of proteins which can belong to a certain pathway from
well-established model organisms. It helps to combine and
transfer information of a certain pathway or biological process
from several reference organisms to one target organism.
Path2PPI only depends on the sequence similarity of the
involved proteins.
biocViews: NetworkInference, SystemsBiology, Network, Proteomics,
Pathways
Author: Oliver Philipp [aut, cre], Ina Koch [ctb]
Maintainer: Oliver Philipp <contact@oliverphilipp.info>
URL: http://www.bioinformatik.uni-frankfurt.de/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Path2PPI
git_branch: RELEASE_3_13
git_last_commit: 0ce380a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Path2PPI_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Path2PPI_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Path2PPI_1.22.0.tgz
vignettes: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html
vignetteTitles: Path2PPI - A brief tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R
dependencyCount: 11
Package: pathifier
Version: 1.30.0
Imports: R.oo, princurve (>= 2.0.4)
License: Artistic-1.0
MD5sum: e3eac28a168eb673142af2eef9b9b07b
NeedsCompilation: no
Title: Quantify deregulation of pathways in cancer
Description: Pathifier is an algorithm that infers pathway deregulation
scores for each tumor sample on the basis of expression data.
This score is determined, in a context-specific manner, for
every particular dataset and type of cancer that is being
investigated. The algorithm transforms gene-level information
into pathway-level information, generating a compact and
biologically relevant representation of each sample.
biocViews: Network
Author: Yotam Drier
Maintainer: Assif Yitzhaky <assif.yitzhaky@weizmann.ac.il>
git_url: https://git.bioconductor.org/packages/pathifier
git_branch: RELEASE_3_13
git_last_commit: 39dcfa0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pathifier_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pathifier_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pathifier_1.30.0.tgz
vignettes: vignettes/pathifier/inst/doc/Overview.pdf
vignetteTitles: Quantify deregulation of pathways in cancer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pathifier/inst/doc/Overview.R
importsMe: lilikoi
dependencyCount: 9
Package: PathNet
Version: 1.32.0
Suggests: PathNetData, RUnit, BiocGenerics
License: GPL-3
MD5sum: 0fbbc2e4e65b4ea3afbad0f1bdc8256c
NeedsCompilation: no
Title: An R package for pathway analysis using topological information
Description: PathNet uses topological information present in pathways
and differential expression levels of genes (obtained from
microarray experiment) to identify pathways that are 1)
significantly enriched and 2) associated with each other in the
context of differential expression. The algorithm is described
in: PathNet: A tool for pathway analysis using topological
information. Dutta B, Wallqvist A, and Reifman J. Source Code
for Biology and Medicine 2012 Sep 24;7(1):10.
biocViews: Pathways, DifferentialExpression, MultipleComparison, KEGG,
NetworkEnrichment, Network
Author: Bhaskar Dutta <bhaskar.dutta@gmail.com>, Anders Wallqvist
<awallqvist@bhsai.org>, and Jaques Reifman <jreifman@bhsai.org>
Maintainer: Ludwig Geistlinger <Ludwig_Geistlinger@hms.harvard.edu>
git_url: https://git.bioconductor.org/packages/PathNet
git_branch: RELEASE_3_13
git_last_commit: 5110ddc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PathNet_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PathNet_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PathNet_1.32.0.tgz
vignettes: vignettes/PathNet/inst/doc/PathNet.pdf
vignetteTitles: PathNet
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PathNet/inst/doc/PathNet.R
dependencyCount: 0
Package: PathoStat
Version: 1.18.0
Depends: R (>= 3.5)
Imports: limma, corpcor,matrixStats, reshape2, scales, ggplot2,
rentrez, DT, tidyr, plyr, dplyr, phyloseq, shiny, stats,
methods, XML, graphics, utils, BiocStyle, edgeR, DESeq2,
ComplexHeatmap, plotly, webshot, vegan, shinyjs, glmnet,
gmodels, ROCR, RColorBrewer, knitr, devtools, ape
Suggests: rmarkdown, testthat
License: GPL (>= 2)
MD5sum: bee74a86f63da811d946b1851b024736
NeedsCompilation: no
Title: PathoStat Statistical Microbiome Analysis Package
Description: The purpose of this package is to perform Statistical
Microbiome Analysis on metagenomics results from sequencing
data samples. In particular, it supports analyses on the
PathoScope generated report files. PathoStat provides various
functionalities including Relative Abundance charts, Diversity
estimates and plots, tests of Differential Abundance, Time
Series visualization, and Core OTU analysis.
biocViews: Microbiome, Metagenomics, GraphAndNetwork, Microarray,
PatternLogic, PrincipalComponent, Sequencing, Software,
Visualization, RNASeq, ImmunoOncology
Author: Solaiappan Manimaran <manimaran_1975@hotmail.com>, Matthew
Bendall <bendall@gwmail.gwu.edu>, Sandro Valenzuela Diaz
<sandrolvalenzuelad@gmail.com>, Eduardo Castro
<castronallar@gmail.com>, Tyler Faits <tfaits@gmail.com>, Yue
Zhao <jasonzhao0307@gmail.com>, Anthony Nicholas Federico
<anfed@bu.edu>, W. Evan Johnson <wej@bu.edu>
Maintainer: Solaiappan Manimaran <manimaran_1975@hotmail.com>, Yue Zhao
<jasonzhao0307@gmail.com>
URL: https://github.com/mani2012/PathoStat
VignetteBuilder: knitr
BugReports: https://github.com/mani2012/PathoStat/issues
git_url: https://git.bioconductor.org/packages/PathoStat
git_branch: RELEASE_3_13
git_last_commit: 9d6981f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PathoStat_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PathoStat_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PathoStat_1.18.0.tgz
vignettes: vignettes/PathoStat/inst/doc/PathoStat-vignette.html
vignetteTitles: PathoStat intro
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PathoStat/inst/doc/PathoStat-vignette.R
dependencyCount: 206
Package: pathRender
Version: 1.60.0
Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods,
stats4
Suggests: ALL, hgu95av2.db
License: LGPL
MD5sum: 8283b3d982ce8da34210ceac192f8a8b
NeedsCompilation: no
Title: Render molecular pathways
Description: build graphs from pathway databases, render them by
Rgraphviz.
biocViews: GraphAndNetwork, Pathways, Visualization
Author: Li Long <lilong@isb-sib.ch>
Maintainer: Vince Carey <stvjc@channing.harvard.edu>
URL: http://www.bioconductor.org
git_url: https://git.bioconductor.org/packages/pathRender
git_branch: RELEASE_3_13
git_last_commit: a976baf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pathRender_1.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pathRender_1.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pathRender_1.60.0.tgz
vignettes: vignettes/pathRender/inst/doc/pathRender.pdf,
vignettes/pathRender/inst/doc/plotExG.pdf
vignetteTitles: pathRender overview, pathway graphs colored by
expression map
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pathRender/inst/doc/pathRender.R,
vignettes/pathRender/inst/doc/plotExG.R
dependencyCount: 51
Package: pathVar
Version: 1.22.0
Depends: R (>= 3.3.0), methods, ggplot2, gridExtra
Imports: EMT, mclust, Matching, data.table, stats, grDevices, graphics,
utils
License: LGPL (>= 2.0)
MD5sum: 1fff1a0e6d801e1dbdea3e552f913d19
NeedsCompilation: no
Title: Methods to Find Pathways with Significantly Different
Variability
Description: This package contains the functions to find the pathways
that have significantly different variability than a reference
gene set. It also finds the categories from this pathway that
are significant where each category is a cluster of genes. The
genes are separated into clusters by their level of
variability.
biocViews: GeneticVariability, GeneSetEnrichment, Pathways
Author: Laurence de Torrente, Samuel Zimmerman, Jessica Mar
Maintainer: Samuel Zimmerman <samuel.e.zimmerman@gmail.com>
git_url: https://git.bioconductor.org/packages/pathVar
git_branch: RELEASE_3_13
git_last_commit: 44f759a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pathVar_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pathVar_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pathVar_1.22.0.tgz
vignettes: vignettes/pathVar/inst/doc/pathVar.pdf
vignetteTitles: Tutorial on How to Use the Functions in the
\texttt{PathVar} Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pathVar/inst/doc/pathVar.R
dependencyCount: 43
Package: pathview
Version: 1.32.0
Depends: R (>= 3.5.0)
Imports: KEGGgraph, XML, Rgraphviz, graph, png, AnnotationDbi,
org.Hs.eg.db, KEGGREST, methods, utils
Suggests: gage, org.Mm.eg.db, RUnit, BiocGenerics
License: GPL (>=3.0)
MD5sum: e65d36d55f4b619f22a498596962e5c0
NeedsCompilation: no
Title: a tool set for pathway based data integration and visualization
Description: Pathview is a tool set for pathway based data integration
and visualization. It maps and renders a wide variety of
biological data on relevant pathway graphs. All users need is
to supply their data and specify the target pathway. Pathview
automatically downloads the pathway graph data, parses the data
file, maps user data to the pathway, and render pathway graph
with the mapped data. In addition, Pathview also seamlessly
integrates with pathway and gene set (enrichment) analysis
tools for large-scale and fully automated analysis.
biocViews: Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment,
DifferentialExpression, GeneExpression, Microarray, RNASeq,
Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing
Author: Weijun Luo
Maintainer: Weijun Luo <luo_weijun@yahoo.com>
URL: https://github.com/datapplab/pathview, https://pathview.uncc.edu/
git_url: https://git.bioconductor.org/packages/pathview
git_branch: RELEASE_3_13
git_last_commit: ec7e0e1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pathview_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pathview_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pathview_1.32.0.tgz
vignettes: vignettes/pathview/inst/doc/pathview.pdf
vignetteTitles: Pathview: pathway based data integration and
visualization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pathview/inst/doc/pathview.R
dependsOnMe: BioNetStat, EGSEA, RNASeqR, SBGNview
importsMe: CompGO, debrowser, EnrichmentBrowser, GDCRNATools,
TCGAbiolinksGUI, TCGAWorkflow, lilikoi
suggestsMe: gage, MAGeCKFlute, TCGAbiolinks, gageData, CAGEWorkflow
dependencyCount: 52
Package: pathwayPCA
Version: 1.8.0
Depends: R (>= 3.1)
Imports: lars, methods, parallel, stats, survival, utils
Suggests: airway, circlize, grDevices, knitr, RCurl, reshape2,
rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse
License: GPL-3
MD5sum: 3cf4334e69e7d7fff8b116698b042179
NeedsCompilation: no
Title: Integrative Pathway Analysis with Modern PCA Methodology and
Gene Selection
Description: pathwayPCA is an integrative analysis tool that implements
the principal component analysis (PCA) based pathway analysis
approaches described in Chen et al. (2008), Chen et al. (2010),
and Chen (2011). pathwayPCA allows users to: (1) Test pathway
association with binary, continuous, or survival phenotypes.
(2) Extract relevant genes in the pathways using the SuperPCA
and AES-PCA approaches. (3) Compute principal components (PCs)
based on the selected genes. These estimated latent variables
represent pathway activities for individual subjects, which can
then be used to perform integrative pathway analysis, such as
multi-omics analysis. (4) Extract relevant genes that drive
pathway significance as well as data corresponding to these
relevant genes for additional in-depth analysis. (5) Perform
analyses with enhanced computational efficiency with parallel
computing and enhanced data safety with S4-class data objects.
(6) Analyze studies with complex experimental designs, with
multiple covariates, and with interaction effects, e.g.,
testing whether pathway association with clinical phenotype is
different between male and female subjects. Citations: Chen et
al. (2008) <https://doi.org/10.1093/bioinformatics/btn458>;
Chen et al. (2010) <https://doi.org/10.1002/gepi.20532>; and
Chen (2011) <https://doi.org/10.2202/1544-6115.1697>.
biocViews: CopyNumberVariation, DNAMethylation, GeneExpression, SNP,
Transcription, GenePrediction, GeneSetEnrichment,
GeneSignaling, GeneTarget, GenomeWideAssociation,
GenomicVariation, CellBiology, Epigenetics, FunctionalGenomics,
Genetics, Lipidomics, Metabolomics, Proteomics, SystemsBiology,
Transcriptomics, Classification, DimensionReduction,
FeatureExtraction, PrincipalComponent, Regression, Survival,
MultipleComparison, Pathways
Author: Gabriel Odom [aut, cre], James Ban [aut], Lizhong Liu [aut],
Lily Wang [aut], Steven Chen [aut]
Maintainer: Gabriel Odom <gabriel.odom@med.miami.edu>
URL: <https://gabrielodom.github.io/pathwayPCA/>
VignetteBuilder: knitr
BugReports: https://github.com/gabrielodom/pathwayPCA/issues
git_url: https://git.bioconductor.org/packages/pathwayPCA
git_branch: RELEASE_3_13
git_last_commit: 4ab8ed1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pathwayPCA_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pathwayPCA_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pathwayPCA_1.8.0.tgz
vignettes:
vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.html,
vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.html,
vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.html,
vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.html,
vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.html,
vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.html
vignetteTitles: Integrative Pathway Analysis with pathwayPCA, Suppl. 1.
Quickstart Guide, Suppl. 2. Importing Data, Suppl. 3. Create
Data Objects, Suppl. 4. Test Pathway Significance, Suppl. 5.
Visualizing the Results
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.R,
vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.R,
vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.R,
vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.R,
vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.R,
vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.R
importsMe: fcoex
dependencyCount: 12
Package: paxtoolsr
Version: 1.26.0
Depends: R (>= 3.2), rJava (>= 0.9-8), methods, XML
Imports: utils, httr, igraph, plyr, rjson, R.utils, jsonlite, readr
Suggests: testthat, knitr, BiocStyle, rmarkdown, RColorBrewer, foreach,
doSNOW, parallel, org.Hs.eg.db, clusterProfiler
License: LGPL-3
MD5sum: 03f0dfcc95b2d4884a31d96a0eddccbc
NeedsCompilation: no
Title: PaxtoolsR: Access Pathways from Multiple Databases through
BioPAX and Pathway Commons
Description: The package provides a set of R functions for interacting
with BioPAX OWL files using Paxtools and the querying Pathway
Commons (PC) molecular interaction database that are hosted by
the Computational Biology Center at Memorial Sloan-Kettering
Cancer Center (MSKCC). Pathway Commons databases include: BIND,
BioGRID, CORUM, CTD, DIP, DrugBank, HPRD, HumanCyc, IntAct,
KEGG, MirTarBase, Panther, PhosphoSitePlus, Reactome, RECON,
TRANSFAC.
biocViews: GeneSetEnrichment, GraphAndNetwork, Pathways, Software,
SystemsBiology, NetworkEnrichment, Network, Reactome, KEGG
Author: Augustin Luna
Maintainer: Augustin Luna <lunaa@cbio.mskcc.org>
URL: https://github.com/BioPAX/paxtoolsr
SystemRequirements: Java (>= 1.6)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/paxtoolsr
git_branch: RELEASE_3_13
git_last_commit: 7635a83
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/paxtoolsr_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/paxtoolsr_1.26.0.zip
vignettes: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html
vignetteTitles: Using PaxtoolsR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.R
suggestsMe: netboxr
dependencyCount: 52
Package: pcaExplorer
Version: 2.18.0
Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges,
S4Vectors, genefilter, ggplot2 (>= 2.0.0), heatmaply, plotly,
scales, NMF, plyr, topGO, limma, GOstats, GO.db, AnnotationDbi,
shiny (>= 0.12.0), shinydashboard, shinyBS, ggrepel, DT,
shinyAce, threejs, biomaRt, pheatmap, knitr, rmarkdown,
base64enc, tidyr, grDevices, methods
Suggests: testthat, BiocStyle, airway, org.Hs.eg.db, htmltools
License: MIT + file LICENSE
MD5sum: dc98c4421449b20754c7e751635531ac
NeedsCompilation: no
Title: Interactive Visualization of RNA-seq Data Using a Principal
Components Approach
Description: This package provides functionality for interactive
visualization of RNA-seq datasets based on Principal Components
Analysis. The methods provided allow for quick information
extraction and effective data exploration. A Shiny application
encapsulates the whole analysis.
biocViews: ImmunoOncology, Visualization, RNASeq, DimensionReduction,
PrincipalComponent, QualityControl, GUI, ReportWriting
Author: Federico Marini [aut, cre]
(<https://orcid.org/0000-0003-3252-7758>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
URL: https://github.com/federicomarini/pcaExplorer,
https://federicomarini.github.io/pcaExplorer/
VignetteBuilder: knitr
BugReports: https://github.com/federicomarini/pcaExplorer/issues
git_url: https://git.bioconductor.org/packages/pcaExplorer
git_branch: RELEASE_3_13
git_last_commit: 724482a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pcaExplorer_2.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pcaExplorer_2.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pcaExplorer_2.18.0.tgz
vignettes: vignettes/pcaExplorer/inst/doc/pcaExplorer.html,
vignettes/pcaExplorer/inst/doc/upandrunning.html
vignetteTitles: pcaExplorer User Guide, Up and running with pcaExplorer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/pcaExplorer/inst/doc/pcaExplorer.R,
vignettes/pcaExplorer/inst/doc/upandrunning.R
importsMe: ideal
dependencyCount: 182
Package: pcaMethods
Version: 1.84.0
Depends: Biobase, methods
Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS
LinkingTo: Rcpp
Suggests: matrixStats, lattice, ggplot2
License: GPL (>= 3)
MD5sum: 8c46bb0890690c684a2eec91e55c8149
NeedsCompilation: yes
Title: A collection of PCA methods
Description: Provides Bayesian PCA, Probabilistic PCA, Nipals PCA,
Inverse Non-Linear PCA and the conventional SVD PCA. A cluster
based method for missing value estimation is included for
comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA
on incomplete data as well as for accurate missing value
estimation. A set of methods for printing and plotting the
results is also provided. All PCA methods make use of the same
data structure (pcaRes) to provide a common interface to the
PCA results. Initiated at the Max-Planck Institute for
Molecular Plant Physiology, Golm, Germany.
biocViews: Bayesian
Author: Wolfram Stacklies, Henning Redestig, Kevin Wright
Maintainer: Henning Redestig <henning.red@gmail.com>
URL: https://github.com/hredestig/pcamethods
SystemRequirements: Rcpp
BugReports: https://github.com/hredestig/pcamethods/issues
git_url: https://git.bioconductor.org/packages/pcaMethods
git_branch: RELEASE_3_13
git_last_commit: 100c80e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pcaMethods_1.84.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pcaMethods_1.84.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pcaMethods_1.84.0.tgz
vignettes: vignettes/pcaMethods/inst/doc/missingValues.pdf,
vignettes/pcaMethods/inst/doc/outliers.pdf,
vignettes/pcaMethods/inst/doc/pcaMethods.pdf
vignetteTitles: Missing value imputation, Data with outliers,
Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R,
vignettes/pcaMethods/inst/doc/outliers.R,
vignettes/pcaMethods/inst/doc/pcaMethods.R
dependsOnMe: DeconRNASeq, crmn, DiffCorr, imputeLCMD
importsMe: autonomics, CompGO, consensusDE, FRASER, MatrixQCvis,
MSnbase, MSPrep, OUTRIDER, PanVizGenerator, PhosR, pmp, scde,
SomaticSignatures, ADAPTS, geneticae, LOST, MetabolomicsBasics,
missCompare, multiDimBio, polyRAD, RAMClustR, santaR, scMappR
suggestsMe: MsCoreUtils, QFeatures, mtbls2, pagoda2
dependencyCount: 10
Package: PCAN
Version: 1.20.0
Depends: R (>= 3.3), BiocParallel
Imports: grDevices, stats
Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb
License: CC BY-NC-ND 4.0
MD5sum: 658b393cc0ce0bacd7a0667c3ff73f4e
NeedsCompilation: no
Title: Phenotype Consensus ANalysis (PCAN)
Description: Phenotypes comparison based on a pathway consensus
approach. Assess the relationship between candidate genes and a
set of phenotypes based on additional genes related to the
candidate (e.g. Pathways or network neighbors).
biocViews: Annotation, Sequencing, Genetics, FunctionalPrediction,
VariantAnnotation, Pathways, Network
Author: Matthew Page and Patrice Godard
Maintainer: Matthew Page <matthew.page@ucb.com> and Patrice Godard
<patrice.godard@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PCAN
git_branch: RELEASE_3_13
git_last_commit: c07417b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PCAN_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PCAN_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PCAN_1.20.0.tgz
vignettes: vignettes/PCAN/inst/doc/PCAN.html
vignetteTitles: Assessing gene relevance for a set of phenotypes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PCAN/inst/doc/PCAN.R
dependencyCount: 12
Package: PCAtools
Version: 2.4.0
Depends: ggplot2, ggrepel
Imports: lattice, grDevices, cowplot, methods, reshape2, stats, Matrix,
DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel,
Rcpp, dqrng
LinkingTo: Rcpp, beachmat, BH, dqrng
Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery,
hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2,
airway, org.Hs.eg.db, magrittr, rmarkdown
License: GPL-3
MD5sum: ab8726a904cc43d5e5fea060c7d09f9f
NeedsCompilation: yes
Title: PCAtools: Everything Principal Components Analysis
Description: Principal Component Analysis (PCA) is a very powerful
technique that has wide applicability in data science,
bioinformatics, and further afield. It was initially developed
to analyse large volumes of data in order to tease out the
differences/relationships between the logical entities being
analysed. It extracts the fundamental structure of the data
without the need to build any model to represent it. This
'summary' of the data is arrived at through a process of
reduction that can transform the large number of variables into
a lesser number that are uncorrelated (i.e. the 'principal
components'), while at the same time being capable of easy
interpretation on the original data. PCAtools provides
functions for data exploration via PCA, and allows the user to
generate publication-ready figures. PCA is performed via
BiocSingular - users can also identify optimal number of
principal components via different metrics, such as elbow
method and Horn's parallel analysis, which has relevance for
data reduction in single-cell RNA-seq (scRNA-seq) and high
dimensional mass cytometry data.
biocViews: RNASeq, ATACSeq, GeneExpression, Transcription, SingleCell,
PrincipalComponent
Author: Kevin Blighe [aut, cre], Anna-Leigh Brown [ctb], Vincent Carey
[ctb], Guido Hooiveld [ctb], Aaron Lun [aut, ctb]
Maintainer: Kevin Blighe <kevin@clinicalbioinformatics.co.uk>
URL: https://github.com/kevinblighe/PCAtools
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PCAtools
git_branch: RELEASE_3_13
git_last_commit: a3863ae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PCAtools_2.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PCAtools_2.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PCAtools_2.4.0.tgz
vignettes: vignettes/PCAtools/inst/doc/PCAtools.html
vignetteTitles: PCAtools: everything Principal Component Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PCAtools/inst/doc/PCAtools.R
dependsOnMe: OSCA.advanced
suggestsMe: scDataviz
dependencyCount: 70
Package: pcxn
Version: 2.14.0
Depends: R (>= 3.4), pcxnData
Imports: methods, grDevices, utils, pheatmap
Suggests: igraph, annotate, org.Hs.eg.db
License: MIT + file LICENSE
MD5sum: 5404bfd70ec3290894c7fcf4c214f763
NeedsCompilation: no
Title: Exploring, analyzing and visualizing functions utilizing the
pcxnData package
Description: Discover the correlated pathways/gene sets of a single
pathway/gene set or discover correlation relationships among
multiple pathways/gene sets. Draw a heatmap or create a network
of your query and extract members of each pathway/gene set
found in the available collections (MSigDB H hallmark, MSigDB
C2 Canonical pathways, MSigDB C5 GO BP and Pathprint).
biocViews: ExperimentData, ExpressionData, MicroarrayData, GEO,
Homo_sapiens_Data, OneChannelData, PathwayInteractionDatabase
Author: Sokratis Kariotis, Yered Pita-Juarez, Winston Hide, Wenbin Wei
Maintainer: Sokratis Kariotis <s.kariotis@sheffield.ac.uk>
git_url: https://git.bioconductor.org/packages/pcxn
git_branch: RELEASE_3_13
git_last_commit: 992d550
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pcxn_2.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pcxn_2.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pcxn_2.14.0.tgz
vignettes: vignettes/pcxn/inst/doc/using_pcxn.pdf
vignetteTitles: pcxn
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/pcxn/inst/doc/using_pcxn.R
suggestsMe: pcxnData
dependencyCount: 20
Package: PDATK
Version: 1.0.2
Depends: R (>= 4.1), SummarizedExperiment
Imports: data.table, MultiAssayExperiment, ConsensusClusterPlus,
igraph, ggplotify, matrixStats, RColorBrewer, clusterRepro,
CoreGx, caret, survminer, methods, S4Vectors, BiocGenerics,
survival, stats, plyr, dplyr, MatrixGenerics, BiocParallel,
rlang, piano, scales, survcomp, genefu, ggplot2, switchBox,
reportROC, pROC, verification, utils
Suggests: testthat (>= 3.0.0), msigdbr, BiocStyle, rmarkdown, knitr,
HDF5Array
License: MIT + file LICENSE
MD5sum: 46112ea3f208daf127a203c98d66e7b7
NeedsCompilation: no
Title: Pancreatic Ductal Adenocarcinoma Tool-Kit
Description: Pancreatic ductal adenocarcinoma (PDA) has a relatively
poor prognosis and is one of the most lethal cancers. Molecular
classification of gene expression profiles holds the potential
to identify meaningful subtypes which can inform therapeutic
strategy in the clinical setting. The Pancreatic Cancer
Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based
interface for performing unsupervised subtype discovery,
cross-cohort meta-clustering, gene-expression-based
classification, and subsequent survival analysis to identify
prognostically useful subtypes in pancreatic cancer and beyond.
Two novel methods, Consensus Subtypes in Pancreatic Cancer
(CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP)
are included for consensus-based meta-clustering and
overall-survival prediction, respectively. Additionally, four
published subtype classifiers and three published prognostic
gene signatures are included to allow users to easily recreate
published results, apply existing classifiers to new data, and
benchmark the relative performance of new methods. The use of
existing Bioconductor classes as input to all PDATK classes and
methods enables integration with existing Bioconductor
datasets, including the 21 pancreatic cancer patient cohorts
available in the MetaGxPancreas data package. PDATK has been
used to replicate results from Sandhu et al (2019)
[https://doi.org/10.1200/cci.18.00102] and an additional paper
is in the works using CSPC to validate subtypes from the
included published classifiers, both of which use the data
available in MetaGxPancreas. The inclusion of subtype centroids
and prognostic gene signatures from these and other
publications will enable researchers and clinicians to classify
novel patient gene expression data, allowing the direct
clinical application of the classifiers included in PDATK.
Overall, PDATK provides a rich set of tools to identify and
validate useful prognostic and molecular subtypes based on
gene-expression data, benchmark new classifiers against
existing ones, and apply discovered classifiers on novel
patient data to inform clinical decision making.
biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics,
Software, Classification, Survival, Clustering, GenePrediction
Author: Vandana Sandhu [aut], Heewon Seo [aut], Christopher Eeles
[aut], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
BugReports: https://github.com/bhklab/PDATK/issues
git_url: https://git.bioconductor.org/packages/PDATK
git_branch: RELEASE_3_13
git_last_commit: 4065ec5
git_last_commit_date: 2021-06-23
Date/Publication: 2021-06-24
source.ver: src/contrib/PDATK_1.0.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PDATK_1.0.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/PDATK_1.0.2.tgz
vignettes: vignettes/PDATK/inst/doc/PCOSP_model_analysis.html,
vignettes/PDATK/inst/doc/PDATK_introduction.html
vignetteTitles: PCOSP_model_analysis.html, PDATK_introduction.html
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PDATK/inst/doc/PCOSP_model_analysis.R,
vignettes/PDATK/inst/doc/PDATK_introduction.R
dependencyCount: 258
Package: pdInfoBuilder
Version: 1.56.0
Depends: R (>= 3.2.0), methods, Biobase (>= 2.27.3), RSQLite (>=
1.0.0), affxparser (>= 1.39.4), oligo (>= 1.31.5)
Imports: Biostrings (>= 2.35.12), BiocGenerics (>= 0.13.11), DBI (>=
0.3.1), IRanges (>= 2.1.43), oligoClasses (>= 1.29.6),
S4Vectors (>= 0.5.22)
License: Artistic-2.0
MD5sum: 7a5cf9b165e30152a04f25158081cb3a
NeedsCompilation: yes
Title: Platform Design Information Package Builder
Description: Builds platform design information packages. These consist
of a SQLite database containing feature-level data such as x, y
position on chip and featureSet ID. The database also
incorporates featureSet-level annotation data. The products of
this packages are used by the oligo pkg.
biocViews: Annotation, Infrastructure
Author: Seth Falcon, Vince Carey, Matt Settles, Kristof de Beuf,
Benilton Carvalho
Maintainer: Benilton Carvalho <benilton@unicamp.br>
git_url: https://git.bioconductor.org/packages/pdInfoBuilder
git_branch: RELEASE_3_13
git_last_commit: 9bbe14d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pdInfoBuilder_1.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pdInfoBuilder_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pdInfoBuilder_1.56.0.tgz
vignettes: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.pdf,
vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.pdf
vignetteTitles: Building Annotation Packages with pdInfoBuilder for Use
with the oligo Package, PDInfo Package Building Affymetrix
Mapping Chips
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R
suggestsMe: maqcExpression4plex, aroma.affymetrix, maGUI
dependencyCount: 54
Package: PeacoQC
Version: 1.2.0
Depends: R (>= 4.0)
Imports: circlize, ComplexHeatmap, flowCore, flowWorkspace, ggplot2,
grDevices, grid, gridExtra, methods, plyr, stats, utils
Suggests: knitr, rmarkdown, BiocStyle
License: GPL (>=3)
Archs: i386, x64
MD5sum: 0dfda5730c25b71ddd0d0625f4dcaad8
NeedsCompilation: no
Title: Peak-based selection of high quality cytometry data
Description: This is a package that includes pre-processing and quality
control functions that can remove margin events, compensate and
transform the data and that will use PeacoQCSignalStability for
quality control. This last function will first detect peaks in
each channel of the flowframe. It will remove anomalies based
on the IsolationTree function and the MAD outlier detection
method. This package can be used for both flow- and mass
cytometry data.
biocViews: FlowCytometry, QualityControl, Preprocessing, PeakDetection
Author: Annelies Emmaneel [aut, cre]
Maintainer: Annelies Emmaneel <annelies.emmaneel@hotmail.com>
URL: http://github.com/saeyslab/PeacoQC
VignetteBuilder: knitr
BugReports: http://github.com/saeyslab/PeacoQC/issues
git_url: https://git.bioconductor.org/packages/PeacoQC
git_branch: RELEASE_3_13
git_last_commit: 0069897
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PeacoQC_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PeacoQC_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PeacoQC_1.2.0.tgz
vignettes: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.html
vignetteTitles: PeacoQC_Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.R
dependencyCount: 97
Package: peakPantheR
Version: 1.6.1
Depends: R (>= 4.0)
Imports: foreach (>= 1.4.4), doParallel (>= 1.0.11), ggplot2 (>=
2.2.1), gridExtra (>= 2.3), MSnbase (>= 2.4.0), mzR (>=
2.12.0), stringr (>= 1.2.0), methods (>= 3.4.0), XML (>=
3.98.1.10), minpack.lm (>= 1.2.1), scales(>= 0.5.0), shiny (>=
1.0.5), shinythemes (>= 1.1.1), shinycssloaders (>= 1.0.0), DT
(>= 0.15), pracma (>= 2.2.3), utils
Suggests: testthat, faahKO, msdata, knitr, rmarkdown, pander, BiocStyle
License: GPL-3
MD5sum: 448e99dce00d3cdc871556e4f9b4b336
NeedsCompilation: no
Title: Peak Picking and Annotation of High Resolution Experiments
Description: An automated pipeline for the detection, integration and
reporting of predefined features across a large number of mass
spectrometry data files. It enables the real time annotation of
multiple compounds in a single file, or the parallel annotation
of multiple compounds in multiple files. A graphical user
interface as well as command line functions will assist in
assessing the quality of annotation and update fitting
parameters until a satisfactory result is obtained.
biocViews: MassSpectrometry, Metabolomics, PeakDetection
Author: Arnaud Wolfer [aut, cre]
(<https://orcid.org/0000-0001-5856-3218>), Goncalo Correia
[aut] (<https://orcid.org/0000-0001-8271-9294>), Jake Pearce
[ctb], Caroline Sands [ctb]
Maintainer: Arnaud Wolfer <adwolfer@gmail.com>
URL: https://github.com/phenomecentre/peakPantheR
VignetteBuilder: knitr
BugReports: https://github.com/phenomecentre/peakPantheR/issues/new
git_url: https://git.bioconductor.org/packages/peakPantheR
git_branch: RELEASE_3_13
git_last_commit: 3308eac
git_last_commit_date: 2021-06-23
Date/Publication: 2021-06-24
source.ver: src/contrib/peakPantheR_1.6.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/peakPantheR_1.6.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/peakPantheR_1.6.1.tgz
vignettes: vignettes/peakPantheR/inst/doc/getting-started.html,
vignettes/peakPantheR/inst/doc/parallel-annotation.html,
vignettes/peakPantheR/inst/doc/peakPantheR-GUI.html,
vignettes/peakPantheR/inst/doc/real-time-annotation.html
vignetteTitles: Getting Started with the peakPantheR package, Parallel
Annotation, peakPantheR Graphical User Interface, Real Time
Annotation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/peakPantheR/inst/doc/getting-started.R,
vignettes/peakPantheR/inst/doc/parallel-annotation.R,
vignettes/peakPantheR/inst/doc/peakPantheR-GUI.R,
vignettes/peakPantheR/inst/doc/real-time-annotation.R
dependencyCount: 109
Package: PECA
Version: 1.28.0
Depends: R (>= 3.3)
Imports: ROTS, limma, affy, genefilter, preprocessCore,
aroma.affymetrix, aroma.core
Suggests: SpikeIn
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 308a5aa91e29d6f9df3513832a04cb87
NeedsCompilation: no
Title: Probe-level Expression Change Averaging
Description: Calculates Probe-level Expression Change Averages (PECA)
to identify differential expression in Affymetrix gene
expression microarray studies or in proteomic studies using
peptide-level mesurements respectively.
biocViews: Software, Proteomics, Microarray, DifferentialExpression,
GeneExpression, ExonArray, DifferentialSplicing
Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo
Maintainer: Tomi Suomi <tomi.suomi@utu.fi>
git_url: https://git.bioconductor.org/packages/PECA
git_branch: RELEASE_3_13
git_last_commit: 08d2016
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PECA_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PECA_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PECA_1.28.0.tgz
vignettes: vignettes/PECA/inst/doc/PECA.pdf
vignetteTitles: PECA: Probe-level Expression Change Averaging
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PECA/inst/doc/PECA.R
dependencyCount: 85
Package: peco
Version: 1.4.0
Depends: R (>= 2.10)
Imports: assertthat, circular, conicfit, doParallel, foreach, genlasso
(>= 1.4), graphics, methods, parallel, scater,
SingleCellExperiment, SummarizedExperiment, stats, utils
Suggests: knitr, rmarkdown
License: GPL (>= 3)
MD5sum: c3ba0460f2d17436dd60f494e3c4cf9f
NeedsCompilation: no
Title: A Supervised Approach for **P**r**e**dicting **c**ell Cycle
Pr**o**gression using scRNA-seq data
Description: Our approach provides a way to assign continuous cell
cycle phase using scRNA-seq data, and consequently, allows to
identify cyclic trend of gene expression levels along the cell
cycle. This package provides method and training data, which
includes scRNA-seq data collected from 6 individual cell lines
of induced pluripotent stem cells (iPSCs), and also continuous
cell cycle phase derived from FUCCI fluorescence imaging data.
biocViews: Sequencing, RNASeq, GeneExpression, Transcriptomics,
SingleCell, Software, StatisticalMethod, Classification,
Visualization
Author: Chiaowen Joyce Hsiao [aut, cre], Matthew Stephens [aut], John
Blischak [ctb], Peter Carbonetto [ctb]
Maintainer: Chiaowen Joyce Hsiao <joyce.hsiao1@gmail.com>
URL: https://github.com/jhsiao999/peco
VignetteBuilder: knitr
BugReports: https://github.com/jhsiao999/peco/issues
git_url: https://git.bioconductor.org/packages/peco
git_branch: RELEASE_3_13
git_last_commit: 32214ef
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/peco_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/peco_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/peco_1.4.0.tgz
vignettes: vignettes/peco/inst/doc/vignette.html
vignetteTitles: An example of predicting cell cycle phase using peco
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/peco/inst/doc/vignette.R
dependencyCount: 95
Package: PepsNMR
Version: 1.10.1
Depends: R (>= 3.6)
Imports: Matrix, ptw, ggplot2, gridExtra, matrixStats, reshape2,
methods, graphics, stats
Suggests: knitr, markdown, rmarkdown, BiocStyle, PepsNMRData
License: GPL-2 | file LICENSE
MD5sum: dee989f0be66797b059e524e63d0afcd
NeedsCompilation: no
Title: Pre-process 1H-NMR FID signals
Description: This package provides R functions for common pre-procssing
steps that are applied on 1H-NMR data. It also provides a
function to read the FID signals directly in the Bruker format.
biocViews: Software, Preprocessing, Visualization, Metabolomics,
DataImport
Author: Manon Martin [aut, cre], Bernadette Govaerts [aut, ths], Benoît
Legat [aut], Paul H.C. Eilers [aut], Pascal de Tullio [dtc],
Bruno Boulanger [ctb], Julien Vanwinsberghe [ctb]
Maintainer: Manon Martin <manon.martin@uclouvain.be>
URL: https://github.com/ManonMartin/PepsNMR
VignetteBuilder: knitr
BugReports: https://github.com/ManonMartin/PepsNMR/issues
git_url: https://git.bioconductor.org/packages/PepsNMR
git_branch: RELEASE_3_13
git_last_commit: 4a75865
git_last_commit_date: 2021-08-02
Date/Publication: 2021-08-03
source.ver: src/contrib/PepsNMR_1.10.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PepsNMR_1.10.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/PepsNMR_1.10.1.tgz
vignettes: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.html
vignetteTitles: Application of PepsNMR on the Human Serum dataset
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.R
importsMe: ASICS
dependencyCount: 48
Package: pepStat
Version: 1.26.0
Depends: R (>= 3.0.0), Biobase, IRanges
Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods,
data.table
Suggests: pepDat, Pviz, knitr, shiny
License: Artistic-2.0
Archs: i386, x64
MD5sum: 2a9d65b6bbcd17f37bd1adf1406ce6af
NeedsCompilation: no
Title: Statistical analysis of peptide microarrays
Description: Statistical analysis of peptide microarrays
biocViews: Microarray, Preprocessing
Author: Raphael Gottardo, Gregory C Imholte, Renan Sauteraud, Mike
Jiang
Maintainer: Gregory C Imholte <gimholte@uw.edu>
URL: https://github.com/RGLab/pepStat
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pepStat
git_branch: RELEASE_3_13
git_last_commit: a3472cb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pepStat_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pepStat_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pepStat_1.26.0.tgz
vignettes: vignettes/pepStat/inst/doc/pepStat.pdf
vignetteTitles: Full peptide microarray analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pepStat/inst/doc/pepStat.R
dependencyCount: 62
Package: pepXMLTab
Version: 1.26.0
Depends: R (>= 3.0.1)
Imports: XML(>= 3.98-1.1)
Suggests: RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: ec1ca84cb7655c63a5e40b34b5456dc9
NeedsCompilation: no
Title: Parsing pepXML files and filter based on peptide FDR.
Description: Parsing pepXML files based one XML package. The package
tries to handle pepXML files generated from different
softwares. The output will be a peptide-spectrum-matching
tabular file. The package also provide function to filter the
PSMs based on FDR.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry
Author: Xiaojing Wang
Maintainer: Xiaojing Wang <xiaojing.wang@vanderbilt.edu>
git_url: https://git.bioconductor.org/packages/pepXMLTab
git_branch: RELEASE_3_13
git_last_commit: 0b4e195
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pepXMLTab_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pepXMLTab_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pepXMLTab_1.26.0.tgz
vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf
vignetteTitles: Introduction to pepXMLTab
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pepXMLTab/inst/doc/pepXMLTab.R
dependencyCount: 3
Package: PERFect
Version: 1.6.0
Depends: R (>= 3.6.0), sn (>= 1.5.2)
Imports: ggplot2 (>= 3.0.0), phyloseq (>= 1.28.0), zoo (>= 1.8.3),
psych (>= 1.8.4), stats (>= 3.6.0), Matrix (>= 1.2.14),
fitdistrplus (>= 1.0.12), parallel (>= 3.6.0)
Suggests: knitr, rmarkdown, kableExtra, ggpubr
License: Artistic-2.0
MD5sum: f6a07b7a6d6f95aecdb0b2f44729dfa9
NeedsCompilation: no
Title: Permutation filtration for microbiome data
Description: PERFect is a novel permutation filtering approach designed
to address two unsolved problems in microbiome data processing:
(i) define and quantify loss due to filtering by implementing
thresholds, and (ii) introduce and evaluate a permutation test
for filtering loss to provide a measure of excessive filtering.
biocViews: Software, Microbiome, Sequencing, Classification,
Metagenomics
Author: Ekaterina Smirnova <ekaterina.smirnova@vcuhealth.org>, Quy Cao
<quy.cao@umontana.edu>
Maintainer: Quy Cao <quy.cao@umontana.edu>
URL: https://github.com/cxquy91/PERFect
VignetteBuilder: knitr
BugReports: https://github.com/cxquy91/PERFect/issues
git_url: https://git.bioconductor.org/packages/PERFect
git_branch: RELEASE_3_13
git_last_commit: 65e5dbb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PERFect_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PERFect_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PERFect_1.6.0.tgz
vignettes: vignettes/PERFect/inst/doc/MethodIllustration.html
vignetteTitles: Method Illustration
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PERFect/inst/doc/MethodIllustration.R
dependencyCount: 90
Package: periodicDNA
Version: 1.2.0
Depends: R (>= 4.0), Biostrings, GenomicRanges, IRanges, BSgenome,
BiocParallel
Imports: S4Vectors, rtracklayer, stats, GenomeInfoDb, magrittr, zoo,
ggplot2, methods, parallel, cowplot
Suggests: BSgenome.Scerevisiae.UCSC.sacCer3,
BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm6,
BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Mmusculus.UCSC.mm10, reticulate, testthat, covr,
knitr, rmarkdown, pkgdown
License: GPL-3 + file LICENSE
MD5sum: 625b2033b41bdd894d2d328697dd8796
NeedsCompilation: no
Title: Set of tools to identify periodic occurrences of k-mers in DNA
sequences
Description: This R package helps the user identify k-mers (e.g. di- or
tri-nucleotides) present periodically in a set of genomic loci
(typically regulatory elements). The functions of this package
provide a straightforward approach to find periodic occurrences
of k-mers in DNA sequences, such as regulatory elements. It is
not aimed at identifying motifs separated by a conserved
distance; for this type of analysis, please visit MEME website.
biocViews: SequenceMatching, MotifDiscovery, MotifAnnotation,
Sequencing, Coverage, Alignment, DataImport
Author: Jacques Serizay [aut, cre]
(<https://orcid.org/0000-0002-4295-0624>)
Maintainer: Jacques Serizay <jacquesserizay@gmail.com>
URL: https://github.com/js2264/periodicDNA
VignetteBuilder: knitr
BugReports: https://github.com/js2264/periodicDNA/issues
git_url: https://git.bioconductor.org/packages/periodicDNA
git_branch: RELEASE_3_13
git_last_commit: 0a265ed
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/periodicDNA_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/periodicDNA_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/periodicDNA_1.2.0.tgz
vignettes: vignettes/periodicDNA/inst/doc/periodicDNA.html
vignetteTitles: Introduction to periodicDNA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/periodicDNA/inst/doc/periodicDNA.R
dependencyCount: 76
Package: perturbatr
Version: 1.12.0
Depends: R (>= 3.5), methods, stats
Imports: dplyr, ggplot2, tidyr, assertthat, lme4, splines, igraph,
foreach, parallel, doParallel, diffusr, lazyeval, tibble, grid,
utils, graphics, scales, magrittr, formula.tools, rlang
Suggests: testthat, lintr, knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 2300078744d8ac59f122d3e6679dfacc
NeedsCompilation: no
Title: Statistical Analysis of High-Throughput Genetic Perturbation
Screens
Description: perturbatr does stage-wise analysis of large-scale genetic
perturbation screens for integrated data sets consisting of
multiple screens. For multiple integrated perturbation screens
a hierarchical model that considers the variance between
different biological conditions is fitted. The resulting list
of gene effects is then further extended using a network
propagation algorithm to correct for false negatives.
biocViews: ImmunoOncology, Regression, CellBasedAssays, Network
Author: Simon Dirmeier [aut, cre]
Maintainer: Simon Dirmeier <simon.dirmeier@web.de>
URL: https://github.com/cbg-ethz/perturbatr
VignetteBuilder: knitr
BugReports: https://github.com/cbg-ethz/perturbatr/issues
git_url: https://git.bioconductor.org/packages/perturbatr
git_branch: RELEASE_3_13
git_last_commit: 43f04ea
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/perturbatr_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/perturbatr_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/perturbatr_1.12.0.tgz
vignettes: vignettes/perturbatr/inst/doc/perturbatr.html
vignetteTitles: perturbatr cookbook
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/perturbatr/inst/doc/perturbatr.R
dependencyCount: 62
Package: PFP
Version: 1.0.0
Depends: R (>= 4.0)
Imports: graph, igraph, KEGGgraph, clusterProfiler, ggplot2, plyr,
tidyr, magrittr, stats, methods, utils
Suggests: knitr, testthat, rmarkdown, org.Hs.eg.db
License: GPL-2
MD5sum: 1b2955d6cb69886a95158764eab245c8
NeedsCompilation: no
Title: Pathway Fingerprint Framework in R
Description: An implementation of the pathway fingerprint framework
that introduced in paper "Pathway Fingerprint: a novel pathway
knowledge and topology based method for biomarker discovery and
characterization". This method provides a systematic
comparisons between a gene set (such as a list of
differentially expressed genes) and well-studied "basic pathway
networks" (KEGG pathways), measuring the importance of pathways
and genes for the gene set. The package is helpful for
researchers to find the biomarkers and its function.
biocViews: Software, Pathways, RNASeq
Author: XC Zhang [aut, cre]
Maintainer: XC Zhang <kunghero@163.com>
URL: https://github.com/aib-group/PFP
VignetteBuilder: knitr
BugReports: https://github.com/aib-group/PFP/issues
git_url: https://git.bioconductor.org/packages/PFP
git_branch: RELEASE_3_13
git_last_commit: 80a0d85
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PFP_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PFP_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PFP_1.0.0.tgz
vignettes: vignettes/PFP/inst/doc/PFP.html
vignetteTitles: Pathway fingerprint: a tool for biomarker discovery
based on gene expression data and pathway knowledge
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PFP/inst/doc/PFP.R
dependencyCount: 130
Package: pgca
Version: 1.16.0
Imports: utils, stats
Suggests: knitr, testthat
License: GPL (>= 2)
MD5sum: ad3e5eaccc28f138193e7f617c0b6689
NeedsCompilation: no
Title: PGCA: An Algorithm to Link Protein Groups Created from MS/MS
Data
Description: Protein Group Code Algorithm (PGCA) is a computationally
inexpensive algorithm to merge protein summaries from multiple
experimental quantitative proteomics data. The algorithm
connects two or more groups with overlapping accession numbers.
In some cases, pairwise groups are mutually exclusive but they
may still be connected by another group (or set of groups) with
overlapping accession numbers. Thus, groups created by PGCA
from multiple experimental runs (i.e., global groups) are
called "connected" groups. These identified global protein
groups enable the analysis of quantitative data available for
protein groups instead of unique protein identifiers.
biocViews:
WorkflowStep,AssayDomain,Proteomics,MassSpectrometry,ImmunoOncology
Author: Gabriela Cohen-Freue <gcohen@stat.ubc.ca>
Maintainer: Gabriela Cohen-Freue <gcohen@stat.ubc.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pgca
git_branch: RELEASE_3_13
git_last_commit: fd2bcd1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pgca_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pgca_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pgca_1.16.0.tgz
vignettes: vignettes/pgca/inst/doc/intro.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pgca/inst/doc/intro.R
dependencyCount: 2
Package: phantasus
Version: 1.12.0
Depends: R (>= 3.5)
Imports: ggplot2, protolite, Biobase, GEOquery, Rook, htmltools,
httpuv, jsonlite, limma, opencpu, assertthat, methods, httr,
rhdf5, utils, parallel, stringr, fgsea (>= 1.9.4), svglite,
gtable, stats, Matrix, pheatmap, scales, ccaPP, grid,
grDevices, AnnotationDbi, DESeq2, curl
Suggests: testthat, BiocStyle, knitr, rmarkdown, data.table
License: MIT + file LICENSE
MD5sum: b16b3606c7d8d58b5677ef250d7ebd41
NeedsCompilation: no
Title: Visual and interactive gene expression analysis
Description: Phantasus is a web-application for visual and interactive
gene expression analysis. Phantasus is based on Morpheus – a
web-based software for heatmap visualisation and analysis,
which was integrated with an R environment via OpenCPU API.
Aside from basic visualization and filtering methods, R-based
methods such as k-means clustering, principal component
analysis or differential expression analysis with limma package
are supported.
biocViews: GeneExpression, GUI, Visualization, DataRepresentation,
Transcriptomics, RNASeq, Microarray, Normalization, Clustering,
DifferentialExpression, PrincipalComponent, ImmunoOncology
Author: Daria Zenkova [aut], Vladislav Kamenev [aut], Rita Sablina
[ctb], Maxim Kleverov [ctb], Maxim Artyomov [aut], Alexey
Sergushichev [aut, cre]
Maintainer: Alexey Sergushichev <alsergbox@gmail.com>
URL: https://genome.ifmo.ru/phantasus,
https://artyomovlab.wustl.edu/phantasus
VignetteBuilder: knitr
BugReports: https://github.com/ctlab/phantasus/issues
git_url: https://git.bioconductor.org/packages/phantasus
git_branch: RELEASE_3_13
git_last_commit: e2115e2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/phantasus_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/phantasus_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/phantasus_1.12.0.tgz
vignettes: vignettes/phantasus/inst/doc/phantasus-tutorial.html
vignetteTitles: Using phantasus application
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/phantasus/inst/doc/phantasus-tutorial.R
dependencyCount: 145
Package: PharmacoGx
Version: 2.4.0
Depends: R (>= 3.6), CoreGx
Imports: BiocGenerics, Biobase, S4Vectors, SummarizedExperiment,
MultiAssayExperiment, BiocParallel, ggplot2, magicaxis,
RColorBrewer, parallel, caTools, methods, downloader, stats,
utils, graphics, grDevices, reshape2, jsonlite, data.table,
glue
Suggests: pander, rmarkdown, knitr, knitcitations, crayon, testthat,
markdown
License: Artistic-2.0
MD5sum: 789a8dd734ee995d78249cd261ed8add
NeedsCompilation: no
Title: Analysis of Large-Scale Pharmacogenomic Data
Description: Contains a set of functions to perform large-scale
analysis of pharmaco-genomic data. These include the
PharmacoSet object for storing the results of pharmacogenomic
experiments, as well as a number of functions for computing
common summaries of drug-dose response and correlating them
with the molecular features in a cancer cell-line.
biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics,
Software, Classification
Author: Petr Smirnov [aut], Zhaleh Safikhani [aut], Christopher Eeles
[aut], Mark Freeman [aut], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
BugReports: https://github.com/bhklab/PharmacoGx/issues
git_url: https://git.bioconductor.org/packages/PharmacoGx
git_branch: RELEASE_3_13
git_last_commit: 1565ba6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PharmacoGx_2.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PharmacoGx_2.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PharmacoGx_2.4.0.tgz
vignettes: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.pdf,
vignettes/PharmacoGx/inst/doc/PharmacoGx.pdf
vignetteTitles: Creating a PharmacoSet Object, PharmacoGx: An R Package
for Analysis of Large Pharmacogenomic Datasets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.R,
vignettes/PharmacoGx/inst/doc/PharmacoGx.R
importsMe: Xeva
suggestsMe: ToxicoGx
dependencyCount: 131
Package: phemd
Version: 1.8.0
Depends: R (>= 3.5), monocle
Imports: SingleCellExperiment, RColorBrewer, igraph, transport, pracma,
cluster, Rtsne, destiny, Seurat, RANN, ggplot2, maptree,
pheatmap, scatterplot3d, VGAM, methods, grDevices, graphics,
stats, utils, cowplot, S4Vectors, BiocGenerics,
SummarizedExperiment, Biobase, phateR, reticulate
Suggests: knitr
License: GPL-2
MD5sum: 8541e8adcac2a16878fe7ec766577bec
NeedsCompilation: no
Title: Phenotypic EMD for comparison of single-cell samples
Description: Package for comparing and generating a low-dimensional
embedding of multiple single-cell samples.
biocViews: Clustering, ComparativeGenomics, Proteomics,
Transcriptomics, Sequencing, DimensionReduction, SingleCell,
DataRepresentation, Visualization, MultipleComparison
Author: William S Chen
Maintainer: William S Chen <wil.yum.chen@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/phemd
git_branch: RELEASE_3_13
git_last_commit: 6c77cb3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/phemd_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/phemd_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/phemd_1.8.0.tgz
vignettes: vignettes/phemd/inst/doc/phemd.html
vignetteTitles: PhEMD vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/phemd/inst/doc/phemd.R
dependencyCount: 184
Package: PhenoGeneRanker
Version: 1.0.0
Imports: igraph, Matrix, foreach, doParallel, dplyr, stats, utils,
parallel
Suggests: knitr, rmarkdown
License: Creative Commons Attribution 4.0 International License
MD5sum: 1d63f3040d5f45c806e5a508c67b1ae7
NeedsCompilation: no
Title: PhenoGeneRanker: A gene and phenotype prioritization tool
Description: This package is a gene/phenotype prioritization tool that
utilizes multiplex heterogeneous gene phenotype network.
PhenoGeneRanker allows multi-layer gene and phenotype networks.
It also calculates empirical p-values of gene/phenotype ranking
using random stratified sampling of genes/phenotypes based on
their connectivity degree in the network.
https://dl.acm.org/doi/10.1145/3307339.3342155.
biocViews: BiomedicalInformatics, GenePrediction, GraphAndNetwork,
Network, NetworkInference, Pathways, Software, SystemsBiology
Author: Cagatay Dursun [aut, cre]
Maintainer: Cagatay Dursun <cagataydursun@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PhenoGeneRanker
git_branch: RELEASE_3_13
git_last_commit: 2cbd92c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PhenoGeneRanker_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PhenoGeneRanker_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PhenoGeneRanker_1.0.0.tgz
vignettes: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.html
vignetteTitles: PhenoGeneRanker
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.R
dependencyCount: 32
Package: phenopath
Version: 1.16.0
Imports: Rcpp (>= 0.12.8), SummarizedExperiment, methods, stats, dplyr,
tibble, ggplot2, tidyr
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, forcats, testthat, BiocStyle,
SingleCellExperiment
License: Apache License (== 2.0)
MD5sum: 2d2739d1f7beb4a7bf6f85877874cbf9
NeedsCompilation: yes
Title: Genomic trajectories with heterogeneous genetic and
environmental backgrounds
Description: PhenoPath infers genomic trajectories (pseudotimes) in the
presence of heterogeneous genetic and environmental backgrounds
and tests for interactions between them.
biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian,
SingleCell, PrincipalComponent
Author: Kieran Campbell
Maintainer: Kieran Campbell <kieranrcampbell@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/phenopath
git_branch: RELEASE_3_13
git_last_commit: de82be6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/phenopath_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/phenopath_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/phenopath_1.16.0.tgz
vignettes: vignettes/phenopath/inst/doc/introduction_to_phenopath.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/phenopath/inst/doc/introduction_to_phenopath.R
suggestsMe: splatter
dependencyCount: 63
Package: phenoTest
Version: 1.40.0
Depends: R (>= 3.6.0), Biobase, methods, annotate, Heatplus, BMA,
ggplot2, Hmisc
Imports: survival, limma, gplots, Category, AnnotationDbi, hopach,
biomaRt, GSEABase, genefilter, xtable, annotate, mgcv,
hgu133a.db, ellipse
Suggests: GSEABase, GO.db
Enhances: parallel, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db,
org.Hs.eg.db, org.Dm.eg.db
License: GPL (>=2)
MD5sum: 3fa53bc402f3f43a69d4a315fa54659b
NeedsCompilation: no
Title: Tools to test association between gene expression and phenotype
in a way that is efficient, structured, fast and scalable. We
also provide tools to do GSEA (Gene set enrichment analysis)
and copy number variation.
Description: Tools to test correlation between gene expression and
phenotype in a way that is efficient, structured, fast and
scalable. GSEA is also provided.
biocViews: Microarray, DifferentialExpression, MultipleComparison,
Clustering, Classification
Author: Evarist Planet
Maintainer: Evarist Planet <evarist.planet@epfl.ch>
git_url: https://git.bioconductor.org/packages/phenoTest
git_branch: RELEASE_3_13
git_last_commit: 2fbc5e7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/phenoTest_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/phenoTest_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/phenoTest_1.40.0.tgz
vignettes: vignettes/phenoTest/inst/doc/phenoTest.pdf
vignetteTitles: Manual for the phenoTest library
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/phenoTest/inst/doc/phenoTest.R
importsMe: canceR
dependencyCount: 139
Package: PhenStat
Version: 2.28.0
Depends: R (>= 3.5.0)
Imports: SmoothWin, methods, car, nlme, nortest, MASS, msgps, logistf,
knitr, tools, pingr, ggplot2, reshape, corrplot, graph, lme4,
graphics, grDevices, utils, stats
Suggests: RUnit, BiocGenerics
License: file LICENSE
MD5sum: 66042ebf2012408bb0629fd662020f72
NeedsCompilation: no
Title: Statistical analysis of phenotypic data
Description: Package contains methods for statistical analysis of
phenotypic data.
biocViews: StatisticalMethod
Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason, Hamed
Haselimashhadi
Maintainer: Hamed Haselimashhadi <hamedhm@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/PhenStat
git_branch: RELEASE_3_13
git_last_commit: d1496a6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PhenStat_2.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PhenStat_2.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PhenStat_2.28.0.tgz
vignettes: vignettes/PhenStat/inst/doc/PhenStat.pdf
vignetteTitles: PhenStat Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PhenStat/inst/doc/PhenStat.R
dependencyCount: 114
Package: philr
Version: 1.18.0
Imports: ape, phangorn, tidyr, ggplot2, ggtree
Suggests: testthat, knitr, rmarkdown, BiocStyle, phyloseq, glmnet,
dplyr
License: GPL-3
MD5sum: 69c2bb6966acb47732a452ad587de901
NeedsCompilation: no
Title: Phylogenetic partitioning based ILR transform for metagenomics
data
Description: PhILR is short for Phylogenetic Isometric Log-Ratio
Transform. This package provides functions for the analysis of
compositional data (e.g., data representing proportions of
different variables/parts). Specifically this package allows
analysis of compositional data where the parts can be related
through a phylogenetic tree (as is common in microbiota survey
data) and makes available the Isometric Log Ratio transform
built from the phylogenetic tree and utilizing a weighted
reference measure.
biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics,
Software
Author: Justin Silverman
Maintainer: Justin Silverman <jsilve24@gmail.com>
URL: https://github.com/jsilve24/philr
VignetteBuilder: knitr
BugReports: https://github.com/jsilve24/philr/issues
git_url: https://git.bioconductor.org/packages/philr
git_branch: RELEASE_3_13
git_last_commit: 5dd64f6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/philr_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/philr_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/philr_1.18.0.tgz
vignettes: vignettes/philr/inst/doc/philr-intro.html
vignetteTitles: Introduction to PhILR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/philr/inst/doc/philr-intro.R
dependencyCount: 63
Package: PhIPData
Version: 1.0.0
Depends: R (>= 4.1.0), SummarizedExperiment (>= 1.3.81)
Imports: BiocGenerics, methods, GenomicRanges, IRanges, S4Vectors,
edgeR, cli, utils
Suggests: BiocStyle, testthat, knitr, rmarkdown, covr, dplyr, readr
License: MIT + file LICENSE
MD5sum: b6fbb6629d9419c0d16e4b387d63e016
NeedsCompilation: no
Title: Container for PhIP-Seq Experiments
Description: PhIPData defines an S4 class for phage-immunoprecipitation
sequencing (PhIP-seq) experiments. Buliding upon the
RangedSummarizedExperiment class, PhIPData enables users to
coordinate metadata with experimental data in analyses.
Additionally, PhIPData provides specialized methods to subset
and identify beads-only samples, subset objects using virus
aliases, and use existing peptide libraries to populate object
parameters.
biocViews: Infrastructure, DataRepresentation, Sequencing, Coverage
Author: Athena Chen [aut, cre]
(<https://orcid.org/0000-0001-6900-2264>), Rob Scharpf [aut],
Ingo Ruczinski [aut]
Maintainer: Athena Chen <achen70@jhu.edu>
VignetteBuilder: knitr
BugReports: https://github.com/athchen/PhIPData/issues
git_url: https://git.bioconductor.org/packages/PhIPData
git_branch: RELEASE_3_13
git_last_commit: 988e473
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PhIPData_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PhIPData_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PhIPData_1.0.0.tgz
vignettes: vignettes/PhIPData/inst/doc/PhIPData.html
vignetteTitles: PhIPData: A Container for PhIP-Seq Experiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PhIPData/inst/doc/PhIPData.R
dependencyCount: 32
Package: phosphonormalizer
Version: 1.16.0
Depends: R (>= 4.0)
Imports: plyr, stats, graphics, matrixStats, methods
Suggests: knitr, rmarkdown, testthat
Enhances: MSnbase
License: GPL (>= 2)
MD5sum: f74bc4b0fc3da39cd114be57dfd28eeb
NeedsCompilation: no
Title: Compensates for the bias introduced by median normalization in
Description: It uses the overlap between enriched and non-enriched
datasets to compensate for the bias introduced in global
phosphorylation after applying median normalization.
biocViews: Software, StatisticalMethod, WorkflowStep, Normalization,
Proteomics
Author: Sohrab Saraei [aut, cre], Tomi Suomi [ctb], Otto Kauko [ctb],
Laura Elo [ths]
Maintainer: Sohrab Saraei <sohrab.saraei@blueprintgenetics.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/phosphonormalizer
git_branch: RELEASE_3_13
git_last_commit: 70319d9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/phosphonormalizer_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/phosphonormalizer_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/phosphonormalizer_1.16.0.tgz
vignettes: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.pdf,
vignettes/phosphonormalizer/inst/doc/vignette.html
vignetteTitles: phosphonormalizer: Phosphoproteomics Normalization,
Pairwise normalization of phosphoproteomics data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.R,
vignettes/phosphonormalizer/inst/doc/vignette.R
dependencyCount: 7
Package: PhosR
Version: 1.2.0
Depends: R (>= 4.1.0)
Imports: ruv, e1071, dendextend, limma, pcaMethods, stats,
RColorBrewer, circlize, dplyr, igraph, pheatmap,
preprocessCore, tidyr, rlang, graphics, grDevices, utils,
SummarizedExperiment, methods, S4Vectors, BiocGenerics,
ggplot2, GGally, ggdendro, ggpubr, network, reshape2, ggtext
Suggests: testthat, knitr, rgl, sna, ClueR, directPA, rmarkdown,
org.Rn.eg.db, org.Mm.eg.db, reactome.db, annotate, BiocStyle,
stringr, calibrate
License: GPL-3 + file LICENSE
Archs: i386, x64
MD5sum: e084d4207b41a2dcb13827f359c0c7b6
NeedsCompilation: no
Title: A set of methods and tools for comprehensive analysis of
phosphoproteomics data
Description: PhosR is a package for the comprenhensive analysis of
phosphoproteomic data. There are two major components to PhosR:
processing and downstream analysis. PhosR consists of various
processing tools for phosphoproteomics data including
filtering, imputation, normalisation, and functional analysis
for inferring active kinases and signalling pathways.
biocViews: Software, ResearchField, Proteomics
Author: Pengyi Yang [aut], Taiyun Kim [aut, cre], Hani Jieun Kim [aut]
Maintainer: Taiyun Kim <taiyun.kim91@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PhosR
git_branch: RELEASE_3_13
git_last_commit: cd5ab36
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PhosR_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PhosR_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PhosR_1.2.0.tgz
vignettes: vignettes/PhosR/inst/doc/PhosR.html
vignetteTitles: An introduction to PhosR package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PhosR/inst/doc/PhosR.R
dependencyCount: 149
Package: PhyloProfile
Version: 1.6.6
Depends: R (>= 4.1.0)
Imports: ape, bioDist, BiocStyle, Biostrings, colourpicker, data.table,
DT, energy, ExperimentHub, ggplot2, gridExtra, pbapply,
RColorBrewer, RCurl, shiny, shinyBS, shinyjs, OmaDB, plyr,
xml2, zoo
Suggests: knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: 818046c5b592fea64a0425ba30c8461f
NeedsCompilation: no
Title: PhyloProfile
Description: PhyloProfile is a tool for exploring complex phylogenetic
profiles. Phylogenetic profiles, presence/absence patterns of
genes over a set of species, are commonly used to trace the
functional and evolutionary history of genes across species and
time. With PhyloProfile we can enrich regular phylogenetic
profiles with further data like sequence/structure similarity,
to make phylogenetic profiling more meaningful. Besides the
interactive visualisation powered by R-Shiny, the package
offers a set of further analysis features to gain insights like
the gene age estimation or core gene identification.
biocViews: Software, Visualization, DataRepresentation,
MultipleComparison, FunctionalPrediction
Author: Vinh Tran [aut, cre], Bastian Greshake Tzovaras [aut], Ingo
Ebersberger [aut], Carla Mölbert [ctb]
Maintainer: Vinh Tran <tran@bio.uni-frankfurt.de>
URL: https://github.com/BIONF/PhyloProfile/
VignetteBuilder: knitr
BugReports: https://github.com/BIONF/PhyloProfile/issues
git_url: https://git.bioconductor.org/packages/PhyloProfile
git_branch: RELEASE_3_13
git_last_commit: 92e2e9e
git_last_commit_date: 2021-06-29
Date/Publication: 2021-07-01
source.ver: src/contrib/PhyloProfile_1.6.6.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PhyloProfile_1.6.6.zip
mac.binary.ver: bin/macosx/contrib/4.1/PhyloProfile_1.6.6.tgz
vignettes: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.html
vignetteTitles: PhyloProfile
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.R
dependencyCount: 138
Package: phyloseq
Version: 1.36.0
Depends: R (>= 3.3.0)
Imports: ade4 (>= 1.7.4), ape (>= 5.0), Biobase (>= 2.36.2),
BiocGenerics (>= 0.22.0), biomformat (>= 1.0.0), Biostrings (>=
2.40.0), cluster (>= 2.0.4), data.table (>= 1.10.4), foreach
(>= 1.4.3), ggplot2 (>= 2.1.0), igraph (>= 1.0.1), methods (>=
3.3.0), multtest (>= 2.28.0), plyr (>= 1.8.3), reshape2 (>=
1.4.1), scales (>= 0.4.0), vegan (>= 2.5)
Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58),
knitr (>= 1.16), magrittr (>= 1.5), metagenomeSeq (>= 1.14),
rmarkdown (>= 1.6), testthat (>= 1.0.2)
Enhances: doParallel (>= 1.0.10)
License: AGPL-3
MD5sum: 5ccca0a61e66d256db1fc40074261b8f
NeedsCompilation: no
Title: Handling and analysis of high-throughput microbiome census data
Description: phyloseq provides a set of classes and tools to facilitate
the import, storage, analysis, and graphical display of
microbiome census data.
biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics,
Clustering, Classification, MultipleComparison,
GeneticVariability
Author: Paul J. McMurdie <joey711@gmail.com>, Susan Holmes
<susan@stat.stanford.edu>, with contributions from Gregory
Jordan and Scott Chamberlain
Maintainer: Paul J. McMurdie <joey711@gmail.com>
URL: http://dx.plos.org/10.1371/journal.pone.0061217
VignetteBuilder: knitr
BugReports: https://github.com/joey711/phyloseq/issues
git_url: https://git.bioconductor.org/packages/phyloseq
git_branch: RELEASE_3_13
git_last_commit: f9af643
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/phyloseq_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/phyloseq_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/phyloseq_1.36.0.tgz
vignettes: vignettes/phyloseq/inst/doc/phyloseq-analysis.html,
vignettes/phyloseq/inst/doc/phyloseq-basics.html,
vignettes/phyloseq/inst/doc/phyloseq-FAQ.html,
vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html
vignetteTitles: analysis vignette, phyloseq basics vignette, phyloseq
Frequently Asked Questions (FAQ), phyloseq and DESeq2 on
Colorectal Cancer Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/phyloseq/inst/doc/phyloseq-analysis.R,
vignettes/phyloseq/inst/doc/phyloseq-basics.R,
vignettes/phyloseq/inst/doc/phyloseq-FAQ.R,
vignettes/phyloseq/inst/doc/phyloseq-mixture-models.R
dependsOnMe: microbiome, SIAMCAT, phyloseqGraphTest
importsMe: ANCOMBC, combi, metavizr, microbiomeDASim, PathoStat,
PERFect, RCM, reconsi, RPA, SPsimSeq, HMP2Data, adaptiveGPCA,
corncob, HTSSIP, microbial, mixKernel, SigTree, treeDA
suggestsMe: decontam, mia, MicrobiotaProcess, MMUPHin, philr,
HMP16SData, file2meco, metacoder, PLNmodels
dependencyCount: 76
Package: Pi
Version: 2.4.0
Depends: igraph, dnet, ggplot2, graphics
Imports: Matrix, GenomicRanges, GenomeInfoDb, supraHex, scales,
grDevices, ggrepel, ROCR, randomForest, glmnet, lattice, caret,
plot3D, stats, methods, MASS, IRanges, BiocGenerics, dplyr,
tidyr, ggnetwork, osfr, RCircos, purrr, readr, tibble
Suggests: foreach, doParallel, BiocStyle, knitr, rmarkdown, png,
GGally, gridExtra, ggforce, fgsea, RColorBrewer, ggpubr,
rtracklayer, ggbio, Gviz, data.tree, jsonlite
License: GPL-3
MD5sum: 0210029f8674b6a748d4451ae9732b75
NeedsCompilation: no
Title: Leveraging Genetic Evidence to Prioritise Drug Targets at the
Gene and Pathway Level
Description: Priority index or Pi is developed as a genomic-led target
prioritisation system. It integrates functional genomic
predictors, knowledge of network connectivity and immune
ontologies to prioritise potential drug targets at the gene and
pathway level.
biocViews: Software, Genetics, GraphAndNetwork, Pathways,
GeneExpression, GeneTarget, GenomeWideAssociation,
LinkageDisequilibrium, Network, HiC
Author: Hai Fang, the ULTRA-DD Consortium, Julian C Knight
Maintainer: Hai Fang <hfang.shanghai@gmail.com>
URL: http://pi314.r-forge.r-project.org
VignetteBuilder: knitr
BugReports: https://github.com/hfang-bristol/Pi/issues
git_url: https://git.bioconductor.org/packages/Pi
git_branch: RELEASE_3_13
git_last_commit: e5fb99d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Pi_2.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Pi_2.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Pi_2.4.0.tgz
vignettes: vignettes/Pi/inst/doc/Pi_vignettes.html
vignetteTitles: Pi User Manual (R/Bioconductor package)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Pi/inst/doc/Pi_vignettes.R
dependencyCount: 140
Package: piano
Version: 2.8.0
Depends: R (>= 3.5)
Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray,
fgsea, shiny, DT, htmlwidgets, shinyjs, shinydashboard,
visNetwork, scales, grDevices, graphics, stats, utils, methods
Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM,
gtools, biomaRt, snowfall, AnnotationDbi, knitr, rmarkdown,
BiocStyle
License: GPL (>=2)
MD5sum: 789531f2ea7d96e9978fc0eac466542b
NeedsCompilation: no
Title: Platform for integrative analysis of omics data
Description: Piano performs gene set analysis using various statistical
methods, from different gene level statistics and a wide range
of gene-set collections. Furthermore, the Piano package
contains functions for combining the results of multiple runs
of gene set analyses.
biocViews: Microarray, Preprocessing, QualityControl,
DifferentialExpression, Visualization, GeneExpression,
GeneSetEnrichment, Pathways
Author: Leif Varemo Wigge <piano.rpkg@gmail.com> and Intawat Nookaew
<piano.rpkg@gmail.com>
Maintainer: Leif Varemo Wigge <piano.rpkg@gmail.com>
URL: http://www.sysbio.se/piano
VignetteBuilder: knitr
BugReports: https://github.com/varemo/piano/issues
git_url: https://git.bioconductor.org/packages/piano
git_branch: RELEASE_3_13
git_last_commit: f9697f5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/piano_2.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/piano_2.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/piano_2.8.0.tgz
vignettes: vignettes/piano/inst/doc/piano-vignette.pdf,
vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.html
vignetteTitles: Piano - Platform for Integrative Analysis of Omics
data, Running gene-set anaysis with piano
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/piano/inst/doc/piano-vignette.R,
vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.R
importsMe: CoreGx, PDATK
suggestsMe: BloodCancerMultiOmics2017
dependencyCount: 93
Package: pickgene
Version: 1.64.0
Imports: graphics, grDevices, MASS, stats, utils
License: GPL (>= 2)
MD5sum: 81d6bac84fae39a36fca1c0271950870
NeedsCompilation: no
Title: Adaptive Gene Picking for Microarray Expression Data Analysis
Description: Functions to Analyze Microarray (Gene Expression) Data.
biocViews: Microarray, DifferentialExpression
Author: Brian S. Yandell <yandell@stat.wisc.edu>
Maintainer: Brian S. Yandell <yandell@stat.wisc.edu>
URL: http://www.stat.wisc.edu/~yandell/statgen
git_url: https://git.bioconductor.org/packages/pickgene
git_branch: RELEASE_3_13
git_last_commit: 94e97ec
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pickgene_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pickgene_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pickgene_1.64.0.tgz
vignettes: vignettes/pickgene/inst/doc/pickgene.pdf
vignetteTitles: Adaptive Gene Picking for Microarray Expression Data
Analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 6
Package: PICS
Version: 2.36.0
Depends: R (>= 3.0.0)
Imports: utils, stats, graphics, grDevices, methods, IRanges,
GenomicRanges, Rsamtools, GenomicAlignments
Suggests: rtracklayer, parallel, knitr
License: Artistic-2.0
MD5sum: 978b26683e09adce6162abc7dc938f1a
NeedsCompilation: yes
Title: Probabilistic inference of ChIP-seq
Description: Probabilistic inference of ChIP-Seq using an empirical
Bayes mixture model approach.
biocViews: Clustering, Visualization, Sequencing, ChIPseq
Author: Xuekui Zhang <xzhang@stat.ubc.ca>, Raphael Gottardo
<rgottard@fhcrc.org>
Maintainer: Renan Sauteraud <renan.sauteraud@gmail.com>
URL: https://github.com/SRenan/PICS
VignetteBuilder: knitr
BugReports: https://github.com/SRenan/PICS/issues
git_url: https://git.bioconductor.org/packages/PICS
git_branch: RELEASE_3_13
git_last_commit: 87607d0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PICS_2.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PICS_2.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PICS_2.36.0.tgz
vignettes: vignettes/PICS/inst/doc/PICS.html
vignetteTitles: The PICS users guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PICS/inst/doc/PICS.R
importsMe: PING
dependencyCount: 38
Package: Pigengene
Version: 1.18.10
Depends: R (>= 4.0.3), graph, BiocStyle (>= 2.18.1)
Imports: bnlearn (>= 4.7), C50 (>= 0.1.2), MASS, matrixStats, partykit,
Rgraphviz, WGCNA, GO.db, impute, preprocessCore, grDevices,
graphics, stats, utils, parallel, pheatmap (>= 1.0.8), dplyr,
gdata
Suggests: org.Hs.eg.db (>= 3.7.0), org.Mm.eg.db (>= 3.7.0), biomaRt (>=
2.30.0), knitr, AnnotationDbi, energy
License: GPL (>=2)
MD5sum: fbb3979a2ca12bb5c606e76cd9cd1044
NeedsCompilation: no
Title: Infers biological signatures from gene expression data
Description: Pigengene package provides an efficient way to infer
biological signatures from gene expression profiles. The
signatures are independent from the underlying platform, e.g.,
the input can be microarray or RNA Seq data. It can even infer
the signatures using data from one platform, and evaluate them
on the other. Pigengene identifies the modules (clusters) of
highly coexpressed genes using coexpression network analysis,
summarizes the biological information of each module in an
eigengene, learns a Bayesian network that models the
probabilistic dependencies between modules, and builds a
decision tree based on the expression of eigengenes.
biocViews: GeneExpression, RNASeq, NetworkInference, Network,
GraphAndNetwork, BiomedicalInformatics, SystemsBiology,
Transcriptomics, Classification, Clustering, DecisionTree,
DimensionReduction, PrincipalComponent, Microarray,
Normalization, ImmunoOncology
Author: Habil Zare, Amir Foroushani, Rupesh Agrahari, and Meghan Short
Maintainer: Habil Zare <zare@u.washington.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Pigengene
git_branch: RELEASE_3_13
git_last_commit: 72e780a
git_last_commit_date: 2021-09-27
Date/Publication: 2021-09-28
source.ver: src/contrib/Pigengene_1.18.10.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Pigengene_1.18.10.zip
mac.binary.ver: bin/macosx/contrib/4.1/Pigengene_1.18.10.tgz
vignettes: vignettes/Pigengene/inst/doc/Pigengene_inference.pdf
vignetteTitles: Pigengene: Computing and using eigengenes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Pigengene/inst/doc/Pigengene_inference.R
dependencyCount: 133
Package: PING
Version: 2.36.0
Depends: R(>= 3.5.0)
Imports: methods, PICS, graphics, grDevices, stats, Gviz, fda,
BSgenome, stats4, BiocGenerics, IRanges, GenomicRanges,
S4Vectors
Suggests: parallel, ShortRead, rtracklayer
License: Artistic-2.0
MD5sum: 56577c708ad1865f5270a82b3a11dd4c
NeedsCompilation: yes
Title: Probabilistic inference for Nucleosome Positioning with
MNase-based or Sonicated Short-read Data
Description: Probabilistic inference of ChIP-Seq using an empirical
Bayes mixture model approach.
biocViews: Clustering, StatisticalMethod, Visualization, Sequencing
Author: Xuekui Zhang <xuezhang@jhsph.edu>, Raphael Gottardo
<rgottard@fredhutch.org>, Sangsoon Woo <swoo@fhcrc.org>
Maintainer: Renan Sauteraud <renan.sauteraud@gmail.com>
git_url: https://git.bioconductor.org/packages/PING
git_branch: RELEASE_3_13
git_last_commit: 02aecc8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PING_2.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PING_2.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PING_2.36.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 162
Package: pipeComp
Version: 1.2.0
Depends: R (>= 4.1)
Imports: BiocParallel, S4Vectors, ComplexHeatmap, SingleCellExperiment,
SummarizedExperiment, Seurat, matrixStats, Matrix, cluster,
aricode, methods, utils, dplyr, grid, scales, scran,
viridisLite, clue, randomcoloR, ggplot2, cowplot,
intrinsicDimension, scater, knitr, reshape2, stats, Rtsne,
uwot, circlize, RColorBrewer
Suggests: BiocStyle, rmarkdown
License: GPL
MD5sum: 5593a60346eda32493ac2740733aecbc
NeedsCompilation: no
Title: pipeComp pipeline benchmarking framework
Description: A simple framework to facilitate the comparison of
pipelines involving various steps and parameters. The
`pipelineDefinition` class represents pipelines as, minimally,
a set of functions consecutively executed on the output of the
previous one, and optionally accompanied by step-wise
evaluation and aggregation functions. Given such an object, a
set of alternative parameters/methods, and benchmark datasets,
the `runPipeline` function then proceeds through all
combinations arguments, avoiding recomputing the same step
twice and compiling evaluations on the fly to avoid storing
potentially large intermediate data.
biocViews: GeneExpression, Transcriptomics, Clustering,
DataRepresentation
Author: Pierre-Luc Germain [cre, aut]
(<https://orcid.org/0000-0003-3418-4218>), Anthony Sonrel [aut]
(<https://orcid.org/0000-0002-2414-715X>), Mark D. Robinson
[aut, fnd] (<https://orcid.org/0000-0002-3048-5518>)
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
URL: https://doi.org/10.1186/s13059-020-02136-7
VignetteBuilder: knitr
BugReports: https://github.com/plger/pipeComp
git_url: https://git.bioconductor.org/packages/pipeComp
git_branch: RELEASE_3_13
git_last_commit: b8a4bed
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pipeComp_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pipeComp_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pipeComp_1.2.0.tgz
vignettes: vignettes/pipeComp/inst/doc/pipeComp_dea.html,
vignettes/pipeComp/inst/doc/pipeComp_scRNA.html,
vignettes/pipeComp/inst/doc/pipeComp.html
vignetteTitles: pipeComp_dea, pipeComp_scRNA, pipeComp
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/pipeComp/inst/doc/pipeComp_dea.R,
vignettes/pipeComp/inst/doc/pipeComp_scRNA.R,
vignettes/pipeComp/inst/doc/pipeComp.R
dependencyCount: 203
Package: pipeFrame
Version: 1.8.0
Depends: R (>= 3.6.1),
Imports: BSgenome, digest, visNetwork, magrittr, methods, Biostrings,
GenomeInfoDb, parallel, stats, utils
Suggests: BiocManager, knitr, rtracklayer, testthat
License: GPL-3
MD5sum: 3269c60b719b00c47c94397134984d21
NeedsCompilation: no
Title: Pipeline framework for bioinformatics in R
Description: pipeFrame is an R package for building a componentized
bioinformatics pipeline. Each step in this pipeline is wrapped
in the framework, so the connection among steps is created
seamlessly and automatically. Users could focus more on
fine-tuning arguments rather than spending a lot of time on
transforming file format, passing task outputs to task inputs
or installing the dependencies. Componentized step elements can
be customized into other new pipelines flexibly as well. This
pipeline can be split into several important functional steps,
so it is much easier for users to understand the complex
arguments from each step rather than parameter combination from
the whole pipeline. At the same time, componentized pipeline
can restart at the breakpoint and avoid rerunning the whole
pipeline, which may save a lot of time for users on pipeline
tuning or such issues as power off or process other interrupts.
biocViews: Software, Infrastructure, WorkflowStep
Author: Zheng Wei, Shining Ma
Maintainer: Zheng Wei <wzweizheng@qq.com>
URL: https://github.com/wzthu/pipeFrame
VignetteBuilder: knitr
BugReports: https://github.com/wzthu/pipeFrame/issues
git_url: https://git.bioconductor.org/packages/pipeFrame
git_branch: RELEASE_3_13
git_last_commit: e733729
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pipeFrame_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pipeFrame_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pipeFrame_1.8.0.tgz
vignettes: vignettes/pipeFrame/inst/doc/pipeFrame.html
vignetteTitles: An Introduction to pipeFrame
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pipeFrame/inst/doc/pipeFrame.R
dependsOnMe: enrichTF, esATAC
dependencyCount: 54
Package: pkgDepTools
Version: 1.58.0
Depends: methods, graph, RBGL
Imports: graph, RBGL
Suggests: Biobase, Rgraphviz, RCurl, BiocManager
License: GPL-2
MD5sum: 7e4fc9f2abc2bd4a0f040a2da2d7fc7e
NeedsCompilation: no
Title: Package Dependency Tools
Description: This package provides tools for computing and analyzing
dependency relationships among R packages. It provides tools
for building a graph-based representation of the dependencies
among all packages in a list of CRAN-style package
repositories. There are also utilities for computing
installation order of a given package. If the RCurl package is
available, an estimate of the download size required to install
a given package and its dependencies can be obtained.
biocViews: Infrastructure, GraphAndNetwork
Author: Seth Falcon [aut], Bioconductor Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/pkgDepTools
git_branch: RELEASE_3_13
git_last_commit: 706a7e5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pkgDepTools_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pkgDepTools_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pkgDepTools_1.58.0.tgz
vignettes: vignettes/pkgDepTools/inst/doc/pkgDepTools.pdf
vignetteTitles: How to Use pkgDepTools
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pkgDepTools/inst/doc/pkgDepTools.R
dependencyCount: 10
Package: planet
Version: 1.0.0
Depends: R (>= 4.0)
Imports: methods, tibble, magrittr, dplyr
Suggests: ggplot2, testthat, tidyr, scales, minfi, EpiDISH, knitr,
rmarkdown
License: GPL-2
Archs: i386, x64
MD5sum: 385afeadea173b9a9c2a2e737671e176
NeedsCompilation: no
Title: Placental DNA methylation analysis tools
Description: This package contains R functions to infer additional
biological variables to supplemental DNA methylation analysis
of placental data. This includes inferring ethnicity/ancestry,
gestational age, and cell composition from placental DNA
methylation array (450k/850k) data. The package comes with an
example processed placental dataset.
biocViews: Software, DifferentialMethylation, Epigenetics, Microarray,
MethylationArray, DNAMethylation, CpGIsland
Author: Victor Yuan [aut, cre], Wendy P. Robinson [ctb]
Maintainer: Victor Yuan <victor.2wy@gmail.com>
URL: https://victor.rbind.io/planet, http://github.com/wvictor14/planet
VignetteBuilder: knitr
BugReports: http://github.com/wvictor14/planet/issues
git_url: https://git.bioconductor.org/packages/planet
git_branch: RELEASE_3_13
git_last_commit: dbb7a2a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/planet_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/planet_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/planet_1.0.0.tgz
vignettes: vignettes/planet/inst/doc/planet.html
vignetteTitles: planet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/planet/inst/doc/planet.R
dependencyCount: 21
Package: plethy
Version: 1.30.0
Depends: R (>= 3.1.0), methods, DBI (>= 0.5-1), RSQLite (>= 1.1),
BiocGenerics, S4Vectors
Imports: Streamer, IRanges, reshape2, plyr, RColorBrewer,ggplot2,
Biobase
Suggests: RUnit, BiocStyle
License: GPL-3
MD5sum: bf603b9888283f47bfa9522dabe4cb35
NeedsCompilation: no
Title: R framework for exploration and analysis of respirometry data
Description: This package provides the infrastructure and tools to
import, query and perform basic analysis of whole body
plethysmography and metabolism data. Currently support is
limited to data derived from Buxco respirometry instruments as
exported by their FinePointe software.
biocViews: DataImport, biocViews, Infastructure,
DataRepresentation,TimeCourse
Author: Daniel Bottomly [aut, cre], Marty Ferris [ctb], Beth Wilmot
[aut], Shannon McWeeney [aut]
Maintainer: Daniel Bottomly <bottomly@ohsu.edu>
git_url: https://git.bioconductor.org/packages/plethy
git_branch: RELEASE_3_13
git_last_commit: fa08dc1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/plethy_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/plethy_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/plethy_1.30.0.tgz
vignettes: vignettes/plethy/inst/doc/plethy.pdf
vignetteTitles: plethy
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/plethy/inst/doc/plethy.R
dependencyCount: 63
Package: plgem
Version: 1.64.0
Depends: R (>= 2.10)
Imports: utils, Biobase (>= 2.5.5), MASS, methods
License: GPL-2
Archs: i386, x64
MD5sum: 4a7ffd8032e65f3c48b8be787bf3dcdc
NeedsCompilation: no
Title: Detect differential expression in microarray and proteomics
datasets with the Power Law Global Error Model (PLGEM)
Description: The Power Law Global Error Model (PLGEM) has been shown to
faithfully model the variance-versus-mean dependence that
exists in a variety of genome-wide datasets, including
microarray and proteomics data. The use of PLGEM has been shown
to improve the detection of differentially expressed genes or
proteins in these datasets.
biocViews: ImmunoOncology, Microarray, DifferentialExpression,
Proteomics, GeneExpression, MassSpectrometry
Author: Mattia Pelizzola <mattia.pelizzola@gmail.com> and Norman
Pavelka <normanpavelka@gmail.com>
Maintainer: Norman Pavelka <normanpavelka@gmail.com>
URL: http://www.genopolis.it
git_url: https://git.bioconductor.org/packages/plgem
git_branch: RELEASE_3_13
git_last_commit: a7b40ff
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/plgem_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/plgem_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/plgem_1.64.0.tgz
vignettes: vignettes/plgem/inst/doc/plgem.pdf
vignetteTitles: An introduction to PLGEM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/plgem/inst/doc/plgem.R
importsMe: INSPEcT
dependencyCount: 9
Package: plier
Version: 1.62.0
Depends: R (>= 2.0), methods
Imports: affy, Biobase, methods
License: GPL (>= 2)
MD5sum: 545bb6c056d9a54e12e1314a321789bc
NeedsCompilation: yes
Title: Implements the Affymetrix PLIER algorithm
Description: The PLIER (Probe Logarithmic Error Intensity Estimate)
method produces an improved signal by accounting for
experimentally observed patterns in probe behavior and handling
error at the appropriately at low and high signal values.
biocViews: Software
Author: Affymetrix Inc., Crispin J Miller, PICR
Maintainer: Crispin Miller <cmiller@picr.man.ac.uk>
git_url: https://git.bioconductor.org/packages/plier
git_branch: RELEASE_3_13
git_last_commit: 405f847
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/plier_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/plier_1.62.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/plier_1.62.0.tgz
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
suggestsMe: piano
dependencyCount: 13
Package: PloGO2
Version: 1.4.0
Depends: R (>= 4.0), GO.db, GOstats
Imports: lattice, httr, openxlsx, xtable
License: GPL-2
MD5sum: 3e7c0fc20bb1f3703001b28175d4b055
NeedsCompilation: no
Title: Plot Gene Ontology and KEGG pathway Annotation and Abundance
Description: Functions for enrichment analysis and plotting gene
ontology or KEGG pathway information for multiple data subsets
at the same time. It also enables encorporating multiple
conditions and abundance data.
biocViews: Annotation, Clustering, GO, GeneSetEnrichment, KEGG,
MultipleComparison, Pathways, Software, Visualization
Author: Dana Pascovici, Jemma Wu
Maintainer: Jemma Wu <jemma.wu@mq.edu.au>, Dana Pascovici
<dana.pascovici@mq.edu.au>
git_url: https://git.bioconductor.org/packages/PloGO2
git_branch: RELEASE_3_13
git_last_commit: ca5de78
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PloGO2_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PloGO2_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PloGO2_1.4.0.tgz
vignettes: vignettes/PloGO2/inst/doc/PloGO2_vignette.pdf,
vignettes/PloGO2/inst/doc/PloGO2_with_WGNCA_vignette.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PloGO2/inst/doc/PloGO2_vignette.R,
vignettes/PloGO2/inst/doc/PloGO2_with_WGNCA_vignette.R
dependencyCount: 67
Package: plotGrouper
Version: 1.10.0
Depends: R (>= 3.5)
Imports: ggplot2 (>= 3.0.0), dplyr (>= 0.7.6), tidyr (>= 0.2.0), tibble
(>= 1.4.2), stringr (>= 1.3.1), readr (>= 1.1.1), readxl (>=
1.1.0), scales (>= 1.0.0), stats, grid, gridExtra (>= 2.3), egg
(>= 0.4.0), gtable (>= 0.2.0), ggpubr (>= 0.1.8), shiny (>=
1.1.0), shinythemes (>= 1.1.1), colourpicker (>= 1.0), magrittr
(>= 1.5), Hmisc (>= 4.1.1), rlang (>= 0.2.2)
Suggests: knitr, htmltools, BiocStyle, rmarkdown, testthat
License: GPL-3
MD5sum: f037c45d3517d904e492917ef14bb3ed
NeedsCompilation: no
Title: Shiny app GUI wrapper for ggplot with built-in statistical
analysis
Description: A shiny app-based GUI wrapper for ggplot with built-in
statistical analysis. Import data from file and use dropdown
menus and checkboxes to specify the plotting variables, graph
type, and look of your plots. Once created, plots can be saved
independently or stored in a report that can be saved as a pdf.
If new data are added to the file, the report can be refreshed
to include new data. Statistical tests can be selected and
added to the graphs. Analysis of flow cytometry data is
especially integrated with plotGrouper. Count data can be
transformed to return the absolute number of cells in a sample
(this feature requires inclusion of the number of beads per
sample and information about any dilution performed).
biocViews: ImmunoOncology, FlowCytometry, GraphAndNetwork,
StatisticalMethod, DataImport, GUI, MultipleComparison
Author: John D. Gagnon [aut, cre]
Maintainer: John D. Gagnon <john.gagnon.42@gmail.com>
URL: https://jdgagnon.github.io/plotGrouper/
VignetteBuilder: knitr
BugReports: https://github.com/jdgagnon/plotGrouper/issues
git_url: https://git.bioconductor.org/packages/plotGrouper
git_branch: RELEASE_3_13
git_last_commit: 0004bdf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/plotGrouper_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/plotGrouper_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/plotGrouper_1.10.0.tgz
vignettes: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.html
vignetteTitles: plotGrouper
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.R
dependencyCount: 141
Package: PLPE
Version: 1.52.0
Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods
License: GPL (>= 2)
MD5sum: 4dd055e1c53be275798214d59e325e3d
NeedsCompilation: no
Title: Local Pooled Error Test for Differential Expression with Paired
High-throughput Data
Description: This package performs tests for paired high-throughput
data.
biocViews: Proteomics, Microarray, DifferentialExpression
Author: HyungJun Cho <hj4cho@korea.ac.kr> and Jae K. Lee
<jaeklee@virginia.edu>
Maintainer: Soo-heang Eo <hanansh@korea.ac.kr>
URL: http://www.korea.ac.kr/~stat2242/
git_url: https://git.bioconductor.org/packages/PLPE
git_branch: RELEASE_3_13
git_last_commit: 0a5e31d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PLPE_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PLPE_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PLPE_1.52.0.tgz
vignettes: vignettes/PLPE/inst/doc/PLPE.pdf
vignetteTitles: PLPE Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PLPE/inst/doc/PLPE.R
dependencyCount: 10
Package: plyranges
Version: 1.12.1
Depends: R (>= 3.5), BiocGenerics, IRanges (>= 2.12.0), GenomicRanges
(>= 1.28.4)
Imports: methods, dplyr, rlang (>= 0.2.0), magrittr, tidyselect (>=
1.0.0), rtracklayer, GenomicAlignments, GenomeInfoDb,
Rsamtools, S4Vectors (>= 0.23.10), utils
Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 2.1.0),
HelloRanges, HelloRangesData, BSgenome.Hsapiens.UCSC.hg19,
pasillaBamSubset, covr, ggplot2
License: Artistic-2.0
MD5sum: 8fdb2f859b880b63204e2a639224d5b3
NeedsCompilation: no
Title: A fluent interface for manipulating GenomicRanges
Description: A dplyr-like interface for interacting with the common
Bioconductor classes Ranges and GenomicRanges. By providing a
grammatical and consistent way of manipulating these classes
their accessiblity for new Bioconductor users is hopefully
increased.
biocViews: Infrastructure, DataRepresentation, WorkflowStep, Coverage
Author: Stuart Lee [aut, cre]
(<https://orcid.org/0000-0003-1179-8436>), Michael Lawrence
[aut, ctb], Dianne Cook [aut, ctb], Spencer Nystrom [ctb]
(<https://orcid.org/0000-0003-1000-1579>)
Maintainer: Stuart Lee <stuart.andrew.lee@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/sa-lee/plyranges
git_url: https://git.bioconductor.org/packages/plyranges
git_branch: RELEASE_3_13
git_last_commit: e3563e8
git_last_commit_date: 2021-06-27
Date/Publication: 2021-06-29
source.ver: src/contrib/plyranges_1.12.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/plyranges_1.12.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/plyranges_1.12.1.tgz
vignettes: vignettes/plyranges/inst/doc/an-introduction.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/plyranges/inst/doc/an-introduction.R
importsMe: BUSpaRse, dasper, InPAS, methylCC, multicrispr, nearBynding,
fluentGenomics
suggestsMe: memes
dependencyCount: 61
Package: pmm
Version: 1.24.0
Depends: R (>= 2.10)
Imports: lme4, splines
License: GPL-3
MD5sum: 55780ca7b526ec1a5b58a6df69315448
NeedsCompilation: no
Title: Parallel Mixed Model
Description: The Parallel Mixed Model (PMM) approach is suitable for
hit selection and cross-comparison of RNAi screens generated in
experiments that are performed in parallel under several
conditions. For example, we could think of the measurements or
readouts from cells under RNAi knock-down, which are infected
with several pathogens or which are grown from different cell
lines.
biocViews: SystemsBiology, Regression
Author: Anna Drewek
Maintainer: Anna Drewek <adrewek@stat.math.ethz.ch>
git_url: https://git.bioconductor.org/packages/pmm
git_branch: RELEASE_3_13
git_last_commit: 3bbc4d2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pmm_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pmm_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pmm_1.24.0.tgz
vignettes: vignettes/pmm/inst/doc/pmm-package.pdf
vignetteTitles: User manual for R-Package PMM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pmm/inst/doc/pmm-package.R
dependencyCount: 18
Package: pmp
Version: 1.4.0
Depends: R (>= 4.0)
Imports: stats, impute, pcaMethods, missForest, ggplot2, methods,
SummarizedExperiment, S4Vectors, matrixStats, grDevices,
reshape2, utils
Suggests: testthat, covr, knitr, rmarkdown, BiocStyle, gridExtra,
magick
License: GPL-3
Archs: i386, x64
MD5sum: 80fe7c817bf9521541623c110667bfd2
NeedsCompilation: no
Title: Peak Matrix Processing and signal batch correction for
metabolomics datasets
Description: Methods and tools for (pre-)processing of metabolomics
datasets (i.e. peak matrices), including filtering,
normalisation, missing value imputation, scaling, and signal
drift and batch effect correction methods. Filtering methods
are based on: the fraction of missing values (across samples or
features); Relative Standard Deviation (RSD) calculated from
the Quality Control (QC) samples; the blank samples.
Normalisation methods include Probabilistic Quotient
Normalisation (PQN) and normalisation to total signal
intensity. A unified user interface for several commonly used
missing value imputation algorithms is also provided. Supported
methods are: k-nearest neighbours (knn), random forests (rf),
Bayesian PCA missing value estimator (bpca), mean or median
value of the given feature and a constant small value. The
generalised logarithm (glog) transformation algorithm is
available to stabilise the variance across low and high
intensity mass spectral features. Finally, this package
provides an implementation of the Quality Control-Robust Spline
Correction (QCRSC) algorithm for signal drift and batch effect
correction of mass spectrometry-based datasets.
biocViews: MassSpectrometry, Metabolomics, Software, QualityControl,
BatchEffect
Author: Andris Jankevics [aut], Gavin Rhys Lloyd [aut, cre], Ralf
Johannes Maria Weber [aut]
Maintainer: Gavin Rhys Lloyd <g.r.lloyd@bham.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pmp
git_branch: RELEASE_3_13
git_last_commit: e0310dc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pmp_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pmp_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pmp_1.4.0.tgz
vignettes:
vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.html,
vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.html,
vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.html
vignetteTitles: Peak Matrix Processing for metabolomics datasets,
Signal drift and batch effect correction and mass spectral
quality assessment, Signal drift and batch effect correction
for mass spectrometry
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.R,
vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.R,
vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.R
suggestsMe: metabolomicsWorkbenchR, structToolbox
dependencyCount: 69
Package: PoDCall
Version: 1.0.0
Depends: R (>= 4.1)
Imports: ggplot2, gridExtra, mclust, diptest, rlist, shiny, DT,
LaplacesDemon, purrr, shinyjs, readr, grDevices, stats, utils
Suggests: knitr, testthat
License: GPL-3
MD5sum: f1a5e11d607baa8567d3af3b3f67cf10
NeedsCompilation: no
Title: Positive Droplet Calling for DNA Methylation Droplet Digital PCR
Description: Reads files exported from 'QuantaSoft' containing
amplitude values from a run of ddPCR (96 well plate) and
robustly sets thresholds to determine positive droplets for
each channel of each individual well. Concentration and
normalized concentration in addition to other metrics is then
calculated for each well. Results are returned as a table,
optionally written to file, as well as optional plots
(scatterplot and histogram) for both channels per well written
to file. The package includes a shiny application which
provides an interactive and user-friendly interface to the full
functionality of PoDCall.
biocViews: Classification, Epigenetics, ddPCR, DifferentialMethylation,
CpGIsland, DNAMethylation,
Author: Hans Petter Brodal [aut, cre], Marine Jeanmougin [aut]
Maintainer: Hans Petter Brodal <hans.petter.brodal@rr-research.no>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PoDCall
git_branch: RELEASE_3_13
git_last_commit: d410b26
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PoDCall_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PoDCall_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PoDCall_1.0.0.tgz
vignettes: vignettes/PoDCall/inst/doc/PoDCall.html
vignetteTitles: PoDCall: Positive Droplet Caller for DNA Methylation
ddPCR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PoDCall/inst/doc/PoDCall.R
dependencyCount: 85
Package: podkat
Version: 1.24.0
Depends: R (>= 3.5.0), methods, Rsamtools (>= 1.99.1), GenomicRanges
Imports: Rcpp (>= 0.11.1), parallel, stats, graphics, grDevices, utils,
Biobase, BiocGenerics, Matrix, GenomeInfoDb, IRanges,
Biostrings, BSgenome (>= 1.32.0)
LinkingTo: Rcpp, Rhtslib (>= 1.15.3)
Suggests: BSgenome.Hsapiens.UCSC.hg38.masked,
TxDb.Hsapiens.UCSC.hg38.knownGene,
BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24),
VariantAnnotation, SummarizedExperiment, knitr
License: GPL (>= 2)
MD5sum: a95c503c337b2c1ade4c5bc15d424e12
NeedsCompilation: yes
Title: Position-Dependent Kernel Association Test
Description: This package provides an association test that is capable
of dealing with very rare and even private variants. This is
accomplished by a kernel-based approach that takes the
positions of the variants into account. The test can be used
for pre-processed matrix data, but also directly for variant
data stored in VCF files. Association testing can be performed
whole-genome, whole-exome, or restricted to pre-defined regions
of interest. The test is complemented by tools for analyzing
and visualizing the results.
biocViews: Genetics, WholeGenome, Annotation, VariantAnnotation,
Sequencing, DataImport
Author: Ulrich Bodenhofer
Maintainer: Ulrich Bodenhofer <bodenhofer@bioinf.jku.at>
URL: http://www.bioinf.jku.at/software/podkat/
https://github.com/UBod/podkat
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/podkat
git_branch: RELEASE_3_13
git_last_commit: 01fa5e3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/podkat_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/podkat_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/podkat_1.24.0.tgz
vignettes: vignettes/podkat/inst/doc/podkat.pdf
vignetteTitles: PODKAT - An R Package for Association Testing Involving
Rare and Private Variants
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/podkat/inst/doc/podkat.R
dependencyCount: 46
Package: pogos
Version: 1.12.2
Depends: R (>= 3.5.0), rjson (>= 0.2.15), httr (>= 1.3.1)
Imports: methods, S4Vectors, utils, shiny, ontoProc, ggplot2, graphics
Suggests: knitr, DT, ontologyPlot, testthat, rmarkdown
License: Artistic-2.0
MD5sum: 316f0f49351dee9356bed1bc12be1a62
NeedsCompilation: no
Title: PharmacOGenomics Ontology Support
Description: Provide simple utilities for querying bhklab PharmacoDB,
modeling API outputs, and integrating to cell and compound
ontologies.
biocViews: Pharmacogenomics, PooledScreens, ImmunoOncology
Author: Vince Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pogos
git_branch: RELEASE_3_13
git_last_commit: 2735342
git_last_commit_date: 2021-08-29
Date/Publication: 2021-08-31
source.ver: src/contrib/pogos_1.12.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pogos_1.12.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/pogos_1.12.2.tgz
vignettes: vignettes/pogos/inst/doc/pogos.html
vignetteTitles: pogos -- simple interface to bhklab PharmacoDB with
emphasis on ontology
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pogos/inst/doc/pogos.R
suggestsMe: BiocOncoTK
dependencyCount: 108
Package: polyester
Version: 1.28.0
Depends: R (>= 3.0.0)
Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma,
zlibbioc
Suggests: knitr, ballgown
License: Artistic-2.0
MD5sum: 92b3f4d340aedcef4e553851967d7675
NeedsCompilation: no
Title: Simulate RNA-seq reads
Description: This package can be used to simulate RNA-seq reads from
differential expression experiments with replicates. The reads
can then be aligned and used to perform comparisons of methods
for differential expression.
biocViews: Sequencing, DifferentialExpression
Author: Alyssa C. Frazee, Andrew E. Jaffe, Rory Kirchner, Jeffrey T.
Leek
Maintainer: Jack Fu <jmfu@jhsph.edu>, Jeff Leek <jtleek@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/polyester
git_branch: RELEASE_3_13
git_last_commit: a71cce4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/polyester_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/polyester_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/polyester_1.28.0.tgz
vignettes: vignettes/polyester/inst/doc/polyester.html
vignetteTitles: The Polyester package for simulating RNA-seq reads
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/polyester/inst/doc/polyester.R
dependencyCount: 21
Package: POMA
Version: 1.2.0
Depends: R (>= 4.0)
Imports: broom, caret, clisymbols, ComplexHeatmap, crayon, dplyr,
e1071, ggcorrplot, ggplot2, ggraph, ggrepel, glasso (>= 1.11),
glmnet, impute, knitr, limma, magrittr, mixOmics, MSnbase (>=
2.12), patchwork, qpdf, randomForest, RankProd (>= 3.14),
rmarkdown, tibble, tidyr, vegan
Suggests: Biobase, BiocStyle, covr, plotly, tidyverse, testthat (>=
2.3.2)
License: GPL-3
MD5sum: bb7d1b259c90d9b5913fa9ff169e9a22
NeedsCompilation: no
Title: User-friendly Workflow for Metabolomics and Proteomics Data
Analysis
Description: A structured, reproducible and easy-to-use workflow for
the visualization, pre-processing, exploratory data analysis,
and statistical analysis of metabolomics and proteomics data.
The main aim of POMA is to enable a flexible data cleaning and
statistical analysis processes in one comprehensible and
user-friendly R package. This package also has a Shiny app
version that implements all POMA functions. See
https://github.com/pcastellanoescuder/POMAShiny.
biocViews: MassSpectrometry, Metabolomics, Proteomics, Software,
Visualization, Preprocessing, Normalization, ReportWriting
Author: Pol Castellano-Escuder [aut, cre]
(<https://orcid.org/0000-0001-6466-877X>), Cristina
Andrés-Lacueva [aut] (<https://orcid.org/0000-0002-8494-4978>),
Alex Sánchez-Pla [aut]
(<https://orcid.org/0000-0002-8673-7737>)
Maintainer: Pol Castellano-Escuder <polcaes@gmail.com>
URL: https://github.com/pcastellanoescuder/POMA
VignetteBuilder: knitr
BugReports: https://github.com/pcastellanoescuder/POMA/issues
git_url: https://git.bioconductor.org/packages/POMA
git_branch: RELEASE_3_13
git_last_commit: a990e43
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/POMA_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/POMA_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/POMA_1.2.0.tgz
vignettes: vignettes/POMA/inst/doc/POMA-demo.html,
vignettes/POMA/inst/doc/POMA-eda.html,
vignettes/POMA/inst/doc/POMA-normalization.html
vignetteTitles: POMA Workflow, POMA EDA Example, POMA Normalization
Methods
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/POMA/inst/doc/POMA-demo.R,
vignettes/POMA/inst/doc/POMA-eda.R,
vignettes/POMA/inst/doc/POMA-normalization.R
suggestsMe: fobitools
dependencyCount: 168
Package: PoTRA
Version: 1.8.2
Depends: R (>= 3.6.0), stats, BiocGenerics, org.Hs.eg.db, igraph,
graph, graphite
Suggests: BiocStyle, knitr, rmarkdown, colr, metap, repmis
License: LGPL
MD5sum: 367140953c28886fb4ebed313d8dbe7b
NeedsCompilation: no
Title: PoTRA: Pathways of Topological Rank Analysis
Description: The PoTRA analysis is based on topological ranks of genes
in biological pathways. PoTRA can be used to detect pathways
involved in disease (Li, Liu & Dinu, 2018). We use PageRank to
measure the relative topological ranks of genes in each
biological pathway, then select hub genes for each pathway, and
use Fishers Exact test to determine if the number of hub genes
in each pathway is altered from normal to cancer (Li, Liu &
Dinu, 2018). Alternatively, if the distribution of topological
ranks of gene in a pathway is altered between normal and
cancer, this pathway might also be involved in cancer (Li, Liu
& Dinu, 2018). Hence, we use the Kolmogorov–Smirnov test to
detect pathways that have an altered distribution of
topological ranks of genes between two phenotypes (Li, Liu &
Dinu, 2018). PoTRA can be used with the KEGG, Reactome, SMPDB
and PharmGKB, Panther, PathBank, etc databases from the devel
graphite library.
biocViews: GraphAndNetwork, StatisticalMethod, GeneExpression,
DifferentialExpression, Pathways, Reactome, Network, KEGG,
PathBank, Panther
Author: Chaoxing Li, Li Liu and Valentin Dinu
Maintainer: Margaret Linan <mlinan@asu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PoTRA
git_branch: RELEASE_3_13
git_last_commit: cf90958
git_last_commit_date: 2021-07-19
Date/Publication: 2021-07-20
source.ver: src/contrib/PoTRA_1.8.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PoTRA_1.8.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/PoTRA_1.8.2.tgz
vignettes: vignettes/PoTRA/inst/doc/PoTRA.html
vignetteTitles: Pathways of Topological Rank Analysis (PoTRA)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PoTRA/inst/doc/PoTRA.R
dependencyCount: 57
Package: powerTCR
Version: 1.12.0
Imports: cubature, doParallel, evmix, foreach, magrittr, methods,
parallel, purrr, stats, truncdist, vegan, VGAM
Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: 322d4e107fb82c8a3ef36f137587cdd3
NeedsCompilation: no
Title: Model-Based Comparative Analysis of the TCR Repertoire
Description: This package provides a model for the clone size
distribution of the TCR repertoire. Further, it permits
comparative analysis of TCR repertoire libraries based on
theoretical model fits.
biocViews: Software, Clustering, BiomedicalInformatics
Author: Hillary Koch
Maintainer: Hillary Koch <hillary.koch01@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/powerTCR
git_branch: RELEASE_3_13
git_last_commit: 7031cbb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/powerTCR_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/powerTCR_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/powerTCR_1.12.0.tgz
vignettes: vignettes/powerTCR/inst/doc/powerTCR.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/powerTCR/inst/doc/powerTCR.R
importsMe: scRepertoire
dependencyCount: 32
Package: POWSC
Version: 1.0.0
Depends: R (>= 4.1), Biobase, SingleCellExperiment, MAST
Imports: pheatmap, ggplot2, RColorBrewer, grDevices,
SummarizedExperiment, limma
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle
License: GPL-2
Archs: i386, x64
MD5sum: e277f0b1a3a20e95f456a8fdd3788b46
NeedsCompilation: no
Title: Simulation, power evaluation, and sample size recommendation for
single cell RNA-seq
Description: Determining the sample size for adequate power to detect
statistical significance is a crucial step at the design stage
for high-throughput experiments. Even though a number of
methods and tools are available for sample size calculation for
microarray and RNA-seq in the context of differential
expression (DE), this topic in the field of single-cell RNA
sequencing is understudied. Moreover, the unique data
characteristics present in scRNA-seq such as sparsity and
heterogeneity increase the challenge. We propose POWSC, a
simulation-based method, to provide power evaluation and sample
size recommendation for single-cell RNA sequencing DE analysis.
POWSC consists of a data simulator that creates realistic
expression data, and a power assessor that provides a
comprehensive evaluation and visualization of the power and
sample size relationship.
biocViews: DifferentialExpression, ImmunoOncology, SingleCell, Software
Author: Kenong Su [aut, cre], Hao Wu [aut]
Maintainer: Kenong Su <kenong.su@emory.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/POWSC
git_branch: RELEASE_3_13
git_last_commit: f67097a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/POWSC_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/POWSC_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/POWSC_1.0.0.tgz
vignettes: vignettes/POWSC/inst/doc/POWSC.html
vignetteTitles: The POWSC User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/POWSC/inst/doc/POWSC.R
dependencyCount: 70
Package: ppcseq
Version: 1.0.0
Depends: R (>= 4.1.0)
Imports: methods, Rcpp (>= 0.12.0), rstan (>= 2.18.1), rstantools (>=
2.0.0), tibble, dplyr, magrittr, purrr, future, furrr, tidyr
(>= 0.8.3.9000), lifecycle, ggplot2, foreach, tidybayes, edgeR,
benchmarkme, parallel, rlang, stats, utils, graphics
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0),
rstan (>= 2.18.1), StanHeaders (>= 2.18.0)
Suggests: knitr, testthat, BiocStyle, rmarkdown
License: GPL-3
MD5sum: a099813690035caa49521425d81458a3
NeedsCompilation: yes
Title: Probabilistic Outlier Identification for RNA Sequencing
Generalized Linear Models
Description: Relative transcript abundance has proven to be a valuable
tool for understanding the function of genes in biological
systems. For the differential analysis of transcript abundance
using RNA sequencing data, the negative binomial model is by
far the most frequently adopted. However, common methods that
are based on a negative binomial model are not robust to
extreme outliers, which we found to be abundant in public
datasets. So far, no rigorous and probabilistic methods for
detection of outliers have been developed for RNA sequencing
data, leaving the identification mostly to visual inspection.
Recent advances in Bayesian computation allow large-scale
comparison of observed data against its theoretical
distribution given in a statistical model. Here we propose
ppcseq, a key quality-control tool for identifying transcripts
that include outlier data points in differential expression
analysis, which do not follow a negative binomial distribution.
Applying ppcseq to analyse several publicly available datasets
using popular tools, we show that from 3 to 10 percent of
differentially abundant transcripts across algorithms and
datasets had statistics inflated by the presence of outliers.
biocViews: RNASeq, DifferentialExpression, GeneExpression,
Normalization, Clustering, QualityControl, Sequencing,
Transcription, Transcriptomics
Author: Stefano Mangiola [aut, cre]
(<https://orcid.org/0000-0001-7474-836X>)
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/stemangiola/ppcseq/issues
git_url: https://git.bioconductor.org/packages/ppcseq
git_branch: RELEASE_3_13
git_last_commit: f8b106a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ppcseq_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ppcseq_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ppcseq_1.0.0.tgz
vignettes: vignettes/ppcseq/inst/doc/introduction.html
vignetteTitles: Overview of the ppcseq package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ppcseq/inst/doc/introduction.R
dependencyCount: 101
Package: PPInfer
Version: 1.18.0
Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb,
yeastExpData
License: Artistic-2.0
MD5sum: fafb3e68f3e3e126f8a8294f6099e90b
NeedsCompilation: no
Title: Inferring functionally related proteins using protein
interaction networks
Description: Interactions between proteins occur in many, if not most,
biological processes. Most proteins perform their functions in
networks associated with other proteins and other biomolecules.
This fact has motivated the development of a variety of
experimental methods for the identification of protein
interactions. This variety has in turn ushered in the
development of numerous different computational approaches for
modeling and predicting protein interactions. Sometimes an
experiment is aimed at identifying proteins closely related to
some interesting proteins. A network based statistical learning
method is used to infer the putative functions of proteins from
the known functions of its neighboring proteins on a PPI
network. This package identifies such proteins often involved
in the same or similar biological functions.
biocViews: Software, StatisticalMethod, Network, GraphAndNetwork,
GeneSetEnrichment, NetworkEnrichment, Pathways
Author: Dongmin Jung, Xijin Ge
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
git_url: https://git.bioconductor.org/packages/PPInfer
git_branch: RELEASE_3_13
git_last_commit: 7713f20
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-20
source.ver: src/contrib/PPInfer_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PPInfer_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PPInfer_1.18.0.tgz
vignettes: vignettes/PPInfer/inst/doc/PPInfer.pdf
vignetteTitles: User manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PPInfer/inst/doc/PPInfer.R
dependsOnMe: gsean
dependencyCount: 116
Package: ppiStats
Version: 1.58.0
Depends: ScISI (>= 1.13.2), lattice, ppiData (>= 0.1.19)
Imports: Biobase, Category, graph, graphics, grDevices, lattice,
methods, RColorBrewer, stats
Suggests: yeastExpData, xtable
License: Artistic-2.0
MD5sum: fa4c2018af7a3223b327bb7a89e3c929
NeedsCompilation: no
Title: Protein-Protein Interaction Statistical Package
Description: Tools for the analysis of protein interaction data.
biocViews: Proteomics, GraphAndNetwork, Network, NetworkInference
Author: T. Chiang and D. Scholtens with contributions from W. Huber and
L. Wang
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/ppiStats
git_branch: RELEASE_3_13
git_last_commit: 0d9ffa4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ppiStats_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ppiStats_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ppiStats_1.58.0.tgz
vignettes: vignettes/ppiStats/inst/doc/ppiStats.pdf
vignetteTitles: ppiStats
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ppiStats/inst/doc/ppiStats.R
suggestsMe: RpsiXML, ppiData
dependencyCount: 69
Package: pqsfinder
Version: 2.8.0
Depends: Biostrings
Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods
LinkingTo: Rcpp, BH (>= 1.69.0)
Suggests: BiocStyle, knitr, Gviz, rtracklayer, ggplot2,
BSgenome.Hsapiens.UCSC.hg38, testthat, stringr, stringi
License: BSD_2_clause + file LICENSE
MD5sum: 91060034bc46b5ffc9f2c6d149e79223
NeedsCompilation: yes
Title: Identification of potential quadruplex forming sequences
Description: Pqsfinder detects DNA and RNA sequence patterns that are
likely to fold into an intramolecular G-quadruplex (G4). Unlike
many other approaches, pqsfinder is able to detect G4s folded
from imperfect G-runs containing bulges or mismatches or G4s
having long loops. Pqsfinder also assigns an integer score to
each hit that was fitted on G4 sequencing data and corresponds
to expected stability of the folded G4.
biocViews: MotifDiscovery, SequenceMatching, GeneRegulation
Author: Jiri Hon, Dominika Labudova, Matej Lexa and Tomas Martinek
Maintainer: Jiri Hon <jiri.hon@gmail.com>
URL: https://pqsfinder.fi.muni.cz
SystemRequirements: GNU make, C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pqsfinder
git_branch: RELEASE_3_13
git_last_commit: 777bb64
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pqsfinder_2.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pqsfinder_2.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pqsfinder_2.8.0.tgz
vignettes: vignettes/pqsfinder/inst/doc/pqsfinder.html
vignetteTitles: pqsfinder: User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R
dependencyCount: 22
Package: pram
Version: 1.8.0
Depends: R (>= 3.6)
Imports: methods, BiocParallel, tools, utils, data.table (>= 1.11.8),
GenomicAlignments (>= 1.16.0), rtracklayer (>= 1.40.6),
BiocGenerics (>= 0.26.0), GenomeInfoDb (>= 1.16.0),
GenomicRanges (>= 1.32.0), IRanges (>= 2.14.12), Rsamtools (>=
1.32.3), S4Vectors (>= 0.18.3)
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: GPL (>= 3)
MD5sum: e461ed4ac45440e9de67c5b110617d0d
NeedsCompilation: no
Title: Pooling RNA-seq datasets for assembling transcript models
Description: Publicly available RNA-seq data is routinely used for
retrospective analysis to elucidate new biology. Novel
transcript discovery enabled by large collections of RNA-seq
datasets has emerged as one of such analysis. To increase the
power of transcript discovery from large collections of RNA-seq
datasets, we developed a new R package named Pooling RNA-seq
and Assembling Models (PRAM), which builds transcript models in
intergenic regions from pooled RNA-seq datasets. This package
includes functions for defining intergenic regions, extracting
and pooling related RNA-seq alignments, predicting, selected,
and evaluating transcript models.
biocViews: Software, Technology, Sequencing, RNASeq,
BiologicalQuestion, GenePrediction, GenomeAnnotation,
ResearchField, Transcriptomics
Author: Peng Liu [aut, cre], Colin N. Dewey [aut], Sündüz Keleş [aut]
Maintainer: Peng Liu <pliu55.wisc@gmail.com>
URL: https://github.com/pliu55/pram
VignetteBuilder: knitr
BugReports: https://github.com/pliu55/pram/issues
git_url: https://git.bioconductor.org/packages/pram
git_branch: RELEASE_3_13
git_last_commit: 3e967c2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pram_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pram_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pram_1.8.0.tgz
vignettes: vignettes/pram/inst/doc/pram.pdf
vignetteTitles: Pooling RNA-seq and Assembling Models
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pram/inst/doc/pram.R
dependencyCount: 45
Package: prebs
Version: 1.32.0
Depends: R (>= 2.14.0), GenomicAlignments, affy, RPA
Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges,
Biobase, GenomeInfoDb, S4Vectors
Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe
License: Artistic-2.0
MD5sum: 4c042c2cf9715532102eef7974091dee
NeedsCompilation: no
Title: Probe region expression estimation for RNA-seq data for improved
microarray comparability
Description: The prebs package aims at making RNA-sequencing (RNA-seq)
data more comparable to microarray data. The comparability is
achieved by summarizing sequencing-based expressions of probe
regions using a modified version of RMA algorithm. The pipeline
takes mapped reads in BAM format as an input and produces
either gene expressions or original microarray probe set
expressions as an output.
biocViews: ImmunoOncology, Microarray, RNASeq, Sequencing,
GeneExpression, Preprocessing
Author: Karolis Uziela and Antti Honkela
Maintainer: Karolis Uziela <karolis.uziela@scilifelab.se>
git_url: https://git.bioconductor.org/packages/prebs
git_branch: RELEASE_3_13
git_last_commit: 67c681b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/prebs_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/prebs_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/prebs_1.32.0.tgz
vignettes: vignettes/prebs/inst/doc/prebs.pdf
vignetteTitles: prebs User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/prebs/inst/doc/prebs.R
dependencyCount: 97
Package: preciseTAD
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: S4Vectors, IRanges, GenomicRanges, randomForest, ModelMetrics,
e1071, PRROC, pROC, caret, utils, cluster, dbscan, doSNOW,
foreach, pbapply, stats, parallel, stats
Suggests: knitr, rmarkdown, testthat, BiocCheck, BiocManager, BiocStyle
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 4fb635344687d590459a6eaa31d2877d
NeedsCompilation: no
Title: preciseTAD: A machine learning framework for precise TAD
boundary prediction
Description: preciseTAD provides functions to predict the location of
boundaries of topologically associated domains (TADs) and
chromatin loops at base-level resolution. As an input, it takes
BED-formatted genomic coordinates of domain boundaries detected
from low-resolution Hi-C data, and coordinates of
high-resolution genomic annotations from ENCODE or other
consortia. preciseTAD employs several feature engineering
strategies and resampling techniques to address class
imbalance, and trains an optimized random forest model for
predicting low-resolution domain boundaries. Translated on a
base-level, preciseTAD predicts the probability for each base
to be a boundary. Density-based clustering and scalable
partitioning techniques are used to detect precise boundary
regions and summit points. Compared with low-resolution
boundaries, preciseTAD boundaries are highly enriched for CTCF,
RAD21, SMC3, and ZNF143 signal and more conserved across cell
lines. The pre-trained model can accurately predict boundaries
in another cell line using CTCF, RAD21, SMC3, and ZNF143
annotation data for this cell line.
biocViews: Software, HiC, Sequencing, Clustering, Classification,
FunctionalGenomics, FeatureExtraction
Author: Spiro Stilianoudakis [aut, cre], Mikhail Dozmorov [aut]
Maintainer: Spiro Stilianoudakis <stilianoudasc@vcu.edu>
URL: https://github.com/dozmorovlab/preciseTAD
VignetteBuilder: knitr
BugReports: https://github.com/dozmorovlab/preciseTAD/issues
git_url: https://git.bioconductor.org/packages/preciseTAD
git_branch: RELEASE_3_13
git_last_commit: c908e5e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/preciseTAD_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/preciseTAD_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/preciseTAD_1.2.0.tgz
vignettes: vignettes/preciseTAD/inst/doc/preciseTAD.html
vignetteTitles: preciseTAD
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/preciseTAD/inst/doc/preciseTAD.R
suggestsMe: preciseTADhub
dependencyCount: 98
Package: PrecisionTrialDrawer
Version: 1.8.0
Depends: R (>= 3.6)
Imports: graphics, grDevices, stats, utils, methods, cgdsr, parallel,
stringr, reshape2, data.table, RColorBrewer, BiocParallel,
magrittr, biomaRt, XML, httr, jsonlite, ggplot2, ggrepel, grid,
S4Vectors, IRanges, GenomicRanges, LowMACAAnnotation,
googleVis, shiny, shinyBS, DT, brglm, matrixStats
Suggests: BiocStyle, knitr, rmarkdown, dplyr
License: GPL-3
MD5sum: 2763ab89fc974a7d3236c8d5dde0a11f
NeedsCompilation: no
Title: A Tool to Analyze and Design NGS Based Custom Gene Panels
Description: A suite of methods to design umbrella and basket trials
for precision oncology.
biocViews: SomaticMutation, WholeGenome, Sequencing, DataImport,
GeneExpression
Author: Giorgio Melloni, Alessandro Guida, Luca Mazzarella
Maintainer: Giorgio Melloni <melloni.giorgio@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PrecisionTrialDrawer
git_branch: RELEASE_3_13
git_last_commit: 8690c66
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PrecisionTrialDrawer_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PrecisionTrialDrawer_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PrecisionTrialDrawer_1.8.0.tgz
vignettes:
vignettes/PrecisionTrialDrawer/inst/doc/PrecisionTrialDrawer.html
vignetteTitles: Bioconductor style for HTML documents
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PrecisionTrialDrawer/inst/doc/PrecisionTrialDrawer.R
dependencyCount: 129
Package: PREDA
Version: 1.38.0
Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats,
methods, annotate
Suggests: quantsmooth, qvalue, limma, caTools, affy, PREDAsampledata
Enhances: Rmpi, rsprng
License: GPL-2
MD5sum: 90ed462fc115af33ef59e81eee705d11
NeedsCompilation: no
Title: Position Related Data Analysis
Description: Package for the position related analysis of quantitative
functional genomics data.
biocViews: Software, CopyNumberVariation, GeneExpression, Genetics
Author: Francesco Ferrari <francesco.ferrari@ifom.eu>
Maintainer: Francesco Ferrari <francesco.ferrari@ifom.eu>
git_url: https://git.bioconductor.org/packages/PREDA
git_branch: RELEASE_3_13
git_last_commit: f207bb8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PREDA_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PREDA_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PREDA_1.38.0.tgz
vignettes: vignettes/PREDA/inst/doc/PREDAclasses.pdf,
vignettes/PREDA/inst/doc/PREDAtutorial.pdf
vignetteTitles: PREDA S4-classes, PREDA tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PREDA/inst/doc/PREDAtutorial.R
dependsOnMe: PREDAsampledata
dependencyCount: 58
Package: predictionet
Version: 1.38.0
Depends: igraph, catnet
Imports: penalized, RBGL, MASS
Suggests: network, minet, knitr
License: Artistic-2.0
MD5sum: 7c64b3c86730c2500c494886b70899c3
NeedsCompilation: yes
Title: Inference for predictive networks designed for (but not limited
to) genomic data
Description: This package contains a set of functions related to
network inference combining genomic data and prior information
extracted from biomedical literature and structured biological
databases. The main function is able to generate networks using
Bayesian or regression-based inference methods; while the
former is limited to < 100 of variables, the latter may infer
networks with hundreds of variables. Several statistics at the
edge and node levels have been implemented (edge stability,
predictive ability of each node, ...) in order to help the user
to focus on high quality subnetworks. Ultimately, this package
is used in the 'Predictive Networks' web application developed
by the Dana-Farber Cancer Institute in collaboration with
Entagen.
biocViews: GraphAndNetwork, NetworkInference
Author: Benjamin Haibe-Kains, Catharina Olsen, Gianluca Bontempi, John
Quackenbush
Maintainer: Benjamin Haibe-Kains <bhaibeka@jimmy.harvard.edu>,
Catharina Olsen <colsen@ulb.ac.be>
URL: http://compbio.dfci.harvard.edu, http://www.ulb.ac.be/di/mlg
git_url: https://git.bioconductor.org/packages/predictionet
git_branch: RELEASE_3_13
git_last_commit: 37c1515
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/predictionet_1.38.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/predictionet_1.38.0.tgz
vignettes: vignettes/predictionet/inst/doc/predictionet.pdf
vignetteTitles: predictionet
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/predictionet/inst/doc/predictionet.R
dependencyCount: 24
Package: preprocessCore
Version: 1.54.0
Imports: stats
License: LGPL (>= 2)
Archs: i386, x64
MD5sum: 1dd8a41fb9ef6b5c819b463b01e81c85
NeedsCompilation: yes
Title: A collection of pre-processing functions
Description: A library of core preprocessing routines.
biocViews: Infrastructure
Author: Ben Bolstad <bmb@bmbolstad.com>
Maintainer: Ben Bolstad <bmb@bmbolstad.com>
URL: https://github.com/bmbolstad/preprocessCore
git_url: https://git.bioconductor.org/packages/preprocessCore
git_branch: RELEASE_3_13
git_last_commit: 66a30ca
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/preprocessCore_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/preprocessCore_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/preprocessCore_1.54.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: affyPLM, cqn, crlmm, RefPlus, SCATE
importsMe: affy, BloodGen3Module, bnbc, cn.farms, EMDomics, ExiMiR,
fastLiquidAssociation, frma, frmaTools, hipathia, iCheck,
ImmuneSpaceR, InPAS, lumi, MADSEQ, MBCB, MBQN, MEDIPS, mimager,
minfi, MSPrep, MSstats, NormalyzerDE, oligo, PECA, PhosR,
Pigengene, proBatch, qPLEXanalyzer, quantiseqr, sesame, soGGi,
tidybulk, yarn, GSE13015, ADAPTS, cinaR, FARDEEP, HEMDAG,
lilikoi, MetaIntegrator, MiDA, noise, noisyr, oncoPredict,
RAMClustR, retriever, SMDIC, WGCNA
suggestsMe: MsCoreUtils, multiClust, QFeatures, scp, splatter,
aroma.affymetrix, aroma.core, glycanr, wrMisc, wrTopDownFrag
linksToMe: affy, affyPLM, crlmm, oligo
dependencyCount: 1
Package: primirTSS
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: GenomicRanges (>= 1.32.2), S4Vectors (>= 0.18.2), rtracklayer
(>= 1.40.3), dplyr (>= 0.7.6), stringr (>= 1.3.1), tidyr (>=
0.8.1), Biostrings (>= 2.48.0), purrr (>= 0.2.5),
BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.1),
phastCons100way.UCSC.hg38 (>= 3.7.1), GenomicScores (>= 1.4.1),
shiny (>= 1.0.5), Gviz (>= 1.24.0), BiocGenerics (>= 0.26.0),
IRanges (>= 2.14.10), TFBSTools (>= 1.18.0), JASPAR2018 (>=
1.1.1), tibble (>= 1.4.2), R.utils (>= 2.6.0), stats, utils
Suggests: knitr, rmarkdown
License: GPL-2
MD5sum: 95d11c2651b5aa8cb404f3dc1d9349c9
NeedsCompilation: no
Title: Prediction of pri-miRNA Transcription Start Site
Description: A fast, convenient tool to identify the TSSs of miRNAs by
integrating the data of H3K4me3 and Pol II as well as combining
the conservation level and sequence feature, provided within
both command-line and graphical interfaces, which achieves a
better performance than the previous non-cell-specific methods
on miRNA TSSs.
biocViews: ImmunoOncology, Sequencing, RNASeq, Genetics, Preprocessing,
Transcription, GeneRegulation
Author: Pumin Li [aut, cre], Qi Xu [aut], Jie Li [aut], Jin Wang [aut]
Maintainer: Pumin Li <ipumin@163.com>
URL: https://github.com/ipumin/primirTSS
VignetteBuilder: knitr
BugReports: http://github.com/ipumin/primirTSS/issues
git_url: https://git.bioconductor.org/packages/primirTSS
git_branch: RELEASE_3_13
git_last_commit: e9cd880
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/primirTSS_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/primirTSS_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/primirTSS_1.10.0.tgz
vignettes: vignettes/primirTSS/inst/doc/primirTSS.html
vignetteTitles: primirTSS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/primirTSS/inst/doc/primirTSS.R
dependencyCount: 190
Package: PrInCE
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: purrr (>= 0.2.4), dplyr (>= 0.7.4), tidyr (>= 0.8.99),
forecast (>= 8.2), progress (>= 1.1.2), Hmisc (>= 4.0),
naivebayes (>= 0.9.1), robustbase (>= 0.92-7), ranger (>=
0.8.0), LiblineaR (>= 2.10-8), speedglm (>= 0.3-2), tester (>=
0.1.7), magrittr (>= 1.5), Biobase (>= 2.40.0), MSnbase (>=
2.8.3), stats, utils, methods, Rdpack (>= 0.7)
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3 + file LICENSE
MD5sum: 43726299baa3e71f611ac393ea463415
NeedsCompilation: no
Title: Predicting Interactomes from Co-Elution
Description: PrInCE (Predicting Interactomes from Co-Elution) uses a
naive Bayes classifier trained on dataset-derived features to
recover protein-protein interactions from co-elution
chromatogram profiles. This package contains the R
implementation of PrInCE.
biocViews: Proteomics, SystemsBiology, NetworkInference
Author: Michael Skinnider [aut, trl, cre], R. Greg Stacey [ctb],
Nichollas Scott [ctb], Anders Kristensen [ctb], Leonard Foster
[aut, led]
Maintainer: Michael Skinnider <michael.skinnider@msl.ubc.ca>
VignetteBuilder: knitr
BugReports: https://github.com/fosterlab/PrInCE/issues
git_url: https://git.bioconductor.org/packages/PrInCE
git_branch: RELEASE_3_13
git_last_commit: 0c2804f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PrInCE_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PrInCE_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PrInCE_1.8.0.tgz
vignettes: vignettes/PrInCE/inst/doc/intro-to-prince.html
vignetteTitles: Interactome reconstruction from co-elution data with
PrInCE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PrInCE/inst/doc/intro-to-prince.R
dependencyCount: 138
Package: proActiv
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: AnnotationDbi, BiocParallel, data.table, dplyr, DESeq2,
IRanges, GenomicRanges, GenomicFeatures, GenomicAlignments,
GenomeInfoDb, ggplot2, Gviz, methods, rlang, S4Vectors,
SummarizedExperiment, stats, tibble
Suggests: testthat, rmarkdown, knitr, Rtsne, gridExtra
License: MIT + file LICENSE
MD5sum: 8316f17a34d6b7ea2800e08f09db8d39
NeedsCompilation: no
Title: Estimate Promoter Activity from RNA-Seq data
Description: Most human genes have multiple promoters that control the
expression of different isoforms. The use of these alternative
promoters enables the regulation of isoform expression
pre-transcriptionally. Alternative promoters have been found to
be important in a wide number of cell types and diseases.
proActiv is an R package that enables the analysis of promoters
from RNA-seq data. proActiv uses aligned reads as input, and
generates counts and normalized promoter activity estimates for
each annotated promoter. In particular, proActiv accepts
junction files from TopHat2 or STAR or BAM files as inputs.
These estimates can then be used to identify which promoter is
active, which promoter is inactive, and which promoters change
their activity across conditions. proActiv also allows
visualization of promoter activity across conditions.
biocViews: RNASeq, GeneExpression, Transcription, AlternativeSplicing,
GeneRegulation, DifferentialSplicing, FunctionalGenomics,
Epigenetics, Transcriptomics, Preprocessing
Author: Deniz Demircioglu [aut]
(<https://orcid.org/0000-0001-7857-0407>), Jonathan Göke [aut],
Joseph Lee [cre]
Maintainer: Joseph Lee <joseph.lee@u.nus.edu>
URL: https://github.com/GoekeLab/proActiv
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/proActiv
git_branch: RELEASE_3_13
git_last_commit: 0a66788
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/proActiv_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/proActiv_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/proActiv_1.2.0.tgz
vignettes: vignettes/proActiv/inst/doc/proActiv.html
vignetteTitles: Identifying Active and Alternative Promoters from
RNA-Seq data with proActiv
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/proActiv/inst/doc/proActiv.R
dependencyCount: 149
Package: proBAMr
Version: 1.26.0
Depends: R (>= 3.0.1), IRanges, AnnotationDbi
Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer
Suggests: RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: d833332419bf398f65e51b5a73fdc56f
NeedsCompilation: no
Title: Generating SAM file for PSMs in shotgun proteomics data
Description: Mapping PSMs back to genome. The package builds SAM file
from shotgun proteomics data The package also provides function
to prepare annotation from GTF file.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Software,
Visualization
Author: Xiaojing Wang
Maintainer: Xiaojing Wang <xiaojing.wang@vanderbilt.edu>
git_url: https://git.bioconductor.org/packages/proBAMr
git_branch: RELEASE_3_13
git_last_commit: cf5184b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/proBAMr_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/proBAMr_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/proBAMr_1.26.0.tgz
vignettes: vignettes/proBAMr/inst/doc/proBAMr.pdf
vignetteTitles: Introduction to proBAMr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/proBAMr/inst/doc/proBAMr.R
dependencyCount: 96
Package: proBatch
Version: 1.8.0
Depends: R (>= 3.6)
Imports: Biobase, corrplot, dplyr, data.table, ggfortify, ggplot2,
grDevices, lazyeval, lubridate, magrittr, pheatmap,
preprocessCore, purrr, pvca, RColorBrewer, reshape2, rlang,
scales, stats, sva, tidyr, tibble, tools, utils, viridis,
wesanderson, WGCNA
Suggests: knitr, rmarkdown, devtools, ggpubr, gtable, gridExtra,
roxygen2, testthat (>= 2.1.0), spelling
License: GPL-3
MD5sum: 05f7714d4cd3085fb3f7143cfcd947b7
NeedsCompilation: no
Title: Tools for Diagnostics and Corrections of Batch Effects in
Proteomics
Description: These tools facilitate batch effects analysis and
correction in high-throughput experiments. It was developed
primarily for mass-spectrometry proteomics (DIA/SWATH), but
could also be applicable to most omic data with minor
adaptations. The package contains functions for diagnostics
(proteome/genome-wide and feature-level), correction
(normalization and batch effects correction) and quality
control. Non-linear fitting based approaches were also included
to deal with complex, mass spectrometry-specific signal drifts.
biocViews: BatchEffect, Normalization, Preprocessing, Software,
MassSpectrometry,Proteomics, QualityControl
Author: Jelena Cuklina <chuklina.jelena@gmail.com>, Chloe H. Lee
<chloe.h.lee94@gmail.com>, Patrick Pedrioli
<pedrioli@gmail.com>
Maintainer: Chloe H. Lee <chloe.h.lee94@gmail.com>
URL: https://github.com/symbioticMe/proBatch
VignetteBuilder: knitr
BugReports: https://github.com/symbioticMe/proBatch/issues
git_url: https://git.bioconductor.org/packages/proBatch
git_branch: RELEASE_3_13
git_last_commit: a577fe3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/proBatch_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/proBatch_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/proBatch_1.8.0.tgz
vignettes: vignettes/proBatch/inst/doc/proBatch.pdf
vignetteTitles: proBatch package overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/proBatch/inst/doc/proBatch.R
dependencyCount: 148
Package: PROcess
Version: 1.68.0
Depends: Icens
Imports: graphics, grDevices, Icens, stats, utils
License: Artistic-2.0
Archs: i386, x64
MD5sum: c5f57457d23548e0f62cecd8531941d7
NeedsCompilation: no
Title: Ciphergen SELDI-TOF Processing
Description: A package for processing protein mass spectrometry data.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics
Author: Xiaochun Li
Maintainer: Xiaochun Li <xiaochun@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/PROcess
git_branch: RELEASE_3_13
git_last_commit: bbbacba
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PROcess_1.68.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PROcess_1.68.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PROcess_1.68.0.tgz
vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf
vignetteTitles: HOWTO PROcess
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PROcess/inst/doc/howtoprocess.R
dependencyCount: 11
Package: procoil
Version: 2.20.0
Depends: R (>= 3.3.0), kebabs
Imports: methods, stats, graphics, S4Vectors, Biostrings, utils
Suggests: knitr
License: GPL (>= 2)
MD5sum: 6730ad49c03faf80e170a8853bad1699
NeedsCompilation: no
Title: Prediction of Oligomerization of Coiled Coil Proteins
Description: The package allows for predicting whether a coiled coil
sequence (amino acid sequence plus heptad register) is more
likely to form a dimer or more likely to form a trimer.
Additionally to the prediction itself, a prediction profile is
computed which allows for determining the strengths to which
the individual residues are indicative for either class.
Prediction profiles can also be visualized as curves or
heatmaps.
biocViews: Proteomics, Classification, SupportVectorMachine
Author: Ulrich Bodenhofer
Maintainer: Ulrich Bodenhofer <bodenhofer@bioinf.jku.at>
URL: http://www.bioinf.jku.at/software/procoil/
https://github.com/UBod/procoil
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/procoil
git_branch: RELEASE_3_13
git_last_commit: 8884085
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/procoil_2.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/procoil_2.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/procoil_2.20.0.tgz
vignettes: vignettes/procoil/inst/doc/procoil.pdf
vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting
the Oligomerization of Coiled-Coil Proteins
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/procoil/inst/doc/procoil.R
dependencyCount: 31
Package: proDA
Version: 1.6.0
Imports: stats, utils, methods, BiocGenerics, SummarizedExperiment,
S4Vectors, extraDistr
Suggests: testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr,
tibble, limma, DEP, numDeriv, pheatmap, knitr, rmarkdown
License: GPL-3
MD5sum: b7d2a891084890707c20df3ed074cccc
NeedsCompilation: no
Title: Differential Abundance Analysis of Label-Free Mass Spectrometry
Data
Description: Account for missing values in label-free mass spectrometry
data without imputation. The package implements a probabilistic
dropout model that ensures that the information from observed
and missing values are properly combined. It adds empirical
Bayesian priors to increase power to detect differentially
abundant proteins.
biocViews: Proteomics, MassSpectrometry, DifferentialExpression,
Bayesian, Regression, Software, Normalization, QualityControl
Author: Constantin Ahlmann-Eltze [aut, cre]
(<https://orcid.org/0000-0002-3762-068X>), Simon Anders [ths]
(<https://orcid.org/0000-0003-4868-1805>)
Maintainer: Constantin Ahlmann-Eltze <artjom31415@googlemail.com>
URL: https://github.com/const-ae/proDA
VignetteBuilder: knitr
BugReports: https://github.com/const-ae/proDA/issues
git_url: https://git.bioconductor.org/packages/proDA
git_branch: RELEASE_3_13
git_last_commit: 73fced0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/proDA_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/proDA_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/proDA_1.6.0.tgz
vignettes: vignettes/proDA/inst/doc/data-import.html,
vignettes/proDA/inst/doc/Introduction.html
vignetteTitles: Data Import, Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/proDA/inst/doc/data-import.R,
vignettes/proDA/inst/doc/Introduction.R
importsMe: MatrixQCvis
suggestsMe: protti
dependencyCount: 28
Package: proFIA
Version: 1.18.0
Depends: R (>= 2.5.0), xcms
Imports: stats, graphics, utils, grDevices, methods, pracma, Biobase,
minpack.lm, BiocParallel, missForest, ropls
Suggests: BiocGenerics, plasFIA, knitr,
License: CeCILL
MD5sum: 202b49958d4f45df837c800b8a85adab
NeedsCompilation: yes
Title: Preprocessing of FIA-HRMS data
Description: Flow Injection Analysis coupled to High-Resolution Mass
Spectrometry is a promising approach for high-throughput
metabolomics. FIA- HRMS data, however, cannot be pre-processed
with current software tools which rely on liquid chromatography
separation, or handle low resolution data only. Here we present
the proFIA package, which implements a new methodology to
pre-process FIA-HRMS raw data (netCDF, mzData, mzXML, and mzML)
including noise modelling and injection peak reconstruction,
and generate the peak table. The workflow includes noise
modelling, band detection and filtering then signal matching
and missing value imputation. The peak table can then be
exported as a .tsv file for further analysis. Visualisations to
assess the quality of the data and of the signal made are
easely produced.
biocViews: MassSpectrometry, Metabolomics, Lipidomics, Preprocessing,
PeakDetection, Proteomics
Author: Alexis Delabriere and Etienne Thevenot.
Maintainer: Alexis Delabriere <alexis.delabriere@outlook.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/proFIA
git_branch: RELEASE_3_13
git_last_commit: ce749bb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/proFIA_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/proFIA_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/proFIA_1.18.0.tgz
vignettes: vignettes/proFIA/inst/doc/proFIA-vignette.html
vignetteTitles: processing FIA-HRMS data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/proFIA/inst/doc/proFIA-vignette.R
dependsOnMe: plasFIA
dependencyCount: 104
Package: profileplyr
Version: 1.8.1
Depends: R (>= 3.6), BiocGenerics, SummarizedExperiment
Imports: GenomicRanges, stats, soGGi, methods, utils, S4Vectors,
R.utils, dplyr, magrittr, tidyr, IRanges, rjson,
ChIPseeker,GenomicFeatures,TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,org.Hs.eg.db,org.Mm.eg.db,rGREAT,
pheatmap, ggplot2, EnrichedHeatmap, ComplexHeatmap, grid,
circlize, BiocParallel, rtracklayer, GenomeInfoDb, grDevices,
rlang, Cairo, tiff, Rsamtools
Suggests: BiocStyle, testthat, knitr, rmarkdown, png
License: GPL (>= 3)
MD5sum: 50559efdcd1eb7cacb903d338627ff37
NeedsCompilation: no
Title: Visualization and annotation of read signal over genomic ranges
with profileplyr
Description: Quick and straightforward visualization of read signal
over genomic intervals is key for generating hypotheses from
sequencing data sets (e.g. ChIP-seq, ATAC-seq,
bisulfite/methyl-seq). Many tools both inside and outside of R
and Bioconductor are available to explore these types of data,
and they typically start with a bigWig or BAM file and end with
some representation of the signal (e.g. heatmap). profileplyr
leverages many Bioconductor tools to allow for both flexibility
and additional functionality in workflows that end with
visualization of the read signal.
biocViews: ChIPSeq, DataImport, Sequencing, ChipOnChip, Coverage
Author: Tom Carroll and Doug Barrows
Maintainer: Tom Carroll <tc.infomatics@gmail.com>, Doug Barrows
<doug.barrows@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/profileplyr
git_branch: RELEASE_3_13
git_last_commit: 7623d2f
git_last_commit_date: 2021-08-09
Date/Publication: 2021-08-10
source.ver: src/contrib/profileplyr_1.8.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/profileplyr_1.8.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/profileplyr_1.8.1.tgz
vignettes: vignettes/profileplyr/inst/doc/profileplyr.html
vignetteTitles: Visualization and annotation of read signal over
genomic ranges with profileplyr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/profileplyr/inst/doc/profileplyr.R
dependencyCount: 189
Package: profileScoreDist
Version: 1.20.0
Depends: R(>= 3.3)
Imports: Rcpp, BiocGenerics, methods, graphics
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, MotifDb
License: MIT + file LICENSE
MD5sum: ad26b816f132151660c2bd2bf49906a2
NeedsCompilation: yes
Title: Profile score distributions
Description: Regularization and score distributions for position count
matrices.
biocViews: Software, GeneRegulation, StatisticalMethod
Author: Paal O. Westermark
Maintainer: Paal O. Westermark <pal-olof.westermark@charite.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/profileScoreDist
git_branch: RELEASE_3_13
git_last_commit: 1095a72
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/profileScoreDist_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/profileScoreDist_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/profileScoreDist_1.20.0.tgz
vignettes:
vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.pdf
vignetteTitles: Using profileScoreDist
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.R
dependencyCount: 7
Package: progeny
Version: 1.14.0
Depends: R (>= 3.6.0)
Imports: Biobase, stats, dplyr, tidyr, ggplot2, ggrepel, gridExtra
Suggests: airway, biomaRt, BiocFileCache, broom, Seurat,
SingleCellExperiment, DESeq2, BiocStyle, knitr, readr, readxl,
pheatmap, tibble, testthat (>= 2.1.0)
License: Apache License (== 2.0) | file LICENSE
MD5sum: 1b479122fe24d3fbb59ec470c24f71b8
NeedsCompilation: no
Title: Pathway RespOnsive GENes for activity inference from gene
expression
Description: This package provides a function to infer pathway activity
from gene expression using PROGENy. It contains the linear
model we inferred in the publication "Perturbation-response
genes reveal signaling footprints in cancer gene expression".
biocViews: SystemsBiology, GeneExpression, FunctionalPrediction,
GeneRegulation
Author: Michael Schubert [aut], Alberto Valdeolivas [cre, ctb]
(<https://orcid.org/0000-0001-5482-9023>), Christian H. Holland
[ctb] (<https://orcid.org/0000-0002-3060-5786>), Igor Bulanov
[ctb], Aurélien Dugourd [ctb]
Maintainer: Alberto Valdeolivas <alvaldeolivas@gmail.com>
URL: https://github.com/saezlab/progeny
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/progeny/issues
git_url: https://git.bioconductor.org/packages/progeny
git_branch: RELEASE_3_13
git_last_commit: 6252912
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/progeny_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/progeny_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/progeny_1.14.0.tgz
vignettes: vignettes/progeny/inst/doc/progenyBulk.html,
vignettes/progeny/inst/doc/ProgenySingleCell.html
vignetteTitles: PROGENy pathway signatures: Application to Bulk
transcriptomics, Applying PROGENy on single-cell RNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/progeny/inst/doc/progenyBulk.R,
vignettes/progeny/inst/doc/ProgenySingleCell.R
suggestsMe: mistyR
dependencyCount: 50
Package: projectR
Version: 1.8.0
Imports: methods, cluster, stats, limma, CoGAPS, NMF, ROCR, ggalluvial,
RColorBrewer, dplyr, reshape2, viridis, scales, ggplot2
Suggests: BiocStyle, gridExtra, grid, testthat, devtools, knitr,
rmarkdown, ComplexHeatmap
License: GPL (==2)
MD5sum: c752e0e502f82018d7e0d67ed1991f77
NeedsCompilation: no
Title: Functions for the projection of weights from PCA, CoGAPS, NMF,
correlation, and clustering
Description: Functions for the projection of data into the spaces
defined by PCA, CoGAPS, NMF, correlation, and clustering.
biocViews: FunctionalPrediction, GeneRegulation, BiologicalQuestion,
Software
Author: Gaurav Sharma, Genevieve Stein-O'Brien
Maintainer: Genevieve Stein-O'Brien <gsteinobrien@gmail.com>
URL: https://github.com/genesofeve/projectR/
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/projectR/
git_url: https://git.bioconductor.org/packages/projectR
git_branch: RELEASE_3_13
git_last_commit: c86e61e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/projectR_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/projectR_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/projectR_1.8.0.tgz
vignettes: vignettes/projectR/inst/doc/projectR.pdf
vignetteTitles: projectR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/projectR/inst/doc/projectR.R
dependencyCount: 102
Package: pRoloc
Version: 1.32.0
Depends: R (>= 3.5), MSnbase (>= 1.19.20), MLInterfaces (>= 1.67.10),
methods, Rcpp (>= 0.10.3), BiocParallel
Imports: stats4, Biobase, mclust (>= 4.3), caret, e1071, sampling,
class, kernlab, lattice, nnet, randomForest, proxy, FNN,
hexbin, BiocGenerics, stats, dendextend, RColorBrewer, scales,
MASS, knitr, mvtnorm, LaplacesDemon, coda, mixtools, gtools,
plyr, ggplot2, biomaRt, utils, grDevices, graphics
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, rmarkdown, pRolocdata (>= 1.9.4), roxygen2, xtable,
rgl, BiocStyle (>= 2.5.19), hpar (>= 1.15.3), dplyr, akima,
fields, vegan, GO.db, AnnotationDbi, Rtsne (>= 0.13), nipals,
reshape, magick
License: GPL-2
MD5sum: 6e8f2b00b5574a341f808460662cb152
NeedsCompilation: yes
Title: A unifying bioinformatics framework for spatial proteomics
Description: The pRoloc package implements machine learning and
visualisation methods for the analysis and interogation of
quantitiative mass spectrometry data to reliably infer protein
sub-cellular localisation.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry,
Classification, Clustering, QualityControl
Author: Laurent Gatto, Oliver Crook and Lisa M. Breckels with
contributions from Thomas Burger and Samuel Wieczorek
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://github.com/lgatto/pRoloc
VignetteBuilder: knitr
Video:
https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow
BugReports: https://github.com/lgatto/pRoloc/issues
git_url: https://git.bioconductor.org/packages/pRoloc
git_branch: RELEASE_3_13
git_last_commit: 637f4c7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pRoloc_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pRoloc_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pRoloc_1.32.0.tgz
vignettes: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.html,
vignettes/pRoloc/inst/doc/v02-pRoloc-ml.html,
vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.html,
vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.html,
vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.html
vignetteTitles: Using pRoloc for spatial proteomics data analysis,
Machine learning techniques available in pRoloc, Bayesian
spatial proteomics with pRoloc, Annotating spatial proteomics
data, A transfer learning algorithm for spatial proteomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.R,
vignettes/pRoloc/inst/doc/v02-pRoloc-ml.R,
vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.R,
vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.R,
vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.R
dependsOnMe: pRolocGUI, proteomics
suggestsMe: MSnbase, pRolocdata, RforProteomics
dependencyCount: 207
Package: pRolocGUI
Version: 2.2.0
Depends: methods, R (>= 3.1.0), pRoloc (>= 1.27.6), Biobase, MSnbase
(>= 2.1.11)
Imports: shiny (>= 0.9.1), scales, dplyr, DT (>= 0.1.40), graphics,
utils, ggplot2, shinydashboardPlus, colourpicker, shinyhelper,
shinyWidgets, shinyjs, colorspace, shinydashboard, stats,
grDevices, grid, BiocGenerics
Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown
License: GPL-2
Archs: i386, x64
MD5sum: 815fd486dffcda5e2b929640642cb54a
NeedsCompilation: no
Title: Interactive visualisation of spatial proteomics data
Description: The package pRolocGUI comprises functions to interactively
visualise organelle (spatial) proteomics data on the basis of
pRoloc, pRolocdata and shiny.
biocViews: Proteomics, Visualization, GUI
Author: Lisa Breckels [aut], Thomas Naake [aut], Laurent Gatto [aut,
cre]
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: http://ComputationalProteomicsUnit.github.io/pRolocGUI/
VignetteBuilder: knitr
Video:
https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow
BugReports:
https://github.com/ComputationalProteomicsUnit/pRolocGUI/issues
git_url: https://git.bioconductor.org/packages/pRolocGUI
git_branch: RELEASE_3_13
git_last_commit: 685e93d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pRolocGUI_2.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pRolocGUI_2.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pRolocGUI_2.2.0.tgz
vignettes: vignettes/pRolocGUI/inst/doc/pRolocGUI.html
vignetteTitles: pRolocGUI - Interactive visualisation of spatial
proteomics data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R
dependencyCount: 220
Package: PROMISE
Version: 1.44.0
Depends: R (>= 3.1.0), Biobase, GSEABase
Imports: Biobase, GSEABase, stats
License: GPL (>= 2)
MD5sum: 8fe7840b94370fcd194d79a53242ff43
NeedsCompilation: no
Title: PRojection Onto the Most Interesting Statistical Evidence
Description: A general tool to identify genomic features with a
specific biologically interesting pattern of associations with
multiple endpoint variables as described in Pounds et. al.
(2009) Bioinformatics 25: 2013-2019
biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression
Author: Stan Pounds <stanley.pounds@stjude.org>, Xueyuan Cao
<xueyuan.cao@stjude.org>
Maintainer: Stan Pounds <stanley.pounds@stjude.org>, Xueyuan Cao
<xueyuan.cao@stjude.org>
git_url: https://git.bioconductor.org/packages/PROMISE
git_branch: RELEASE_3_13
git_last_commit: ae08e9e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PROMISE_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PROMISE_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PROMISE_1.44.0.tgz
vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf
vignetteTitles: An introduction to PROMISE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PROMISE/inst/doc/PROMISE.R
dependsOnMe: CCPROMISE
dependencyCount: 51
Package: PROPER
Version: 1.24.0
Depends: R (>= 3.3)
Imports: edgeR
Suggests: BiocStyle,DESeq2,DSS,knitr
License: GPL
Archs: i386, x64
MD5sum: 0cefab8cd0b8de9c003f316b1f80853d
NeedsCompilation: no
Title: PROspective Power Evaluation for RNAseq
Description: This package provide simulation based methods for
evaluating the statistical power in differential expression
analysis from RNA-seq data.
biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression
Author: Hao Wu
Maintainer: Hao Wu <hao.wu@emory.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PROPER
git_branch: RELEASE_3_13
git_last_commit: 04d073c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PROPER_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PROPER_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PROPER_1.24.0.tgz
vignettes: vignettes/PROPER/inst/doc/PROPER.pdf
vignetteTitles: Power and Sample size analysis for gene expression from
RNA-seq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PROPER/inst/doc/PROPER.R
dependencyCount: 11
Package: PROPS
Version: 1.14.0
Imports: bnlearn, reshape2, sva, stats, utils, Biobase
Suggests: knitr, rmarkdown
License: GPL-2
MD5sum: 7480b8dee78d01fa76357951c1f82914
NeedsCompilation: no
Title: PRObabilistic Pathway Score (PROPS)
Description: This package calculates probabilistic pathway scores using
gene expression data. Gene expression values are aggregated
into pathway-based scores using Bayesian network
representations of biological pathways.
biocViews: Classification, Bayesian, GeneExpression
Author: Lichy Han
Maintainer: Lichy Han <lhan2@stanford.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PROPS
git_branch: RELEASE_3_13
git_last_commit: 042cf5f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PROPS_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PROPS_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PROPS_1.14.0.tgz
vignettes: vignettes/PROPS/inst/doc/props.html
vignetteTitles: PRObabilistic Pathway Scores (PROPS)
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PROPS/inst/doc/props.R
dependencyCount: 75
Package: Prostar
Version: 1.24.8
Depends: R (>= 4.1.0)
Imports: DAPAR (>= 1.24.5), DAPARdata (>= 1.22.2), rhandsontable,
data.table, shinyjs, DT, shiny, shinyBS, shinyAce, highcharter,
htmlwidgets, webshot, R.utils, shinythemes, XML,later,
rclipboard, shinycssloaders, future, promises, colourpicker,
BiocManager, shinyjqui,shinyTree, shinyWidgets, sass, tibble
Suggests: BiocStyle, testthat
License: Artistic-2.0
MD5sum: d5c0fe52e729647c694a933a17ed3a75
NeedsCompilation: no
Title: Provides a GUI for DAPAR
Description: This package provides a GUI interface for DAPAR.
biocViews: Proteomics, MassSpectrometry, Normalization, Preprocessing,
ImmunoOncology, R.utils, GO, GUI
Author: Samuel Wieczorek [cre, aut], Thomas Burger [aut], Enora Fremy
[aut]
Maintainer: Samuel Wieczorek <samuel.wieczorek@cea.fr>
URL: http://www.prostar-proteomics.org/
BugReports: https://github.com/samWieczorek/Prostar/issues
git_url: https://git.bioconductor.org/packages/Prostar
git_branch: RELEASE_3_13
git_last_commit: f1507c2
git_last_commit_date: 2021-08-21
Date/Publication: 2021-08-22
source.ver: src/contrib/Prostar_1.24.8.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Prostar_1.24.8.zip
mac.binary.ver: bin/macosx/contrib/4.1/Prostar_1.24.8.tgz
vignettes: vignettes/Prostar/inst/doc/Prostar_UserManual.pdf
vignetteTitles: Prostar user manual
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Prostar/inst/doc/Prostar_UserManual.R
dependencyCount: 323
Package: proteinProfiles
Version: 1.32.0
Depends: R (>= 2.15.2)
Imports: graphics, stats
Suggests: testthat
License: GPL-3
Archs: i386, x64
MD5sum: 93fcce6ae6d58060d910b38fd311f3a1
NeedsCompilation: no
Title: Protein Profiling
Description: Significance assessment for distance measures of
time-course protein profiles
Author: Julian Gehring
Maintainer: Julian Gehring <jg-bioc@gmx.com>
git_url: https://git.bioconductor.org/packages/proteinProfiles
git_branch: RELEASE_3_13
git_last_commit: 92c9be6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/proteinProfiles_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/proteinProfiles_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/proteinProfiles_1.32.0.tgz
vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf
vignetteTitles: The proteinProfiles package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/proteinProfiles/inst/doc/proteinProfiles.R
dependencyCount: 2
Package: ProteomicsAnnotationHubData
Version: 1.22.0
Depends: AnnotationHub (>= 2.1.45), AnnotationHubData,
Imports: mzR (>= 2.3.2), MSnbase, Biostrings, GenomeInfoDb, utils,
Biobase, BiocManager, RCurl
Suggests: knitr, BiocStyle, rmarkdown, testthat
License: Artistic-2.0
MD5sum: 8e74ee610e39e82c067c40cb43842c2e
NeedsCompilation: no
Title: Transform public proteomics data resources into Bioconductor
Data Structures
Description: These recipes convert a variety and a growing number of
public proteomics data sets into easily-used standard
Bioconductor data structures.
biocViews: DataImport, Proteomics
Author: Gatto Laurent [aut, cre], Sonali Arora [aut]
Maintainer: Laurent Gatto <lg390@cam.ac.uk>
URL: https://github.com/lgatto/ProteomicsAnnotationHubData
VignetteBuilder: knitr
BugReports:
https://github.com/lgatto/ProteomicsAnnotationHubData/issues
git_url:
https://git.bioconductor.org/packages/ProteomicsAnnotationHubData
git_branch: RELEASE_3_13
git_last_commit: f070c64
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ProteomicsAnnotationHubData_1.22.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/ProteomicsAnnotationHubData_1.22.0.zip
mac.binary.ver:
bin/macosx/contrib/4.1/ProteomicsAnnotationHubData_1.22.0.tgz
vignettes:
vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.html
vignetteTitles: Proteomics Data in Annotation Hub
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.R
dependencyCount: 169
Package: ProteoMM
Version: 1.10.0
Depends: R (>= 3.5)
Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats,
graphics
Suggests: BiocStyle, knitr, rmarkdown
License: MIT
MD5sum: 64c9fe8723bee1f9ab67033034dd1ff2
NeedsCompilation: no
Title: Multi-Dataset Model-based Differential Expression Proteomics
Analysis Platform
Description: ProteoMM is a statistical method to perform model-based
peptide-level differential expression analysis of single or
multiple datasets. For multiple datasets ProteoMM produces a
single fold change and p-value for each protein across multiple
datasets. ProteoMM provides functionality for normalization,
missing value imputation and differential expression.
Model-based peptide-level imputation and differential
expression analysis component of package follows the analysis
described in “A statistical framework for protein quantitation
in bottom-up MS based proteomics" (Karpievitch et al.
Bioinformatics 2009). EigenMS normalisation is implemented as
described in "Normalization of peak intensities in bottom-up
MS-based proteomics using singular value decomposition."
(Karpievitch et al. Bioinformatics 2009).
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Normalization,
DifferentialExpression
Author: Yuliya V Karpievitch, Tim Stuart and Sufyaan Mohamed
Maintainer: Yuliya V Karpievitch <yuliya.k@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ProteoMM
git_branch: RELEASE_3_13
git_last_commit: 0521464
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ProteoMM_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ProteoMM_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ProteoMM_1.10.0.tgz
vignettes: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.html
vignetteTitles: Multi-Dataset Model-based Differential Expression
Proteomics Platform
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.R
dependencyCount: 93
Package: ProtGenerics
Version: 1.24.0
Depends: methods
Suggests: testthat
License: Artistic-2.0
MD5sum: 07e73a9c97addf4d99af00f4d7f9491a
NeedsCompilation: no
Title: Generic infrastructure for Bioconductor mass spectrometry
packages
Description: S4 generic functions and classes needed by Bioconductor
proteomics packages.
biocViews: Infrastructure, Proteomics, MassSpectrometry
Author: Laurent Gatto <laurent.gatto@uclouvain.be>, Johannes Rainer
<johannes.rainer@eurac.edu>
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://github.com/lgatto/ProtGenerics
git_url: https://git.bioconductor.org/packages/ProtGenerics
git_branch: RELEASE_3_13
git_last_commit: 50e7e66
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ProtGenerics_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ProtGenerics_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ProtGenerics_1.24.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: Cardinal, MSnbase, Spectra, tofsims, topdownr
importsMe: ensembldb, matter, MsBackendMassbank, MsFeatures, MSGFplus,
MSnID, mzID, mzR, QFeatures, xcms
dependencyCount: 1
Package: PSEA
Version: 1.26.0
Imports: Biobase, MASS
Suggests: BiocStyle
License: Artistic-2.0
MD5sum: b078593db29e4ce343c3aec7760bf2da
NeedsCompilation: no
Title: Population-Specific Expression Analysis.
Description: Deconvolution of gene expression data by
Population-Specific Expression Analysis (PSEA).
biocViews: Software
Author: Alexandre Kuhn <alexandre.m.kuhn@gmail.com>
Maintainer: Alexandre Kuhn <alexandre.m.kuhn@gmail.com>
git_url: https://git.bioconductor.org/packages/PSEA
git_branch: RELEASE_3_13
git_last_commit: 9f69d31
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PSEA_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PSEA_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PSEA_1.26.0.tgz
vignettes: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.pdf,
vignettes/PSEA/inst/doc/PSEA.pdf
vignetteTitles: PSEA: Deconvolution of RNA mixtures in Nature Methods
paper, PSEA: Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.R,
vignettes/PSEA/inst/doc/PSEA.R
dependencyCount: 9
Package: psichomics
Version: 1.18.6
Depends: R (>= 4.0), shiny (>= 1.7.0), shinyBS
Imports: AnnotationDbi, AnnotationHub, BiocFileCache, cluster,
colourpicker, data.table, digest, dplyr, DT (>= 0.2), edgeR,
fastICA, fastmatch, ggplot2, ggrepel, graphics, grDevices,
highcharter (>= 0.5.0), htmltools, httr, jsonlite, limma,
pairsD3, plyr, purrr, Rcpp (>= 0.12.14), recount, Rfast,
R.utils, reshape2, shinyjs, stringr, stats,
SummarizedExperiment, survival, tools, utils, XML, xtable,
methods
LinkingTo: Rcpp
Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr,
car, rstudioapi, spelling
License: MIT + file LICENSE
MD5sum: 92a127db88b09a5cd9b80fa572686f5b
NeedsCompilation: yes
Title: Graphical Interface for Alternative Splicing Quantification,
Analysis and Visualisation
Description: Interactive R package with an intuitive Shiny-based
graphical interface for alternative splicing quantification and
integrative analyses of alternative splicing and gene
expression based on The Cancer Genome Atlas (TCGA), the
Genotype-Tissue Expression project (GTEx), Sequence Read
Archive (SRA) and user-provided data. The tool interactively
performs survival, dimensionality reduction and median- and
variance-based differential splicing and gene expression
analyses that benefit from the incorporation of clinical and
molecular sample-associated features (such as tumour stage or
survival). Interactive visual access to genomic mapping and
functional annotation of selected alternative splicing events
is also included.
biocViews: Sequencing, RNASeq, AlternativeSplicing,
DifferentialSplicing, Transcription, GUI, PrincipalComponent,
Survival, BiomedicalInformatics, Transcriptomics,
ImmunoOncology, Visualization, MultipleComparison,
GeneExpression, DifferentialExpression
Author: Nuno Saraiva-Agostinho [aut, cre]
(<https://orcid.org/0000-0002-5549-105X>), Nuno LuÃs
Barbosa-Morais [aut, led, ths]
(<https://orcid.org/0000-0002-1215-0538>), André Falcão [ths],
Lina Gallego Paez [ctb], Marie Bordone [ctb], Teresa Maia
[ctb], Mariana Ferreira [ctb], Ana Carolina Leote [ctb],
Bernardo de Almeida [ctb]
Maintainer: Nuno Saraiva-Agostinho <nunodanielagostinho@gmail.com>
URL: https://nuno-agostinho.github.io/psichomics/,
https://github.com/nuno-agostinho/psichomics/
VignetteBuilder: knitr
BugReports: https://github.com/nuno-agostinho/psichomics/issues
git_url: https://git.bioconductor.org/packages/psichomics
git_branch: RELEASE_3_13
git_last_commit: 2ffd219
git_last_commit_date: 2021-10-04
Date/Publication: 2021-10-07
source.ver: src/contrib/psichomics_1.18.6.tar.gz
win.binary.ver: bin/windows/contrib/4.1/psichomics_1.18.6.zip
mac.binary.ver: bin/macosx/contrib/4.1/psichomics_1.18.6.tgz
vignettes: vignettes/psichomics/inst/doc/AS_events_preparation.html,
vignettes/psichomics/inst/doc/CLI_tutorial.html,
vignettes/psichomics/inst/doc/custom_data.html,
vignettes/psichomics/inst/doc/GUI_tutorial.html
vignetteTitles: Preparing an Alternative Splicing Annotation for
psichomics, Case study: command-line interface (CLI) tutorial,
Loading user-provided data, Case study: visual interface
tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/psichomics/inst/doc/AS_events_preparation.R,
vignettes/psichomics/inst/doc/CLI_tutorial.R,
vignettes/psichomics/inst/doc/custom_data.R,
vignettes/psichomics/inst/doc/GUI_tutorial.R
dependencyCount: 203
Package: PSICQUIC
Version: 1.30.0
Depends: R (>= 3.2.0), methods, IRanges, biomaRt (>= 2.34.1),
BiocGenerics, httr, plyr
Imports: RCurl
Suggests: org.Hs.eg.db
License: Apache License 2.0
MD5sum: 8ff9da1007880816ed7a117cb969d67b
NeedsCompilation: no
Title: Proteomics Standard Initiative Common QUery InterfaCe
Description: PSICQUIC is a project within the HUPO Proteomics Standard
Initiative (HUPO-PSI). It standardises programmatic access to
molecular interaction databases.
biocViews: DataImport, GraphAndNetwork, ThirdPartyClient
Author: Paul Shannon
Maintainer: Paul Shannon<pshannon@systemsbiology.org>
git_url: https://git.bioconductor.org/packages/PSICQUIC
git_branch: RELEASE_3_13
git_last_commit: 3d77738
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PSICQUIC_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PSICQUIC_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PSICQUIC_1.30.0.tgz
vignettes: vignettes/PSICQUIC/inst/doc/PSICQUIC.pdf
vignetteTitles: PSICQUIC
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PSICQUIC/inst/doc/PSICQUIC.R
dependencyCount: 73
Package: psygenet2r
Version: 1.24.0
Depends: R (>= 3.4)
Imports: stringr, RCurl, igraph, ggplot2, reshape2, grid, parallel,
biomaRt, BgeeDB, topGO, Biobase, labeling, GO.db
Suggests: testthat, knitr
License: MIT + file LICENSE
MD5sum: b94279f21051e274ec525a2620d7311e
NeedsCompilation: no
Title: psygenet2r - An R package for querying PsyGeNET and to perform
comorbidity studies in psychiatric disorders
Description: Package to retrieve data from PsyGeNET database
(www.psygenet.org) and to perform comorbidity studies with
PsyGeNET's and user's data.
biocViews: Software, BiomedicalInformatics, Genetics, Infrastructure,
DataImport, DataRepresentation
Author: Alba Gutierrez-Sacristan [aut, cre], Carles Hernandez-Ferrer
[aut], Jaun R. Gonzalez [aut], Laura I. Furlong [aut]
Maintainer: Alba Gutierrez-Sacristan <a.gutierrez.sacristan@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/psygenet2r
git_branch: RELEASE_3_13
git_last_commit: 7a013f8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-20
source.ver: src/contrib/psygenet2r_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/psygenet2r_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/psygenet2r_1.24.0.tgz
vignettes: vignettes/psygenet2r/inst/doc/case_study.html,
vignettes/psygenet2r/inst/doc/general_overview.html
vignetteTitles: psygenet2r: Case study on GWAS on bipolar disorder,
psygenet2r: An R package for querying PsyGeNET and to perform
comorbidity studies in psychiatric disorders
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/psygenet2r/inst/doc/case_study.R,
vignettes/psygenet2r/inst/doc/general_overview.R
dependencyCount: 104
Package: ptairMS
Version: 1.0.1
Imports: Biobase, bit64, chron, data.table, doParallel, DT, enviPat,
foreach, ggplot2, graphics, grDevices, ggpubr, gridExtra,
Hmisc, methods, minpack.lm, MSnbase, parallel, plotly, rhdf5,
rlang, Rcpp, shiny, shinyscreenshot, signal, scales, stats,
utils
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), ptairData,
ropls
License: GPL-3
MD5sum: 051298e64f5629ec2faa9ae91cacde9a
NeedsCompilation: yes
Title: Pre-processing PTR-TOF-MS Data
Description: This package implements a suite of methods to preprocess
data from PTR-TOF-MS instruments (HDF5 format) and generates
the 'sample by features' table of peak intensities in addition
to the sample and feature metadata (as a single ExpressionSet
object for subsequent statistical analysis). This package also
permit usefull tools for cohorts management as analyzing data
progressively, visualization tools and quality control. The
steps include calibration, expiration detection, peak detection
and quantification, feature alignment, missing value imputation
and feature annotation. Applications to exhaled air and cell
culture in headspace are described in the vignettes and
examples. This package was used for data analysis of Gassin
Delyle study on adults undergoing invasive mechanical
ventilation in the intensive care unit due to severe COVID-19
or non-COVID-19 acute respiratory distress syndrome (ARDS), and
permit to identfy four potentiel biomarquers of the infection.
biocViews: Software, MassSpectrometry, Preprocessing, Metabolomics,
PeakDetection, Alignment
Author: camille Roquencourt [aut, cre]
Maintainer: camille Roquencourt <camille.roquencourt@hotmail.fr>
VignetteBuilder: knitr
BugReports: https://github.com/camilleroquencourt/ptairMS/issues
git_url: https://git.bioconductor.org/packages/ptairMS
git_branch: RELEASE_3_13
git_last_commit: 701b577
git_last_commit_date: 2021-09-27
Date/Publication: 2021-09-28
source.ver: src/contrib/ptairMS_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ptairMS_1.0.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ptairMS_1.0.1.tgz
vignettes: vignettes/ptairMS/inst/doc/ptairMS.html
vignetteTitles: ptaiMS: Processing and analysis of PTR-TOF-MS data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ptairMS/inst/doc/ptairMS.R
dependencyCount: 188
Package: PubScore
Version: 1.4.0
Depends: R (>= 4.0.0)
Imports: ggplot2, igraph, ggrepel,rentrez, progress, graphics, dplyr,
utils, methods, intergraph, network, sna
Suggests: FCBF, plotly, SummarizedExperiment, SingleCellExperiment,
knitr, rmarkdown, testthat (>= 2.1.0), BiocManager, biomaRt
License: MIT + file LICENSE
MD5sum: bf094e8316bc8f110429a8b95203da71
NeedsCompilation: no
Title: Automatic calculation of literature relevance of genes
Description: Calculates the importance score for a given gene. The
importance score is the total counts of articles in the pubmed
database that are a result for that gene AND each term of a
list.
biocViews: GeneSetEnrichment, GeneExpression, SystemsBiology, Genetics,
Epigenetics, BiomedicalInformatics, Visualization, SingleCell
Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths]
Maintainer: Tiago Lubiana <tiago.lubiana.alves@usp.br>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PubScore
git_branch: RELEASE_3_13
git_last_commit: 5e46d7d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/PubScore_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PubScore_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/PubScore_1.4.0.tgz
vignettes: vignettes/PubScore/inst/doc/PubScore_vignette.html
vignetteTitles: FCBF : Fast Correlation Based Filter for Feature
Selection
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PubScore/inst/doc/PubScore_vignette.R
dependencyCount: 64
Package: pulsedSilac
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: robustbase, methods, R.utils, taRifx, S4Vectors,
SummarizedExperiment, ggplot2, ggridges, stats, utils, UpSetR,
cowplot, grid, MuMIn
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, gridExtra
License: GPL-3
MD5sum: 9311a33821dcfecffb4f3809a09446b0
NeedsCompilation: no
Title: Analysis of pulsed-SILAC quantitative proteomics data
Description: This package provides several tools for pulsed-SILAC data
analysis. Functions are provided to organize the data,
calculate isotope ratios, isotope fractions, model protein
turnover, compare turnover models, estimate cell growth and
estimate isotope recycling. Several visualization tools are
also included to do basic data exploration, quality control,
condition comparison, individual model inspection and model
comparison.
biocViews: Proteomics
Author: Marc Pagès-Gallego, Tobias B. Dansen
Maintainer: Marc Pagès-Gallego <M.PagesGallego@umcutrecht.nl>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pulsedSilac
git_branch: RELEASE_3_13
git_last_commit: 33c3efa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pulsedSilac_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pulsedSilac_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pulsedSilac_1.6.0.tgz
vignettes: vignettes/pulsedSilac/inst/doc/pulsedsilac.html
vignetteTitles: Pulsed-SILAC data analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pulsedSilac/inst/doc/pulsedsilac.R
dependencyCount: 72
Package: puma
Version: 3.34.0
Depends: R (>= 3.2.0), oligo (>= 1.32.0),graphics,grDevices, methods,
stats, utils, mclust, oligoClasses
Imports: Biobase (>= 2.5.5), affy (>= 1.46.0), affyio, oligoClasses
Suggests: pumadata, affydata, snow, limma, ROCR,annotate
License: LGPL
Archs: i386, x64
MD5sum: 62ab124df86604ff17b50f9de00a496b
NeedsCompilation: yes
Title: Propagating Uncertainty in Microarray Analysis(including
Affymetrix tranditional 3' arrays and exon arrays and Human
Transcriptome Array 2.0)
Description: Most analyses of Affymetrix GeneChip data (including
tranditional 3' arrays and exon arrays and Human Transcriptome
Array 2.0) are based on point estimates of expression levels
and ignore the uncertainty of such estimates. By propagating
uncertainty to downstream analyses we can improve results from
microarray analyses. For the first time, the puma package makes
a suite of uncertainty propagation methods available to a
general audience. In additon to calculte gene expression from
Affymetrix 3' arrays, puma also provides methods to process
exon arrays and produces gene and isoform expression for
alternative splicing study. puma also offers improvements in
terms of scope and speed of execution over previously available
uncertainty propagation methods. Included are summarisation,
differential expression detection, clustering and PCA methods,
together with useful plotting functions.
biocViews: Microarray, OneChannel, Preprocessing,
DifferentialExpression, Clustering, ExonArray, GeneExpression,
mRNAMicroarray, ChipOnChip, AlternativeSplicing,
DifferentialSplicing, Bayesian, TwoChannel, DataImport, HTA2.0
Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo,
Neil D. Lawrence, Guido Sanguinetti, Li Zhang
Maintainer: Xuejun Liu <xuejun.liu@nuaa.edu.cn>
URL: http://umber.sbs.man.ac.uk/resources/puma
git_url: https://git.bioconductor.org/packages/puma
git_branch: RELEASE_3_13
git_last_commit: 8863f78
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/puma_3.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/puma_3.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/puma_3.34.0.tgz
vignettes: vignettes/puma/inst/doc/puma.pdf
vignetteTitles: puma User Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/puma/inst/doc/puma.R
suggestsMe: tigre
dependencyCount: 56
Package: PureCN
Version: 1.22.2
Depends: R (>= 3.5.0), DNAcopy, VariantAnnotation (>= 1.14.1)
Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer,
S4Vectors, data.table, grDevices, graphics, stats, utils,
SummarizedExperiment, GenomeInfoDb, GenomicFeatures, Rsamtools,
Biostrings, BiocGenerics, rtracklayer, ggplot2, gridExtra,
futile.logger, VGAM, tools, methods, rhdf5, Matrix
Suggests: BiocParallel, BiocStyle, PSCBS,
TxDb.Hsapiens.UCSC.hg19.knownGene, copynumber, covr, knitr,
optparse, org.Hs.eg.db, jsonlite, rmarkdown, testthat
Enhances: genomicsdb (>= 0.0.3)
License: Artistic-2.0
MD5sum: 24d24c1686aeb9110002fd07c0e4c407
NeedsCompilation: no
Title: Copy number calling and SNV classification using targeted short
read sequencing
Description: This package estimates tumor purity, copy number, and loss
of heterozygosity (LOH), and classifies single nucleotide
variants (SNVs) by somatic status and clonality. PureCN is
designed for targeted short read sequencing data, integrates
well with standard somatic variant detection and copy number
pipelines, and has support for tumor samples without matching
normal samples.
biocViews: CopyNumberVariation, Software, Sequencing,
VariantAnnotation, VariantDetection, Coverage, ImmunoOncology
Author: Markus Riester [aut, cre]
(<https://orcid.org/0000-0002-4759-8332>), Angad P. Singh [aut]
Maintainer: Markus Riester <markus.riester@novartis.com>
URL: https://github.com/lima1/PureCN
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PureCN
git_branch: RELEASE_3_13
git_last_commit: 1d595f4
git_last_commit_date: 2021-07-01
Date/Publication: 2021-07-04
source.ver: src/contrib/PureCN_1.22.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PureCN_1.22.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/PureCN_1.22.2.tgz
vignettes: vignettes/PureCN/inst/doc/PureCN.pdf,
vignettes/PureCN/inst/doc/Quick.html
vignetteTitles: Overview of the PureCN R package, Best practices,,
quick start and command line usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PureCN/inst/doc/PureCN.R,
vignettes/PureCN/inst/doc/Quick.R
dependencyCount: 119
Package: pvac
Version: 1.40.0
Depends: R (>= 2.8.0)
Imports: affy (>= 1.20.0), stats, Biobase
Suggests: pbapply, affydata, ALLMLL, genefilter
License: LGPL (>= 2.0)
MD5sum: 8899f92241d865ff3951ba7a04c79713
NeedsCompilation: no
Title: PCA-based gene filtering for Affymetrix arrays
Description: The package contains the function for filtering genes by
the proportion of variation accounted for by the first
principal component (PVAC).
biocViews: Microarray, OneChannel, QualityControl
Author: Jun Lu and Pierre R. Bushel
Maintainer: Jun Lu <jlu276@gmail.com>, Pierre R. Bushel
<bushel@niehs.nih.gov>
git_url: https://git.bioconductor.org/packages/pvac
git_branch: RELEASE_3_13
git_last_commit: 2500206
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pvac_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pvac_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pvac_1.40.0.tgz
vignettes: vignettes/pvac/inst/doc/pvac.pdf
vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pvac/inst/doc/pvac.R
dependencyCount: 13
Package: pvca
Version: 1.32.0
Depends: R (>= 2.15.1)
Imports: Matrix, Biobase, vsn, stats, lme4
Suggests: golubEsets
License: LGPL (>= 2.0)
Archs: i386, x64
MD5sum: c434aecf981feb769c45dec3c83a959d
NeedsCompilation: no
Title: Principal Variance Component Analysis (PVCA)
Description: This package contains the function to assess the batch
sourcs by fitting all "sources" as random effects including
two-way interaction terms in the Mixed Model(depends on lme4
package) to selected principal components, which were obtained
from the original data correlation matrix. This package
accompanies the book "Batch Effects and Noise in Microarray
Experiements, chapter 12.
biocViews: Microarray, BatchEffect
Author: Pierre Bushel <bushel@niehs.nih.gov>
Maintainer: Jianying LI <li11@niehs.nih.gov>
git_url: https://git.bioconductor.org/packages/pvca
git_branch: RELEASE_3_13
git_last_commit: 7a06e6c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pvca_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pvca_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pvca_1.32.0.tgz
vignettes: vignettes/pvca/inst/doc/pvca.pdf
vignetteTitles: Batch effect estimation in Microarray data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pvca/inst/doc/pvca.R
importsMe: proBatch, ExpressionNormalizationWorkflow, statVisual
dependencyCount: 54
Package: Pviz
Version: 1.26.0
Depends: R(>= 3.0.0), Gviz(>= 1.7.10)
Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table,
methods
Suggests: knitr, pepDat
License: Artistic-2.0
MD5sum: ba715fc85dae76e6c9abb56631f351d7
NeedsCompilation: no
Title: Peptide Annotation and Data Visualization using Gviz
Description: Pviz adapts the Gviz package for protein sequences and
data.
biocViews: Visualization, Proteomics, Microarray
Author: Renan Sauteraud, Mike Jiang, Raphael Gottardo
Maintainer: Renan Sauteraud <rsautera@fhcrc.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Pviz
git_branch: RELEASE_3_13
git_last_commit: dcce10c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Pviz_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Pviz_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Pviz_1.26.0.tgz
vignettes: vignettes/Pviz/inst/doc/Pviz.pdf
vignetteTitles: The Pviz users guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Pviz/inst/doc/Pviz.R
suggestsMe: pepStat
dependencyCount: 142
Package: PWMEnrich
Version: 4.28.1
Depends: R (>= 3.5.0), methods, grid, BiocGenerics, Biostrings
Imports: seqLogo, gdata, evd, S4Vectors
Suggests: MotifDb, BSgenome, BSgenome.Dmelanogaster.UCSC.dm3,
PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel,
PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background,
BiocStyle, knitr
License: LGPL (>= 2)
MD5sum: a6b765f59d51a0d1932c9a71fc8e4826
NeedsCompilation: no
Title: PWM enrichment analysis
Description: A toolkit of high-level functions for DNA motif scanning
and enrichment analysis built upon Biostrings. The main
functionality is PWM enrichment analysis of already known PWMs
(e.g. from databases such as MotifDb), but the package also
implements high-level functions for PWM scanning and
visualisation. The package does not perform "de novo" motif
discovery, but is instead focused on using motifs that are
either experimentally derived or computationally constructed by
other tools.
biocViews: MotifAnnotation, SequenceMatching, Software
Author: Robert Stojnic, Diego Diez
Maintainer: Diego Diez <diego10ruiz@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PWMEnrich
git_branch: RELEASE_3_13
git_last_commit: 7ca4ee9
git_last_commit_date: 2021-05-25
Date/Publication: 2021-05-25
source.ver: src/contrib/PWMEnrich_4.28.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/PWMEnrich_4.28.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/PWMEnrich_4.28.1.tgz
vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf
vignetteTitles: Overview of the 'PWMEnrich' package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PWMEnrich/inst/doc/PWMEnrich.R
dependsOnMe: PWMEnrich.Dmelanogaster.background,
PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background
suggestsMe: rTRM
dependencyCount: 24
Package: pwOmics
Version: 1.24.0
Depends: R (>= 3.2)
Imports: data.table, rBiopaxParser, igraph, STRINGdb, graphics, gplots,
Biobase, BiocGenerics, AnnotationDbi, biomaRt, AnnotationHub,
GenomicRanges, graph, grDevices, stats, utils
Suggests: ebdbNet, longitudinal, Mfuzz
License: GPL (>= 2)
Archs: i386, x64
MD5sum: c09010e93d3a3d6fec681653787ebf63
NeedsCompilation: no
Title: Pathway-based data integration of omics data
Description: pwOmics performs pathway-based level-specific data
comparison of matching omics data sets based on pre-analysed
user-specified lists of differential genes/transcripts and
phosphoproteins. A separate downstream analysis of
phosphoproteomic data including pathway identification,
transcription factor identification and target gene
identification is opposed to the upstream analysis starting
with gene or transcript information as basis for identification
of upstream transcription factors and potential proteomic
regulators. The cross-platform comparative analysis allows for
comprehensive analysis of single time point experiments and
time-series experiments by providing static and dynamic
analysis tools for data integration. In addition, it provides
functions to identify individual signaling axes based on data
integration.
biocViews: SystemsBiology, Transcription, GeneTarget, GeneSignaling
Author: Astrid Wachter <Astrid.Wachter@med.uni-goettingen.de>
Maintainer: Maren Sitte <Maren.Sitte@med.uni-goettingen.de>
git_url: https://git.bioconductor.org/packages/pwOmics
git_branch: RELEASE_3_13
git_last_commit: 9b1e369
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pwOmics_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pwOmics_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pwOmics_1.24.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 116
Package: pwrEWAS
Version: 1.6.0
Depends: shinyBS, foreach
Imports: doParallel, abind, truncnorm, CpGassoc, shiny, ggplot2,
parallel, shinyWidgets, BiocManager, doSNOW, limma, genefilter,
stats, grDevices, methods, utils, graphics, pwrEWAS.data
Suggests: knitr, RUnit, BiocGenerics, rmarkdown
License: Artistic-2.0
MD5sum: 38ff9f5f016353818c0cf0bd32771bba
NeedsCompilation: no
Title: A user-friendly tool for comprehensive power estimation for
epigenome wide association studies (EWAS)
Description: pwrEWAS is a user-friendly tool to assists researchers in
the design and planning of EWAS to help circumvent under- and
overpowered studies.
biocViews: DNAMethylation, Microarray, DifferentialMethylation,
TissueMicroarray
Author: Stefan Graw
Maintainer: Stefan Graw <shgraw@uams.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pwrEWAS
git_branch: RELEASE_3_13
git_last_commit: 48c3ab7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/pwrEWAS_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/pwrEWAS_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/pwrEWAS_1.6.0.tgz
vignettes: vignettes/pwrEWAS/inst/doc/pwrEWAS.pdf
vignetteTitles: pwrEWAS User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pwrEWAS/inst/doc/pwrEWAS.R
dependencyCount: 122
Package: qckitfastq
Version: 1.8.0
Imports: magrittr, ggplot2, dplyr, seqTools, zlibbioc, data.table,
reshape2, grDevices, graphics, stats, utils, Rcpp, rlang,
RSeqAn
LinkingTo: Rcpp, RSeqAn
Suggests: knitr, rmarkdown, kableExtra, testthat
License: Artistic-2.0
MD5sum: adee9fa23af7b0aee6aee2d689a8e6b7
NeedsCompilation: yes
Title: FASTQ Quality Control
Description: Assessment of FASTQ file format with multiple metrics
including quality score, sequence content, overrepresented
sequence and Kmers.
biocViews: Software,QualityControl,Sequencing
Author: Wenyue Xing [aut], August Guang [aut, cre]
Maintainer: August Guang <august.guang@gmail.com>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/qckitfastq
git_branch: RELEASE_3_13
git_last_commit: 6e68a18
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/qckitfastq_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/qckitfastq_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/qckitfastq_1.8.0.tgz
vignettes: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.pdf
vignetteTitles: Quality control analysis and visualization using
qckitfastq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.R
dependencyCount: 52
Package: qcmetrics
Version: 1.30.0
Depends: R (>= 3.3)
Imports: Biobase, methods, knitr, tools, xtable, pander, S4Vectors
Suggests: affy, MSnbase, ggplot2, lattice, mzR, BiocStyle
License: GPL-2
MD5sum: 1222688a063c9bf8a3ca78dd2d9e1f5c
NeedsCompilation: no
Title: A Framework for Quality Control
Description: The package provides a framework for generic quality
control of data. It permits to create, manage and visualise
individual or sets of quality control metrics and generate
quality control reports in various formats.
biocViews: ImmunoOncology, Software, QualityControl, Proteomics,
Microarray, MassSpectrometry, Visualization, ReportWriting
Author: Laurent Gatto [aut, cre]
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: http://lgatto.github.io/qcmetrics/articles/qcmetrics.html
VignetteBuilder: knitr
BugReports: https://github.com/lgatto/qcmetrics/issues
git_url: https://git.bioconductor.org/packages/qcmetrics
git_branch: RELEASE_3_13
git_last_commit: ba3f35c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/qcmetrics_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/qcmetrics_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/qcmetrics_1.30.0.tgz
vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.html
vignetteTitles: Index file for the qcmetrics package vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R
importsMe: MSstatsQC
dependencyCount: 24
Package: QDNAseq
Version: 1.28.0
Depends: R (>= 3.1.0)
Imports: graphics, methods, stats, utils, Biobase (>= 2.18.0), CGHbase
(>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0),
GenomicRanges (>= 1.20), IRanges (>= 2.2), matrixStats (>=
0.54.0), R.utils (>= 2.9.0), Rsamtools (>= 1.20), future (>=
1.14.0), future.apply (>= 1.3.0)
Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>=
0.6.20), GenomeInfoDb (>= 1.6.0), R.cache (>= 0.13.0),
QDNAseq.hg19, QDNAseq.mm10
License: GPL
MD5sum: b754d3b0af4bf772106b1e2c80cc8201
NeedsCompilation: no
Title: Quantitative DNA Sequencing for Chromosomal Aberrations
Description: Quantitative DNA sequencing for chromosomal aberrations.
The genome is divided into non-overlapping fixed-sized bins,
number of sequence reads in each counted, adjusted with a
simultaneous two-dimensional loess correction for sequence
mappability and GC content, and filtered to remove spurious
regions in the genome. Downstream steps of segmentation and
calling are also implemented via packages DNAcopy and CGHcall,
respectively.
biocViews: CopyNumberVariation, DNASeq, Genetics, GenomeAnnotation,
Preprocessing, QualityControl, Sequencing
Author: Ilari Scheinin [aut], Daoud Sie [aut, cre], Henrik Bengtsson
[aut], Erik van Dijk [ctb]
Maintainer: Daoud Sie <d.sie@vumc.nl>
URL: https://github.com/ccagc/QDNAseq
BugReports: https://github.com/ccagc/QDNAseq/issues
git_url: https://git.bioconductor.org/packages/QDNAseq
git_branch: RELEASE_3_13
git_last_commit: 3ca285f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/QDNAseq_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/QDNAseq_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/QDNAseq_1.28.0.tgz
vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf
vignetteTitles: Introduction to QDNAseq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/QDNAseq/inst/doc/QDNAseq.R
dependsOnMe: GeneBreak, QDNAseq.hg19, QDNAseq.mm10
importsMe: ACE, biscuiteer, HiCcompare
dependencyCount: 48
Package: QFeatures
Version: 1.2.0
Depends: R (>= 4.0), MultiAssayExperiment
Imports: methods, stats, utils, S4Vectors, IRanges,
SummarizedExperiment, BiocGenerics, ProtGenerics (>= 1.19.3),
AnnotationFilter, lazyeval, Biobase, MsCoreUtils (>= 1.1.2),
Suggests: SingleCellExperiment, HDF5Array, msdata, ggplot2, gplots,
dplyr, limma, magrittr, DT, shiny, shinydashboard, testthat,
knitr, BiocStyle, rmarkdown, vsn, preprocessCore, matrixStats,
imputeLCMD, pcaMethods, impute, norm
License: Artistic-2.0
MD5sum: 817b47db795215763ea73dfeb0c65a23
NeedsCompilation: no
Title: Quantitative features for mass spectrometry data
Description: The QFeatures infrastructure enables the management and
processing of quantitative features for high-throughput mass
spectrometry assays. It provides a familiar Bioconductor user
experience to manages quantitative data across different assay
levels (such as peptide spectrum matches, peptides and
proteins) in a coherent and tractable format.
biocViews: Infrastructure, MassSpectrometry, Proteomics, Metabolomics
Author: Laurent Gatto [aut, cre]
(<https://orcid.org/0000-0002-1520-2268>), Christophe Vanderaa
[aut] (<https://orcid.org/0000-0001-7443-5427>)
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://github.com/RforMassSpectrometry/QFeatures
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/QFeatures/issues
git_url: https://git.bioconductor.org/packages/QFeatures
git_branch: RELEASE_3_13
git_last_commit: e858c2b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/QFeatures_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/QFeatures_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/QFeatures_1.2.0.tgz
vignettes: vignettes/QFeatures/inst/doc/Processing.html,
vignettes/QFeatures/inst/doc/QFeatures.html
vignetteTitles: Processing quantitative proteomics data with QFeatures,
Quantitative features for mass spectrometry data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/QFeatures/inst/doc/Processing.R,
vignettes/QFeatures/inst/doc/QFeatures.R
dependsOnMe: msqrob2, scp, scpdata
dependencyCount: 55
Package: qpcrNorm
Version: 1.50.0
Depends: methods, Biobase, limma, affy
License: LGPL (>= 2)
MD5sum: 0e2dca0a3208efda256517f35cb44ce6
NeedsCompilation: no
Title: Data-driven normalization strategies for high-throughput qPCR
data.
Description: The package contains functions to perform normalization of
high-throughput qPCR data. Basic functions for processing raw
Ct data plus functions to generate diagnostic plots are also
available.
biocViews: Preprocessing, GeneExpression
Author: Jessica Mar
Maintainer: Jessica Mar <jess@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/qpcrNorm
git_branch: RELEASE_3_13
git_last_commit: 9538a20
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/qpcrNorm_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/qpcrNorm_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/qpcrNorm_1.50.0.tgz
vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf
vignetteTitles: qPCR Normalization Example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qpcrNorm/inst/doc/qpcrNorm.R
dependencyCount: 14
Package: qpgraph
Version: 2.26.0
Depends: R (>= 3.5)
Imports: methods, parallel, Matrix (>= 1.0), grid, annotate, graph (>=
1.45.1), Biobase, S4Vectors, BiocParallel, AnnotationDbi,
IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, mvtnorm,
qtl, Rgraphviz
Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db,
rlecuyer, snow, Category, GOstats
License: GPL (>= 2)
MD5sum: 400aad4dc4be7b00dff738355d10e40e
NeedsCompilation: yes
Title: Estimation of genetic and molecular regulatory networks from
high-throughput genomics data
Description: Estimate gene and eQTL networks from high-throughput
expression and genotyping assays.
biocViews: Microarray, GeneExpression, Transcription, Pathways,
NetworkInference, GraphAndNetwork, GeneRegulation, Genetics,
GeneticVariability, SNP, Software
Author: Robert Castelo [aut, cre], Alberto Roverato [aut]
Maintainer: Robert Castelo <robert.castelo@upf.edu>
URL: https://github.com/rcastelo/qpgraph
BugReports: https://github.com/rcastelo/rcastelo/issues
git_url: https://git.bioconductor.org/packages/qpgraph
git_branch: RELEASE_3_13
git_last_commit: 80062b5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/qpgraph_2.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/qpgraph_2.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/qpgraph_2.26.0.tgz
vignettes: vignettes/qpgraph/inst/doc/BasicUsersGuide.pdf,
vignettes/qpgraph/inst/doc/eQTLnetworks.pdf,
vignettes/qpgraph/inst/doc/qpgraphSimulate.pdf,
vignettes/qpgraph/inst/doc/qpTxRegNet.pdf
vignetteTitles: BasicUsersGuide.pdf, Estimate eQTL networks using
qpgraph, Simulating molecular regulatory networks using
qpgraph, Reverse-engineer transcriptional regulatory networks
using qpgraph
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qpgraph/inst/doc/eQTLnetworks.R,
vignettes/qpgraph/inst/doc/qpgraphSimulate.R,
vignettes/qpgraph/inst/doc/qpTxRegNet.R
importsMe: clipper, topologyGSA
dependencyCount: 102
Package: qPLEXanalyzer
Version: 1.10.0
Depends: R (>= 4.0), Biobase, MSnbase
Imports: assertthat, BiocGenerics, Biostrings, dplyr (>= 1.0.0),
ggdendro, ggplot2, graphics, grDevices, IRanges, limma,
magrittr, preprocessCore, purrr, RColorBrewer, readr, rlang,
scales, stats, stringr, tibble, tidyr, tidyselect, utils
Suggests: gridExtra, knitr, qPLEXdata, rmarkdown, testthat, UniProt.ws,
vdiffr
License: GPL-2
Archs: i386, x64
MD5sum: ea25b1fe9f0e3ec7aa22f6cfec12a5a4
NeedsCompilation: no
Title: Tools for qPLEX-RIME data analysis
Description: Tools for quantitative proteomics data analysis generated
from qPLEX-RIME method.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Normalization,
Preprocessing, QualityControl, DataImport
Author: Matthew Eldridge [aut], Kamal Kishore [aut], Ashley Sawle [aut,
cre]
Maintainer: Ashley Sawle <ads2202cu@gmail.com>
VignetteBuilder: knitr
BugReports:
https://github.com/crukci-bioinformatics/qPLEXanalyzer/issues
git_url: https://git.bioconductor.org/packages/qPLEXanalyzer
git_branch: RELEASE_3_13
git_last_commit: 1edf1ad
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/qPLEXanalyzer_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/qPLEXanalyzer_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/qPLEXanalyzer_1.10.0.tgz
vignettes: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.html
vignetteTitles: qPLEXanalyzer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.R
dependsOnMe: qPLEXdata
dependencyCount: 103
Package: qrqc
Version: 1.46.0
Depends: reshape, ggplot2, Biostrings, biovizBase, brew, xtable,
testthat
Imports: reshape, ggplot2, Biostrings, biovizBase, graphics, methods,
plyr, stats
LinkingTo: Rhtslib (>= 1.15.3)
License: GPL (>=2)
MD5sum: 5cb6ae5e4d44f9f39a0b67b9b18062a6
NeedsCompilation: yes
Title: Quick Read Quality Control
Description: Quickly scans reads and gathers statistics on base and
quality frequencies, read length, k-mers by position, and
frequent sequences. Produces graphical output of statistics for
use in quality control pipelines, and an optional HTML quality
report. S4 SequenceSummary objects allow specific tests and
functionality to be written around the data collected.
biocViews: Sequencing, QualityControl, DataImport, Preprocessing,
Visualization
Author: Vince Buffalo
Maintainer: Vince Buffalo <vsbuffalo@ucdavis.edu>
URL: http://github.com/vsbuffalo/qrqc
SystemRequirements: GNU make
git_url: https://git.bioconductor.org/packages/qrqc
git_branch: RELEASE_3_13
git_last_commit: 891dd79
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/qrqc_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/qrqc_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/qrqc_1.46.0.tgz
vignettes: vignettes/qrqc/inst/doc/qrqc.pdf
vignetteTitles: Using the qrqc package to gather information about
sequence qualities
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qrqc/inst/doc/qrqc.R
dependencyCount: 157
Package: qsea
Version: 1.18.0
Depends: R (>= 3.5)
Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy,
rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges,
limma, GenomeInfoDb, BiocGenerics, grDevices, zoo,
BiocParallel, KernSmooth, MASS
Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle,
knitr, rmarkdown, BiocManager
License: GPL (>=2)
MD5sum: ea6d1d296558d3f212c7f7eb48fb00cb
NeedsCompilation: yes
Title: IP-seq data analysis and vizualization
Description: qsea (quantitative sequencing enrichment analysis) was
developed as the successor of the MEDIPS package for analyzing
data derived from methylated DNA immunoprecipitation (MeDIP)
experiments followed by sequencing (MeDIP-seq). However, qsea
provides several functionalities for the analysis of other
kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq,
CMS-seq and others) including calculation of differential
enrichment between groups of samples.
biocViews: Sequencing, DNAMethylation, CpGIsland, ChIPSeq,
Preprocessing, Normalization, QualityControl, Visualization,
CopyNumberVariation, ChipOnChip, DifferentialMethylation
Author: Matthias Lienhard, Lukas Chavez, Ralf Herwig
Maintainer: Matthias Lienhard <lienhard@molgen.mpg.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/qsea
git_branch: RELEASE_3_13
git_last_commit: f23006b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/qsea_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/qsea_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/qsea_1.18.0.tgz
vignettes: vignettes/qsea/inst/doc/qsea_tutorial.html
vignetteTitles: qsea
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qsea/inst/doc/qsea_tutorial.R
dependencyCount: 52
Package: qsmooth
Version: 1.8.0
Depends: R (>= 4.0)
Imports: SummarizedExperiment, utils, sva, stats, methods, graphics
Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat
License: CC BY 4.0
MD5sum: 5989be7d169c31df4aa9271b5ccf180a
NeedsCompilation: no
Title: Smooth quantile normalization
Description: Smooth quantile normalization is a generalization of
quantile normalization, which is average of the two types of
assumptions about the data generation process: quantile
normalization and quantile normalization between groups.
biocViews: Normalization, Preprocessing, MultipleComparison,
Microarray, Sequencing, RNASeq, BatchEffect
Author: Stephanie C. Hicks [aut, cre]
(<https://orcid.org/0000-0002-7858-0231>), Kwame Okrah [aut],
Hector Corrada Bravo [aut]
(<https://orcid.org/0000-0002-1255-4444>), Rafael Irizarry
[aut] (<https://orcid.org/0000-0002-3944-4309>)
Maintainer: Stephanie C. Hicks <shicks19@jhu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/qsmooth
git_branch: RELEASE_3_13
git_last_commit: 85b51b0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/qsmooth_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/qsmooth_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/qsmooth_1.8.0.tgz
vignettes: vignettes/qsmooth/inst/doc/qsmooth.html
vignetteTitles: The qsmooth user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qsmooth/inst/doc/qsmooth.R
dependencyCount: 73
Package: QSutils
Version: 1.10.0
Depends: R (>= 3.5), Biostrings, BiocGenerics,methods
Imports: ape, stats, psych
Suggests: BiocStyle, knitr, rmarkdown, ggplot2
License: file LICENSE
MD5sum: ea3bb2b6ae5cd784fa6ed413cb750dee
NeedsCompilation: no
Title: Quasispecies Diversity
Description: Set of utility functions for viral quasispecies analysis
with NGS data. Most functions are equally useful for
metagenomic studies. There are three main types: (1) data
manipulation and exploration—functions useful for converting
reads to haplotypes and frequencies, repairing reads,
intersecting strand haplotypes, and visualizing haplotype
alignments. (2) diversity indices—functions to compute
diversity and entropy, in which incidence, abundance, and
functional indices are considered. (3) data
simulation—functions useful for generating random viral
quasispecies data.
biocViews: Software, Genetics, DNASeq, GeneticVariability, Sequencing,
Alignment, SequenceMatching, DataImport
Author: Mercedes Guerrero-Murillo [cre, aut]
(<https://orcid.org/0000-0002-5556-2460>), Josep Gregori i Font
[aut] (<https://orcid.org/0000-0002-4253-8015>)
Maintainer: Mercedes Guerrero-Murillo <mergumu@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/QSutils
git_branch: RELEASE_3_13
git_last_commit: 74dbca7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/QSutils_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/QSutils_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/QSutils_1.10.0.tgz
vignettes: vignettes/QSutils/inst/doc/QSUtils-Alignment.html,
vignettes/QSutils/inst/doc/QSutils-Diversity.html,
vignettes/QSutils/inst/doc/QSutils-Simulation.html
vignetteTitles: QSUtils-Alignment, QSutils-Diversity,
QSutils-Simulation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/QSutils/inst/doc/QSUtils-Alignment.R,
vignettes/QSutils/inst/doc/QSutils-Diversity.R,
vignettes/QSutils/inst/doc/QSutils-Simulation.R
dependencyCount: 27
Package: Qtlizer
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: httr, curl, GenomicRanges, stringi
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: GPL-3
Archs: i386, x64
MD5sum: 219f5c3fe790be9612b54a29761b86e8
NeedsCompilation: no
Title: Comprehensive QTL annotation of GWAS results
Description: This R package provides access to the Qtlizer web server.
Qtlizer annotates lists of common small variants (mainly SNPs)
and genes in humans with associated changes in gene expression
using the most comprehensive database of published quantitative
trait loci (QTLs).
biocViews: GenomeWideAssociation, SNP, Genetics, LinkageDisequilibrium
Author: Matthias Munz [aut, cre]
(<https://orcid.org/0000-0002-4728-3357>), Julia Remes [aut]
Maintainer: Matthias Munz <matthias.munz@gmx.de>
VignetteBuilder: knitr
BugReports: https://github.com/matmu/Qtlizer/issues
git_url: https://git.bioconductor.org/packages/Qtlizer
git_branch: RELEASE_3_13
git_last_commit: 7da775a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Qtlizer_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Qtlizer_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Qtlizer_1.6.0.tgz
vignettes: vignettes/Qtlizer/inst/doc/Qtlizer.html
vignetteTitles: Qtlizer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Qtlizer/inst/doc/Qtlizer.R
dependencyCount: 26
Package: quantiseqr
Version: 1.0.0
Depends: R (>= 4.1.0)
Imports: Biobase, limSolve, MASS, methods, preprocessCore, stats,
SummarizedExperiment, ggplot2, tidyr, rlang, utils
Suggests: AnnotationDbi, BiocStyle, dplyr, ExperimentHub, GEOquery,
knitr, macrophage, org.Hs.eg.db, reshape2, rmarkdown, testthat,
tibble
License: GPL-3
MD5sum: 0d0ae8bcb7a80fd8a2268d5fcff2d706
NeedsCompilation: no
Title: Quantification of the Tumor Immune contexture from RNA-seq data
Description: This package provides a streamlined workflow for the
quanTIseq method, developed to perform the quantification of
the Tumor Immune contexture from RNA-seq data. The
quantification is performed against the TIL10 signature
(dissecting the contributions of ten immune cell types),
carefully crafted from a collection of human RNA-seq samples.
The TIL10 signature has been extensively validated using
simulated, flow cytometry, and immunohistochemistry data.
biocViews: GeneExpression, Software, Transcription, Transcriptomics,
Sequencing, Microarray, Visualization, Annotation,
ImmunoOncology, FeatureExtraction, Classification,
StatisticalMethod, ExperimentHubSoftware, FlowCytometry
Author: Federico Marini [aut, cre]
(<https://orcid.org/0000-0003-3252-7758>), Francesca Finotello
[aut] (<https://orcid.org/0000-0003-0712-4658>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/quantiseqr
git_branch: RELEASE_3_13
git_last_commit: 4408bea
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/quantiseqr_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/quantiseqr_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/quantiseqr_1.0.0.tgz
vignettes: vignettes/quantiseqr/inst/doc/using_quantiseqr.html
vignetteTitles: Using quantiseqr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/quantiseqr/inst/doc/using_quantiseqr.R
dependencyCount: 66
Package: quantro
Version: 1.26.0
Depends: R (>= 4.0)
Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2,
methods, RColorBrewer
Suggests: knitr, RUnit, BiocGenerics, BiocStyle
License: GPL (>=3)
Archs: i386, x64
MD5sum: 65057a21fec4146761dbc396f65fbd74
NeedsCompilation: no
Title: A test for when to use quantile normalization
Description: A data-driven test for the assumptions of quantile
normalization using raw data such as objects that inherit eSets
(e.g. ExpressionSet, MethylSet). Group level information about
each sample (such as Tumor / Normal status) must also be
provided because the test assesses if there are global
differences in the distributions between the user-defined
groups.
biocViews: Normalization, Preprocessing, MultipleComparison,
Microarray, Sequencing
Author: Stephanie Hicks [aut, cre]
(<https://orcid.org/0000-0002-7858-0231>), Rafael Irizarry
[aut] (<https://orcid.org/0000-0002-3944-4309>)
Maintainer: Stephanie Hicks <shicks19@jhu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/quantro
git_branch: RELEASE_3_13
git_last_commit: b93958c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/quantro_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/quantro_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/quantro_1.26.0.tgz
vignettes: vignettes/quantro/inst/doc/quantro.html
vignetteTitles: The quantro user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/quantro/inst/doc/quantro.R
importsMe: yarn
suggestsMe: qsmooth
dependencyCount: 150
Package: quantsmooth
Version: 1.58.0
Depends: R(>= 2.10.0), quantreg, grid
License: GPL-2
Archs: i386, x64
MD5sum: 4218abedc14f09aa3e0bc795cd6f6262
NeedsCompilation: no
Title: Quantile smoothing and genomic visualization of array data
Description: Implements quantile smoothing as introduced in: Quantile
smoothing of array CGH data; Eilers PH, de Menezes RX;
Bioinformatics. 2005 Apr 1;21(7):1146-53.
biocViews: Visualization, CopyNumberVariation
Author: Jan Oosting, Paul Eilers, Renee Menezes
Maintainer: Jan Oosting <j.oosting@lumc.nl>
git_url: https://git.bioconductor.org/packages/quantsmooth
git_branch: RELEASE_3_13
git_last_commit: 6446b0c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/quantsmooth_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/quantsmooth_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/quantsmooth_1.58.0.tgz
vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf
vignetteTitles: quantsmooth
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/quantsmooth/inst/doc/quantsmooth.R
dependsOnMe: beadarraySNP
importsMe: GWASTools, SIM
suggestsMe: PREDA
dependencyCount: 15
Package: QuartPAC
Version: 1.24.0
Depends: iPAC, GraphPAC, SpacePAC, data.table
Suggests: RUnit, BiocGenerics, rgl
License: GPL-2
MD5sum: ee226e9e546083d3c1b44438617767d0
NeedsCompilation: no
Title: Identification of mutational clusters in protein quaternary
structures.
Description: Identifies clustering of somatic mutations in proteins
over the entire quaternary structure.
biocViews: Clustering, Proteomics, SomaticMutation
Author: Gregory Ryslik, Yuwei Cheng, Hongyu Zhao
Maintainer: Gregory Ryslik <gregory.ryslik@yale.edu>
git_url: https://git.bioconductor.org/packages/QuartPAC
git_branch: RELEASE_3_13
git_last_commit: 5debb20
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/QuartPAC_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/QuartPAC_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/QuartPAC_1.24.0.tgz
vignettes: vignettes/QuartPAC/inst/doc/QuartPAC.pdf
vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein
space using simulation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/QuartPAC/inst/doc/QuartPAC.R
dependencyCount: 43
Package: QuasR
Version: 1.32.0
Depends: R (>= 4.0), parallel, GenomicRanges, Rbowtie
Imports: methods, grDevices, graphics, utils, BiocGenerics, S4Vectors,
IRanges, BiocManager, Biobase, Biostrings, BSgenome, Rsamtools,
GenomicFeatures, ShortRead, BiocParallel, GenomeInfoDb,
rtracklayer, GenomicFiles, AnnotationDbi, tools
LinkingTo: Rhtslib
Suggests: Gviz, BiocStyle, GenomicAlignments, Rhisat2, knitr,
rmarkdown, covr, testthat
License: GPL-2
MD5sum: 110512509111ecfe022e39d0a2724531
NeedsCompilation: yes
Title: Quantify and Annotate Short Reads in R
Description: This package provides a framework for the quantification
and analysis of Short Reads. It covers a complete workflow
starting from raw sequence reads, over creation of alignments
and quality control plots, to the quantification of genomic
regions of interest.
biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq,
MethylSeq, Coverage, Alignment, QualityControl, ImmunoOncology
Author: Anita Lerch [aut], Charlotte Soneson [aut]
(<https://orcid.org/0000-0003-3833-2169>), Dimos Gaidatzis
[aut], Michael Stadler [aut, cre]
(<https://orcid.org/0000-0002-2269-4934>)
Maintainer: Michael Stadler <michael.stadler@fmi.ch>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/QuasR
git_branch: RELEASE_3_13
git_last_commit: b24d275
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/QuasR_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/QuasR_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/QuasR_1.32.0.tgz
vignettes: vignettes/QuasR/inst/doc/QuasR.html
vignetteTitles: An introduction to QuasR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/QuasR/inst/doc/QuasR.R
importsMe: SingleMoleculeFootprinting
suggestsMe: eisaR
dependencyCount: 106
Package: QuaternaryProd
Version: 1.26.0
Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18)
LinkingTo: Rcpp
Suggests: knitr
License: GPL (>=3)
MD5sum: 076da9acaa60eac2ef2a2ba8309b092d
NeedsCompilation: yes
Title: Computes the Quaternary Dot Product Scoring Statistic for Signed
and Unsigned Causal Graphs
Description: QuaternaryProd is an R package that performs causal
reasoning on biological networks, including publicly available
networks such as STRINGdb. QuaternaryProd is an open-source
alternative to commercial products such as Inginuity Pathway
Analysis. For a given a set of differentially expressed genes,
QuaternaryProd computes the significance of upstream regulators
in the network by performing causal reasoning using the
Quaternary Dot Product Scoring Statistic (Quaternary
Statistic), Ternary Dot product Scoring Statistic (Ternary
Statistic) and Fisher's exact test (Enrichment test). The
Quaternary Statistic handles signed, unsigned and ambiguous
edges in the network. Ambiguity arises when the direction of
causality is unknown, or when the source node (e.g., a protein)
has edges with conflicting signs for the same target gene. On
the other hand, the Ternary Statistic provides causal reasoning
using the signed and unambiguous edges only. The Vignette
provides more details on the Quaternary Statistic and
illustrates an example of how to perform causal reasoning using
STRINGdb.
biocViews: GraphAndNetwork, GeneExpression, Transcription
Author: Carl Tony Fakhry [cre, aut], Ping Chen [ths], Kourosh
Zarringhalam [aut, ths]
Maintainer: Carl Tony Fakhry <cfakhry@cs.umb.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/QuaternaryProd
git_branch: RELEASE_3_13
git_last_commit: 86cd5be
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/QuaternaryProd_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/QuaternaryProd_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/QuaternaryProd_1.26.0.tgz
vignettes: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.pdf
vignetteTitles: <span style="color:red">QuaternaryProdVignette</span>
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.R
dependencyCount: 23
Package: QUBIC
Version: 1.20.1
Depends: R (>= 3.1), biclust
Imports: Rcpp (>= 0.11.0), methods, Matrix
LinkingTo: Rcpp, RcppArmadillo
Suggests: QUBICdata, qgraph, fields, knitr, rmarkdown
Enhances: RColorBrewer
License: CC BY-NC-ND 4.0 + file LICENSE
MD5sum: a3189dbbeb015579ec70b70d158d8ac7
NeedsCompilation: yes
Title: An R package for qualitative biclustering in support of gene
co-expression analyses
Description: The core function of this R package is to provide the
implementation of the well-cited and well-reviewed QUBIC
algorithm, aiming to deliver an effective and efficient
biclustering capability. This package also includes the
following related functions: (i) a qualitative representation
of the input gene expression data, through a well-designed
discretization way considering the underlying data property,
which can be directly used in other biclustering programs; (ii)
visualization of identified biclusters using heatmap in support
of overall expression pattern analysis; (iii) bicluster-based
co-expression network elucidation and visualization, where
different correlation coefficient scores between a pair of
genes are provided; and (iv) a generalize output format of
biclusters and corresponding network can be freely downloaded
so that a user can easily do following comprehensive functional
enrichment analysis (e.g. DAVID) and advanced network
visualization (e.g. Cytoscape).
biocViews: StatisticalMethod, Microarray, DifferentialExpression,
MultipleComparison, Clustering, Visualization, GeneExpression,
Network
Author: Yu Zhang [aut, cre], Qin Ma [aut]
Maintainer: Yu Zhang <zy26@jlu.edu.cn>
URL: http://github.com/zy26/QUBIC
SystemRequirements: C++11, Rtools (>= 3.1)
VignetteBuilder: knitr
BugReports: http://github.com/zy26/QUBIC/issues
git_url: https://git.bioconductor.org/packages/QUBIC
git_branch: RELEASE_3_13
git_last_commit: 0217483
git_last_commit_date: 2021-07-27
Date/Publication: 2021-07-29
source.ver: src/contrib/QUBIC_1.20.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/QUBIC_1.20.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/QUBIC_1.20.1.tgz
vignettes: vignettes/QUBIC/inst/doc/qubic_vignette.pdf
vignetteTitles: QUBIC Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/QUBIC/inst/doc/qubic_vignette.R
suggestsMe: runibic
dependencyCount: 53
Package: qusage
Version: 2.26.0
Depends: R (>= 2.10), limma (>= 3.14), methods
Imports: utils, Biobase, nlme, emmeans, fftw
License: GPL (>= 2)
MD5sum: 9f4e20aa4e014ed5bdd0337c308d4b79
NeedsCompilation: no
Title: qusage: Quantitative Set Analysis for Gene Expression
Description: This package is an implementation the Quantitative Set
Analysis for Gene Expression (QuSAGE) method described in
(Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene
Set Enrichment-type test, which is designed to provide a
faster, more accurate, and easier to understand test for gene
expression studies. qusage accounts for inter-gene correlations
using the Variance Inflation Factor technique proposed by Wu et
al. (Nucleic Acids Res, 2012). In addition, rather than simply
evaluating the deviation from a null hypothesis with a single
number (a P value), qusage quantifies gene set activity with a
complete probability density function (PDF). From this PDF, P
values and confidence intervals can be easily extracted.
Preserving the PDF also allows for post-hoc analysis (e.g.,
pair-wise comparisons of gene set activity) while maintaining
statistical traceability. Finally, while qusage is compatible
with individual gene statistics from existing methods (e.g.,
LIMMA), a Welch-based method is implemented that is shown to
improve specificity. The QuSAGE package also includes a mixed
effects model implementation, as described in (Turner JA et al,
BMC Bioinformatics, 2015), and a meta-analysis framework as
described in (Meng H, et al. PLoS Comput Biol. 2019). For
questions, contact Chris Bolen (cbolen1@gmail.com) or Steven
Kleinstein (steven.kleinstein@yale.edu)
biocViews: GeneSetEnrichment, Microarray, RNASeq, Software,
ImmunoOncology
Author: Christopher Bolen and Gur Yaari, with contributions from Juilee
Thakar, Hailong Meng, Jacob Turner, Derek Blankenship, and
Steven Kleinstein
Maintainer: Christopher Bolen <cbolen1@gmail.com>
URL: http://clip.med.yale.edu/qusage
git_url: https://git.bioconductor.org/packages/qusage
git_branch: RELEASE_3_13
git_last_commit: 599ffb5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/qusage_2.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/qusage_2.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/qusage_2.26.0.tgz
vignettes: vignettes/qusage/inst/doc/qusage.pdf
vignetteTitles: Running qusage
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qusage/inst/doc/qusage.R
dependsOnMe: DrInsight
importsMe: mExplorer
suggestsMe: SigCheck
dependencyCount: 18
Package: qvalue
Version: 2.24.0
Depends: R(>= 2.10)
Imports: splines, ggplot2, grid, reshape2
Suggests: knitr
License: LGPL
MD5sum: 414eba00a898b375b1ce0870c31cb8ce
NeedsCompilation: no
Title: Q-value estimation for false discovery rate control
Description: This package takes a list of p-values resulting from the
simultaneous testing of many hypotheses and estimates their
q-values and local FDR values. The q-value of a test measures
the proportion of false positives incurred (called the false
discovery rate) when that particular test is called
significant. The local FDR measures the posterior probability
the null hypothesis is true given the test's p-value. Various
plots are automatically generated, allowing one to make
sensible significance cut-offs. Several mathematical results
have recently been shown on the conservative accuracy of the
estimated q-values from this software. The software can be
applied to problems in genomics, brain imaging, astrophysics,
and data mining.
biocViews: MultipleComparisons
Author: John D. Storey [aut, cre], Andrew J. Bass [aut], Alan Dabney
[aut], David Robinson [aut], Gregory Warnes [ctb]
Maintainer: John D. Storey <jstorey@princeton.edu>, Andrew J. Bass
<ajbass@princeton.edu>
URL: http://github.com/jdstorey/qvalue
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/qvalue
git_branch: RELEASE_3_13
git_last_commit: a64acae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/qvalue_2.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/qvalue_2.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/qvalue_2.24.0.tgz
vignettes: vignettes/qvalue/inst/doc/qvalue.pdf
vignetteTitles: qvalue Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qvalue/inst/doc/qvalue.R
dependsOnMe: anota, DEGseq, DrugVsDisease, r3Cseq, webbioc, BonEV,
cp4p, isva
importsMe: Anaquin, anota, clusterProfiler, derfinder, DOSE, edge,
epihet, erccdashboard, EventPointer, fishpond, metaseqR2,
methylKit, MOMA, msmsTests, MWASTools, netresponse, normr,
OPWeight, PAST, RiboDiPA, RNAsense, Rnits, SDAMS, sights,
signatureSearch, subSeq, trigger, webbioc, IHWpaper, AEenrich,
armada, cancerGI, DGEobj.utils, fdrDiscreteNull, glmmSeq,
groupedSurv, jaccard, jackstraw, NBPSeq, SeqFeatR, ssizeRNA
suggestsMe: biobroom, LBE, maanova, PREDA, RnBeads,
SummarizedBenchmark, swfdr, RNAinteractMAPK, BootstrapQTL,
CpGassoc, dartR, easylabel, matR, mutoss, Rediscover,
seqgendiff, wrMisc
dependencyCount: 44
Package: R3CPET
Version: 1.24.0
Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods
Imports: methods, parallel, ggplot2, pheatmap, clValid, igraph,
data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors,
IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8),
ggbio
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene,
biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny,
ChIPpeakAnno
License: GPL (>=2)
MD5sum: b7355090c26b15f4fe5b9f0c66e6d46a
NeedsCompilation: yes
Title: 3CPET: Finding Co-factor Complexes in Chia-PET experiment using
a Hierarchical Dirichlet Process
Description: The package provides a method to infer the set of proteins
that are more probably to work together to maintain chormatin
interaction given a ChIA-PET experiment results.
biocViews: NetworkInference, GenePrediction, Bayesian, GraphAndNetwork,
Network, GeneExpression, HiC
Author: Djekidel MN, Yang Chen et al.
Maintainer: Mohamed Nadhir Djekidel <djek.nad@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/sirusb/R3CPET/issues
git_url: https://git.bioconductor.org/packages/R3CPET
git_branch: RELEASE_3_13
git_last_commit: 577029f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/R3CPET_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/R3CPET_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/R3CPET_1.24.0.tgz
vignettes: vignettes/R3CPET/inst/doc/R3CPET.pdf
vignetteTitles: 3CPET: Finding Co-factor Complexes maintaining Chia-PET
interactions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/R3CPET/inst/doc/R3CPET.R
dependencyCount: 157
Package: r3Cseq
Version: 1.38.0
Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue
Imports: methods, GenomeInfoDb, IRanges, Biostrings, data.table, sqldf,
RColorBrewer
Suggests: BSgenome.Mmusculus.UCSC.mm9.masked,
BSgenome.Mmusculus.UCSC.mm10.masked,
BSgenome.Hsapiens.UCSC.hg18.masked,
BSgenome.Hsapiens.UCSC.hg19.masked,
BSgenome.Rnorvegicus.UCSC.rn5.masked
License: GPL-3
Archs: x64
MD5sum: 51b9ff25c910ff657113add7d91e9e2f
NeedsCompilation: no
Title: Analysis of Chromosome Conformation Capture and Next-generation
Sequencing (3C-seq)
Description: This package is used for the analysis of long-range
chromatin interactions from 3C-seq assay.
biocViews: Preprocessing, Sequencing
Author: Supat Thongjuea, MRC WIMM Centre for Computational Biology,
Weatherall Institute of Molecular Medicine, University of
Oxford, UK <supat.thongjuea@imm.ox.ac.uk>
Maintainer: Supat Thongjuea <supat.thongjuea@imm.ox.ac.uk> or
<supat.thongjuea@gmail.com>
URL: http://r3cseq.genereg.net,https://github.com/supatt-lab/r3Cseq/
git_url: https://git.bioconductor.org/packages/r3Cseq
git_branch: RELEASE_3_13
git_last_commit: f159c04
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/r3Cseq_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/r3Cseq_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/r3Cseq_1.38.0.tgz
vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf
vignetteTitles: r3Cseq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/r3Cseq/inst/doc/r3Cseq.R
dependencyCount: 94
Package: R453Plus1Toolbox
Version: 1.42.0
Depends: R (>= 2.12.0), methods, VariantAnnotation (>= 1.25.11),
Biostrings (>= 2.47.6), Biobase
Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML,
TeachingDemos, BiocGenerics, S4Vectors (>= 0.17.25), IRanges
(>= 2.13.12), XVector, GenomicRanges (>= 1.31.8),
SummarizedExperiment, biomaRt, BSgenome (>= 1.47.3), Rsamtools,
ShortRead (>= 1.37.1)
Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Scerevisiae.UCSC.sacCer2
License: LGPL-3
Archs: i386, x64
MD5sum: edfb53cc94b4e88737eb13552e25485b
NeedsCompilation: yes
Title: A package for importing and analyzing data from Roche's Genome
Sequencer System
Description: The R453Plus1 Toolbox comprises useful functions for the
analysis of data generated by Roche's 454 sequencing platform.
It adds functions for quality assurance as well as for
annotation and visualization of detected variants,
complementing the software tools shipped by Roche with their
product. Further, a pipeline for the detection of structural
variants is provided.
biocViews: Sequencing, Infrastructure, DataImport, DataRepresentation,
Visualization, QualityControl, ReportWriting
Author: Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert
Maintainer: Hans-Ulrich Klein <h.klein@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/R453Plus1Toolbox
git_branch: RELEASE_3_13
git_last_commit: 02482b8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/R453Plus1Toolbox_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/R453Plus1Toolbox_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/R453Plus1Toolbox_1.42.0.tgz
vignettes: vignettes/R453Plus1Toolbox/inst/doc/vignette.pdf
vignetteTitles: A package for importing and analyzing data from Roche's
Genome Sequencer System
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/R453Plus1Toolbox/inst/doc/vignette.R
dependencyCount: 106
Package: R4RNA
Version: 1.20.0
Depends: R (>= 3.2.0), Biostrings (>= 2.38.0)
License: GPL-3
Archs: i386, x64
MD5sum: 0b2ef6501976971d80ea80736eb0d5a5
NeedsCompilation: no
Title: An R package for RNA visualization and analysis
Description: A package for RNA basepair analysis, including the
visualization of basepairs as arc diagrams for easy comparison
and annotation of sequence and structure. Arc diagrams can
additionally be projected onto multiple sequence alignments to
assess basepair conservation and covariation, with numerical
methods for computing statistics for each.
biocViews: Alignment, MultipleSequenceAlignment, Preprocessing,
Visualization, DataImport, DataRepresentation,
MultipleComparison
Author: Daniel Lai, Irmtraud Meyer <irmtraud.meyer@cantab.net>
Maintainer: Daniel Lai <jujubix@cs.ubc.ca>
URL: http://www.e-rna.org/r-chie/
git_url: https://git.bioconductor.org/packages/R4RNA
git_branch: RELEASE_3_13
git_last_commit: d3707fc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/R4RNA_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/R4RNA_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/R4RNA_1.20.0.tgz
vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R
suggestsMe: rfaRm
dependencyCount: 19
Package: RadioGx
Version: 1.2.0
Depends: R (>= 4.1), CoreGx
Imports: SummarizedExperiment, S4Vectors, Biobase, parallel,
BiocParallel, RColorBrewer, caTools, magicaxis, methods,
reshape2, scales, grDevices, graphics, stats, utils,
assertthat, matrixStats, downloader
Suggests: rmarkdown, BiocStyle, knitr, pander, markdown
License: GPL-3
MD5sum: 18ba8feaeda039e367fad1ed8e8d701d
NeedsCompilation: no
Title: Analysis of Large-Scale Radio-Genomic Data
Description: Computational tool box for radio-genomic analysis which
integrates radio-response data, radio-biological modelling and
comprehensive cell line annotations for hundreds of cancer cell
lines. The 'RadioSet' class enables creation and manipulation
of standardized datasets including information about cancer
cells lines, radio-response assays and dose-response
indicators. Included methods allow fitting and plotting
dose-response data using established radio-biological models
along with quality control to validate results. Additional
functions related to fitting and plotting dose response curves,
quantifying statistical correlation and calculating area under
the curve (AUC) or survival fraction (SF) are included. For
more details please see the included documentation, references,
as well as: Manem, V. et al (2018) <doi:10.1101/449793>.
biocViews: Software, Pharmacogenetics, QualityControl, Survival,
Pharmacogenomics, Classification
Author: Venkata Manem [aut], Petr Smirnov [aut], Ian Smith [aut],
Meghan Lambie [aut], Christopher Eeles [aut], Scott Bratman
[aut], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RadioGx
git_branch: RELEASE_3_13
git_last_commit: fdb4a45
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RadioGx_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RadioGx_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RadioGx_1.2.0.tgz
vignettes: vignettes/RadioGx/inst/doc/RadioGx.html
vignetteTitles: RadioGx: An R Package for Analysis of Large
Radiogenomic Datasets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RadioGx/inst/doc/RadioGx.R
dependencyCount: 132
Package: RaggedExperiment
Version: 1.16.0
Depends: R (>= 3.6.0), GenomicRanges (>= 1.37.17)
Imports: BiocGenerics, GenomeInfoDb, IRanges, Matrix, MatrixGenerics,
methods, S4Vectors, stats, SummarizedExperiment
Suggests: BiocStyle, knitr, rmarkdown, testthat, MultiAssayExperiment
License: Artistic-2.0
MD5sum: ca43bb0598ab1e28857155c2c3d42268
NeedsCompilation: no
Title: Representation of Sparse Experiments and Assays Across Samples
Description: This package provides a flexible representation of copy
number, mutation, and other data that fit into the ragged array
schema for genomic location data. The basic representation of
such data provides a rectangular flat table interface to the
user with range information in the rows and samples/specimen in
the columns.
biocViews: Infrastructure, DataRepresentation
Author: Martin Morgan [aut, cre], Marcel Ramos [aut]
Maintainer: Martin Morgan <martin.morgan@roswellpark.org>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/RaggedExperiment/issues
git_url: https://git.bioconductor.org/packages/RaggedExperiment
git_branch: RELEASE_3_13
git_last_commit: a1c10f7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RaggedExperiment_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RaggedExperiment_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RaggedExperiment_1.16.0.tgz
vignettes: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.html
vignetteTitles: RaggedExperiment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.R
dependsOnMe: CNVRanger, compartmap
importsMe: cBioPortalData, omicsPrint, RTCGAToolbox, TCGAutils
suggestsMe: maftools, MultiAssayExperiment, MultiDataSet,
curatedTCGAData, SingleCellMultiModal
dependencyCount: 26
Package: rain
Version: 1.26.0
Depends: R (>= 2.10), gmp, multtest
Suggests: lattice, BiocStyle
License: GPL-2
Archs: i386, x64
MD5sum: c79afd2b827d918d64ce368ed8c065f7
NeedsCompilation: no
Title: Rhythmicity Analysis Incorporating Non-parametric Methods
Description: This package uses non-parametric methods to detect rhythms
in time series. It deals with outliers, missing values and is
optimized for time series comprising 10-100 measurements. As it
does not assume expect any distinct waveform it is optimal or
detecting oscillating behavior (e.g. circadian or cell cycle)
in e.g. genome- or proteome-wide biological measurements such
as: micro arrays, proteome mass spectrometry, or metabolome
measurements.
biocViews: TimeCourse, Genetics, SystemsBiology, Proteomics,
Microarray, MultipleComparison
Author: Paul F. Thaben, PÃ¥l O. Westermark
Maintainer: Paul F. Thaben <paul.thaben@charite.de>
git_url: https://git.bioconductor.org/packages/rain
git_branch: RELEASE_3_13
git_last_commit: 03a40f6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rain_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rain_1.26.0.zip
vignettes: vignettes/rain/inst/doc/rain.pdf
vignetteTitles: Rain Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rain/inst/doc/rain.R
dependencyCount: 17
Package: rama
Version: 1.66.0
Depends: R(>= 2.5.0)
License: GPL (>= 2)
MD5sum: 7ee6e76082f305b188a58718e73e0cd5
NeedsCompilation: yes
Title: Robust Analysis of MicroArrays
Description: Robust estimation of cDNA microarray intensities with
replicates. The package uses a Bayesian hierarchical model for
the robust estimation. Outliers are modeled explicitly using a
t-distribution, and the model also addresses classical issues
such as design effects, normalization, transformation, and
nonconstant variance.
biocViews: Microarray, TwoChannel, QualityControl, Preprocessing
Author: Raphael Gottardo
Maintainer: Raphael Gottardo <raph@stat.ubc.ca>
git_url: https://git.bioconductor.org/packages/rama
git_branch: RELEASE_3_13
git_last_commit: 8d29b43
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rama_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rama_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rama_1.66.0.tgz
vignettes: vignettes/rama/inst/doc/rama.pdf
vignetteTitles: rama Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rama/inst/doc/rama.R
dependsOnMe: bridge
dependencyCount: 0
Package: ramr
Version: 1.0.3
Depends: R (>= 4.1), GenomicRanges, parallel, doParallel, foreach,
doRNG, methods
Imports: IRanges, BiocGenerics, ggplot2, reshape2, EnvStats, ExtDist,
matrixStats, S4Vectors
Suggests: RUnit, knitr, rmarkdown, gridExtra, annotatr, LOLA,
org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene
License: Artistic-2.0
Archs: i386, x64
MD5sum: 7a373f8eabfded74ea2b7c788fd07cba
NeedsCompilation: no
Title: Detection of Rare Aberrantly Methylated Regions in Array and NGS
Data
Description: ramr is an R package for detection of low-frequency
aberrant methylation events in large data sets obtained by
methylation profiling using array or high-throughput bisulfite
sequencing. In addition, package provides functions to
visualize found aberrantly methylated regions (AMRs), to
generate sets of all possible regions to be used as reference
sets for enrichment analysis, and to generate biologically
relevant test data sets for performance evaluation of AMR/DMR
search algorithms.
biocViews: DNAMethylation, DifferentialMethylation, Epigenetics,
MethylationArray, MethylSeq
Author: Oleksii Nikolaienko [aut, cre]
(<https://orcid.org/0000-0002-5910-4934>)
Maintainer: Oleksii Nikolaienko <oleksii.nikolaienko@gmail.com>
URL: https://github.com/BBCG/ramr
VignetteBuilder: knitr
BugReports: https://github.com/BBCG/ramr/issues
git_url: https://git.bioconductor.org/packages/ramr
git_branch: RELEASE_3_13
git_last_commit: 7bf9acb
git_last_commit_date: 2021-08-13
Date/Publication: 2021-08-15
source.ver: src/contrib/ramr_1.0.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ramr_1.0.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/ramr_1.0.3.tgz
vignettes: vignettes/ramr/inst/doc/ramr.html
vignetteTitles: ramr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ramr/inst/doc/ramr.R
dependencyCount: 68
Package: ramwas
Version: 1.16.0
Depends: R (>= 3.3.0), methods, filematrix
Imports: graphics, stats, utils, digest, glmnet, KernSmooth, grDevices,
GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings,
BiocGenerics
Suggests: knitr, rmarkdown, pander, BiocStyle,
BSgenome.Ecoli.NCBI.20080805
License: LGPL-3
MD5sum: 2c5da015b7562cfbd5226edb7075b9c7
NeedsCompilation: yes
Title: Fast Methylome-Wide Association Study Pipeline for Enrichment
Platforms
Description: A complete toolset for methylome-wide association studies
(MWAS). It is specifically designed for data from enrichment
based methylation assays, but can be applied to other data as
well. The analysis pipeline includes seven steps: (1) scanning
aligned reads from BAM files, (2) calculation of quality
control measures, (3) creation of methylation score (coverage)
matrix, (4) principal component analysis for capturing batch
effects and detection of outliers, (5) association analysis
with respect to phenotypes of interest while correcting for top
PCs and known covariates, (6) annotation of significant
findings, and (7) multi-marker analysis (methylation risk
score) using elastic net. Additionally, RaMWAS include tools
for joint analysis of methlyation and genotype data. This work
is published in Bioinformatics, Shabalin et al. (2018)
<doi:10.1093/bioinformatics/bty069>.
biocViews: DNAMethylation, Sequencing, QualityControl, Coverage,
Preprocessing, Normalization, BatchEffect, PrincipalComponent,
DifferentialMethylation, Visualization
Author: Andrey A Shabalin [aut, cre]
(<https://orcid.org/0000-0003-0309-6821>), Shaunna L Clark
[aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J
C G van den Oord [aut]
Maintainer: Andrey A Shabalin <andrey.shabalin@gmail.com>
URL: https://bioconductor.org/packages/ramwas/
VignetteBuilder: knitr
BugReports: https://github.com/andreyshabalin/ramwas/issues
git_url: https://git.bioconductor.org/packages/ramwas
git_branch: RELEASE_3_13
git_last_commit: 239ad35
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ramwas_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ramwas_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ramwas_1.16.0.tgz
vignettes: vignettes/ramwas/inst/doc/RW1_intro.html,
vignettes/ramwas/inst/doc/RW2_CpG_sets.html,
vignettes/ramwas/inst/doc/RW3_BAM_QCs.html,
vignettes/ramwas/inst/doc/RW4_SNPs.html,
vignettes/ramwas/inst/doc/RW5a_matrix.html,
vignettes/ramwas/inst/doc/RW5c_matrix.html,
vignettes/ramwas/inst/doc/RW6_param.html
vignetteTitles: 1. Overview, 2. CpG sets, 3. BAM Quality Control
Measures, 4. Joint Analysis of Methylation and Genotype Data,
5.a. Analyzing Illumina Methylation Array Data, 5.c. Analyzing
data from other sources, 6. RaMWAS parameters
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ramwas/inst/doc/RW1_intro.R,
vignettes/ramwas/inst/doc/RW2_CpG_sets.R,
vignettes/ramwas/inst/doc/RW3_BAM_QCs.R,
vignettes/ramwas/inst/doc/RW4_SNPs.R,
vignettes/ramwas/inst/doc/RW5a_matrix.R,
vignettes/ramwas/inst/doc/RW5c_matrix.R,
vignettes/ramwas/inst/doc/RW6_param.R
dependencyCount: 99
Package: RandomWalkRestartMH
Version: 1.12.0
Depends: R(>= 3.5.0)
Imports: igraph, Matrix, dnet, methods
Suggests: BiocStyle, testthat
License: GPL (>= 2)
MD5sum: bbe63a366139398db97e38e442589a90
NeedsCompilation: no
Title: Random walk with restart on multiplex and heterogeneous Networks
Description: This package performs Random Walk with Restart on
multiplex and heterogeneous networks. It is described in the
following article: "Random Walk With Restart On Multiplex And
Heterogeneous Biological Networks".
https://www.biorxiv.org/content/early/2017/08/30/134734 .
biocViews: GenePrediction, NetworkInference, SomaticMutation,
BiomedicalInformatics, MathematicalBiology, SystemsBiology,
GraphAndNetwork, Pathways, BioCarta, KEGG, Reactome, Network
Author: Alberto Valdeolivas Urbelz <alvaldeolivas@gmail.com>
Maintainer: Alberto Valdeolivas Urbelz <alvaldeolivas@gmail.com>
URL: https://www.biorxiv.org/content/early/2017/08/30/134734
git_url: https://git.bioconductor.org/packages/RandomWalkRestartMH
git_branch: RELEASE_3_13
git_last_commit: 0e1c73a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RandomWalkRestartMH_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RandomWalkRestartMH_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RandomWalkRestartMH_1.12.0.tgz
vignettes:
vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH1.pdf
vignetteTitles: Random Walk with Restart on Multiplex and Heterogeneous
Networks
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH1.R
dependencyCount: 54
Package: randPack
Version: 1.38.0
Depends: methods
Imports: Biobase
License: Artistic 2.0
MD5sum: 233caac538598949d61c572dc9c3cfd4
NeedsCompilation: no
Title: Randomization routines for Clinical Trials
Description: A suite of classes and functions for randomizing patients
in clinical trials.
biocViews: StatisticalMethod
Author: Vincent Carey <stvjc@channing.harvard.edu> and Robert Gentleman
Maintainer: Robert Gentleman <rgentlem@gmail.com>
git_url: https://git.bioconductor.org/packages/randPack
git_branch: RELEASE_3_13
git_last_commit: b9875fd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/randPack_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/randPack_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/randPack_1.38.0.tgz
vignettes: vignettes/randPack/inst/doc/randPack.pdf
vignetteTitles: Clinical trial randomization infrastructure
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/randPack/inst/doc/randPack.R
dependencyCount: 7
Package: randRotation
Version: 1.4.0
Imports: methods, graphics, utils, stats, Rdpack (>= 0.7)
Suggests: knitr, BiocParallel, lme4, nlme, rmarkdown, BiocStyle,
testthat (>= 2.1.0), limma, sva
License: GPL-3
MD5sum: e8c3dc391600580c31171637d20c59fa
NeedsCompilation: no
Title: Random Rotation Methods for High Dimensional Data with Batch
Structure
Description: A collection of methods for performing random rotations on
high-dimensional, normally distributed data (e.g. microarray or
RNA-seq data) with batch structure. The random rotation
approach allows exact testing of dependent test statistics with
linear models following arbitrary batch effect correction
methods.
biocViews: Software, Sequencing, BatchEffect, BiomedicalInformatics,
RNASeq, Preprocessing, Microarray, DifferentialExpression,
GeneExpression, Genetics, MicroRNAArray, Normalization,
StatisticalMethod
Author: Peter Hettegger [aut, cre]
(<https://orcid.org/0000-0001-8557-588X>)
Maintainer: Peter Hettegger <p.hettegger@gmail.com>
URL: https://github.com/phettegger/randRotation
VignetteBuilder: knitr
BugReports: https://github.com/phettegger/randRotation/issues
git_url: https://git.bioconductor.org/packages/randRotation
git_branch: RELEASE_3_13
git_last_commit: cf2c134
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/randRotation_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/randRotation_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/randRotation_1.4.0.tgz
vignettes: vignettes/randRotation/inst/doc/randRotationIntro.pdf
vignetteTitles: Random Rotation Package Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/randRotation/inst/doc/randRotationIntro.R
dependencyCount: 7
Package: RankProd
Version: 3.18.0
Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp
Imports: graphics
License: file LICENSE
License_restricts_use: yes
Archs: i386, x64
MD5sum: 938364c7ab29d7ae889454035f5eafde
NeedsCompilation: no
Title: Rank Product method for identifying differentially expressed
genes with application in meta-analysis
Description: Non-parametric method for identifying differentially
expressed (up- or down- regulated) genes based on the estimated
percentage of false predictions (pfp). The method can combine
data sets from different origins (meta-analysis) to increase
the power of the identification.
biocViews: DifferentialExpression, StatisticalMethod, Software,
ResearchField, Metabolomics, Lipidomics, Proteomics,
SystemsBiology, GeneExpression, Microarray, GeneSignaling
Author: Francesco Del Carratore
<francesco.delcarratore@manchester.ac.uk>, Andris Jankevics
<andris.jankevics@gmail.com> Fangxin Hong
<fxhong@jimmy.harvard.edu>, Ben Wittner
<Wittner.Ben@mgh.harvard.edu>, Rainer Breitling
<rainer.breitling@manchester.ac.uk>, and Florian Battke
<battke@informatik.uni-tuebingen.de>
Maintainer: Francesco Del Carratore <francescodc87@gmail.com>
git_url: https://git.bioconductor.org/packages/RankProd
git_branch: RELEASE_3_13
git_last_commit: 394f13f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RankProd_3.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RankProd_3.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RankProd_3.18.0.tgz
vignettes: vignettes/RankProd/inst/doc/RankProd.pdf
vignetteTitles: RankProd Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RankProd/inst/doc/RankProd.R
dependsOnMe: tRanslatome
importsMe: POMA, synlet, INCATome, sigQC
dependencyCount: 6
Package: RareVariantVis
Version: 2.20.0
Depends: BiocGenerics, VariantAnnotation, googleVis, GenomicFeatures
Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, gtools,
BSgenome, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19,
SummarizedExperiment, GenomicScores
Suggests: knitr
License: Artistic-2.0
MD5sum: 6a05d74c6f68618a51af79e2e6d523e5
NeedsCompilation: no
Title: A suite for analysis of rare genomic variants in whole genome
sequencing data
Description: Second version of RareVariantVis package aims to provide
comprehensive information about rare variants for your genome
data. It annotates, filters and presents genomic variants
(especially rare ones) in a global, per chromosome way. For
discovered rare variants CRISPR guide RNAs are designed, so the
user can plan further functional studies. Large structural
variants, including copy number variants are also supported.
Package accepts variants directly from variant caller - for
example GATK or Speedseq. Output of package are lists of
variants together with adequate visualization. Visualization of
variants is performed in two ways - standard that outputs png
figures and interactive that uses JavaScript d3 package.
Interactive visualization allows to analyze trio/family data,
for example in search for causative variants in rare Mendelian
diseases, in point-and-click interface. The package includes
homozygous region caller and allows to analyse whole human
genomes in less than 30 minutes on a desktop computer.
RareVariantVis disclosed novel causes of several rare monogenic
disorders, including one with non-coding causative variant -
keratolythic winter erythema.
biocViews: GenomicVariation, Sequencing, WholeGenome
Author: Adam Gudys and Tomasz Stokowy
Maintainer: Tomasz Stokowy <tomasz.stokowy@k2.uib.no>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RareVariantVis
git_branch: RELEASE_3_13
git_last_commit: 2e6f87c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RareVariantVis_2.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RareVariantVis_2.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RareVariantVis_2.20.0.tgz
vignettes: vignettes/RareVariantVis/inst/doc/RareVariantsVis.pdf
vignetteTitles: RareVariantVis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RareVariantVis/inst/doc/RareVariantsVis.R
dependencyCount: 130
Package: rawrr
Version: 1.0.3
Depends: R (>= 4.1)
Imports: grDevices, graphics, stats, utils
Suggests: BiocStyle (>= 2.5), ExperimentHub, knitr, protViz (>= 0.6),
rmarkdown, tartare (>= 1.5), testthat
License: GPL-3
Archs: i386, x64
MD5sum: b609da350ee5071730ce95cba1563734
NeedsCompilation: no
Title: Direct Access to Orbitrap Data and Beyond
Description: This package wraps the functionality of the RawFileReader
.NET assembly. Within the R environment, spectra and
chromatograms are represented by S3 objects (Kockmann T. et al.
(2020) <doi:10.1101/2020.10.30.362533>). The package provides
basic functions to download and install the required
third-party libraries. The package is developed, tested, and
used at the Functional Genomics Center Zurich, Switzerland
<https://fgcz.ch>.
biocViews: MassSpectrometry, Proteomics, Metabolomics
Author: Christian Panse [aut, cre]
(<https://orcid.org/0000-0003-1975-3064>), Tobias Kockmann
[aut] (<https://orcid.org/0000-0002-1847-885X>)
Maintainer: Christian Panse <cp@fgcz.ethz.ch>
URL: https://github.com/fgcz/rawrr/
SystemRequirements: mono-runtime 4.x or higher (including System.Data
library) on Linux/macOS, .Net Framework (>= 4.5.1) on Microsoft
Windows.
VignetteBuilder: knitr
BugReports: https://github.com/fgcz/rawrr/issues
git_url: https://git.bioconductor.org/packages/rawrr
git_branch: RELEASE_3_13
git_last_commit: 57ff81e
git_last_commit_date: 2021-09-14
Date/Publication: 2021-09-16
source.ver: src/contrib/rawrr_1.0.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rawrr_1.0.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/rawrr_1.0.3.tgz
vignettes: vignettes/rawrr/inst/doc/rawrr.html
vignetteTitles: Direct Access to Orbitrap Data and Beyond
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/rawrr/inst/doc/rawrr.R
dependencyCount: 4
Package: RbcBook1
Version: 1.60.0
Depends: R (>= 2.10), Biobase, graph, rpart
License: Artistic-2.0
MD5sum: 2beab9248a1c09a560761948414a3780
NeedsCompilation: no
Title: Support for Springer monograph on Bioconductor
Description: tools for building book
biocViews: Software
Author: Vince Carey <stvjc@channing.harvard.edu> and Wolfgang Huber
<huber@ebi.ac.uk>
Maintainer: Vince Carey <stvjc@channing.harvard.edu>
URL: http://www.biostat.harvard.edu/~carey
git_url: https://git.bioconductor.org/packages/RbcBook1
git_branch: RELEASE_3_13
git_last_commit: f563f7a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RbcBook1_1.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RbcBook1_1.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RbcBook1_1.60.0.tgz
vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf
vignetteTitles: RbcBook1 Primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RbcBook1/inst/doc/RbcBook1.R
dependencyCount: 11
Package: Rbec
Version: 1.0.0
Imports: Rcpp (>= 1.0.6), dada2, ggplot2, readr, doParallel, foreach,
grDevices, stats, utils
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
License: LGPL-3
MD5sum: b535d65f1ad72920538d01b80c1c8922
NeedsCompilation: yes
Title: Rbec: a tool for analysis of amplicon sequencing data from
synthetic microbial communities
Description: Rbec is a adapted version of DADA2 for analyzing amplicon
sequencing data from synthetic communities (SynComs), where the
reference sequences for each strain exists. Rbec can not only
accurately profile the microbial compositions in SynComs, but
also predict the contaminants in SynCom samples.
biocViews: Sequencing, MicrobialStrain, Microbiome
Author: Pengfan Zhang [aut, cre]
Maintainer: Pengfan Zhang <pzhang@mpipz.mpg.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rbec
git_branch: RELEASE_3_13
git_last_commit: 8dd1cc7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rbec_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rbec_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rbec_1.0.0.tgz
vignettes: vignettes/Rbec/inst/doc/Rbec.html
vignetteTitles: Rbec
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rbec/inst/doc/Rbec.R
dependencyCount: 95
Package: RBGL
Version: 1.68.0
Depends: graph, methods
Imports: methods
LinkingTo: BH
Suggests: Rgraphviz, XML, RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: 0b158610548cfe967c20fddb67f66e58
NeedsCompilation: yes
Title: An interface to the BOOST graph library
Description: A fairly extensive and comprehensive interface to the
graph algorithms contained in the BOOST library.
biocViews: GraphAndNetwork, Network
Author: Vince Carey <stvjc@channing.harvard.edu>, Li Long
<li.long@isb-sib.ch>, R. Gentleman
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: http://www.bioconductor.org
git_url: https://git.bioconductor.org/packages/RBGL
git_branch: RELEASE_3_13
git_last_commit: 9433738
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RBGL_1.68.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RBGL_1.68.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RBGL_1.68.0.tgz
vignettes: vignettes/RBGL/inst/doc/RBGL.pdf
vignetteTitles: RBGL Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RBGL/inst/doc/RBGL.R
dependsOnMe: apComplex, BioNet, CellNOptR, fgga, pkgDepTools,
archeofrag, PerfMeas, QuACN, RSeed, SubpathwayLNCE
importsMe: alpine, BiocPkgTools, biocViews, CAMERA, Category,
ChIPpeakAnno, CHRONOS, clipper, CytoML, DEGraph, DEsubs,
EventPointer, flowWorkspace, GAPGOM, GOSim, GOstats, MIGSA,
NCIgraph, OrganismDbi, pkgDepTools, predictionet, RpsiXML,
Streamer, VariantFiltering, BiDAG, eff2, gRbase, HEMDAG,
netgwas, pcalg, rags2ridges, RANKS, SID, wiseR
suggestsMe: DEGraph, GeneNetworkBuilder, graph, gwascat, KEGGgraph,
rBiopaxParser, VariantTools, yeastExpData, gRc, maGUI
dependencyCount: 9
Package: RBioinf
Version: 1.52.0
Depends: graph, methods
Suggests: Rgraphviz
License: Artistic-2.0
MD5sum: a01408a2af3ade5fada9bc9d630356c0
NeedsCompilation: yes
Title: RBioinf
Description: Functions and datasets and examples to accompany the
monograph R For Bioinformatics.
biocViews: GeneExpression, Microarray, Preprocessing, QualityControl,
Classification, Clustering, MultipleComparison, Annotation
Author: Robert Gentleman
Maintainer: Robert Gentleman <rgentlem@gmail.com>
git_url: https://git.bioconductor.org/packages/RBioinf
git_branch: RELEASE_3_13
git_last_commit: 4edfe70
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RBioinf_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RBioinf_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RBioinf_1.52.0.tgz
vignettes: vignettes/RBioinf/inst/doc/RBioinf.pdf
vignetteTitles: RBioinf Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RBioinf/inst/doc/RBioinf.R
dependencyCount: 8
Package: rBiopaxParser
Version: 2.32.0
Depends: R (>= 4.0), data.table
Imports: XML
Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, RBGL, igraph
License: GPL (>= 2)
MD5sum: 58d4d1509bad4925397468157cda82e9
NeedsCompilation: no
Title: Parses BioPax files and represents them in R
Description: Parses BioPAX files and represents them in R, at the
moment BioPAX level 2 and level 3 are supported.
biocViews: DataRepresentation
Author: Frank Kramer
Maintainer: Frank Kramer <frank.kramer@informatik.uni-augsburg.de>
URL: https://github.com/frankkramer-lab/rBiopaxParser
git_url: https://git.bioconductor.org/packages/rBiopaxParser
git_branch: RELEASE_3_13
git_last_commit: ed5cf40
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rBiopaxParser_2.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rBiopaxParser_2.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rBiopaxParser_2.32.0.tgz
vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf
vignetteTitles: rBiopaxParser Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.R
importsMe: pwOmics
suggestsMe: AnnotationHub, NetPathMiner
dependencyCount: 4
Package: RBM
Version: 1.24.0
Depends: R (>= 3.2.0), limma, marray
License: GPL (>= 2)
MD5sum: e11e45d1431c56e6f76e12bc1e9ed0a3
NeedsCompilation: no
Title: RBM: a R package for microarray and RNA-Seq data analysis
Description: Use A Resampling-Based Empirical Bayes Approach to Assess
Differential Expression in Two-Color Microarrays and RNA-Seq
data sets.
biocViews: Microarray, DifferentialExpression
Author: Dongmei Li and Chin-Yuan Liang
Maintainer: Dongmei Li <Dongmei_Li@urmc.rochester.edu>
git_url: https://git.bioconductor.org/packages/RBM
git_branch: RELEASE_3_13
git_last_commit: f2a8092
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RBM_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RBM_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RBM_1.24.0.tgz
vignettes: vignettes/RBM/inst/doc/RBM.pdf
vignetteTitles: RBM
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RBM/inst/doc/RBM.R
dependencyCount: 7
Package: Rbowtie
Version: 1.32.0
Suggests: testthat, parallel, BiocStyle, knitr, rmarkdown
License: Artistic-2.0 | file LICENSE
MD5sum: 760690b36a0df89de021c4d4f98ac0a4
NeedsCompilation: yes
Title: R bowtie wrapper
Description: This package provides an R wrapper around the popular
bowtie short read aligner and around SpliceMap, a de novo
splice junction discovery and alignment tool. The package is
used by the QuasR bioconductor package. We recommend to use the
QuasR package instead of using Rbowtie directly.
biocViews: Sequencing, Alignment
Author: Florian Hahne, Anita Lerch, Michael B Stadler
Maintainer: Michael Stadler <michael.stadler@fmi.ch>
URL: https://github.com/fmicompbio/Rbowtie
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/fmicompbio/Rbowtie/issues
git_url: https://git.bioconductor.org/packages/Rbowtie
git_branch: RELEASE_3_13
git_last_commit: 1a56c71
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rbowtie_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rbowtie_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rbowtie_1.32.0.tgz
vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.html
vignetteTitles: An introduction to Rbowtie
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R
dependsOnMe: QuasR
importsMe: MACPET, multicrispr
suggestsMe: eisaR
dependencyCount: 0
Package: Rbowtie2
Version: 1.14.0
Depends: R (>= 3.5)
Suggests: knitr
License: GPL (>= 3)
Archs: i386, x64
MD5sum: 05117fd43ec80d985054f28180e66709
NeedsCompilation: yes
Title: An R Wrapper for Bowtie2 and AdapterRemoval
Description: This package provides an R wrapper of the popular bowtie2
sequencing reads aligner and AdapterRemoval, a convenient tool
for rapid adapter trimming, identification, and read merging.
biocViews: Sequencing, Alignment, Preprocessing
Author: Zheng Wei, Wei Zhang
Maintainer: Zheng Wei <wzweizheng@qq.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rbowtie2
git_branch: RELEASE_3_13
git_last_commit: f15bc6e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rbowtie2_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rbowtie2_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rbowtie2_1.14.0.tgz
vignettes: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.html
vignetteTitles: An Introduction to Rbowtie2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.R
importsMe: esATAC, UMI4Cats
dependencyCount: 0
Package: rbsurv
Version: 2.50.0
Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival
License: GPL (>= 2)
Archs: i386, x64
MD5sum: ba02c02c758c7d1027dd9078286db488
NeedsCompilation: no
Title: Robust likelihood-based survival modeling with microarray data
Description: This package selects genes associated with survival.
biocViews: Microarray
Author: HyungJun Cho <hj4cho@korea.ac.kr>, Sukwoo Kim
<s4kim@korea.ac.kr>, Soo-heang Eo <hanansh@korea.ac.kr>, Jaewoo
Kang <kangj@korea.ac.kr>
Maintainer: Soo-heang Eo <hanansh@korea.ac.kr>
URL: http://www.korea.ac.kr/~stat2242/
git_url: https://git.bioconductor.org/packages/rbsurv
git_branch: RELEASE_3_13
git_last_commit: bd8165b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rbsurv_2.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rbsurv_2.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rbsurv_2.50.0.tgz
vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf
vignetteTitles: Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rbsurv/inst/doc/rbsurv.R
dependencyCount: 13
Package: Rcade
Version: 1.34.0
Depends: R (>= 3.5.0), methods, GenomicRanges, Rsamtools, baySeq
Imports: utils, grDevices, stats, graphics, rgl, plotrix, S4Vectors (>=
0.23.19), IRanges, GenomeInfoDb, GenomicAlignments
Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle
License: GPL-2
Archs: i386, x64
MD5sum: e8039915bc93bc5ab61e749a97c91e8a
NeedsCompilation: no
Title: R-based analysis of ChIP-seq And Differential Expression - a
tool for integrating a count-based ChIP-seq analysis with
differential expression summary data
Description: Rcade (which stands for "R-based analysis of ChIP-seq And
Differential Expression") is a tool for integrating ChIP-seq
data with differential expression summary data, through a
Bayesian framework. A key application is in identifing the
genes targeted by a transcription factor of interest - that is,
we collect genes that are associated with a ChIP-seq peak, and
differential expression under some perturbation related to that
TF.
biocViews: DifferentialExpression, GeneExpression, Transcription,
ChIPSeq, Sequencing, Genetics
Author: Jonathan Cairns
Maintainer: Jonathan Cairns <jmcairns200@gmail.com>
git_url: https://git.bioconductor.org/packages/Rcade
git_branch: RELEASE_3_13
git_last_commit: d311ac7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rcade_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rcade_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rcade_1.34.0.tgz
vignettes: vignettes/Rcade/inst/doc/Rcade.pdf
vignetteTitles: Rcade Vignette
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rcade/inst/doc/Rcade.R
dependencyCount: 65
Package: RCAS
Version: 1.18.0
Depends: R (>= 3.3.0), plotly (>= 4.5.2), DT (>= 0.2), data.table,
Imports: GenomicRanges, IRanges, BSgenome, BSgenome.Hsapiens.UCSC.hg19,
GenomeInfoDb (>= 1.12.0), Biostrings, rtracklayer,
GenomicFeatures, rmarkdown (>= 0.9.5), genomation (>= 1.5.5),
knitr (>= 1.12.3), BiocGenerics, S4Vectors, plotrix, pbapply,
RSQLite, proxy, pheatmap, ggplot2, cowplot, ggseqlogo, utils,
ranger, gprofiler2
Suggests: testthat, covr
License: Artistic-2.0
MD5sum: b61db623dadb9092911793d350f7364c
NeedsCompilation: no
Title: RNA Centric Annotation System
Description: RCAS is an R/Bioconductor package designed as a generic
reporting tool for the functional analysis of
transcriptome-wide regions of interest detected by
high-throughput experiments. Such transcriptomic regions could
be, for instance, signal peaks detected by CLIP-Seq analysis
for protein-RNA interaction sites, RNA modification sites
(alias the epitranscriptome), CAGE-tag locations, or any other
collection of query regions at the level of the transcriptome.
RCAS produces in-depth annotation summaries and coverage
profiles based on the distribution of the query regions with
respect to transcript features (exons, introns, 5'/3' UTR
regions, exon-intron boundaries, promoter regions). Moreover,
RCAS can carry out functional enrichment analyses and
discriminative motif discovery.
biocViews: Software, GeneTarget, MotifAnnotation, MotifDiscovery, GO,
Transcriptomics, GenomeAnnotation, GeneSetEnrichment, Coverage
Author: Bora Uyar [aut, cre], Dilmurat Yusuf [aut], Ricardo Wurmus
[aut], Altuna Akalin [aut]
Maintainer: Bora Uyar <bora.uyar@mdc-berlin.de>
SystemRequirements: pandoc (>= 1.12.3)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RCAS
git_branch: RELEASE_3_13
git_last_commit: ffebafc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RCAS_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RCAS_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RCAS_1.18.0.tgz
vignettes: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.html,
vignettes/RCAS/inst/doc/RCAS.vignette.html
vignetteTitles: How to do meta-analysis of multiple samples,
Introduction - single sample analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.R,
vignettes/RCAS/inst/doc/RCAS.vignette.R
dependencyCount: 152
Package: RCASPAR
Version: 1.38.0
License: GPL (>=3)
MD5sum: 058c2a9c63b836dc60c0d8e595dcefcf
NeedsCompilation: no
Title: A package for survival time prediction based on a piecewise
baseline hazard Cox regression model.
Description: The package is the R-version of the C-based software
\bold{CASPAR} (Kaderali,2006:
\url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}).
It is meant to help predict survival times in the presence of
high-dimensional explanatory covariates. The model is a
piecewise baseline hazard Cox regression model with an Lq-norm
based prior that selects for the most important regression
coefficients, and in turn the most relevant covariates for
survival analysis. It was primarily tried on gene expression
and aCGH data, but can be used on any other type of
high-dimensional data and in disciplines other than biology and
medicine.
biocViews: aCGH, GeneExpression, Genetics, Proteomics, Visualization
Author: Douaa Mugahid, Lars Kaderali
Maintainer: Douaa Mugahid <douaa.mugahid@gmail.com>, Lars Kaderali
<lars.kaderali@uni-greifswald.de>
git_url: https://git.bioconductor.org/packages/RCASPAR
git_branch: RELEASE_3_13
git_last_commit: fa5ea8c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RCASPAR_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RCASPAR_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RCASPAR_1.38.0.tgz
vignettes: vignettes/RCASPAR/inst/doc/RCASPAR.pdf
vignetteTitles: RCASPAR: Software for high-dimentional-data driven
survival time prediction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RCASPAR/inst/doc/RCASPAR.R
dependencyCount: 0
Package: rcellminer
Version: 2.14.0
Depends: R (>= 3.2), Biobase, rcellminerData (>= 2.0.0)
Imports: stringr, gplots, ggplot2, methods, stats, utils, shiny
Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat,
BiocStyle, jsonlite, heatmaply, glmnet, foreach, doSNOW,
parallel
License: LGPL-3 + file LICENSE
Archs: x64
MD5sum: a3996a2b0c2006cd0ed079b71a711a2f
NeedsCompilation: no
Title: rcellminer: Molecular Profiles, Drug Response, and Chemical
Structures for the NCI-60 Cell Lines
Description: The NCI-60 cancer cell line panel has been used over the
course of several decades as an anti-cancer drug screen. This
panel was developed as part of the Developmental Therapeutics
Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National
Cancer Institute (NCI). Thousands of compounds have been tested
on the NCI-60, which have been extensively characterized by
many platforms for gene and protein expression, copy number,
mutation, and others (Reinhold, et al., 2012). The purpose of
the CellMiner project (http://discover.nci.nih.gov/ cellminer)
has been to integrate data from multiple platforms used to
analyze the NCI-60 and to provide a powerful suite of tools for
exploration of NCI-60 data.
biocViews: aCGH, CellBasedAssays, CopyNumberVariation, GeneExpression,
Pharmacogenomics, Pharmacogenetics, miRNA, Cheminformatics,
Visualization, Software, SystemsBiology
Author: Augustin Luna, Vinodh Rajapakse, Fabricio Sousa
Maintainer: Augustin Luna <lunaa@cbio.mskcc.org>, Vinodh Rajapakse
<vinodh.rajapakse@nih.gov>, Fathi Elloumi
<fathi.elloumi@nih.gov>
URL: http://discover.nci.nih.gov/cellminer/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rcellminer
git_branch: RELEASE_3_13
git_last_commit: 65b993c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rcellminer_2.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rcellminer_2.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rcellminer_2.14.0.tgz
vignettes: vignettes/rcellminer/inst/doc/rcellminerUsage.html
vignetteTitles: Using rcellminer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R
suggestsMe: rcellminerData
dependencyCount: 70
Package: rCGH
Version: 1.22.0
Depends: R (>= 3.4),methods,stats,utils,graphics
Imports: plyr,DNAcopy,lattice,ggplot2,grid,shiny (>= 0.11.1),
limma,affy,mclust,TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene,
org.Hs.eg.db,GenomicFeatures,GenomeInfoDb,GenomicRanges,AnnotationDbi,
parallel,IRanges,grDevices,aCGH
Suggests: BiocStyle, knitr, BiocGenerics, RUnit
License: Artistic-2.0
Archs: x64
MD5sum: b876882ffcce83408b8fe829a19c7557
NeedsCompilation: no
Title: Comprehensive Pipeline for Analyzing and Visualizing Array-Based
CGH Data
Description: A comprehensive pipeline for analyzing and interactively
visualizing genomic profiles generated through commercial or
custom aCGH arrays. As inputs, rCGH supports Agilent dual-color
Feature Extraction files (.txt), from 44 to 400K, Affymetrix
SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt
files exported from ChAS or Affymetrix Power Tools. rCGH also
supports custom arrays, provided data complies with the
expected format. This package takes over all the steps required
for individual genomic profiles analysis, from reading files to
profiles segmentation and gene annotations. This package also
provides several visualization functions (static or
interactive) which facilitate individual profiles
interpretation. Input files can be in compressed format, e.g.
.bz2 or .gz.
biocViews: aCGH,CopyNumberVariation,Preprocessing,FeatureExtraction
Author: Frederic Commo [aut, cre]
Maintainer: Frederic Commo <fredcommo@gmail.com>
URL: https://github.com/fredcommo/rCGH
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rCGH
git_branch: RELEASE_3_13
git_last_commit: 23845ef
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rCGH_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rCGH_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rCGH_1.22.0.tgz
vignettes: vignettes/rCGH/inst/doc/rCGH.pdf
vignetteTitles: using rCGH package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rCGH/inst/doc/rCGH.R
dependencyCount: 140
Package: RcisTarget
Version: 1.12.0
Depends: R (>= 3.5.0)
Imports: AUCell (>= 1.1.6), BiocGenerics, data.table, feather,
graphics, GenomeInfoDb, GenomicRanges, arrow (>= 2.0.0), dplyr,
tibble, GSEABase, methods, R.utils, stats,
SummarizedExperiment, utils
Suggests: Biobase, BiocStyle, BiocParallel, doParallel, DT, foreach,
gplots, rtracklayer, igraph, knitr,
RcisTarget.hg19.motifDBs.cisbpOnly.500bp, rmarkdown, testthat,
visNetwork
Enhances: doMC, doRNG, zoo
License: GPL-3
MD5sum: 79998857883f879dcc9ecacdc6e755bc
NeedsCompilation: no
Title: RcisTarget Identify transcription factor binding motifs enriched
on a list of genes or genomic regions
Description: RcisTarget identifies transcription factor binding motifs
(TFBS) over-represented on a gene list. In a first step,
RcisTarget selects DNA motifs that are significantly
over-represented in the surroundings of the transcription start
site (TSS) of the genes in the gene-set. This is achieved by
using a database that contains genome-wide cross-species
rankings for each motif. The motifs that are then annotated to
TFs and those that have a high Normalized Enrichment Score
(NES) are retained. Finally, for each motif and gene-set,
RcisTarget predicts the candidate target genes (i.e. genes in
the gene-set that are ranked above the leading edge).
biocViews: GeneRegulation, MotifAnnotation, Transcriptomics,
Transcription, GeneSetEnrichment, GeneTarget
Author: Sara Aibar, Gert Hulselmans, Stein Aerts. Laboratory of
Computational Biology. VIB-KU Leuven Center for Brain & Disease
Research. Leuven, Belgium
Maintainer: Sara Aibar <sara.aibar@kuleuven.vib.be>
URL: http://scenic.aertslab.org
VignetteBuilder: knitr
BugReports: https://github.com/aertslab/RcisTarget/issues
git_url: https://git.bioconductor.org/packages/RcisTarget
git_branch: RELEASE_3_13
git_last_commit: da8bb0d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RcisTarget_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RcisTarget_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RcisTarget_1.12.0.tgz
vignettes: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.html,
vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.html,
vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.html
vignetteTitles: RcisTarget: Transcription factor binding motif
enrichment, RcisTarget - on regiions, RcisTarget - with
background
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.R,
vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.R,
vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.R
dependencyCount: 101
Package: RCM
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: RColorBrewer, alabama, edgeR, reshape2, tseries, VGAM, ggplot2
(>= 2.2.1.9000), nleqslv, phyloseq, tensor, MASS, stats,
grDevices, graphics, methods
Suggests: knitr, rmarkdown, testthat
License: GPL-2
MD5sum: 06262b9b1e96b071e61d1ece6293689a
NeedsCompilation: no
Title: Fit row-column association models with the negative binomial
distribution for the microbiome
Description: Combine ideas of log-linear analysis of contingency table,
flexible response function estimation and empirical Bayes
dispersion estimation for explorative visualization of
microbiome datasets. The package includes unconstrained as well
as constrained analysis.
biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization
Author: Stijn Hawinkel <stijn.hawinkel@ugent.be>
Maintainer: Joris Meys <joris.meys@ugent.be>
URL:
https://bioconductor.org/packages/release/bioc/vignettes/RCM/inst/doc/RCMvignette.html/
VignetteBuilder: knitr
BugReports: https://github.com/CenterForStatistics-UGent/RCM/issues
git_url: https://git.bioconductor.org/packages/RCM
git_branch: RELEASE_3_13
git_last_commit: b1cf974
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RCM_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RCM_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RCM_1.8.0.tgz
vignettes: vignettes/RCM/inst/doc/RCMvignette.html
vignetteTitles: Manual for the RCM pacakage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RCM/inst/doc/RCMvignette.R
dependencyCount: 92
Package: Rcpi
Version: 1.28.0
Depends: R (>= 3.0.2)
Imports: stats, utils, methods, RCurl, rjson, foreach, doParallel,
Biostrings, GOSemSim, ChemmineR, fmcsR, rcdk (>= 3.3.8)
Suggests: knitr, rmarkdown, RUnit, BiocGenerics
Enhances: ChemmineOB
License: Artistic-2.0 | file LICENSE
MD5sum: 77e97ca511bbace0d45db1bc9922b99d
NeedsCompilation: no
Title: Molecular Informatics Toolkit for Compound-Protein Interaction
in Drug Discovery
Description: Rcpi offers a molecular informatics toolkit with a
comprehensive integration of bioinformatics and
chemoinformatics tools for drug discovery.
biocViews: Software, DataImport, DataRepresentation, FeatureExtraction,
Cheminformatics, BiomedicalInformatics, Proteomics, GO,
SystemsBiology
Author: Nan Xiao [aut, cre], Dong-Sheng Cao [aut], Qing-Song Xu [aut]
Maintainer: Nan Xiao <me@nanx.me>
URL: https://nanx.me/Rcpi/, https://github.com/nanxstats/Rcpi
VignetteBuilder: knitr
BugReports: https://github.com/nanxstats/Rcpi/issues
git_url: https://git.bioconductor.org/packages/Rcpi
git_branch: RELEASE_3_13
git_last_commit: 422fb52
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rcpi_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rcpi_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rcpi_1.28.0.tgz
vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.html,
vignettes/Rcpi/inst/doc/Rcpi.html
vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package
as an Integrated Informatics Platform for Drug Discovery
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rcpi/inst/doc/Rcpi.R
dependencyCount: 100
Package: RCSL
Version: 1.0.0
Depends: R (>= 4.1)
Imports: RcppAnnoy, igraph, NbClust, Rtsne, ggplot2, methods, pracma,
umap, grDevices, graphics, stats
Suggests: knitr, rmarkdown, mclust, RcppAnnoy
License: GPL-3
MD5sum: 110e206d5ca2f232936cf7097c0890cb
NeedsCompilation: no
Title: Rank Constrained Similarity Learning for single cell RNA
sequencing data
Description: A novel clustering algorithm and toolkit RCSL (Rank
Constrained Similarity Learning) to accurately identify various
cell types using scRNA-seq data from a complex tissue. RCSL
considers both lo-cal similarity and global similarity among
the cells to discern the subtle differences among cells of the
same type as well as larger differences among cells of
different types. RCSL uses Spearman’s rank correlations of a
cell’s expression vector with those of other cells to measure
its global similar-ity, and adaptively learns neighbour
representation of a cell as its local similarity. The overall
similar-ity of a cell to other cells is a linear combination of
its global similarity and local similarity.
biocViews: SingleCell, Software, Clustering, DimensionReduction,
RNASeq, Visualization, Sequencing
Author: Qinglin Mei [cre, aut], Guojun Li [fnd], Zhengchang Su [fnd]
Maintainer: Qinglin Mei <meiqinglinkf@163.com>
URL: https://github.com/QinglinMei/RCSL
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RCSL
git_branch: RELEASE_3_13
git_last_commit: 51b4de1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RCSL_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RCSL_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RCSL_1.0.0.tgz
vignettes: vignettes/RCSL/inst/doc/RCSL.html
vignetteTitles: RCSL package manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RCSL/inst/doc/RCSL.R
dependencyCount: 56
Package: Rcwl
Version: 1.8.1
Depends: R (>= 3.6), yaml, methods, S4Vectors
Imports: utils, stats, BiocParallel, batchtools, DiagrammeR, shiny,
R.utils, codetools, basilisk
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-2 | file LICENSE
MD5sum: 7b1f0bf236c65fa5bd80fb23959694ef
NeedsCompilation: no
Title: An R interface to the Common Workflow Language
Description: The Common Workflow Language (CWL) is an open standard for
development of data analysis workflows that is portable and
scalable across different tools and working environments. Rcwl
provides a simple way to wrap command line tools and build CWL
data analysis pipelines programmatically within R. It increases
the ease of usage, development, and maintenance of CWL
pipelines.
biocViews: Software, WorkflowStep, ImmunoOncology
Author: Qiang Hu [aut, cre], Qian Liu [aut]
Maintainer: Qiang Hu <qiang.hu@roswellpark.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rcwl
git_branch: RELEASE_3_13
git_last_commit: 04dd98c
git_last_commit_date: 2021-06-29
Date/Publication: 2021-07-01
source.ver: src/contrib/Rcwl_1.8.1.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/Rcwl_1.8.1.tgz
vignettes: vignettes/Rcwl/inst/doc/Rcwl.html
vignetteTitles: Rcwl: An R interface to the Common Workflow Language
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rcwl/inst/doc/Rcwl.R
dependsOnMe: RcwlPipelines
dependencyCount: 115
Package: RcwlPipelines
Version: 1.8.0
Depends: R (>= 3.6), Rcwl, BiocFileCache
Imports: rappdirs, methods, utils, git2r, httr, S4Vectors
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-2
MD5sum: 9cb057b02cd082a95feaf7529db991fb
NeedsCompilation: no
Title: Bioinformatics pipelines based on Rcwl
Description: A collection of Bioinformatics tools and pipelines based
on R and the Common Workflow Language.
biocViews: Software, WorkflowStep, Alignment, Preprocessing,
QualityControl, DNASeq, RNASeq, DataImport, ImmunoOncology
Author: Qiang Hu [aut, cre], Qian Liu [aut], Shuang Gao [aut]
Maintainer: Qiang Hu <qiang.hu@roswellpark.org>
SystemRequirements: nodejs
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RcwlPipelines
git_branch: RELEASE_3_13
git_last_commit: 4e08b9e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RcwlPipelines_1.8.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/RcwlPipelines_1.8.0.tgz
vignettes: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.html
vignetteTitles: RcwlPipelines
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.R
dependencyCount: 130
Package: RCy3
Version: 2.12.4
Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, igraph,
stats, graph, R.utils, dplR, uchardet, glue, RCurl, base64url,
base64enc, IRkernel, RColorBrewer
Suggests: BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 1d5cade6f792b43e0d759738bca6899b
NeedsCompilation: no
Title: Functions to Access and Control Cytoscape
Description: Vizualize, analyze and explore networks using Cytoscape
via R. Anything you can do using the graphical user interface
of Cytoscape, you can now do with a single RCy3 function.
biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network
Author: Alex Pico [aut, cre] (<https://orcid.org/0000-0001-5706-2163>),
Tanja Muetze [aut], Paul Shannon [aut], Ruth Isserlin [ctb],
Shraddha Pai [ctb], Julia Gustavsen [ctb], Georgi Kolishovski
[ctb], Yihang Xin [ctb]
Maintainer: Alex Pico <alex.pico@gladstone.ucsf.edu>
URL: https://github.com/cytoscape/RCy3
SystemRequirements: Cytoscape (>= 3.7.1), CyREST (>= 3.8.0)
VignetteBuilder: knitr
BugReports: https://github.com/cytoscape/RCy3/issues
git_url: https://git.bioconductor.org/packages/RCy3
git_branch: RELEASE_3_13
git_last_commit: 5bb5011
git_last_commit_date: 2021-08-09
Date/Publication: 2021-08-10
source.ver: src/contrib/RCy3_2.12.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RCy3_2.12.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/RCy3_2.12.4.tgz
vignettes: vignettes/RCy3/inst/doc/Cancer-networks-and-data.html,
vignettes/RCy3/inst/doc/Custom-Graphics.html,
vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.html,
vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.html,
vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.html,
vignettes/RCy3/inst/doc/Filtering-Networks.html,
vignettes/RCy3/inst/doc/Group-nodes.html,
vignettes/RCy3/inst/doc/Identifier-mapping.html,
vignettes/RCy3/inst/doc/Importing-data.html,
vignettes/RCy3/inst/doc/Network-functions-and-visualization.html,
vignettes/RCy3/inst/doc/Overview-of-RCy3.html,
vignettes/RCy3/inst/doc/Phylogenetic-trees.html,
vignettes/RCy3/inst/doc/Upgrading-existing-scripts.html
vignetteTitles: 06. Cancer networks and data ~40 min, 11. Custom
Graphics and Labels ~10 min, 03. Cytoscape and graphNEL ~5 min,
02. Cytoscape and igraph ~5 min, 09. Cytoscape and NDEx ~20
min, 12. Filtering Networks ~10 min, 10. Group nodes ~15 min,
07. Identifier mapping ~20 min, 04. Importing data ~5 min, 05.
Network functions and visualization ~15 min, 01. Overview of
RCy3 ~25 min, 13. Phylogenetic Trees ~3 min, 08. Upgrading
existing scripts ~15 min
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RCy3/inst/doc/Cancer-networks-and-data.R,
vignettes/RCy3/inst/doc/Custom-Graphics.R,
vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.R,
vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.R,
vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.R,
vignettes/RCy3/inst/doc/Filtering-Networks.R,
vignettes/RCy3/inst/doc/Group-nodes.R,
vignettes/RCy3/inst/doc/Identifier-mapping.R,
vignettes/RCy3/inst/doc/Importing-data.R,
vignettes/RCy3/inst/doc/Network-functions-and-visualization.R,
vignettes/RCy3/inst/doc/Overview-of-RCy3.R,
vignettes/RCy3/inst/doc/Phylogenetic-trees.R,
vignettes/RCy3/inst/doc/Upgrading-existing-scripts.R
importsMe: categoryCompare, CeTF, fedup, MOGAMUN, NCIgraph, regutools,
TimiRGeN, transomics2cytoscape, lilikoi, netgsa, ScriptMapR
suggestsMe: graphite, rScudo, sparsebnUtils
dependencyCount: 64
Package: RCyjs
Version: 2.14.0
Depends: R (>= 3.5.0), BrowserViz (>= 2.7.18), graph (>= 1.56.0)
Imports: methods, httpuv (>= 1.5.0), BiocGenerics, base64enc, utils
Suggests: RUnit, BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 4d55c49172f728bf81a166e158d36b2c
NeedsCompilation: no
Title: Display and manipulate graphs in cytoscape.js
Description: Interactive viewing and exploration of graphs, connecting
R to Cytoscape.js, using websockets.
biocViews: Visualization, GraphAndNetwork, ThirdPartyClient
Author: Paul Shannon
Maintainer: Paul Shannon <paul.thurmond.shannon@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RCyjs
git_branch: RELEASE_3_13
git_last_commit: d93cc23
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RCyjs_2.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RCyjs_2.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RCyjs_2.14.0.tgz
vignettes: vignettes/RCyjs/inst/doc/RCyjs.html
vignetteTitles: "RCyjs: programmatic access to the web browser-based
network viewer,, cytoscape.js"
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R
dependencyCount: 18
Package: rDGIdb
Version: 1.18.0
Imports: jsonlite,httr,methods,graphics
Suggests: BiocStyle,knitr,testthat
License: MIT + file LICENSE
MD5sum: 0b443707d6ea816330ddfbe45bcac0c4
NeedsCompilation: no
Title: R Wrapper for DGIdb
Description: The rDGIdb package provides a wrapper for the Drug Gene
Interaction Database (DGIdb). For simplicity, the wrapper query
function and output resembles the user interface and results
format provided on the DGIdb website (https://www.dgidb.org/).
biocViews: Software,ResearchField,Pharmacogenetics,Pharmacogenomics,
FunctionalGenomics,WorkflowStep,Annotation
Author: Thomas Thurnherr, Franziska Singer, Daniel J. Stekhoven, and
Niko Beerenwinkel
Maintainer: Lars Bosshard <bosshard@nexus.ethz.ch>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rDGIdb
git_branch: RELEASE_3_13
git_last_commit: 586ff45
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rDGIdb_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rDGIdb_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rDGIdb_1.18.0.tgz
vignettes: vignettes/rDGIdb/inst/doc/vignette.pdf
vignetteTitles: Query DGIdb using R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rDGIdb/inst/doc/vignette.R
dependencyCount: 11
Package: Rdisop
Version: 1.52.0
Depends: R (>= 2.0.0), Rcpp
LinkingTo: Rcpp
Suggests: RUnit
License: GPL-2
MD5sum: 1027528cd90138e1b951427e564c65f4
NeedsCompilation: yes
Title: Decomposition of Isotopic Patterns
Description: Identification of metabolites using high precision mass
spectrometry. MS Peaks are used to derive a ranked list of sum
formulae, alternatively for a given sum formula the theoretical
isotope distribution can be calculated to search in MS peak
lists.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics
Author: Anton Pervukhin <apervukh@minet.uni-jena.de>, Steffen Neumann
<sneumann@ipb-halle.de>
Maintainer: Steffen Neumann <sneumann@ipb-halle.de>
URL: https://github.com/sneumann/Rdisop
SystemRequirements: None
BugReports: https://github.com/sneumann/Rdisop/issues/new
git_url: https://git.bioconductor.org/packages/Rdisop
git_branch: RELEASE_3_13
git_last_commit: 2f422e0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rdisop_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rdisop_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rdisop_1.52.0.tgz
vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf
vignetteTitles: Molecule Identification with Rdisop
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: TRUE
hasLICENSE: FALSE
importsMe: enviGCMS, HiResTEC, InterpretMSSpectrum, MetaDBparse
suggestsMe: adductomicsR, MSnbase, RforProteomics
dependencyCount: 3
Package: RDRToolbox
Version: 1.42.0
Depends: R (>= 2.9.0)
Imports: graphics, grDevices, methods, stats, MASS, rgl
Suggests: golubEsets
License: GPL (>= 2)
MD5sum: df8ca3cf85f599659c6ff10186bdc280
NeedsCompilation: no
Title: A package for nonlinear dimension reduction with Isomap and LLE.
Description: A package for nonlinear dimension reduction using the
Isomap and LLE algorithm. It also includes a routine for
computing the Davis-Bouldin-Index for cluster validation, a
plotting tool and a data generator for microarray gene
expression data and for the Swiss Roll dataset.
biocViews: DimensionReduction, FeatureExtraction, Visualization,
Clustering, Microarray
Author: Christoph Bartenhagen
Maintainer: Christoph Bartenhagen <c.bartenhagen@uni-koeln.de>
git_url: https://git.bioconductor.org/packages/RDRToolbox
git_branch: RELEASE_3_13
git_last_commit: e16efe3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RDRToolbox_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RDRToolbox_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RDRToolbox_1.42.0.tgz
vignettes: vignettes/RDRToolbox/inst/doc/vignette.pdf
vignetteTitles: A package for nonlinear dimension reduction with Isomap
and LLE.
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RDRToolbox/inst/doc/vignette.R
suggestsMe: loon
dependencyCount: 27
Package: ReactomeContentService4R
Version: 1.0.0
Imports: httr, jsonlite, utils, magick (>= 2.5.1), data.table,
doParallel, foreach, parallel
Suggests: pdftools, testthat, knitr, rmarkdown
License: Apache License (>= 2.0) | file LICENSE
MD5sum: 07f9f703d348cbbd06ae6c580c9a872f
NeedsCompilation: no
Title: Interface for the Reactome Content Service
Description: Reactome is a free, open-source, open access, curated and
peer-reviewed knowledgebase of bio-molecular pathways. This
package is to interact with the Reactome Content Service API.
Pre-built functions would allow users to retrieve data and
images that consist of proteins, pathways, and other molecules
related to a specific gene or entity in Reactome.
biocViews: DataImport, Pathways, Reactome
Author: Chi-Lam Poon [aut, cre]
(<https://orcid.org/0000-0001-6298-7099>), Reactome [cph]
Maintainer: Chi-Lam Poon <clpoon807@gmail.com>
URL: https://github.com/reactome/ReactomeContentService4R
VignetteBuilder: knitr
BugReports: https://github.com/reactome/ReactomeContentService4R/issues
git_url: https://git.bioconductor.org/packages/ReactomeContentService4R
git_branch: RELEASE_3_13
git_last_commit: 02b2833
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ReactomeContentService4R_1.0.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/ReactomeContentService4R_1.0.0.zip
mac.binary.ver:
bin/macosx/contrib/4.1/ReactomeContentService4R_1.0.0.tgz
vignettes:
vignettes/ReactomeContentService4R/inst/doc/ReactomeContentService4R.html
vignetteTitles: ReactomeContentService4R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/ReactomeContentService4R/inst/doc/ReactomeContentService4R.R
importsMe: ReactomeGraph4R
dependencyCount: 20
Package: ReactomeGraph4R
Version: 1.0.0
Depends: R (>= 4.1)
Imports: neo4r, utils, getPass, jsonlite, purrr, magrittr, data.table,
rlang, ReactomeContentService4R, doParallel, parallel, foreach
Suggests: knitr, rmarkdown, testthat, stringr, networkD3, visNetwork,
wesanderson
License: Apache License (>= 2)
MD5sum: a95778fd920af8536d2e5fa885e54311
NeedsCompilation: no
Title: Interface for the Reactome Graph Database
Description: Pathways, reactions, and biological entities in Reactome
knowledge are systematically represented as an ordered network.
Instances are represented as nodes and relationships between
instances as edges; they are all stored in the Reactome Graph
Database. This package serves as an interface to query the
interconnected data from a local Neo4j database, with the aim
of minimizing the usage of Neo4j Cypher queries.
biocViews: DataImport, Pathways, Reactome, Network, GraphAndNetwork
Author: Chi-Lam Poon [aut, cre]
(<https://orcid.org/0000-0001-6298-7099>), Reactome [cph]
Maintainer: Chi-Lam Poon <clpoon807@gmail.com>
URL: https://github.com/reactome/ReactomeGraph4R
VignetteBuilder: knitr
BugReports: https://github.com/reactome/ReactomeGraph4R/issues
git_url: https://git.bioconductor.org/packages/ReactomeGraph4R
git_branch: RELEASE_3_13
git_last_commit: 526e3dc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ReactomeGraph4R_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ReactomeGraph4R_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ReactomeGraph4R_1.0.0.tgz
vignettes: vignettes/ReactomeGraph4R/inst/doc/Introduction.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ReactomeGraph4R/inst/doc/Introduction.R
dependencyCount: 69
Package: ReactomeGSA
Version: 1.6.1
Imports: jsonlite, httr, progress, ggplot2, methods, gplots,
RColorBrewer
Suggests: testthat, knitr, rmarkdown, ReactomeGSA.data, Biobase,
devtools
Enhances: limma, edgeR, Seurat (>= 3.0), scater
License: MIT + file LICENSE
MD5sum: 38717409f1ae023b0790a99e04684d0f
NeedsCompilation: no
Title: Client for the Reactome Analysis Service for comparative
multi-omics gene set analysis
Description: The ReactomeGSA packages uses Reactome's online analysis
service to perform a multi-omics gene set analysis. The main
advantage of this package is, that the retrieved results can be
visualized using REACTOME's powerful webapplication. Since
Reactome's analysis service also uses R to perfrom the actual
gene set analysis you will get similar results when using the
same packages (such as limma and edgeR) locally. Therefore, if
you only require a gene set analysis, different packages are
more suited.
biocViews: GeneSetEnrichment, Proteomics, Transcriptomics,
SystemsBiology, GeneExpression, Reactome
Author: Johannes Griss [aut, cre]
(<https://orcid.org/0000-0003-2206-9511>)
Maintainer: Johannes Griss <johannes.griss@meduniwien.ac.at>
URL: https://github.com/reactome/ReactomeGSA
VignetteBuilder: knitr
BugReports: https://github.com/reactome/ReactomeGSA/issues
git_url: https://git.bioconductor.org/packages/ReactomeGSA
git_branch: RELEASE_3_13
git_last_commit: 0fa14a1
git_last_commit_date: 2021-09-10
Date/Publication: 2021-09-23
source.ver: src/contrib/ReactomeGSA_1.6.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ReactomeGSA_1.6.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ReactomeGSA_1.6.1.tgz
vignettes: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.html,
vignettes/ReactomeGSA/inst/doc/using-reactomegsa.html
vignetteTitles: Analysing single-cell RNAseq data, Using the
ReactomeGSA package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.R,
vignettes/ReactomeGSA/inst/doc/using-reactomegsa.R
dependsOnMe: ReactomeGSA.data
dependencyCount: 54
Package: ReactomePA
Version: 1.36.0
Depends: R (>= 3.4.0)
Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2, ggraph,
reactome.db, igraph, graphite
Suggests: BiocStyle, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db,
prettydoc, testthat
License: GPL-2
MD5sum: fddc702a3c4569a7d44efb200b20f632
NeedsCompilation: no
Title: Reactome Pathway Analysis
Description: This package provides functions for pathway analysis based
on REACTOME pathway database. It implements enrichment
analysis, gene set enrichment analysis and several functions
for visualization.
biocViews: Pathways, Visualization, Annotation, MultipleComparison,
GeneSetEnrichment, Reactome
Author: Guangchuang Yu [aut, cre], Vladislav Petyuk [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/biomedical-knowledge-mining-book/
VignetteBuilder: knitr
BugReports: https://github.com/GuangchuangYu/ReactomePA/issues
git_url: https://git.bioconductor.org/packages/ReactomePA
git_branch: RELEASE_3_13
git_last_commit: 78eb2e3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ReactomePA_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ReactomePA_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ReactomePA_1.36.0.tgz
vignettes: vignettes/ReactomePA/inst/doc/ReactomePA.html
vignetteTitles: An R package for Reactome Pathway Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R
dependsOnMe: maEndToEnd
importsMe: bioCancer, epihet, miRspongeR, multiSight, scTensor,
ExpHunterSuite
suggestsMe: ChIPseeker, CINdex, clusterProfiler, cola, scGPS
dependencyCount: 130
Package: ReadqPCR
Version: 1.38.0
Depends: R(>= 2.14.0), Biobase, methods
Suggests: qpcR
License: LGPL-3
Archs: i386, x64
MD5sum: aaf3540de3eff6451b550dbee2b541e4
NeedsCompilation: no
Title: Read qPCR data
Description: The package provides functions to read raw RT-qPCR data of
different platforms.
biocViews: DataImport, MicrotitrePlateAssay, GeneExpression, qPCR
Author: James Perkins, Matthias Kohl, Nor Izayu Abdul Rahman
Maintainer: James Perkins <jimrperkins@gmail.com>
URL:
http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html
git_url: https://git.bioconductor.org/packages/ReadqPCR
git_branch: RELEASE_3_13
git_last_commit: d387f65
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ReadqPCR_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ReadqPCR_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ReadqPCR_1.38.0.tgz
vignettes: vignettes/ReadqPCR/inst/doc/ReadqPCR.pdf
vignetteTitles: Functions to load RT-qPCR data into R
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ReadqPCR/inst/doc/ReadqPCR.R
dependsOnMe: NormqPCR
dependencyCount: 7
Package: REBET
Version: 1.10.0
Depends: ASSET
Imports: stats, utils
Suggests: RUnit, BiocGenerics
License: GPL-2
MD5sum: 416cbe93c0e2e09b3a2e96b5de61e609
NeedsCompilation: yes
Title: The subREgion-based BurdEn Test (REBET)
Description: There is an increasing focus to investigate the
association between rare variants and diseases. The REBET
package implements the subREgion-based BurdEn Test which is a
powerful burden test that simultaneously identifies
susceptibility loci and sub-regions.
biocViews: Software, VariantAnnotation, SNP
Author: Bill Wheeler [cre], Bin Zhu [aut], Lisa Mirabello [ctb],
Nilanjan Chatterjee [ctb]
Maintainer: Bill Wheeler <wheelerb@imsweb.com>
git_url: https://git.bioconductor.org/packages/REBET
git_branch: RELEASE_3_13
git_last_commit: fc89bb9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/REBET_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/REBET_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/REBET_1.10.0.tgz
vignettes: vignettes/REBET/inst/doc/vignette.pdf
vignetteTitles: REBET Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/REBET/inst/doc/vignette.R
dependencyCount: 16
Package: rebook
Version: 1.2.1
Imports: utils, methods, knitr (>= 1.32), rmarkdown, CodeDepends,
dir.expiry, filelock, BiocStyle
Suggests: testthat, igraph, XML, BiocManager, RCurl, bookdown,
rappdirs, yaml, BiocParallel, OSCA.intro, OSCA.workflows
License: GPL-3
MD5sum: 88e285d04b040ebaa0bf088be1346d8a
NeedsCompilation: no
Title: Re-using Content in Bioconductor Books
Description: Provides utilities to re-use content across chapters of a
Bioconductor book. This is mostly based on functionality
developed while writing the OSCA book, but generalized for
potential use in other large books with heavy compute. Also
contains some functions to assist book deployment.
biocViews: Software, Infrastructure, ReportWriting
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rebook
git_branch: RELEASE_3_13
git_last_commit: fd68600
git_last_commit_date: 2021-08-28
Date/Publication: 2021-08-29
source.ver: src/contrib/rebook_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rebook_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/rebook_1.2.1.tgz
vignettes: vignettes/rebook/inst/doc/userguide.html
vignetteTitles: Reusing book content
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rebook/inst/doc/userguide.R
dependsOnMe: csawBook, OSCA, OSCA.advanced, OSCA.basic, OSCA.intro,
OSCA.multisample, OSCA.workflows
suggestsMe: SingleRBook
dependencyCount: 36
Package: receptLoss
Version: 1.4.0
Depends: R (>= 3.6.0)
Imports: dplyr, ggplot2, magrittr, tidyr, SummarizedExperiment
Suggests: knitr, rmarkdown, testthat (>= 2.1.0), here
License: GPL-3 + file LICENSE
MD5sum: 2c49e0beeaebdbbe2490f804ef1524f9
NeedsCompilation: no
Title: Unsupervised Identification of Genes with Expression Loss in
Subsets of Tumors
Description: receptLoss identifies genes whose expression is lost in
subsets of tumors relative to normal tissue. It is particularly
well-suited in cases where the number of normal tissue samples
is small, as the distribution of gene expression in normal
tissue samples is approximated by a Gaussian. Originally
designed for identifying nuclear hormone receptor expression
loss but can be applied transcriptome wide as well.
biocViews: GeneExpression, StatisticalMethod
Author: Daniel Pique, John Greally, Jessica Mar
Maintainer: Daniel Pique <daniel.pique@med.einstein.yu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/receptLoss
git_branch: RELEASE_3_13
git_last_commit: 76bd045
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/receptLoss_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/receptLoss_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/receptLoss_1.4.0.tgz
vignettes: vignettes/receptLoss/inst/doc/receptLoss.html
vignetteTitles: receptLoss
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/receptLoss/inst/doc/receptLoss.R
dependencyCount: 62
Package: reconsi
Version: 1.4.0
Imports: phyloseq, KernSmooth, reshape2, ggplot2, stats, methods,
graphics, grDevices, matrixStats
Suggests: knitr, rmarkdown, testthat
License: GPL-2
MD5sum: 9ca7148d5f5b8cb2dc2f40013facd9b6
NeedsCompilation: no
Title: Resampling Collapsed Null Distributions for Simultaneous
Inference
Description: Improves simultaneous inference under dependence of tests
by estimating a collapsed null distribution through resampling.
Accounting for the dependence between tests increases the power
while reducing the variability of the false discovery
proportion. This dependence is common in genomics applications,
e.g. when combining flow cytometry measurements with microbiome
sequence counts.
biocViews: Metagenomics, Microbiome, MultipleComparison, FlowCytometry
Author: Stijn Hawinkel <stijn.hawinkel@ugent.be>
Maintainer: Joris Meys <joris.meys@ugent.be>
VignetteBuilder: knitr
BugReports: https://github.com/CenterForStatistics-UGent/reconsi/issues
git_url: https://git.bioconductor.org/packages/reconsi
git_branch: RELEASE_3_13
git_last_commit: 4f305fe
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/reconsi_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/reconsi_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/reconsi_1.4.0.tgz
vignettes: vignettes/reconsi/inst/doc/reconsiVignette.html
vignetteTitles: Manual for the RCM pacakage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/reconsi/inst/doc/reconsiVignette.R
dependencyCount: 79
Package: recount
Version: 1.18.1
Depends: R (>= 3.3.0), SummarizedExperiment
Imports: BiocParallel, derfinder, downloader, GEOquery, GenomeInfoDb,
GenomicRanges, IRanges, methods, RCurl, rentrez, rtracklayer
(>= 1.35.3), S4Vectors, stats, utils
Suggests: AnnotationDbi, BiocManager, BiocStyle (>= 2.5.19), DESeq2,
sessioninfo, EnsDb.Hsapiens.v79, GenomicFeatures, knitr (>=
1.6), org.Hs.eg.db, RefManageR, regionReport (>= 1.9.4),
rmarkdown (>= 0.9.5), testthat (>= 2.1.0), covr, pheatmap, DT,
edgeR, ggplot2, RColorBrewer
License: Artistic-2.0
MD5sum: 1c592a6bb5c3298aba8bed4cafd4c7ae
NeedsCompilation: no
Title: Explore and download data from the recount project
Description: Explore and download data from the recount project
available at https://jhubiostatistics.shinyapps.io/recount/.
Using the recount package you can download
RangedSummarizedExperiment objects at the gene, exon or
exon-exon junctions level, the raw counts, the phenotype
metadata used, the urls to the sample coverage bigWig files or
the mean coverage bigWig file for a particular study. The
RangedSummarizedExperiment objects can be used by different
packages for performing differential expression analysis. Using
http://bioconductor.org/packages/derfinder you can perform
annotation-agnostic differential expression analyses with the
data from the recount project as described at
http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html.
biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq,
Sequencing, Software, DataImport, ImmunoOncology
Author: Leonardo Collado-Torres [aut, cre]
(<https://orcid.org/0000-0003-2140-308X>), Abhinav Nellore
[ctb], Andrew E. Jaffe [ctb]
(<https://orcid.org/0000-0001-6886-1454>), Margaret A. Taub
[ctb], Kai Kammers [ctb], Shannon E. Ellis [ctb]
(<https://orcid.org/0000-0002-9231-0481>), Kasper Daniel Hansen
[ctb] (<https://orcid.org/0000-0003-0086-0687>), Ben Langmead
[ctb] (<https://orcid.org/0000-0003-2437-1976>), Jeffrey T.
Leek [aut, ths] (<https://orcid.org/0000-0002-2873-2671>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/leekgroup/recount
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/recount/
git_url: https://git.bioconductor.org/packages/recount
git_branch: RELEASE_3_13
git_last_commit: 64a92e6
git_last_commit_date: 2021-08-09
Date/Publication: 2021-08-10
source.ver: src/contrib/recount_1.18.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/recount_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/recount_1.18.1.tgz
vignettes: vignettes/recount/inst/doc/recount-quickstart.html,
vignettes/recount/inst/doc/SRP009615-results.html
vignetteTitles: recount quick start guide, Basic DESeq2 results
exploration
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/recount/inst/doc/recount-quickstart.R,
vignettes/recount/inst/doc/SRP009615-results.R
importsMe: psichomics, RNAAgeCalc, recountWorkflow
suggestsMe: dasper, recount3
dependencyCount: 158
Package: recount3
Version: 1.2.6
Depends: SummarizedExperiment
Imports: BiocFileCache, methods, rtracklayer, S4Vectors, utils, RCurl,
data.table, R.utils, Matrix, GenomicRanges, sessioninfo, tools
Suggests: BiocStyle, covr, knitcitations, knitr, RefManageR, rmarkdown,
testthat, pryr, interactiveDisplayBase, recount
License: Artistic-2.0
MD5sum: fbf5ec3ebdc6d1af56477dc8f936588f
NeedsCompilation: no
Title: Explore and download data from the recount3 project
Description: The recount3 package enables access to a large amount of
uniformly processed RNA-seq data from human and mouse. You can
download RangedSummarizedExperiment objects at the gene, exon
or exon-exon junctions level with sample metadata and QC
statistics. In addition we provide access to sample coverage
BigWig files.
biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq,
Sequencing, Software, DataImport
Author: Leonardo Collado-Torres [aut, cre]
(<https://orcid.org/0000-0003-2140-308X>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/LieberInstitute/recount3
VignetteBuilder: knitr
BugReports: https://github.com/LieberInstitute/recount3/issues
git_url: https://git.bioconductor.org/packages/recount3
git_branch: RELEASE_3_13
git_last_commit: b41fa0f
git_last_commit_date: 2021-09-27
Date/Publication: 2021-09-28
source.ver: src/contrib/recount3_1.2.6.tar.gz
win.binary.ver: bin/windows/contrib/4.1/recount3_1.2.6.zip
mac.binary.ver: bin/macosx/contrib/4.1/recount3_1.2.6.tgz
vignettes: vignettes/recount3/inst/doc/recount3-quickstart.html
vignetteTitles: recount3 quick start guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/recount3/inst/doc/recount3-quickstart.R
dependencyCount: 89
Package: recountmethylation
Version: 1.2.3
Depends: R (>= 4.0.0)
Imports: minfi, HDF5Array, rhdf5, S4Vectors, utils, methods, RCurl,
R.utils, BiocFileCache
Suggests: knitr, testthat, ggplot2, gridExtra, rmarkdown, BiocStyle,
GenomicRanges, limma, ExperimentHub, AnnotationHub
License: Artistic-2.0
Archs: i386, x64
MD5sum: a3eefc03810def61b36b94bc6b72edbc
NeedsCompilation: no
Title: Access and analyze DNA methylation database compilations
Description: Access cross-study compilations of DNA methylation array
databases. Database files can be downloaded and accessed using
provided functions. Background about database file types (HDF5
and HDF5-SummarizedExperiment), SummarizedExperiment classes,
and examples for data handling, validation, and analyses, can
be found in the package vignettes. Note the disclaimer on
package load, and consult the main manuscript for further info.
biocViews: DNAMethylation, Epigenetics, Microarray, MethylationArray,
ExperimentHub
Author: Sean K Maden [cre, aut]
(<https://orcid.org/0000-0002-2212-4894>), Reid F Thompson
[aut] (<https://orcid.org/0000-0003-3661-5296>), Kasper D
Hansen [aut] (<https://orcid.org/0000-0003-0086-0687>), Abhinav
Nellore [aut] (<https://orcid.org/0000-0001-8145-1484>)
Maintainer: Sean K Maden <maden@ohsu.edu>
URL: https://github.com/metamaden/recountmethylation
VignetteBuilder: knitr
BugReports: https://github.com/metamaden/recountmethylation/issues
git_url: https://git.bioconductor.org/packages/recountmethylation
git_branch: RELEASE_3_13
git_last_commit: 3557cec
git_last_commit_date: 2021-10-11
Date/Publication: 2021-10-12
source.ver: src/contrib/recountmethylation_1.2.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/recountmethylation_1.2.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/recountmethylation_1.2.3.tgz
vignettes:
vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.html,
vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.html
vignetteTitles: Data Analyses, recountmethylation User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.R,
vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.R
dependencyCount: 142
Package: recoup
Version: 1.20.1
Depends: R (>= 4.0.0), GenomicRanges, GenomicAlignments, ggplot2,
ComplexHeatmap
Imports: BiocGenerics, biomaRt, Biostrings, circlize, GenomeInfoDb,
GenomicFeatures, graphics, grDevices, httr, IRanges, methods,
parallel, RSQLite, Rsamtools, rtracklayer, S4Vectors, stats,
stringr, utils
Suggests: grid, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocManager,
BSgenome, RMySQL
License: GPL (>= 3)
MD5sum: 02055c4a12a40772626f2b390e68a3d9
NeedsCompilation: no
Title: An R package for the creation of complex genomic profile plots
Description: recoup calculates and plots signal profiles created from
short sequence reads derived from Next Generation Sequencing
technologies. The profiles provided are either sumarized curve
profiles or heatmap profiles. Currently, recoup supports
genomic profile plots for reads derived from ChIP-Seq and
RNA-Seq experiments. The package uses ggplot2 and
ComplexHeatmap graphics facilities for curve and heatmap
coverage profiles respectively.
biocViews: ImmunoOncology, Software, GeneExpression, Preprocessing,
QualityControl, RNASeq, ChIPSeq, Sequencing, Coverage, ATACSeq,
ChipOnChip, Alignment, DataImport
Author: Panagiotis Moulos <moulos@fleming.gr>
Maintainer: Panagiotis Moulos <moulos@fleming.gr>
URL: https://github.com/pmoulos/recoup
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/recoup
git_branch: RELEASE_3_13
git_last_commit: ab6d090
git_last_commit_date: 2021-10-06
Date/Publication: 2021-10-07
source.ver: src/contrib/recoup_1.20.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/recoup_1.20.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/recoup_1.20.1.tgz
vignettes: vignettes/recoup/inst/doc/recoup_intro.html
vignetteTitles: Introduction to the recoup package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/recoup/inst/doc/recoup_intro.R
dependencyCount: 122
Package: RedeR
Version: 1.40.5
Depends: R (>= 3.5), methods
Imports: igraph
Suggests: pvclust, BiocStyle, knitr, rmarkdown
License: GPL (>= 2)
MD5sum: a7a744f788e9d7ea507da65a7be9d591
NeedsCompilation: no
Title: Interactive visualization and manipulation of nested networks
Description: RedeR is an R-based package combined with a stand-alone
Java application for interactive visualization and manipulation
of modular structures, nested networks and multiple levels of
hierarchical associations.
biocViews: Infrastructure, GraphAndNetwork, Software, Network,
Visualization, DataRepresentation
Author: Mauro Castro, Xin Wang, Florian Markowetz
Maintainer: Mauro Castro <mauro.a.castro@gmail.com>
URL: http://genomebiology.com/2012/13/4/R29
SystemRequirements: Java Runtime Environment (>= 8)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RedeR
git_branch: RELEASE_3_13
git_last_commit: f5bb912
git_last_commit_date: 2021-09-25
Date/Publication: 2021-09-26
source.ver: src/contrib/RedeR_1.40.5.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RedeR_1.40.5.zip
mac.binary.ver: bin/macosx/contrib/4.1/RedeR_1.40.5.tgz
vignettes: vignettes/RedeR/inst/doc/RedeR.html
vignetteTitles: "RedeR: hierarchical network representation"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RedeR/inst/doc/RedeR.R
dependsOnMe: Fletcher2013b, dc3net
importsMe: PANR, RTN, transcriptogramer, TreeAndLeaf
dependencyCount: 11
Package: REDseq
Version: 1.38.0
Depends: R (>= 3.5.0), BiocGenerics, BSgenome.Celegans.UCSC.ce2,
multtest, Biostrings, BSgenome, ChIPpeakAnno
Imports: AnnotationDbi, graphics, IRanges (>= 1.13.5), stats, utils
License: GPL (>=2)
MD5sum: 1a16f75bc097dcf0b5f494cdbcd54058
NeedsCompilation: no
Title: Analysis of high-throughput sequencing data processed by
restriction enzyme digestion
Description: The package includes functions to build restriction enzyme
cut site (RECS) map, distribute mapped sequences on the map
with five different approaches, find enriched/depleted RECSs
for a sample, and identify differentially enriched/depleted
RECSs between samples.
biocViews: Sequencing, SequenceMatching, Preprocessing
Author: Lihua Julie Zhu, Junhui Li and Thomas Fazzio
Maintainer: Lihua Julie Zhu <julie.zhu@umassmed.edu>
git_url: https://git.bioconductor.org/packages/REDseq
git_branch: RELEASE_3_13
git_last_commit: 4c3b25a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/REDseq_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/REDseq_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/REDseq_1.38.0.tgz
vignettes: vignettes/REDseq/inst/doc/REDseq.pdf
vignetteTitles: REDseq Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/REDseq/inst/doc/REDseq.R
dependencyCount: 124
Package: RefPlus
Version: 1.62.0
Depends: R (>= 2.8.0), Biobase (>= 2.1.0), affy (>= 1.20.0), affyPLM
(>= 1.18.0), preprocessCore (>= 1.4.0)
Suggests: affydata
License: GPL (>= 2)
MD5sum: b9f508c0383d99086600b547f33c94e6
NeedsCompilation: no
Title: A function set for the Extrapolation Strategy (RMA+) and
Extrapolation Averaging (RMA++) methods.
Description: The package contains functions for pre-processing
Affymetrix data using the RMA+ and the RMA++ methods.
biocViews: Microarray, OneChannel, Preprocessing
Author: Kai-Ming Chang <kaiming@gmail.com>, Chris Harbron
<Chris.Harbron@astrazeneca.com>, Marie C South
<Marie.C.South@astrazeneca.com>
Maintainer: Kai-Ming Chang <kaiming@gmail.com>
git_url: https://git.bioconductor.org/packages/RefPlus
git_branch: RELEASE_3_13
git_last_commit: 9fed9a4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RefPlus_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RefPlus_1.62.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RefPlus_1.62.0.tgz
vignettes: vignettes/RefPlus/inst/doc/RefPlus.pdf
vignetteTitles: RefPlus Manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RefPlus/inst/doc/RefPlus.R
dependencyCount: 27
Package: RegEnrich
Version: 1.2.0
Depends: R (>= 4.0.0), S4Vectors, dplyr, tibble, BiocSet,
SummarizedExperiment
Imports: randomForest, fgsea, DOSE, BiocParallel, DESeq2, limma, WGCNA,
ggplot2 (>= 2.2.0), methods, reshape2, magrittr
Suggests: GEOquery, rmarkdown, knitr, BiocManager, testthat
License: GPL (>= 2)
MD5sum: c7fbaf01d62af23e1f2c22e2190fd3a1
NeedsCompilation: no
Title: Gene regulator enrichment analysis
Description: This package is a pipeline to identify the key gene
regulators in a biological process, for example in cell
differentiation and in cell development after stimulation.
There are four major steps in this pipeline: (1) differential
expression analysis; (2) regulator-target network inference;
(3) enrichment analysis; and (4) regulators scoring and
ranking.
biocViews: GeneExpression, Transcriptomics, RNASeq, TwoChannel,
Transcription, GeneTarget, NetworkEnrichment,
DifferentialExpression, Network, NetworkInference,
GeneSetEnrichment, FunctionalPrediction
Author: Weiyang Tao [cre, aut], Aridaman Pandit [aut]
Maintainer: Weiyang Tao <w.tao-2@umcutrecht.nl>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RegEnrich
git_branch: RELEASE_3_13
git_last_commit: 65372ae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RegEnrich_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RegEnrich_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RegEnrich_1.2.0.tgz
vignettes: vignettes/RegEnrich/inst/doc/RegEnrich.html
vignetteTitles: Gene regulator enrichment with RegEnrich
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RegEnrich/inst/doc/RegEnrich.R
dependencyCount: 147
Package: regioneR
Version: 1.24.0
Depends: GenomicRanges
Imports: memoise, GenomicRanges, IRanges, BSgenome, Biostrings,
rtracklayer, parallel, graphics, stats, utils, methods,
GenomeInfoDb, S4Vectors, tools
Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19.masked,
testthat
License: Artistic-2.0
MD5sum: 14f99bc90bbd64747350e57a45480d36
NeedsCompilation: no
Title: Association analysis of genomic regions based on permutation
tests
Description: regioneR offers a statistical framework based on
customizable permutation tests to assess the association
between genomic region sets and other genomic features.
biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation
Author: Anna Diez-Villanueva <adiez@iconcologia.net>, Roberto
Malinverni <rmalinverni@carrerasresearch.org> and Bernat Gel
<bgel@igtp.cat>
Maintainer: Bernat Gel <bgel@imppc.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/regioneR
git_branch: RELEASE_3_13
git_last_commit: 3cbcdab
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/regioneR_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/regioneR_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/regioneR_1.24.0.tgz
vignettes: vignettes/regioneR/inst/doc/regioneR.html
vignetteTitles: regioneR vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/regioneR/inst/doc/regioneR.R
dependsOnMe: karyoploteR
importsMe: annotatr, ChIPpeakAnno, CNVfilteR, CopyNumberPlots,
karyoploteR, RIPAT, UMI4Cats
suggestsMe: CNVRanger
dependencyCount: 49
Package: regionReport
Version: 1.26.0
Depends: R(>= 3.2)
Imports: BiocStyle (>= 2.5.19), derfinder (>= 1.25.3), DEFormats,
DESeq2, GenomeInfoDb, GenomicRanges, knitr (>= 1.6),
knitrBootstrap (>= 0.9.0), methods, RefManageR, rmarkdown (>=
0.9.5), S4Vectors, SummarizedExperiment, utils
Suggests: BiocManager, biovizBase, bumphunter (>= 1.7.6), derfinderPlot
(>= 1.3.2), sessioninfo, DT, edgeR, ggbio (>= 1.35.2), ggplot2,
grid, gridExtra, IRanges, mgcv, pasilla, pheatmap,
RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, whisker
License: Artistic-2.0
MD5sum: 156dc71053ca6e9c2540b5e10ab01888
NeedsCompilation: no
Title: Generate HTML or PDF reports for a set of genomic regions or
DESeq2/edgeR results
Description: Generate HTML or PDF reports to explore a set of regions
such as the results from annotation-agnostic expression
analysis of RNA-seq data at base-pair resolution performed by
derfinder. You can also create reports for DESeq2 or edgeR
results.
biocViews: DifferentialExpression, Sequencing, RNASeq, Software,
Visualization, Transcription, Coverage, ReportWriting,
DifferentialMethylation, DifferentialPeakCalling,
ImmunoOncology, QualityControl
Author: Leonardo Collado-Torres [aut, cre]
(<https://orcid.org/0000-0003-2140-308X>), Andrew E. Jaffe
[aut] (<https://orcid.org/0000-0001-6886-1454>), Jeffrey T.
Leek [aut, ths] (<https://orcid.org/0000-0002-2873-2671>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/leekgroup/regionReport
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/regionReport/
git_url: https://git.bioconductor.org/packages/regionReport
git_branch: RELEASE_3_13
git_last_commit: 985fa5a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/regionReport_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/regionReport_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/regionReport_1.26.0.tgz
vignettes: vignettes/regionReport/inst/doc/bumphunterExample.html,
vignettes/regionReport/inst/doc/regionReport.html
vignetteTitles: Example report using bumphunter results, Introduction
to regionReport
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/regionReport/inst/doc/bumphunterExample.R,
vignettes/regionReport/inst/doc/regionReport.R
importsMe: recountWorkflow
suggestsMe: recount
dependencyCount: 168
Package: regsplice
Version: 1.18.0
Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats,
pbapply, utils, methods
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 3571d0ae3cfdeb096b84c10f87a462fa
NeedsCompilation: no
Title: L1-regularization based methods for detection of differential
splicing
Description: Statistical methods for detection of differential splicing
(differential exon usage) in RNA-seq and exon microarray data,
using L1-regularization (lasso) to improve power.
biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression,
DifferentialSplicing, Sequencing, RNASeq, Microarray,
ExonArray, ExperimentalDesign, Software
Author: Lukas M. Weber [aut, cre]
Maintainer: Lukas M. Weber <lukas.weber.edu@gmail.com>
URL: https://github.com/lmweber/regsplice
VignetteBuilder: knitr
BugReports: https://github.com/lmweber/regsplice/issues
git_url: https://git.bioconductor.org/packages/regsplice
git_branch: RELEASE_3_13
git_last_commit: d2c7809
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/regsplice_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/regsplice_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/regsplice_1.18.0.tgz
vignettes: vignettes/regsplice/inst/doc/regsplice-workflow.html
vignetteTitles: regsplice workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/regsplice/inst/doc/regsplice-workflow.R
dependencyCount: 38
Package: regutools
Version: 1.4.0
Depends: R (>= 4.0)
Imports: AnnotationDbi, AnnotationHub, Biostrings, DBI, GenomicRanges,
Gviz, IRanges, RCy3, RSQLite, S4Vectors, methods, stats, utils,
BiocFileCache
Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo,
testthat (>= 2.1.0), covr
License: Artistic-2.0
MD5sum: cef7cb481f9ae34d7a287fa28aad0a5a
NeedsCompilation: no
Title: regutools: an R package for data extraction from RegulonDB
Description: RegulonDB has collected, harmonized and centralized data
from hundreds of experiments for nearly two decades and is
considered a point of reference for transcriptional regulation
in Escherichia coli K12. Here, we present the regutools R
package to facilitate programmatic access to RegulonDB data in
computational biology. regutools provides researchers with the
possibility of writing reproducible workflows with automated
queries to RegulonDB. The regutools package serves as a bridge
between RegulonDB data and the Bioconductor ecosystem by
reusing the data structures and statistical methods powered by
other Bioconductor packages. We demonstrate the integration of
regutools with Bioconductor by analyzing transcription factor
DNA binding sites and transcriptional regulatory networks from
RegulonDB. We anticipate that regutools will serve as a useful
building block in our progress to further our understanding of
gene regulatory networks.
biocViews: GeneRegulation, GeneExpression, SystemsBiology,
Network,NetworkInference,Visualization, Transcription
Author: Joselyn Chavez [aut, cre]
(<https://orcid.org/0000-0002-4974-4591>), Carmina
Barberena-Jonas [aut]
(<https://orcid.org/0000-0001-7413-638X>), Jesus E.
Sotelo-Fonseca [aut] (<https://orcid.org/0000-0003-1600-2396>),
Jose Alquicira-Hernandez [ctb]
(<https://orcid.org/0000-0002-9049-7780>), Heladia Salgado
[ctb] (<https://orcid.org/0000-0002-3166-5801>), Leonardo
Collado-Torres [aut] (<https://orcid.org/0000-0003-2140-308X>),
Alejandro Reyes [aut] (<https://orcid.org/0000-0001-8717-6612>)
Maintainer: Joselyn Chavez <joselynchavezf@gmail.com>
URL: https://github.com/ComunidadBioInfo/regutools
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/regutools
git_url: https://git.bioconductor.org/packages/regutools
git_branch: RELEASE_3_13
git_last_commit: fe87b86
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/regutools_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/regutools_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/regutools_1.4.0.tgz
vignettes: vignettes/regutools/inst/doc/regutools.html
vignetteTitles: regutools: an R package for data extraction from
RegulonDB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/regutools/inst/doc/regutools.R
dependencyCount: 177
Package: REMP
Version: 1.16.0
Depends: R (>= 3.6), SummarizedExperiment(>= 1.1.6), minfi (>= 1.22.0)
Imports: readr, rtracklayer, graphics, stats, utils, methods, settings,
BiocGenerics, S4Vectors, Biostrings, GenomicRanges, IRanges,
GenomeInfoDb, BiocParallel, doParallel, parallel, foreach,
caret, kernlab, ranger, BSgenome, AnnotationHub, org.Hs.eg.db,
impute, iterators
Suggests: IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b2.hg19,
BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38,
knitr, rmarkdown, minfiDataEPIC
License: GPL-3
MD5sum: dd8f75dfd8f98a100a906c7adbaa47ae
NeedsCompilation: no
Title: Repetitive Element Methylation Prediction
Description: Machine learning-based tools to predict DNA methylation of
locus-specific repetitive elements (RE) by learning surrounding
genetic and epigenetic information. These tools provide
genomewide and single-base resolution of DNA methylation
prediction on RE that are difficult to measure using
array-based or sequencing-based platforms, which enables
epigenome-wide association study (EWAS) and differentially
methylated region (DMR) analysis on RE.
biocViews: DNAMethylation, Microarray, MethylationArray, Sequencing,
GenomeWideAssociation, Epigenetics, Preprocessing,
MultiChannel, TwoChannel, DifferentialMethylation,
QualityControl, DataImport
Author: Yinan Zheng [aut, cre], Lei Liu [aut], Wei Zhang [aut], Warren
Kibbe [aut], Lifang Hou [aut, cph]
Maintainer: Yinan Zheng <y-zheng@northwestern.edu>
URL: https://github.com/YinanZheng/REMP
BugReports: https://github.com/YinanZheng/REMP/issues
git_url: https://git.bioconductor.org/packages/REMP
git_branch: RELEASE_3_13
git_last_commit: 9be0aa3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/REMP_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/REMP_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/REMP_1.16.0.tgz
vignettes: vignettes/REMP/inst/doc/REMP.pdf
vignetteTitles: An Introduction to the REMP Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/REMP/inst/doc/REMP.R
dependencyCount: 203
Package: Repitools
Version: 1.38.0
Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.8.0)
Imports: parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12),
GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools,
GenomicAlignments, rtracklayer, BSgenome (>= 1.47.3), gplots,
grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Ringo, Rsolnp,
cluster
Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18
License: LGPL (>= 2)
MD5sum: 44fe96822d6dea94cb33bcb9473b32ce
NeedsCompilation: yes
Title: Epigenomic tools
Description: Tools for the analysis of enrichment-based epigenomic
data. Features include summarization and visualization of
epigenomic data across promoters according to gene expression
context, finding regions of differential methylation/binding,
BayMeth for quantifying methylation etc.
biocViews: DNAMethylation, GeneExpression, MethylSeq
Author: Mark Robinson <mark.robinson@imls.uzh.ch>, Dario Strbenac
<dario.strbenac@sydney.edu.au>, Aaron Statham
<a.statham@garvan.org.au>, Andrea Riebler
<andrea.riebler@math.ntnu.no>
Maintainer: Mark Robinson <mark.robinson@imls.uzh.ch>
git_url: https://git.bioconductor.org/packages/Repitools
git_branch: RELEASE_3_13
git_last_commit: dc767d1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Repitools_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Repitools_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Repitools_1.38.0.tgz
vignettes: vignettes/Repitools/inst/doc/Repitools_vignette.pdf
vignetteTitles: Using Repitools for Epigenomic Sequencing Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Repitools/inst/doc/Repitools_vignette.R
dependencyCount: 116
Package: ReportingTools
Version: 2.32.1
Depends: methods, knitr, utils
Imports: Biobase,hwriter,Category,GOstats,limma(>=
3.17.5),lattice,AnnotationDbi,edgeR, annotate,PFAM.db,
GSEABase, BiocGenerics(>= 0.1.6), grid, XML, R.utils, DESeq2(>=
1.3.41), ggplot2, ggbio, IRanges
Suggests: RUnit, ALL, hgu95av2.db, org.Mm.eg.db, shiny, pasilla,
org.Sc.sgd.db, rmarkdown
License: Artistic-2.0
MD5sum: 719c879c706aee4b0b3770c27adbd2b8
NeedsCompilation: no
Title: Tools for making reports in various formats
Description: The ReportingTools software package enables users to
easily display reports of analysis results generated from
sources such as microarray and sequencing data. The package
allows users to create HTML pages that may be viewed on a web
browser such as Safari, or in other formats readable by
programs such as Excel. Users can generate tables with
sortable and filterable columns, make and display plots, and
link table entries to other data sources such as NCBI or larger
plots within the HTML page. Using the package, users can also
produce a table of contents page to link various reports
together for a particular project that can be viewed in a web
browser. For more examples, please visit our site: http://
research-pub.gene.com/ReportingTools.
biocViews: ImmunoOncology, Software, Visualization, Microarray, RNASeq,
GO, DataRepresentation, GeneSetEnrichment
Author: Jason A. Hackney, Melanie Huntley, Jessica L. Larson, Christina
Chaivorapol, Gabriel Becker, and Josh Kaminker
Maintainer: Jason A. Hackney <hackney.jason@gene.com>, Gabriel Becker
<becker.gabe@gene.com>, Jessica L. Larson
<larson.jessica@gmail.com>
VignetteBuilder: utils, knitr
git_url: https://git.bioconductor.org/packages/ReportingTools
git_branch: RELEASE_3_13
git_last_commit: 158e4a4
git_last_commit_date: 2021-07-26
Date/Publication: 2021-07-27
source.ver: src/contrib/ReportingTools_2.32.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ReportingTools_2.32.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ReportingTools_2.32.1.tgz
vignettes: vignettes/ReportingTools/inst/doc/basicReportingTools.pdf,
vignettes/ReportingTools/inst/doc/microarrayAnalysis.pdf,
vignettes/ReportingTools/inst/doc/rnaseqAnalysis.pdf,
vignettes/ReportingTools/inst/doc/shiny.pdf,
vignettes/ReportingTools/inst/doc/knitr.html
vignetteTitles: ReportingTools basics, Reporting on microarray
differential expression, Reporting on RNA-seq differential
expression, ReportingTools shiny, Knitr and ReportingTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ReportingTools/inst/doc/basicReportingTools.R,
vignettes/ReportingTools/inst/doc/knitr.R,
vignettes/ReportingTools/inst/doc/microarrayAnalysis.R,
vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R,
vignettes/ReportingTools/inst/doc/shiny.R
dependsOnMe: rnaseqGene
importsMe: affycoretools
suggestsMe: cpvSNP, EnrichmentBrowser, GSEABase, npGSEA
dependencyCount: 173
Package: RepViz
Version: 1.8.0
Depends: R (>= 3.5.1), GenomicRanges (>= 1.30.0), Rsamtools (>=
1.34.1), IRanges (>= 2.14.0), biomaRt (>= 2.36.0), S4Vectors
(>= 0.18.0), graphics, grDevices, utils
Suggests: knitr, testthat
License: GPL-3
MD5sum: 6cb6c81098fd39b8eb1a88f469bb56f9
NeedsCompilation: no
Title: Replicate oriented Visualization of a genomic region
Description: RepViz enables the view of a genomic region in a simple
and efficient way. RepViz allows simultaneous viewing of both
intra- and intergroup variation in sequencing counts of the
studied conditions, as well as their comparison to the output
features (e.g. identified peaks) from user selected data
analysis methods.The RepViz tool is primarily designed for
chromatin data such as ChIP-seq and ATAC-seq, but can also be
used with other sequencing data such as RNA-seq, or
combinations of different types of genomic data.
biocViews: WorkflowStep, Visualization, Sequencing, ChIPSeq, ATACSeq,
Software, Coverage, GenomicVariation
Author: Thomas Faux, Kalle Rytkönen, Asta Laiho, Laura L. Elo
Maintainer: Thomas Faux, Asta Laiho <faux.thomas1@gmail.com>
<asta.laiho@utu.fi>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RepViz
git_branch: RELEASE_3_13
git_last_commit: 700aac6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RepViz_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RepViz_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RepViz_1.8.0.tgz
vignettes: vignettes/RepViz/inst/doc/RepViz.html
vignetteTitles: RepViz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RepViz/inst/doc/RepViz.R
dependencyCount: 82
Package: ReQON
Version: 1.38.0
Depends: R (>= 3.0.2), Rsamtools, seqbias
Imports: rJava, graphics, stats, utils, grDevices
Suggests: BiocStyle
License: GPL-2
MD5sum: 3fb48a41689780090debbf2dc690592a
NeedsCompilation: no
Title: Recalibrating Quality Of Nucleotides
Description: Algorithm for recalibrating the base quality scores for
aligned sequencing data in BAM format.
biocViews: Sequencing, HighThroughputSequencing, Preprocessing,
QualityControl
Author: Christopher Cabanski, Keary Cavin, Chris Bizon
Maintainer: Christopher Cabanski <cabanskc@gmail.com>
SystemRequirements: Java version >= 1.6
git_url: https://git.bioconductor.org/packages/ReQON
git_branch: RELEASE_3_13
git_last_commit: 01c27aa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ReQON_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ReQON_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ReQON_1.38.0.tgz
vignettes: vignettes/ReQON/inst/doc/ReQON.pdf
vignetteTitles: ReQON Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ReQON/inst/doc/ReQON.R
dependencyCount: 31
Package: ResidualMatrix
Version: 1.2.0
Imports: methods, Matrix, S4Vectors, DelayedArray
Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular
License: GPL-3
MD5sum: 9ee1962a7b02ffd916eaa4af46eea880
NeedsCompilation: no
Title: Creating a DelayedMatrix of Regression Residuals
Description: Provides delayed computation of a matrix of residuals
after fitting a linear model to each column of an input matrix.
Also supports partial computation of residuals where selected
factors are to be preserved in the output matrix. Implements a
number of efficient methods for operating on the delayed matrix
of residuals, most notably matrix multiplication and
calculation of row/column sums or means.
biocViews: Software, DataRepresentation, Regression, BatchEffect,
ExperimentalDesign
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/ResidualMatrix
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/ResidualMatrix/issues
git_url: https://git.bioconductor.org/packages/ResidualMatrix
git_branch: RELEASE_3_13
git_last_commit: a4d7553
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ResidualMatrix_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ResidualMatrix_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ResidualMatrix_1.2.0.tgz
vignettes: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.html
vignetteTitles: Using the ResidualMatrix
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.R
importsMe: batchelor
suggestsMe: BiocSingular, scran
dependencyCount: 16
Package: restfulSE
Version: 1.14.2
Depends: R (>= 3.6), SummarizedExperiment,DelayedArray
Imports: utils, stats, methods, S4Vectors, Biobase,reshape2,
AnnotationDbi, DBI, GO.db, rhdf5client, dplyr (>= 0.7.1),
magrittr, bigrquery, ExperimentHub, AnnotationHub, rlang
Suggests: knitr, testthat, Rtsne, org.Mm.eg.db, org.Hs.eg.db,
BiocStyle, restfulSEData, rmarkdown
License: Artistic-2.0
MD5sum: a6f58ff9a60bb09af635611ed59eb0f2
NeedsCompilation: no
Title: Access matrix-like HDF5 server content or BigQuery content
through a SummarizedExperiment interface
Description: This package provides functions and classes to interface
with remote data stores by operating on
SummarizedExperiment-like objects.
biocViews: Infrastructure, SingleCell, Transcriptomics, Sequencing,
Coverage
Author: Vincent Carey [aut], Shweta Gopaulakrishnan [cre, aut]
Maintainer: Shweta Gopaulakrishnan <shwetagopaul92@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/restfulSE
git_branch: RELEASE_3_13
git_last_commit: 9a05544
git_last_commit_date: 2021-08-20
Date/Publication: 2021-08-22
source.ver: src/contrib/restfulSE_1.14.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/restfulSE_1.14.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/restfulSE_1.14.2.tgz
vignettes: vignettes/restfulSE/inst/doc/restfulSE.pdf
vignetteTitles: restfulSE -- experiments with SE interface to remote
HDF5
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/restfulSE/inst/doc/restfulSE.R
dependsOnMe: tenXplore
suggestsMe: BiocOncoTK, BiocSklearn
dependencyCount: 110
Package: rexposome
Version: 1.14.1
Depends: R (>= 3.5), Biobase
Imports: methods, utils, stats, lsr, FactoMineR, stringr, circlize,
corrplot, ggplot2, reshape2, pryr, S4Vectors, imputeLCMD,
scatterplot3d, glmnet, gridExtra, grid, Hmisc, gplots, gtools,
scales, lme4, grDevices, graphics, ggrepel, mice
Suggests: mclust, flexmix, testthat, BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 7ace3ae903b10f14023b2b36b4ec7605
NeedsCompilation: no
Title: Exposome exploration and outcome data analysis
Description: Package that allows to explore the exposome and to perform
association analyses between exposures and health outcomes.
biocViews: Software, BiologicalQuestion, Infrastructure, DataImport,
DataRepresentation, BiomedicalInformatics, ExperimentalDesign,
MultipleComparison, Classification, Clustering
Author: Carles Hernandez-Ferrer [aut, cre], Juan R. Gonzalez [aut],
Xavier Escribà -Montagut [aut]
Maintainer: Xavier Escribà Montagut <xavier.escriba@isglobal.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rexposome
git_branch: RELEASE_3_13
git_last_commit: 8a968e3
git_last_commit_date: 2021-07-12
Date/Publication: 2021-07-13
source.ver: src/contrib/rexposome_1.14.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rexposome_1.14.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/rexposome_1.14.1.tgz
vignettes: vignettes/rexposome/inst/doc/exposome_data_analysis.html,
vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.html
vignetteTitles: Exposome Data Analysis, Dealing with Multiple
Imputations
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rexposome/inst/doc/exposome_data_analysis.R,
vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.R
importsMe: omicRexposome
suggestsMe: brgedata
dependencyCount: 157
Package: rfaRm
Version: 1.4.3
Imports: httr, stringi, rsvg, magick, data.table, Biostrings, utils,
rvest, xml2, IRanges, S4Vectors
Suggests: R4RNA, treeio, knitr, BiocStyle, rmarkdown, BiocGenerics
License: GPL-3
MD5sum: 049de8fb214ed5b5fde0d1f793d1f009
NeedsCompilation: no
Title: An R interface to the Rfam database
Description: rfaRm provides a client interface to the Rfam database of
RNA families. Data that can be retrieved include RNA families,
secondary structure images, covariance models, sequences within
each family, alignments leading to the identification of a
family and secondary structures in the dot-bracket format.
biocViews: FunctionalGenomics, DataImport, ThirdPartyClient,
Visualization, MultipleSequenceAlignment
Author: Lara Selles Vidal, Rafael Ayala, Guy-Bart Stan, Rodrigo
Ledesma-Amaro
Maintainer: Lara Selles Vidal <lara.selles@oist.jp>, Rafael Ayala
<rafael.ayala@oist.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rfaRm
git_branch: RELEASE_3_13
git_last_commit: f034172
git_last_commit_date: 2021-08-04
Date/Publication: 2021-08-05
source.ver: src/contrib/rfaRm_1.4.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rfaRm_1.4.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/rfaRm_1.4.3.tgz
vignettes: vignettes/rfaRm/inst/doc/rfaRm.html
vignetteTitles: rfaRm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rfaRm/inst/doc/rfaRm.R
dependencyCount: 48
Package: Rfastp
Version: 1.2.0
Imports: Rcpp, rjson, ggplot2, reshape2
LinkingTo: Rcpp, Rhtslib, zlibbioc
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: GPL-3 + file LICENSE
MD5sum: 180c823697b47e6fde3adaaca870f61c
NeedsCompilation: yes
Title: An Ultra-Fast and All-in-One Fastq Preprocessor (Quality
Control, Adapter, low quality and polyX trimming) and UMI
Sequence Parsing).
Description: Rfastp is an R wrapper of fastp developed in c++. fastp
performs quality control for fastq files. including low quality
bases trimming, polyX trimming, adapter auto-detection and
trimming, paired-end reads merging, UMI sequence/id handling.
Rfastp can concatenate multiple files into one file (like shell
command cat) and accept multiple files as input.
biocViews: QualityControl, Sequencing, Preprocessing, Software
Author: Wei Wang [aut] (<https://orcid.org/0000-0002-3216-7118>),
Ji-Dung Luo [ctb] (<https://orcid.org/0000-0003-0150-1440>),
Thomas Carroll [cre, aut]
(<https://orcid.org/0000-0002-0073-1714>)
Maintainer: Thomas Carroll <tc.infomatics@gmail.com>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rfastp
git_branch: RELEASE_3_13
git_last_commit: c35bc0b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rfastp_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rfastp_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rfastp_1.2.0.tgz
vignettes: vignettes/Rfastp/inst/doc/Rfastp.html
vignetteTitles: Rfastp
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rfastp/inst/doc/Rfastp.R
dependencyCount: 47
Package: rfPred
Version: 1.30.0
Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods,
parallel
Suggests: BiocStyle
License: GPL (>=2 )
MD5sum: c357f93b066edaf7d27cfe054bb761b5
NeedsCompilation: yes
Title: Assign rfPred functional prediction scores to a missense
variants list
Description: Based on external numerous data files where rfPred scores
are pre-calculated on all genomic positions of the human exome,
the package gives rfPred scores to missense variants identified
by the chromosome, the position (hg19 version), the referent
and alternative nucleotids and the uniprot identifier of the
protein. Note that for using the package, the user has to be
connected on the Internet or to download the TabixFile and
index (approximately 3.3 Go).
biocViews: Software, Annotation, Classification
Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais
Maintainer: Hugo Varet <varethugo@gmail.com>
URL: http://www.sbim.fr/rfPred
git_url: https://git.bioconductor.org/packages/rfPred
git_branch: RELEASE_3_13
git_last_commit: 1289b9d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rfPred_1.30.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/rfPred_1.30.0.tgz
vignettes: vignettes/rfPred/inst/doc/vignette.pdf
vignetteTitles: CalculatingrfPredscoreswithpackagerfPred
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rfPred/inst/doc/vignette.R
dependencyCount: 30
Package: rGADEM
Version: 2.40.0
Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo
Imports: Biostrings, GenomicRanges, methods, graphics, seqLogo
Suggests: BSgenome.Hsapiens.UCSC.hg19, rtracklayer
License: Artistic-2.0
MD5sum: 00d2cecbac2928f58586042f869b7204
NeedsCompilation: yes
Title: de novo motif discovery
Description: rGADEM is an efficient de novo motif discovery tool for
large-scale genomic sequence data. It is an open-source R
package, which is based on the GADEM software.
biocViews: Microarray, ChIPchip, Sequencing, ChIPSeq, MotifDiscovery
Author: Arnaud Droit, Raphael Gottardo, Gordon Robertson and Leiping Li
Maintainer: Arnaud Droit <arnaud.droit@crchuq.ulaval.ca>
git_url: https://git.bioconductor.org/packages/rGADEM
git_branch: RELEASE_3_13
git_last_commit: 3d0cf8b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rGADEM_2.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rGADEM_2.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rGADEM_2.40.0.tgz
vignettes: vignettes/rGADEM/inst/doc/rGADEM.pdf
vignetteTitles: The rGADEM users guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rGADEM/inst/doc/rGADEM.R
importsMe: TCGAWorkflow
dependencyCount: 46
Package: RGalaxy
Version: 1.36.0
Depends: XML, methods, tools, optparse
Imports: BiocGenerics, Biobase, roxygen2
Suggests: RUnit, hgu95av2.db, AnnotationDbi, knitr, formatR, Rserve
Enhances: RSclient
License: Artistic-2.0
MD5sum: ede1a992a6e921881c2cd487352349ce
NeedsCompilation: no
Title: Make an R function available in the Galaxy web platform
Description: Given an R function and its manual page, make the
documented function available in Galaxy.
biocViews: Infrastructure
Author: Dan Tenenbaum
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RGalaxy
git_branch: RELEASE_3_13
git_last_commit: 0fa126d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RGalaxy_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RGalaxy_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RGalaxy_1.36.0.tgz
vignettes: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html
vignetteTitles: Introduction to RGalaxy
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.R
dependencyCount: 37
Package: Rgin
Version: 1.12.0
Depends: R (>= 3.5)
LinkingTo: RcppEigen (>= 0.3.3.5.0)
Suggests: knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 4f3a8e0f045f6b63d3d988f148b12ee8
NeedsCompilation: yes
Title: gin in R
Description: C++ implementation of SConES.
biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability,
Genetics, FeatureExtraction, GraphAndNetwork, Network
Author: Hector Climente-Gonzalez [aut, cre], Dominik Gerhard Grimm
[aut], Chloe-Agathe Azencott [aut]
Maintainer: Hector Climente-Gonzalez <hector.climente@curie.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rgin
git_branch: RELEASE_3_13
git_last_commit: 71ce57c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rgin_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rgin_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rgin_1.12.0.tgz
vignettes: vignettes/Rgin/inst/doc/Rgin-UsingCppLibraries.html
vignetteTitles: Using Rgin C++ libraries
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependencyCount: 10
Package: RGMQL
Version: 1.12.4
Depends: R(>= 3.4.2), RGMQLlib
Imports: httr, rJava, GenomicRanges, rtracklayer, data.table, utils,
plyr, xml2, methods, S4Vectors, dplyr, stats, glue,
BiocGenerics
Suggests: BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 9e913b52bd31aa4f16c7c3c749ca3e1d
NeedsCompilation: no
Title: GenoMetric Query Language for R/Bioconductor
Description: This package brings the GenoMetric Query Language (GMQL)
functionalities into the R environment. GMQL is a high-level,
declarative language to manage heterogeneous genomic datasets
for biomedical purposes, using simple queries to process
genomic regions and their metadata and properties. GMQL adopts
algorithms efficiently designed for big data using
cloud-computing technologies (like Apache Hadoop and Spark)
allowing GMQL to run on modern infrastructures, in order to
achieve scalability and high performance. It allows to create,
manipulate and extract genomic data from different data sources
both locally and remotely. Our RGMQL functions allow complex
queries and processing leveraging on the R idiomatic paradigm.
The RGMQL package also provides a rich set of ancillary classes
that allow sophisticated input/output management and sorting,
such as: ASC, DESC, BAG, MIN, MAX, SUM, AVG, MEDIAN, STD, Q1,
Q2, Q3 (and many others). Note that many RGMQL functions are
not directly executed in R environment, but are deferred until
real execution is issued.
biocViews: Software, Infrastructure, DataImport, Network,
ImmunoOncology, SingleCell
Author: Simone Pallotta [aut, cre], Marco Masseroli [aut]
Maintainer: Simone Pallotta <simonepallotta@hotmail.com>
URL: http://www.bioinformatics.deib.polimi.it/genomic_computing/GMQL/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RGMQL
git_branch: RELEASE_3_13
git_last_commit: b816075
git_last_commit_date: 2021-07-19
Date/Publication: 2021-07-20
source.ver: src/contrib/RGMQL_1.12.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RGMQL_1.12.4.zip
vignettes: vignettes/RGMQL/inst/doc/RGMQL-vignette.html
vignetteTitles: RGMQL: GenoMetric Query Language for R/Bioconductor
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RGMQL/inst/doc/RGMQL-vignette.R
dependencyCount: 74
Package: RGraph2js
Version: 1.20.0
Imports: utils, whisker, rjson, digest, graph
Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna
License: GPL-2
MD5sum: 76681245f32e67085cc8c4488b5ef382
NeedsCompilation: no
Title: Convert a Graph into a D3js Script
Description: Generator of web pages which display interactive
network/graph visualizations with D3js, jQuery and Raphael.
biocViews: Visualization, Network, GraphAndNetwork, ThirdPartyClient
Author: Stephane Cano [aut, cre], Sylvain Gubian [aut], Florian Martin
[aut]
Maintainer: Stephane Cano <DL.RSupport@pmi.com>
SystemRequirements: jQuery, jQueryUI, qTip2, D3js and Raphael are
required Javascript libraries made available via the online
CDNJS service (http://cdnjs.cloudflare.com).
git_url: https://git.bioconductor.org/packages/RGraph2js
git_branch: RELEASE_3_13
git_last_commit: 3a5e683
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RGraph2js_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RGraph2js_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RGraph2js_1.20.0.tgz
vignettes: vignettes/RGraph2js/inst/doc/RGraph2js.pdf
vignetteTitles: RGraph2js
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RGraph2js/inst/doc/RGraph2js.R
dependencyCount: 11
Package: Rgraphviz
Version: 2.36.0
Depends: R (>= 2.6.0), methods, utils, graph, grid
Imports: stats4, graphics, grDevices
Suggests: RUnit, BiocGenerics, XML
License: EPL
Archs: i386, x64
MD5sum: 4c108dc2322a36b751bc9ecbfd79b715
NeedsCompilation: yes
Title: Provides plotting capabilities for R graph objects
Description: Interfaces R with the AT and T graphviz library for
plotting R graph objects from the graph package.
biocViews: GraphAndNetwork, Visualization
Author: Kasper Daniel Hansen [cre, aut], Jeff Gentry [aut], Li Long
[aut], Robert Gentleman [aut], Seth Falcon [aut], Florian Hahne
[aut], Deepayan Sarkar [aut]
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
SystemRequirements: optionally Graphviz (>= 2.16)
git_url: https://git.bioconductor.org/packages/Rgraphviz
git_branch: RELEASE_3_13
git_last_commit: 1ea05ef
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rgraphviz_2.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rgraphviz_2.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rgraphviz_2.36.0.tgz
vignettes: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.pdf,
vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf
vignetteTitles: A New Interface to Plot Graphs Using Rgraphviz, How To
Plot A Graph Using Rgraphviz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.R,
vignettes/Rgraphviz/inst/doc/Rgraphviz.R
dependsOnMe: biocGraph, BioMVCClass, CellNOptR, flowCL, MineICA,
netresponse, paircompviz, pathRender, ROntoTools,
SplicingGraphs, TDARACNE, maEndToEnd, abn, dlsem, geneNetBP,
gridGraphviz, GUIProfiler, hasseDiagram
importsMe: apComplex, biocGraph, BiocOncoTK, bnem, chimeraviz, CompGO,
CytoML, dce, DEGraph, EnrichmentBrowser, flowWorkspace,
GeneNetworkBuilder, GOstats, hyperdraw, KEGGgraph, MIGSA,
mirIntegrator, mnem, OncoSimulR, ontoProc, paircompviz,
pathview, Pigengene, qpgraph, SplicingGraphs, trackViewer,
TRONCO, BiDAG, bnpa, ceg, CePa, classGraph, cogmapr, dnet,
gRain, gRbase, gRim, hmma, hpoPlot, maGUI, MetaClean,
ontologyPlot, SEMgraph, stablespec, wiseR
suggestsMe: a4, altcdfenvs, annotate, Category, CNORfeeder, CNORfuzzy,
DEGraph, flowCore, geneplotter, GlobalAncova, globaltest,
GSEABase, MLP, NCIgraph, pkgDepTools, RBGL, RBioinf,
rBiopaxParser, RpsiXML, Rtreemix, safe, SPIA, SRAdb, Streamer,
topGO, ViSEAGO, vtpnet, NCIgraphData, SNAData, arulesViz,
BayesNetBP, bnclassify, bnlearn, bnstruct, bsub, ChoR,
CodeDepends, gbutils, GeneNet, gRc, HEMDAG, iTOP, kpcalg, kst,
lava, loon, MCDA, msSurv, multiplex, ParallelPC, pcalg, psych,
relations, rEMM, rPref, RSeed, SCCI, sisal, SourceSet,
textplot, tm, topologyGSA, unifDAG, zenplots
dependencyCount: 10
Package: rGREAT
Version: 1.24.0
Depends: R (>= 3.1.2), GenomicRanges, IRanges, methods
Imports: rjson, GetoptLong (>= 0.0.9), RCurl, utils, stats
Suggests: testthat (>= 0.3), knitr, circlize (>= 0.4.8), rmarkdown
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: b556d14d66cb85e7b56e1685eaa08fff
NeedsCompilation: no
Title: Client for GREAT Analysis
Description: This package makes GREAT (Genomic Regions Enrichment of
Annotations Tool) analysis automatic by constructing a HTTP
POST request according to user's input and automatically
retrieving results from GREAT web server.
biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing,
WholeGenome, GenomeAnnotation, Coverage
Author: Zuguang Gu
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/rGREAT,
http://great.stanford.edu/public/html/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rGREAT
git_branch: RELEASE_3_13
git_last_commit: d3aad8c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rGREAT_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rGREAT_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rGREAT_1.24.0.tgz
vignettes: vignettes/rGREAT/inst/doc/rGREAT.html
vignetteTitles: Analyze with GREAT
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rGREAT/inst/doc/rGREAT.R
suggestsMe: TADCompare
dependencyCount: 22
Package: RGSEA
Version: 1.26.0
Depends: R(>= 2.10.0)
Imports: BiocGenerics
Suggests: BiocStyle, GEOquery, knitr, RUnit
License: GPL(>=3)
MD5sum: 9d77540acb31e08246934f20314ed7d4
NeedsCompilation: no
Title: Random Gene Set Enrichment Analysis
Description: Combining bootstrap aggregating and Gene set enrichment
analysis (GSEA), RGSEA is a classfication algorithm with high
robustness and no over-fitting problem. It performs well
especially for the data generated from different exprements.
biocViews: GeneSetEnrichment, StatisticalMethod, Classification
Author: Chengcheng Ma
Maintainer: Chengcheng Ma <ccma@sibs.ac.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RGSEA
git_branch: RELEASE_3_13
git_last_commit: 8c2b030
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RGSEA_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RGSEA_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RGSEA_1.26.0.tgz
vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf
vignetteTitles: Introduction to RGSEA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RGSEA/inst/doc/RGSEA.R
dependencyCount: 6
Package: rgsepd
Version: 1.24.0
Depends: R (>= 4.0.0), DESeq2, goseq (>= 1.28)
Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment,
hash, AnnotationDbi
Suggests: boot, tools, BiocGenerics, knitr, xtable
License: GPL-3
Archs: i386, x64
MD5sum: 9c6d537dc167ad2f8a2afd13c4a7a689
NeedsCompilation: no
Title: Gene Set Enrichment / Projection Displays
Description: R/GSEPD is a bioinformatics package for R to help
disambiguate transcriptome samples (a matrix of RNA-Seq counts
at transcript IDs) by automating differential expression (with
DESeq2), then gene set enrichment (with GOSeq), and finally a
N-dimensional projection to quantify in which ways each sample
is like either treatment group.
biocViews: ImmunoOncology, Software, DifferentialExpression,
GeneSetEnrichment, RNASeq
Author: Karl Stamm
Maintainer: Karl Stamm <karl.stamm@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rgsepd
git_branch: RELEASE_3_13
git_last_commit: a30ee67
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rgsepd_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rgsepd_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rgsepd_1.24.0.tgz
vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf
vignetteTitles: An Introduction to the rgsepd package
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R
dependencyCount: 128
Package: rhdf5
Version: 2.36.0
Depends: R (>= 4.0.0), methods
Imports: Rhdf5lib (>= 1.13.4), rhdf5filters
LinkingTo: Rhdf5lib
Suggests: bit64, BiocStyle, knitr, rmarkdown, testthat, microbenchmark,
dplyr, ggplot2
License: Artistic-2.0
MD5sum: 05cfc23c6275c019e9aba5ba37325adc
NeedsCompilation: yes
Title: R Interface to HDF5
Description: This package provides an interface between HDF5 and R.
HDF5's main features are the ability to store and access very
large and/or complex datasets and a wide variety of metadata on
mass storage (disk) through a completely portable file format.
The rhdf5 package is thus suited for the exchange of large
and/or complex datasets between R and other software package,
and for letting R applications work on datasets that are larger
than the available RAM.
biocViews: Infrastructure, DataImport
Author: Bernd Fischer [aut], Mike Smith [aut, cre]
(<https://orcid.org/0000-0002-7800-3848>), Gregoire Pau [aut],
Martin Morgan [ctb], Daniel van Twisk [ctb]
Maintainer: Mike Smith <mike.smith@embl.de>
URL: https://github.com/grimbough/rhdf5
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/grimbough/rhdf5/issues
git_url: https://git.bioconductor.org/packages/rhdf5
git_branch: RELEASE_3_13
git_last_commit: 4dc527f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rhdf5_2.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rhdf5_2.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rhdf5_2.36.0.tgz
vignettes: vignettes/rhdf5/inst/doc/practical_tips.html,
vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.html,
vignettes/rhdf5/inst/doc/rhdf5.html
vignetteTitles: rhdf5 Practical Tips, Reading HDF5 Files In The Cloud,
rhdf5 - HDF5 interface for R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rhdf5/inst/doc/practical_tips.R,
vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.R,
vignettes/rhdf5/inst/doc/rhdf5.R
dependsOnMe: GenoGAM, GSCA, HDF5Array, HiCBricks, LoomExperiment
importsMe: BayesSpace, BgeeCall, biomformat, bnbc, bsseq, CiteFuse,
cmapR, CoGAPS, CopyNumberPlots, cTRAP, cytomapper, diffHic,
DropletUtils, epigraHMM, EventPointer, FRASER, GenomicScores,
gep2pep, h5vc, HiCcompare, IONiseR, MOFA2, phantasus, ptairMS,
PureCN, recountmethylation, ribor, scCB2, scone,
signatureSearch, slinky, trackViewer, MafH5.gnomAD.r3.0.GRCh38,
MafH5.gnomAD.v3.1.1.GRCh38, DmelSGI, MethylSeqData, ptairData,
signatureSearchData, bioRad, file2meco, NEONiso, ondisc, smapr
suggestsMe: edgeR, rhdf5filters, slalom, Spectra, SummarizedExperiment,
tximport, zellkonverter, antaresProcessing, antaresRead,
antaresViz, conos, digitalDLSorteR, hadron, io, isoreader,
MplusAutomation, neonstore, neonUtilities, rbiom, SignacX
dependencyCount: 3
Package: rhdf5client
Version: 1.14.2
Depends: R (>= 3.6), methods, DelayedArray
Imports: S4Vectors, httr, R6, rjson, utils
Suggests: knitr, testthat, BiocStyle, DT, reticulate, rmarkdown
License: Artistic-2.0
MD5sum: 15ca37c93e1c5b12bdbff7ba950d1268
NeedsCompilation: yes
Title: Access HDF5 content from h5serv
Description: Provides functionality for reading data from h5serv server
from within R.
biocViews: DataImport, Software
Author: Samuela Pollack [aut], Shweta Gopaulakrishnan [aut], Vincent
Carey [cre, aut]
Maintainer: Vincent Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rhdf5client
git_branch: RELEASE_3_13
git_last_commit: a24e7d8
git_last_commit_date: 2021-06-23
Date/Publication: 2021-06-24
source.ver: src/contrib/rhdf5client_1.14.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rhdf5client_1.14.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/rhdf5client_1.14.2.tgz
vignettes: vignettes/rhdf5client/inst/doc/delayed-array.html
vignetteTitles: HSDSArray DelayedArray backend
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rhdf5client/inst/doc/delayed-array.R
importsMe: restfulSE
suggestsMe: BiocOncoTK, HumanTranscriptomeCompendium
dependencyCount: 26
Package: rhdf5filters
Version: 1.4.0
LinkingTo: Rhdf5lib
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), rhdf5 (>=
2.34.0)
License: BSD_2_clause + file LICENSE
Archs: i386, x64
MD5sum: 14ad76dc3ecffedd8d273cb2b0836e6b
NeedsCompilation: yes
Title: HDF5 Compression Filters
Description: Provides a collection of compression filters for use with
HDF5 datasets.
biocViews: Infrastructure, DataImport
Author: Mike Smith [aut, cre] (<https://orcid.org/0000-0002-7800-3848>)
Maintainer: Mike Smith <grimbough@gmail.com>
URL: https://github.com/grimbough/rhdf5filters
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/grimbough/rhdf5filters
git_url: https://git.bioconductor.org/packages/rhdf5filters
git_branch: RELEASE_3_13
git_last_commit: c55c70e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rhdf5filters_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rhdf5filters_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rhdf5filters_1.4.0.tgz
vignettes: vignettes/rhdf5filters/inst/doc/rhdf5filters.html
vignetteTitles: HDF5 Compression Filters
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rhdf5filters/inst/doc/rhdf5filters.R
importsMe: HDF5Array, rhdf5
dependencyCount: 1
Package: Rhdf5lib
Version: 1.14.2
Depends: R (>= 4.0.0)
Suggests: BiocStyle, knitr, rmarkdown, tinytest, mockery
License: Artistic-2.0
MD5sum: 1778d6ec886c02ea2be4c33c824f4469
NeedsCompilation: yes
Title: hdf5 library as an R package
Description: Provides C and C++ hdf5 libraries.
biocViews: Infrastructure
Author: Mike Smith [ctb, cre]
(<https://orcid.org/0000-0002-7800-3848>), The HDF Group [cph]
Maintainer: Mike Smith <grimbough@gmail.com>
URL: https://github.com/grimbough/Rhdf5lib
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/grimbough/Rhdf5lib
git_url: https://git.bioconductor.org/packages/Rhdf5lib
git_branch: RELEASE_3_13
git_last_commit: 500fdf6
git_last_commit_date: 2021-07-05
Date/Publication: 2021-07-06
source.ver: src/contrib/Rhdf5lib_1.14.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rhdf5lib_1.14.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rhdf5lib_1.14.2.tgz
vignettes: vignettes/Rhdf5lib/inst/doc/downloadHDF5.html,
vignettes/Rhdf5lib/inst/doc/Rhdf5lib.html
vignetteTitles: Creating this HDF5 distribution, Linking to Rhdf5lib
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rhdf5lib/inst/doc/downloadHDF5.R,
vignettes/Rhdf5lib/inst/doc/Rhdf5lib.R
importsMe: epigraHMM, rhdf5
suggestsMe: mbkmeans
linksToMe: CytoML, DropletUtils, epigraHMM, HDF5Array, mbkmeans, mzR,
ncdfFlow, rhdf5, rhdf5filters, ondisc
dependencyCount: 0
Package: Rhisat2
Version: 1.8.0
Depends: R (>= 3.6)
Imports: GenomicFeatures, SGSeq, GenomicRanges, methods, utils
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 1ee04280788332000d59337b9e53a021
NeedsCompilation: yes
Title: R Wrapper for HISAT2 Aligner
Description: An R interface to the HISAT2 spliced short-read aligner by
Kim et al. (2015). The package contains wrapper functions to
create a genome index and to perform the read alignment to the
generated index.
biocViews: Alignment, Sequencing, SplicedAlignment
Author: Charlotte Soneson [aut, cre]
(<https://orcid.org/0000-0003-3833-2169>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/fmicompbio/Rhisat2
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/fmicompbio/Rhisat2/issues
git_url: https://git.bioconductor.org/packages/Rhisat2
git_branch: RELEASE_3_13
git_last_commit: d0f4299
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rhisat2_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rhisat2_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rhisat2_1.8.0.tgz
vignettes: vignettes/Rhisat2/inst/doc/Rhisat2.html
vignetteTitles: Rhisat2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rhisat2/inst/doc/Rhisat2.R
suggestsMe: QuasR
dependencyCount: 99
Package: Rhtslib
Version: 1.24.0
Imports: zlibbioc
LinkingTo: zlibbioc
Suggests: BiocStyle, knitr
License: LGPL (>= 2)
MD5sum: 07b6e66499a3f97e30404b6950381249
NeedsCompilation: yes
Title: HTSlib high-throughput sequencing library as an R package
Description: This package provides version 1.7 of the 'HTSlib' C
library for high-throughput sequence analysis. The package is
primarily useful to developers of other R packages who wish to
make use of HTSlib. Motivation and instructions for use of this
package are in the vignette, vignette(package="Rhtslib",
"Rhtslib").
biocViews: DataImport, Sequencing
Author: Nathaniel Hayden [led, aut], Martin Morgan [aut], Bioconductor
Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/Rhtslib, http://www.htslib.org/
SystemRequirements: libbz2 & liblzma & libcurl (with header files), GNU
make
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/Rhtslib/issues
git_url: https://git.bioconductor.org/packages/Rhtslib
git_branch: RELEASE_3_13
git_last_commit: 28051cc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rhtslib_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rhtslib_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rhtslib_1.24.0.tgz
vignettes: vignettes/Rhtslib/inst/doc/Rhtslib.html
vignetteTitles: Motivation and use of Rhtslib
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R
importsMe: deepSNV, diffHic, maftools, scPipe
linksToMe: ArrayExpressHTS, bamsignals, BitSeq, csaw, deepSNV,
DiffBind, diffHic, h5vc, maftools, methylKit, podkat, qrqc,
QuasR, Rfastp, Rsamtools, scPipe, seqbias, ShortRead,
TransView, VariantAnnotation, jackalope
dependencyCount: 1
Package: RiboDiPA
Version: 1.0.0
Depends: R (>= 4.1), Rsamtools, GenomicFeatures, GenomicAlignments
Imports: Rcpp (>= 1.0.2), graphics, stats, data.table, elitism,
methods, S4Vectors, IRanges, GenomicRanges, matrixStats,
reldist, doParallel, foreach, parallel, qvalue, DESeq2,
ggplot2, BiocFileCache
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
License: LGPL (>= 3)
Archs: i386, x64
MD5sum: 4e200b83d08d7a201a629d40d42e1665
NeedsCompilation: yes
Title: Differential pattern analysis for Ribo-seq data
Description: This package performs differential pattern analysis for
Ribo-seq data. It identifies genes with significantly different
patterns in the ribosome footprint between two conditions.
RiboDiPA contains five major components including bam file
processing, P-site mapping, data binning, differential pattern
analysis and footprint visualization.
biocViews: RiboSeq, GeneExpression, GeneRegulation,
DifferentialExpression, Sequencing, Coverage, Alignment,
RNASeq, ImmunoOncology, QualityControl, DataImport, Software,
Normalization
Author: Keren Li [aut], Matt Hope [aut], Xiaozhong Wang [aut], Ji-Ping
Wang [aut, cre]
Maintainer: Ji-Ping Wang <jzwang@northwestern.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RiboDiPA
git_branch: RELEASE_3_13
git_last_commit: a39b378
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RiboDiPA_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RiboDiPA_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RiboDiPA_1.0.0.tgz
vignettes: vignettes/RiboDiPA/inst/doc/RiboDiPA.html
vignetteTitles: RiboDiPA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RiboDiPA/inst/doc/RiboDiPA.R
dependencyCount: 149
Package: RiboProfiling
Version: 1.22.0
Depends: R (>= 3.2.2), Biostrings
Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, IRanges, reshape2,
GenomicFeatures, grid, plyr, S4Vectors, GenomicAlignments,
ggplot2, ggbio, Rsamtools, rtracklayer, data.table, sqldf
Suggests: knitr, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene,
BSgenome.Hsapiens.UCSC.hg19, testthat, SummarizedExperiment
License: GPL-3
Archs: i386, x64
MD5sum: 698e3608e00736a4587d3416ca957c90
NeedsCompilation: no
Title: Ribosome Profiling Data Analysis: from BAM to Data
Representation and Interpretation
Description: Starting with a BAM file, this package provides the
necessary functions for quality assessment, read start position
recalibration, the counting of reads on CDS, 3'UTR, and 5'UTR,
plotting of count data: pairs, log fold-change, codon frequency
and coverage assessment, principal component analysis on codon
coverage.
biocViews: RiboSeq, Sequencing, Coverage, Alignment, QualityControl,
Software, PrincipalComponent
Author: Alexandra Popa
Maintainer: A. Popa <alexandra.mariela.popa@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RiboProfiling
git_branch: RELEASE_3_13
git_last_commit: a315cdd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RiboProfiling_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RiboProfiling_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RiboProfiling_1.22.0.tgz
vignettes: vignettes/RiboProfiling/inst/doc/RiboProfiling.pdf
vignetteTitles: Analysing Ribo-Seq data with the "RiboProfiling"
package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RiboProfiling/inst/doc/RiboProfiling.R
dependencyCount: 157
Package: ribor
Version: 1.4.0
Depends: R (>= 3.6.0)
Imports: dplyr, ggplot2, hash, methods, rhdf5, rlang, stats, S4Vectors,
tidyr, tools, yaml
Suggests: testthat, knitr, rmarkdown
License: GPL-3
Archs: i386, x64
MD5sum: 24898cae5c4bdc309055eb6ba2fa57d9
NeedsCompilation: no
Title: An R Interface for Ribo Files
Description: The ribor package provides an R Interface for .ribo files.
It provides functionality to read the .ribo file, which is of
HDF5 format, and performs common analyses on its contents.
biocViews: Software, Infrastructure
Author: Michael Geng [cre, aut], Hakan Ozadam [aut], Can Cenik [aut]
Maintainer: Michael Geng <michaelgeng@utexas.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ribor
git_branch: RELEASE_3_13
git_last_commit: ca9bd2a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ribor_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ribor_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ribor_1.4.0.tgz
vignettes: vignettes/ribor/inst/doc/ribor.html
vignetteTitles: A Walkthrough of RiboR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ribor/inst/doc/ribor.R
dependencyCount: 54
Package: riboSeqR
Version: 1.26.0
Depends: R (>= 3.0.2), methods, GenomicRanges, abind
Imports: Rsamtools, IRanges, baySeq, GenomeInfoDb, seqLogo
Suggests: BiocStyle, RUnit, BiocGenerics
License: GPL-3
Archs: i386, x64
MD5sum: f909b5445ef6129533f3b63966527990
NeedsCompilation: no
Title: Analysis of sequencing data from ribosome profiling experiments
Description: Plotting functions, frameshift detection and parsing of
sequencing data from ribosome profiling experiments.
biocViews: Sequencing,Genetics,Visualization,RiboSeq
Author: Thomas J. Hardcastle
Maintainer: Thomas J. Hardcastle <tjh48@cam.ac.uk>
git_url: https://git.bioconductor.org/packages/riboSeqR
git_branch: RELEASE_3_13
git_last_commit: 8c2c4ed
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/riboSeqR_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/riboSeqR_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/riboSeqR_1.26.0.tgz
vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf
vignetteTitles: riboSeqR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/riboSeqR/inst/doc/riboSeqR.R
dependencyCount: 38
Package: ribosomeProfilingQC
Version: 1.4.0
Depends: R (>= 4.0), GenomicRanges
Imports: AnnotationDbi, BiocGenerics, Biostrings, BSgenome, EDASeq,
GenomicAlignments, GenomicFeatures, GenomeInfoDb, IRanges,
methods, motifStack, rtracklayer, Rsamtools, RUVSeq, Rsubread,
S4Vectors, XVector, ggplot2, ggfittext, scales, ggrepel, utils,
cluster, stats, graphics, grid
Suggests: RUnit, BiocStyle, knitr, BSgenome.Drerio.UCSC.danRer10,
edgeR, limma, testthat, rmarkdown
License: GPL (>=3) + file LICENSE
Archs: x64
MD5sum: 5a53b4ef437403860a445b0a27d63806
NeedsCompilation: no
Title: Ribosome Profiling Quality Control
Description: Ribo-Seq (also named ribosome profiling or footprinting)
measures translatome (unlike RNA-Seq, which sequences the
transcriptome) by direct quantification of the
ribosome-protected fragments (RPFs). This package provides the
tools for quality assessment of ribosome profiling. In
addition, it can preprocess Ribo-Seq data for subsequent
differential analysis.
biocViews: RiboSeq, Sequencing, GeneRegulation, QualityControl,
Visualization, Coverage
Author: Jianhong Ou [aut, cre]
(<https://orcid.org/0000-0002-8652-2488>), Mariah Hoye [aut]
Maintainer: Jianhong Ou <jianhong.ou@duke.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ribosomeProfilingQC
git_branch: RELEASE_3_13
git_last_commit: 482e0e7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ribosomeProfilingQC_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ribosomeProfilingQC_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ribosomeProfilingQC_1.4.0.tgz
vignettes:
vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.html
vignetteTitles: ribosomeProfilingQC Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.R
dependencyCount: 137
Package: RImmPort
Version: 1.20.0
Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools,
utils, RSQLite
Suggests: knitr
License: GPL-3
Archs: i386, x64
MD5sum: 85d863549a5ae08a7449e79716412daf
NeedsCompilation: no
Title: RImmPort: Enabling Ready-for-analysis Immunology Research Data
Description: The RImmPort package simplifies access to ImmPort data for
analysis in the R environment. It provides a standards-based
interface to the ImmPort study data that is in a proprietary
format.
biocViews: BiomedicalInformatics, DataImport, DataRepresentation
Author: Ravi Shankar <rshankar@stanford.edu>
Maintainer: Zicheng Hu <Zicheng.Hu@ucsf.edu>, Ravi Shankar
<rshankar@stanford.edu>
URL: http://bioconductor.org/packages/RImmPort/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RImmPort
git_branch: RELEASE_3_13
git_last_commit: 1565e15
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RImmPort_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RImmPort_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RImmPort_1.20.0.tgz
vignettes: vignettes/RImmPort/inst/doc/RImmPort_Article.pdf,
vignettes/RImmPort/inst/doc/RImmPort_QuickStart.pdf
vignetteTitles: RImmPort: Enabling ready-for-analysis immunology
research data, RImmPort: Quick Start Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RImmPort/inst/doc/RImmPort_Article.R,
vignettes/RImmPort/inst/doc/RImmPort_QuickStart.R
dependencyCount: 43
Package: Ringo
Version: 1.56.0
Depends: methods, Biobase (>= 1.14.1), RColorBrewer, limma, Matrix,
grid, lattice
Imports: BiocGenerics (>= 0.1.11), genefilter, limma, vsn, stats4
Suggests: rtracklayer (>= 1.3.1), mclust, topGO (>= 1.15.0)
License: Artistic-2.0
Archs: i386, x64
MD5sum: 6c8786653858874d0391bf885321ccb4
NeedsCompilation: yes
Title: R Investigation of ChIP-chip Oligoarrays
Description: The package Ringo facilitates the primary analysis of
ChIP-chip data. The main functionalities of the package are
data read-in, quality assessment, data visualisation and
identification of genomic regions showing enrichment in
ChIP-chip. The package has functions to deal with two-color
oligonucleotide microarrays from NimbleGen used in ChIP-chip
projects, but also contains more general functions for
ChIP-chip data analysis, given that the data is supplied as
RGList (raw) or ExpressionSet (pre- processed). The package
employs functions from various other packages of the
Bioconductor project and provides additional ChIP-chip-specific
and NimbleGen-specific functionalities.
biocViews:
Microarray,TwoChannel,DataImport,QualityControl,Preprocessing
Author: Joern Toedling, Oleg Sklyar, Tammo Krueger, Matt Ritchie,
Wolfgang Huber
Maintainer: J. Toedling <jtoedling@yahoo.de>
git_url: https://git.bioconductor.org/packages/Ringo
git_branch: RELEASE_3_13
git_last_commit: 3da76e3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Ringo_1.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Ringo_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Ringo_1.56.0.tgz
vignettes: vignettes/Ringo/inst/doc/Ringo.pdf
vignetteTitles: R Investigation of NimbleGen Oligoarrays
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Ringo/inst/doc/Ringo.R
dependsOnMe: SimBindProfiles, ccTutorial
importsMe: Repitools
dependencyCount: 83
Package: RIPAT
Version: 1.2.0
Depends: R (>= 4.0)
Imports: biomaRt (>= 2.38.0), GenomicRanges (>= 1.34.0), ggplot2 (>=
3.1.0), grDevices (>= 3.5.3), IRanges (>= 2.16.0), karyoploteR
(>= 1.6.3), openxlsx (>= 4.1.4), plyr (>= 1.8.4), regioneR (>=
1.12.0), rtracklayer (>= 1.42.2), stats (>= 3.5.3), stringr (>=
1.3.1), utils (>= 3.5.3)
Suggests: knitr (>= 1.28)
License: Artistic-2.0
MD5sum: 686e26b9b385bd45439e6786d9c943ae
NeedsCompilation: no
Title: Retroviral Integration Pattern Analysis Tool (RIPAT)
Description: RIPAT is developed as an R package for retroviral
integration sites annotation and distribution analysis. RIPAT
needs local alignment results from BLAST and BLAT. Specific
input format is depicted in RIPAT manual. RIPAT provides RV
integration pattern analysis result as forms of R objects,
excel file with multiple sheets and plots.
biocViews: Annotation
Author: Min-Jeong Baek [aut, cre]
Maintainer: Min-Jeong Baek <mjbaek16@korea.ac.kr>
URL: https://github.com/bioinfo16/RIPAT/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RIPAT
git_branch: RELEASE_3_13
git_last_commit: 1c308b1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RIPAT_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RIPAT_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RIPAT_1.2.0.tgz
vignettes: vignettes/RIPAT/inst/doc/RIPAT_manual_v0.99.8.html
vignetteTitles: RIPAT : Retroviral Integration Pattern Analysis Tool
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RIPAT/inst/doc/RIPAT_manual_v0.99.8.R
dependencyCount: 148
Package: Risa
Version: 1.34.0
Depends: R (>= 2.0.9), Biobase (>= 2.4.0), methods, Rcpp (>= 0.9.13),
biocViews, affy
Imports: xcms
Suggests: faahKO (>= 1.2.11)
License: LGPL
MD5sum: 180f218cd1f7ebe26f091723686a667b
NeedsCompilation: no
Title: Converting experimental metadata from ISA-tab into Bioconductor
data structures
Description: The Investigation / Study / Assay (ISA) tab-delimited
format is a general purpose framework with which to collect and
communicate complex metadata (i.e. sample characteristics,
technologies used, type of measurements made) from experiments
employing a combination of technologies, spanning from
traditional approaches to high-throughput techniques. Risa
allows to access metadata/data in ISA-Tab format and build
Bioconductor data structures. Currently, data generated from
microarray, flow cytometry and metabolomics-based (i.e. mass
spectrometry) assays are supported. The package is extendable
and efforts are undergoing to support metadata associated to
proteomics assays.
biocViews: Annotation, DataImport, MassSpectrometry
Author: Alejandra Gonzalez-Beltran, Audrey Kauffmann, Steffen Neumann,
Gabriella Rustici, ISA Team
Maintainer: Alejandra Gonzalez-Beltran
<alejandra.gonzalez.beltran@gmail.com>
URL: http://www.isa-tools.org/
BugReports: https://github.com/ISA-tools/Risa/issues
git_url: https://git.bioconductor.org/packages/Risa
git_branch: RELEASE_3_13
git_last_commit: 681a687
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Risa_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Risa_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Risa_1.34.0.tgz
vignettes: vignettes/Risa/inst/doc/Risa.pdf
vignetteTitles: Risa: converts experimental metadata from ISA-tab into
Bioconductor data structures
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Risa/inst/doc/Risa.R
suggestsMe: mtbls2
dependencyCount: 98
Package: RITAN
Version: 1.16.0
Depends: R (>= 3.4),
Imports: graphics, stats, utils, grid, gridExtra, reshape2, gplots,
ggplot2, plotrix, RColorBrewer, STRINGdb, MCL, linkcomm,
dynamicTreeCut, gsubfn, hash, png, sqldf, igraph, BgeeDB,
knitr, RITANdata
Suggests: rmarkdown
License: file LICENSE
MD5sum: af5bb336831a4ccfd4fb8b4e0b0c2e0b
NeedsCompilation: no
Title: Rapid Integration of Term Annotation and Network resources
Description: Tools for comprehensive gene set enrichment and extraction
of multi-resource high confidence subnetworks. RITAN
facilitates bioinformatic tasks for enabling network biology
research.
biocViews: QualityControl, Network, NetworkEnrichment,
NetworkInference, GeneSetEnrichment, FunctionalGenomics
Author: Michael Zimmermann
Maintainer: Michael Zimmermann <mtzimmermann@mcw.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RITAN
git_branch: RELEASE_3_13
git_last_commit: ded8612
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RITAN_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RITAN_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RITAN_1.16.0.tgz
vignettes: vignettes/RITAN/inst/doc/choosing_resources.html,
vignettes/RITAN/inst/doc/enrichment.html,
vignettes/RITAN/inst/doc/multi_tissue_analysis.html,
vignettes/RITAN/inst/doc/resource_relationships.html,
vignettes/RITAN/inst/doc/subnetworks.html
vignetteTitles: Choosing Resources, Enrichment Vignette, Multi-Tissue
Analysis, Relationships Among Resources, Network Biology
Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RITAN/inst/doc/choosing_resources.R,
vignettes/RITAN/inst/doc/enrichment.R,
vignettes/RITAN/inst/doc/multi_tissue_analysis.R,
vignettes/RITAN/inst/doc/resource_relationships.R,
vignettes/RITAN/inst/doc/subnetworks.R
dependencyCount: 115
Package: RIVER
Version: 1.16.0
Depends: R (>= 3.3.2)
Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods,
utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools
License: GPL (>= 2)
MD5sum: b5a073c98aa199f46c2a11e6da49e6e0
NeedsCompilation: no
Title: R package for RIVER (RNA-Informed Variant Effect on Regulation)
Description: An implementation of a probabilistic modeling framework
that jointly analyzes personal genome and transcriptome data to
estimate the probability that a variant has regulatory impact
in that individual. It is based on a generative model that
assumes that genomic annotations, such as the location of a
variant with respect to regulatory elements, determine the
prior probability that variant is a functional regulatory
variant, which is an unobserved variable. The functional
regulatory variant status then influences whether nearby genes
are likely to display outlier levels of gene expression in that
person. See the RIVER website for more information,
documentation and examples.
biocViews: GeneExpression, GeneticVariability, SNP, Transcription,
FunctionalPrediction, GeneRegulation, GenomicVariation,
BiomedicalInformatics, FunctionalGenomics, Genetics,
SystemsBiology, Transcriptomics, Bayesian, Clustering,
TranscriptomeVariant, Regression
Author: Yungil Kim [aut, cre], Alexis Battle [aut]
Maintainer: Yungil Kim <ipw012@gmail.com>
URL: https://github.com/ipw012/RIVER
VignetteBuilder: knitr
BugReports: https://github.com/ipw012/RIVER/issues
git_url: https://git.bioconductor.org/packages/RIVER
git_branch: RELEASE_3_13
git_last_commit: ed4a4d4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RIVER_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RIVER_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RIVER_1.16.0.tgz
vignettes: vignettes/RIVER/inst/doc/RIVER.html
vignetteTitles: RIVER
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RIVER/inst/doc/RIVER.R
dependencyCount: 50
Package: RJMCMCNucleosomes
Version: 1.16.0
Depends: R (>= 3.4), IRanges, GenomicRanges
Imports: Rcpp (>= 0.12.5), consensusSeekeR, BiocGenerics, GenomeInfoDb,
S4Vectors (>= 0.23.10), BiocParallel, stats, graphics, methods,
grDevices
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, nucleoSim, RUnit
License: Artistic-2.0
MD5sum: e704e632c1631b7deb9a52fc85ab2ca6
NeedsCompilation: yes
Title: Bayesian hierarchical model for genome-wide nucleosome
positioning with high-throughput short-read data (MNase-Seq)
Description: This package does nucleosome positioning using informative
Multinomial-Dirichlet prior in a t-mixture with reversible jump
estimation of nucleosome positions for genome-wide profiling.
biocViews: BiologicalQuestion, ChIPSeq, NucleosomePositioning,
Software, StatisticalMethod, Bayesian, Sequencing, Coverage
Author: Pascal Belleau [aut], Rawane Samb [aut], Astrid Deschênes [cre,
aut], Khader Khadraoui [aut], Lajmi Lakhal-Chaieb [aut], Arnaud
Droit [aut]
Maintainer: Astrid Deschênes <adeschen@hotmail.com>
URL: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes
SystemRequirements: Rcpp
VignetteBuilder: knitr
BugReports: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues
git_url: https://git.bioconductor.org/packages/RJMCMCNucleosomes
git_branch: RELEASE_3_13
git_last_commit: ff26964
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RJMCMCNucleosomes_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RJMCMCNucleosomes_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RJMCMCNucleosomes_1.16.0.tgz
vignettes: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.html
vignetteTitles: Nucleosome Positioning
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.R
dependencyCount: 50
Package: RLassoCox
Version: 1.0.0
Depends: R (>= 4.1), glmnet
Imports: Matrix, igraph, survival, stats
Suggests: knitr
License: Artistic-2.0
Archs: i386, x64
MD5sum: e4e9a5008bb9b8f8d22257b58da29424
NeedsCompilation: no
Title: A reweighted Lasso-Cox by integrating gene interaction
information
Description: RLassoCox is a package that implements the RLasso-Cox
model proposed by Wei Liu. The RLasso-Cox model integrates gene
interaction information into the Lasso-Cox model for accurate
survival prediction and survival biomarker discovery. It is
based on the hypothesis that topologically important genes in
the gene interaction network tend to have stable expression
changes. The RLasso-Cox model uses random walk to evaluate the
topological weight of genes, and then highlights topologically
important genes to improve the generalization ability of the
Lasso-Cox model. The RLasso-Cox model has the advantage of
identifying small gene sets with high prognostic performance on
independent datasets, which may play an important role in
identifying robust survival biomarkers for various cancer
types.
biocViews: Survival, Regression, GeneExpression, GenePrediction,
Network
Author: Wei Liu [cre, aut] (<https://orcid.org/0000-0002-5496-3641>)
Maintainer: Wei Liu <freelw@qq.com>
VignetteBuilder: knitr
BugReports: https://github.com/weiliu123/RLassoCox/issues
git_url: https://git.bioconductor.org/packages/RLassoCox
git_branch: RELEASE_3_13
git_last_commit: 81d0c12
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RLassoCox_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RLassoCox_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RLassoCox_1.0.0.tgz
vignettes: vignettes/RLassoCox/inst/doc/RLassoCox.pdf
vignetteTitles: RLassoCox
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RLassoCox/inst/doc/RLassoCox.R
dependencyCount: 18
Package: RLMM
Version: 1.54.0
Depends: R (>= 2.1.0)
Imports: graphics, grDevices, MASS, stats, utils
License: LGPL (>= 2)
MD5sum: 6dc1480a1db043c92153a5c1b5ef4f0f
NeedsCompilation: no
Title: A Genotype Calling Algorithm for Affymetrix SNP Arrays
Description: A classification algorithm, based on a multi-chip,
multi-SNP approach for Affymetrix SNP arrays. Using a large
training sample where the genotype labels are known, this
aglorithm will obtain more accurate classification results on
new data. RLMM is based on a robust, linear model and uses the
Mahalanobis distance for classification. The chip-to-chip
non-biological variation is removed through normalization. This
model-based algorithm captures the similarities across genotype
groups and probes, as well as thousands other SNPs for accurate
classification. NOTE: 100K-Xba only at for now.
biocViews: Microarray, OneChannel, SNP, GeneticVariability
Author: Nusrat Rabbee <nrabbee@post.harvard.edu>, Gary Wong
<wongg62@berkeley.edu>
Maintainer: Nusrat Rabbee <nrabbee@post.harvard.edu>
URL: http://www.stat.berkeley.edu/users/nrabbee/RLMM
SystemRequirements: Internal files Xba.CQV, Xba.regions (or other
regions file)
git_url: https://git.bioconductor.org/packages/RLMM
git_branch: RELEASE_3_13
git_last_commit: 76954af
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RLMM_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RLMM_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RLMM_1.54.0.tgz
vignettes: vignettes/RLMM/inst/doc/RLMM.pdf
vignetteTitles: RLMM Doc
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RLMM/inst/doc/RLMM.R
dependencyCount: 6
Package: Rmagpie
Version: 1.48.0
Depends: R (>= 2.6.1), Biobase (>= 2.5.5)
Imports: Biobase (>= 2.5.5), e1071, graphics, grDevices, kernlab,
methods, pamr, stats, utils
Suggests: xtable
License: GPL (>= 3)
MD5sum: 16473f5d6dc5a716d3f8e62634a62337
NeedsCompilation: no
Title: MicroArray Gene-expression-based Program In Error rate
estimation
Description: Microarray Classification is designed for both biologists
and statisticians. It offers the ability to train a classifier
on a labelled microarray dataset and to then use that
classifier to predict the class of new observations. A range of
modern classifiers are available, including support vector
machines (SVMs), nearest shrunken centroids (NSCs)... Advanced
methods are provided to estimate the predictive error rate and
to report the subset of genes which appear essential in
discriminating between classes.
biocViews: Microarray, Classification
Author: Camille Maumet <Rmagpie@gmail.com>, with contributions from C.
Ambroise J. Zhu
Maintainer: Camille Maumet <Rmagpie@gmail.com>
URL: http://www.bioconductor.org/
git_url: https://git.bioconductor.org/packages/Rmagpie
git_branch: RELEASE_3_13
git_last_commit: a465b4a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rmagpie_1.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rmagpie_1.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rmagpie_1.48.0.tgz
vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf
vignetteTitles: Rmagpie Examples
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rmagpie/inst/doc/Magpie_examples.R
dependencyCount: 20
Package: RMassBank
Version: 3.2.0
Depends: Rcpp
Imports: XML,rjson,S4Vectors,digest,
rcdk,yaml,mzR,methods,Biobase,MSnbase,httr, enviPat,assertthat
Suggests: BiocStyle,gplots,RMassBankData, xcms (>= 1.37.1), CAMERA,
RUnit, knitr
License: Artistic-2.0
MD5sum: e1240514bc1fa76e6d9f55e3e88e5743
NeedsCompilation: no
Title: Workflow to process tandem MS files and build MassBank records
Description: Workflow to process tandem MS files and build MassBank
records. Functions include automated extraction of tandem MS
spectra, formula assignment to tandem MS fragments,
recalibration of tandem MS spectra with assigned fragments,
spectrum cleanup, automated retrieval of compound information
from Internet databases, and export to MassBank records.
biocViews: ImmunoOncology, Bioinformatics, MassSpectrometry,
Metabolomics, Software
Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller,
with contributions from Tobias Schulze
Maintainer: RMassBank at Eawag <massbank@eawag.ch>
SystemRequirements: OpenBabel
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RMassBank
git_branch: RELEASE_3_13
git_last_commit: ba135d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RMassBank_3.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RMassBank_3.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RMassBank_3.2.0.tgz
vignettes: vignettes/RMassBank/inst/doc/RMassBank.html,
vignettes/RMassBank/inst/doc/RMassBankNonstandard.html,
vignettes/RMassBank/inst/doc/RMassBankXCMS.html
vignetteTitles: RMassBank: The workflow by example, RMassBank:
Non-standard usage, RMassBank for XCMS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R,
vignettes/RMassBank/inst/doc/RMassBankNonstandard.R,
vignettes/RMassBank/inst/doc/RMassBankXCMS.R
suggestsMe: RMassBankData
dependencyCount: 95
Package: rmelting
Version: 1.8.0
Depends: R (>= 3.6)
Imports: Rdpack, rJava (>= 0.5-0)
Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat
License: GPL-2 | GPL-3
MD5sum: 693b7fe40c244a229091f3e6ead566c9
NeedsCompilation: no
Title: R Interface to MELTING 5
Description: R interface to the MELTING 5 program
(https://www.ebi.ac.uk/biomodels-static/tools/melting/) to
compute melting temperatures of nucleic acid duplexes along
with other thermodynamic parameters.
biocViews: BiomedicalInformatics, Cheminformatics,
Author: J. Aravind [aut, cre]
(<https://orcid.org/0000-0002-4791-442X>), G. K. Krishna [aut],
Bob Rudis [ctb] (melting5jars), Nicolas Le Novère [ctb]
(MELTING 5 Java Library), Marine Dumousseau [ctb] (MELTING 5
Java Library), William John Gowers [ctb] (MELTING 5 Java
Library)
Maintainer: J. Aravind <j.aravind@icar.gov.in>
URL: https://github.com/aravind-j/rmelting,
https://aravind-j.github.io/rmelting/
SystemRequirements: Java
VignetteBuilder: knitr
BugReports: https://github.com/aravind-j/rmelting/issues
git_url: https://git.bioconductor.org/packages/rmelting
git_branch: RELEASE_3_13
git_last_commit: 856c547
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rmelting_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rmelting_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rmelting_1.8.0.tgz
vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf
vignetteTitles: Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 6
Package: RmiR
Version: 1.48.0
Depends: R (>= 2.7.0), RmiR.Hs.miRNA, RSVGTipsDevice
Imports: DBI, methods, stats
Suggests: hgug4112a.db,org.Hs.eg.db
License: Artistic-2.0
MD5sum: c415005570b732cd9e7ccaf316c95b7e
NeedsCompilation: no
Title: Package to work with miRNAs and miRNA targets with R
Description: Useful functions to merge microRNA and respective targets
using differents databases
biocViews: Software,GeneExpression,Microarray,TimeCourse,Visualization
Author: Francesco Favero
Maintainer: Francesco Favero <favero.francesco@gmail.com>
git_url: https://git.bioconductor.org/packages/RmiR
git_branch: RELEASE_3_13
git_last_commit: 590c194
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RmiR_1.48.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/RmiR_1.48.0.tgz
vignettes: vignettes/RmiR/inst/doc/RmiR.pdf
vignetteTitles: RmiR Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RmiR/inst/doc/RmiR.R
dependencyCount: 48
Package: Rmmquant
Version: 1.10.0
Depends: R (>= 3.6)
Imports: Rcpp (>= 0.12.8), methods, S4Vectors, GenomicRanges,
SummarizedExperiment, devtools, TBX20BamSubset,
TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, DESeq2,
BiocStyle
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat
License: GPL-3
Archs: i386, x64
MD5sum: 3272ce8255166240a2f064f777881f40
NeedsCompilation: yes
Title: RNA-Seq multi-mapping Reads Quantification Tool
Description: RNA-Seq is currently used routinely, and it provides
accurate information on gene transcription. However, the method
cannot accurately estimate duplicated genes expression. Several
strategies have been previously used, but all of them provide
biased results. With Rmmquant, if a read maps at different
positions, the tool detects that the corresponding genes are
duplicated; it merges the genes and creates a merged gene. The
counts of ambiguous reads is then based on the input genes and
the merged genes. Rmmquant is a drop-in replacement of the
widely used tools findOverlaps and featureCounts that handles
multi-mapping reads in an unabiased way.
biocViews: GeneExpression, Transcription
Author: Zytnicki Matthias [aut, cre]
Maintainer: Zytnicki Matthias <matthias.zytnicki@inra.fr>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rmmquant
git_branch: RELEASE_3_13
git_last_commit: 5bad2b4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rmmquant_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rmmquant_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rmmquant_1.10.0.tgz
vignettes: vignettes/Rmmquant/inst/doc/Rmmquant.html
vignetteTitles: The Rmmquant package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rmmquant/inst/doc/Rmmquant.R
dependencyCount: 167
Package: RNAAgeCalc
Version: 1.4.0
Depends: R (>= 3.6)
Imports: ggplot2, recount, impute, AnnotationDbi, org.Hs.eg.db, stats,
SummarizedExperiment, methods
Suggests: knitr, rmarkdown, testthat
License: GPL-2
MD5sum: 557659d457e513be84a4367ad5ca4035
NeedsCompilation: no
Title: A multi-tissue transcriptional age calculator
Description: It has been shown that both DNA methylation and RNA
transcription are linked to chronological age and age related
diseases. Several estimators have been developed to predict
human aging from DNA level and RNA level. Most of the human
transcriptional age predictor are based on microarray data and
limited to only a few tissues. To date, transcriptional studies
on aging using RNASeq data from different human tissues is
limited. The aim of this package is to provide a tool for
across-tissue and tissue-specific transcriptional age
calculation based on GTEx RNASeq data.
biocViews: RNASeq,GeneExpression
Author: Xu Ren [aut, cre], Pei Fen Kuan [aut]
Maintainer: Xu Ren <xuren2120@gmail.com>
URL: https://github.com/reese3928/RNAAgeCalc
VignetteBuilder: knitr
BugReports: https://github.com/reese3928/RNAAgeCalc/issues
git_url: https://git.bioconductor.org/packages/RNAAgeCalc
git_branch: RELEASE_3_13
git_last_commit: a4fe63d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RNAAgeCalc_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RNAAgeCalc_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RNAAgeCalc_1.4.0.tgz
vignettes: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.html
vignetteTitles: RNAAgeCalc
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.R
dependencyCount: 161
Package: RNAdecay
Version: 1.12.0
Depends: R (>= 3.5)
Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr,
scales
Suggests: parallel, knitr, reshape2, rmarkdown
License: GPL-2
MD5sum: bdac324f4b2309641fb7496c3596acc1
NeedsCompilation: yes
Title: Maximum Likelihood Decay Modeling of RNA Degradation Data
Description: RNA degradation is monitored through measurement of RNA
abundance after inhibiting RNA synthesis. This package has
functions and example scripts to facilitate (1) data
normalization, (2) data modeling using constant decay rate or
time-dependent decay rate models, (3) the evaluation of
treatment or genotype effects, and (4) plotting of the data and
models. Data Normalization: functions and scripts make easy the
normalization to the initial (T0) RNA abundance, as well as a
method to correct for artificial inflation of Reads per Million
(RPM) abundance in global assessments as the total size of the
RNA pool decreases. Modeling: Normalized data is then modeled
using maximum likelihood to fit parameters. For making
treatment or genotype comparisons (up to four), the modeling
step models all possible treatment effects on each gene by
repeating the modeling with constraints on the model parameters
(i.e., the decay rate of treatments A and B are modeled once
with them being equal and again allowing them to both vary
independently). Model Selection: The AICc value is calculated
for each model, and the model with the lowest AICc is chosen.
Modeling results of selected models are then compiled into a
single data frame. Graphical Plotting: functions are provided
to easily visualize decay data model, or half-life
distributions using ggplot2 package functions.
biocViews: ImmunoOncology, Software, GeneExpression, GeneRegulation,
DifferentialExpression, Transcription, Transcriptomics,
TimeCourse, Regression, RNASeq, Normalization, WorkflowStep
Author: Reed Sorenson [aut, cre], Katrina Johnson [aut], Frederick
Adler [aut], Leslie Sieburth [aut]
Maintainer: Reed Sorenson <reedssorenson@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RNAdecay
git_branch: RELEASE_3_13
git_last_commit: cf277c6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RNAdecay_1.12.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/RNAdecay_1.12.0.tgz
vignettes: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.html
vignetteTitles: RNAdecay
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.R
dependencyCount: 47
Package: rnaEditr
Version: 1.2.0
Depends: R (>= 4.0)
Imports: GenomicRanges, IRanges, BiocGenerics, GenomeInfoDb,
bumphunter, S4Vectors, stats, survival, logistf, plyr, corrplot
Suggests: knitr, rmarkdown, testthat
License: GPL-3
MD5sum: d6ebd73aeb8187b2e6f91cb5cb9bf83c
NeedsCompilation: no
Title: Statistical analysis of RNA editing sites and hyper-editing
regions
Description: RNAeditr analyzes site-specific RNA editing events, as
well as hyper-editing regions. The editing frequencies can be
tested against binary, continuous or survival outcomes.
Multiple covariate variables as well as interaction effects can
also be incorporated in the statistical models.
biocViews: GeneTarget, Epigenetics, DimensionReduction,
FeatureExtraction, Regression, Survival, RNASeq
Author: Lanyu Zhang [aut, cre], Gabriel Odom [aut], Tiago Silva [aut],
Lissette Gomez [aut], Lily Wang [aut]
Maintainer: Lanyu Zhang <jennyzly2016@gmail.com>
URL: https://github.com/TransBioInfoLab/rnaEditr
VignetteBuilder: knitr
BugReports: https://github.com/TransBioInfoLab/rnaEditr/issues
git_url: https://git.bioconductor.org/packages/rnaEditr
git_branch: RELEASE_3_13
git_last_commit: 377f691
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rnaEditr_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rnaEditr_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rnaEditr_1.2.0.tgz
vignettes: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.html
vignetteTitles: Introduction to rnaEditr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.R
dependencyCount: 118
Package: RNAinteract
Version: 1.40.0
Depends: R (>= 2.12.0),
Imports: RColorBrewer, ICS, ICSNP, cellHTS2, geneplotter, gplots, grid,
hwriter, lattice, latticeExtra, limma, methods, splots (>=
1.13.12), abind, locfit, Biobase
License: Artistic-2.0
MD5sum: 097eca2bfde88621ff9518defcbd450e
NeedsCompilation: no
Title: Estimate Pairwise Interactions from multidimensional features
Description: RNAinteract estimates genetic interactions from
multi-dimensional read-outs like features extracted from
images. The screen is assumed to be performed in multi-well
plates or similar designs. Starting from a list of features
(e.g. cell number, area, fluorescence intensity) per well,
genetic interactions are estimated. The packages provides
functions for reporting interacting gene pairs, plotting
heatmaps and double RNAi plots. An HTML report can be written
for quality control and analysis.
biocViews: ImmunoOncology, CellBasedAssays, QualityControl,
Preprocessing, Visualization
Author: Bernd Fischer [aut], Wolfgang Huber [ctb], Mike Smith [cre]
Maintainer: Mike Smith <mike.smith@embl.de>
git_url: https://git.bioconductor.org/packages/RNAinteract
git_branch: RELEASE_3_13
git_last_commit: e2582be
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RNAinteract_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RNAinteract_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RNAinteract_1.40.0.tgz
vignettes: vignettes/RNAinteract/inst/doc/RNAinteract.pdf
vignetteTitles: RNAinteract
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAinteract/inst/doc/RNAinteract.R
dependsOnMe: RNAinteractMAPK
dependencyCount: 107
Package: RNAmodR
Version: 1.6.0
Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9),
GenomicRanges, Modstrings
Imports: methods, stats, grDevices, matrixStats, BiocGenerics,
Biostrings (>= 2.57.2), BiocParallel, GenomicFeatures,
GenomicAlignments, GenomeInfoDb, rtracklayer, Rsamtools,
BSgenome, RColorBrewer, colorRamps, ggplot2, Gviz (>= 1.31.0),
reshape2, graphics, ROCR
Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data
License: Artistic-2.0
MD5sum: a47b7616b5227f365b1a4a0507d04b1c
NeedsCompilation: no
Title: Detection of post-transcriptional modifications in high
throughput sequencing data
Description: RNAmodR provides classes and workflows for
loading/aggregation data from high througput sequencing aimed
at detecting post-transcriptional modifications through
analysis of specific patterns. In addition, utilities are
provided to validate and visualize the results. The RNAmodR
package provides a core functionality from which specific
analysis strategies can be easily implemented as a seperate
package.
biocViews: Software, Infrastructure, WorkflowStep, Visualization,
Sequencing
Author: Felix G.M. Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>), Denis L.J.
Lafontaine [ctb, fnd]
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/RNAmodR
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/RNAmodR/issues
git_url: https://git.bioconductor.org/packages/RNAmodR
git_branch: RELEASE_3_13
git_last_commit: c7b25bf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RNAmodR_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RNAmodR_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR_1.6.0.tgz
vignettes: vignettes/RNAmodR/inst/doc/RNAmodR.creation.html,
vignettes/RNAmodR/inst/doc/RNAmodR.html
vignetteTitles: RNAmodR - creating new classes for a new detection
strategy, RNAmodR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAmodR/inst/doc/RNAmodR.creation.R,
vignettes/RNAmodR/inst/doc/RNAmodR.R
dependsOnMe: RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq
dependencyCount: 151
Package: RNAmodR.AlkAnilineSeq
Version: 1.6.0
Depends: R (>= 4.0), RNAmodR (>= 1.5.3)
Imports: methods, S4Vectors, IRanges, BiocGenerics, GenomicRanges, Gviz
Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer,
Biostrings, RNAmodR.Data
License: Artistic-2.0
MD5sum: 4b42200276e241092fb35ead97f1d0e7
NeedsCompilation: no
Title: Detection of m7G, m3C and D modification by AlkAnilineSeq
Description: RNAmodR.AlkAnilineSeq implements the detection of m7G, m3C
and D modifications on RNA from experimental data generated
with the AlkAnilineSeq protocol. The package builds on the core
functionality of the RNAmodR package to detect specific
patterns of the modifications in high throughput sequencing
data.
biocViews: Software, WorkflowStep, Visualization, Sequencing
Author: Felix G.M. Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>), Denis L.J.
Lafontaine [ctb, fnd]
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq/issues
git_url: https://git.bioconductor.org/packages/RNAmodR.AlkAnilineSeq
git_branch: RELEASE_3_13
git_last_commit: cd821d4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RNAmodR.AlkAnilineSeq_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RNAmodR.AlkAnilineSeq_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR.AlkAnilineSeq_1.6.0.tgz
vignettes:
vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.html
vignetteTitles: RNAmodR.AlkAnilineSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.R
suggestsMe: RNAmodR.ML
dependencyCount: 152
Package: RNAmodR.ML
Version: 1.6.0
Depends: R (>= 3.6), RNAmodR
Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges,
stats, ranger
Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data,
RNAmodR.AlkAnilineSeq, GenomicFeatures, Rsamtools, rtracklayer,
keras
License: Artistic-2.0
Archs: i386, x64
MD5sum: 3d31450cd675cfa5c454825722fb288b
NeedsCompilation: no
Title: Detecting patterns of post-transcriptional modifications using
machine learning
Description: RNAmodR.ML extend the functionality of the RNAmodR package
and classical detection strategies towards detection through
machine learning models. RNAmodR.ML provides classes, functions
and an example workflow to establish a detection stratedy,
which can be packaged.
biocViews: Software, Infrastructure, WorkflowStep, Visualization,
Sequencing
Author: Felix G.M. Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>), Denis L.J.
Lafontaine [ctb]
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/RNAmodR.ML
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/RNAmodR.ML/issues
git_url: https://git.bioconductor.org/packages/RNAmodR.ML
git_branch: RELEASE_3_13
git_last_commit: 70709a5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RNAmodR.ML_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RNAmodR.ML_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR.ML_1.6.0.tgz
vignettes: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.html
vignetteTitles: RNAmodR.ML
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.R
dependencyCount: 154
Package: RNAmodR.RiboMethSeq
Version: 1.6.0
Depends: R (>= 4.0), RNAmodR (>= 1.5.3)
Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomicRanges, Gviz
Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer,
RNAmodR.Data
License: Artistic-2.0
MD5sum: 1fbbbb124520af2c643317179191c9d3
NeedsCompilation: no
Title: Detection of 2'-O methylations by RiboMethSeq
Description: RNAmodR.RiboMethSeq implements the detection of 2'-O
methylations on RNA from experimental data generated with the
RiboMethSeq protocol. The package builds on the core
functionality of the RNAmodR package to detect specific
patterns of the modifications in high throughput sequencing
data.
biocViews: Software, WorkflowStep, Visualization, Sequencing
Author: Felix G.M. Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>), Denis L.J.
Lafontaine [ctb, fnd]
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/RNAmodR.RiboMethSeq
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/RNAmodR.RiboMethSeq/issues
git_url: https://git.bioconductor.org/packages/RNAmodR.RiboMethSeq
git_branch: RELEASE_3_13
git_last_commit: b1876ea
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RNAmodR.RiboMethSeq_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RNAmodR.RiboMethSeq_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR.RiboMethSeq_1.6.0.tgz
vignettes:
vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.html
vignetteTitles: RNAmodR.RiboMethSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.R
dependencyCount: 152
Package: RNAsense
Version: 1.6.0
Depends: R (>= 3.6)
Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment,
stats, utils, methods
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 7ec214cba04237e97edc1dc7410f0bbe
NeedsCompilation: no
Title: Analysis of Time-Resolved RNA-Seq Data
Description: RNA-sense tool compares RNA-seq time curves in two
experimental conditions, i.e. wild-type and mutant, and works
in three steps. At Step 1, it builds expression profile for
each transcript in one condition (i.e. wild-type) and tests if
the transcript abundance grows or decays significantly.
Dynamic transcripts are then sorted to non-overlapping groups
(time profiles) by the time point of switch up or down. At Step
2, RNA-sense outputs the groups of differentially expressed
transcripts, which are up- or downregulated in the mutant
compared to the wild-type at each time point. At Step 3,
Correlations (Fisher's exact test) between the outputs of Step
1 (switch up- and switch down- time profile groups) and the
outputs of Step2 (differentially expressed transcript groups)
are calculated. The results of the correlation analysis are
printed as two-dimensional color plot, with time profiles and
differential expression groups at y- and x-axis, respectively,
and facilitates the biological interpretation of the data.
biocViews: RNASeq, GeneExpression, DifferentialExpression
Author: Marcus Rosenblatt [cre], Gao Meijang [aut], Helge Hass [aut],
Daria Onichtchouk [aut]
Maintainer: Marcus Rosenblatt <marcus.rosenblatt@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/marcusrosenblatt/RNAsense
git_url: https://git.bioconductor.org/packages/RNAsense
git_branch: RELEASE_3_13
git_last_commit: d486415
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RNAsense_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RNAsense_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RNAsense_1.6.0.tgz
vignettes: vignettes/RNAsense/inst/doc/example.html
vignetteTitles: Put the title of your vignette here
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAsense/inst/doc/example.R
dependencyCount: 63
Package: rnaseqcomp
Version: 1.22.0
Depends: R (>= 3.2.0)
Imports: RColorBrewer, methods
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 25c9be05e53840d535dd2fcb7052d5eb
NeedsCompilation: no
Title: Benchmarks for RNA-seq Quantification Pipelines
Description: Several quantitative and visualized benchmarks for RNA-seq
quantification pipelines. Two-condition quantifications for
genes, transcripts, junctions or exons by each pipeline with
necessary meta information should be organized into numeric
matrices in order to proceed the evaluation.
biocViews: RNASeq, Visualization, QualityControl
Author: Mingxiang Teng and Rafael A. Irizarry
Maintainer: Mingxiang Teng <tengmx@gmail.com>
URL: https://github.com/tengmx/rnaseqcomp
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rnaseqcomp
git_branch: RELEASE_3_13
git_last_commit: 53163e9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rnaseqcomp_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rnaseqcomp_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rnaseqcomp_1.22.0.tgz
vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.html
vignetteTitles: The rnaseqcomp user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.R
suggestsMe: SummarizedBenchmark
dependencyCount: 2
Package: RNASeqPower
Version: 1.32.0
License: LGPL (>=2)
MD5sum: 823dc610c7cf53fb5054e4ca7b3bc3b2
NeedsCompilation: no
Title: Sample size for RNAseq studies
Description: RNA-seq, sample size
biocViews: ImmunoOncology, RNASeq
Author: Terry M Therneau [aut, cre], Hart Stephen [ctb]
Maintainer: Terry M Therneau <therneau.terry@mayo.edu>
git_url: https://git.bioconductor.org/packages/RNASeqPower
git_branch: RELEASE_3_13
git_last_commit: aab663a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RNASeqPower_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RNASeqPower_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RNASeqPower_1.32.0.tgz
vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf
vignetteTitles: RNAseq samplesize
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNASeqPower/inst/doc/samplesize.R
importsMe: DGEobj.utils
dependencyCount: 0
Package: RNASeqR
Version: 1.10.0
Depends: R(>= 3.5.0), ggplot2, pathview, edgeR, methods
Imports: Rsamtools, tools, reticulate, ballgown, gridExtra, rafalib,
FactoMineR, factoextra, corrplot, PerformanceAnalytics,
reshape2, DESeq2, systemPipeR, systemPipeRdata,
clusterProfiler, org.Hs.eg.db, org.Sc.sgd.db, stringr,
pheatmap, grDevices, graphics, stats, utils, DOSE, Biostrings,
parallel
Suggests: knitr, png, grid, RNASeqRData
License: Artistic-2.0
MD5sum: 8b67f30235b26eb354499f4a852e75c6
NeedsCompilation: no
Title: RNASeqR: an R package for automated two-group RNA-Seq analysis
workflow
Description: This R package is designed for case-control RNA-Seq
analysis (two-group). There are six steps: "RNASeqRParam S4
Object Creation", "Environment Setup", "Quality Assessment",
"Reads Alignment & Quantification", "Gene-level Differential
Analyses" and "Functional Analyses". Each step corresponds to a
function in this package. After running functions in order, a
basic RNASeq analysis would be done easily.
biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq,
GeneExpression, GeneSetEnrichment, Alignment, QualityControl,
DifferentialExpression, FunctionalPrediction,
ExperimentalDesign, GO, KEGG, Visualization, Normalization,
Pathways, Clustering, ImmunoOncology
Author: Kuan-Hao Chao
Maintainer: Kuan-Hao Chao <ntueeb05howard@gmail.com>
URL: https://github.com/HowardChao/RNASeqR
SystemRequirements: RNASeqR only support Linux and macOS. Window is not
supported. Python2 is highly recommended. If your machine is
Python3, make sure '2to3' command is available.
VignetteBuilder: knitr
BugReports: https://github.com/HowardChao/RNASeqR/issues
git_url: https://git.bioconductor.org/packages/RNASeqR
git_branch: RELEASE_3_13
git_last_commit: c89d5c1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RNASeqR_1.10.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/RNASeqR_1.10.0.tgz
vignettes: vignettes/RNASeqR/inst/doc/RNASeqR.html
vignetteTitles: RNA-Seq analysis based on one independent variable
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNASeqR/inst/doc/RNASeqR.R
dependencyCount: 269
Package: RnaSeqSampleSize
Version: 2.2.0
Depends: R (>= 4.0.0), RnaSeqSampleSizeData
Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,methods,grDevices,
graphics, stats, utils,Rcpp (>= 0.11.2)
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, testthat
License: GPL (>= 2)
MD5sum: a783d469c60bc8d94d2f90c044322b33
NeedsCompilation: yes
Title: RnaSeqSampleSize
Description: RnaSeqSampleSize package provides a sample size
calculation method based on negative binomial model and the
exact test for assessing differential expression analysis of
RNA-seq data. It controls FDR for multiple testing and utilizes
the average read count and dispersion distributions from real
data to estimate a more reliable sample size. It is also
equipped with several unique features, including estimation for
interested genes or pathway, power curve visualization, and
parameter optimization.
biocViews: ImmunoOncology, ExperimentalDesign, Sequencing, RNASeq,
GeneExpression, DifferentialExpression
Author: Shilin Zhao Developer [aut, cre], Chung-I Li Developer [aut],
Yan Guo Developer [aut], Quanhu Sheng Developer [aut], Yu Shyr
Developer [aut]
Maintainer: Shilin Zhao Developer <zhaoshilin@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RnaSeqSampleSize
git_branch: RELEASE_3_13
git_last_commit: 88b5b96
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RnaSeqSampleSize_2.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RnaSeqSampleSize_2.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RnaSeqSampleSize_2.2.0.tgz
vignettes: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.pdf
vignetteTitles: RnaSeqSampleSize: Sample size estimation by real data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.R
dependencyCount: 81
Package: RnBeads
Version: 2.10.0
Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25),
GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2),
gplots, gridExtra, limma, matrixStats, methods, illuminaio,
methylumi, plyr
Imports: IRanges
Suggests: Category, GOstats, Gviz,
IlluminaHumanMethylation450kmanifest, RPMM, RefFreeEWAS,
RnBeads.hg19, RnBeads.mm9, XML, annotate, biomaRt, foreach,
doParallel, ggbio, isva, mclust, mgcv, minfi, nlme,
org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, quadprog,
rtracklayer, qvalue, sva, wateRmelon, wordcloud, qvalue,
argparse, glmnet, GLAD,
IlluminaHumanMethylation450kanno.ilmn12.hg19, scales,
missMethyl, impute, shiny, shinyjs, plotrix, hexbin, RUnit,
MethylSeekR, sesame
License: GPL-3
MD5sum: dc4cdda2c94dd19e817d2ba788505264
NeedsCompilation: no
Title: RnBeads
Description: RnBeads facilitates comprehensive analysis of various
types of DNA methylation data at the genome scale.
biocViews: DNAMethylation, MethylationArray, MethylSeq, Epigenetics,
QualityControl, Preprocessing, BatchEffect,
DifferentialMethylation, Sequencing, CpGIsland, ImmunoOncology,
TwoChannel, DataImport
Author: Yassen Assenov [aut], Christoph Bock [aut], Pavlo Lutsik [aut],
Michael Scherer [aut], Fabian Mueller [aut, cre]
Maintainer: Fabian Mueller <team@rnbeads.org>
git_url: https://git.bioconductor.org/packages/RnBeads
git_branch: RELEASE_3_13
git_last_commit: f6ec71a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RnBeads_2.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RnBeads_2.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RnBeads_2.10.0.tgz
vignettes: vignettes/RnBeads/inst/doc/RnBeads_Annotations.pdf,
vignettes/RnBeads/inst/doc/RnBeads.pdf
vignetteTitles: RnBeads Annotation, Comprehensive DNA Methylation
Analysis with RnBeads
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RnBeads/inst/doc/RnBeads_Annotations.R,
vignettes/RnBeads/inst/doc/RnBeads.R
dependsOnMe: MAGAR
suggestsMe: RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9,
RnBeads.rn5
dependencyCount: 166
Package: Rnits
Version: 1.26.0
Depends: R (>= 3.6.0), Biobase, ggplot2, limma, methods
Imports: affy, boot, impute, splines, graphics, qvalue, reshape2
Suggests: BiocStyle, knitr, GEOquery, stringr
License: GPL-3
MD5sum: c8228028dba9209474344b12ecd76789
NeedsCompilation: no
Title: R Normalization and Inference of Time Series data
Description: R/Bioconductor package for normalization, curve
registration and inference in time course gene expression data.
biocViews: GeneExpression, Microarray, TimeCourse,
DifferentialExpression, Normalization
Author: Dipen P. Sangurdekar <dipen.sangurdekar@gmail.com>
Maintainer: Dipen P. Sangurdekar <dipen.sangurdekar@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rnits
git_branch: RELEASE_3_13
git_last_commit: 756cbae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rnits_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rnits_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rnits_1.26.0.tgz
vignettes: vignettes/Rnits/inst/doc/Rnits-vignette.pdf
vignetteTitles: R/Bioconductor package for normalization and
differential expression inference in time series gene
expression microarray data.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rnits/inst/doc/Rnits-vignette.R
dependencyCount: 56
Package: roar
Version: 1.28.0
Depends: R (>= 3.0.1)
Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges,
SummarizedExperiment, GenomicAlignments (>= 0.99.4),
rtracklayer, GenomeInfoDb
Suggests: RNAseqData.HNRNPC.bam.chr14, testthat
License: GPL-3
MD5sum: d2c43f6a4e6db4c2ff20c2a84ab52ce3
NeedsCompilation: no
Title: Identify differential APA usage from RNA-seq alignments
Description: Identify preferential usage of APA sites, comparing two
biological conditions, starting from known alternative sites
and alignments obtained from standard RNA-seq experiments.
biocViews: Sequencing, HighThroughputSequencing, RNAseq, Transcription
Author: Elena Grassi
Maintainer: Elena Grassi <grassi.e@gmail.com>
URL: https://github.com/vodkatad/roar/
git_url: https://git.bioconductor.org/packages/roar
git_branch: RELEASE_3_13
git_last_commit: 34c7fa7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/roar_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/roar_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/roar_1.28.0.tgz
vignettes: vignettes/roar/inst/doc/roar.pdf
vignetteTitles: Identify differential APA usage from RNA-seq alignments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/roar/inst/doc/roar.R
dependencyCount: 44
Package: ROC
Version: 1.68.1
Depends: R (>= 1.9.0), utils, methods
Imports: knitr
Suggests: rmarkdown, Biobase
License: Artistic-2.0
MD5sum: 33bd40ed90664477d759991d5dd0e0c1
NeedsCompilation: yes
Title: utilities for ROC, with microarray focus
Description: Provide utilities for ROC, with microarray focus.
biocViews: DifferentialExpression
Author: Vince Carey <stvjc@channing.harvard.edu>, Henning Redestig for
C++ language enhancements
Maintainer: Vince Carey <stvjc@channing.harvard.edu>
URL: http://www.bioconductor.org
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ROC
git_branch: RELEASE_3_13
git_last_commit: 2c48100
git_last_commit_date: 2021-07-28
Date/Publication: 2021-07-29
source.ver: src/contrib/ROC_1.68.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ROC_1.68.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ROC_1.68.1.tgz
vignettes: vignettes/ROC/inst/doc/ROCnotes.html
vignetteTitles: Notes on ROC package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: TCC, wateRmelon
importsMe: clst, rMisbeta
suggestsMe: genefilter
dependencyCount: 13
Package: ROCpAI
Version: 1.4.0
Depends: boot, SummarizedExperiment, fission, knitr, methods
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: ddbb9e8df20b649a8fd975de94832dc1
NeedsCompilation: no
Title: Receiver Operating Characteristic Partial Area Indexes for
evaluating classifiers
Description: The package analyzes the Curve ROC, identificates it among
different types of Curve ROC and calculates the area under de
curve through the method that is most accuracy. This package is
able to standarizate proper and improper pAUC.
biocViews: Software, StatisticalMethod, Classification
Author: Juan-Pedro Garcia [aut, cre], Manuel Franco [aut], Juana-MarÃa
Vivo [aut]
Maintainer: Juan-Pedro Garcia <juanpedro.garcia4@um.es>
VignetteBuilder: knitr
BugReports: https://github.com/juanpegarcia/ROCpAI/tree/master/issues
git_url: https://git.bioconductor.org/packages/ROCpAI
git_branch: RELEASE_3_13
git_last_commit: 137d7ce
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ROCpAI_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ROCpAI_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ROCpAI_1.4.0.tgz
vignettes: vignettes/ROCpAI/inst/doc/vignettes.html
vignetteTitles: ROC Partial Area Indexes for evaluating classifiers
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ROCpAI/inst/doc/vignettes.R
dependencyCount: 37
Package: rols
Version: 2.20.1
Depends: methods
Imports: httr, progress, jsonlite, utils, Biobase, BiocGenerics (>=
0.23.1)
Suggests: GO.db, knitr (>= 1.1.0), BiocStyle (>= 2.5.19), testthat,
lubridate, DT, rmarkdown,
License: GPL-2
Archs: i386, x64
MD5sum: 154d463ba878e933eda6e9a993d92528
NeedsCompilation: no
Title: An R interface to the Ontology Lookup Service
Description: The rols package is an interface to the Ontology Lookup
Service (OLS) to access and query hundred of ontolgies directly
from R.
biocViews: ImmunoOncology, Software, Annotation, MassSpectrometry, GO
Author: Laurent Gatto [aut, cre], Tiage Chedraoui Silva [ctb], Andrew
Clugston [ctb]
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: http://lgatto.github.com/rols/
VignetteBuilder: knitr
BugReports: https://github.com/lgatto/rols/issues
git_url: https://git.bioconductor.org/packages/rols
git_branch: RELEASE_3_13
git_last_commit: 2148a4e
git_last_commit_date: 2021-06-15
Date/Publication: 2021-06-15
source.ver: src/contrib/rols_2.20.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rols_2.20.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/rols_2.20.1.tgz
vignettes: vignettes/rols/inst/doc/rols.html
vignetteTitles: An R interface to the Ontology Lookup Service
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rols/inst/doc/rols.R
dependsOnMe: proteomics
importsMe: spatialHeatmap
suggestsMe: MSnbase, RforProteomics
dependencyCount: 27
Package: ROntoTools
Version: 2.20.0
Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz
Suggests: RUnit, BiocGenerics
License: CC BY-NC-ND 4.0 + file LICENSE
MD5sum: 15cad42fc80ae42690d7e37596c6eb44
NeedsCompilation: no
Title: R Onto-Tools suite
Description: Suite of tools for functional analysis.
biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks
Author: Calin Voichita <calin@wayne.edu> and Sahar Ansari
<saharansari@wayne.edu> and Sorin Draghici <sorin@wayne.edu>
Maintainer: Calin Voichita <calin@wayne.edu>
git_url: https://git.bioconductor.org/packages/ROntoTools
git_branch: RELEASE_3_13
git_last_commit: b1691c3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ROntoTools_2.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ROntoTools_2.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ROntoTools_2.20.0.tgz
vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf
vignetteTitles: ROntoTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ROntoTools/inst/doc/rontotools.R
dependsOnMe: BLMA
dependencyCount: 35
Package: ropls
Version: 1.24.0
Depends: Biobase
Imports: graphics, grDevices, methods, MultiDataSet, stats
Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4,
rmarkdown, testthat
License: CeCILL
MD5sum: 5addfe868974e8544677ee26e1420255
NeedsCompilation: no
Title: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and
feature selection of omics data
Description: Latent variable modeling with Principal Component
Analysis(PCA) and Partial Least Squares (PLS) are powerful
methods for visualization, regression, classification, and
feature selection of omics data where the number of variables
exceeds the number of samples and with multicollinearity among
variables. Orthogonal Partial Least Squares (OPLS) enables to
separately model the variation correlated (predictive) to the
factor of interest and the uncorrelated (orthogonal) variation.
While performing similarly to PLS, OPLS facilitates
interpretation. Successful applications of these chemometrics
techniques include spectroscopic data such as Raman
spectroscopy, nuclear magnetic resonance (NMR), mass
spectrometry (MS) in metabolomics and proteomics, but also
transcriptomics data. In addition to scores, loadings and
weights plots, the package provides metrics and graphics to
determine the optimal number of components (e.g. with the R2
and Q2 coefficients), check the validity of the model by
permutation testing, detect outliers, and perform feature
selection (e.g. with Variable Importance in Projection or
regression coefficients). The package can be accessed via a
user interface on the Workflow4Metabolomics.org online resource
for computational metabolomics (built upon the Galaxy
environment).
biocViews: Regression, Classification, PrincipalComponent,
Transcriptomics, Proteomics, Metabolomics, Lipidomics,
MassSpectrometry, ImmunoOncology
Author: Etienne A. Thevenot <etienne.thevenot@cea.fr>
Maintainer: Etienne A. Thevenot <etienne.thevenot@cea.fr>
URL: http://dx.doi.org/10.1021/acs.jproteome.5b00354
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ropls
git_branch: RELEASE_3_13
git_last_commit: 9442690
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ropls_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ropls_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ropls_1.24.0.tgz
vignettes: vignettes/ropls/inst/doc/ropls-vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ropls/inst/doc/ropls-vignette.R
dependsOnMe: biosigner
importsMe: ASICS, lipidr, MultiBaC, proFIA, MetabolomicsBasics
suggestsMe: autonomics, ptairMS, structToolbox
dependencyCount: 62
Package: ROSeq
Version: 1.4.0
Depends: R (>= 4.0)
Imports: pbmcapply, edgeR, limma
Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics
License: GPL-3
MD5sum: 91040e48e12b28cd1f49b37dd40441bf
NeedsCompilation: no
Title: Modeling expression ranks for noise-tolerant differential
expression analysis of scRNA-Seq data
Description: ROSeq - A rank based approach to modeling gene expression
with filtered and normalized read count matrix. ROSeq takes
filtered and normalized read matrix and
cell-annotation/condition as input and determines the
differentially expressed genes between the contrasting groups
of single cells. One of the input parameters is the number of
cores to be used.
biocViews: GeneExpression, DifferentialExpression, SingleCell
Author: Krishan Gupta [aut, cre], Manan Lalit [aut], Aditya Biswas
[aut], Abhik Ghosh [aut], Debarka Sengupta [aut]
Maintainer: Krishan Gupta <krishang@iiitd.ac.in>
URL: https://github.com/krishan57gupta/ROSeq
VignetteBuilder: knitr
BugReports: https://github.com/krishan57gupta/ROSeq/issues
git_url: https://git.bioconductor.org/packages/ROSeq
git_branch: RELEASE_3_13
git_last_commit: 78c7da3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ROSeq_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ROSeq_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ROSeq_1.4.0.tgz
vignettes: vignettes/ROSeq/inst/doc/ROSeq.html
vignetteTitles: ROSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ROSeq/inst/doc/ROSeq.R
dependencyCount: 13
Package: ROTS
Version: 1.20.0
Depends: R (>= 3.3)
Imports: Rcpp, stats, Biobase, methods
LinkingTo: Rcpp
Suggests: testthat
License: GPL (>= 2)
Archs: i386, x64
MD5sum: b9ac0318c3a12a049d768a178e1a0bb3
NeedsCompilation: yes
Title: Reproducibility-Optimized Test Statistic
Description: Calculates the Reproducibility-Optimized Test Statistic
(ROTS) for differential testing in omics data.
biocViews: Software, GeneExpression, DifferentialExpression,
Microarray, RNASeq, Proteomics, ImmunoOncology
Author: Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo
Maintainer: Tomi Suomi <tomi.suomi@utu.fi>
git_url: https://git.bioconductor.org/packages/ROTS
git_branch: RELEASE_3_13
git_last_commit: d24bde8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ROTS_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ROTS_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ROTS_1.20.0.tgz
vignettes: vignettes/ROTS/inst/doc/ROTS.pdf
vignetteTitles: ROTS: Reproducibility Optimized Test Statistic
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ROTS/inst/doc/ROTS.R
importsMe: PECA
suggestsMe: wrProteo
dependencyCount: 8
Package: RPA
Version: 1.48.0
Depends: R (>= 3.1.1), affy, BiocGenerics, methods
Imports: phyloseq
Suggests: affydata, knitr, parallel
License: BSD_2_clause + file LICENSE
MD5sum: dd977c85f0fc792b0375e029f2e74c86
NeedsCompilation: no
Title: RPA: Robust Probabilistic Averaging for probe-level analysis
Description: Probabilistic analysis of probe reliability and
differential gene expression on short oligonucleotide arrays.
biocViews: GeneExpression, Microarray, Preprocessing, QualityControl
Author: Leo Lahti [aut, cre]
Maintainer: Leo Lahti <leo.lahti@iki.fi>
URL: https://github.com/antagomir/RPA
VignetteBuilder: knitr
BugReports: https://github.com/antagomir/RPA
git_url: https://git.bioconductor.org/packages/RPA
git_branch: RELEASE_3_13
git_last_commit: be1119f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RPA_1.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RPA_1.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RPA_1.48.0.tgz
vignettes: vignettes/RPA/inst/doc/RPA.html
vignetteTitles: RPA R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependsOnMe: prebs
dependencyCount: 81
Package: RProtoBufLib
Version: 2.4.0
License: BSD_3_clause
MD5sum: 3857ba944035b429c6b029076b98e021
NeedsCompilation: yes
Title: C++ headers and static libraries of Protocol buffers
Description: This package provides the headers and static library of
Protocol buffers for other R packages to compile and link
against.
biocViews: Infrastructure
Author: Mike Jiang
Maintainer: Mike Jiang <mike@ozette.ai>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RProtoBufLib
git_branch: RELEASE_3_13
git_last_commit: 49aa129
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RProtoBufLib_2.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RProtoBufLib_2.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RProtoBufLib_2.4.0.tgz
vignettes: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.html
vignetteTitles: Using RProtoBufLib
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.R
importsMe: cytolib, flowWorkspace
linksToMe: cytolib, CytoML, flowCore, flowWorkspace
dependencyCount: 0
Package: RpsiXML
Version: 2.34.0
Depends: methods, XML (>= 2.4.0), utils
Imports: annotate (>= 1.21.0), graph (>= 1.21.0), Biobase, RBGL (>=
1.17.0), hypergraph (>= 1.15.2), AnnotationDbi
Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Dm.eg.db, org.Rn.eg.db,
org.Sc.sgd.db, Rgraphviz, ppiStats, ScISI, testthat
License: LGPL-3
MD5sum: 17b0f2a3a6500cc987a1b7af9488b7c6
NeedsCompilation: no
Title: R interface to PSI-MI 2.5 files
Description: Queries, data structure and interface to visualization of
interaction datasets. This package inplements the PSI-MI 2.5
standard and supports up to now 8 databases. Further databases
supporting PSI-MI 2.5 standard will be added continuously.
biocViews: Infrastructure, Proteomics
Author: Jitao David Zhang [aut, cre, ctb]
(<https://orcid.org/0000-0002-3085-0909>), Stefan Wiemann
[ctb], Marc Carlson [ctb], Tony Chiang [ctb]
Maintainer: Jitao David Zhang <jitao_david.zhang@roche.com>
URL: http://www.bioconductor.org
git_url: https://git.bioconductor.org/packages/RpsiXML
git_branch: RELEASE_3_13
git_last_commit: 94db0a3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RpsiXML_2.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RpsiXML_2.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RpsiXML_2.34.0.tgz
vignettes: vignettes/RpsiXML/inst/doc/RpsiXML.pdf,
vignettes/RpsiXML/inst/doc/RpsiXMLApp.pdf
vignetteTitles: Reading PSI-25 XML files, Application Examples of
RpsiXML package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RpsiXML/inst/doc/RpsiXML.R,
vignettes/RpsiXML/inst/doc/RpsiXMLApp.R
dependsOnMe: ScISI
importsMe: ScISI
dependencyCount: 53
Package: rpx
Version: 2.0.3
Depends: methods
Imports: BiocFileCache, jsonlite, xml2, RCurl, utils
Suggests: Biostrings, BiocStyle, testthat, knitr, rmarkdown
License: GPL-2
MD5sum: d8ba4fd736403469af562c4278ba60fa
NeedsCompilation: no
Title: R Interface to the ProteomeXchange Repository
Description: The rpx package implements an interface to proteomics data
submitted to the ProteomeXchange consortium.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DataImport,
ThirdPartyClient
Author: Laurent Gatto
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://github.com/lgatto/rpx
VignetteBuilder: knitr
BugReports: https://github.com/lgatto/rpx/issues
git_url: https://git.bioconductor.org/packages/rpx
git_branch: RELEASE_3_13
git_last_commit: 9d84fbe
git_last_commit_date: 2021-08-17
Date/Publication: 2021-08-17
source.ver: src/contrib/rpx_2.0.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rpx_2.0.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/rpx_2.0.3.tgz
vignettes: vignettes/rpx/inst/doc/rpx.html
vignetteTitles: An R interface to the ProteomeXchange repository
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rpx/inst/doc/rpx.R
dependsOnMe: proteomics
importsMe: MBQN
suggestsMe: MSnbase, RforProteomics
dependencyCount: 50
Package: Rqc
Version: 1.26.0
Depends: BiocParallel, ShortRead, ggplot2
Imports: BiocGenerics (>= 0.25.1), Biostrings, IRanges, methods,
S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid,
reshape2, Rcpp (>= 0.11.6), biovizBase, shiny, Rsamtools,
GenomicAlignments, GenomicFiles
LinkingTo: Rcpp
Suggests: testthat
License: GPL (>= 2)
MD5sum: 845fda7a4202781771c53b7dc79259b6
NeedsCompilation: yes
Title: Quality Control Tool for High-Throughput Sequencing Data
Description: Rqc is an optimised tool designed for quality control and
assessment of high-throughput sequencing data. It performs
parallel processing of entire files and produces a report which
contains a set of high-resolution graphics.
biocViews: Sequencing, QualityControl, DataImport
Author: Welliton Souza, Benilton Carvalho <beniltoncarvalho@gmail.com>
Maintainer: Welliton Souza <well309@gmail.com>
URL: https://github.com/labbcb/Rqc
VignetteBuilder: knitr
BugReports: https://github.com/labbcb/Rqc/issues
git_url: https://git.bioconductor.org/packages/Rqc
git_branch: RELEASE_3_13
git_last_commit: 1c1c91a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rqc_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rqc_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rqc_1.26.0.tgz
vignettes: vignettes/Rqc/inst/doc/Rqc.html
vignetteTitles: Using Rqc
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rqc/inst/doc/Rqc.R
dependencyCount: 164
Package: rqt
Version: 1.18.0
Depends: R (>= 3.4), SummarizedExperiment
Imports:
stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls
Suggests: BiocStyle, knitr, rmarkdown
License: GPL
Archs: i386, x64
MD5sum: bedd8b92012ebd56a3a14eee6f2d7884
NeedsCompilation: no
Title: rqt: utilities for gene-level meta-analysis
Description: Despite the recent advances of modern GWAS methods, it
still remains an important problem of addressing calculation an
effect size and corresponding p-value for the whole gene rather
than for single variant. The R- package rqt offers gene-level
GWAS meta-analysis. For more information, see: "Gene-set
association tests for next-generation sequencing data" by Lee
et al (2016), Bioinformatics, 32(17), i611-i619,
<doi:10.1093/bioinformatics/btw429>.
biocViews: GenomeWideAssociation, Regression, Survival,
PrincipalComponent, StatisticalMethod, Sequencing
Author: I. Y. Zhbannikov, K. G. Arbeev, A. I. Yashin.
Maintainer: Ilya Y. Zhbannikov <ilya.zhbannikov@duke.edu>
URL: https://github.com/izhbannikov/rqt
VignetteBuilder: knitr
BugReports: https://github.com/izhbannikov/rqt/issues
git_url: https://git.bioconductor.org/packages/rqt
git_branch: RELEASE_3_13
git_last_commit: bcd7dae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rqt_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rqt_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rqt_1.18.0.tgz
vignettes: vignettes/rqt/inst/doc/rqt-vignette.html
vignetteTitles: Tutorial for rqt package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rqt/inst/doc/rqt-vignette.R
dependencyCount: 141
Package: rqubic
Version: 1.38.0
Imports: methods, Biobase, BiocGenerics, biclust
Suggests: RColorBrewer
License: GPL-2
MD5sum: 8801b9993cab1e9f9e5a0d303b10a476
NeedsCompilation: yes
Title: Qualitative biclustering algorithm for expression data analysis
in R
Description: This package implements the QUBIC algorithm introduced by
Li et al. for the qualitative biclustering with gene expression
data.
biocViews: Clustering
Author: Jitao David Zhang [aut, cre, ctb]
(<https://orcid.org/0000-0002-3085-0909>)
Maintainer: Jitao David Zhang <jitao_david.zhang@roche.com>
git_url: https://git.bioconductor.org/packages/rqubic
git_branch: RELEASE_3_13
git_last_commit: 8d90216
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rqubic_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rqubic_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rqubic_1.38.0.tgz
vignettes: vignettes/rqubic/inst/doc/rqubic.pdf
vignetteTitles: Qualitative Biclustering with Bioconductor Package
rqubic
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rqubic/inst/doc/rqubic.R
importsMe: miRSM
suggestsMe: RcmdrPlugin.BiclustGUI
dependencyCount: 53
Package: rRDP
Version: 1.26.0
Depends: Biostrings (>= 2.26.2)
Suggests: rRDPData
License: GPL-2 | file LICENSE
Archs: i386, x64
MD5sum: 604e10f8d22a640176d0893549460ffc
NeedsCompilation: no
Title: Interface to the RDP Classifier
Description: Seamlessly interfaces RDP classifier (version 2.9).
biocViews: Genetics, Sequencing, Infrastructure, Classification,
Microbiome, ImmunoOncology
Author: Michael Hahsler, Anurag Nagar
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>
SystemRequirements: Java
git_url: https://git.bioconductor.org/packages/rRDP
git_branch: RELEASE_3_13
git_last_commit: 2a5507e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rRDP_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rRDP_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rRDP_1.26.0.tgz
vignettes: vignettes/rRDP/inst/doc/rRDP.pdf
vignetteTitles: rRDP: Interface to the RDP Classifier
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rRDP/inst/doc/rRDP.R
dependsOnMe: rRDPData
dependencyCount: 19
Package: RRHO
Version: 1.32.0
Depends: R (>= 2.10), grid
Imports: VennDiagram
Suggests: lattice
License: GPL-2
MD5sum: 3c89e53ddcb6feed739a2b78c245b208
NeedsCompilation: no
Title: Inference on agreement between ordered lists
Description: The package is aimed at inference on the amount of
agreement in two sorted lists using the Rank-Rank
Hypergeometric Overlap test.
biocViews: Genetics, SequenceMatching, Microarray, Transcription
Author: Jonathan Rosenblatt and Jason Stein
Maintainer: Jonathan Rosenblatt <john.ros.work@gmail.com>
git_url: https://git.bioconductor.org/packages/RRHO
git_branch: RELEASE_3_13
git_last_commit: 589c56d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RRHO_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RRHO_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RRHO_1.32.0.tgz
vignettes: vignettes/RRHO/inst/doc/RRHO.pdf
vignetteTitles: RRHO
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RRHO/inst/doc/RRHO.R
dependencyCount: 7
Package: rrvgo
Version: 1.4.4
Imports: GOSemSim, AnnotationDbi, GO.db, pheatmap, ggplot2, ggrepel,
treemap, tm, wordcloud, shiny, grDevices, grid, stats, methods
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0),
shinydashboard, DT, plotly, heatmaply, magrittr, utils,
clusterProfiler, DOSE, slam, org.Ag.eg.db, org.At.tair.db,
org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db,
org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db,
org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pf.plasmo.db,
org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db,
org.Xl.eg.db
License: GPL-3
MD5sum: 6775da27b493200fff5d6a829591b5d6
NeedsCompilation: no
Title: Reduce + Visualize GO
Description: Reduce and visualize lists of Gene Ontology terms by
identifying redudance based on semantic similarity.
biocViews: Annotation, Clustering, GO, Network, Pathways, Software
Author: Sergi Sayols [aut, cre]
Maintainer: Sergi Sayols <sergisayolspuig@gmail.com>
URL: https://www.bioconductor.org/packages/rrvgo,
https://ssayols.github.io/rrvgo/index.html
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rrvgo
git_branch: RELEASE_3_13
git_last_commit: 60a1114
git_last_commit_date: 2021-07-28
Date/Publication: 2021-07-29
source.ver: src/contrib/rrvgo_1.4.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rrvgo_1.4.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/rrvgo_1.4.4.tgz
vignettes: vignettes/rrvgo/inst/doc/rrvgo.html
vignetteTitles: Using rrvgo
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rrvgo/inst/doc/rrvgo.R
dependencyCount: 101
Package: Rsamtools
Version: 2.8.0
Depends: methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8),
Biostrings (>= 2.47.6), R (>= 3.5.0)
Imports: utils, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25),
IRanges (>= 2.13.12), XVector (>= 0.19.7), zlibbioc, bitops,
BiocParallel, stats
LinkingTo: Rhtslib (>= 1.17.7), S4Vectors, IRanges, XVector, Biostrings
Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures,
TxDb.Dmelanogaster.UCSC.dm3.ensGene,
TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14,
BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocStyle
License: Artistic-2.0 | file LICENSE
Archs: i386, x64
MD5sum: 804ede99db45d6eb2b8b72da107bf016
NeedsCompilation: yes
Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix
file import
Description: This package provides an interface to the 'samtools',
'bcftools', and 'tabix' utilities for manipulating SAM
(Sequence Alignment / Map), FASTA, binary variant call (BCF)
and compressed indexed tab-delimited (tabix) files.
biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl
Author: Martin Morgan, Hervé Pagès, Valerie Obenchain, Nathaniel Hayden
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/Rsamtools
SystemRequirements: GNU make
Video:
https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q
BugReports: https://github.com/Bioconductor/Rsamtools/issues
git_url: https://git.bioconductor.org/packages/Rsamtools
git_branch: RELEASE_3_13
git_last_commit: 45d46ab
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rsamtools_2.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rsamtools_2.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rsamtools_2.8.0.tgz
vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.pdf
vignetteTitles: An introduction to Rsamtools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R
dependsOnMe: ArrayExpressHTS, BitSeq, CODEX, contiBAIT, CoverageView,
esATAC, exomeCopy, FRASER, GenomicAlignments, GenomicFiles,
girafe, gmapR, HelloRanges, IntEREst, MEDIPS, methylPipe,
MMDiff2, podkat, r3Cseq, Rcade, RepViz, ReQON, rfPred,
RiboDiPA, SCOPE, SGSeq, ShortRead, SICtools, SNPhood,
systemPipeR, TarSeqQC, TEQC, VariantAnnotation, wavClusteR,
leeBamViews, TBX20BamSubset, sequencing, csawBook, Brundle
importsMe: AllelicImbalance, alpine, AneuFinder, annmap,
AnnotationHubData, APAlyzer, appreci8R, ArrayExpressHTS,
ASpediaFI, ASpli, ATACseqQC, BadRegionFinder, bambu,
BBCAnalyzer, biovizBase, biscuiteer, breakpointR, BRGenomics,
BSgenome, CAGEr, casper, cellbaseR, ChIC, chimeraviz,
ChIPexoQual, ChIPpeakAnno, ChIPQC, ChromSCape, chromstaR,
chromVAR, cn.mops, CNVfilteR, CNVPanelizer, CNVrd2,
compEpiTools, consensusDE, CopyNumberPlots, CopywriteR,
CrispRVariants, csaw, CSSQ, customProDB, DAMEfinder, DegNorm,
derfinder, DEXSeq, DiffBind, diffHic, easyRNASeq, EDASeq,
ensembldb, epialleleR, epigenomix, epigraHMM, eudysbiome,
FilterFFPE, FunChIP, gcapc, GeneGeneInteR, GenoGAM, genomation,
GenomicAlignments, GenomicInteractions, GenVisR, ggbio, gmoviz,
GOTHiC, GreyListChIP, GUIDEseq, Gviz, h5vc, HTSeqGenie, icetea,
IMAS, INSPEcT, karyoploteR, ldblock, MACPET, MADSEQ, MDTS,
metagene, metagene2, metaseqR2, methylKit, MMAPPR2, mosaics,
motifmatchr, msgbsR, NADfinder, NanoMethViz, nearBynding,
nucleR, ORFik, panelcn.mops, PICS, plyranges, pram,
profileplyr, PureCN, QDNAseq, qsea, QuasR, R453Plus1Toolbox,
ramwas, recoup, Repitools, RiboProfiling, riboSeqR,
ribosomeProfilingQC, RNAmodR, RNASeqR, Rqc, rtracklayer,
scruff, segmentSeq, seqsetvis, SimFFPE, sitadela, soGGi,
SplicingGraphs, srnadiff, strandCheckR, TCseq, TFutils,
tracktables, trackViewer, transcriptR, tRNAscanImport,
TSRchitect, TVTB, UMI4Cats, uncoverappLib, VariantFiltering,
VariantTools, VaSP, VCFArray, VplotR, chipseqDBData,
LungCancerLines, MMAPPR2data, systemPipeRdata, BinQuasi,
ExomeDepth, hoardeR, intePareto, kibior, MAAPER, MicroSEC,
NIPTeR, noisyr, PlasmaMutationDetector, pulseTD, RAPIDR,
Signac, spp, VALERIE
suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocGenerics,
BiocParallel, biomvRCNS, Chicago, epivizrChart, gage,
GenomeInfoDb, GenomicDataCommons, GenomicFeatures,
GenomicRanges, gwascat, IRanges, omicsPrint, RNAmodR.ML,
SeqArray, seqbias, SigFuge, similaRpeak, Streamer,
GeuvadisTranscriptExpr, NanoporeRNASeq, parathyroidSE,
chipseqDB, polyRAD, seqmagick
dependencyCount: 28
Package: rsbml
Version: 2.50.0
Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils
Imports: BiocGenerics, graph, utils
License: Artistic-2.0
MD5sum: a2a2dec3103708182f9291252ddb2079
NeedsCompilation: yes
Title: R support for SBML, using libsbml
Description: Links R to libsbml for SBML parsing, validating output,
provides an S4 SBML DOM, converts SBML to R graph objects.
Optionally links to the SBML ODE Solver Library (SOSLib) for
simulating models.
biocViews: GraphAndNetwork, Pathways, Network
Author: Michael Lawrence <michafla@gene.com>
Maintainer: Michael Lawrence <michafla@gene.com>
URL: http://www.sbml.org
SystemRequirements: libsbml (==5.10.2)
git_url: https://git.bioconductor.org/packages/rsbml
git_branch: RELEASE_3_13
git_last_commit: 84b2f61
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rsbml_2.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rsbml_2.50.0.zip
vignettes: vignettes/rsbml/inst/doc/quick-start.pdf
vignetteTitles: Quick start for rsbml
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/rsbml/inst/doc/quick-start.R
dependsOnMe: BiGGR
suggestsMe: piano, SBMLR, seeds
dependencyCount: 8
Package: rScudo
Version: 1.8.0
Depends: R (>= 3.6)
Imports: methods, stats, igraph, stringr, grDevices, Biobase,
S4Vectors, SummarizedExperiment, BiocGenerics
Suggests: testthat, BiocStyle, knitr, rmarkdown, ALL, RCy3, caret,
e1071, parallel, doParallel
License: GPL-3
Archs: i386, x64
MD5sum: 91d707d01c714e3cacb31c7440e36ed9
NeedsCompilation: no
Title: Signature-based Clustering for Diagnostic Purposes
Description: SCUDO (Signature-based Clustering for Diagnostic Purposes)
is a rank-based method for the analysis of gene expression
profiles for diagnostic and classification purposes. It is
based on the identification of sample-specific gene signatures
composed of the most up- and down-regulated genes for that
sample. Starting from gene expression data, functions in this
package identify sample-specific gene signatures and use them
to build a graph of samples. In this graph samples are joined
by edges if they have a similar expression profile, according
to a pre-computed similarity matrix. The similarity between the
expression profiles of two samples is computed using a method
similar to GSEA. The graph of samples can then be used to
perform community clustering or to perform supervised
classification of samples in a testing set.
biocViews: GeneExpression, DifferentialExpression,
BiomedicalInformatics, Classification, Clustering,
GraphAndNetwork, Network, Proteomics, Transcriptomics,
SystemsBiology, FeatureExtraction
Author: Matteo Ciciani [aut, cre], Thomas Cantore [aut], Enrica
Colasurdo [ctb], Mario Lauria [ctb]
Maintainer: Matteo Ciciani <matteo.ciciani@gmail.com>
URL: https://github.com/Matteo-Ciciani/scudo
VignetteBuilder: knitr
BugReports: https://github.com/Matteo-Ciciani/scudo/issues
git_url: https://git.bioconductor.org/packages/rScudo
git_branch: RELEASE_3_13
git_last_commit: 673d670
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rScudo_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rScudo_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rScudo_1.8.0.tgz
vignettes: vignettes/rScudo/inst/doc/rScudo-vignette.html
vignetteTitles: Signature-based Clustering for Diagnostic Purposes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rScudo/inst/doc/rScudo-vignette.R
dependencyCount: 32
Package: rsemmed
Version: 1.2.0
Depends: R (>= 4.0), igraph
Imports: methods, magrittr, stringr, dplyr
Suggests: testthat, knitr, BiocStyle, rmarkdown
License: Artistic-2.0
MD5sum: 8e43f99e8300d66c413de9b5630693b5
NeedsCompilation: no
Title: An interface to the Semantic MEDLINE database
Description: A programmatic interface to the Semantic MEDLINE database.
It provides functions for searching the database for concepts
and finding paths between concepts. Path searching can also be
tailored to user specifications, such as placing restrictions
on concept types and the type of link between concepts. It also
provides functions for summarizing and visualizing those paths.
biocViews: Software, Annotation, Pathways, SystemsBiology
Author: Leslie Myint [aut, cre]
(<https://orcid.org/0000-0003-2478-0331>)
Maintainer: Leslie Myint <leslie.myint@gmail.com>
URL: https://github.com/lmyint/rsemmed
VignetteBuilder: knitr
BugReports: https://github.com/lmyint/rsemmed/issues
git_url: https://git.bioconductor.org/packages/rsemmed
git_branch: RELEASE_3_13
git_last_commit: 786285d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rsemmed_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rsemmed_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rsemmed_1.2.0.tgz
vignettes: vignettes/rsemmed/inst/doc/rsemmed_user_guide.html
vignetteTitles: rsemmed User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rsemmed/inst/doc/rsemmed_user_guide.R
dependencyCount: 30
Package: RSeqAn
Version: 1.12.0
Imports: Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat
License: BSD_3_clause + file LICENSE
MD5sum: 9e98137e5d831265830b7e2386e33243
NeedsCompilation: yes
Title: R SeqAn
Description: Headers and some wrapper functions from the SeqAn C++
library for ease of usage in R.
biocViews: Infrastructure, Software
Author: August Guang [aut, cre]
Maintainer: August Guang <august.guang@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/compbiocore/RSeqAn/issues
git_url: https://git.bioconductor.org/packages/RSeqAn
git_branch: RELEASE_3_13
git_last_commit: c59a5d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RSeqAn_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RSeqAn_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RSeqAn_1.12.0.tgz
vignettes: vignettes/RSeqAn/inst/doc/first_example.html
vignetteTitles: Introduction to Using RSeqAn
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RSeqAn/inst/doc/first_example.R
importsMe: qckitfastq
linksToMe: qckitfastq
dependencyCount: 3
Package: Rsubread
Version: 2.6.4
Imports: grDevices, stats, utils, Matrix
License: GPL (>=3)
MD5sum: 8c60914f66d294da4ab72053466c91c5
NeedsCompilation: yes
Title: Mapping, quantification and variant analysis of sequencing data
Description: Alignment, quantification and analysis of RNA sequencing
data (including both bulk RNA-seq and scRNA-seq) and DNA
sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc).
Includes functionality for read mapping, read counting, SNP
calling, structural variant detection and gene fusion
discovery. Can be applied to all major sequencing techologies
and to both short and long sequence reads.
biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq,
SingleCell, GeneExpression, GeneRegulation, Genetics,
ImmunoOncology, SNP, GeneticVariability, Preprocessing,
QualityControl, GenomeAnnotation, GeneFusionDetection,
IndelDetection, VariantAnnotation, VariantDetection,
MultipleSequenceAlignment
Author: Wei Shi, Yang Liao and Gordon K Smyth with contributions from
Jenny Dai
Maintainer: Wei Shi <wei.shi@onjcri.org.au>, Yang Liao
<yang.liao@onjcri.org.au> and Gordon K Smyth
<smyth@wehi.edu.au>
URL: http://bioconductor.org/packages/Rsubread
git_url: https://git.bioconductor.org/packages/Rsubread
git_branch: RELEASE_3_13
git_last_commit: 77deb77
git_last_commit_date: 2021-07-14
Date/Publication: 2021-07-15
source.ver: src/contrib/Rsubread_2.6.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rsubread_2.6.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rsubread_2.6.4.tgz
vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf,
vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf
vignetteTitles: Rsubread Vignette, SubreadUsersGuide.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R
dependsOnMe: ExCluster
importsMe: APAlyzer, diffUTR, dupRadar, FRASER, ribosomeProfilingQC,
scruff, SEAA
suggestsMe: autonomics, icetea, scPipe, singleCellTK, tidybulk
dependencyCount: 8
Package: RSVSim
Version: 1.32.0
Depends: R (>= 3.0.0), Biostrings, GenomicRanges
Imports: methods, IRanges, ShortRead
Suggests: BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer
License: LGPL-3
Archs: i386, x64
MD5sum: d4ebf308413b7594f932a4d340b12bb8
NeedsCompilation: no
Title: RSVSim: an R/Bioconductor package for the simulation of
structural variations
Description: RSVSim is a package for the simulation of deletions,
insertions, inversion, tandem-duplications and translocations
of various sizes in any genome available as FASTA-file or
BSgenome data package. SV breakpoints can be placed uniformly
accross the whole genome, with a bias towards repeat regions
and regions of high homology (for hg19) or at user-supplied
coordinates.
biocViews: Sequencing
Author: Christoph Bartenhagen
Maintainer: Christoph Bartenhagen <c.bartenhagen@uni-koeln.de>
git_url: https://git.bioconductor.org/packages/RSVSim
git_branch: RELEASE_3_13
git_last_commit: 7c52c69
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RSVSim_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RSVSim_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RSVSim_1.32.0.tgz
vignettes: vignettes/RSVSim/inst/doc/vignette.pdf
vignetteTitles: RSVSim: an R/Bioconductor package for the simulation of
structural variations
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RSVSim/inst/doc/vignette.R
dependencyCount: 44
Package: rSWeeP
Version: 1.4.0
Depends: R (>= 4.0)
Imports: pracma, stats
Suggests: Biostrings, methods, knitr, rmarkdown, BiocStyle
License: GPL-3
Archs: i386, x64
MD5sum: db04a8b079c4f4d16918a92ffab270f3
NeedsCompilation: no
Title: Functions to creation of low dimensional comparative matrices of
Amino Acid Sequence occurrences
Description: The SWeeP method was developed to favor the analizes
between amino acids sequences and to assist alignment free
phylogenetic studies. This method is based on the concept of
sparse words, which is applied in the scan of biological
sequences and its the conversion in a matrix of ocurrences.
Aiming the generation of low dimensional matrices of Amino Acid
Sequence occurrences.
biocViews:
Software,StatisticalMethod,SupportVectorMachine,Technology,Sequencing,Genetics,
Alignment
Author: Danrley R. Fernandes [com, cre, aut]
Maintainer: Danrley R. Fernandes <DanrleyRF@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rSWeeP
git_branch: RELEASE_3_13
git_last_commit: e8a62ab
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rSWeeP_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rSWeeP_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rSWeeP_1.4.0.tgz
vignettes: vignettes/rSWeeP/inst/doc/rSWeeP.html
vignetteTitles: rSWeeP
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rSWeeP/inst/doc/rSWeeP.R
dependencyCount: 5
Package: RTCA
Version: 1.44.0
Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools
Suggests: xtable
License: LGPL-3
MD5sum: e3d2dbe9c371296fb7111dd8f0f7ca53
NeedsCompilation: no
Title: Open-source toolkit to analyse data from xCELLigence System
(RTCA)
Description: Import, analyze and visualize data from Roche(R)
xCELLigence RTCA systems. The package imports real-time cell
electrical impedance data into R. As an alternative to
commercial software shipped along the system, the Bioconductor
package RTCA provides several unique transformation
(normalization) strategies and various visualization tools.
biocViews: ImmunoOncology, CellBasedAssays, Infrastructure,
Visualization, TimeCourse
Author: Jitao David Zhang
Maintainer: Jitao David Zhang <davidvonpku@gmail.com>
URL:
http://code.google.com/p/xcelligence/,http://www.xcelligence.roche.com/,http://www.nextbiomotif.com/Home/scientific-programming
git_url: https://git.bioconductor.org/packages/RTCA
git_branch: RELEASE_3_13
git_last_commit: 0e775e9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RTCA_1.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RTCA_1.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RTCA_1.44.0.tgz
vignettes: vignettes/RTCA/inst/doc/aboutRTCA.pdf,
vignettes/RTCA/inst/doc/RTCAtransformation.pdf
vignetteTitles: Introduction to Data Analysis of the Roche xCELLigence
System with RTCA Package, RTCAtransformation: Discussion of
transformation methods of RTCA data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R,
vignettes/RTCA/inst/doc/RTCAtransformation.R
dependencyCount: 9
Package: RTCGA
Version: 1.22.0
Depends: R (>= 3.3.0)
Imports: XML, assertthat, stringi, rvest, data.table, xml2, dplyr,
purrr, survival, survminer, ggplot2, ggthemes, viridis, knitr,
scales
Suggests: devtools, testthat, pander, Biobase, GenomicRanges, IRanges,
S4Vectors, RTCGA.rnaseq, RTCGA.clinical, RTCGA.mutations,
RTCGA.RPPA, RTCGA.mRNA, RTCGA.miRNASeq, RTCGA.methylation,
RTCGA.CNV, RTCGA.PANCAN12, magrittr, tidyr
License: GPL-2
MD5sum: d1678bd83ffaddf7ae3d37e48530e585
NeedsCompilation: no
Title: The Cancer Genome Atlas Data Integration
Description: The Cancer Genome Atlas (TCGA) Data Portal provides a
platform for researchers to search, download, and analyze data
sets generated by TCGA. It contains clinical information,
genomic characterization data, and high level sequence analysis
of the tumor genomes. The key is to understand genomics to
improve cancer care. RTCGA package offers download and
integration of the variety and volume of TCGA data using
patient barcode key, what enables easier data possession. This
may have an benefcial infuence on impact on development of
science and improvement of patients' treatment. Furthermore,
RTCGA package transforms TCGA data to tidy form which is
convenient to use.
biocViews: ImmunoOncology, Software, DataImport, DataRepresentation,
Preprocessing, RNASeq
Author: Marcin Kosinski <m.p.kosinski@gmail.com>, Przemyslaw Biecek
<przemyslaw.biecek@gmail.com>
Maintainer: Marcin Kosinski <m.p.kosinski@gmail.com>
URL: https://rtcga.github.io/RTCGA
VignetteBuilder: knitr
BugReports: https://github.com/RTCGA/RTCGA/issues
git_url: https://git.bioconductor.org/packages/RTCGA
git_branch: RELEASE_3_13
git_last_commit: 98a46dc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RTCGA_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RTCGA_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RTCGA_1.22.0.tgz
vignettes: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html
vignetteTitles: Integrating TCGA Data - RTCGA Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: RTCGA.clinical, RTCGA.CNV, RTCGA.methylation,
RTCGA.miRNASeq, RTCGA.mRNA, RTCGA.mutations, RTCGA.PANCAN12,
RTCGA.rnaseq, RTCGA.RPPA
dependencyCount: 134
Package: RTCGAToolbox
Version: 2.22.1
Depends: R (>= 3.5.0)
Imports: BiocGenerics, data.table, DelayedArray, GenomicRanges,
GenomeInfoDb, httr, limma, methods, RaggedExperiment, RCircos,
RCurl, RJSONIO, S4Vectors (>= 0.23.10), stats, stringr,
SummarizedExperiment, survival, TCGAutils (>= 1.9.4), XML
Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown
License: file LICENSE
MD5sum: 350d89cfdb2302f4058bb4c5c381d978
NeedsCompilation: no
Title: A new tool for exporting TCGA Firehose data
Description: Managing data from large scale projects such as The Cancer
Genome Atlas (TCGA) for further analysis is an important and
time consuming step for research projects. Several efforts,
such as Firehose project, make TCGA pre-processed data publicly
available via web services and data portals but it requires
managing, downloading and preparing the data for following
steps. We developed an open source and extensible R based data
client for Firehose pre-processed data and demonstrated its use
with sample case studies. Results showed that RTCGAToolbox
could improve data management for researchers who are
interested with TCGA data. In addition, it can be integrated
with other analysis pipelines for following data analysis.
biocViews: DifferentialExpression, GeneExpression, Sequencing
Author: Mehmet Samur [aut], Marcel Ramos [aut, cre], Ludwig Geistlinger
[ctb]
Maintainer: Marcel Ramos <marcel.ramos@roswellpark.org>
URL: http://mksamur.github.io/RTCGAToolbox/
VignetteBuilder: knitr
BugReports: https://github.com/mksamur/RTCGAToolbox/issues
git_url: https://git.bioconductor.org/packages/RTCGAToolbox
git_branch: RELEASE_3_13
git_last_commit: adc18d9
git_last_commit_date: 2021-06-14
Date/Publication: 2021-06-15
source.ver: src/contrib/RTCGAToolbox_2.22.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RTCGAToolbox_2.22.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/RTCGAToolbox_2.22.1.tgz
vignettes: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html
vignetteTitles: RTCGAToolbox Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R
importsMe: cBioPortalData, TCGAWorkflow
suggestsMe: TCGAutils
dependencyCount: 114
Package: RTN
Version: 2.16.0
Depends: R (>= 3.6.3), methods,
Imports: RedeR, minet, viper, mixtools, snow, stats, limma, data.table,
IRanges, igraph, S4Vectors, SummarizedExperiment, car, pwr,
pheatmap, grDevices, graphics, utils
Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: 6b560e9070f77e9b60aa4ef1b3d85395
NeedsCompilation: no
Title: RTN: Reconstruction of Transcriptional regulatory Networks and
analysis of regulons
Description: A transcriptional regulatory network (TRN) consists of a
collection of transcription factors (TFs) and the regulated
target genes. TFs are regulators that recognize specific DNA
sequences and guide the expression of the genome, either
activating or repressing the expression the target genes. The
set of genes controlled by the same TF forms a regulon. This
package provides classes and methods for the reconstruction of
TRNs and analysis of regulons.
biocViews: Transcription, Network, NetworkInference, NetworkEnrichment,
GeneRegulation, GeneExpression, GraphAndNetwork,
GeneSetEnrichment, GeneticVariability
Author: Clarice Groeneveld [ctb], Gordon Robertson [ctb], Xin Wang
[aut], Michael Fletcher [aut], Florian Markowetz [aut], Kerstin
Meyer [aut], and Mauro Castro [aut]
Maintainer: Mauro Castro <mauro.a.castro@gmail.com>
URL: http://dx.doi.org/10.1038/ncomms3464
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RTN
git_branch: RELEASE_3_13
git_last_commit: ae869f1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RTN_2.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RTN_2.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RTN_2.16.0.tgz
vignettes: vignettes/RTN/inst/doc/RTN.html
vignetteTitles: "RTN: reconstruction of transcriptional regulatory
networks and analysis of regulons.""
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RTN/inst/doc/RTN.R
dependsOnMe: RTNduals, RTNsurvival, Fletcher2013b
suggestsMe: geneplast
dependencyCount: 124
Package: RTNduals
Version: 1.16.0
Depends: R(>= 3.6.3), RTN(>= 2.14.1), methods
Imports: graphics, grDevices, stats, utils
Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: 4e44633e931651d7bc7ac2c99e0a87a0
NeedsCompilation: no
Title: Analysis of co-regulation and inference of 'dual regulons'
Description: RTNduals is a tool that searches for possible
co-regulatory loops between regulon pairs generated by the RTN
package. It compares the shared targets in order to infer 'dual
regulons', a new concept that tests whether regulators can
co-operate or compete in influencing targets.
biocViews: GeneRegulation, GeneExpression, NetworkEnrichment,
NetworkInference, GraphAndNetwork
Author: Vinicius S. Chagas, Clarice S. Groeneveld, Gordon Robertson,
Kerstin B. Meyer, Mauro A. A. Castro
Maintainer: Mauro Castro <mauro.a.castro@gmail.com>, Clarice Groeneveld
<clari.groeneveld@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RTNduals
git_branch: RELEASE_3_13
git_last_commit: b30aac2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RTNduals_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RTNduals_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RTNduals_1.16.0.tgz
vignettes: vignettes/RTNduals/inst/doc/RTNduals.html
vignetteTitles: "RTNduals: analysis of co-regulation and inference of
dual regulons."
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RTNduals/inst/doc/RTNduals.R
dependsOnMe: RTNsurvival
dependencyCount: 125
Package: RTNsurvival
Version: 1.16.0
Depends: R(>= 3.6.3), RTN(>= 2.14.1), RTNduals(>= 1.14.1), methods
Imports: survival, RColorBrewer, grDevices, graphics, stats, utils,
scales, data.table, egg, ggplot2, pheatmap, dunn.test
Suggests: Fletcher2013b, knitr, rmarkdown, BiocStyle, RUnit,
BiocGenerics
License: Artistic-2.0
MD5sum: e21b24efa66b7efbd53a7bbb6bc41d32
NeedsCompilation: no
Title: Survival analysis using transcriptional networks inferred by the
RTN package
Description: RTNsurvival is a tool for integrating regulons generated
by the RTN package with survival information. For a given
regulon, the 2-tailed GSEA approach computes a differential
Enrichment Score (dES) for each individual sample, and the dES
distribution of all samples is then used to assess the survival
statistics for the cohort. There are two main survival analysis
workflows: a Cox Proportional Hazards approach used to model
regulons as predictors of survival time, and a Kaplan-Meier
analysis assessing the stratification of a cohort based on the
regulon activity. All plots can be fine-tuned to the user's
specifications.
biocViews: NetworkEnrichment, Survival, GeneRegulation,
GeneSetEnrichment, NetworkInference, GraphAndNetwork
Author: Clarice S. Groeneveld, Vinicius S. Chagas, Mauro A. A. Castro
Maintainer: Clarice Groeneveld <clari.groeneveld@gmail.com>, Mauro A.
A. Castro <mauro.a.castro@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RTNsurvival
git_branch: RELEASE_3_13
git_last_commit: 981ddae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RTNsurvival_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RTNsurvival_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RTNsurvival_1.16.0.tgz
vignettes: vignettes/RTNsurvival/inst/doc/RTNsurvival.html
vignetteTitles: "RTNsurvival: multivariate survival analysis using
transcriptional networks and regulons."
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RTNsurvival/inst/doc/RTNsurvival.R
dependencyCount: 132
Package: RTopper
Version: 1.38.0
Depends: R (>= 2.12.0), Biobase
Imports: limma, multtest
Suggests: org.Hs.eg.db, KEGGREST, GO.db
License: GPL (>= 3) + file LICENSE
MD5sum: 6e9af6b8c5830b6f523d99d3da5882f2
NeedsCompilation: no
Title: This package is designed to perform Gene Set Analysis across
multiple genomic platforms
Description: the RTopper package is designed to perform and integrate
gene set enrichment results across multiple genomic platforms.
biocViews: Microarray
Author: Luigi Marchionni <marchion@jhu.edu>, Svitlana Tyekucheva
<svitlana@jimmy.harvard.edu>
Maintainer: Luigi Marchionni <marchion@jhu.edu>
git_url: https://git.bioconductor.org/packages/RTopper
git_branch: RELEASE_3_13
git_last_commit: 1c351a3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RTopper_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RTopper_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RTopper_1.38.0.tgz
vignettes: vignettes/RTopper/inst/doc/RTopper.pdf
vignetteTitles: RTopper user's manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RTopper/inst/doc/RTopper.R
dependencyCount: 17
Package: Rtpca
Version: 1.2.0
Depends: R (>= 4.0.0), stats, dplyr, tidyr
Imports: Biobase, methods, ggplot2, pROC, fdrtool, splines, utils,
tibble
Suggests: knitr, BiocStyle, TPP, testthat, rmarkdown
License: GPL-3
MD5sum: 507b686e12027997cfb795e1bf50ce04
NeedsCompilation: no
Title: Thermal proximity co-aggregation with R
Description: R package for performing thermal proximity co-aggregation
analysis with thermal proteome profiling datasets to analyse
protein complex assembly and (differential) protein-protein
interactions across conditions.
biocViews: Software, Proteomics, DataImport
Author: Nils Kurzawa [aut, cre], André Mateus [aut], Mikhail M.
Savitski [aut]
Maintainer: Nils Kurzawa <nils.kurzawa@embl.de>
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/Rtpca
git_branch: RELEASE_3_13
git_last_commit: acf0a22
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rtpca_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rtpca_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rtpca_1.2.0.tgz
vignettes: vignettes/Rtpca/inst/doc/Rtpca.html
vignetteTitles: Introduction to Rtpca
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rtpca/inst/doc/Rtpca.R
dependencyCount: 51
Package: rtracklayer
Version: 1.52.1
Depends: R (>= 3.3), methods, GenomicRanges (>= 1.37.2)
Imports: XML (>= 1.98-0), BiocGenerics (>= 0.35.3), S4Vectors (>=
0.23.18), IRanges (>= 2.13.13), XVector (>= 0.19.7),
GenomeInfoDb (>= 1.15.2), Biostrings (>= 2.47.6), zlibbioc,
RCurl (>= 1.4-2), Rsamtools (>= 1.31.2), GenomicAlignments (>=
1.15.6), BiocIO, tools, restfulr (>= 0.0.13)
LinkingTo: S4Vectors, IRanges, XVector
Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1),
genefilter, limma, org.Hs.eg.db, hgu133plus2.db,
GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit
License: Artistic-2.0 + file LICENSE
MD5sum: 9ed518471b5dc09acd93be49bcd7fe42
NeedsCompilation: yes
Title: R interface to genome annotation files and the UCSC genome
browser
Description: Extensible framework for interacting with multiple genome
browsers (currently UCSC built-in) and manipulating annotation
tracks in various formats (currently GFF, BED, bedGraph, BED15,
WIG, BigWig and 2bit built-in). The user may export/import
tracks to/from the supported browsers, as well as query and
modify the browser state, such as the current viewport.
biocViews: Annotation,Visualization,DataImport
Author: Michael Lawrence, Vince Carey, Robert Gentleman
Maintainer: Michael Lawrence <michafla@gene.com>
git_url: https://git.bioconductor.org/packages/rtracklayer
git_branch: RELEASE_3_13
git_last_commit: 20a3831
git_last_commit_date: 2021-08-12
Date/Publication: 2021-08-15
source.ver: src/contrib/rtracklayer_1.52.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rtracklayer_1.52.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/rtracklayer_1.52.1.tgz
vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf
vignetteTitles: rtracklayer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: TRUE
Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R
dependsOnMe: BRGenomics, BSgenome, CAGEfightR, CoverageView, CSSQ,
cummeRbund, ExCluster, geneXtendeR, GenomicFiles, groHMM,
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chromswitch, circRNAprofiler, CNEr, coMET, compartmap, CompGO,
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dmrseq, ELMER, enrichTF, ensembldb, epidecodeR, epigraHMM,
erma, esATAC, fcScan, genbankr, geneAttribution, genomation,
GenomicFeatures, GenomicInteractions, genotypeeval, ggbio,
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HTSeqGenie, icetea, igvR, INSPEcT, IsoformSwitchAnalyzeR,
karyoploteR, MACPET, MADSEQ, maser, MEDIPS, metagene,
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motifbreakR, MotifDb, multicrispr, NADfinder, nanotatoR,
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plyranges, pram, primirTSS, proBAMr, profileplyr, PureCN, qsea,
QuasR, RCAS, recount, recount3, recoup, regioneR, REMP,
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crispRdesignR, GALLO, kibior, PlasmaMutationDetector,
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suggestsMe: alpine, AnnotationHub, autonomics, BiocFileCache,
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interactiveDisplay, megadepth, methylumi, miRBaseConverter,
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RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq,
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GeuvadisTranscriptExpr, nanotubes, PasillaTranscriptExpr,
spatialLIBD, chipseqDB, gkmSVM, LDheatmap, RTIGER, Seurat,
Signac
dependencyCount: 43
Package: Rtreemix
Version: 1.54.0
Depends: R (>= 2.5.0)
Imports: methods, graph, Biobase, Hmisc
Suggests: Rgraphviz
License: LGPL
MD5sum: b6f182c241cdd5a1437959e31e9eed26
NeedsCompilation: yes
Title: Rtreemix: Mutagenetic trees mixture models.
Description: Rtreemix is a package that offers an environment for
estimating the mutagenetic trees mixture models from
cross-sectional data and using them for various predictions. It
includes functions for fitting the trees mixture models,
likelihood computations, model comparisons, waiting time
estimations, stability analysis, etc.
biocViews: StatisticalMethod
Author: Jasmina Bogojeska
Maintainer: Jasmina Bogojeska <jasmina.bogojeska@gmail.com>
git_url: https://git.bioconductor.org/packages/Rtreemix
git_branch: RELEASE_3_13
git_last_commit: 5821181
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Rtreemix_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Rtreemix_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Rtreemix_1.54.0.tgz
vignettes: vignettes/Rtreemix/inst/doc/Rtreemix.pdf
vignetteTitles: Rtreemix
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rtreemix/inst/doc/Rtreemix.R
dependencyCount: 73
Package: rTRM
Version: 1.30.0
Depends: R (>= 2.10), igraph (>= 1.0)
Imports: AnnotationDbi, DBI, RSQLite
Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt,
knitr, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked,
org.Hs.eg.db, org.Mm.eg.db, ggplot2
License: GPL-3
MD5sum: c359ccfe2da7b349aed33690fdf1dce0
NeedsCompilation: no
Title: Identification of transcriptional regulatory modules from PPI
networks
Description: rTRM identifies transcriptional regulatory modules (TRMs)
from protein-protein interaction networks.
biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork
Author: Diego Diez
Maintainer: Diego Diez <diego10ruiz@gmail.com>
URL: https://github.com/ddiez/rTRM
VignetteBuilder: knitr
BugReports: https://github.com/ddiez/rTRM/issues
git_url: https://git.bioconductor.org/packages/rTRM
git_branch: RELEASE_3_13
git_last_commit: 4d427e2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rTRM_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rTRM_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rTRM_1.30.0.tgz
vignettes: vignettes/rTRM/inst/doc/rTRM_Introduction.pdf
vignetteTitles: Introduction to rTRM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rTRM/inst/doc/rTRM_Introduction.R
importsMe: rTRMui
dependencyCount: 51
Package: rTRMui
Version: 1.30.0
Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db
License: GPL-3
MD5sum: 6027308b3166ecff26f61e845d3c6410
NeedsCompilation: no
Title: A shiny user interface for rTRM
Description: This package provides a web interface to compute
transcriptional regulatory modules with rTRM.
biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork, GUI
Author: Diego Diez
Maintainer: Diego Diez <diego10ruiz@gmail.com>
URL: https://github.com/ddiez/rTRMui
BugReports: https://github.com/ddiez/rTRMui/issues
git_url: https://git.bioconductor.org/packages/rTRMui
git_branch: RELEASE_3_13
git_last_commit: d5e4738
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/rTRMui_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rTRMui_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rTRMui_1.30.0.tgz
vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf
vignetteTitles: Introduction to rTRMui
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rTRMui/inst/doc/rTRMui.R
dependencyCount: 96
Package: runibic
Version: 1.14.0
Depends: R (>= 3.4.0), biclust, SummarizedExperiment
Imports: Rcpp (>= 0.12.12), testthat, methods
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, GEOquery, affy, airway, QUBIC
License: MIT + file LICENSE
MD5sum: abd73d123157f9285601c441d88d3b53
NeedsCompilation: yes
Title: runibic: row-based biclustering algorithm for analysis of gene
expression data in R
Description: This package implements UbiBic algorithm in R. This
biclustering algorithm for analysis of gene expression data was
introduced by Zhenjia Wang et al. in 2016. It is currently
considered the most promising biclustering method for
identification of meaningful structures in complex and noisy
data.
biocViews: Microarray, Clustering, GeneExpression, Sequencing, Coverage
Author: Patryk Orzechowski, Artur Pańszczyk
Maintainer: Patryk Orzechowski <patryk.orzechowski@gmail.com>
URL: http://github.com/athril/runibic
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
BugReports: http://github.com/athril/runibic/issues
git_url: https://git.bioconductor.org/packages/runibic
git_branch: RELEASE_3_13
git_last_commit: 71ead1e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/runibic_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/runibic_1.14.0.zip
vignettes: vignettes/runibic/inst/doc/runibic.html
vignetteTitles: runibic: UniBic in R Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependencyCount: 83
Package: RUVcorr
Version: 1.24.0
Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra,
snowfall, psych, BiocParallel, grid, bladderbatch, reshape2,
graphics
Suggests: knitr, hgu133a2.db
License: GPL-2
MD5sum: 9aef327980f82d51b0ac0939ee2b33f9
NeedsCompilation: no
Title: Removal of unwanted variation for gene-gene correlations and
related analysis
Description: RUVcorr allows to apply global removal of unwanted
variation (ridged version of RUV) to real and simulated gene
expression data.
biocViews: GeneExpression, Normalization
Author: Saskia Freytag
Maintainer: Saskia Freytag <saskia.freytag@perkins.uwa.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RUVcorr
git_branch: RELEASE_3_13
git_last_commit: 2673612
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RUVcorr_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RUVcorr_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RUVcorr_1.24.0.tgz
vignettes: vignettes/RUVcorr/inst/doc/Vignette.html
vignetteTitles: Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RUVcorr/inst/doc/Vignette.R
dependencyCount: 35
Package: RUVnormalize
Version: 1.26.0
Depends: R (>= 2.10.0)
Imports: RUVnormalizeData, Biobase
Enhances: spams
License: GPL-3
Archs: i386, x64
MD5sum: 25d4f664fdb539a7f28bacd5d2d55aff
NeedsCompilation: no
Title: RUV for normalization of expression array data
Description: RUVnormalize is meant to remove unwanted variation from
gene expression data when the factor of interest is not
defined, e.g., to clean up a dataset for general use or to do
any kind of unsupervised analysis.
biocViews: StatisticalMethod, Normalization
Author: Laurent Jacob
Maintainer: Laurent Jacob <laurent.jacob@univ-lyon1.fr>
git_url: https://git.bioconductor.org/packages/RUVnormalize
git_branch: RELEASE_3_13
git_last_commit: b11bbc7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RUVnormalize_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RUVnormalize_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RUVnormalize_1.26.0.tgz
vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf
vignetteTitles: RUVnormalize
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RUVnormalize/inst/doc/RUVnormalize.R
dependencyCount: 8
Package: RUVSeq
Version: 1.26.0
Depends: Biobase, EDASeq (>= 1.99.1), edgeR
Imports: methods, MASS
Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2
License: Artistic-2.0
Archs: i386, x64
MD5sum: 3f44498d8b275c806e011f62ab6591f9
NeedsCompilation: no
Title: Remove Unwanted Variation from RNA-Seq Data
Description: This package implements the remove unwanted variation
(RUV) methods of Risso et al. (2014) for the normalization of
RNA-Seq read counts between samples.
biocViews: ImmunoOncology, DifferentialExpression, Preprocessing,
RNASeq, Software
Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Lorena
Pantano [ctb], Kamil Slowikowski [ctb]
Maintainer: Davide Risso <risso.davide@gmail.com>
URL: https://github.com/drisso/RUVSeq
VignetteBuilder: knitr
BugReports: https://github.com/drisso/RUVSeq/issues
git_url: https://git.bioconductor.org/packages/RUVSeq
git_branch: RELEASE_3_13
git_last_commit: b6d90ae
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RUVSeq_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RUVSeq_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RUVSeq_1.26.0.tgz
vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.pdf
vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R
dependsOnMe: rnaseqGene
importsMe: consensusDE, ribosomeProfilingQC, scone
suggestsMe: DEScan2
dependencyCount: 111
Package: RVS
Version: 1.14.0
Depends: R (>= 3.5.0)
Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils
Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation
License: GPL-2
MD5sum: 941bf58b0376cc66796fe480cc487a01
NeedsCompilation: no
Title: Computes estimates of the probability of related individuals
sharing a rare variant
Description: Rare Variant Sharing (RVS) implements tests of association
and linkage between rare genetic variant genotypes and a
dichotomous phenotype, e.g. a disease status, in family
samples. The tests are based on probabilities of rare variant
sharing by relatives under the null hypothesis of absence of
linkage and association between the rare variants and the
phenotype and apply to single variants or multiple variants in
a region (e.g. gene-based test).
biocViews: ImmunoOncology, Genetics, GenomeWideAssociation,
VariantDetection, ExomeSeq, WholeGenome
Author: Alexandre Bureau, Ingo Ruczinski, Samuel Younkin, Thomas
Sherman
Maintainer: Thomas Sherman <tomsherman159@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RVS
git_branch: RELEASE_3_13
git_last_commit: 02ba79e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/RVS_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/RVS_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/RVS_1.14.0.tgz
vignettes: vignettes/RVS/inst/doc/RVS.html
vignetteTitles: The RVS Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RVS/inst/doc/RVS.R
dependencyCount: 35
Package: rWikiPathways
Version: 1.12.0
Imports: httr, utils, XML, rjson, data.table, tidyr, RCurl
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 7dde9ce483c83f5451681526d9989581
NeedsCompilation: no
Title: rWikiPathways - R client library for the WikiPathways API
Description: Use this package to interface with the WikiPathways API.
It provides programmatic access to WikiPathways content in
multiple data and image formats, including official monthly
release files and convenient GMT read/write functions.
biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network,
Metabolomics
Author: Egon Willighagen [aut, cre]
(<https://orcid.org/0000-0001-7542-0286>), Alex Pico [aut]
(<https://orcid.org/0000-0001-5706-2163>)
Maintainer: Egon Willighagen <egon.willighagen@gmail.com>
URL: https://github.com/wikipathways/rwikipathways
VignetteBuilder: knitr
BugReports: https://github.com/wikipathways/rwikipathways/issues
git_url: https://git.bioconductor.org/packages/rWikiPathways
git_branch: RELEASE_3_13
git_last_commit: 1b68185
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-20
source.ver: src/contrib/rWikiPathways_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/rWikiPathways_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/rWikiPathways_1.12.0.tgz
vignettes: vignettes/rWikiPathways/inst/doc/Overview.html,
vignettes/rWikiPathways/inst/doc/Pathway-Analysis.html,
vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.html,
vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.html
vignetteTitles: 1. Overview, 4. Pathway Analysis, 2. rWikiPathways and
BridgeDbR, 3. rWikiPathways and RCy3
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rWikiPathways/inst/doc/Overview.R,
vignettes/rWikiPathways/inst/doc/Pathway-Analysis.R,
vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.R,
vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.R
importsMe: famat, multiSight, TimiRGeN, RVA
suggestsMe: TRONCO
dependencyCount: 36
Package: S4Vectors
Version: 0.30.2
Depends: R (>= 4.0.0), methods, utils, stats, stats4, BiocGenerics (>=
0.37.0)
Suggests: IRanges, GenomicRanges, SummarizedExperiment, Matrix,
DelayedArray, ShortRead, graph, data.table, RUnit, BiocStyle
License: Artistic-2.0
Archs: i386, x64
MD5sum: 7f6216bf0994150690c6dbbe9202eb57
NeedsCompilation: yes
Title: Foundation of vector-like and list-like containers in
Bioconductor
Description: The S4Vectors package defines the Vector and List virtual
classes and a set of generic functions that extend the semantic
of ordinary vectors and lists in R. Package developers can
easily implement vector-like or list-like objects as concrete
subclasses of Vector or List. In addition, a few low-level
concrete subclasses of general interest (e.g. DataFrame, Rle,
and Hits) are implemented in the S4Vectors package itself (many
more are implemented in the IRanges package and in other
Bioconductor infrastructure packages).
biocViews: Infrastructure, DataRepresentation
Author: H. Pagès, M. Lawrence and P. Aboyoun
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/S4Vectors
BugReports: https://github.com/Bioconductor/S4Vectors/issues
git_url: https://git.bioconductor.org/packages/S4Vectors
git_branch: RELEASE_3_13
git_last_commit: 87b7827
git_last_commit_date: 2021-09-30
Date/Publication: 2021-10-03
source.ver: src/contrib/S4Vectors_0.30.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/S4Vectors_0.30.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/S4Vectors_0.30.2.tgz
vignettes: vignettes/S4Vectors/inst/doc/RleTricks.pdf,
vignettes/S4Vectors/inst/doc/S4QuickOverview.pdf,
vignettes/S4Vectors/inst/doc/S4VectorsOverview.pdf
vignetteTitles: Rle Tips and Tricks, A quick overview of the S4 class
system, An Overview of the S4Vectors package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R,
vignettes/S4Vectors/inst/doc/S4QuickOverview.R,
vignettes/S4Vectors/inst/doc/S4VectorsOverview.R
dependsOnMe: altcdfenvs, AnnotationHubData, ATACseqQC, bambu,
Biostrings, BiSeq, BRGenomics, BSgenome, bumphunter, Cardinal,
CellMapper, chimeraviz, ChIPpeakAnno, chipseq, ChIPseqR,
ClassifyR, CODEX, coseq, CSAR, CSSQ, DelayedArray,
DelayedDataFrame, DESeq2, DEXSeq, DirichletMultinomial, DMCFB,
DMCHMM, DMRcaller, epigenomix, epihet, ExperimentHubData,
ExpressionAtlas, fCCAC, GA4GHclient, GenoGAM, GenomeInfoDb,
GenomicAlignments, GenomicFeatures, GenomicRanges,
GenomicScores, GenomicTuples, girafe, groHMM, Gviz,
HelloRanges, InPAS, InTAD, IntEREst, IRanges, LoomExperiment,
MetNet, MotifDb, MSnbase, NADfinder, NanoStringNCTools,
NBAMSeq, OTUbase, padma, plethy, Rcwl, RegEnrich, RepViz,
RNAmodR, RnBeads, scDataviz, segmentSeq, SeqGate, Spectra,
SQLDataFrame, Structstrings, SummarizedBenchmark,
TimeSeriesExperiment, topdownr, TreeSummarizedExperiment,
triplex, VariantExperiment, VariantTools, vulcan, XVector,
pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501,
pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine,
pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine,
pd.canine.2, pd.celegans, pd.chicken, pd.chigene.1.0.st,
pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st,
pd.citrus, pd.clariom.d.human, pd.clariom.s.human,
pd.clariom.s.human.ht, pd.clariom.s.mouse,
pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht,
pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st,
pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array,
pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1,
pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2,
pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st,
pd.equgene.1.1.st, pd.felgene.1.0.st, pd.felgene.1.1.st,
pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5,
pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st,
pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a,
pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219,
pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d,
pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm,
pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800,
pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1,
pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize,
pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240,
pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st,
pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a,
pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c,
pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0,
pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1,
pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st,
pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0,
pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180,
pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st,
pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine,
pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st,
pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1,
pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st,
pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st,
pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c,
pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice,
pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0,
pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean,
pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato,
pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2,
pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98,
pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish,
SNPlocs.Hsapiens.dbSNP141.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, FlowSorted.Blood.EPIC,
generegulation, pagoo
importsMe: ADImpute, affycoretools, aggregateBioVar, airpart, ALDEx2,
AllelicImbalance, alpine, AlpsNMR, amplican, AneuFinder,
animalcules, AnnotationDbi, AnnotationForge, AnnotationHub,
annotatr, appreci8R, ASpediaFI, ASpli, AUCell, autonomics,
BadRegionFinder, ballgown, barcodetrackR, BASiCS, batchelor,
BayesSpace, BiocIO, BiocNeighbors, BiocOncoTK, BiocSet,
BiocSingular, biotmle, biovizBase, biscuiteer, BiSeq, BitSeq,
bluster, bnbc, BPRMeth, BrainSABER, branchpointer, breakpointR,
BSgenome, bsseq, BumpyMatrix, BUSpaRse, CAGEfightR, CAGEr,
casper, CATALYST, cBioPortalData, ccfindR, celaref, celda,
CellaRepertorium, censcyt, CeTF, CHETAH, ChIC, chipenrich,
ChIPexoQual, ChIPQC, ChIPseeker, ChromSCape, chromstaR,
chromswitch, chromVAR, cicero, circRNAprofiler, CiteFuse,
cleaver, CluMSID, clusterExperiment, clustifyr, cn.mops, CNEr,
CNVPanelizer, CNVRanger, COCOA, CoGAPS, coMET, compEpiTools,
consensusDE, consensusSeekeR, contiBAIT, copynumber,
CopywriteR, CoreGx, CoverageView, CRISPRseek, CrispRVariants,
csaw, CTDquerier, cummeRbund, customProDB, cydar, cytomapper,
DAMEfinder, dasper, debrowser, DECIPHER, decompTumor2Sig,
DEFormats, DegNorm, DEGreport, DelayedMatrixStats, derfinder,
derfinderHelper, derfinderPlot, DEScan2, DEWSeq, DiffBind,
diffcyt, diffHic, diffloop, diffUTR, DiscoRhythm, dittoSeq,
DMRcate, dmrseq, doseR, DRIMSeq, DropletUtils,
drugTargetInteractions, easyRNASeq, eegc, eisaR, ELMER,
EnrichmentBrowser, enrichTF, ensembldb, ensemblVEP, epigraHMM,
EpiTxDb, epivizr, epivizrData, epivizrStandalone, erma, esATAC,
EventPointer, ExperimentHub, ExperimentSubset,
ExploreModelMatrix, FastqCleaner, fastseg, FilterFFPE,
FindMyFriends, fishpond, flowCore, flowWorkspace, FRASER,
GA4GHshiny, gcapc, GDSArray, genbankr, GeneRegionScan, GENESIS,
GeneTonic, genomation, genomeIntervals, GenomicAlignments,
GenomicDataCommons, GenomicFiles, GenomicInteractions,
GenomicOZone, GenomicSuperSignature, GeomxTools, ggbio, Glimma,
gmapR, gmoviz, GOpro, GOTHiC, GRmetrics, GSEABenchmarkeR, GSVA,
GUIDEseq, gwascat, h5vc, HDF5Array, HiCBricks, HiCcompare,
HiLDA, hipathia, hmdbQuery, HTSeqGenie,
HumanTranscriptomeCompendium, icetea, ideal, ILoReg, IMAS,
INSPEcT, InteractionSet, InteractiveComplexHeatmap, InterMineR,
iSEE, iSEEu, isomiRs, IVAS, ivygapSE, karyoploteR, kebabs,
lionessR, lipidr, lisaClust, loci2path, LOLA, MACPET, MACSr,
MADSEQ, marr, MAST, MatrixQCvis, mbkmeans, mCSEA, MEAL, meshr,
MesKit, metabCombiner, metaseqR2, MetCirc, MethCP,
methInheritSim, MethReg, methylCC, methylInheritance,
methylKit, methylPipe, methylSig, methylumi, mia, miaViz,
midasHLA, miloR, mimager, minfi, MinimumDistance, MIRA, MiRaGE,
missMethyl, missRows, MMAPPR2, MMDiff2, moanin, Modstrings,
mosaics, MOSim, motifbreakR, motifmatchr, mpra, msa,
MsBackendMassbank, MsBackendMgf, MsCoreUtils, msgbsR, MSPrep,
MultiAssayExperiment, MultiDataSet, mumosa, muscat, musicatk,
MutationalPatterns, mygene, myvariant, NanoMethViz, ncRNAtools,
nearBynding, nucleoSim, nucleR, oligoClasses, ontoProc,
openPrimeR, ORFik, Organism.dplyr, OrganismDbi, OUTRIDER,
packFinder, PAIRADISE, panelcn.mops, PAST, pcaExplorer, PDATK,
pdInfoBuilder, periodicDNA, PharmacoGx, phemd, PhIPData, PhosR,
PING, pipeComp, plyranges, pmp, pogos, polyester, pqsfinder,
pram, prebs, preciseTAD, PrecisionTrialDrawer, primirTSS,
proActiv, procoil, proDA, profileplyr, pulsedSilac, PureCN,
PWMEnrich, qcmetrics, QFeatures, qpgraph, QuasR, R3CPET,
R453Plus1Toolbox, RadioGx, RaggedExperiment, ramr,
RareVariantVis, Rcade, RCAS, RcwlPipelines, recount, recount3,
recountmethylation, recoup, regioneR, regionReport, regsplice,
regutools, REMP, Repitools, ResidualMatrix, restfulSE,
rexposome, rfaRm, RGMQL, rhdf5client, RiboDiPA, RiboProfiling,
ribor, ribosomeProfilingQC, RJMCMCNucleosomes, Rmmquant,
rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML,
RNAmodR.RiboMethSeq, roar, Rqc, Rsamtools, rScudo,
RTCGAToolbox, RTN, rtracklayer, SC3, ScaledMatrix, SCArray,
scater, scClassify, scDblFinder, scDD, scds, scHOT, scmap,
scMerge, SCnorm, SCOPE, scp, scPipe, scran, scruff, scTensor,
scTGIF, scuttle, sechm, SeqArray, seqCAT, seqsetvis, SeqSQC,
SeqVarTools, sesame, SEtools, sevenbridges, sevenC, SGSeq,
ShortRead, SingleCellExperiment, singleCellTK, SingleR,
singscore, sitadela, skewr, slingshot, SMITE, SNPhood, soGGi,
SomaticSignatures, Spaniel, SpatialExperiment, spatialHeatmap,
spicyR, splatter, SplicingGraphs, SPLINTER, sRACIPE, srnadiff,
STAN, strandCheckR, struct, StructuralVariantAnnotation,
SummarizedExperiment, SynExtend, systemPipeR, TAPseq, TarSeqQC,
TBSignatureProfiler, TCGAbiolinks, TCGAutils, TFBSTools, TFHAZ,
tidySingleCellExperiment, tidySummarizedExperiment,
TileDBArray, TnT, ToxicoGx, trackViewer, tradeSeq,
TrajectoryUtils, transcriptR, TransView, Trendy, tricycle,
tRNA, tRNAdbImport, tRNAscanImport, TSCAN, tscR, TSRchitect,
TVTB, twoddpcr, tximeta, Ularcirc, UMI4Cats, universalmotif,
VanillaICE, VariantAnnotation, VariantFiltering, VaSP,
VCFArray, velociraptor, VplotR, wavClusteR, weitrix,
wiggleplotr, XCIR, xcms, XNAString, XVector, yamss,
zellkonverter, fitCons.UCSC.hg19,
MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5,
MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5,
MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5,
MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5,
MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5,
MafDb.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38,
MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19,
MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38,
MafH5.gnomAD.v3.1.1.GRCh38, phastCons100way.UCSC.hg19,
phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38,
SNPlocs.Hsapiens.dbSNP.20101109,
SNPlocs.Hsapiens.dbSNP141.GRCh38,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP151.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP141.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, celldex, chipenrich.data,
chipseqDBData, curatedMetagenomicData, curatedTCGAData,
DropletTestFiles, HighlyReplicatedRNASeq, HMP16SData, HMP2Data,
imcdatasets, leeBamViews, MetaGxPancreas, MethylSeqData,
MouseGastrulationData, MouseThymusAgeing, pd.atdschip.tiling,
scpdata, scRNAseq, SimBenchData, SingleCellMultiModal,
SomaticCancerAlterations, spatialLIBD, ActiveDriverWGS,
BinQuasi, crispRdesignR, digitalDLSorteR, driveR, genBaRcode,
geno2proteo, hoardeR, imcExperiment, LoopRig, microbial,
NIPTeR, oncoPredict, PlasmaMutationDetector, pulseTD, restfulr,
rsolr, SC.MEB, Signac
suggestsMe: BiocGenerics, conclus, dearseq, epivizrChart, globalSeq,
GWASTools, GWENA, maftools, martini, MicrobiotaProcess,
MungeSumstats, RTCGA, TFEA.ChIP, TFutils, tidybulk,
alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38,
curatedAdipoChIP, curatedAdipoRNA, ObMiTi, cancerTiming,
GeoTcgaData, gkmSVM, polyRAD, rliger, Seurat, valr
linksToMe: Biostrings, CNEr, DECIPHER, DelayedArray, GenomicAlignments,
GenomicRanges, HDF5Array, IRanges, kebabs, MatrixRider,
Rsamtools, rtracklayer, ShortRead, Structstrings, triplex,
VariantAnnotation, VariantFiltering, XVector
dependencyCount: 7
Package: safe
Version: 3.32.0
Depends: R (>= 2.4.0), AnnotationDbi, Biobase, methods, SparseM
Suggests: GO.db, PFAM.db, reactome.db, hgu133a.db, breastCancerUPP,
survival, foreach, doRNG, Rgraphviz, GOstats
License: GPL (>= 2)
MD5sum: c4234f9a536190ca17b236f9a49f1eed
NeedsCompilation: no
Title: Significance Analysis of Function and Expression
Description: SAFE is a resampling-based method for testing functional
categories in gene expression experiments. SAFE can be applied
to 2-sample and multi-class comparisons, or simple linear
regressions. Other experimental designs can also be
accommodated through user-defined functions.
biocViews: DifferentialExpression, Pathways, GeneSetEnrichment,
StatisticalMethod, Software
Author: William T. Barry
Maintainer: Ludwig Geistlinger <ludwig_geistlinger@hms.harvard.edu>
git_url: https://git.bioconductor.org/packages/safe
git_branch: RELEASE_3_13
git_last_commit: 0919b3a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/safe_3.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/safe_3.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/safe_3.32.0.tgz
vignettes: vignettes/safe/inst/doc/SAFEmanual3.pdf
vignetteTitles: SAFE manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/safe/inst/doc/SAFEmanual3.R
dependsOnMe: PCGSE
importsMe: EGSEA, EnrichmentBrowser
dependencyCount: 47
Package: sagenhaft
Version: 1.62.0
Depends: R (>= 2.10), SparseM (>= 0.73), methods
Imports: graphics, stats, utils
License: GPL (>= 2)
MD5sum: 9d32ad1afefafe45883a555c875b699a
NeedsCompilation: no
Title: Collection of functions for reading and comparing SAGE libraries
Description: This package implements several functions useful for
analysis of gene expression data by sequencing tags as done in
SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction
of a SAGE library from sequence files, sequence error
correction, library comparison. Sequencing error correction is
implementing using an Expectation Maximization Algorithm based
on a Mixture Model of tag counts.
biocViews: SAGE
Author: Tim Beissbarth <tim.beissbarth@bioinf.med.uni-goettingen.de>,
with contributions from Gordon Smyth <smyth@wehi.edu.au>
Maintainer: Tim Beissbarth
<tim.beissbarth@bioinf.med.uni-goettingen.de>
URL: http://www.bioinf.med.uni-goettingen.de
git_url: https://git.bioconductor.org/packages/sagenhaft
git_branch: RELEASE_3_13
git_last_commit: 202f5f0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sagenhaft_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sagenhaft_1.62.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sagenhaft_1.62.0.tgz
vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf
vignetteTitles: SAGEnhaft
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sagenhaft/inst/doc/SAGEnhaft.R
dependencyCount: 5
Package: SAIGEgds
Version: 1.6.0
Depends: R (>= 3.5.0), gdsfmt (>= 1.20.0), SeqArray (>= 1.31.8), Rcpp
Imports: methods, stats, utils, RcppParallel, SPAtest (>= 3.0.0)
LinkingTo: Rcpp, RcppArmadillo, RcppParallel (>= 5.0.0)
Suggests: parallel, crayon, RUnit, knitr, markdown, rmarkdown,
BiocGenerics, SNPRelate
License: GPL-3
MD5sum: d27b0b17e0e084f536d1dc1546924d1f
NeedsCompilation: yes
Title: Scalable Implementation of Generalized mixed models using GDS
files in Phenome-Wide Association Studies
Description: Scalable implementation of generalized mixed models with
highly optimized C++ implementation and integration with
Genomic Data Structure (GDS) files. It is designed for single
variant tests in large-scale phenome-wide association studies
(PheWAS) with millions of variants and samples, controlling for
sample structure and case-control imbalance. The implementation
is based on the original SAIGE R package (v0.29.4.4 for single
variant tests, Zhou et al. 2018). SAIGEgds also implements some
of the SPAtest functions in C to speed up the calculation of
Saddlepoint approximation. Benchmarks show that SAIGEgds is 5
to 6 times faster than the original SAIGE R package.
biocViews: Software, Genetics, StatisticalMethod
Author: Xiuwen Zheng [aut, cre]
(<https://orcid.org/0000-0002-1390-0708>), Wei Zhou [ctb] (the
original author of the SAIGE R package), J. Wade Davis [ctb]
Maintainer: Xiuwen Zheng <xiuwen.zheng@abbvie.com>
URL: https://github.com/AbbVie-ComputationalGenomics/SAIGEgds
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SAIGEgds
git_branch: RELEASE_3_13
git_last_commit: bbfe3bd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SAIGEgds_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SAIGEgds_1.5.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/SAIGEgds_1.6.0.tgz
vignettes: vignettes/SAIGEgds/inst/doc/SAIGEgds.html
vignetteTitles: SAIGEgds Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SAIGEgds/inst/doc/SAIGEgds.R
dependencyCount: 26
Package: sampleClassifier
Version: 1.16.0
Depends: R (>= 4.0), MGFM, MGFR, annotate
Imports: e1071, ggplot2, stats, utils
Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db
License: Artistic-2.0
MD5sum: 602ce94c491830329450300e397090f6
NeedsCompilation: no
Title: Sample Classifier
Description: The package is designed to classify microarray RNA-seq
gene expression profiles.
biocViews: ImmunoOncology, Classification, Microarray, RNASeq,
GeneExpression
Author: Khadija El Amrani [aut, cre]
Maintainer: Khadija El Amrani <a.khadija@gmx.de>
git_url: https://git.bioconductor.org/packages/sampleClassifier
git_branch: RELEASE_3_13
git_last_commit: 2f67267
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sampleClassifier_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sampleClassifier_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sampleClassifier_1.16.0.tgz
vignettes: vignettes/sampleClassifier/inst/doc/sampleClassifier.pdf
vignetteTitles: sampleClassifier Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sampleClassifier/inst/doc/sampleClassifier.R
dependencyCount: 96
Package: SamSPECTRAL
Version: 1.46.0
Depends: R (>= 3.3.3)
Imports: methods
License: GPL (>= 2)
MD5sum: fe0a4da1747f344a41cd66de3ed9eaab
NeedsCompilation: yes
Title: Identifies cell population in flow cytometry data.
Description: Samples large data such that spectral clustering is
possible while preserving density information in edge weights.
More specifically, given a matrix of coordinates as input,
SamSPECTRAL first builds the communities to sample the data
points. Then, it builds a graph and after weighting the edges
by conductance computation, the graph is passed to a classic
spectral clustering algorithm to find the spectral clusters.
The last stage of SamSPECTRAL is to combine the spectral
clusters. The resulting "connected components" estimate
biological cell populations in the data. See the vignette for
more details on how to use this package, some illustrations,
and simple examples.
biocViews: FlowCytometry, CellBiology, Clustering, Cancer,
FlowCytometry, StemCells, HIV, ImmunoOncology
Author: Habil Zare and Parisa Shooshtari
Maintainer: Habil <zare@u.washington.edu>
git_url: https://git.bioconductor.org/packages/SamSPECTRAL
git_branch: RELEASE_3_13
git_last_commit: 89f0f33
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SamSPECTRAL_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SamSPECTRAL_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SamSPECTRAL_1.46.0.tgz
vignettes: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.pdf
vignetteTitles: A modified spectral clustering method for clustering
Flow Cytometry Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.R
importsMe: ddPCRclust
dependencyCount: 1
Package: sangeranalyseR
Version: 1.2.0
Depends: R (>= 4.0.0), stringr, ape, Biostrings, DECIPHER, parallel,
reshape2, phangorn, sangerseqR, gridExtra, shiny,
shinydashboard, shinyjs, data.table, plotly, DT, zeallot,
excelR, shinycssloaders, ggdendro, shinyWidgets, openxlsx,
tools, rmarkdown, kableExtra, seqinr, BiocStyle, logger
Suggests: knitr, testthat (>= 2.1.0)
License: GPL-2
Archs: i386, x64
MD5sum: 211e06e9893d60dc7bc1549975938d7d
NeedsCompilation: no
Title: sangeranalyseR: a suite of functions for the analysis of Sanger
sequence data in R
Description: This package builds on sangerseqR to allow users to create
contigs from collections of Sanger sequencing reads. It
provides a wide range of options for a number of
commonly-performed actions including read trimming, detecting
secondary peaks, and detecting indels using a reference
sequence. All parameters can be adjusted interactively either
in R or in the associated Shiny applications. There is
extensive online documentation, and the package can outputs
detailed HTML reports, including chromatograms.
biocViews: Genetics, Alignment, Sequencing, SangerSeq, Preprocessing,
QualityControl, Visualization, GUI
Author: Rob Lanfear <rob.lanfear@gmail.com>, Kuan-Hao Chao
<ntueeb05howard@gmail.com>
Maintainer: Kuan-Hao Chao <ntueeb05howard@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sangeranalyseR
git_branch: RELEASE_3_13
git_last_commit: 1dc9873
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sangeranalyseR_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sangeranalyseR_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sangeranalyseR_1.2.0.tgz
vignettes: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.html
vignetteTitles: sangeranalyseR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.R
dependencyCount: 143
Package: sangerseqR
Version: 1.28.0
Depends: R (>= 3.0.2), Biostrings
Imports: methods, shiny
Suggests: BiocStyle, knitr, RUnit, BiocGenerics
License: GPL-2
MD5sum: 9012cee809f4f5c86766e2aba477be5c
NeedsCompilation: no
Title: Tools for Sanger Sequencing Data in R
Description: This package contains several tools for analyzing Sanger
Sequencing data files in R, including reading .scf and .ab1
files, making basecalls and plotting chromatograms.
biocViews: Sequencing, SNP, Visualization
Author: Jonathon T. Hill, Bradley Demarest
Maintainer: Jonathon Hill <jhill@byu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sangerseqR
git_branch: RELEASE_3_13
git_last_commit: cdb10aa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sangerseqR_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sangerseqR_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sangerseqR_1.28.0.tgz
vignettes: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.pdf
vignetteTitles: sangerseqR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.R
dependsOnMe: sangeranalyseR
suggestsMe: CrispRVariants, bold
dependencyCount: 47
Package: SANTA
Version: 2.28.0
Depends: R (>= 4.1), igraph
Imports: graphics, Matrix, methods, stats
Suggests: RUnit, BiocGenerics, knitr, formatR, org.Sc.sgd.db, BioNet,
DLBCL, msm
License: GPL (>= 2)
MD5sum: ab3171d3a6cd965007b1243ff6b5b63a
NeedsCompilation: yes
Title: Spatial Analysis of Network Associations
Description: This package provides methods for measuring the strength
of association between a network and a phenotype. It does this
by measuring clustering of the phenotype across the network
(Knet). Vertices can also be individually ranked by their
strength of association with high-weight vertices (Knode).
biocViews: Network, NetworkEnrichment, Clustering
Author: Alex Cornish [cre, aut]
Maintainer: Alex Cornish <alex.cornish@icr.ac.uk>
VignetteBuilder: knitr
BugReports: https://github.com/alexjcornish/SANTA
git_url: https://git.bioconductor.org/packages/SANTA
git_branch: RELEASE_3_13
git_last_commit: 574349d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SANTA_2.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SANTA_2.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SANTA_2.28.0.tgz
vignettes: vignettes/SANTA/inst/doc/SANTA-vignette.html
vignetteTitles: Introduction to SANTA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R
dependencyCount: 11
Package: sarks
Version: 1.4.0
Depends: R (>= 4.0)
Imports: rJava, Biostrings, IRanges, utils, stats, cluster, binom
Suggests: RUnit, BiocGenerics, ggplot2
License: BSD_3_clause + file LICENSE
Archs: i386, x64
MD5sum: 06edd5d7bd169c7134266a62c5f06a70
NeedsCompilation: no
Title: Suffix Array Kernel Smoothing for discovery of correlative
sequence motifs and multi-motif domains
Description: Suffix Array Kernel Smoothing (see
https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797),
or SArKS, identifies sequence motifs whose presence correlates
with numeric scores (such as differential expression
statistics) assigned to the sequences (such as gene promoters).
SArKS smooths over sequence similarity, quantified by location
within a suffix array based on the full set of input sequences.
A second round of smoothing over spatial proximity within
sequences reveals multi-motif domains. Discovered motifs can
then be merged or extended based on adjacency within MMDs.
False positive rates are estimated and controlled by
permutation testing.
biocViews: MotifDiscovery, GeneRegulation, GeneExpression,
Transcriptomics, RNASeq, DifferentialExpression,
FeatureExtraction
Author: Dennis Wylie [aut, cre]
(<https://orcid.org/0000-0003-0380-3549>)
Maintainer: Dennis Wylie <denniscwylie@gmail.com>
URL:
https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797,
https://github.com/denniscwylie/sarks
SystemRequirements: Java (>= 1.8)
BugReports: https://github.com/denniscwylie/sarks/issues
git_url: https://git.bioconductor.org/packages/sarks
git_branch: RELEASE_3_13
git_last_commit: 47f8e9b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sarks_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sarks_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sarks_1.4.0.tgz
vignettes: vignettes/sarks/inst/doc/sarks-vignette.pdf
vignetteTitles: sarks-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sarks/inst/doc/sarks-vignette.R
dependencyCount: 22
Package: satuRn
Version: 1.0.0
Depends: R (>= 4.1)
Imports: locfdr, SummarizedExperiment, BiocParallel, limma, pbapply,
ggplot2, boot, Matrix, stats, methods, graphics
Suggests: knitr, rmarkdown, testthat, covr, BiocStyle, AnnotationHub,
ensembldb, edgeR, DEXSeq, stageR, DelayedArray
License: Artistic-2.0
MD5sum: d1f906084636cce8a0591e92072871d8
NeedsCompilation: no
Title: Scalable Analysis of Differential Transcript Usage for Bulk and
Single-Cell RNA-sequencing Applications
Description: satuRn provides a higly performant and scalable framework
for performing differential transcript usage analyses. The
package consists of three main functions. The first function,
fitDTU, fits quasi-binomial generalized linear models that
model transcript usage in different groups of interest. The
second function, testDTU, tests for differential usage of
transcripts between groups of interest. Finally, plotDTU
visualizes the usage profiles of transcripts in groups of
interest.
biocViews: Regression, ExperimentalDesign, DifferentialExpression,
GeneExpression, RNASeq, Sequencing, Software, SingleCell,
Transcriptomics, MultipleComparison, Visualization
Author: Jeroen Gilis [aut, cre], Kristoffer Vitting-Seerup [ctb], Koen
Van den Berge [ctb], Lieven Clement [ctb]
Maintainer: Jeroen Gilis <jeroen.gilis@ugent.be>
URL: https://github.com/statOmics/satuRn
VignetteBuilder: knitr
BugReports: https://github.com/statOmics/satuRn/issues
git_url: https://git.bioconductor.org/packages/satuRn
git_branch: RELEASE_3_13
git_last_commit: d3fe0b2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/satuRn_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/satuRn_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/satuRn_1.0.0.tgz
vignettes: vignettes/satuRn/inst/doc/Vignette.html
vignetteTitles: satuRn - vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/satuRn/inst/doc/Vignette.R
dependencyCount: 67
Package: savR
Version: 1.30.0
Depends: ggplot2
Imports: methods, reshape2, scales, gridExtra, XML
Suggests: Cairo, testthat
License: AGPL-3
Archs: i386, x64
MD5sum: 1c4e64ccd32332f676cce28df324a293
NeedsCompilation: no
Title: Parse and analyze Illumina SAV files
Description: Parse Illumina Sequence Analysis Viewer (SAV) files,
access data, and generate QC plots.
biocViews: Sequencing
Author: R. Brent Calder
Maintainer: R. Brent Calder <brent.calder@einstein.yu.edu>
URL: https://github.com/bcalder/savR
BugReports: https://github.com/bcalder/savR/issues
git_url: https://git.bioconductor.org/packages/savR
git_branch: RELEASE_3_13
git_last_commit: 2e0da91
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/savR_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/savR_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/savR_1.30.0.tgz
vignettes: vignettes/savR/inst/doc/savR.pdf
vignetteTitles: Using savR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/savR/inst/doc/savR.R
dependencyCount: 46
Package: SBGNview
Version: 1.6.0
Depends: R (>= 3.6), pathview, SBGNview.data
Imports: Rdpack, grDevices, methods, stats, utils, xml2, rsvg, igraph,
rmarkdown, knitr, SummarizedExperiment, AnnotationDbi, httr,
KEGGREST, bookdown
Suggests: testthat, gage
License: AGPL-3
MD5sum: 9cf5eef8e82aa1c2e96cf4c2a6041f2b
NeedsCompilation: no
Title: "SBGNview: Data Analysis, Integration and Visualization on SBGN
Pathways"
Description: SBGNview is a tool set for pathway based data
visalization, integration and analysis. SBGNview is similar and
complementary to the widely used Pathview, with the following
key features: 1. Pathway definition by the widely adopted
Systems Biology Graphical Notation (SBGN); 2. Supports multiple
major pathway databases beyond KEGG (Reactome, MetaCyc, SMPDB,
PANTHER, METACROP) and user defined pathways; 3. Covers 5,200
reference pathways and over 3,000 species by default; 4.
Extensive graphics controls, including glyph and edge
attributes, graph layout and sub-pathway highlight; 5. SBGN
pathway data manipulation, processing, extraction and analysis.
biocViews: GeneTarget, Pathways, GraphAndNetwork, Visualization,
GeneSetEnrichment, DifferentialExpression, GeneExpression,
Microarray, RNASeq, Genetics, Metabolomics, Proteomics,
SystemsBiology, Sequencing, GeneTarget
Author: Xiaoxi Dong*, Kovidh Vegesna*, Weijun Luo
Maintainer: Weijun Luo <luo_weijun@yahoo.com>
URL: https://github.com/datapplab/SBGNview
VignetteBuilder: knitr
BugReports: https://github.com/datapplab/SBGNview/issues
git_url: https://git.bioconductor.org/packages/SBGNview
git_branch: RELEASE_3_13
git_last_commit: 7fc4f04
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SBGNview_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SBGNview_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SBGNview_1.6.0.tgz
vignettes:
vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.html,
vignettes/SBGNview/inst/doc/SBGNview.quick.start.html,
vignettes/SBGNview/inst/doc/SBGNview.Vignette.html
vignetteTitles: Pathway analysis using SBGNview gene set, Quick start
SBGNview, SBGNview functions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.R,
vignettes/SBGNview/inst/doc/SBGNview.quick.start.R,
vignettes/SBGNview/inst/doc/SBGNview.Vignette.R
dependencyCount: 81
Package: SBMLR
Version: 1.88.0
Depends: XML, deSolve
Suggests: rsbml
License: GPL-2
MD5sum: ff7ae234895c5a9acb744ab453342f8a
NeedsCompilation: no
Title: SBML-R Interface and Analysis Tools
Description: This package contains a systems biology markup language
(SBML) interface to R.
biocViews: GraphAndNetwork, Pathways, Network
Author: Tomas Radivoyevitch, Vishak Venkateswaran
Maintainer: Tomas Radivoyevitch <radivot@gmail.com>
URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html
git_url: https://git.bioconductor.org/packages/SBMLR
git_branch: RELEASE_3_13
git_last_commit: e247ba0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SBMLR_1.88.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SBMLR_1.88.0.zip
vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf
vignetteTitles: Quick intro to SBMLR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SBMLR/inst/doc/quick-start.R
dependencyCount: 7
Package: SC3
Version: 1.20.0
Depends: R(>= 3.3)
Imports: graphics, stats, utils, methods, e1071, parallel, foreach,
doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8), ROCR,
robustbase, rrcov, cluster, WriteXLS, Rcpp (>= 0.11.1),
SummarizedExperiment, SingleCellExperiment, BiocGenerics,
S4Vectors
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, mclust, scater
License: GPL-3
Archs: i386, x64
MD5sum: 468b95a01770bc43e20cbdd3a34cc52f
NeedsCompilation: yes
Title: Single-Cell Consensus Clustering
Description: A tool for unsupervised clustering and analysis of single
cell RNA-Seq data.
biocViews: ImmunoOncology, SingleCell, Software, Classification,
Clustering, DimensionReduction, SupportVectorMachine, RNASeq,
Visualization, Transcriptomics, DataRepresentation, GUI,
DifferentialExpression, Transcription
Author: Vladimir Kiselev
Maintainer: Vladimir Kiselev <vladimir.yu.kiselev@gmail.com>
URL: https://github.com/hemberg-lab/SC3
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/sc3/
git_url: https://git.bioconductor.org/packages/SC3
git_branch: RELEASE_3_13
git_last_commit: 2fc947c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SC3_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SC3_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SC3_1.20.0.tgz
vignettes: vignettes/SC3/inst/doc/SC3.html
vignetteTitles: SC3 package manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SC3/inst/doc/SC3.R
importsMe: FEAST
suggestsMe: InteractiveComplexHeatmap
dependencyCount: 100
Package: Scale4C
Version: 1.14.0
Depends: R (>= 3.4), smoothie, GenomicRanges, IRanges,
SummarizedExperiment
Imports: methods, grDevices, graphics, utils
License: LGPL-3
MD5sum: 8a2a06a4a228e6db3a08cff9a703fda2
NeedsCompilation: no
Title: Scale4C: an R/Bioconductor package for scale-space
transformation of 4C-seq data
Description: Scale4C is an R/Bioconductor package for scale-space
transformation and visualization of 4C-seq data. The
scale-space transformation is a multi-scale visualization
technique to transform a 2D signal (e.g. 4C-seq reads on a
genomic interval of choice) into a tesselation in the scale
space (2D, genomic position x scale factor) by applying
different smoothing kernels (Gauss, with increasing sigma).
This transformation allows for explorative analysis and
comparisons of the data's structure with other samples.
biocViews: Visualization, QualityControl, DataImport, Sequencing,
Coverage
Author: Carolin Walter
Maintainer: Carolin Walter <carolin.walter@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/Scale4C
git_branch: RELEASE_3_13
git_last_commit: c276a31
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Scale4C_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Scale4C_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Scale4C_1.14.0.tgz
vignettes: vignettes/Scale4C/inst/doc/vignette.pdf
vignetteTitles: Scale4C: an R/Bioconductor package for scale-space
transformation of 4C-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Scale4C/inst/doc/vignette.R
dependencyCount: 27
Package: ScaledMatrix
Version: 1.0.0
Imports: methods, Matrix, S4Vectors, DelayedArray
Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular
License: GPL-3
MD5sum: ebde9157e2153e388c704a3fcf837dd4
NeedsCompilation: no
Title: Creating a DelayedMatrix of Scaled and Centered Values
Description: Provides delayed computation of a matrix of scaled and
centered values. The result is equivalent to using the scale()
function but avoids explicit realization of a dense matrix
during block processing. This permits greater efficiency in
common operations, most notably matrix multiplication.
biocViews: Software, DataRepresentation
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/ScaledMatrix
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/ScaledMatrix/issues
git_url: https://git.bioconductor.org/packages/ScaledMatrix
git_branch: RELEASE_3_13
git_last_commit: 84cb9ed
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ScaledMatrix_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ScaledMatrix_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ScaledMatrix_1.0.0.tgz
vignettes: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.html
vignetteTitles: Using the ScaledMatrix
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.R
importsMe: batchelor, BiocSingular, mumosa, scPCA
suggestsMe: scran
dependencyCount: 16
Package: scAlign
Version: 1.6.0
Depends: R (>= 3.6), SingleCellExperiment (>= 1.4), Seurat (>= 2.3.4),
tensorflow, purrr, irlba, Rtsne, ggplot2, methods, utils, FNN
Suggests: knitr, rmarkdown, testthat
License: GPL-3
MD5sum: f22bab87829351b77dcc701be7454cd9
NeedsCompilation: no
Title: An alignment and integration method for single cell genomics
Description: An unsupervised deep learning method for data alignment,
integration and estimation of per-cell differences in -omic
data (e.g. gene expression) across datasets (conditions,
tissues, species). See Johansen and Quon (2019)
<doi:10.1101/504944> for more details.
biocViews: SingleCell, Transcriptomics, DimensionReduction,
NeuralNetwork
Author: Nelson Johansen [aut, cre], Gerald Quon [aut]
Maintainer: Nelson Johansen <njjohansen@ucdavis.edu>
URL: https://github.com/quon-titative-biology/scAlign
SystemRequirements: python (< 3.7), tensorflow
VignetteBuilder: knitr
BugReports: https://github.com/quon-titative-biology/scAlign/issues
git_url: https://git.bioconductor.org/packages/scAlign
git_branch: RELEASE_3_13
git_last_commit: 8ef122b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scAlign_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scAlign_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scAlign_1.6.0.tgz
vignettes: vignettes/scAlign/inst/doc/scAlign.pdf
vignetteTitles: alignment_tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scAlign/inst/doc/scAlign.R
dependencyCount: 165
Package: SCAN.UPC
Version: 2.34.0
Depends: R (>= 2.14.0), Biobase (>= 2.6.0), oligo, Biostrings,
GEOquery, affy, affyio, foreach, sva
Imports: utils, methods, MASS, tools, IRanges
Suggests: pd.hg.u95a
License: MIT
MD5sum: 5d2e6f1d56006e1506d44cce5c1bb10b
NeedsCompilation: no
Title: Single-channel array normalization (SCAN) and Universal
exPression Codes (UPC)
Description: SCAN is a microarray normalization method to facilitate
personalized-medicine workflows. Rather than processing
microarray samples as groups, which can introduce biases and
present logistical challenges, SCAN normalizes each sample
individually by modeling and removing probe- and array-specific
background noise using only data from within each array. SCAN
can be applied to one-channel (e.g., Affymetrix) or two-channel
(e.g., Agilent) microarrays. The Universal exPression Codes
(UPC) method is an extension of SCAN that estimates whether a
given gene/transcript is active above background levels in a
given sample. The UPC method can be applied to one-channel or
two-channel microarrays as well as to RNA-Seq read counts.
Because UPC values are represented on the same scale and have
an identical interpretation for each platform, they can be used
for cross-platform data integration.
biocViews: ImmunoOncology, Software, Microarray, Preprocessing, RNASeq,
TwoChannel, OneChannel
Author: Stephen R. Piccolo and Andrea H. Bild and W. Evan Johnson
Maintainer: Stephen R. Piccolo <stephen_piccolo@byu.edu>
URL: http://bioconductor.org, http://jlab.bu.edu/software/scan-upc
git_url: https://git.bioconductor.org/packages/SCAN.UPC
git_branch: RELEASE_3_13
git_last_commit: 03c0797
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SCAN.UPC_2.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SCAN.UPC_2.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SCAN.UPC_2.34.0.tgz
vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf
vignetteTitles: Primer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.R
dependencyCount: 108
Package: SCANVIS
Version: 1.6.0
Depends: R (>= 3.6)
Imports: IRanges,plotrix,RCurl,rtracklayer
Suggests: knitr, rmarkdown
License: file LICENSE
MD5sum: df370188ad88be90e3ad81a51810d65f
NeedsCompilation: no
Title: SCANVIS - a tool for SCoring, ANnotating and VISualizing splice
junctions
Description: SCANVIS is a set of annotation-dependent tools for
analyzing splice junctions and their read support as
predetermined by an alignment tool of choice (for example, STAR
aligner). SCANVIS assesses each junction's relative read
support (RRS) by relating to the context of local split reads
aligning to annotated transcripts. SCANVIS also annotates each
splice junction by indicating whether the junction is supported
by annotation or not, and if not, what type of junction it is
(e.g. exon skipping, alternative 5' or 3' events, Novel Exons).
Unannotated junctions are also futher annotated by indicating
whether it induces a frame shift or not. SCANVIS includes a
visualization function to generate static sashimi-style plots
depicting relative read support and number of split reads using
arc thickness and arc heights, making it easy for users to spot
well-supported junctions. These plots also clearly delineate
unannotated junctions from annotated ones using designated
color schemes, and users can also highlight splice junctions of
choice. Variants and/or a read profile are also incoroporated
into the plot if the user supplies variants in bed format
and/or the BAM file. One further feature of the visualization
function is that users can submit multiple samples of a certain
disease or cohort to generate a single plot - this occurs via a
"merge" function wherein junction details over multiple samples
are merged to generate a single sashimi plot, which is useful
when contrasting cohorots (eg. disease vs control).
biocViews:
Software,ResearchField,Transcriptomics,WorkflowStep,Annotation,Visualization
Author: Phaedra Agius <pagius@nygenome.org>
Maintainer: Phaedra Agius <pagius@nygenome.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SCANVIS
git_branch: RELEASE_3_13
git_last_commit: 17a205b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SCANVIS_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SCANVIS_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SCANVIS_1.6.0.tgz
vignettes: vignettes/SCANVIS/inst/doc/runningSCANVIS.pdf
vignetteTitles: SCANVIS
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SCANVIS/inst/doc/runningSCANVIS.R
dependencyCount: 45
Package: SCArray
Version: 1.0.0
Depends: R (>= 3.5.0), gdsfmt (>= 1.27.4), methods, DelayedArray
Imports: BiocGenerics, S4Vectors, IRanges, utils, SummarizedExperiment,
SingleCellExperiment
Suggests: Matrix, DelayedMatrixStats, scater, uwot, RUnit, knitr,
markdown, rmarkdown
License: GPL-3
Archs: i386, x64
MD5sum: 3e2aae1500df295c9f7ab32f0d132353
NeedsCompilation: no
Title: Large-scale single-cell RNA-seq data manipulation with GDS files
Description: Provides large-scale single-cell RNA-seq data manipulation
using Genomic Data Structure (GDS) files. It combines dense and
sparse matrices stored in GDS files and the Bioconductor
infrastructure framework (SingleCellExperiment and
DelayedArray) to provide out-of-memory data storage and
large-scale manipulation using the R programming language.
biocViews: Infrastructure, DataRepresentation, DataImport, SingleCell,
RNASeq
Author: Xiuwen Zheng [aut, cre]
(<https://orcid.org/0000-0002-1390-0708>)
Maintainer: Xiuwen Zheng <xiuwen.zheng@abbvie.com>
URL: https://github.com/AbbVie-ComputationalGenomics/SCArray
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SCArray
git_branch: RELEASE_3_13
git_last_commit: 0faf080
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SCArray_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SCArray_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SCArray_1.0.0.tgz
vignettes: vignettes/SCArray/inst/doc/SCArray.html
vignetteTitles: Single-cell RNA-seq data manipulation using GDS files
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCArray/inst/doc/SCArray.R
dependencyCount: 28
Package: SCATE
Version: 1.2.0
Depends: parallel, preprocessCore, splines, splines2, xgboost,
SCATEData, Rtsne, mclust
Imports: utils, stats, GenomicAlignments, GenomicRanges
Suggests: rmarkdown, ggplot2, knitr
License: MIT + file LICENSE
MD5sum: d7f1a1de6fd7ed20b04eee2438f298fb
NeedsCompilation: no
Title: SCATE: Single-cell ATAC-seq Signal Extraction and Enhancement
Description: SCATE is a software tool for extracting and enhancing the
sparse and discrete Single-cell ATAC-seq Signal. Single-cell
sequencing assay for transposase-accessible chromatin
(scATAC-seq) is the state-of-the-art technology for analyzing
genome-wide regulatory landscapes in single cells. Single-cell
ATAC-seq data are sparse and noisy, and analyzing such data is
challenging. Existing computational methods cannot accurately
reconstruct activities of individual cis-regulatory elements
(CREs) in individual cells or rare cell subpopulations. SCATE
was developed to adaptively integrate information from
co-activated CREs, similar cells, and publicly available
regulome data and substantially increase the accuracy for
estimating activities of individual CREs. We demonstrate that
SCATE can be used to better reconstruct the regulatory
landscape of a heterogeneous sample.
biocViews: ExperimentHub, ExperimentData, Genome, SequencingData,
SingleCellData, SNPData
Author: Zhicheng Ji [aut], Weiqiang Zhou [aut], Wenpin Hou [cre, aut]
(<https://orcid.org/0000-0003-0972-2192>), Hongkai Ji [aut]
Maintainer: Wenpin Hou <wp.hou3@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/Winnie09/SCATE/issues
git_url: https://git.bioconductor.org/packages/SCATE
git_branch: RELEASE_3_13
git_last_commit: f1f5a43
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SCATE_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SCATE_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SCATE_1.2.0.tgz
vignettes: vignettes/SCATE/inst/doc/SCATE.html
vignetteTitles: 1. SCATE package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SCATE/inst/doc/SCATE.R
dependencyCount: 116
Package: scater
Version: 1.20.1
Depends: SingleCellExperiment, scuttle, ggplot2
Imports: stats, utils, methods, grid, gridExtra, Matrix, BiocGenerics,
S4Vectors, SummarizedExperiment, DelayedArray,
DelayedMatrixStats, beachmat, BiocNeighbors, BiocSingular,
BiocParallel, rlang, ggbeeswarm, viridis, Rtsne, RColorBrewer
Suggests: BiocStyle, biomaRt, cowplot, destiny, knitr, scRNAseq,
robustbase, rmarkdown, uwot, NMF, testthat, pheatmap, snifter,
Biobase
License: GPL-3
MD5sum: 0ebf286f6adb03eef17024a4de3af76a
NeedsCompilation: no
Title: Single-Cell Analysis Toolkit for Gene Expression Data in R
Description: A collection of tools for doing various analyses of
single-cell RNA-seq gene expression data, with a focus on
quality control and visualization.
biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl,
Preprocessing, Normalization, Visualization,
DimensionReduction, Transcriptomics, GeneExpression,
Sequencing, Software, DataImport, DataRepresentation,
Infrastructure, Coverage
Author: Davis McCarthy [aut], Kieran Campbell [aut], Aaron Lun [aut,
ctb], Quin Wills [aut], Vladimir Kiselev [ctb], Felix G.M.
Ernst [ctb], Alan O'Callaghan [ctb, cre]
Maintainer: Alan O'Callaghan <alan.ocallaghan@outlook.com>
URL: http://bioconductor.org/packages/scater/
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/scater
git_branch: RELEASE_3_13
git_last_commit: 67e2515
git_last_commit_date: 2021-05-24
Date/Publication: 2021-06-15
source.ver: src/contrib/scater_1.20.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scater_1.20.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/scater_1.20.1.tgz
vignettes: vignettes/scater/inst/doc/overview.html
vignetteTitles: Overview of scater functionality
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scater/inst/doc/overview.R
dependsOnMe: netSmooth, OSCA.advanced, OSCA.basic, OSCA.intro,
OSCA.multisample, OSCA.workflows
importsMe: airpart, BayesSpace, CATALYST, celda, CelliD, CellMixS,
ChromSCape, conclus, distinct, IRISFGM, mia, miaViz, muscat,
netDx, peco, pipeComp, scDblFinder, scPipe, singleCellTK,
Spaniel, splatter, tricycle, spatialLIBD, SC.MEB
suggestsMe: batchelor, bluster, CellaRepertorium, CellTrails, CiteFuse,
dittoSeq, ExperimentSubset, fcoex, InteractiveComplexHeatmap,
iSEE, iSEEu, M3Drop, MAST, mbkmeans, miloR, miQC, monocle,
mumosa, Nebulosa, SC3, SCArray, scds, schex, scHOT, scMerge,
scone, scp, scran, scRepertoire, SingleR, slalom, snifter,
SummarizedBenchmark, tidySingleCellExperiment, velociraptor,
waddR, curatedMetagenomicData, DuoClustering2018, HCAData,
muscData, SingleCellMultiModal, TabulaMurisData,
simpleSingleCell, SingleRBook, bcTSNE
dependencyCount: 81
Package: scBFA
Version: 1.6.0
Depends: R (>= 3.6)
Imports: SingleCellExperiment, SummarizedExperiment, Seurat, MASS,
zinbwave, stats, copula, ggplot2, DESeq2, utils, grid, methods,
Matrix
Suggests: knitr, rmarkdown, testthat, Rtsne
License: GPL-3 + file LICENSE
MD5sum: e6033ef482f6bc377d88ea907a6a6dc3
NeedsCompilation: no
Title: A dimensionality reduction tool using gene detection pattern to
mitigate noisy expression profile of scRNA-seq
Description: This package is designed to model gene detection pattern
of scRNA-seq through a binary factor analysis model. This model
allows user to pass into a cell level covariate matrix X and
gene level covariate matrix Q to account for nuisance
variance(e.g batch effect), and it will output a low
dimensional embedding matrix for downstream analysis.
biocViews: SingleCell, Transcriptomics,
DimensionReduction,GeneExpression, ATACSeq, BatchEffect, KEGG,
QualityControl
Author: Ruoxin Li [aut, cre], Gerald Quon [aut]
Maintainer: Ruoxin Li <uskli@ucdavis.edu>
URL: https://github.com/ucdavis/quon-titative-biology/BFA
VignetteBuilder: knitr
BugReports: https://github.com/ucdavis/quon-titative-biology/BFA/issues
git_url: https://git.bioconductor.org/packages/scBFA
git_branch: RELEASE_3_13
git_last_commit: 7c8cbac
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scBFA_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scBFA_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scBFA_1.6.0.tgz
vignettes: vignettes/scBFA/inst/doc/vignette.html
vignetteTitles: Gene Detection Analysis for scRNA-seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scBFA/inst/doc/vignette.R
dependencyCount: 190
Package: SCBN
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: stats
Suggests: knitr,rmarkdown
License: GPL-2
MD5sum: 7c1632832efb8cb66d02267e1b3191d7
NeedsCompilation: no
Title: A statistical normalization method and differential expression
analysis for RNA-seq data between different species
Description: This package provides a scale based normalization (SCBN)
method to identify genes with differential expression between
different species. It takes into account the available
knowledge of conserved orthologous genes and the hypothesis
testing framework to detect differentially expressed
orthologous genes. The method on this package are described in
the article 'A statistical normalization method and
differential expression analysis for RNA-seq data between
different species' by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui
Wang, Bingqing Lin, Jun Zhang (2018, pending publication).
biocViews: DifferentialExpression, GeneExpression, Normalization
Author: Yan Zhou
Maintainer: Yan Zhou <2160090406@email.szu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SCBN
git_branch: RELEASE_3_13
git_last_commit: 772ef87
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SCBN_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SCBN_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SCBN_1.10.0.tgz
vignettes: vignettes/SCBN/inst/doc/SCBN.html
vignetteTitles: SCBN Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCBN/inst/doc/SCBN.R
dependencyCount: 1
Package: scCB2
Version: 1.2.0
Depends: R (>= 3.6.0)
Imports: SingleCellExperiment, SummarizedExperiment, Matrix, methods,
utils, stats, edgeR, rhdf5, parallel, DropletUtils, doParallel,
iterators, foreach, Seurat
Suggests: testthat (>= 2.1.0), KernSmooth, beachmat, knitr, BiocStyle,
rmarkdown
License: GPL-3
MD5sum: 622d6e39e455e2a1d058df2ae078574e
NeedsCompilation: yes
Title: CB2 improves power of cell detection in droplet-based
single-cell RNA sequencing data
Description: scCB2 is an R package implementing CB2 for distinguishing
real cells from empty droplets in droplet-based single cell
RNA-seq experiments (especially for 10x Chromium). It is based
on clustering similar barcodes and calculating Monte-Carlo
p-value for each cluster to test against background
distribution. This cluster-level test outperforms
single-barcode-level tests in dealing with low count barcodes
and homogeneous sequencing library, while keeping FDR well
controlled.
biocViews: DataImport, RNASeq, SingleCell, Sequencing, GeneExpression,
Transcriptomics, Preprocessing, Clustering
Author: Zijian Ni [aut, cre], Shuyang Chen [ctb], Christina Kendziorski
[ctb]
Maintainer: Zijian Ni <zni25@wisc.edu>
URL: https://github.com/zijianni/scCB2
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/zijianni/scCB2/issues
git_url: https://git.bioconductor.org/packages/scCB2
git_branch: RELEASE_3_13
git_last_commit: 6a7ccb6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scCB2_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scCB2_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scCB2_1.2.0.tgz
vignettes: vignettes/scCB2/inst/doc/scCB2.html
vignetteTitles: CB2 improves power of cell detection in droplet-based
single-cell RNA sequencing data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scCB2/inst/doc/scCB2.R
dependencyCount: 180
Package: scClassifR
Version: 1.0.0
Depends: R (>= 4.1), Seurat, SingleCellExperiment, SummarizedExperiment
Imports: dplyr, ggplot2, caret, ROCR, pROC, data.tree, methods, stats,
e1071, ape, kernlab, utils
Suggests: knitr, scRNAseq, testthat
License: MIT + file LICENSE
MD5sum: 41f0161d0a18d7c1aed2eff1ff9e5469
NeedsCompilation: no
Title: Pretrained learning models for cell type prediction on single
cell RNA-sequencing data
Description: The package comprises a set of pretrained machine learning
models to predict basic immune cell types. This enables all
users to quickly get a first annotation of the cell types
present in their dataset without requiring prior knowledge.
scClassifR also allows users to train their own models to
predict new cell types based on specific research needs.
biocViews: SingleCell, Transcriptomics, GeneExpression,
SupportVectorMachine, Classification, Software
Author: Vy Nguyen [aut] (<https://orcid.org/0000-0003-3436-3662>),
Johannes Griss [cre] (<https://orcid.org/0000-0003-2206-9511>)
Maintainer: Johannes Griss <johannes.griss@meduniwien.ac.at>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scClassifR
git_branch: RELEASE_3_13
git_last_commit: 2be48cc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scClassifR_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scClassifR_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scClassifR_1.0.0.tgz
vignettes: vignettes/scClassifR/inst/doc/classifying-cells.html,
vignettes/scClassifR/inst/doc/training-basic-model.html,
vignettes/scClassifR/inst/doc/training-child-model.html
vignetteTitles: 1. Introduction to scClassifR, 2. Training basic model,
3. Training child model
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scClassifR/inst/doc/classifying-cells.R,
vignettes/scClassifR/inst/doc/training-basic-model.R,
vignettes/scClassifR/inst/doc/training-child-model.R
dependencyCount: 178
Package: scClassify
Version: 1.4.0
Depends: R (>= 4.0)
Imports: S4Vectors, limma, ggraph, igraph, methods, cluster,
minpack.lm, mixtools, BiocParallel, proxy, proxyC, Matrix,
ggplot2, hopach, diptest, mgcv, stats, graphics, statmod
Suggests: knitr, rmarkdown, BiocStyle, pkgdown
License: GPL-3
MD5sum: 50f7bc00f4f0a12587fd6a46c4543090
NeedsCompilation: no
Title: scClassify: single-cell Hierarchical Classification
Description: scClassify is a multiscale classification framework for
single-cell RNA-seq data based on ensemble learning and cell
type hierarchies, enabling sample size estimation required for
accurate cell type classification and joint classification of
cells using multiple references.
biocViews: SingleCell, GeneExpression, Classification
Author: Yingxin Lin
Maintainer: Yingxin Lin <yingxin.lin@sydney.edu.au>
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/scClassify/issues
git_url: https://git.bioconductor.org/packages/scClassify
git_branch: RELEASE_3_13
git_last_commit: 11b64e3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scClassify_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scClassify_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scClassify_1.4.0.tgz
vignettes: vignettes/scClassify/inst/doc/pretrainedModel.html,
vignettes/scClassify/inst/doc/scClassify.html
vignetteTitles: pretrainedModel, scClassify
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scClassify/inst/doc/pretrainedModel.R,
vignettes/scClassify/inst/doc/scClassify.R
dependencyCount: 83
Package: scDataviz
Version: 1.2.0
Depends: R (>= 4.0), S4Vectors, SingleCellExperiment,
Imports: ggplot2, ggrepel, flowCore, umap, Seurat, reshape2, scales,
RColorBrewer, corrplot, stats, grDevices, graphics, utils,
MASS, matrixStats, methods
Suggests: PCAtools, cowplot, BiocGenerics, RUnit, knitr, kableExtra,
rmarkdown
License: GPL-3
MD5sum: 590db66adf9c047bffbcc5a8120a6e0b
NeedsCompilation: no
Title: scDataviz: single cell dataviz and downstream analyses
Description: In the single cell World, which includes flow cytometry,
mass cytometry, single-cell RNA-seq (scRNA-seq), and others,
there is a need to improve data visualisation and to bring
analysis capabilities to researchers even from non-technical
backgrounds. scDataviz attempts to fit into this space, while
also catering for advanced users. Additonally, due to the way
that scDataviz is designed, which is based on
SingleCellExperiment, it has a 'plug and play' feel, and
immediately lends itself as flexibile and compatibile with
studies that go beyond scDataviz. Finally, the graphics in
scDataviz are generated via the ggplot engine, which means that
users can 'add on' features to these with ease.
biocViews: SingleCell, ImmunoOncology, RNASeq, GeneExpression,
Transcription, FlowCytometry, MassSpectrometry, DataImport
Author: Kevin Blighe [aut, cre]
Maintainer: Kevin Blighe <kevin@clinicalbioinformatics.co.uk>
URL: https://github.com/kevinblighe/scDataviz
VignetteBuilder: knitr
BugReports: https://github.com/kevinblighe/scDataviz/issues
git_url: https://git.bioconductor.org/packages/scDataviz
git_branch: RELEASE_3_13
git_last_commit: 0b82f98
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scDataviz_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scDataviz_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scDataviz_1.2.0.tgz
vignettes: vignettes/scDataviz/inst/doc/scDataviz.html
vignetteTitles: scDataviz: single cell dataviz and downstream analyses
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scDataviz/inst/doc/scDataviz.R
dependencyCount: 163
Package: scDblFinder
Version: 1.6.0
Depends: R (>= 4.1)
Imports: igraph, Matrix, BiocGenerics, BiocParallel, BiocNeighbors,
BiocSingular, S4Vectors, SummarizedExperiment,
SingleCellExperiment, scran, scater, scuttle, bluster, methods,
DelayedArray, xgboost, stats, utils, mbkmeans
Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, circlize,
ComplexHeatmap, ggplot2, dplyr, MASS, viridisLite
License: GPL-3
MD5sum: 2e855dc2b7ae7ad798d8524ba1799624
NeedsCompilation: no
Title: scDblFinder
Description: The scDblFinder package gathers various methods for the
detection and handling of doublets/multiplets in single-cell
RNA sequencing data (i.e. multiple cells captured within the
same droplet or reaction volume). It includes methods formerly
found in the scran package, and the new fast and comprehensive
scDblFinder method.
biocViews: Preprocessing, SingleCell, RNASeq
Author: Pierre-Luc Germain [cre, aut]
(<https://orcid.org/0000-0003-3418-4218>), Aaron Lun [ctb]
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
URL: https://github.com/plger/scDblFinder
VignetteBuilder: knitr
BugReports: https://github.com/plger/scDblFinder/issues
git_url: https://git.bioconductor.org/packages/scDblFinder
git_branch: RELEASE_3_13
git_last_commit: e5c1a83
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scDblFinder_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scDblFinder_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scDblFinder_1.6.0.tgz
vignettes: vignettes/scDblFinder/inst/doc/computeDoubletDensity.html,
vignettes/scDblFinder/inst/doc/findDoubletClusters.html,
vignettes/scDblFinder/inst/doc/introduction.html,
vignettes/scDblFinder/inst/doc/recoverDoublets.html,
vignettes/scDblFinder/inst/doc/scDblFinder.html
vignetteTitles: 4_computeDoubletDensity, 3_findDoubletClusters,
1_introduction, 5_recoverDoublets, 2_scDblFinder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scDblFinder/inst/doc/computeDoubletDensity.R,
vignettes/scDblFinder/inst/doc/findDoubletClusters.R,
vignettes/scDblFinder/inst/doc/introduction.R,
vignettes/scDblFinder/inst/doc/scDblFinder.R
dependsOnMe: OSCA.advanced
importsMe: singleCellTK
dependencyCount: 119
Package: scDD
Version: 1.16.0
Depends: R (>= 3.4)
Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm,
SingleCellExperiment, SummarizedExperiment, grDevices,
graphics, stats, S4Vectors, scran
Suggests: BiocStyle, knitr, gridExtra
License: GPL-2
MD5sum: 68fc22a6d4eac35af3dfd4dd10241857
NeedsCompilation: yes
Title: Mixture modeling of single-cell RNA-seq data to identify genes
with differential distributions
Description: This package implements a method to analyze single-cell
RNA- seq Data utilizing flexible Dirichlet Process mixture
models. Genes with differential distributions of expression are
classified into several interesting patterns of differences
between two conditions. The package also includes functions for
simulating data with these patterns from negative binomial
distributions.
biocViews: ImmunoOncology, Bayesian, Clustering, RNASeq, SingleCell,
MultipleComparison, Visualization, DifferentialExpression
Author: Keegan Korthauer [cre, aut]
(<https://orcid.org/0000-0002-4565-1654>)
Maintainer: Keegan Korthauer <keegan@stat.ubc.ca>
URL: https://github.com/kdkorthauer/scDD
VignetteBuilder: knitr
BugReports: https://github.com/kdkorthauer/scDD/issues
git_url: https://git.bioconductor.org/packages/scDD
git_branch: RELEASE_3_13
git_last_commit: 05c6b7b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scDD_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scDD_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scDD_1.16.0.tgz
vignettes: vignettes/scDD/inst/doc/scDD.pdf
vignetteTitles: scDD Quickstart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scDD/inst/doc/scDD.R
suggestsMe: splatter
dependencyCount: 123
Package: scde
Version: 2.20.0
Depends: R (>= 3.0.0), flexmix
Imports: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook,
rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods,
nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, cba, fastcluster, WGCNA, GO.db, org.Hs.eg.db,
rmarkdown
License: GPL-2
MD5sum: 95a128d4b0dd851fd367988088c649e8
NeedsCompilation: yes
Title: Single Cell Differential Expression
Description: The scde package implements a set of statistical methods
for analyzing single-cell RNA-seq data. scde fits individual
error models for single-cell RNA-seq measurements. These models
can then be used for assessment of differential expression
between groups of cells, as well as other types of analysis.
The scde package also contains the pagoda framework which
applies pathway and gene set overdispersion analysis to
identify and characterize putative cell subpopulations based on
transcriptional signatures. The overall approach to the
differential expression analysis is detailed in the following
publication: "Bayesian approach to single-cell differential
expression analysis" (Kharchenko PV, Silberstein L, Scadden DT,
Nature Methods, doi: 10.1038/nmeth.2967). The overall approach
to subpopulation identification and characterization is
detailed in the following pre-print: "Characterizing
transcriptional heterogeneity through pathway and gene set
overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G,
Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and
Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734).
biocViews: ImmunoOncology, RNASeq, StatisticalMethod,
DifferentialExpression, Bayesian, Transcription, Software
Author: Peter Kharchenko [aut, cre], Jean Fan [aut]
Maintainer: Jean Fan <jeanfan@jhu.edu>
URL: http://pklab.med.harvard.edu/scde
VignetteBuilder: knitr
BugReports: https://github.com/hms-dbmi/scde/issues
git_url: https://git.bioconductor.org/packages/scde
git_branch: RELEASE_3_13
git_last_commit: d19d2a0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scde_2.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scde_2.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scde_2.20.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
suggestsMe: pagoda2
dependencyCount: 47
Package: scds
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: Matrix, S4Vectors, SingleCellExperiment, SummarizedExperiment,
xgboost, methods, stats, dplyr, pROC
Suggests: BiocStyle, knitr, rsvd, Rtsne, scater, cowplot
License: MIT + file LICENSE
MD5sum: 60e2437f8b806f35a64f19cfc4a4bf34
NeedsCompilation: no
Title: In-Silico Annotation of Doublets for Single Cell RNA Sequencing
Data
Description: In single cell RNA sequencing (scRNA-seq) data
combinations of cells are sometimes considered a single cell
(doublets). The scds package provides methods to annotate
doublets in scRNA-seq data computationally.
biocViews: SingleCell, RNASeq, QualityControl, Preprocessing,
Transcriptomics, GeneExpression, Sequencing, Software,
Classification
Author: Dennis Kostka [aut, cre], Bais Abha [aut]
Maintainer: Dennis Kostka <kostka@pitt.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scds
git_branch: RELEASE_3_13
git_last_commit: 844eec6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scds_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scds_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scds_1.8.0.tgz
vignettes: vignettes/scds/inst/doc/scds.html
vignetteTitles: Introduction to the scds package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scds/inst/doc/scds.R
importsMe: singleCellTK
suggestsMe: ExperimentSubset, muscData
dependencyCount: 51
Package: SCFA
Version: 1.2.0
Depends: R (>= 4.0)
Imports: matrixStats, keras, tensorflow, BiocParallel, igraph, Matrix,
cluster, clusterCrit, psych, glmnet, RhpcBLASctl, stats, utils,
methods, survival
Suggests: knitr
License: LGPL
MD5sum: b3441c0ae2d04fc4db37404e3d9ad17d
NeedsCompilation: no
Title: SCFA: Subtyping via Consensus Factor Analysis
Description: Subtyping via Consensus Factor Analysis (SCFA) can
efficiently remove noisy signals from consistent molecular
patterns in multi-omics data. SCFA first uses an autoencoder to
select only important features and then repeatedly performs
factor analysis to represent the data with different numbers of
factors. Using these representations, it can reliably identify
cancer subtypes and accurately predict risk scores of patients.
biocViews: Survival, Clustering, Classification
Author: Duc Tran [aut, cre], Hung Nguyen [aut], Tin Nguyen [fnd]
Maintainer: Duc Tran <duct@nevada.unr.edu>
URL: https://github.com/duct317/SCFA
VignetteBuilder: knitr
BugReports: https://github.com/duct317/SCFA/issues
git_url: https://git.bioconductor.org/packages/SCFA
git_branch: RELEASE_3_13
git_last_commit: 9619dda
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SCFA_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SCFA_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SCFA_1.2.0.tgz
vignettes: vignettes/SCFA/inst/doc/Example.html
vignetteTitles: SCFA package manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCFA/inst/doc/Example.R
dependencyCount: 63
Package: scFeatureFilter
Version: 1.12.0
Depends: R (>= 3.6)
Imports: dplyr (>= 0.7.3), ggplot2 (>= 2.1.0), magrittr (>= 1.5), rlang
(>= 0.1.2), tibble (>= 1.3.4), stats, methods
Suggests: testthat, knitr, rmarkdown, BiocStyle, SingleCellExperiment,
SummarizedExperiment, scRNAseq, cowplot
License: MIT + file LICENSE
MD5sum: abff913301bcd3c3fc76d3ee00dba659
NeedsCompilation: no
Title: A correlation-based method for quality filtering of single-cell
RNAseq data
Description: An R implementation of the correlation-based method
developed in the Joshi laboratory to analyse and filter
processed single-cell RNAseq data. It returns a filtered
version of the data containing only genes expression values
unaffected by systematic noise.
biocViews: ImmunoOncology, SingleCell, RNASeq, Preprocessing,
GeneExpression
Author: Angeles Arzalluz-Luque [aut], Guillaume Devailly [aut, cre],
Anagha Joshi [aut]
Maintainer: Guillaume Devailly <gdevailly@hotmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scFeatureFilter
git_branch: RELEASE_3_13
git_last_commit: 5b723a0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scFeatureFilter_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scFeatureFilter_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scFeatureFilter_1.12.0.tgz
vignettes: vignettes/scFeatureFilter/inst/doc/Introduction.html
vignetteTitles: Introduction to scFeatureFilter
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scFeatureFilter/inst/doc/Introduction.R
dependencyCount: 42
Package: scGPS
Version: 1.6.0
Depends: R (>= 3.6), SummarizedExperiment, dynamicTreeCut,
SingleCellExperiment
Imports: glmnet (> 2.0), caret (>= 6.0), ggplot2 (>= 2.2.1),
fastcluster, dplyr, Rcpp, RcppArmadillo, RcppParallel,
grDevices, graphics, stats, utils, DESeq2, locfit
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: Matrix (>= 1.2), testthat, knitr, parallel, rmarkdown,
RColorBrewer, ReactomePA, clusterProfiler, cowplot,
org.Hs.eg.db, reshape2, xlsx, dendextend, networkD3, Rtsne,
BiocParallel, e1071, WGCNA, devtools, DOSE
License: GPL-3
MD5sum: c78da3ac75f576b67b47bff580eb72cf
NeedsCompilation: yes
Title: A complete analysis of single cell subpopulations, from
identifying subpopulations to analysing their relationship
(scGPS = single cell Global Predictions of Subpopulation)
Description: The package implements two main algorithms to answer two
key questions: a SCORE (Stable Clustering at Optimal
REsolution) to find subpopulations, followed by scGPS to
investigate the relationships between subpopulations.
biocViews: SingleCell, Clustering, DataImport, Sequencing, Coverage
Author: Quan Nguyen [aut, cre], Michael Thompson [aut], Anne Senabouth
[aut]
Maintainer: Quan Nguyen <quan.nguyen@uq.edu.au>
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports:
https://github.com/IMB-Computational-Genomics-Lab/scGPS/issues
git_url: https://git.bioconductor.org/packages/scGPS
git_branch: RELEASE_3_13
git_last_commit: 503aaf0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scGPS_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scGPS_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scGPS_1.6.0.tgz
vignettes: vignettes/scGPS/inst/doc/vignette.html
vignetteTitles: single cell Global fate Potential of Subpopulations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scGPS/inst/doc/vignette.R
dependencyCount: 137
Package: schex
Version: 1.6.3
Depends: SingleCellExperiment (>= 1.7.4), Seurat, ggplot2 (>= 3.2.1),
shiny
Imports: hexbin, stats, methods, cluster, dplyr, entropy, ggforce,
scales, grid, concaveman
Suggests: ggrepel, knitr, rmarkdown, testthat (>= 2.1.0), covr,
TENxPBMCData, scater, shinydashboard, iSEE, igraph, scran
License: GPL-3
MD5sum: ae53ffc0722a9ba087638ddfc3954d07
NeedsCompilation: no
Title: Hexbin plots for single cell omics data
Description: Builds hexbin plots for variables and dimension reduction
stored in single cell omics data such as SingleCellExperiment
and SeuratObject. The ideas used in this package are based on
the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin
Maechler and Thomas Lumley.
biocViews: Software, Sequencing, SingleCell, DimensionReduction,
Visualization
Author: Saskia Freytag
Maintainer: Saskia Freytag <saskia.freytag@perkins.uwa.edu.au>
URL: https://github.com/SaskiaFreytag/schex
VignetteBuilder: knitr
BugReports: https://github.com/SaskiaFreytag/schex/issues
git_url: https://git.bioconductor.org/packages/schex
git_branch: RELEASE_3_13
git_last_commit: 4c45aec
git_last_commit_date: 2021-06-06
Date/Publication: 2021-06-06
source.ver: src/contrib/schex_1.6.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/schex_1.6.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/schex_1.6.3.tgz
vignettes: vignettes/schex/inst/doc/multi_modal_schex.html,
vignettes/schex/inst/doc/picking_the_right_resolution.html,
vignettes/schex/inst/doc/Seurat_schex.html,
vignettes/schex/inst/doc/shiny_schex.html,
vignettes/schex/inst/doc/using_schex.html
vignetteTitles: multi_modal_schex, picking_the_right_resolution,
Seurat_schex, shiny_schhex, using_schex
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/schex/inst/doc/multi_modal_schex.R,
vignettes/schex/inst/doc/picking_the_right_resolution.R,
vignettes/schex/inst/doc/Seurat_schex.R,
vignettes/schex/inst/doc/shiny_schex.R,
vignettes/schex/inst/doc/using_schex.R
importsMe: scTensor, scTGIF
suggestsMe: fcoex
dependencyCount: 171
Package: scHOT
Version: 1.4.0
Depends: R (>= 4.0)
Imports: S4Vectors (>= 0.24.3), SingleCellExperiment, Matrix,
SummarizedExperiment, IRanges, methods, stats, BiocParallel,
reshape, ggplot2, igraph, grDevices, ggforce, graphics
Suggests: knitr, rmarkdown, scater, scattermore, scales, matrixStats,
deldir
License: GPL-3
Archs: i386, x64
MD5sum: f1bfdf8dc88c71c36d04549683178b47
NeedsCompilation: no
Title: single-cell higher order testing
Description: Single cell Higher Order Testing (scHOT) is an R package
that facilitates testing changes in higher order structure of
gene expression along either a developmental trajectory or
across space. scHOT is general and modular in nature, can be
run in multiple data contexts such as along a continuous
trajectory, between discrete groups, and over spatial
orientations; as well as accommodate any higher order
measurement such as variability or correlation. scHOT
meaningfully adds to first order effect testing, such as
differential expression, and provides a framework for
interrogating higher order interactions from single cell data.
biocViews: GeneExpression, RNASeq, Sequencing, SingleCell, Software,
Transcriptomics
Author: Shila Ghazanfar [aut, cre], Yingxin Lin [aut]
Maintainer: Shila Ghazanfar <shazanfar@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scHOT
git_branch: RELEASE_3_13
git_last_commit: 55a2ef4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scHOT_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scHOT_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scHOT_1.4.0.tgz
vignettes: vignettes/scHOT/inst/doc/scHOT.html
vignetteTitles: Getting started: scHOT
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scHOT/inst/doc/scHOT.R
dependencyCount: 74
Package: ScISI
Version: 1.64.0
Depends: R (>= 2.10), GO.db, RpsiXML, annotate, apComplex
Imports: AnnotationDbi, GO.db, RpsiXML, annotate, methods,
org.Sc.sgd.db, utils
Suggests: ppiData, xtable
License: LGPL
MD5sum: 7e978cc955282913f10616dc8bffab0e
NeedsCompilation: no
Title: In Silico Interactome
Description: Package to create In Silico Interactomes
biocViews: GraphAndNetwork, Proteomics, NetworkInference, DecisionTree
Author: Tony Chiang <tchiang@fhcrc.org>
Maintainer: Tony Chiang <tchiang@fhcrc.org>
git_url: https://git.bioconductor.org/packages/ScISI
git_branch: RELEASE_3_13
git_last_commit: 1ee59b4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ScISI_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ScISI_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ScISI_1.64.0.tgz
vignettes: vignettes/ScISI/inst/doc/vignette.pdf
vignetteTitles: ScISI Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ScISI/inst/doc/vignette.R
dependsOnMe: ppiStats, SLGI
importsMe: SLGI
suggestsMe: RpsiXML
dependencyCount: 59
Package: scMAGeCK
Version: 1.4.0
Imports: Seurat, stats, utils
Suggests: knitr, rmarkdown
License: BSD_2_clause
MD5sum: 9cd3fdd39c41b289d598c2cd1a0d8ade
NeedsCompilation: yes
Title: Identify genes associated with multiple expression phenotypes in
single-cell CRISPR screening data
Description: scMAGeCK is a computational model to identify genes
associated with multiple expression phenotypes from CRISPR
screening coupled with single-cell RNA sequencing data
(CROP-seq)
biocViews: CRISPR, SingleCell, RNASeq, PooledScreens, Transcriptomics,
GeneExpression, Regression
Author: Wei Li, Xiaolong Cheng
Maintainer: Xiaolong Cheng <xiaolongcheng1120@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scMAGeCK
git_branch: RELEASE_3_13
git_last_commit: c09bcb9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scMAGeCK_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scMAGeCK_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scMAGeCK_1.4.0.tgz
vignettes: vignettes/scMAGeCK/inst/doc/scMAGeCK.html
vignetteTitles: scMAGeCK
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scMAGeCK/inst/doc/scMAGeCK.R
dependencyCount: 141
Package: scmap
Version: 1.14.0
Depends: R(>= 3.4)
Imports: Biobase, SingleCellExperiment, SummarizedExperiment,
BiocGenerics, S4Vectors, dplyr, reshape2, matrixStats, proxy,
utils, googleVis, ggplot2, methods, stats, e1071, randomForest,
Rcpp (>= 0.12.12)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 8e1c116cdbd89fc9cc26b8c733d70577
NeedsCompilation: yes
Title: A tool for unsupervised projection of single cell RNA-seq data
Description: Single-cell RNA-seq (scRNA-seq) is widely used to
investigate the composition of complex tissues since the
technology allows researchers to define cell-types using
unsupervised clustering of the transcriptome. However, due to
differences in experimental methods and computational analyses,
it is often challenging to directly compare the cells
identified in two different experiments. scmap is a method for
projecting cells from a scRNA-seq experiment on to the
cell-types or individual cells identified in a different
experiment.
biocViews: ImmunoOncology, SingleCell, Software, Classification,
SupportVectorMachine, RNASeq, Visualization, Transcriptomics,
DataRepresentation, Transcription, Sequencing, Preprocessing,
GeneExpression, DataImport
Author: Vladimir Kiselev
Maintainer: Vladimir Kiselev <vladimir.yu.kiselev@gmail.com>
URL: https://github.com/hemberg-lab/scmap
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/scmap/
git_url: https://git.bioconductor.org/packages/scmap
git_branch: RELEASE_3_13
git_last_commit: e59738b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scmap_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scmap_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scmap_1.14.0.tgz
vignettes: vignettes/scmap/inst/doc/scmap.html
vignetteTitles: `scmap` package vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scmap/inst/doc/scmap.R
dependencyCount: 73
Package: scMerge
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: BiocParallel, BiocSingular, cluster, DelayedArray,
DelayedMatrixStats, distr, igraph, M3Drop (>= 1.9.4), parallel,
pdist, proxy, ruv, S4Vectors (>= 0.23.19), SingleCellExperiment
(>= 1.7.3), SummarizedExperiment
Suggests: BiocStyle, covr, HDF5Array, knitr, Matrix, rmarkdown, scales,
scater, testthat, badger
License: GPL-3
Archs: i386, x64
MD5sum: 8ebd646fddb169fe46d19b178f32f53b
NeedsCompilation: no
Title: scMerge: Merging multiple batches of scRNA-seq data
Description: Like all gene expression data, single-cell RNA-seq
(scRNA-Seq) data suffers from batch effects and other unwanted
variations that makes accurate biological interpretations
difficult. The scMerge method leverages factor analysis, stably
expressed genes (SEGs) and (pseudo-) replicates to remove
unwanted variations and merge multiple scRNA-Seq data. This
package contains all the necessary functions in the scMerge
pipeline, including the identification of SEGs,
replication-identification methods, and merging of scRNA-Seq
data.
biocViews: BatchEffect, GeneExpression, Normalization, RNASeq,
Sequencing, SingleCell, Software, Transcriptomics
Author: Yingxin Lin [aut, cre], Kevin Wang [aut], Sydney Bioinformatics
and Biometrics Group [fnd]
Maintainer: Yingxin Lin <yingxin.lin@sydney.edu.au>
URL: https://github.com/SydneyBioX/scMerge
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/scMerge/issues
git_url: https://git.bioconductor.org/packages/scMerge
git_branch: RELEASE_3_13
git_last_commit: b558998
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scMerge_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scMerge_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scMerge_1.8.0.tgz
vignettes: vignettes/scMerge/inst/doc/scMerge.html
vignetteTitles: scMerge
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scMerge/inst/doc/scMerge.R
importsMe: singleCellTK
dependencyCount: 119
Package: scmeth
Version: 1.12.0
Depends: R (>= 3.5.0)
Imports: knitr, rmarkdown, bsseq, AnnotationHub, GenomicRanges,
reshape2, stats, utils, BSgenome, DelayedArray (>= 0.5.15),
annotatr, SummarizedExperiment (>= 1.5.6), GenomeInfoDb,
Biostrings, DT, HDF5Array (>= 1.7.5)
Suggests: BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.NCBI.GRCh38,
TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, Biobase,
BiocGenerics, ggplot2, ggthemes
License: GPL-2
MD5sum: 8324c4f5e8fea3cfd9500e04572c2a05
NeedsCompilation: no
Title: Functions to conduct quality control analysis in methylation
data
Description: Functions to analyze methylation data can be found here.
Some functions are relevant for single cell methylation data
but most other functions can be used for any methylation data.
Highlight of this workflow is the comprehensive quality control
report.
biocViews: DNAMethylation, QualityControl, Preprocessing, SingleCell,
ImmunoOncology
Author: Divy Kangeyan <divyswar01@g.harvard.edu>
Maintainer: Divy Kangeyan <divyswar01@g.harvard.edu>
VignetteBuilder: knitr
BugReports: https://github.com/aryeelab/scmeth/issues
git_url: https://git.bioconductor.org/packages/scmeth
git_branch: RELEASE_3_13
git_last_commit: 1ad2a18
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scmeth_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scmeth_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scmeth_1.12.0.tgz
vignettes: vignettes/scmeth/inst/doc/my-vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scmeth/inst/doc/my-vignette.R
suggestsMe: biscuiteer
dependencyCount: 164
Package: SCnorm
Version: 1.14.0
Depends: R (>= 3.4.0),
Imports: SingleCellExperiment, SummarizedExperiment, stats, methods,
graphics, grDevices, parallel, quantreg, cluster, moments,
data.table, BiocParallel, S4Vectors, ggplot2, forcats,
BiocGenerics
Suggests: BiocStyle, knitr, rmarkdown, devtools
License: GPL (>= 2)
MD5sum: defac70a40da768a842e11fe58718322
NeedsCompilation: no
Title: Normalization of single cell RNA-seq data
Description: This package implements SCnorm — a method to normalize
single-cell RNA-seq data.
biocViews: Normalization, RNASeq, SingleCell, ImmunoOncology
Author: Rhonda Bacher
Maintainer: Rhonda Bacher <rbacher@ufl.edu>
URL: https://github.com/rhondabacher/SCnorm
VignetteBuilder: knitr
BugReports: https://github.com/rhondabacher/SCnorm/issues
git_url: https://git.bioconductor.org/packages/SCnorm
git_branch: RELEASE_3_13
git_last_commit: b6a7f61
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SCnorm_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SCnorm_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SCnorm_1.14.0.tgz
vignettes: vignettes/SCnorm/inst/doc/SCnorm.pdf
vignetteTitles: SCnorm Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCnorm/inst/doc/SCnorm.R
dependencyCount: 74
Package: scone
Version: 1.16.1
Depends: R (>= 3.4), methods, SummarizedExperiment
Imports: graphics, stats, utils, aroma.light, BiocParallel, class,
cluster, compositions, diptest, edgeR, fpc, gplots, grDevices,
hexbin, limma, matrixStats, mixtools, RColorBrewer, boot,
rhdf5, RUVSeq, rARPACK, MatrixGenerics, SingleCellExperiment
Suggests: BiocStyle, DT, ggplot2, knitr, miniUI, NMF, plotly, reshape2,
rmarkdown, scran, scRNAseq, shiny, testthat, visNetwork,
doParallel, BatchJobs, splatter, scater, kableExtra, mclust,
TENxPBMCData
License: Artistic-2.0
MD5sum: 94c09080148101f98b2db581c07957d9
NeedsCompilation: no
Title: Single Cell Overview of Normalized Expression data
Description: SCONE is an R package for comparing and ranking the
performance of different normalization schemes for single-cell
RNA-seq and other high-throughput analyses.
biocViews: ImmunoOncology, Normalization, Preprocessing,
QualityControl, GeneExpression, RNASeq, Software,
Transcriptomics, Sequencing, SingleCell, Coverage
Author: Michael Cole [aut, cph], Davide Risso [aut, cre, cph], Matteo
Borella [ctb], Chiara Romualdi [ctb]
Maintainer: Davide Risso <risso.davide@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/YosefLab/scone/issues
git_url: https://git.bioconductor.org/packages/scone
git_branch: RELEASE_3_13
git_last_commit: e028b2e
git_last_commit_date: 2021-07-21
Date/Publication: 2021-07-22
source.ver: src/contrib/scone_1.16.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scone_1.16.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/scone_1.16.1.tgz
vignettes: vignettes/scone/inst/doc/PsiNorm.html,
vignettes/scone/inst/doc/sconeTutorial.html
vignetteTitles: PsiNorm normalization, Introduction to SCONE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scone/inst/doc/PsiNorm.R,
vignettes/scone/inst/doc/sconeTutorial.R
dependencyCount: 145
Package: Sconify
Version: 1.12.0
Depends: R (>= 3.5)
Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils,
stats, readr
Suggests: knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: acca6b2284760c68abe5bbe3e9e22cf9
NeedsCompilation: no
Title: A toolkit for performing KNN-based statistics for flow and mass
cytometry data
Description: This package does k-nearest neighbor based statistics and
visualizations with flow and mass cytometery data. This gives
tSNE maps"fold change" functionality and provides a data
quality metric by assessing manifold overlap between fcs files
expected to be the same. Other applications using this package
include imputation, marker redundancy, and testing the relative
information loss of lower dimension embeddings compared to the
original manifold.
biocViews: ImmunoOncology, SingleCell, FlowCytometry, Software,
MultipleComparison, Visualization
Author: Tyler J Burns
Maintainer: Tyler J Burns <burns.tyler@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Sconify
git_branch: RELEASE_3_13
git_last_commit: 68c5316
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Sconify_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Sconify_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Sconify_1.12.0.tgz
vignettes: vignettes/Sconify/inst/doc/DataQuality.html,
vignettes/Sconify/inst/doc/FindingIdealK.html,
vignettes/Sconify/inst/doc/Step1.PreProcessing.html,
vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.html,
vignettes/Sconify/inst/doc/Step3.PostProcessing.html
vignetteTitles: Data Quality, Finding Ideal K, How to process FCS files
for downstream use in R, General Scone Analysis, Final
Post-Processing Steps for Scone
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Sconify/inst/doc/DataQuality.R,
vignettes/Sconify/inst/doc/FindingIdealK.R,
vignettes/Sconify/inst/doc/Step1.PreProcessing.R,
vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.R,
vignettes/Sconify/inst/doc/Step3.PostProcessing.R
dependencyCount: 68
Package: SCOPE
Version: 1.4.0
Depends: R (>= 3.6.0), GenomicRanges, IRanges, Rsamtools, GenomeInfoDb,
BSgenome.Hsapiens.UCSC.hg19
Imports: stats, grDevices, graphics, utils, DescTools, RColorBrewer,
gplots, foreach, parallel, doParallel, DNAcopy, BSgenome,
Biostrings, BiocGenerics, S4Vectors
Suggests: knitr, rmarkdown, WGSmapp, BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Mmusculus.UCSC.mm10, testthat (>= 2.1.0)
License: GPL-2
Archs: i386, x64
MD5sum: dd464cf8efc725ca60996c1b7551f0c2
NeedsCompilation: no
Title: A normalization and copy number estimation method for
single-cell DNA sequencing
Description: Whole genome single-cell DNA sequencing (scDNA-seq)
enables characterization of copy number profiles at the
cellular level. This circumvents the averaging effects
associated with bulk-tissue sequencing and has increased
resolution yet decreased ambiguity in deconvolving cancer
subclones and elucidating cancer evolutionary history.
ScDNA-seq data is, however, sparse, noisy, and highly variable
even within a homogeneous cell population, due to the biases
and artifacts that are introduced during the library
preparation and sequencing procedure. Here, we propose SCOPE, a
normalization and copy number estimation method for scDNA-seq
data. The distinguishing features of SCOPE include: (i)
utilization of cell-specific Gini coefficients for quality
controls and for identification of normal/diploid cells, which
are further used as negative control samples in a Poisson
latent factor model for normalization; (ii) modeling of GC
content bias using an expectation-maximization algorithm
embedded in the Poisson generalized linear models, which
accounts for the different copy number states along the genome;
(iii) a cross-sample iterative segmentation procedure to
identify breakpoints that are shared across cells from the same
genetic background.
biocViews: SingleCell, Normalization, CopyNumberVariation, Sequencing,
WholeGenome, Coverage, Alignment, QualityControl, DataImport,
DNASeq
Author: Rujin Wang, Danyu Lin, Yuchao Jiang
Maintainer: Rujin Wang <rujin@email.unc.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SCOPE
git_branch: RELEASE_3_13
git_last_commit: 16e6b55
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SCOPE_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SCOPE_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SCOPE_1.4.0.tgz
vignettes: vignettes/SCOPE/inst/doc/SCOPE_vignette.html
vignetteTitles: SCOPE: Single-cell Copy Number Estimation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCOPE/inst/doc/SCOPE_vignette.R
dependencyCount: 72
Package: scoreInvHap
Version: 1.14.0
Depends: R (>= 3.6.0)
Imports: Biostrings, methods, snpStats, VariantAnnotation,
GenomicRanges, BiocParallel, graphics, SummarizedExperiment
Suggests: testthat, knitr, BiocStyle, rmarkdown
License: file LICENSE
MD5sum: 97a4e44f2d9b0fd3621d400bda5d5bc9
NeedsCompilation: no
Title: Get inversion status in predefined regions
Description: scoreInvHap can get the samples' inversion status of known
inversions. scoreInvHap uses SNP data as input and requires the
following information about the inversion: genotype frequencies
in the different haplotypes, R2 between the region SNPs and
inversion status and heterozygote genotypes in the reference.
The package include this data for 21 inversions.
biocViews: SNP, Genetics, GenomicVariation
Author: Carlos Ruiz [aut], Dolors Pelegrà [aut], Juan R. Gonzalez [aut,
cre]
Maintainer: Dolors Pelegri-Siso <dolors.pelegri@isglobal.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scoreInvHap
git_branch: RELEASE_3_13
git_last_commit: 484fe25
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scoreInvHap_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scoreInvHap_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scoreInvHap_1.14.0.tgz
vignettes: vignettes/scoreInvHap/inst/doc/scoreInvHap.html
vignetteTitles: Inversion genotyping with scoreInvHap
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scoreInvHap/inst/doc/scoreInvHap.R
dependencyCount: 101
Package: scp
Version: 1.2.0
Depends: R (>= 4.0), QFeatures
Imports: methods, stats, utils, SingleCellExperiment,
SummarizedExperiment, MultiAssayExperiment, MsCoreUtils,
matrixStats, S4Vectors, dplyr, magrittr, rlang
Suggests: testthat, knitr, BiocStyle, BiocCheck, rmarkdown, patchwork,
ggplot2, impute, scater, sva, preprocessCore, vsn, uwot
License: Artistic-2.0
MD5sum: ce40d2b7defc237d08a75d7e7440b408
NeedsCompilation: no
Title: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis
Description: Utility functions for manipulating, processing, and
analyzing mass spectrometry-based single-cell proteomics (SCP)
data. The package is an extension to the 'QFeatures' package
designed for SCP applications.
biocViews: GeneExpression, Proteomics, SingleCell, MassSpectrometry,
Preprocessing, CellBasedAssays
Author: Christophe Vanderaa [aut, cre]
(<https://orcid.org/0000-0001-7443-5427>), Laurent Gatto [aut]
(<https://orcid.org/0000-0002-1520-2268>)
Maintainer: Christophe Vanderaa <christophe.vanderaa@uclouvain.be>
URL: https://UCLouvain-CBIO.github.io/scp
VignetteBuilder: knitr
BugReports: https://github.com/UCLouvain-CBIO/scp/issues
git_url: https://git.bioconductor.org/packages/scp
git_branch: RELEASE_3_13
git_last_commit: a7c883a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scp_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scp_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scp_1.2.0.tgz
vignettes: vignettes/scp/inst/doc/scp.html
vignetteTitles: Single Cell Proteomics data processing and analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scp/inst/doc/scp.R
suggestsMe: scpdata
dependencyCount: 57
Package: scPCA
Version: 1.6.2
Depends: R (>= 4.0.2)
Imports: stats, methods, assertthat, tibble, dplyr, purrr, stringr,
Rdpack, matrixStats, BiocParallel, elasticnet, sparsepca,
cluster, kernlab, origami, RSpectra, coop, Matrix,
DelayedArray, ScaledMatrix, MatrixGenerics
Suggests: DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0),
covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter,
SingleCellExperiment, microbenchmark
License: MIT + file LICENSE
MD5sum: cbb8f818a16a60ed343937b6cc016853
NeedsCompilation: no
Title: Sparse Contrastive Principal Component Analysis
Description: A toolbox for sparse contrastive principal component
analysis (scPCA) of high-dimensional biological data. scPCA
combines the stability and interpretability of sparse PCA with
contrastive PCA's ability to disentangle biological signal from
unwanted variation through the use of control data. Also
implements and extends cPCA.
biocViews: PrincipalComponent, GeneExpression, DifferentialExpression,
Sequencing, Microarray, RNASeq
Author: Philippe Boileau [aut, cre, cph]
(<https://orcid.org/0000-0002-4850-2507>), Nima Hejazi [aut]
(<https://orcid.org/0000-0002-7127-2789>), Sandrine Dudoit
[ctb, ths] (<https://orcid.org/0000-0002-6069-8629>)
Maintainer: Philippe Boileau <philippe_boileau@berkeley.edu>
URL: https://github.com/PhilBoileau/scPCA
VignetteBuilder: knitr
BugReports: https://github.com/PhilBoileau/scPCA/issues
git_url: https://git.bioconductor.org/packages/scPCA
git_branch: RELEASE_3_13
git_last_commit: 2f31700
git_last_commit_date: 2021-05-26
Date/Publication: 2021-05-27
source.ver: src/contrib/scPCA_1.6.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scPCA_1.6.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/scPCA_1.6.2.tgz
vignettes: vignettes/scPCA/inst/doc/scpca_intro.html
vignetteTitles: Sparse contrastive principal component analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scPCA/inst/doc/scpca_intro.R
dependsOnMe: OSCA.advanced, OSCA.workflows
dependencyCount: 68
Package: scPipe
Version: 1.14.0
Depends: R (>= 3.4), ggplot2, methods, SingleCellExperiment
Imports: Rhtslib, biomaRt, GGally, MASS, mclust, Rcpp (>= 0.11.3),
reshape, BiocGenerics, robustbase, scales, utils, stats,
S4Vectors, SummarizedExperiment, AnnotationDbi, org.Hs.eg.db,
org.Mm.eg.db, stringr, rtracklayer, hash, dplyr, GenomicRanges,
magrittr, glue (>= 1.3.0), rlang, scater (>= 1.11.0)
LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc, testthat
Suggests: Rsubread, knitr, rmarkdown, testthat
License: GPL (>= 2)
MD5sum: 7c177e26437e7a8576024b5227bf90bd
NeedsCompilation: yes
Title: pipeline for single cell RNA-seq data analysis
Description: A preprocessing pipeline for single cell RNA-seq data that
starts from the fastq files and produces a gene count matrix
with associated quality control information. It can process
fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium
10x and SMART-seq protocols.
biocViews: ImmunoOncology, Software, Sequencing, RNASeq,
GeneExpression, SingleCell, Visualization, SequenceMatching,
Preprocessing, QualityControl, GenomeAnnotation
Author: Luyi Tian
Maintainer: Luyi Tian <tian.l@wehi.edu.au>
URL: https://github.com/LuyiTian/scPipe
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
BugReports: https://github.com/LuyiTian/scPipe
git_url: https://git.bioconductor.org/packages/scPipe
git_branch: RELEASE_3_13
git_last_commit: 998ffca
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scPipe_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scPipe_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scPipe_1.14.0.tgz
vignettes: vignettes/scPipe/inst/doc/scPipe_tutorial.html
vignetteTitles: scPipe: flexible data preprocessing pipeline for 3' end
scRNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scPipe/inst/doc/scPipe_tutorial.R
dependencyCount: 153
Package: scran
Version: 1.20.1
Depends: SingleCellExperiment, scuttle
Imports: SummarizedExperiment, S4Vectors, BiocGenerics, BiocParallel,
Rcpp, stats, methods, utils, Matrix, edgeR, limma, igraph,
statmod, DelayedArray, DelayedMatrixStats, BiocSingular,
bluster, metapod, dqrng, beachmat
LinkingTo: Rcpp, beachmat, BH, dqrng, scuttle
Suggests: testthat, BiocStyle, knitr, rmarkdown, HDF5Array, scRNAseq,
dynamicTreeCut, ResidualMatrix, ScaledMatrix, DESeq2, monocle,
Biobase, pheatmap, scater
License: GPL-3
MD5sum: 435a34857a13ee9a6594cfe2efca28bf
NeedsCompilation: yes
Title: Methods for Single-Cell RNA-Seq Data Analysis
Description: Implements miscellaneous functions for interpretation of
single-cell RNA-seq data. Methods are provided for assignment
of cell cycle phase, detection of highly variable and
significantly correlated genes, identification of marker genes,
and other common tasks in routine single-cell analysis
workflows.
biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software,
GeneExpression, Transcriptomics, SingleCell, Clustering
Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim
[ctb], Antonio Scialdone [ctb]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scran
git_branch: RELEASE_3_13
git_last_commit: 5fcaf5b
git_last_commit_date: 2021-05-24
Date/Publication: 2021-05-24
source.ver: src/contrib/scran_1.20.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scran_1.20.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/scran_1.20.1.tgz
vignettes: vignettes/scran/inst/doc/scran.html
vignetteTitles: Using scran to analyze scRNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scran/inst/doc/scran.R
dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample,
OSCA.workflows
importsMe: BASiCS, BayesSpace, celda, ChromSCape, CiteFuse, conclus,
IRISFGM, msImpute, mumosa, pipeComp, scDblFinder, scDD,
SingleCellSignalR, singleCellTK, Spaniel, SC.MEB
suggestsMe: batchelor, bluster, CellTrails, clusterExperiment,
dittoSeq, ExperimentSubset, fcoex, glmGamPoi, iSEEu, miloR,
Nebulosa, PCAtools, schex, scone, scuttle, SingleR, snifter,
splatter, tidySingleCellExperiment, TSCAN, velociraptor,
HCAData, SingleCellMultiModal, TabulaMurisData,
simpleSingleCell, SingleRBook
dependencyCount: 57
Package: scRecover
Version: 1.8.0
Depends: R (>= 3.4.0)
Imports: stats, utils, methods, graphics, doParallel, foreach,
parallel, penalized, kernlab, rsvd, Matrix (>= 1.2-14), MASS
(>= 7.3-45), pscl (>= 1.4.9), bbmle (>= 1.0.18), gamlss (>=
4.4-0), preseqR (>= 4.0.0), SAVER (>= 1.1.1), Rmagic (>=
1.3.0), BiocParallel (>= 1.12.0)
Suggests: knitr, rmarkdown, SingleCellExperiment, testthat
License: GPL
MD5sum: 6dbad72419123ad24c2ed6379233aa95
NeedsCompilation: no
Title: scRecover for imputation of single-cell RNA-seq data
Description: scRecover is an R package for imputation of single-cell
RNA-seq (scRNA-seq) data. It will detect and impute dropout
values in a scRNA-seq raw read counts matrix while keeping the
real zeros unchanged, since there are both dropout zeros and
real zeros in scRNA-seq data. By combination with scImpute,
SAVER and MAGIC, scRecover not only detects dropout and real
zeros at higher accuracy, but also improve the downstream
clustering and visualization results.
biocViews: GeneExpression, SingleCell, RNASeq, Transcriptomics,
Sequencing, Preprocessing, Software
Author: Zhun Miao, Xuegong Zhang <zhangxg@tsinghua.edu.cn>
Maintainer: Zhun Miao <miaoz13@tsinghua.org.cn>
URL: https://miaozhun.github.io/scRecover
VignetteBuilder: knitr
BugReports: https://github.com/miaozhun/scRecover/issues
git_url: https://git.bioconductor.org/packages/scRecover
git_branch: RELEASE_3_13
git_last_commit: ac1750d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scRecover_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scRecover_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scRecover_1.8.0.tgz
vignettes: vignettes/scRecover/inst/doc/scRecover.html
vignetteTitles: scRecover
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scRecover/inst/doc/scRecover.R
dependencyCount: 78
Package: scRepertoire
Version: 1.2.0
Depends: ggplot2, R (>= 4.0)
Imports: Biostrings, dplyr, reshape2, ggalluvial, stringr, vegan,
powerTCR, SummarizedExperiment, plyr, parallel, doParallel,
methods, utils, rlang
Suggests: knitr, rmarkdown, BiocStyle, scater, circlize, scales, Seurat
License: Apache License 2.0
MD5sum: c2880c8369e012b0bd134117efc77a3b
NeedsCompilation: no
Title: A toolkit for single-cell immune receptor profiling
Description: scRepertoire was built to process data derived from the
10x Genomics Chromium Immune Profiling for both T-cell receptor
(TCR) and immunoglobulin (Ig) enrichment workflows and
subsequently interacts with the popular Seurat and
SingleCellExperiment R packages. It also allows for general
analysis of single-cell clonotype information without the use
of expression information. The package functions as a wrapper
for Startrac and powerTCR R packages.
biocViews: Software, ImmunoOncology, SingleCell, Classification,
Annotation, Sequencing
Author: Nick Borcherding [aut, cre]
Maintainer: Nick Borcherding <ncborch@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scRepertoire
git_branch: RELEASE_3_13
git_last_commit: 1daa495
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scRepertoire_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scRepertoire_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scRepertoire_1.2.0.tgz
vignettes: vignettes/scRepertoire/inst/doc/vignette.html
vignetteTitles: Using scRepertoire
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scRepertoire/inst/doc/vignette.R
dependencyCount: 85
Package: scruff
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: data.table, GenomicAlignments, GenomicFeatures, GenomicRanges,
Rsamtools, ShortRead, parallel, plyr, BiocGenerics,
BiocParallel, S4Vectors, AnnotationDbi, Biostrings, methods,
ggplot2, ggthemes, scales, GenomeInfoDb, stringdist, ggbio,
rtracklayer, SingleCellExperiment, SummarizedExperiment,
Rsubread
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: b7b5876be10e87f597631689e20bda8a
NeedsCompilation: no
Title: Single Cell RNA-Seq UMI Filtering Facilitator (scruff)
Description: A pipeline which processes single cell RNA-seq (scRNA-seq)
reads from CEL-seq and CEL-seq2 protocols. Demultiplex
scRNA-seq FASTQ files, align reads to reference genome using
Rsubread, and generate UMI filtered count matrix. Also provide
visualizations of read alignments and pre- and post-alignment
QC metrics.
biocViews: Software, Technology, Sequencing, Alignment, RNASeq,
SingleCell, WorkflowStep, Preprocessing, QualityControl,
Visualization, ImmunoOncology
Author: Zhe Wang [aut, cre], Junming Hu [aut], Joshua Campbell [aut]
Maintainer: Zhe Wang <zhe@bu.edu>
VignetteBuilder: knitr
BugReports: https://github.com/campbio/scruff/issues
git_url: https://git.bioconductor.org/packages/scruff
git_branch: RELEASE_3_13
git_last_commit: 027572f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scruff_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scruff_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scruff_1.10.0.tgz
vignettes: vignettes/scruff/inst/doc/scruff.html
vignetteTitles: Process Single Cell RNA-Seq reads using scruff
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scruff/inst/doc/scruff.R
dependencyCount: 159
Package: scry
Version: 1.4.0
Depends: R (>= 4.0), stats, methods
Imports: DelayedArray, glmpca (>= 0.2.0), HDF5Array, Matrix,
SingleCellExperiment, SummarizedExperiment, BiocSingular
Suggests: BiocGenerics, covr, DuoClustering2018, ggplot2, knitr,
rmarkdown, TENxPBMCData, testthat
License: Artistic-2.0
MD5sum: c7661bbefcc9573eef5286b01f462da9
NeedsCompilation: no
Title: Small-Count Analysis Methods for High-Dimensional Data
Description: Many modern biological datasets consist of small counts
that are not well fit by standard linear-Gaussian methods such
as principal component analysis. This package provides
implementations of count-based feature selection and dimension
reduction algorithms. These methods can be used to facilitate
unsupervised analysis of any high-dimensional data such as
single-cell RNA-seq.
biocViews: DimensionReduction, GeneExpression, Normalization,
PrincipalComponent, RNASeq, Software, Sequencing, SingleCell,
Transcriptomics
Author: Kelly Street [aut, cre], F. William Townes [aut, cph], Davide
Risso [aut], Stephanie Hicks [aut]
Maintainer: Kelly Street <street.kelly@gmail.com>
URL: https://bioconductor.org/packages/scry.html
VignetteBuilder: knitr
BugReports: https://github.com/kstreet13/scry/issues
git_url: https://git.bioconductor.org/packages/scry
git_branch: RELEASE_3_13
git_last_commit: e03f3cb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scry_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scry_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scry_1.4.0.tgz
vignettes: vignettes/scry/inst/doc/bigdata.html,
vignettes/scry/inst/doc/scry.html
vignetteTitles: Scry Methods For Larger Datasets, Overview of Scry
Methods
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scry/inst/doc/bigdata.R,
vignettes/scry/inst/doc/scry.R
dependencyCount: 46
Package: scTensor
Version: 2.2.0
Depends: R (>= 3.5.0)
Imports: methods, RSQLite, igraph, S4Vectors, plotly, reactome.db,
AnnotationDbi, SummarizedExperiment, SingleCellExperiment,
nnTensor, rTensor, abind, plotrix, heatmaply, tagcloud,
rmarkdown, BiocStyle, knitr, AnnotationHub, MeSHDbi, grDevices,
graphics, stats, utils, outliers, Category, meshr, GOstats,
ReactomePA, DOSE, crayon, checkmate, BiocManager, visNetwork,
schex, ggplot2
Suggests: testthat, LRBase.Hsa.eg.db, LRBase.Mmu.eg.db, LRBaseDbi,
Seurat, scTGIF, Homo.sapiens
License: Artistic-2.0
Archs: i386, x64
MD5sum: 65c04027bab742332c076821697b6039
NeedsCompilation: no
Title: Detection of cell-cell interaction from single-cell RNA-seq
dataset by tensor decomposition
Description: The algorithm is based on the non-negative tucker
decomposition (NTD2) of nnTensor.
biocViews: DimensionReduction, SingleCell, Software, GeneExpression
Author: Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut]
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scTensor
git_branch: RELEASE_3_13
git_last_commit: adb0ecb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scTensor_2.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scTensor_2.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scTensor_2.2.0.tgz
vignettes:
vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.html,
vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.html,
vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.html,
vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.html,
vignettes/scTensor/inst/doc/scTensor.html
vignetteTitles: scTensor: 1. Data format and ID conversion, scTensor:
2. Interpretation of HTML report, scTensor: 3. Simulation of
CCI, scTensor: 4. Reanalysis of the results of scTensor,
scTensor
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.R,
vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.R,
vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.R,
vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.R,
vignettes/scTensor/inst/doc/scTensor.R
dependencyCount: 312
Package: scTGIF
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: GSEABase, Biobase, SingleCellExperiment, BiocStyle, plotly,
tagcloud, rmarkdown, Rcpp, grDevices, graphics, utils, knitr,
S4Vectors, SummarizedExperiment, RColorBrewer, nnTensor,
methods, scales, msigdbr, schex, tibble, ggplot2, igraph
Suggests: testthat
License: Artistic-2.0
Archs: i386, x64
MD5sum: 695dcf8f94dd67eab96dc0dcdce679b6
NeedsCompilation: no
Title: Cell type annotation for unannotated single-cell RNA-Seq data
Description: scTGIF connects the cells and the related gene functions
without cell type label.
biocViews: DimensionReduction, QualityControl, SingleCell, Software,
GeneExpression
Author: Koki Tsuyuzaki [aut, cre]
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scTGIF
git_branch: RELEASE_3_13
git_last_commit: 8463e50
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scTGIF_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scTGIF_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scTGIF_1.6.0.tgz
vignettes: vignettes/scTGIF/inst/doc/scTGIF.html
vignetteTitles: scTGIF
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scTGIF/inst/doc/scTGIF.R
suggestsMe: scTensor
dependencyCount: 207
Package: scTHI
Version: 1.4.0
Depends: R (>= 4.0)
Imports: BiocParallel, Rtsne, grDevices, graphics, stats
Suggests: scTHI.data, knitr, rmarkdown
License: GPL-2
MD5sum: e70e426c5e6a1602bef23ee7a214e484
NeedsCompilation: no
Title: Indentification of significantly activated ligand-receptor
interactions across clusters of cells from single-cell RNA
sequencing data
Description: scTHI is an R package to identify active pairs of
ligand-receptors from single cells in order to study,among
others, tumor-host interactions. scTHI contains a set of
signatures to classify cells from the tumor microenvironment.
biocViews: Software,SingleCell
Author: Francesca Pia Caruso [aut], Michele Ceccarelli [aut, cre]
Maintainer: Michele Ceccarelli <m.ceccarelli@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/miccec/scTHI/issues
git_url: https://git.bioconductor.org/packages/scTHI
git_branch: RELEASE_3_13
git_last_commit: 7d5a24a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/scTHI_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scTHI_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/scTHI_1.4.0.tgz
vignettes: vignettes/scTHI/inst/doc/vignette.html
vignetteTitles: Using scTHI
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scTHI/inst/doc/vignette.R
dependencyCount: 15
Package: scuttle
Version: 1.2.1
Depends: SingleCellExperiment
Imports: methods, utils, stats, Matrix, Rcpp, BiocGenerics, S4Vectors,
BiocParallel, GenomicRanges, SummarizedExperiment,
DelayedArray, DelayedMatrixStats, beachmat
LinkingTo: Rcpp, beachmat
Suggests: BiocStyle, knitr, scRNAseq, rmarkdown, testthat, scran
License: GPL-3
MD5sum: b0271029c4db5f943c0832a78b9536bd
NeedsCompilation: yes
Title: Single-Cell RNA-Seq Analysis Utilities
Description: Provides basic utility functions for performing
single-cell analyses, focusing on simple normalization, quality
control and data transformations. Also provides some helper
functions to assist development of other packages.
biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl,
Preprocessing, Normalization, Transcriptomics, GeneExpression,
Sequencing, Software, DataImport
Author: Aaron Lun [aut, cre], Davis McCarthy [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scuttle
git_branch: RELEASE_3_13
git_last_commit: 56a8a81
git_last_commit_date: 2021-08-04
Date/Publication: 2021-08-05
source.ver: src/contrib/scuttle_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/scuttle_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/scuttle_1.2.1.tgz
vignettes: vignettes/scuttle/inst/doc/misc.html,
vignettes/scuttle/inst/doc/norm.html,
vignettes/scuttle/inst/doc/qc.html
vignetteTitles: 3. Other functions, 2. Normalization, 1. Quality
control
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scuttle/inst/doc/misc.R,
vignettes/scuttle/inst/doc/norm.R,
vignettes/scuttle/inst/doc/qc.R
dependsOnMe: scater, scran, OSCA.advanced, OSCA.basic, OSCA.intro,
OSCA.multisample, OSCA.workflows
importsMe: BASiCS, batchelor, DropletUtils, mia, mumosa, muscat,
scDblFinder, velociraptor
suggestsMe: bluster, SingleR, snifter, splatter, TSCAN, HCAData,
MouseThymusAgeing, SingleRBook
linksToMe: DropletUtils, scran
dependencyCount: 38
Package: SDAMS
Version: 1.12.0
Depends: R(>= 3.5), SummarizedExperiment
Imports: trust, qvalue, methods, stats, utils
Suggests: testthat
License: GPL
MD5sum: efda431db8c119d83464084835525b8d
NeedsCompilation: no
Title: Differential Abundant/Expression Analysis for Metabolomics,
Proteomics and single-cell RNA sequencing Data
Description: This Package utilizes a Semi-parametric Differential
Abundance/expression analysis (SDA) method for metabolomics and
proteomics data from mass spectrometry as well as single-cell
RNA sequencing data. SDA is able to robustly handle
non-normally distributed data and provides a clear
quantification of the effect size.
biocViews: ImmunoOncology, DifferentialExpression, Metabolomics,
Proteomics, MassSpectrometry, SingleCell
Author: Yuntong Li <yuntong.li@uky.edu>, Chi Wang <chi.wang@uky.edu>,
Li Chen <lichenuky@uky.edu>
Maintainer: Yuntong Li <yuntong.li@uky.edu>
git_url: https://git.bioconductor.org/packages/SDAMS
git_branch: RELEASE_3_13
git_last_commit: 0dc63b2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SDAMS_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SDAMS_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SDAMS_1.12.0.tgz
vignettes: vignettes/SDAMS/inst/doc/SDAMS.pdf
vignetteTitles: SDAMS Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SDAMS/inst/doc/SDAMS.R
dependencyCount: 63
Package: sechm
Version: 1.0.0
Depends: R (>= 4.1)
Imports: S4Vectors, SummarizedExperiment, seriation, ComplexHeatmap,
circlize, methods, randomcoloR, stats, grid, grDevices
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: bf04e5d9c73bc928a529cde0bd769b4c
NeedsCompilation: no
Title: sechm: Complex Heatmaps from a SummarizedExperiment
Description: sechm provides a simple interface between
SummarizedExperiment objects and the ComplexHeatmap package. It
enables plotting annotated heatmaps from SE objects, with easy
access to rowData and colData columns, and implements a number
of features to make the generation of heatmaps easier and more
flexible. These functionalities used to be part of the SEtools
package.
biocViews: GeneExpression, Visualization
Author: Pierre-Luc Germain [cre, aut]
(<https://orcid.org/0000-0003-3418-4218>)
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
VignetteBuilder: knitr
BugReports: https://github.com/plger/sechm
git_url: https://git.bioconductor.org/packages/sechm
git_branch: RELEASE_3_13
git_last_commit: a0a1394
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sechm_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sechm_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sechm_1.0.0.tgz
vignettes: vignettes/sechm/inst/doc/sechm.html
vignetteTitles: sechm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sechm/inst/doc/sechm.R
dependencyCount: 68
Package: segmentSeq
Version: 2.26.0
Depends: R (>= 3.0.0), methods, baySeq (>= 2.9.0), S4Vectors, parallel,
GenomicRanges, ShortRead, stats
Imports: Rsamtools, IRanges, GenomeInfoDb, graphics, grDevices, utils,
abind
Suggests: BiocStyle, BiocGenerics
License: GPL-3
MD5sum: f51561954ad849fee2835cebd23e81e5
NeedsCompilation: no
Title: Methods for identifying small RNA loci from high-throughput
sequencing data
Description: High-throughput sequencing technologies allow the
production of large volumes of short sequences, which can be
aligned to the genome to create a set of matches to the genome.
By looking for regions of the genome which to which there are
high densities of matches, we can infer a segmentation of the
genome into regions of biological significance. The methods in
this package allow the simultaneous segmentation of data from
multiple samples, taking into account replicate data, in order
to create a consensus segmentation. This has obvious
applications in a number of classes of sequencing experiments,
particularly in the discovery of small RNA loci and novel mRNA
transcriptome discovery.
biocViews: MultipleComparison, Sequencing, Alignment,
DifferentialExpression, QualityControl, DataImport
Author: Thomas J. Hardcastle
Maintainer: Thomas J. Hardcastle <tjh48@cam.ac.uk>
git_url: https://git.bioconductor.org/packages/segmentSeq
git_branch: RELEASE_3_13
git_last_commit: 5fe3637
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/segmentSeq_2.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/segmentSeq_2.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/segmentSeq_2.26.0.tgz
vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.pdf,
vignettes/segmentSeq/inst/doc/segmentSeq.pdf
vignetteTitles: segmentsSeq: Methylation locus identification,
segmentSeq: small RNA locus detection
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/segmentSeq/inst/doc/methylationAnalysis.R,
vignettes/segmentSeq/inst/doc/segmentSeq.R
dependencyCount: 50
Package: selectKSigs
Version: 1.4.0
Depends: R(>= 3.6)
Imports: HiLDA, magrittr, gtools, methods, Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, dplyr, tidyr
License: GPL-3
MD5sum: fba91fbf9649aa4accf23487d9c8facb
NeedsCompilation: yes
Title: Selecting the number of mutational signatures using a
perplexity-based measure and cross-validation
Description: A package to suggest the number of mutational signatures
in a collection of somatic mutations using calculating the
cross-validated perplexity score.
biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod,
Clustering
Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb]
Maintainer: Zhi Yang <zyang895@gmail.com>
URL: https://github.com/USCbiostats/selectKSigs
VignetteBuilder: knitr
BugReports: https://github.com/USCbiostats/HiLDA/selectKSigs
git_url: https://git.bioconductor.org/packages/selectKSigs
git_branch: RELEASE_3_13
git_last_commit: 803d736
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/selectKSigs_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/selectKSigs_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/selectKSigs_1.4.0.tgz
vignettes: vignettes/selectKSigs/inst/doc/selectKSigs.html
vignetteTitles: An introduction to HiLDA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/selectKSigs/inst/doc/selectKSigs.R
dependencyCount: 125
Package: SELEX
Version: 1.24.0
Depends: rJava (>= 0.5-0), Biostrings (>= 2.26.0)
Imports: stats, utils
License: GPL (>=2)
MD5sum: 706272d74f4c881307821a0b3337f5d0
NeedsCompilation: no
Title: Functions for analyzing SELEX-seq data
Description: Tools for quantifying DNA binding specificities based on
SELEX-seq data.
biocViews: Software, MotifDiscovery, MotifAnnotation, GeneRegulation,
Transcription
Author: Chaitanya Rastogi, Dahong Liu, Lucas Melo, and Harmen J.
Bussemaker
Maintainer: Harmen J. Bussemaker <hjb2004@columbia.edu>
URL: https://bussemakerlab.org/site/software/
SystemRequirements: Java (>= 1.5)
git_url: https://git.bioconductor.org/packages/SELEX
git_branch: RELEASE_3_13
git_last_commit: d37bba9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SELEX_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SELEX_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SELEX_1.24.0.tgz
vignettes: vignettes/SELEX/inst/doc/SELEX.pdf
vignetteTitles: Motif Discovery with SELEX-seq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SELEX/inst/doc/SELEX.R
dependencyCount: 20
Package: SemDist
Version: 1.26.0
Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate
Suggests: GOSemSim
License: GPL (>= 2)
Archs: i386, x64
MD5sum: d5798bdf57dd47e0a65f77d6416d4f6b
NeedsCompilation: no
Title: Information Accretion-based Function Predictor Evaluation
Description: This package implements methods to calculate information
accretion for a given version of the gene ontology and uses
this data to calculate remaining uncertainty, misinformation,
and semantic similarity for given sets of predicted annotations
and true annotations from a protein function predictor.
biocViews: Classification, Annotation, GO, Software
Author: Ian Gonzalez and Wyatt Clark
Maintainer: Ian Gonzalez <gonzalez.isv@gmail.com>
URL: http://github.com/iangonzalez/SemDist
git_url: https://git.bioconductor.org/packages/SemDist
git_branch: RELEASE_3_13
git_last_commit: d508e59
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SemDist_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SemDist_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SemDist_1.26.0.tgz
vignettes: vignettes/SemDist/inst/doc/introduction.pdf
vignetteTitles: introduction.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SemDist/inst/doc/introduction.R
dependencyCount: 50
Package: semisup
Version: 1.16.0
Depends: R (>= 3.0.0)
Imports: VGAM
Suggests: knitr, testthat, SummarizedExperiment
License: GPL-3
MD5sum: 96636288ae16aeae3bac66d95f963fdb
NeedsCompilation: no
Title: Semi-Supervised Mixture Model
Description: Implements a parametric semi-supervised mixture model. The
permutation test detects markers with main or interactive
effects, without distinguishing them. Possible applications
include genome-wide association analysis and differential
expression analysis.
biocViews: SNP, GenomicVariation, SomaticMutation, Genetics,
Classification, Clustering, DNASeq, Microarray,
MultipleComparison
Author: Armin Rauschenberger [aut, cre]
Maintainer: Armin Rauschenberger <armin.rauschenberger@uni.lu>
URL: https://github.com/rauschenberger/semisup
VignetteBuilder: knitr
BugReports: https://github.com/rauschenberger/semisup/issues
git_url: https://git.bioconductor.org/packages/semisup
git_branch: RELEASE_3_13
git_last_commit: 6853d6d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/semisup_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/semisup_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/semisup_1.16.0.tgz
vignettes: vignettes/semisup/inst/doc/semisup.pdf,
vignettes/semisup/inst/doc/article.html,
vignettes/semisup/inst/doc/vignette.html
vignetteTitles: vignette source, article frame, vignette frame
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/semisup/inst/doc/semisup.R
dependencyCount: 5
Package: SEPIRA
Version: 1.12.0
Depends: R (>= 3.5.0)
Imports: limma (>= 3.32.5), corpcor (>= 1.6.9), parallel (>= 3.3.1),
stats
Suggests: knitr, rmarkdown, testthat, igraph
License: GPL-3
MD5sum: 184b7c8d42e71e351a4c7b1d94009aa3
NeedsCompilation: no
Title: Systems EPigenomics Inference of Regulatory Activity
Description: SEPIRA (Systems EPigenomics Inference of Regulatory
Activity) is an algorithm that infers sample-specific
transcription factor activity from the genome-wide expression
or DNA methylation profile of the sample.
biocViews: GeneExpression, Transcription, GeneRegulation, GeneTarget,
NetworkInference, Network, Software
Author: Yuting Chen [aut, cre], Andrew Teschendorff [aut]
Maintainer: Yuting Chen <cytwarmmay@hotmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SEPIRA
git_branch: RELEASE_3_13
git_last_commit: 6fe3b44
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SEPIRA_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SEPIRA_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SEPIRA_1.12.0.tgz
vignettes: vignettes/SEPIRA/inst/doc/SEPIRA.html
vignetteTitles: Introduction to `SEPIRA`
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SEPIRA/inst/doc/SEPIRA.R
dependencyCount: 8
Package: seq2pathway
Version: 1.24.0
Depends: R (>= 3.6.2)
Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data
License: GPL-2
MD5sum: f009a447b9f202320947cf5015def361
NeedsCompilation: no
Title: a novel tool for functional gene-set (or termed as pathway)
analysis of next-generation sequencing data
Description: Seq2pathway is a novel tool for functional gene-set (or
termed as pathway) analysis of next-generation sequencing data,
consisting of "seq2gene" and "gene2path" components. The
seq2gene links sequence-level measurements of genomic regions
(including SNPs or point mutation coordinates) to gene-level
scores, and the gene2pathway summarizes gene scores to
pathway-scores for each sample. The seq2gene has the
feasibility to assign both coding and non-exon regions to a
broader range of neighboring genes than only the nearest one,
thus facilitating the study of functional non-coding regions.
The gene2pathway takes into account the quantity of
significance for gene members within a pathway compared those
outside a pathway. The output of seq2pathway is a general
structure of quantitative pathway-level scores, thus allowing
one to functional interpret such datasets as RNA-seq, ChIP-seq,
GWAS, and derived from other next generational sequencing
experiments.
biocViews: Software
Author: Xinan Yang <xyang2@uchicago.edu>; Bin Wang <binw@uchicago.edu>
Maintainer: Arjun Kinstlick <akinstlick@uchicago.edu>
git_url: https://git.bioconductor.org/packages/seq2pathway
git_branch: RELEASE_3_13
git_last_commit: 61596ef
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/seq2pathway_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/seq2pathway_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/seq2pathway_1.24.0.tgz
vignettes: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.pdf
vignetteTitles: An R package for sequence
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.R
dependencyCount: 127
Package: SeqArray
Version: 1.32.0
Depends: R (>= 3.5.0), gdsfmt (>= 1.23.5)
Imports: methods, parallel, IRanges, GenomicRanges, GenomeInfoDb,
Biostrings, S4Vectors
LinkingTo: gdsfmt
Suggests: Biobase, BiocGenerics, BiocParallel, RUnit, Rcpp, SNPRelate,
digest, crayon, knitr, markdown, rmarkdown, Rsamtools,
VariantAnnotation
License: GPL-3
Archs: i386, x64
MD5sum: 442b2eea24758d0c388293e7c1f4aef6
NeedsCompilation: yes
Title: Data management of large-scale whole-genome sequence variant
calls
Description: Data management of large-scale whole-genome sequencing
variant calls with thousands of individuals: genotypic data
(e.g., SNVs, indels and structural variation calls) and
annotations in SeqArray GDS files are stored in an
array-oriented and compressed manner, with efficient data
access using the R programming language.
biocViews: Infrastructure, DataRepresentation, Sequencing, Genetics
Author: Xiuwen Zheng [aut, cre]
(<https://orcid.org/0000-0002-1390-0708>), Stephanie Gogarten
[aut], David Levine [ctb], Cathy Laurie [ctb]
Maintainer: Xiuwen Zheng <zhengx@u.washington.edu>
URL: http://github.com/zhengxwen/SeqArray
VignetteBuilder: knitr
BugReports: http://github.com/zhengxwen/SeqArray/issues
git_url: https://git.bioconductor.org/packages/SeqArray
git_branch: RELEASE_3_13
git_last_commit: 8fa3c99
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SeqArray_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SeqArray_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SeqArray_1.32.0.tgz
vignettes: vignettes/SeqArray/inst/doc/OverviewSlides.html,
vignettes/SeqArray/inst/doc/SeqArray.html,
vignettes/SeqArray/inst/doc/SeqArrayTutorial.html
vignetteTitles: SeqArray Overview, R Integration, SeqArray Data Format
and Access
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SeqArray/inst/doc/SeqArray.R,
vignettes/SeqArray/inst/doc/SeqArrayTutorial.R
dependsOnMe: SAIGEgds, SeqVarTools
importsMe: GDSArray, GENESIS, VariantExperiment, GMMAT, MAGEE
suggestsMe: DelayedDataFrame, HIBAG, VCFArray
dependencyCount: 21
Package: seqbias
Version: 1.40.0
Depends: R (>= 3.0.2), GenomicRanges (>= 0.1.0), Biostrings (>=
2.15.0), methods
LinkingTo: Rhtslib (>= 1.15.3)
Suggests: Rsamtools, ggplot2
License: LGPL-3
MD5sum: ef4494289d1301883e1c57eec705cc86
NeedsCompilation: yes
Title: Estimation of per-position bias in high-throughput sequencing
data
Description: This package implements a model of per-position sequencing
bias in high-throughput sequencing data using a simple Bayesian
network, the structure and parameters of which are trained on a
set of aligned reads and a reference genome sequence.
biocViews: Sequencing
Author: Daniel Jones <dcjones@cs.washington.edu>
Maintainer: Daniel Jones <dcjones@cs.washington.edu>
SystemRequirements: GNU make
git_url: https://git.bioconductor.org/packages/seqbias
git_branch: RELEASE_3_13
git_last_commit: c9d8e78
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/seqbias_1.40.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/seqbias_1.40.0.tgz
vignettes: vignettes/seqbias/inst/doc/overview.pdf
vignetteTitles: Assessing and Adjusting for Technical Bias in High
Throughput Sequencing Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqbias/inst/doc/overview.R
dependsOnMe: ReQON
dependencyCount: 21
Package: seqCAT
Version: 1.14.1
Depends: R (>= 3.6), GenomicRanges (>= 1.26.4), VariantAnnotation(>=
1.20.3)
Imports: dplyr (>= 0.5.0), GenomeInfoDb (>= 1.13.4), ggplot2 (>=
2.2.1), grid (>= 3.5.0), IRanges (>= 2.8.2), methods,
rtracklayer, rlang, scales (>= 0.4.1), S4Vectors (>= 0.12.2),
stats, SummarizedExperiment (>= 1.4.0), tidyr (>= 0.6.1), utils
Suggests: knitr, BiocStyle, rmarkdown, testthat, BiocManager
License: MIT + file LICENCE
MD5sum: ed8a64f4a0f611321e3dd1ccef035ea7
NeedsCompilation: no
Title: High Throughput Sequencing Cell Authentication Toolkit
Description: The seqCAT package uses variant calling data (in the form
of VCF files) from high throughput sequencing technologies to
authenticate and validate the source, function and
characteristics of biological samples used in scientific
endeavours.
biocViews: Coverage, GenomicVariation, Sequencing, VariantAnnotation
Author: Erik Fasterius [aut, cre]
Maintainer: Erik Fasterius <erik.fasterius@outlook.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/seqCAT
git_branch: RELEASE_3_13
git_last_commit: a806d9f
git_last_commit_date: 2021-10-10
Date/Publication: 2021-10-12
source.ver: src/contrib/seqCAT_1.14.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/seqCAT_1.14.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/seqCAT_1.14.1.tgz
vignettes: vignettes/seqCAT/inst/doc/seqCAT.html
vignetteTitles: seqCAT: The High Throughput Sequencing Cell
Authentication Toolkit
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqCAT/inst/doc/seqCAT.R
dependencyCount: 114
Package: seqCNA
Version: 1.38.0
Depends: R (>= 3.0), GLAD (>= 2.14), doSNOW (>= 1.0.5), adehabitatLT
(>= 0.3.4), seqCNA.annot (>= 0.99), methods
License: GPL-3
MD5sum: dca2bdb34175f6a6714568930f55c109
NeedsCompilation: yes
Title: Copy number analysis of high-throughput sequencing cancer data
Description: Copy number analysis of high-throughput sequencing cancer
data with fast summarization, extensive filtering and improved
normalization
biocViews: CopyNumberVariation, Genetics, Sequencing
Author: David Mosen-Ansorena
Maintainer: David Mosen-Ansorena <dmosen.gn@cicbiogune.es>
SystemRequirements: samtools
git_url: https://git.bioconductor.org/packages/seqCNA
git_branch: RELEASE_3_13
git_last_commit: 9827ffc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/seqCNA_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/seqCNA_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/seqCNA_1.38.0.tgz
vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R
suggestsMe: Herper
dependencyCount: 26
Package: seqcombo
Version: 1.14.1
Depends: R (>= 3.4.0)
Imports: Biostrings, cowplot, dplyr, ggplot2, grid, igraph, magrittr,
methods, utils, yulab.utils
Suggests: emojifont, knitr, rmarkdown, prettydoc, tibble
License: Artistic-2.0
MD5sum: 41787b7e2ad5a2fdbf3d5907b03c8e11
NeedsCompilation: no
Title: Visualization Tool for Sequence Recombination and Reassortment
Description: Provides useful functions for visualizing sequence
recombination and virus reassortment events.
biocViews: Alignment, Software, Visualization
Author: Guangchuang Yu [aut, cre]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/GuangchuangYu/seqcombo/issues
git_url: https://git.bioconductor.org/packages/seqcombo
git_branch: RELEASE_3_13
git_last_commit: 11fdb25
git_last_commit_date: 2021-08-20
Date/Publication: 2021-08-22
source.ver: src/contrib/seqcombo_1.14.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/seqcombo_1.14.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/seqcombo_1.14.1.tgz
vignettes: vignettes/seqcombo/inst/doc/reassortment.html,
vignettes/seqcombo/inst/doc/seqcombo.html
vignetteTitles: Reassortment, seqcombo introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqcombo/inst/doc/reassortment.R,
vignettes/seqcombo/inst/doc/seqcombo.R
dependencyCount: 58
Package: SeqGate
Version: 1.2.0
Depends: S4Vectors, SummarizedExperiment, GenomicRanges
Imports: stats, methods, BiocManager
Suggests: testthat (>= 3.0.0), edgeR, BiocStyle, knitr, rmarkdown
License: GPL (>= 2.0)
MD5sum: fb84b76f0ebe5901e63169ea98d88299
NeedsCompilation: no
Title: Filtering of Lowly Expressed Features
Description: Filtering of lowly expressed features (e.g. genes) is a
common step before performing statistical analysis, but an
arbitrary threshold is generally chosen. SeqGate implements a
method that rationalize this step by the analysis of the
distibution of counts in replicate samples. The gate is the
threshold above which sequenced features can be considered as
confidently quantified.
biocViews: DifferentialExpression, GeneExpression, Transcriptomics,
Sequencing, RNASeq
Author: Christelle Reynès [aut], Stéphanie Rialle [aut, cre]
Maintainer: Stéphanie Rialle <stephanie.rialle@mgx.cnrs.fr>
VignetteBuilder: knitr
BugReports: https://github.com/srialle/SeqGate/issues
git_url: https://git.bioconductor.org/packages/SeqGate
git_branch: RELEASE_3_13
git_last_commit: 7196a0b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SeqGate_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SeqGate_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SeqGate_1.2.0.tgz
vignettes: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.html
vignetteTitles: SeqGate: Filter lowly expressed features
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.R
dependencyCount: 27
Package: SeqGSEA
Version: 1.32.0
Depends: Biobase, doParallel, DESeq2
Imports: methods, biomaRt
Suggests: GenomicRanges
License: GPL (>= 3)
MD5sum: 671ace3299db344c315c79e224fbe39f
NeedsCompilation: no
Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating
differential expression and splicing
Description: The package generally provides methods for gene set
enrichment analysis of high-throughput RNA-Seq data by
integrating differential expression and splicing. It uses
negative binomial distribution to model read count data, which
accounts for sequencing biases and biological variation. Based
on permutation tests, statistical significance can also be
achieved regarding each gene's differential expression and
splicing, respectively.
biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression,
DifferentialExpression, DifferentialSplicing, ImmunoOncology
Author: Xi Wang <Xi.Wang@newcastle.edu.au>
Maintainer: Xi Wang <Xi.Wang@dkfz.de>
git_url: https://git.bioconductor.org/packages/SeqGSEA
git_branch: RELEASE_3_13
git_last_commit: 2ecabac
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SeqGSEA_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SeqGSEA_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SeqGSEA_1.32.0.tgz
vignettes: vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf
vignetteTitles: Gene set enrichment analysis of RNA-Seq data with the
SeqGSEA package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SeqGSEA/inst/doc/SeqGSEA.R
dependencyCount: 113
Package: seqLogo
Version: 1.58.0
Depends: methods, grid
Imports: stats4, grDevices
Suggests: knitr, BiocStyle, rmarkdown, testthat
License: LGPL (>= 2)
Archs: i386, x64
MD5sum: 5173c200a36b6225023304bf88e64d08
NeedsCompilation: no
Title: Sequence logos for DNA sequence alignments
Description: seqLogo takes the position weight matrix of a DNA sequence
motif and plots the corresponding sequence logo as introduced
by Schneider and Stephens (1990).
biocViews: SequenceMatching
Author: Oliver Bembom [aut], Robert Ivanek [aut, cre]
(<https://orcid.org/0000-0002-8403-056X>)
Maintainer: Robert Ivanek <robert.ivanek@unibas.ch>
VignetteBuilder: knitr
BugReports: https://github.com/ivanek/seqLogo/issues
git_url: https://git.bioconductor.org/packages/seqLogo
git_branch: RELEASE_3_13
git_last_commit: acb7150
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/seqLogo_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/seqLogo_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/seqLogo_1.58.0.tgz
vignettes: vignettes/seqLogo/inst/doc/seqLogo.html
vignetteTitles: Sequence logos for DNA sequence alignments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R
dependsOnMe: rGADEM, generegulation
importsMe: igvR, IntEREst, PWMEnrich, rGADEM, riboSeqR, SPLINTER,
TFBSTools
suggestsMe: BCRANK, DiffLogo, MAGAR, motifcounter, MotifDb,
universalmotif, phangorn
dependencyCount: 4
Package: seqPattern
Version: 1.24.0
Depends: methods, R (>= 2.15.0)
Imports: Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix
Suggests: BSgenome.Drerio.UCSC.danRer7, CAGEr, RUnit, BiocGenerics,
BiocStyle
Enhances: parallel
License: GPL-3
MD5sum: eacbee961c406327b499b87af45ddc67
NeedsCompilation: no
Title: Visualising oligonucleotide patterns and motif occurrences
across a set of sorted sequences
Description: Visualising oligonucleotide patterns and sequence motifs
occurrences across a large set of sequences centred at a common
reference point and sorted by a user defined feature.
biocViews: Visualization, SequenceMatching
Author: Vanja Haberle <vanja.haberle@gmail.com>
Maintainer: Vanja Haberle <vanja.haberle@gmail.com>
git_url: https://git.bioconductor.org/packages/seqPattern
git_branch: RELEASE_3_13
git_last_commit: 21a9de6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/seqPattern_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/seqPattern_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/seqPattern_1.24.0.tgz
vignettes: vignettes/seqPattern/inst/doc/seqPattern.pdf
vignetteTitles: seqPattern
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqPattern/inst/doc/seqPattern.R
importsMe: genomation
dependencyCount: 22
Package: seqsetvis
Version: 1.12.0
Depends: R (>= 3.6), ggplot2
Imports: data.table, eulerr, GenomeInfoDb, GenomicAlignments,
GenomicRanges, ggplotify, grDevices, grid, IRanges, limma,
methods, pbapply, pbmcapply, png, RColorBrewer, Rsamtools,
rtracklayer, S4Vectors, stats, UpSetR
Suggests: BiocFileCache, BiocManager, BiocStyle, ChIPpeakAnno, covr,
cowplot, knitr, rmarkdown, testthat
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 21f0219336bb7f1aa4895d0e600e2998
NeedsCompilation: no
Title: Set Based Visualizations for Next-Gen Sequencing Data
Description: seqsetvis enables the visualization and analysis of sets
of genomic sites in next gen sequencing data. Although
seqsetvis was designed for the comparison of mulitple ChIP-seq
samples, this package is domain-agnostic and allows the
processing of multiple genomic coordinate files (bed-like
files) and signal files (bigwig files pileups from bam file).
biocViews: Software, ChIPSeq, MultipleComparison, Sequencing,
Visualization
Author: Joseph R Boyd [aut, cre]
Maintainer: Joseph R Boyd <jrboyd@uvm.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/seqsetvis
git_branch: RELEASE_3_13
git_last_commit: f9979c2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/seqsetvis_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/seqsetvis_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/seqsetvis_1.12.0.tgz
vignettes: vignettes/seqsetvis/inst/doc/seqsetvis_overview.html
vignetteTitles: Overview and Use Cases
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/seqsetvis/inst/doc/seqsetvis_overview.R
dependencyCount: 90
Package: SeqSQC
Version: 1.14.0
Depends: R (>= 3.4), ExperimentHub (>= 1.3.7), SNPRelate (>= 1.10.2)
Imports: e1071, GenomicRanges, gdsfmt, ggplot2, GGally, IRanges,
methods, rbokeh, RColorBrewer, reshape2, rmarkdown, S4Vectors,
stats, utils
Suggests: BiocStyle, knitr, testthat
License: GPL-3
MD5sum: df93718f9a6f180e3d6a331897ebb905
NeedsCompilation: no
Title: A bioconductor package for sample quality check with next
generation sequencing data
Description: The SeqSQC is designed to identify problematic samples in
NGS data, including samples with gender mismatch,
contamination, cryptic relatedness, and population outlier.
biocViews: Experiment Data, Homo_sapiens_Data, Sequencing Data,
Project1000genomes, Genome
Author: Qian Liu [aut, cre]
Maintainer: Qian Liu <qliu7@buffalo.edu>
URL: https://github.com/Liubuntu/SeqSQC
VignetteBuilder: knitr
BugReports: https://github.com/Liubuntu/SeqSQC/issues
git_url: https://git.bioconductor.org/packages/SeqSQC
git_branch: RELEASE_3_13
git_last_commit: 098b7a8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SeqSQC_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SeqSQC_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SeqSQC_1.14.0.tgz
vignettes: vignettes/SeqSQC/inst/doc/vignette.html
vignetteTitles: Sample Quality Check for Next-Generation Sequencing
Data with SeqSQC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SeqSQC/inst/doc/vignette.R
dependencyCount: 140
Package: seqTools
Version: 1.26.0
Depends: methods,utils,zlibbioc
LinkingTo: zlibbioc
Suggests: RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: 9e5ca21ca6164308d9057fd6763423d5
NeedsCompilation: yes
Title: Analysis of nucleotide, sequence and quality content on fastq
files
Description: Analyze read length, phred scores and alphabet frequency
and DNA k-mers on uncompressed and compressed fastq files.
biocViews: QualityControl,Sequencing
Author: Wolfgang Kaisers
Maintainer: Wolfgang Kaisers <kaisers@med.uni-duesseldorf.de>
git_url: https://git.bioconductor.org/packages/seqTools
git_branch: RELEASE_3_13
git_last_commit: f74d126
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/seqTools_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/seqTools_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/seqTools_1.26.0.tgz
vignettes: vignettes/seqTools/inst/doc/seqTools_qual_report.pdf,
vignettes/seqTools/inst/doc/seqTools.pdf
vignetteTitles: seqTools_qual_report, Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqTools/inst/doc/seqTools_qual_report.R,
vignettes/seqTools/inst/doc/seqTools.R
importsMe: qckitfastq
dependencyCount: 3
Package: SeqVarTools
Version: 1.30.0
Depends: SeqArray
Imports: grDevices, graphics, stats, methods, Biobase, BiocGenerics,
gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW,
logistf, Matrix, data.table,
Suggests: BiocStyle, RUnit, stringr
License: GPL-3
MD5sum: 167a615f78e4bba965f975c1b9e4fa4e
NeedsCompilation: no
Title: Tools for variant data
Description: An interface to the fast-access storage format for VCF
data provided in SeqArray, with tools for common operations and
analysis.
biocViews: SNP, GeneticVariability, Sequencing, Genetics
Author: Stephanie M. Gogarten, Xiuwen Zheng, Adrienne Stilp
Maintainer: Stephanie M. Gogarten <sdmorris@uw.edu>
URL: https://github.com/smgogarten/SeqVarTools
git_url: https://git.bioconductor.org/packages/SeqVarTools
git_branch: RELEASE_3_13
git_last_commit: 0617a90
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SeqVarTools_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SeqVarTools_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SeqVarTools_1.30.0.tgz
vignettes: vignettes/SeqVarTools/inst/doc/Iterators.pdf,
vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf
vignetteTitles: Iterators in SeqVarTools, Introduction to SeqVarTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SeqVarTools/inst/doc/Iterators.R,
vignettes/SeqVarTools/inst/doc/SeqVarTools.R
importsMe: GENESIS, VariantExperiment, GMMAT, MAGEE
dependencyCount: 59
Package: sesame
Version: 1.10.5
Depends: R (>= 4.1), sesameData, methods
Imports: BiocParallel, grDevices, utils, stringr, tibble, illuminaio,
MASS, GenomicRanges, IRanges, grid, preprocessCore, S4Vectors,
randomForest, wheatmap, ggplot2, KernSmooth, graphics,
parallel, matrixStats, DNAcopy, stats, SummarizedExperiment
Suggests: scales, knitr, rmarkdown, testthat, dplyr, tidyr, BiocStyle,
IlluminaHumanMethylation450kmanifest, minfi,
FlowSorted.CordBloodNorway.450k, FlowSorted.Blood.450k,
HDF5Array
License: MIT + file LICENSE
MD5sum: b5868347f3d4c1cc447538a0d05b417b
NeedsCompilation: no
Title: SEnsible Step-wise Analysis of DNA MEthylation BeadChips
Description: Tools For analyzing Illumina Infinium DNA methylation
arrays.SeSAMe provides utilities to support analyses of
multiple generations of Infinium DNA methylation BeadChips,
including preprocessing, quality control, visualization and
inference. SeSAMe features more accurate detection calling,
intelligenet inference of ethnicity, sex and advanced quality
control routines.
biocViews: DNAMethylation, MethylationArray, Preprocessing,
QualityControl
Author: Wanding Zhou [aut, cre], Hui Shen [aut], Timothy Triche [ctb],
Bret Barnes [ctb]
Maintainer: Wanding Zhou <zhouwanding@gmail.com>
URL: https://github.com/zwdzwd/sesame
VignetteBuilder: knitr
BugReports: https://github.com/zwdzwd/sesame/issues
git_url: https://git.bioconductor.org/packages/sesame
git_branch: RELEASE_3_13
git_last_commit: 867cb46
git_last_commit_date: 2021-10-07
Date/Publication: 2021-10-10
source.ver: src/contrib/sesame_1.10.5.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sesame_1.10.5.zip
mac.binary.ver: bin/macosx/contrib/4.1/sesame_1.10.5.tgz
vignettes: vignettes/sesame/inst/doc/inferences.html,
vignettes/sesame/inst/doc/modeling.html,
vignettes/sesame/inst/doc/nonhuman.html,
vignettes/sesame/inst/doc/other.html,
vignettes/sesame/inst/doc/QC.html,
vignettes/sesame/inst/doc/sesame.html
vignetteTitles: "4. Data Inference", 3. Modeling, 2. Non-human Array,
5. Other Features, 1. Quality Control, "0. Basic Usage"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sesame/inst/doc/inferences.R,
vignettes/sesame/inst/doc/modeling.R,
vignettes/sesame/inst/doc/nonhuman.R,
vignettes/sesame/inst/doc/other.R,
vignettes/sesame/inst/doc/QC.R,
vignettes/sesame/inst/doc/sesame.R
importsMe: TCGAbiolinksGUI
suggestsMe: MethReg, RnBeads, TCGAbiolinks, sesameData
dependencyCount: 133
Package: SEtools
Version: 1.6.0
Depends: R (>= 4.0)
Imports: S4Vectors, SummarizedExperiment, data.table, seriation,
ComplexHeatmap, circlize, methods, BiocParallel, randomcoloR,
edgeR, openxlsx, sva, stats, DESeq2, Matrix, grid
Suggests: BiocStyle, knitr, rmarkdown, ggplot2, pheatmap
License: GPL
MD5sum: 83a46f296d560d66298c6e161e719b08
NeedsCompilation: no
Title: SEtools: tools for working with SummarizedExperiment
Description: This includes a set of tools for working with the
SummarizedExperiment class, including merging, melting,
aggregation and plotting functions. In particular, SEtools
offers a simple interface for plotting complex heatmaps from SE
objects.
biocViews: GeneExpression, Visualization
Author: Pierre-Luc Germain [cre, aut]
(<https://orcid.org/0000-0003-3418-4218>)
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
VignetteBuilder: knitr
BugReports: https://github.com/plger/SEtools
git_url: https://git.bioconductor.org/packages/SEtools
git_branch: RELEASE_3_13
git_last_commit: 486c252
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SEtools_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SEtools_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SEtools_1.6.0.tgz
vignettes: vignettes/SEtools/inst/doc/SEtools.html
vignetteTitles: SEtools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SEtools/inst/doc/SEtools.R
dependencyCount: 122
Package: sevenbridges
Version: 1.22.0
Depends: methods, utils, stats
Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors,
docopt, curl, uuid, data.table
Suggests: knitr, rmarkdown, testthat, readr
License: Apache License 2.0 | file LICENSE
MD5sum: f3dafe195ba0481c624f78c1ec3e126f
NeedsCompilation: no
Title: Seven Bridges Platform API Client and Common Workflow Language
Tool Builder in R
Description: R client and utilities for Seven Bridges platform API,
from Cancer Genomics Cloud to other Seven Bridges supported
platforms.
biocViews: Software, DataImport, ThirdPartyClient
Author: Soner Koc [aut, cre], Nan Xiao [aut], Tengfei Yin [aut], Dusan
Randjelovic [ctb], Emile Young [ctb], Seven Bridges Genomics
[cph, fnd]
Maintainer: Soner Koc <soner.koc@sevenbridges.com>
URL: https://www.sevenbridges.com,
https://sbg.github.io/sevenbridges-r/,
https://github.com/sbg/sevenbridges-r
VignetteBuilder: knitr
BugReports: https://github.com/sbg/sevenbridges-r/issues
git_url: https://git.bioconductor.org/packages/sevenbridges
git_branch: RELEASE_3_13
git_last_commit: 5515fa3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sevenbridges_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sevenbridges_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sevenbridges_1.22.0.tgz
vignettes: vignettes/sevenbridges/inst/doc/api.html,
vignettes/sevenbridges/inst/doc/apps.html,
vignettes/sevenbridges/inst/doc/bioc-workflow.html,
vignettes/sevenbridges/inst/doc/cgc-datasets.html,
vignettes/sevenbridges/inst/doc/docker.html,
vignettes/sevenbridges/inst/doc/rstudio.html
vignetteTitles: Complete Guide for Seven Bridges API R Client, Describe
and Execute CWL Tools/Workflows in R, Master Tutorial: Use R
for Cancer Genomics Cloud, Find Data on CGC via Data Browser
and Datasets API, Creating Your Docker Container and Command
Line Interface (with docopt), IDE Container: RStudio Server,,
Shiny Server,, and More
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sevenbridges/inst/doc/api.R,
vignettes/sevenbridges/inst/doc/apps.R,
vignettes/sevenbridges/inst/doc/bioc-workflow.R,
vignettes/sevenbridges/inst/doc/cgc-datasets.R,
vignettes/sevenbridges/inst/doc/docker.R,
vignettes/sevenbridges/inst/doc/rstudio.R
dependencyCount: 27
Package: sevenC
Version: 1.12.0
Depends: R (>= 3.5), InteractionSet (>= 1.2.0)
Imports: rtracklayer (>= 1.34.1), BiocGenerics (>= 0.22.0),
GenomeInfoDb (>= 1.12.2), GenomicRanges (>= 1.28.5), IRanges
(>= 2.10.3), S4Vectors (>= 0.14.4), readr (>= 1.1.0), purrr (>=
0.2.2), data.table (>= 1.10.4), boot (>= 1.3-20), methods (>=
3.4.1)
Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicInteractions,
covr
License: GPL-3
MD5sum: 56aad61b2fe15a4e2d50663d6e4e0faf
NeedsCompilation: no
Title: Computational Chromosome Conformation Capture by Correlation of
ChIP-seq at CTCF motifs
Description: Chromatin looping is an essential feature of eukaryotic
genomes and can bring regulatory sequences, such as enhancers
or transcription factor binding sites, in the close physical
proximity of regulated target genes. Here, we provide sevenC,
an R package that uses protein binding signals from ChIP-seq
and sequence motif information to predict chromatin looping
events. Cross-linking of proteins that bind close to loop
anchors result in ChIP-seq signals at both anchor loci. These
signals are used at CTCF motif pairs together with their
distance and orientation to each other to predict whether they
interact or not. The resulting chromatin loops might be used to
associate enhancers or transcription factor binding sites
(e.g., ChIP-seq peaks) to regulated target genes.
biocViews: DNA3DStructure, ChIPchip, Coverage, DataImport, Epigenetics,
FunctionalGenomics, Classification, Regression, ChIPSeq, HiC,
Annotation
Author: Jonas Ibn-Salem [aut, cre]
Maintainer: Jonas Ibn-Salem <jonas.ibn-salem@tron-mainz.de>
URL: https://github.com/ibn-salem/sevenC
VignetteBuilder: knitr
BugReports: https://github.com/ibn-salem/sevenC/issues
git_url: https://git.bioconductor.org/packages/sevenC
git_branch: RELEASE_3_13
git_last_commit: 04cd8e5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sevenC_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sevenC_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sevenC_1.12.0.tgz
vignettes: vignettes/sevenC/inst/doc/sevenC.html
vignetteTitles: Introduction to sevenC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sevenC/inst/doc/sevenC.R
dependencyCount: 74
Package: SGSeq
Version: 1.26.0
Depends: R (>= 4.0), IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10),
Rsamtools (>= 1.31.2), SummarizedExperiment, methods
Imports: AnnotationDbi, BiocGenerics (>= 0.31.5), Biostrings (>=
2.47.6), GenomicAlignments (>= 1.15.7), GenomicFeatures (>=
1.31.5), GenomeInfoDb, RUnit, S4Vectors (>= 0.23.19),
grDevices, graphics, igraph, parallel, rtracklayer (>= 1.39.7),
stats
Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: 73bad464689f411b2087fda1dae915a2
NeedsCompilation: no
Title: Splice event prediction and quantification from RNA-seq data
Description: SGSeq is a software package for analyzing splice events
from RNA-seq data. Input data are RNA-seq reads mapped to a
reference genome in BAM format. Genes are represented as a
splice graph, which can be obtained from existing annotation or
predicted from the mapped sequence reads. Splice events are
identified from the graph and are quantified locally using
structurally compatible reads at the start or end of each
splice variant. The software includes functions for splice
event prediction, quantification, visualization and
interpretation.
biocViews: AlternativeSplicing, ImmunoOncology, RNASeq, Transcription
Author: Leonard Goldstein [cre, aut]
Maintainer: Leonard Goldstein <ldgoldstein@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SGSeq
git_branch: RELEASE_3_13
git_last_commit: d7ca914
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SGSeq_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SGSeq_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SGSeq_1.26.0.tgz
vignettes: vignettes/SGSeq/inst/doc/SGSeq.html
vignetteTitles: SGSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R
dependsOnMe: EventPointer
importsMe: Rhisat2
dependencyCount: 98
Package: SharedObject
Version: 1.6.0
Depends: R (>= 3.6.0)
Imports: Rcpp, methods, stats, BiocGenerics
LinkingTo: BH, Rcpp
Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 023ed3087c1fce2f829e7e4093ddb629
NeedsCompilation: yes
Title: Sharing R objects across multiple R processes without memory
duplication
Description: This package is developed for facilitating parallel
computing in R. It is capable to create an R object in the
shared memory space and share the data across multiple R
processes. It avoids the overhead of memory dulplication and
data transfer, which make sharing big data object across many
clusters possible.
biocViews: Infrastructure
Author: Jiefei Wang [aut, cre]
Maintainer: Jiefei Wang <jwang96@buffalo.edu>
SystemRequirements: GNU make, C++11
VignetteBuilder: knitr
BugReports: https://github.com/Jiefei-Wang/SharedObject/issues
git_url: https://git.bioconductor.org/packages/SharedObject
git_branch: RELEASE_3_13
git_last_commit: a29d26f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SharedObject_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SharedObject_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SharedObject_1.6.0.tgz
vignettes:
vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.html,
vignettes/SharedObject/inst/doc/quick_start_guide.html
vignetteTitles: quickStartChinese, quickStart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.R,
vignettes/SharedObject/inst/doc/quick_start_guide.R
importsMe: NewWave
dependencyCount: 8
Package: shinyepico
Version: 1.0.0
Depends: R (>= 4.0.0)
Imports: DT (>= 0.15.0), data.table (>= 1.13.0), doParallel (>= 1.0.0),
dplyr (>= 1.0.0), foreach (>= 1.5.0), GenomicRanges (>=
1.38.0), ggplot2 (>= 3.3.0), gplots (>= 3.0.0), heatmaply (>=
1.1.0), limma (>= 3.42.0), minfi (>= 1.32.0), plotly (>=
4.9.2), reshape2 (>= 1.4.0), rlang (>= 0.4.0), rmarkdown (>=
2.3.0), rtracklayer (>= 1.46.0), shiny (>= 1.5.0), shinyWidgets
(>= 0.5.0), shinycssloaders (>= 0.3.0), shinyjs (>= 1.1.0),
shinythemes (>= 1.1.0), statmod (>= 1.4.0), tidyr (>= 1.1.0),
zip (>= 2.1.0)
Suggests: knitr (>= 1.30.0), mCSEA (>= 1.10.0),
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19,
IlluminaHumanMethylationEPICmanifest, testthat, minfiData
License: AGPL-3 + file LICENSE
MD5sum: 55b3939a53b97ddab73c66d563c540e3
NeedsCompilation: no
Title: ShinyÉPICo
Description: ShinyÉPICo is a graphical pipeline to analyze Illumina DNA
methylation arrays (450k or EPIC). It allows to calculate
differentially methylated positions and differentially
methylated regions in a user-friendly interface. Moreover, it
includes several options to export the results and obtain files
to perform downstream analysis.
biocViews:
DifferentialMethylation,DNAMethylation,Microarray,Preprocessing,QualityControl
Author: Octavio Morante-Palacios [cre, aut]
Maintainer: Octavio Morante-Palacios <octaviompa@gmail.com>
URL: https://github.com/omorante/shiny_epico
VignetteBuilder: knitr
BugReports: https://github.com/omorante/shiny_epico/issues
git_url: https://git.bioconductor.org/packages/shinyepico
git_branch: RELEASE_3_13
git_last_commit: 42ea0c9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/shinyepico_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/shinyepico_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/shinyepico_1.0.0.tgz
vignettes: vignettes/shinyepico/inst/doc/shiny_epico.html
vignetteTitles: shinyepico
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/shinyepico/inst/doc/shiny_epico.R
dependencyCount: 201
Package: shinyMethyl
Version: 1.28.0
Depends: methods, BiocGenerics (>= 0.3.2), shiny (>= 0.13.2), minfi (>=
1.18.2), IlluminaHumanMethylation450kmanifest, matrixStats, R
(>= 3.0.0)
Imports: RColorBrewer
Suggests: shinyMethylData, minfiData, BiocStyle, RUnit, digest, knitr
License: Artistic-2.0
MD5sum: 3881862cf3146db20b449950b0727153
NeedsCompilation: no
Title: Interactive visualization for Illumina methylation arrays
Description: Interactive tool for visualizing Illumina methylation
array data. Both the 450k and EPIC array are supported.
biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing,
QualityControl
Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut]
Maintainer: Jean-Philippe Fortin <jfortin@jhsph.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/shinyMethyl
git_branch: RELEASE_3_13
git_last_commit: 2634641
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/shinyMethyl_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/shinyMethyl_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/shinyMethyl_1.28.0.tgz
vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.pdf
vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K
methylation arrays
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/shinyMethyl/inst/doc/shinyMethyl.R
dependencyCount: 153
Package: ShortRead
Version: 1.50.0
Depends: BiocGenerics (>= 0.23.3), BiocParallel, Biostrings (>=
2.47.6), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6)
Imports: Biobase, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12),
GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.31.8), hwriter,
methods, zlibbioc, lattice, latticeExtra,
LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib, zlibbioc
Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi
License: Artistic-2.0
MD5sum: 35f3f8799221414b14887fa77c2b12ae
NeedsCompilation: yes
Title: FASTQ input and manipulation
Description: This package implements sampling, iteration, and input of
FASTQ files. The package includes functions for filtering and
trimming reads, and for generating a quality assessment report.
Data are represented as DNAStringSet-derived objects, and
easily manipulated for a diversity of purposes. The package
also contains legacy support for early single-end, ungapped
alignment formats.
biocViews: DataImport, Sequencing, QualityControl
Author: Martin Morgan, Michael Lawrence, Simon Anders
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/ShortRead
git_branch: RELEASE_3_13
git_last_commit: 31dea4d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ShortRead_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ShortRead_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ShortRead_1.50.0.tgz
vignettes: vignettes/ShortRead/inst/doc/Overview.pdf
vignetteTitles: An introduction to ShortRead
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ShortRead/inst/doc/Overview.R
dependsOnMe: chipseq, EDASeq, esATAC, girafe, HTSeqGenie, OTUbase, Rqc,
segmentSeq, systemPipeR, EatonEtAlChIPseq, NestLink,
sequencing, SimRAD, STRMPS
importsMe: amplican, ArrayExpressHTS, basecallQC, BEAT, chipseq,
ChIPseqR, ChIPsim, dada2, easyRNASeq, FastqCleaner, GOTHiC,
icetea, IONiseR, MACPET, nucleR, QuasR, R453Plus1Toolbox,
RSVSim, scruff, UMI4Cats, systemPipeRdata, genBaRcode
suggestsMe: BiocParallel, CSAR, GenomicAlignments, PING, Repitools,
Rsamtools, S4Vectors, HiCDataLymphoblast, yeastRNASeq
dependencyCount: 43
Package: SIAMCAT
Version: 1.12.0
Depends: R (>= 3.6.0), mlr, phyloseq
Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase,
gridExtra, LiblineaR, matrixStats, methods, ParamHelpers, pROC,
PRROC, RColorBrewer, scales, stats, stringr, utils, infotheo,
progress, corrplot
Suggests: BiocStyle, optparse, testthat, knitr, rmarkdown, tidyverse,
ggpubr
License: GPL-3
MD5sum: 1308dd5fe458e7128042b17e5eb242dd
NeedsCompilation: no
Title: Statistical Inference of Associations between Microbial
Communities And host phenoTypes
Description: Pipeline for Statistical Inference of Associations between
Microbial Communities And host phenoTypes (SIAMCAT). A primary
goal of analyzing microbiome data is to determine changes in
community composition that are associated with environmental
factors. In particular, linking human microbiome composition to
host phenotypes such as diseases has become an area of intense
research. For this, robust statistical modeling and biomarker
extraction toolkits are crucially needed. SIAMCAT provides a
full pipeline supporting data preprocessing, statistical
association testing, statistical modeling (LASSO logistic
regression) including tools for evaluation and interpretation
of these models (such as cross validation, parameter selection,
ROC analysis and diagnostic model plots).
biocViews: ImmunoOncology, Metagenomics, Classification, Microbiome,
Sequencing, Preprocessing, Clustering, FeatureExtraction,
GeneticVariability, MultipleComparison,Regression
Author: Konrad Zych [aut] (<https://orcid.org/0000-0001-7426-0516>),
Jakob Wirbel [aut, cre]
(<https://orcid.org/0000-0002-4073-3562>), Georg Zeller [aut]
(<https://orcid.org/0000-0003-1429-7485>), Morgan Essex [ctb],
Nicolai Karcher [ctb], Kersten Breuer [ctb]
Maintainer: Jakob Wirbel <jakob.wirbel@embl.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SIAMCAT
git_branch: RELEASE_3_13
git_last_commit: fb65d54
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SIAMCAT_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SIAMCAT_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SIAMCAT_1.12.0.tgz
vignettes: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.html,
vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.html,
vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.html,
vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.html,
vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.html,
vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.html
vignetteTitles: SIAMCAT confounder example, SIAMCAT holdout testing,
SIAMCAT meta-analysis, SIAMCAT ML pitfalls, SIAMCAT input,
SIAMCAT basic vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.R,
vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.R,
vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.R,
vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.R,
vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.R,
vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.R
dependencyCount: 99
Package: SICtools
Version: 1.22.0
Depends: R (>= 3.0.0), methods, Rsamtools (>= 1.18.1), doParallel (>=
1.0.8), Biostrings (>= 2.32.1), stringr (>= 0.6.2), matrixStats
(>= 0.10.0), plyr (>= 1.8.3), GenomicRanges (>= 1.22.4),
IRanges (>= 2.4.8)
Suggests: knitr, RUnit, BiocGenerics
License: GPL (>=2)
MD5sum: b10eff32562f9c91bcf01c0298bb1f9d
NeedsCompilation: yes
Title: Find SNV/Indel differences between two bam files with near
relationship
Description: This package is to find SNV/Indel differences between two
bam files with near relationship in a way of pairwise
comparison thourgh each base position across the genome region
of interest. The difference is inferred by fisher test and
euclidean distance, the input of which is the base count
(A,T,G,C) in a given position and read counts for indels that
span no less than 2bp on both sides of indel region.
biocViews: Alignment, Sequencing, Coverage, SequenceMatching,
QualityControl, DataImport, Software, SNP, VariantDetection
Author: Xiaobin Xing, Wu Wei
Maintainer: Xiaobin Xing <xiaobinxing0316@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SICtools
git_branch: RELEASE_3_13
git_last_commit: b9dc057
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SICtools_1.22.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/SICtools_1.22.0.tgz
vignettes: vignettes/SICtools/inst/doc/SICtools.pdf
vignetteTitles: Using SICtools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SICtools/inst/doc/SICtools.R
dependencyCount: 40
Package: SigCheck
Version: 2.24.0
Depends: R (>= 3.2.0), MLInterfaces, Biobase, e1071, BiocParallel,
survival
Imports: graphics, stats, utils, methods
Suggests: BiocStyle, breastCancerNKI, qusage
License: Artistic-2.0
MD5sum: 3bb323ac98bf808e836812d4d86ced19
NeedsCompilation: no
Title: Check a gene signature's prognostic performance against random
signatures, known signatures, and permuted data/metadata
Description: While gene signatures are frequently used to predict
phenotypes (e.g. predict prognosis of cancer patients), it it
not always clear how optimal or meaningful they are (cf David
Venet, Jacques E. Dumont, and Vincent Detours' paper "Most
Random Gene Expression Signatures Are Significantly Associated
with Breast Cancer Outcome"). Based on suggestions in that
paper, SigCheck accepts a data set (as an ExpressionSet) and a
gene signature, and compares its performance on survival and/or
classification tasks against a) random gene signatures of the
same length; b) known, related and unrelated gene signatures;
and c) permuted data and/or metadata.
biocViews: GeneExpression, Classification, GeneSetEnrichment
Author: Rory Stark <rory.stark@cruk.cam.ac.uk> and Justin Norden
Maintainer: Rory Stark <rory.stark@cruk.cam.ac.uk>
git_url: https://git.bioconductor.org/packages/SigCheck
git_branch: RELEASE_3_13
git_last_commit: 09d85c0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SigCheck_2.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SigCheck_2.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SigCheck_2.24.0.tgz
vignettes: vignettes/SigCheck/inst/doc/SigCheck.pdf
vignetteTitles: Checking gene expression signatures against random and
known signatures with SigCheck
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SigCheck/inst/doc/SigCheck.R
dependencyCount: 122
Package: sigFeature
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer,
Matrix, SparseM, graphics, stats, utils, SummarizedExperiment,
BiocParallel, methods
Suggests: RUnit, BiocGenerics, knitr
License: GPL
Archs: i386, x64
MD5sum: 4f3bb4146ceb9cda1c4a1009669d6552
NeedsCompilation: no
Title: sigFeature: Significant feature selection using SVM-RFE &
t-statistic
Description: This package provides a novel feature selection algorithm
for binary classification using support vector machine
recursive feature elimination SVM-RFE and t-statistic. In this
feature selection process, the selected features are
differentially significant between the two classes and also
they are good classifier with higher degree of classification
accuracy.
biocViews: FeatureExtraction, GeneExpression, Microarray,
Transcription, mRNAMicroarray, GenePrediction, Normalization,
Classification, SupportVectorMachine
Author: Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User
[ctb], Dr. Sucheta Tripathy User [ctb]
Maintainer: Pijush Das Developer <topijush@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sigFeature
git_branch: RELEASE_3_13
git_last_commit: 61ee3c8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sigFeature_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sigFeature_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sigFeature_1.10.0.tgz
vignettes: vignettes/sigFeature/inst/doc/vignettes.pdf
vignetteTitles: sigFeature
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sigFeature/inst/doc/vignettes.R
dependencyCount: 62
Package: SigFuge
Version: 1.30.0
Depends: R (>= 3.1.1), GenomicRanges
Imports: ggplot2, matlab, reshape, sigclust
Suggests: org.Hs.eg.db, prebsdata, Rsamtools (>= 1.17.0),
TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle
License: GPL-3
MD5sum: 7a85ad9f5437edf8513e5487ba514612
NeedsCompilation: no
Title: SigFuge
Description: Algorithm for testing significance of clustering in
RNA-seq data.
biocViews: Clustering, Visualization, RNASeq, ImmunoOncology
Author: Patrick Kimes, Christopher Cabanski
Maintainer: Patrick Kimes <patrick.kimes@gmail.com>
git_url: https://git.bioconductor.org/packages/SigFuge
git_branch: RELEASE_3_13
git_last_commit: bccaaff
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SigFuge_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SigFuge_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SigFuge_1.30.0.tgz
vignettes: vignettes/SigFuge/inst/doc/SigFuge.pdf
vignetteTitles: SigFuge Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SigFuge/inst/doc/SigFuge.R
dependencyCount: 56
Package: siggenes
Version: 1.66.0
Depends: Biobase, multtest, splines, methods
Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5)
Suggests: affy, annotate, genefilter, KernSmooth
License: LGPL (>= 2)
MD5sum: 2b7ffc929994423c2d5a12f44f1c6575
NeedsCompilation: no
Title: Multiple Testing using SAM and Efron's Empirical Bayes
Approaches
Description: Identification of differentially expressed genes and
estimation of the False Discovery Rate (FDR) using both the
Significance Analysis of Microarrays (SAM) and the Empirical
Bayes Analyses of Microarrays (EBAM).
biocViews: MultipleComparison, Microarray, GeneExpression, SNP,
ExonArray, DifferentialExpression
Author: Holger Schwender
Maintainer: Holger Schwender <holger.schw@gmx.de>
git_url: https://git.bioconductor.org/packages/siggenes
git_branch: RELEASE_3_13
git_last_commit: 7784d06
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/siggenes_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/siggenes_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/siggenes_1.66.0.tgz
vignettes: vignettes/siggenes/inst/doc/siggenes.pdf,
vignettes/siggenes/inst/doc/siggenesRnews.pdf,
vignettes/siggenes/inst/doc/identify.sam.html,
vignettes/siggenes/inst/doc/plot.ebam.html,
vignettes/siggenes/inst/doc/plot.finda0.html,
vignettes/siggenes/inst/doc/plot.sam.html,
vignettes/siggenes/inst/doc/print.ebam.html,
vignettes/siggenes/inst/doc/print.finda0.html,
vignettes/siggenes/inst/doc/print.sam.html,
vignettes/siggenes/inst/doc/summary.ebam.html,
vignettes/siggenes/inst/doc/summary.sam.html
vignetteTitles: siggenes Manual, siggenesRnews.pdf, identify.sam.html,
plot.ebam.html, plot.finda0.html, plot.sam.html,
print.ebam.html, print.finda0.html, print.sam.html,
summary.ebam.html, summary.sam.html
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/siggenes/inst/doc/siggenes.R
dependsOnMe: KCsmart
importsMe: coexnet, DAPAR, minfi, trio, XDE, DeSousa2013, INCATome
suggestsMe: GCSscore, logicFS
dependencyCount: 17
Package: sights
Version: 1.18.0
Depends: R(>= 3.3)
Imports: MASS(>= 7.3), qvalue(>= 2.2), ggplot2(>= 2.0), reshape2(>=
1.4), lattice(>= 0.2), stats(>= 3.3)
Suggests: testthat, knitr, rmarkdown, ggthemes, gridExtra, xlsx
License: GPL-3 | file LICENSE
Archs: i386, x64
MD5sum: e083c06de9dbd3bf9fc9cee06bf5492a
NeedsCompilation: no
Title: Statistics and dIagnostic Graphs for HTS
Description: SIGHTS is a suite of normalization methods, statistical
tests, and diagnostic graphical tools for high throughput
screening (HTS) assays. HTS assays use microtitre plates to
screen large libraries of compounds for their biological,
chemical, or biochemical activity.
biocViews: ImmunoOncology, CellBasedAssays, MicrotitrePlateAssay,
Normalization, MultipleComparison, Preprocessing,
QualityControl, BatchEffect, Visualization
Author: Elika Garg [aut, cre], Carl Murie [aut], Heydar Ensha [ctb],
Robert Nadon [aut]
Maintainer: Elika Garg <elika.garg@mail.mcgill.ca>
URL: https://eg-r.github.io/sights/
VignetteBuilder: knitr
BugReports: https://github.com/eg-r/sights/issues
git_url: https://git.bioconductor.org/packages/sights
git_branch: RELEASE_3_13
git_last_commit: f914a9b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sights_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sights_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sights_1.18.0.tgz
vignettes: vignettes/sights/inst/doc/sights.html
vignetteTitles: Using **SIGHTS** R-package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sights/inst/doc/sights.R
dependencyCount: 45
Package: signatureSearch
Version: 1.6.3
Depends: R(>= 3.6.0), Rcpp, SummarizedExperiment
Imports: AnnotationDbi, ggplot2, data.table, ExperimentHub, HDF5Array,
magrittr, RSQLite, dplyr, fgsea, scales, methods, qvalue,
stats, utils, reshape2, visNetwork, BiocParallel, fastmatch,
reactome.db, Matrix, clusterProfiler, readr, DOSE, rhdf5,
GSEABase, DelayedArray, BiocGenerics
LinkingTo: Rcpp
Suggests: knitr, testthat, rmarkdown, BiocStyle, org.Hs.eg.db,
signatureSearchData, DT
License: Artistic-2.0
MD5sum: 53ab9b18308a9bcc86e6b838dc4f3d04
NeedsCompilation: yes
Title: Environment for Gene Expression Searching Combined with
Functional Enrichment Analysis
Description: This package implements algorithms and data structures for
performing gene expression signature (GES) searches, and
subsequently interpreting the results functionally with
specialized enrichment methods.
biocViews: Software, GeneExpression, GO, KEGG, NetworkEnrichment,
Sequencing, Coverage, DifferentialExpression
Author: Yuzhu Duan [cre, aut], Thomas Girke [aut]
Maintainer: Yuzhu Duan <yduan004@ucr.edu>
URL: https://github.com/yduan004/signatureSearch/
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/yduan004/signatureSearch/issues
git_url: https://git.bioconductor.org/packages/signatureSearch
git_branch: RELEASE_3_13
git_last_commit: 5c1eebb
git_last_commit_date: 2021-08-29
Date/Publication: 2021-08-31
source.ver: src/contrib/signatureSearch_1.6.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/signatureSearch_1.6.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/signatureSearch_1.6.3.tgz
vignettes: vignettes/signatureSearch/inst/doc/signatureSearch.html
vignetteTitles: signatureSearch
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/signatureSearch/inst/doc/signatureSearch.R
importsMe: signatureSearchData
dependencyCount: 176
Package: signeR
Version: 1.18.1
Depends: VariantAnnotation, NMF
Imports: BiocGenerics, Biostrings, class, graphics, grDevices,
GenomeInfoDb, GenomicRanges, IRanges, nloptr, methods, stats,
utils, PMCMRplus
LinkingTo: Rcpp, RcppArmadillo (>= 0.7.100)
Suggests: knitr, rtracklayer, BSgenome.Hsapiens.UCSC.hg19
License: GPL-3
MD5sum: 7284cac3e0cdb1e94e55f6f7f39ced5a
NeedsCompilation: yes
Title: Empirical Bayesian approach to mutational signature discovery
Description: The signeR package provides an empirical Bayesian approach
to mutational signature discovery. It is designed to analyze
single nucleotide variaton (SNV) counts in cancer genomes, but
can also be applied to other features as well. Functionalities
to characterize signatures or genome samples according to
exposure patterns are also provided.
biocViews: GenomicVariation, SomaticMutation, StatisticalMethod,
Visualization
Author: Rafael Rosales, Rodrigo Drummond, Renan Valieris, Israel Tojal
da Silva
Maintainer: Renan Valieris <renan.valieris@accamargo.org.br>
URL: https://github.com/rvalieris/signeR
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/signeR
git_branch: RELEASE_3_13
git_last_commit: 9672acc
git_last_commit_date: 2021-10-07
Date/Publication: 2021-10-10
source.ver: src/contrib/signeR_1.18.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/signeR_1.18.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/signeR_1.18.1.tgz
vignettes: vignettes/signeR/inst/doc/signeR-vignette.html
vignetteTitles: signeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R
dependencyCount: 137
Package: sigPathway
Version: 1.60.0
Depends: R (>= 2.10)
Suggests: hgu133a.db (>= 1.10.0), XML (>= 1.6-3), AnnotationDbi (>=
1.3.12)
License: GPL-2
MD5sum: 44f6036756791646d6149bc56f8b2bc7
NeedsCompilation: yes
Title: Pathway Analysis
Description: Conducts pathway analysis by calculating the NT_k and NE_k
statistics as described in Tian et al. (2005)
biocViews: DifferentialExpression, MultipleComparison
Author: Weil Lai (optimized R and C code), Lu Tian and Peter Park
(algorithm development and initial R code)
Maintainer: Weil Lai <wlai@alum.mit.edu>
URL: http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102,
http://www.chip.org/~ppark/Supplements/PNAS05.html
git_url: https://git.bioconductor.org/packages/sigPathway
git_branch: RELEASE_3_13
git_last_commit: da5e41f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sigPathway_1.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sigPathway_1.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sigPathway_1.60.0.tgz
vignettes: vignettes/sigPathway/inst/doc/sigPathway-vignette.pdf
vignetteTitles: sigPathway
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sigPathway/inst/doc/sigPathway-vignette.R
dependsOnMe: tRanslatome
dependencyCount: 0
Package: SigsPack
Version: 1.6.0
Depends: R (>= 3.6)
Imports: quadprog (>= 1.5-5), methods, Biobase, BSgenome (>= 1.46.0),
VariantAnnotation (>= 1.24.5), Biostrings, GenomeInfoDb,
GenomicRanges, rtracklayer, SummarizedExperiment, graphics,
stats, utils
Suggests: IRanges, BSgenome.Hsapiens.UCSC.hg19, BiocStyle, knitr,
rmarkdown
License: GPL-3
MD5sum: 0a2c71697f0936e0ad2b5c730b213763
NeedsCompilation: no
Title: Mutational Signature Estimation for Single Samples
Description: Single sample estimation of exposure to mutational
signatures. Exposures to known mutational signatures are
estimated for single samples, based on quadratic programming
algorithms. Bootstrapping the input mutational catalogues
provides estimations on the stability of these exposures. The
effect of the sequence composition of mutational context can be
taken into account by normalising the catalogues.
biocViews: SomaticMutation, SNP, VariantAnnotation,
BiomedicalInformatics, DNASeq
Author: Franziska Schumann <franziska.schumann@student.hpi.de>
Maintainer: Franziska Schumann <franziska.schumann@student.hpi.de>
URL: https://github.com/bihealth/SigsPack
VignetteBuilder: knitr
BugReports: https://github.com/bihealth/SigsPack/issues
git_url: https://git.bioconductor.org/packages/SigsPack
git_branch: RELEASE_3_13
git_last_commit: 1f4a968
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SigsPack_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SigsPack_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SigsPack_1.6.0.tgz
vignettes: vignettes/SigsPack/inst/doc/SigsPack.html
vignetteTitles: Introduction to SigsPack
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SigsPack/inst/doc/SigsPack.R
dependencyCount: 99
Package: sigsquared
Version: 1.24.0
Depends: R (>= 3.2.0), methods
Imports: Biobase, survival
Suggests: RUnit, BiocGenerics
License: GPL version 3
MD5sum: 13203fae17cd1b6b11e42374b21f4f35
NeedsCompilation: no
Title: Gene signature generation for functionally validated signaling
pathways
Description: By leveraging statistical properties (log-rank test for
survival) of patient cohorts defined by binary thresholds,
poor-prognosis patients are identified by the sigsquared
package via optimization over a cost function reducing type I
and II error.
Author: UnJin Lee
Maintainer: UnJin Lee <unjin@uchicago.edu>
git_url: https://git.bioconductor.org/packages/sigsquared
git_branch: RELEASE_3_13
git_last_commit: a15362d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sigsquared_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sigsquared_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sigsquared_1.24.0.tgz
vignettes: vignettes/sigsquared/inst/doc/sigsquared.pdf
vignetteTitles: SigSquared
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sigsquared/inst/doc/sigsquared.R
dependencyCount: 13
Package: SIM
Version: 1.62.0
Depends: R (>= 3.5), quantreg
Imports: graphics, stats, globaltest, quantsmooth
Suggests: biomaRt, RColorBrewer
License: GPL (>= 2)
MD5sum: 2e9af981114a628b765c5706f4fdd462
NeedsCompilation: yes
Title: Integrated Analysis on two human genomic datasets
Description: Finds associations between two human genomic datasets.
biocViews: Microarray, Visualization
Author: Renee X. de Menezes and Judith M. Boer
Maintainer: Renee X. de Menezes <r.menezes@nki.nl>
git_url: https://git.bioconductor.org/packages/SIM
git_branch: RELEASE_3_13
git_last_commit: bb5067c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SIM_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SIM_1.62.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SIM_1.62.0.tgz
vignettes: vignettes/SIM/inst/doc/SIM.pdf
vignetteTitles: SIM vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SIM/inst/doc/SIM.R
dependencyCount: 62
Package: SIMAT
Version: 1.24.0
Depends: R (>= 3.5.0), Rcpp (>= 0.11.3)
Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils
Suggests: RUnit, BiocGenerics
License: GPL-2
Archs: i386, x64
MD5sum: c04b51976afea5e0d799eca4bf95b577
NeedsCompilation: no
Title: GC-SIM-MS data processing and alaysis tool
Description: This package provides a pipeline for analysis of GC-MS
data acquired in selected ion monitoring (SIM) mode. The tool
also provides a guidance in choosing appropriate fragments for
the targets of interest by using an optimization algorithm.
This is done by considering overlapping peaks from a provided
library by the user.
biocViews: ImmunoOncology, Software, Metabolomics, MassSpectrometry
Author: M. R. Nezami Ranjbar <nranjbar@vt.edu>
Maintainer: M. R. Nezami Ranjbar <nranjbar@vt.edu>
URL: http://omics.georgetown.edu/SIMAT.html
git_url: https://git.bioconductor.org/packages/SIMAT
git_branch: RELEASE_3_13
git_last_commit: 07b989a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SIMAT_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SIMAT_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SIMAT_1.24.0.tgz
vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf
vignetteTitles: SIMAT Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SIMAT/inst/doc/SIMAT-vignette.R
dependencyCount: 52
Package: SimBindProfiles
Version: 1.30.0
Depends: R (>= 2.10), methods, Ringo
Imports: limma, mclust, Biobase
License: GPL-3
Archs: i386, x64
MD5sum: 3933dd9a99d914399277577aaec98399
NeedsCompilation: no
Title: Similar Binding Profiles
Description: SimBindProfiles identifies common and unique binding
regions in genome tiling array data. This package does not rely
on peak calling, but directly compares binding profiles
processed on the same array platform. It implements a simple
threshold approach, thus allowing retrieval of commonly and
differentially bound regions between datasets as well as events
of compensation and increased binding.
biocViews: Microarray, Software
Author: Bettina Fischer, Enrico Ferrero, Robert Stojnic, Steve Russell
Maintainer: Bettina Fischer <bef22@cam.ac.uk>
git_url: https://git.bioconductor.org/packages/SimBindProfiles
git_branch: RELEASE_3_13
git_last_commit: be78d26
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SimBindProfiles_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SimBindProfiles_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SimBindProfiles_1.30.0.tgz
vignettes: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.pdf
vignetteTitles: SimBindProfiles: Similar Binding Profiles,, identifies
common and unique regions in array genome tiling array data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.R
dependencyCount: 85
Package: SIMD
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: edgeR, statmod, methylMnM, stats, utils
Suggests: BiocStyle, knitr,rmarkdown
License: GPL-3
Archs: i386, x64
MD5sum: c099c6048117198b55bd6a23460901db
NeedsCompilation: yes
Title: Statistical Inferences with MeDIP-seq Data (SIMD) to infer the
methylation level for each CpG site
Description: This package provides a inferential analysis method for
detecting differentially expressed CpG sites in MeDIP-seq data.
It uses statistical framework and EM algorithm, to identify
differentially expressed CpG sites. The methods on this package
are described in the article 'Methylation-level Inferences and
Detection of Differential Methylation with Medip-seq Data' by
Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang
and Xiyan Yang (2018, pending publication).
biocViews: ImmunoOncology, DifferentialMethylation,SingleCell,
DifferentialExpression
Author: Yan Zhou
Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SIMD
git_branch: RELEASE_3_13
git_last_commit: 8cdcfb9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SIMD_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SIMD_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SIMD_1.10.0.tgz
vignettes: vignettes/SIMD/inst/doc/SIMD.html
vignetteTitles: SIMD Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SIMD/inst/doc/SIMD.R
dependencyCount: 13
Package: SimFFPE
Version: 1.4.0
Depends: Biostrings
Imports: dplyr, foreach, doParallel, truncnorm, GenomicRanges, IRanges,
Rsamtools, parallel, graphics, stats, utils, methods
Suggests: BiocStyle
License: LGPL-3
MD5sum: 77b81fd8b2e10b8988888ec32c3a1f1d
NeedsCompilation: no
Title: NGS Read Simulator for FFPE Tissue
Description: The NGS (Next-Generation Sequencing) reads from FFPE
(Formalin-Fixed Paraffin-Embedded) samples contain numerous
artifact chimeric reads (ACRS), which can lead to false
positive structural variant calls. These ACRs are derived from
the combination of two single-stranded DNA (ss-DNA) fragments
with short reverse complementary regions (SRCRs). This package
simulates these artifact chimeric reads as well as normal reads
for FFPE samples on the whole genome / several chromosomes /
large regions.
biocViews: Sequencing, Alignment, MultipleComparison, SequenceMatching,
DataImport
Author: Lanying Wei [aut, cre]
(<https://orcid.org/0000-0002-4281-8017>)
Maintainer: Lanying Wei <lanying.wei@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/SimFFPE
git_branch: RELEASE_3_13
git_last_commit: 13e0b8e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SimFFPE_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SimFFPE_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SimFFPE_1.4.0.tgz
vignettes: vignettes/SimFFPE/inst/doc/SimFFPE.pdf
vignetteTitles: An introduction to SimFFPE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SimFFPE/inst/doc/SimFFPE.R
dependencyCount: 51
Package: similaRpeak
Version: 1.24.0
Depends: R6 (>= 2.0)
Imports: stats
Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments,
rtracklayer, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 8406f9754e95113adfaf740e874729ca
NeedsCompilation: no
Title: Metrics to estimate a level of similarity between two ChIP-Seq
profiles
Description: This package calculates metrics which assign a level of
similarity between ChIP-Seq profiles.
biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison,
DifferentialExpression
Author: Astrid Deschenes [cre, aut], Elsa Bernatchez [aut], Charles
Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb
[aut], Pascal Belleau [aut], Arnaud Droit [aut]
Maintainer: Astrid Deschenes <adeschen@hotmail.com>
URL: https://github.com/adeschen/similaRpeak
VignetteBuilder: knitr
BugReports: https://github.com/adeschen/similaRpeak/issues
git_url: https://git.bioconductor.org/packages/similaRpeak
git_branch: RELEASE_3_13
git_last_commit: d820374
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/similaRpeak_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/similaRpeak_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/similaRpeak_1.24.0.tgz
vignettes: vignettes/similaRpeak/inst/doc/similaRpeak.html
vignetteTitles: Similarity between two ChIP-Seq profiles
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R
suggestsMe: metagene
dependencyCount: 2
Package: SIMLR
Version: 1.18.0
Depends: R (>= 4.0.0),
Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy,
RSpectra
LinkingTo: Rcpp
Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph
License: file LICENSE
Archs: i386, x64
MD5sum: 734b3b5c22a8f8e21c2844d468256c1a
NeedsCompilation: yes
Title: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR)
Description: Single-cell RNA-seq technologies enable high throughput
gene expression measurement of individual cells, and allow the
discovery of heterogeneity within cell populations. Measurement
of cell-to-cell gene expression similarity is critical for the
identification, visualization and analysis of cell populations.
However, single-cell data introduce challenges to conventional
measures of gene expression similarity because of the high
level of noise, outliers and dropouts. We develop a novel
similarity-learning framework, SIMLR (Single-cell
Interpretation via Multi-kernel LeaRning), which learns an
appropriate distance metric from the data for dimension
reduction, clustering and visualization.
biocViews: ImmunoOncology, Clustering, GeneExpression, Sequencing,
SingleCell
Author: Daniele Ramazzotti [cre, aut]
(<https://orcid.org/0000-0002-6087-2666>), Bo Wang [aut], Luca
De Sano [aut] (<https://orcid.org/0000-0002-9618-3774>),
Serafim Batzoglou [ctb]
Maintainer: Luca De Sano <luca.desano@gmail.com>
URL: https://github.com/BatzoglouLabSU/SIMLR
VignetteBuilder: knitr
BugReports: https://github.com/BatzoglouLabSU/SIMLR
git_url: https://git.bioconductor.org/packages/SIMLR
git_branch: RELEASE_3_13
git_last_commit: e3f8bf7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SIMLR_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SIMLR_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SIMLR_1.18.0.tgz
vignettes: vignettes/SIMLR/inst/doc/vignette.pdf
vignetteTitles: Single-cell Interpretation via Multi-kernel LeaRning
(\Biocpkg{SIMLR})
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SIMLR/inst/doc/vignette.R
importsMe: SingleCellSignalR
dependencyCount: 14
Package: simplifyEnrichment
Version: 1.2.0
Depends: R (>= 3.6.0), BiocGenerics, grid
Imports: GOSemSim, ComplexHeatmap (>= 2.7.4), circlize, GetoptLong,
digest, tm, GO.db, org.Hs.eg.db, AnnotationDbi, slam, methods,
clue, grDevices, graphics, stats, utils, proxyC, Matrix,
cluster (>= 1.14.2)
Suggests: knitr, ggplot2, cowplot, mclust, apcluster, MCL, dbscan,
igraph, gridExtra, dynamicTreeCut, testthat, gridGraphics,
clusterProfiler, msigdbr, DOSE, DO.db, reactome.db, flexclust,
BiocManager, InteractiveComplexHeatmap (>= 0.99.11), shiny,
shinydashboard, cola, hu6800.db, rmarkdown
License: MIT + file LICENSE
MD5sum: ebadd3d3382e872cd815e610dd819f2b
NeedsCompilation: no
Title: Simplify Functional Enrichment Results
Description: A new clustering algorithm, binary cut, for clustering
similarity matrices of functional terms is implemeted in this
package. It also provideds functionalities for visualizing,
summarizing and comparing the clusterings.
biocViews: Software, Visualization, GO, Clustering, GeneSetEnrichment
Author: Zuguang Gu [aut, cre] (<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/simplifyEnrichment,
https://simplifyEnrichment.github.io
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/simplifyEnrichment
git_branch: RELEASE_3_13
git_last_commit: 02e5cb4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/simplifyEnrichment_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/simplifyEnrichment_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/simplifyEnrichment_1.2.0.tgz
vignettes: vignettes/simplifyEnrichment/inst/doc/interactive.html,
vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.html,
vignettes/simplifyEnrichment/inst/doc/word_cloud_anno.html
vignetteTitles: 3. A Shiny app to interactively visualize clustering
results, 1. Simplify Functional Enrichment Results, 2. Word
Cloud Annotation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/simplifyEnrichment/inst/doc/interactive.R,
vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.R,
vignettes/simplifyEnrichment/inst/doc/word_cloud_anno.R
suggestsMe: cola, InteractiveComplexHeatmap
dependencyCount: 77
Package: sincell
Version: 1.24.0
Depends: R (>= 3.0.2), igraph
Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2,
reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster,
statmod
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, biomaRt, stringr, monocle
License: GPL (>= 2)
MD5sum: cf6a32fe3b98a9a1ba156ce6f6617a9c
NeedsCompilation: yes
Title: R package for the statistical assessment of cell state
hierarchies from single-cell RNA-seq data
Description: Cell differentiation processes are achieved through a
continuum of hierarchical intermediate cell-states that might
be captured by single-cell RNA seq. Existing computational
approaches for the assessment of cell-state hierarchies from
single-cell data might be formalized under a general workflow
composed of i) a metric to assess cell-to-cell similarities
(combined or not with a dimensionality reduction step), and ii)
a graph-building algorithm (optionally making use of a
cells-clustering step). Sincell R package implements a
methodological toolbox allowing flexible workflows under such
framework. Furthermore, Sincell contributes new algorithms to
provide cell-state hierarchies with statistical support while
accounting for stochastic factors in single-cell RNA seq.
Graphical representations and functional association tests are
provided to interpret hierarchies.
biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering,
GraphAndNetwork, Visualization, GeneExpression,
GeneSetEnrichment, BiomedicalInformatics, CellBiology,
FunctionalGenomics, SystemsBiology
Author: Miguel Julia <migueljuliamolina@gmail.com>, Amalio Telenti
<atelenti@jcvi.org>, Antonio Rausell
<antonio.rausell@isb-sib.ch>
Maintainer: Miguel Julia <migueljuliamolina@gmail.com>, Antonio
Rausell<antonio.rausell@isb-sib.ch>
URL: http://bioconductor.org/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sincell
git_branch: RELEASE_3_13
git_last_commit: 726ccb9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sincell_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sincell_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sincell_1.24.0.tgz
vignettes: vignettes/sincell/inst/doc/sincell-vignette.pdf
vignetteTitles: Sincell: Analysis of cell state hierarchies from
single-cell RNA-seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sincell/inst/doc/sincell-vignette.R
importsMe: ctgGEM
dependencyCount: 63
Package: SingleCellExperiment
Version: 1.14.1
Depends: SummarizedExperiment
Imports: methods, utils, stats, S4Vectors, BiocGenerics, GenomicRanges,
DelayedArray
Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, scRNAseq,
Rtsne
License: GPL-3
Archs: i386, x64
MD5sum: a05d5bafecd62bc1657cef4762ffa200
NeedsCompilation: no
Title: S4 Classes for Single Cell Data
Description: Defines a S4 class for storing data from single-cell
experiments. This includes specialized methods to store and
retrieve spike-in information, dimensionality reduction
coordinates and size factors for each cell, along with the
usual metadata for genes and libraries.
biocViews: ImmunoOncology, DataRepresentation, DataImport,
Infrastructure, SingleCell
Author: Aaron Lun [aut, cph], Davide Risso [aut, cre, cph], Keegan
Korthauer [ctb], Kevin Rue-Albrecht [ctb]
Maintainer: Davide Risso <risso.davide@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SingleCellExperiment
git_branch: RELEASE_3_13
git_last_commit: 5357eff
git_last_commit_date: 2021-05-21
Date/Publication: 2021-05-21
source.ver: src/contrib/SingleCellExperiment_1.14.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SingleCellExperiment_1.14.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/SingleCellExperiment_1.14.1.tgz
vignettes: vignettes/SingleCellExperiment/inst/doc/apply.html,
vignettes/SingleCellExperiment/inst/doc/devel.html,
vignettes/SingleCellExperiment/inst/doc/intro.html
vignetteTitles: 2. Applying over a SingleCellExperiment object, 3.
Developing around the SingleCellExperiment class, 1. An
introduction to the SingleCellExperiment class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SingleCellExperiment/inst/doc/apply.R,
vignettes/SingleCellExperiment/inst/doc/devel.R,
vignettes/SingleCellExperiment/inst/doc/intro.R
dependsOnMe: BASiCS, batchelor, BayesSpace, CATALYST, CellBench,
CelliD, CellTrails, CHETAH, clusterExperiment, cydar,
cytomapper, DropletUtils, ExperimentSubset, iSEE,
LoomExperiment, MAST, mia, mumosa, POWSC, scAlign, scater,
scClassifR, scGPS, schex, scPipe, scran, scuttle, singleCellTK,
SpatialExperiment, splatter, switchde,
tidySingleCellExperiment, TrajectoryUtils,
TreeSummarizedExperiment, tricycle, TSCAN, zinbwave, HCAData,
imcdatasets, MouseGastrulationData, MouseThymusAgeing,
muscData, scRNAseq, TENxBrainData, TENxPBMCData, TMExplorer,
OSCA.intro, DIscBIO, imcExperiment
importsMe: ADImpute, aggregateBioVar, airpart, bayNorm, BEARscc,
ccfindR, celda, CellMixS, ChromSCape, CiteFuse, clustifyr,
CoGAPS, conclus, condiments, corral, distinct, dittoSeq,
escape, fcoex, FEAST, GSVA, HIPPO, ILoReg, infercnv, IRISFGM,
iSEEu, LineagePulse, mbkmeans, MetaNeighbor, miloR, miQC,
muscat, Nebulosa, netSmooth, NewWave, peco, phemd, pipeComp,
SC3, SCArray, scBFA, scCB2, scDblFinder, scDD, scds, scHOT,
scmap, scMerge, SCnorm, scone, scp, scruff, scry, scTensor,
scTGIF, slalom, slingshot, Spaniel, SPsimSeq, tradeSeq,
treekoR, velociraptor, waddR, zellkonverter, scpdata,
SingleCellMultiModal, spatialLIBD, digitalDLSorteR, SC.MEB
suggestsMe: CellaRepertorium, DEsingle, EWCE, FCBF, fishpond,
HDF5Array, InteractiveComplexHeatmap, M3Drop, mistyR, MOFA2,
ontoProc, phenopath, progeny, PubScore, QFeatures,
scFeatureFilter, scPCA, scRecover, SingleR, dorothea,
DuoClustering2018, TabulaMurisData, simpleSingleCell, clustree,
dyngen, Seurat, singleCellHaystack
dependencyCount: 26
Package: SingleCellSignalR
Version: 1.4.0
Depends: R (>= 4.0)
Imports: BiocManager, circlize, limma, igraph, gplots, grDevices,
edgeR, SIMLR, data.table, pheatmap, stats, Rtsne, graphics,
stringr, foreach, multtest, scran, utils,
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: d6b1cbf7ec7cf62be10f10e26a78dd99
NeedsCompilation: no
Title: Cell Signalling Using Single Cell RNAseq Data Analysis
Description: Allows single cell RNA seq data analysis, clustering,
creates internal network and infers cell-cell interactions.
biocViews: SingleCell, Network, Clustering, RNASeq, Classification
Author: Simon Cabello-Aguilar [aut], Jacques Colinge [cre, aut]
Maintainer: Jacques Colinge <jacques.colinge@inserm.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SingleCellSignalR
git_branch: RELEASE_3_13
git_last_commit: 7bf26c8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SingleCellSignalR_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SingleCellSignalR_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SingleCellSignalR_1.4.0.tgz
vignettes: vignettes/SingleCellSignalR/inst/doc/UsersGuide.html
vignetteTitles: my-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SingleCellSignalR/inst/doc/UsersGuide.R
suggestsMe: tidySingleCellExperiment, scDiffCom
dependencyCount: 95
Package: singleCellTK
Version: 2.2.0
Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment,
DelayedArray, Biobase
Imports: ape, batchelor, BiocParallel, celldex, colourpicker,
colorspace, cowplot, cluster, ComplexHeatmap, data.table,
DelayedMatrixStats, DESeq2, dplyr, DT, ExperimentHub, fields,
ggplot2, ggplotify, ggrepel, ggtree, gridExtra, GSVA (>=
1.26.0), GSVAdata, igraph, KernSmooth, limma, MAST, Matrix,
matrixStats, methods, msigdbr, multtest, plotly, RColorBrewer,
ROCR, Rtsne, S4Vectors, scater, scMerge (>= 1.2.0), scran,
Seurat (>= 3.1.3), shiny, shinyjs, SingleR, sva, reshape2,
AnnotationDbi, shinyalert, circlize, enrichR, celda,
shinycssloaders, uwot, DropletUtils, scds (>= 1.2.0),
reticulate (>= 1.14), tools, tximport, fishpond, withr,
GSEABase, R.utils, zinbwave, scRNAseq (>= 2.0.2), TENxPBMCData,
yaml, rmarkdown, magrittr, scDblFinder, metap
Suggests: testthat, Rsubread, BiocStyle, knitr, lintr, xtable,
spelling, org.Mm.eg.db, stringr, kableExtra, shinythemes,
shinyBS, shinyjqui, shinyWidgets, shinyFiles, BiocGenerics
License: MIT + file LICENSE
MD5sum: 3e67e0d2a11756025f15cc2b140b6f2c
NeedsCompilation: no
Title: Comprehensive and Interactive Analysis of Single Cell RNA-Seq
Data
Description: Run common single cell analysis in the R console or
directly through your browser. Includes many functions for
import, quality control, normalization, batch correction,
clustering, differential expression, and visualization..
biocViews: SingleCell, GeneExpression, DifferentialExpression,
Alignment, Clustering, ImmunoOncology
Author: David Jenkins [aut] (<https://orcid.org/0000-0002-7451-4288>),
Vidya Akavoor [aut], Salam Alabdullatif [aut], Shruthi
Bandyadka [aut], Emma Briars [aut]
(<https://orcid.org/0000-0001-9350-5523>), Xinyun Cao [aut],
Sebastian Carrasco Pro [aut], Tyler Faits [aut], Rui Hong
[aut], Mohammed Muzamil Khan [aut], Yusuke Koga [aut, cre],
Anastasia Leshchyk [aut], Irzam Sarfraz [aut], Yichen Wang
[aut], Zhe Wang [aut], W. Evan Johnson [aut]
(<https://orcid.org/0000-0002-6247-6595>), Joshua David
Campbell [aut]
Maintainer: Yusuke Koga <ykoga07@bu.edu>
URL: https://compbiomed.github.io/sctk_docs/
VignetteBuilder: knitr
BugReports: https://github.com/compbiomed/singleCellTK/issues
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: RELEASE_3_13
git_last_commit: 9fdc1a1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/singleCellTK_2.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/singleCellTK_2.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/singleCellTK_2.2.0.tgz
vignettes: vignettes/singleCellTK/inst/doc/singleCellTK.html
vignetteTitles: 1. Introduction to singleCellTK
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/singleCellTK/inst/doc/singleCellTK.R
suggestsMe: celda
dependencyCount: 351
Package: SingleMoleculeFootprinting
Version: 1.0.0
Depends: R (>= 4.1.0)
Imports: BiocGenerics, Biostrings, BSgenome, GenomeInfoDb,
GenomicRanges, data.table, grDevices, plyr, IRanges,
RColorBrewer, stats, QuasR
Suggests: BSgenome.Mmusculus.UCSC.mm10, devtools, ExperimentHub, knitr,
parallel, rmarkdown, readr, SingleMoleculeFootprintingData,
testthat (>= 3.0.0)
License: GPL-3
Archs: i386, x64
MD5sum: 36073f2fa22ff5a8137ef7c7e7de5e82
NeedsCompilation: no
Title: Analysis tools for Single Molecule Footprinting (SMF) data
Description: SingleMoleculeFootprinting is an R package providing
functions to analyze Single Molecule Footprinting (SMF) data.
Following the workflow exemplified in its vignette, the user
will be able to perform basic data analysis of SMF data with
minimal coding effort. Starting from an aligned bam file, we
show how to perform quality controls over sequencing libraries,
extract methylation information at the single molecule level
accounting for the two possible kind of SMF experiments (single
enzyme or double enzyme), classify single molecules based on
their patterns of molecular occupancy, plot SMF information at
a given genomic location
biocViews: DNAMethylation, Coverage, NucleosomePositioning,
DataRepresentation, Epigenetics, MethylSeq, QualityControl
Author: Guido Barzaghi [aut, cre]
(<https://orcid.org/0000-0001-6066-3920>), Arnaud Krebs [aut]
(<https://orcid.org/0000-0001-7999-6127>), Mike Smith [ctb]
(<https://orcid.org/0000-0002-7800-3848>)
Maintainer: Guido Barzaghi <guido.barzaghi@embl.de>
VignetteBuilder: knitr
BugReports:
https://github.com/Krebslabrep/SingleMoleculeFootprinting/issues
git_url:
https://git.bioconductor.org/packages/SingleMoleculeFootprinting
git_branch: RELEASE_3_13
git_last_commit: 398e2fb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SingleMoleculeFootprinting_1.0.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/SingleMoleculeFootprinting_1.0.0.zip
mac.binary.ver:
bin/macosx/contrib/4.1/SingleMoleculeFootprinting_1.0.0.tgz
vignettes:
vignettes/SingleMoleculeFootprinting/inst/doc/SingleMoleculeFootprinting.html
vignetteTitles: SingleMoleculeFootprinting
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/SingleMoleculeFootprinting/inst/doc/SingleMoleculeFootprinting.R
dependencyCount: 109
Package: SingleR
Version: 1.6.1
Depends: SummarizedExperiment
Imports: methods, Matrix, S4Vectors, DelayedArray, DelayedMatrixStats,
BiocNeighbors, BiocParallel, BiocSingular, stats, utils, Rcpp,
beachmat, parallel
LinkingTo: Rcpp, beachmat
Suggests: testthat, knitr, rmarkdown, BiocStyle, BiocGenerics,
SingleCellExperiment, scuttle, scater, scran, scRNAseq,
ggplot2, pheatmap, grDevices, gridExtra, viridis, celldex
License: GPL-3 + file LICENSE
MD5sum: ad8acc6b74914c72fd362292b7531708
NeedsCompilation: yes
Title: Reference-Based Single-Cell RNA-Seq Annotation
Description: Performs unbiased cell type recognition from single-cell
RNA sequencing data, by leveraging reference transcriptomic
datasets of pure cell types to infer the cell of origin of each
single cell independently.
biocViews: Software, SingleCell, GeneExpression, Transcriptomics,
Classification, Clustering, Annotation
Author: Dvir Aran [aut, cph], Aaron Lun [ctb, cre], Daniel Bunis [ctb],
Jared Andrews [ctb], Friederike Dündar [ctb]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/SingleR
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/SingleR
git_branch: RELEASE_3_13
git_last_commit: edbe717
git_last_commit_date: 2021-05-20
Date/Publication: 2021-05-20
source.ver: src/contrib/SingleR_1.6.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SingleR_1.6.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/SingleR_1.6.1.tgz
vignettes: vignettes/SingleR/inst/doc/SingleR.html
vignetteTitles: Annotating scRNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SingleR/inst/doc/SingleR.R
dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.multisample,
OSCA.workflows
importsMe: singleCellTK
suggestsMe: tidySingleCellExperiment, SingleRBook, tidyseurat
dependencyCount: 43
Package: singscore
Version: 1.12.0
Depends: R (>= 3.6)
Imports: methods, stats, graphics, ggplot2, grDevices, ggrepel,
GSEABase, plotly, tidyr, plyr, magrittr, reshape, edgeR,
RColorBrewer, Biobase, BiocParallel, SummarizedExperiment,
matrixStats, reshape2, S4Vectors
Suggests: knitr, rmarkdown, testthat
License: GPL-3
Archs: i386, x64
MD5sum: 44aa4b9d0f5a7b9dc97e0998816e1225
NeedsCompilation: no
Title: Rank-based single-sample gene set scoring method
Description: A simple single-sample gene signature scoring method that
uses rank-based statistics to analyze the sample's gene
expression profile. It scores the expression activities of gene
sets at a single-sample level.
biocViews: Software, GeneExpression, GeneSetEnrichment
Author: Ruqian Lyu [aut, ctb], Momeneh Foroutan [aut, ctb]
(<https://orcid.org/0000-0002-1440-0457>), Dharmesh D. Bhuva
[aut, cre] (<https://orcid.org/0000-0002-6398-9157>)
Maintainer: Dharmesh D. Bhuva <bhuva.d@wehi.edu.au>
URL: https://davislaboratory.github.io/singscore
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/singscore/issues
git_url: https://git.bioconductor.org/packages/singscore
git_branch: RELEASE_3_13
git_last_commit: 60052d6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/singscore_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/singscore_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/singscore_1.12.0.tgz
vignettes: vignettes/singscore/inst/doc/singscore.html
vignetteTitles: Single sample scoring
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/singscore/inst/doc/singscore.R
importsMe: TBSignatureProfiler, SingscoreAMLMutations, clustermole
suggestsMe: vissE, msigdb
dependencyCount: 114
Package: SISPA
Version: 1.22.0
Depends: R (>= 3.5),genefilter,GSVA,changepoint
Imports: data.table, plyr, ggplot2
Suggests: knitr
License: GPL-2
MD5sum: ee1eabc775184418f1a14960602c3fbb
NeedsCompilation: no
Title: SISPA: Method for Sample Integrated Set Profile Analysis
Description: Sample Integrated Set Profile Analysis (SISPA) is a method
designed to define sample groups with similar gene set
enrichment profiles.
biocViews: GeneSetEnrichment,GenomeWideAssociation
Author: Bhakti Dwivedi and Jeanne Kowalski
Maintainer: Bhakti Dwivedi <bhakti.dwivedi@emory.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SISPA
git_branch: RELEASE_3_13
git_last_commit: 9b3f6c1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SISPA_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SISPA_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SISPA_1.22.0.tgz
vignettes: vignettes/SISPA/inst/doc/SISPA.html
vignetteTitles: SISPA:Method for Sample Integrated Set Profile Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SISPA/inst/doc/SISPA.R
dependencyCount: 108
Package: sitadela
Version: 1.0.1
Depends: R (>= 4.1.0)
Imports: Biobase, BiocGenerics, biomaRt, Biostrings, GenomeInfoDb,
GenomicFeatures, GenomicRanges, IRanges, methods, parallel,
Rsamtools, RSQLite, rtracklayer, S4Vectors, tools, utils
Suggests: BSgenome, knitr, RMySQL, RUnit
License: Artistic-2.0
MD5sum: 0bf90d38f37d397a68a067543fb5eea5
NeedsCompilation: no
Title: An R package for the easy provision of simple but complete
tab-delimited genomic annotation from a variety of sources and
organisms
Description: Provides an interface to build a unified database of
genomic annotations and their coordinates (gene, transcript and
exon levels). It is aimed to be used when simple tab-delimited
annotations (or simple GRanges objects) are required instead of
the more complex annotation Bioconductor packages. Also useful
when combinatorial annotation elements are reuired, such as
RefSeq coordinates with Ensembl biotypes. Finally, it can
download, construct and handle annotations with versioned genes
and transcripts (where available, e.g. RefSeq and latest
Ensembl). This is particularly useful in precision medicine
applications where the latter must be reported.
biocViews: Software, WorkflowStep, RNASeq, Transcription, Sequencing,
Transcriptomics, BiomedicalInformatics, FunctionalGenomics,
SystemsBiology, AlternativeSplicing, DataImport, ChIPSeq
Author: Panagiotis Moulos [aut, cre]
Maintainer: Panagiotis Moulos <moulos@fleming.gr>
URL: https://github.com/pmoulos/sitadela
VignetteBuilder: knitr
BugReports: https://github.com/pmoulos/sitadela/issues
git_url: https://git.bioconductor.org/packages/sitadela
git_branch: RELEASE_3_13
git_last_commit: 73efbf8
git_last_commit_date: 2021-10-06
Date/Publication: 2021-10-07
source.ver: src/contrib/sitadela_1.0.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sitadela_1.0.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/sitadela_1.0.2.tgz
vignettes: vignettes/sitadela/inst/doc/sitadela.html
vignetteTitles: Building a simple annotation database
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sitadela/inst/doc/sitadela.R
dependencyCount: 96
Package: sitePath
Version: 1.8.4
Depends: R (>= 4.1)
Imports: RColorBrewer, Rcpp, ape, aplot, ggplot2, ggrepel, ggtree,
graphics, grDevices, gridExtra, methods, parallel, seqinr,
stats, tidytree, utils
LinkingTo: Rcpp
Suggests: BiocStyle, devtools, knitr, magick, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: de1f65084b37fc26f12c9780d5ee9caf
NeedsCompilation: yes
Title: Phylogenetic pathway–dependent recognition of fixed
substitutions and parallel mutations
Description: The package does hierarchical search for fixation and
parallel mutations given multiple sequence alignment and
phylogenetic tree. The package also provides visualization of
these mutations on the tree.
biocViews: Alignment, MultipleSequenceAlignment, Phylogenetics, SNP,
Software
Author: Chengyang Ji [aut, cre, cph]
(<https://orcid.org/0000-0001-9258-5453>), Hangyu Zhou [ths],
Aiping Wu [ths]
Maintainer: Chengyang Ji <chengyang.ji12@alumni.xjtlu.edu.cn>
URL: https://wuaipinglab.github.io/sitePath/
VignetteBuilder: knitr
BugReports: https://github.com/wuaipinglab/sitePath/issues
git_url: https://git.bioconductor.org/packages/sitePath
git_branch: RELEASE_3_13
git_last_commit: cb9e231
git_last_commit_date: 2021-09-16
Date/Publication: 2021-09-16
source.ver: src/contrib/sitePath_1.8.4.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sitePath_1.8.4.zip
mac.binary.ver: bin/macosx/contrib/4.1/sitePath_1.8.4.tgz
vignettes: vignettes/sitePath/inst/doc/sitePath.html
vignetteTitles: An introduction to sitePath
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sitePath/inst/doc/sitePath.R
dependencyCount: 66
Package: sizepower
Version: 1.62.0
Depends: stats
License: LGPL
MD5sum: 92f042ebf78560dcf33d88bb9902cd33
NeedsCompilation: no
Title: Sample Size and Power Calculation in Micorarray Studies
Description: This package has been prepared to assist users in
computing either a sample size or power value for a microarray
experimental study. The user is referred to the cited
references for technical background on the methodology
underpinning these calculations. This package provides support
for five types of sample size and power calculations. These
five types can be adapted in various ways to encompass many of
the standard designs encountered in practice.
biocViews: Microarray
Author: Weiliang Qiu <weiliang.qiu@gmail.com> and Mei-Ling Ting Lee
<meilinglee@sph.osu.edu> and George Alex Whitmore
<george.whitmore@mcgill.ca>
Maintainer: Weiliang Qiu <weiliang.qiu@gmail.com>
git_url: https://git.bioconductor.org/packages/sizepower
git_branch: RELEASE_3_13
git_last_commit: babef3f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sizepower_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sizepower_1.62.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sizepower_1.62.0.tgz
vignettes: vignettes/sizepower/inst/doc/sizepower.pdf
vignetteTitles: Sample Size and Power Calculation in Microarray Studies
Using the \Rpackage{sizepower} package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sizepower/inst/doc/sizepower.R
dependencyCount: 1
Package: skewr
Version: 1.24.0
Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn,
IlluminaHumanMethylation450kmanifest
Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer
Suggests: GEOquery, knitr, minfiData
License: GPL-2
Archs: i386, x64
MD5sum: 4a7c02b74107d623429d5360f860fc7d
NeedsCompilation: no
Title: Visualize Intensities Produced by Illumina's Human Methylation
450k BeadChip
Description: The skewr package is a tool for visualizing the output of
the Illumina Human Methylation 450k BeadChip to aid in quality
control. It creates a panel of nine plots. Six of the plots
represent the density of either the methylated intensity or the
unmethylated intensity given by one of three subsets of the
485,577 total probes. These subsets include Type I-red, Type
I-green, and Type II.The remaining three distributions give the
density of the Beta-values for these same three subsets. Each
of the nine plots optionally displays the distributions of the
"rs" SNP probes and the probes associated with imprinted genes
as series of 'tick' marks located above the x-axis.
biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl
Author: Ryan Putney [cre, aut], Steven Eschrich [aut], Anders Berglund
[aut]
Maintainer: Ryan Putney <ryanputney@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/skewr
git_branch: RELEASE_3_13
git_last_commit: 0f47238
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/skewr_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/skewr_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/skewr_1.24.0.tgz
vignettes: vignettes/skewr/inst/doc/skewr.pdf
vignetteTitles: An Introduction to the skewr Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/skewr/inst/doc/skewr.R
dependencyCount: 170
Package: slalom
Version: 1.14.0
Depends: R (>= 3.4)
Imports: Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase,
methods, rsvd, SingleCellExperiment, SummarizedExperiment,
stats
LinkingTo: Rcpp, RcppArmadillo, BH
Suggests: knitr, rhdf5, scater, testthat
License: GPL-2
MD5sum: f295ea653da321e38b62b073241d31c4
NeedsCompilation: yes
Title: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data
Description: slalom is a scalable modelling framework for single-cell
RNA-seq data that uses gene set annotations to dissect
single-cell transcriptome heterogeneity, thereby allowing to
identify biological drivers of cell-to-cell variability and
model confounding factors.
biocViews: ImmunoOncology, SingleCell, RNASeq, Normalization,
Visualization, DimensionReduction, Transcriptomics,
GeneExpression, Sequencing, Software, Reactome
Author: Florian Buettner [aut], Naruemon Pratanwanich [aut], Davis
McCarthy [aut, cre], John Marioni [aut], Oliver Stegle [aut]
Maintainer: Davis McCarthy <davis@ebi.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/slalom
git_branch: RELEASE_3_13
git_last_commit: 926621e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/slalom_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/slalom_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/slalom_1.14.0.tgz
vignettes: vignettes/slalom/inst/doc/vignette.html
vignetteTitles: Introduction to slalom
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/slalom/inst/doc/vignette.R
dependencyCount: 86
Package: SLGI
Version: 1.52.0
Depends: R (>= 2.10), ScISI, lattice
Imports: AnnotationDbi, Biobase, GO.db, ScISI, graphics, lattice,
methods, stats, BiocGenerics
Suggests: GO.db, org.Sc.sgd.db
License: Artistic-2.0
MD5sum: eaa2edf430afbe3d0059d03cb35de9bf
NeedsCompilation: no
Title: Synthetic Lethal Genetic Interaction
Description: A variety of data files and functions for the analysis of
genetic interactions
biocViews: GraphAndNetwork, Proteomics, Genetics, Network
Author: Nolwenn LeMeur, Zhen Jiang, Ting-Yuan Liu, Jess Mar and Robert
Gentleman
Maintainer: Nolwenn Le Meur <nlemeur@gmail.com>
git_url: https://git.bioconductor.org/packages/SLGI
git_branch: RELEASE_3_13
git_last_commit: cdf0469
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SLGI_1.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SLGI_1.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SLGI_1.52.0.tgz
vignettes: vignettes/SLGI/inst/doc/SLGI.pdf
vignetteTitles: SLGI Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SLGI/inst/doc/SLGI.R
dependencyCount: 61
Package: slingshot
Version: 2.0.0
Depends: R (>= 4.0), princurve (>= 2.0.4), stats, TrajectoryUtils
Imports: graphics, grDevices, igraph, matrixStats, methods, S4Vectors,
SingleCellExperiment, SummarizedExperiment
Suggests: BiocGenerics, BiocStyle, clusterExperiment, knitr, mclust,
mgcv, RColorBrewer, rgl, rmarkdown, testthat, uwot, covr
License: Artistic-2.0
MD5sum: 285520de7c3556790e5ccf8e5172fe7f
NeedsCompilation: no
Title: Tools for ordering single-cell sequencing
Description: Provides functions for inferring continuous, branching
lineage structures in low-dimensional data. Slingshot was
designed to model developmental trajectories in single-cell RNA
sequencing data and serve as a component in an analysis
pipeline after dimensionality reduction and clustering. It is
flexible enough to handle arbitrarily many branching events and
allows for the incorporation of prior knowledge through
supervised graph construction.
biocViews: Clustering, DifferentialExpression, GeneExpression, RNASeq,
Sequencing, Software, Sequencing, SingleCell, Transcriptomics,
Visualization
Author: Kelly Street [aut, cre, cph], Davide Risso [aut], Diya Das
[aut], Sandrine Dudoit [ths], Koen Van den Berge [ctb],
Robrecht Cannoodt [ctb]
(<https://orcid.org/0000-0003-3641-729X>, rcannood)
Maintainer: Kelly Street <street.kelly@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/kstreet13/slingshot/issues
git_url: https://git.bioconductor.org/packages/slingshot
git_branch: RELEASE_3_13
git_last_commit: ffcbc53
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/slingshot_2.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/slingshot_2.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/slingshot_2.0.0.tgz
vignettes: vignettes/slingshot/inst/doc/conditionsVignette.html,
vignettes/slingshot/inst/doc/vignette.html
vignetteTitles: Differential Topology: Comparing Conditions along a
Trajectory, Slingshot: Trajectory Inference for Single-Cell
Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/slingshot/inst/doc/conditionsVignette.R,
vignettes/slingshot/inst/doc/vignette.R
dependsOnMe: OSCA.advanced
importsMe: condiments, tradeSeq
dependencyCount: 33
Package: slinky
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: SummarizedExperiment, curl, dplyr, foreach, httr, stats,
utils, methods, readr, rhdf5, jsonlite, tidyr
Suggests: GeoDE, doParallel, testthat, knitr, rmarkdown, ggplot2,
Rtsne, Biobase, BiocStyle
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: e4120476b853a0c9b0768849d2269c60
NeedsCompilation: no
Title: Putting the fun in LINCS L1000 data analysis
Description: Wrappers to query the L1000 metadata available via the
clue.io REST API as well as helpers for dealing with LINCS gctx
files, extracting data sets of interest, converting to
SummarizedExperiment objects, and some facilities for
performing streamlined differential expression analysis of
these data sets.
biocViews: DataImport, ThirdPartyClient, GeneExpression,
DifferentialExpression, GeneSetEnrichment, PatternLogic
Author: Eric J. Kort
Maintainer: Eric J. Kort <eric.kort@vai.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/slinky
git_branch: RELEASE_3_13
git_last_commit: 53b81e6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/slinky_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/slinky_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/slinky_1.10.0.tgz
vignettes: vignettes/slinky/inst/doc/LINCS-analysis.html
vignetteTitles: "LINCS analysis with slinky"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/slinky/inst/doc/LINCS-analysis.R
dependencyCount: 69
Package: SLqPCR
Version: 1.58.0
Depends: R(>= 2.4.0)
Imports: stats
Suggests: RColorBrewer
License: GPL (>= 2)
MD5sum: 5672ec5e6653c7498c8bb5609a3f4400
NeedsCompilation: no
Title: Functions for analysis of real-time quantitative PCR data at
SIRS-Lab GmbH
Description: Functions for analysis of real-time quantitative PCR data
at SIRS-Lab GmbH
biocViews: MicrotitrePlateAssay, qPCR
Author: Matthias Kohl
Maintainer: Matthias Kohl <kohl@sirs-lab.com>
git_url: https://git.bioconductor.org/packages/SLqPCR
git_branch: RELEASE_3_13
git_last_commit: b95b1ed
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SLqPCR_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SLqPCR_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SLqPCR_1.58.0.tgz
vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf
vignetteTitles: SLqPCR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SLqPCR/inst/doc/SLqPCR.R
dependencyCount: 1
Package: SMAD
Version: 1.8.0
Depends: R (>= 3.6.0), RcppAlgos
Imports: magrittr (>= 1.5), dplyr, stats, tidyr, utils, Rcpp (>= 1.0.0)
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: MIT + file LICENSE
MD5sum: f94e7e036785cc89499eb93002b6d9a0
NeedsCompilation: yes
Title: Statistical Modelling of AP-MS Data (SMAD)
Description: Assigning probability scores to prey proteins captured in
affinity purification mass spectrometry (AP-MS) expriments to
infer protein-protein interactions. The output would facilitate
non-specific background removal as contaminants are commonly
found in AP-MS data.
biocViews: MassSpectrometry, Proteomics, Software
Author: Qingzhou Zhang [aut, cre]
Maintainer: Qingzhou Zhang <zqzneptune@hotmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SMAD
git_branch: RELEASE_3_13
git_last_commit: 5591339
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SMAD_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SMAD_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SMAD_1.8.0.tgz
vignettes: vignettes/SMAD/inst/doc/quickstart.html
vignetteTitles: SMAD Quick Start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SMAD/inst/doc/quickstart.R
dependencyCount: 28
Package: SMAP
Version: 1.56.0
Depends: R (>= 2.10), methods
License: GPL-2
Archs: i386, x64
MD5sum: 42f2e52005be69f997d48d904b204f54
NeedsCompilation: yes
Title: A Segmental Maximum A Posteriori Approach to Array-CGH Copy
Number Profiling
Description: Functions and classes for DNA copy number profiling of
array-CGH data
biocViews: Microarray, TwoChannel, CopyNumberVariation
Author: Robin Andersson <robin.andersson@lcb.uu.se>
Maintainer: Robin Andersson <robin.andersson@lcb.uu.se>
git_url: https://git.bioconductor.org/packages/SMAP
git_branch: RELEASE_3_13
git_last_commit: c2fb485
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SMAP_1.56.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SMAP_1.56.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SMAP_1.56.0.tgz
vignettes: vignettes/SMAP/inst/doc/SMAP.pdf
vignetteTitles: SMAP
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SMAP/inst/doc/SMAP.R
dependencyCount: 1
Package: SMITE
Version: 1.20.0
Depends: R (>= 3.3), GenomicRanges
Imports: scales, plyr, Hmisc, AnnotationDbi, org.Hs.eg.db, ggplot2,
reactome.db, KEGGREST, BioNet, goseq, methods, IRanges, igraph,
Biobase,tools, S4Vectors, geneLenDataBase, grDevices, graphics,
stats, utils
Suggests: knitr
License: GPL (>=2)
MD5sum: ebd37540c10d608dfe409448f16541a4
NeedsCompilation: no
Title: Significance-based Modules Integrating the Transcriptome and
Epigenome
Description: This package builds on the Epimods framework which
facilitates finding weighted subnetworks ("modules") on
Illumina Infinium 27k arrays using the SpinGlass algorithm, as
implemented in the iGraph package. We have created a class of
gene centric annotations associated with p-values and effect
sizes and scores from any researchers prior statistical results
to find functional modules.
biocViews: ImmunoOncology, DifferentialMethylation,
DifferentialExpression, SystemsBiology,
NetworkEnrichment,GenomeAnnotation,Network, Sequencing, RNASeq,
Coverage
Author: Neil Ari Wijetunga, Andrew Damon Johnston, John Murray Greally
Maintainer: Neil Ari Wijetunga <Neil.Wijetunga@med.einstein.yu.edu>,
Andrew Damon Johnston <Andrew.Johnston@med.einstein.yu.edu>
URL: https://github.com/GreallyLab/SMITE
VignetteBuilder: knitr
BugReports: https://github.com/GreallyLab/SMITE/issues
git_url: https://git.bioconductor.org/packages/SMITE
git_branch: RELEASE_3_13
git_last_commit: 7acefe7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SMITE_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SMITE_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SMITE_1.20.0.tgz
vignettes: vignettes/SMITE/inst/doc/SMITE.pdf
vignetteTitles: SMITE Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SMITE/inst/doc/SMITE.R
dependencyCount: 144
Package: SNAGEE
Version: 1.32.0
Depends: R (>= 2.6.0), SNAGEEdata
Suggests: ALL, hgu95av2.db
Enhances: parallel
License: Artistic-2.0
MD5sum: bb43a927814ef1b2d2b2b6571876a50b
NeedsCompilation: no
Title: Signal-to-Noise applied to Gene Expression Experiments
Description: Signal-to-Noise applied to Gene Expression Experiments.
Signal-to-noise ratios can be used as a proxy for quality of
gene expression studies and samples. The SNRs can be calculated
on any gene expression data set as long as gene IDs are
available, no access to the raw data files is necessary. This
allows to flag problematic studies and samples in any public
data set.
biocViews: Microarray, OneChannel, TwoChannel, QualityControl
Author: David Venet <davenet@ulb.ac.be>
Maintainer: David Venet <davenet@ulb.ac.be>
URL: http://bioconductor.org/
git_url: https://git.bioconductor.org/packages/SNAGEE
git_branch: RELEASE_3_13
git_last_commit: 09f2b4b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SNAGEE_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SNAGEE_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SNAGEE_1.32.0.tgz
vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf
vignetteTitles: SNAGEE Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SNAGEE/inst/doc/SNAGEE.R
suggestsMe: SNAGEEdata
dependencyCount: 1
Package: snapCGH
Version: 1.62.0
Depends: R (>= 3.5.0)
Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma,
methods, stats, tilingArray, utils
License: GPL
MD5sum: dc4c555093b2f68a6484634bd0bca107
NeedsCompilation: yes
Title: Segmentation, normalisation and processing of aCGH data
Description: Methods for segmenting, normalising and processing aCGH
data; including plotting functions for visualising raw and
segmented data for individual and multiple arrays.
biocViews: Microarray, CopyNumberVariation, TwoChannel, Preprocessing
Author: Mike L. Smith, John C. Marioni, Steven McKinney, Thomas
Hardcastle, Natalie P. Thorne
Maintainer: John Marioni <marioni@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/snapCGH
git_branch: RELEASE_3_13
git_last_commit: 17b1083
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/snapCGH_1.62.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/snapCGH_1.62.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/snapCGH_1.62.0.tgz
vignettes: vignettes/snapCGH/inst/doc/snapCGHguide.pdf
vignetteTitles: Segmentation Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/snapCGH/inst/doc/snapCGHguide.R
importsMe: ADaCGH2
suggestsMe: beadarraySNP
dependencyCount: 96
Package: snapcount
Version: 1.4.0
Depends: R (>= 4.0.0)
Imports: R6, httr, rlang, purrr, jsonlite, assertthat, data.table,
Matrix, magrittr, methods, stringr, stats, IRanges,
GenomicRanges, SummarizedExperiment
Suggests: BiocManager, bit64, covr, knitcitations, knitr (>= 1.6),
devtools, BiocStyle (>= 2.5.19), rmarkdown (>= 0.9.5), testthat
(>= 2.1.0)
License: MIT + file LICENSE
MD5sum: 15f53bc108ad315fea67c1a4d9ed1808
NeedsCompilation: no
Title: R/Bioconductor Package for interfacing with Snaptron for rapid
querying of expression counts
Description: snapcount is a client interface to the Snaptron
webservices which support querying by gene name or genomic
region. Results include raw expression counts derived from
alignment of RNA-seq samples and/or various summarized measures
of expression across one or more regions/genes per-sample (e.g.
percent spliced in).
biocViews: Coverage, GeneExpression, RNASeq, Sequencing, Software,
DataImport
Author: Rone Charles [aut, cre]
Maintainer: Rone Charles <rcharle8@jh.edu>
URL: https://github.com/langmead-lab/snapcount
VignetteBuilder: knitr
BugReports: https://github.com/langmead-lab/snapcount/issues
git_url: https://git.bioconductor.org/packages/snapcount
git_branch: RELEASE_3_13
git_last_commit: 148c7b4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/snapcount_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/snapcount_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/snapcount_1.4.0.tgz
vignettes: vignettes/snapcount/inst/doc/snapcount_vignette.html
vignetteTitles: snapcount quick start guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/snapcount/inst/doc/snapcount_vignette.R
dependencyCount: 42
Package: snifter
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: basilisk, reticulate, assertthat
Suggests: knitr, rmarkdown, scRNAseq, BiocStyle, scater, scran,
scuttle, ggplot2, testthat (>= 3.0.0)
License: GPL-3
Archs: i386, x64
MD5sum: 3dc71264942cc698f0728f08261758c3
NeedsCompilation: no
Title: R wrapper for the python openTSNE library
Description: Provides an R wrapper for the implementation of FI-tSNE
from the python package openTNSE. See PoliÄar et al. (2019)
<doi:10.1101/731877> and the algorithm described by Linderman
et al. (2018) <doi:10.1038/s41592-018-0308-4>.
biocViews: DimensionReduction, Visualization, Software, SingleCell,
Sequencing
Author: Alan O'Callaghan [aut, cre], Aaron Lun [aut]
Maintainer: Alan O'Callaghan <alan.ocallaghan@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/Alanocallaghan/snifter/issues
git_url: https://git.bioconductor.org/packages/snifter
git_branch: RELEASE_3_13
git_last_commit: 4e71e65
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/snifter_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/snifter_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/snifter_1.2.0.tgz
vignettes: vignettes/snifter/inst/doc/snifter.html
vignetteTitles: Introduction to snifter
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/snifter/inst/doc/snifter.R
dependsOnMe: OSCA.advanced
suggestsMe: scater
dependencyCount: 23
Package: snm
Version: 1.40.0
Depends: R (>= 2.12.0)
Imports: corpcor, lme4 (>= 1.0), splines
License: LGPL
Archs: i386, x64
MD5sum: 384731e7fda3384194097aa6838e8157
NeedsCompilation: no
Title: Supervised Normalization of Microarrays
Description: SNM is a modeling strategy especially designed for
normalizing high-throughput genomic data. The underlying
premise of our approach is that your data is a function of what
we refer to as study-specific variables. These variables are
either biological variables that represent the target of the
statistical analysis, or adjustment variables that represent
factors arising from the experimental or biological setting the
data is drawn from. The SNM approach aims to simultaneously
model all study-specific variables in order to more accurately
characterize the biological or clinical variables of interest.
biocViews: Microarray, OneChannel, TwoChannel, MultiChannel,
DifferentialExpression, ExonArray, GeneExpression,
Transcription, MultipleComparison, Preprocessing,
QualityControl
Author: Brig Mecham and John D. Storey <jstorey@princeton.edu>
Maintainer: John D. Storey <jstorey@princeton.edu>
git_url: https://git.bioconductor.org/packages/snm
git_branch: RELEASE_3_13
git_last_commit: c2f5188
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/snm_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/snm_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/snm_1.40.0.tgz
vignettes: vignettes/snm/inst/doc/snm.pdf
vignetteTitles: snm Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/snm/inst/doc/snm.R
importsMe: edge, ExpressionNormalizationWorkflow
dependencyCount: 19
Package: SNPediaR
Version: 1.18.0
Depends: R (>= 3.0.0)
Imports: RCurl, jsonlite
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-2
MD5sum: e612b1e7391ee48ac39c544992b17721
NeedsCompilation: no
Title: Query data from SNPedia
Description: SNPediaR provides some tools for downloading and parsing
data from the SNPedia web site <http://www.snpedia.com>. The
implemented functions allow users to import the wiki text
available in SNPedia pages and to extract the most relevant
information out of them. If some information in the downloaded
pages is not automatically processed by the library functions,
users can easily implement their own parsers to access it in an
efficient way.
biocViews: SNP, VariantAnnotation
Author: David Montaner [aut, cre]
Maintainer: David Montaner <david.montaner@gmail.com>
URL: https://github.com/genometra/SNPediaR
VignetteBuilder: knitr
BugReports: https://github.com/genometra/SNPediaR/issues
git_url: https://git.bioconductor.org/packages/SNPediaR
git_branch: RELEASE_3_13
git_last_commit: eda562e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SNPediaR_1.18.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/SNPediaR_1.18.0.tgz
vignettes: vignettes/SNPediaR/inst/doc/SNPediaR.html
vignetteTitles: SNPediaR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SNPediaR/inst/doc/SNPediaR.R
dependencyCount: 4
Package: SNPhood
Version: 1.22.0
Depends: R (>= 3.1), GenomicRanges, Rsamtools, data.table, checkmate
Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb, BiocParallel,
VariantAnnotation, BiocGenerics, IRanges, methods,
SummarizedExperiment, RColorBrewer, Biostrings, grDevices,
gridExtra, stats, grid, utils, reshape2, scales, S4Vectors
Suggests: BiocStyle, knitr, pryr, rmarkdown, SNPhoodData, corrplot
License: LGPL (>= 3)
MD5sum: 32d0ca4a035248b699ac1a28b1ef438d
NeedsCompilation: no
Title: SNPhood: Investigate, quantify and visualise the epigenomic
neighbourhood of SNPs using NGS data
Description: To date, thousands of single nucleotide polymorphisms
(SNPs) have been found to be associated with complex traits and
diseases. However, the vast majority of these
disease-associated SNPs lie in the non-coding part of the
genome, and are likely to affect regulatory elements, such as
enhancers and promoters, rather than function of a protein.
Thus, to understand the molecular mechanisms underlying genetic
traits and diseases, it becomes increasingly important to study
the effect of a SNP on nearby molecular traits such as
chromatin environment or transcription factor (TF) binding.
Towards this aim, we developed SNPhood, a user-friendly
*Bioconductor* R package to investigate and visualize the local
neighborhood of a set of SNPs of interest for NGS data such as
chromatin marks or transcription factor binding sites from
ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of
easy-to-use functions to extract, normalize and summarize reads
for a genomic region, perform various data quality checks,
normalize read counts using additional input files, and to
cluster and visualize the regions according to the binding
pattern. The regions around each SNP can be binned in a
user-defined fashion to allow for analysis of very broad
patterns as well as a detailed investigation of specific
binding shapes. Furthermore, SNPhood supports the integration
with genotype information to investigate and visualize
genotype-specific binding patterns. Finally, SNPhood can be
employed for determining, investigating, and visualizing
allele-specific binding patterns around the SNPs of interest.
biocViews: Software
Author: Christian Arnold [aut, cre], Pooja Bhat [aut], Judith Zaugg
[aut]
Maintainer: Christian Arnold <christian.arnold@embl.de>
URL: https://bioconductor.org/packages/SNPhood
VignetteBuilder: knitr
BugReports: christian.arnold@embl.de
git_url: https://git.bioconductor.org/packages/SNPhood
git_branch: RELEASE_3_13
git_last_commit: 562991f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SNPhood_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SNPhood_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SNPhood_1.22.0.tgz
vignettes: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.html,
vignettes/SNPhood/inst/doc/workflow.html
vignetteTitles: Introduction and Methodological Details, Workflow
example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.R,
vignettes/SNPhood/inst/doc/workflow.R
dependencyCount: 127
Package: SNPRelate
Version: 1.26.0
Depends: R (>= 2.15), gdsfmt (>= 1.8.3)
Imports: methods
LinkingTo: gdsfmt
Suggests: parallel, Matrix, RUnit, knitr, rmarkdown, MASS, BiocGenerics
Enhances: SeqArray (>= 1.12.0)
License: GPL-3
MD5sum: 85ffb42150027aaa9d509c93e83247e8
NeedsCompilation: yes
Title: Parallel Computing Toolset for Relatedness and Principal
Component Analysis of SNP Data
Description: Genome-wide association studies (GWAS) are widely used to
investigate the genetic basis of diseases and traits, but they
pose many computational challenges. We developed an R package
SNPRelate to provide a binary format for single-nucleotide
polymorphism (SNP) data in GWAS utilizing CoreArray Genomic
Data Structure (GDS) data files. The GDS format offers the
efficient operations specifically designed for integers with
two bits, since a SNP could occupy only two bits. SNPRelate is
also designed to accelerate two key computations on SNP data
using parallel computing for multi-core symmetric
multiprocessing computer architectures: Principal Component
Analysis (PCA) and relatedness analysis using
Identity-By-Descent measures. The SNP GDS format is also used
by the GWASTools package with the support of S4 classes and
generic functions. The extended GDS format is implemented in
the SeqArray package to support the storage of single
nucleotide variations (SNVs), insertion/deletion polymorphism
(indel) and structural variation calls in whole-genome and
whole-exome variant data.
biocViews: Infrastructure, Genetics, StatisticalMethod,
PrincipalComponent
Author: Xiuwen Zheng [aut, cre, cph]
(<https://orcid.org/0000-0002-1390-0708>), Stephanie Gogarten
[ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths]
(<https://orcid.org/0000-0002-4883-1247>)
Maintainer: Xiuwen Zheng <zhengx@u.washington.edu>
URL: http://github.com/zhengxwen/SNPRelate
VignetteBuilder: knitr
BugReports: http://github.com/zhengxwen/SNPRelate/issues
git_url: https://git.bioconductor.org/packages/SNPRelate
git_branch: RELEASE_3_13
git_last_commit: 8998076
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SNPRelate_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SNPRelate_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SNPRelate_1.26.0.tgz
vignettes: vignettes/SNPRelate/inst/doc/SNPRelate.html
vignetteTitles: SNPRelate Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SNPRelate/inst/doc/SNPRelate.R
dependsOnMe: SeqSQC
importsMe: CNVRanger, GDSArray, GENESIS, gwasurvivr, VariantExperiment,
dartR, EthSEQ, R.SamBada, simplePHENOTYPES
suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray
dependencyCount: 2
Package: snpStats
Version: 1.42.0
Depends: R(>= 2.10.0), survival, Matrix, methods
Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc
Suggests: hexbin
License: GPL-3
Archs: i386, x64
MD5sum: e48c8a8ac7cca7491974954aff591fd7
NeedsCompilation: yes
Title: SnpMatrix and XSnpMatrix classes and methods
Description: Classes and statistical methods for large SNP association
studies. This extends the earlier snpMatrix package, allowing
for uncertainty in genotypes.
biocViews: Microarray, SNP, GeneticVariability
Author: David Clayton <dc208@cam.ac.uk>
Maintainer: David Clayton <dc208@cam.ac.uk>
git_url: https://git.bioconductor.org/packages/snpStats
git_branch: RELEASE_3_13
git_last_commit: 93cc0eb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/snpStats_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/snpStats_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/snpStats_1.42.0.tgz
vignettes: vignettes/snpStats/inst/doc/data-input-vignette.pdf,
vignettes/snpStats/inst/doc/differences.pdf,
vignettes/snpStats/inst/doc/Fst-vignette.pdf,
vignettes/snpStats/inst/doc/imputation-vignette.pdf,
vignettes/snpStats/inst/doc/ld-vignette.pdf,
vignettes/snpStats/inst/doc/pca-vignette.pdf,
vignettes/snpStats/inst/doc/snpStats-vignette.pdf,
vignettes/snpStats/inst/doc/tdt-vignette.pdf
vignetteTitles: Data input, snpMatrix-differences, Fst, Imputation and
meta-analysis, LD statistics, Principal components analysis,
snpStats introduction, TDT tests
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/snpStats/inst/doc/data-input-vignette.R,
vignettes/snpStats/inst/doc/Fst-vignette.R,
vignettes/snpStats/inst/doc/imputation-vignette.R,
vignettes/snpStats/inst/doc/ld-vignette.R,
vignettes/snpStats/inst/doc/pca-vignette.R,
vignettes/snpStats/inst/doc/snpStats-vignette.R,
vignettes/snpStats/inst/doc/tdt-vignette.R
dependsOnMe: MAGAR, snpStatsWriter
importsMe: DExMA, GeneGeneInteR, gwascat, ldblock, martini, RVS,
scoreInvHap, GenomicTools, GenomicTools.fileHandler,
GWASbyCluster, LDheatmap, PhenotypeSimulator, snpEnrichment,
TriadSim
suggestsMe: crlmm, GenomicFiles, GWASTools, omicRexposome, omicsPrint,
VariantAnnotation, adjclust, coloc, genio, pegas
dependencyCount: 13
Package: soGGi
Version: 1.24.1
Depends: R (>= 3.2.0), BiocGenerics, SummarizedExperiment
Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, GenomeInfoDb,
GenomicRanges, Biostrings, Rsamtools, GenomicAlignments,
rtracklayer, preprocessCore, chipseq, BiocParallel
Suggests: testthat, BiocStyle, knitr
License: GPL (>= 3)
MD5sum: 952dfafc6f1447cd464c4ad1e07614d1
NeedsCompilation: no
Title: Visualise ChIP-seq, MNase-seq and motif occurrence as aggregate
plots Summarised Over Grouped Genomic Intervals
Description: The soGGi package provides a toolset to create genomic
interval aggregate/summary plots of signal or motif occurence
from BAM and bigWig files as well as PWM, rlelist, GRanges and
GAlignments Bioconductor objects. soGGi allows for
normalisation, transformation and arithmetic operation on and
between summary plot objects as well as grouping and subsetting
of plots by GRanges objects and user supplied metadata. Plots
are created using the GGplot2 libary to allow user defined
manipulation of the returned plot object. Coupled together,
soGGi features a broad set of methods to visualise genomics
data in the context of groups of genomic intervals such as
genes, superenhancers and transcription factor binding events.
biocViews: Sequencing, ChIPSeq, Coverage
Author: Gopuraja Dharmalingam, Doug Barrows, Tom Carroll
Maintainer: Tom Carroll <tc.infomatics@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/soGGi
git_branch: RELEASE_3_13
git_last_commit: 24358f6
git_last_commit_date: 2021-08-27
Date/Publication: 2021-08-29
source.ver: src/contrib/soGGi_1.24.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/soGGi_1.24.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/soGGi_1.24.1.tgz
vignettes: vignettes/soGGi/inst/doc/soggi.pdf
vignetteTitles: soggi
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/soGGi/inst/doc/soggi.R
importsMe: profileplyr
dependencyCount: 85
Package: sojourner
Version: 1.6.0
Imports:
ggplot2,dplyr,reshape2,gridExtra,EBImage,MASS,R.matlab,Rcpp,fitdistrplus,mclust,minpack.lm,mixtools,mltools,nls2,plyr,sampSurf,scales,shiny,shinyjs,sp,truncnorm,utils,stats,pixmap,rlang,graphics,grDevices,grid,compiler,lattice
Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: bcf2df30289e6f702f3c548f4436d676
NeedsCompilation: no
Title: Statistical analysis of single molecule trajectories
Description: Single molecule tracking has evolved as a novel new
approach complementing genomic sequencing, it reports live
biophysical properties of molecules being investigated besides
properties relating their coding sequence; here we provided
"sojourner" package, to address statistical and bioinformatic
needs related to the analysis and comprehension of high
throughput single molecule tracking data.
biocViews: Technology, WorkflowStep
Author: Sheng Liu [aut], Sun Jay Yoo [aut], Xiao Na Tang [aut], Young
Soo Sung [aut], Carl Wu [aut], Anand Ranjan [ctb], Vu Nguyen
[ctb], Sojourner Developer [cre]
Maintainer: Sojourner Developer <sojourner.developer@outlook.com>
URL: https://github.com/sheng-liu/sojourner
VignetteBuilder: knitr
BugReports: https://github.com/sheng-liu/sojourner/issues
git_url: https://git.bioconductor.org/packages/sojourner
git_branch: RELEASE_3_13
git_last_commit: 2ea1727
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sojourner_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sojourner_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sojourner_1.6.0.tgz
vignettes: vignettes/sojourner/inst/doc/sojourner-vignette.html
vignetteTitles: Sojourner: an R package for statistical analysis of
single molecule trajectories
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sojourner/inst/doc/sojourner-vignette.R
dependencyCount: 109
Package: SomaticSignatures
Version: 2.28.0
Depends: R (>= 3.1.0), VariantAnnotation, GenomicRanges, NMF
Imports: S4Vectors, IRanges, GenomeInfoDb, Biostrings, ggplot2, ggbio,
reshape2, NMF, pcaMethods, Biobase, methods, proxy
Suggests: testthat, knitr, parallel,
BSgenome.Hsapiens.1000genomes.hs37d5, SomaticCancerAlterations,
ggdendro, fastICA, sva
License: MIT + file LICENSE
MD5sum: 9e06f28572384842d5fc399c7d9c07bd
NeedsCompilation: no
Title: Somatic Signatures
Description: The SomaticSignatures package identifies mutational
signatures of single nucleotide variants (SNVs). It provides a
infrastructure related to the methodology described in
Nik-Zainal (2012, Cell), with flexibility in the matrix
decomposition algorithms.
biocViews: Sequencing, SomaticMutation, Visualization, Clustering,
GenomicVariation, StatisticalMethod
Author: Julian Gehring
Maintainer: Julian Gehring <jg-bioc@gmx.com>
URL: https://github.com/juliangehring/SomaticSignatures
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org
git_url: https://git.bioconductor.org/packages/SomaticSignatures
git_branch: RELEASE_3_13
git_last_commit: 3c360f9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SomaticSignatures_2.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SomaticSignatures_2.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SomaticSignatures_2.28.0.tgz
vignettes:
vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html
vignetteTitles: SomaticSignatures
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R
importsMe: YAPSA
dependencyCount: 165
Package: SOMNiBUS
Version: 1.0.0
Depends: R (>= 4.1.0)
Imports: graphics, Matrix, mgcv, stats, VGAM
Suggests: BiocStyle, covr, devtools, dplyr, knitr, magick, rmarkdown,
testthat
License: MIT + file LICENSE
MD5sum: de5afe5f5c5acc1c93f2be3a1a187ae1
NeedsCompilation: no
Title: Smooth modeling of bisulfite sequencing
Description: This package aims to analyse count-based methylation data
on predefined genomic regions, such as those obtained by
targeted sequencing, and thus to identify differentially
methylated regions (DMRs) that are associated with phenotypes
or traits. The method is built a rich flexible model that
allows for the effects, on the methylation levels, of multiple
covariates to vary smoothly along genomic regions. At the same
time, this method also allows for sequencing errors and can
adjust for variability in cell type mixture.
biocViews: DNAMethylation, Regression, Epigenetics,
DifferentialMethylation, Sequencing, FunctionalPrediction
Author: Kaiqiong Zhao [aut], Kathleen Klein [cre]
Maintainer: Kathleen Klein <kathleen.klein@mail.mcgill.ca>
URL: https://github.com/kaiqiong/SOMNiBUS
VignetteBuilder: knitr
BugReports: https://github.com/kaiqiong/SOMNiBUS/issues
git_url: https://git.bioconductor.org/packages/SOMNiBUS
git_branch: RELEASE_3_13
git_last_commit: c53c179
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SOMNiBUS_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SOMNiBUS_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SOMNiBUS_1.0.0.tgz
vignettes: vignettes/SOMNiBUS/inst/doc/SOMNiBUS.html
vignetteTitles: Analyzing Targeted Bisulfite Sequencing data with
SOMNiBUS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SOMNiBUS/inst/doc/SOMNiBUS.R
dependencyCount: 13
Package: SpacePAC
Version: 1.30.0
Depends: R(>= 2.15),iPAC
Suggests: RUnit, BiocGenerics, rgl
License: GPL-2
Archs: i386, x64
MD5sum: fdf79dade82a14613924df9a120110b9
NeedsCompilation: no
Title: Identification of Mutational Clusters in 3D Protein Space via
Simulation.
Description: Identifies clustering of somatic mutations in proteins via
a simulation approach while considering the protein's tertiary
structure.
biocViews: Clustering, Proteomics
Author: Gregory Ryslik, Hongyu Zhao
Maintainer: Gregory Ryslik <gregory.ryslik@yale.edu>
git_url: https://git.bioconductor.org/packages/SpacePAC
git_branch: RELEASE_3_13
git_last_commit: 9f7b13a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SpacePAC_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SpacePAC_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SpacePAC_1.30.0.tgz
vignettes: vignettes/SpacePAC/inst/doc/SpacePAC.pdf
vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein
space using simulation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SpacePAC/inst/doc/SpacePAC.R
dependsOnMe: QuartPAC
dependencyCount: 31
Package: Spaniel
Version: 1.6.0
Depends: R (>= 4.0)
Imports: Seurat, SingleCellExperiment, SummarizedExperiment, dplyr,
methods, ggplot2, scater (>= 1.13), scran, igraph, shiny, jpeg,
magrittr, utils, S4Vectors, DropletUtils, jsonlite, png
Suggests: knitr, rmarkdown, testthat, devtools
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: d13f7bcc9299051c5042cf731b16a078
NeedsCompilation: no
Title: Spatial Transcriptomics Analysis
Description: Spaniel includes a series of tools to aid the quality
control and analysis of Spatial Transcriptomics data. Spaniel
can import data from either the original Spatial
Transcriptomics system or 10X Visium technology. The package
contains functions to create a SingleCellExperiment Seurat
object and provides a method of loading a histologial image
into R. The spanielPlot function allows visualisation of
metrics contained within the S4 object overlaid onto the image
of the tissue.
biocViews: SingleCell, RNASeq, QualityControl, Preprocessing,
Normalization, Visualization, Transcriptomics, GeneExpression,
Sequencing, Software, DataImport, DataRepresentation,
Infrastructure, Coverage, Clustering
Author: Rachel Queen [aut, cre]
Maintainer: Rachel Queen <rachel.queen@newcastle.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Spaniel
git_branch: RELEASE_3_13
git_last_commit: 6303e34
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Spaniel_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Spaniel_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Spaniel_1.6.0.tgz
vignettes: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.html
vignetteTitles: Spaniel 10X Visium
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.R
dependencyCount: 192
Package: sparseDOSSA
Version: 1.16.0
Imports: stats, utils, optparse, MASS, tmvtnorm (>= 1.4.10), MCMCpack
Suggests: knitr, BiocStyle, BiocGenerics, rmarkdown
License: MIT + file LICENSE
MD5sum: cb124cef82cc38cef5dc3b66f3fd3d6e
NeedsCompilation: no
Title: Sparse Data Observations for Simulating Synthetic Abundance
Description: The package is to provide a model based Bayesian method to
characterize and simulate microbiome data. sparseDOSSA's model
captures the marginal distribution of each microbial feature as
a truncated, zero-inflated log-normal distribution, with
parameters distributed as a parent log-normal distribution. The
model can be effectively fit to reference microbial datasets in
order to parameterize their microbes and communities, or to
simulate synthetic datasets of similar population structure.
Most importantly, it allows users to include both known
feature-feature and feature-metadata correlation structures and
thus provides a gold standard to enable benchmarking of
statistical methods for metagenomic data analysis.
biocViews: ImmunoOncology, Bayesian, Microbiome, Metagenomics, Software
Author: Boyu Ren<bor158@mail.harvard.edu>, Emma
Schwager<emma.schwager@gmail.com>, Timothy
Tickle<ttickle@hsph.harvard.edu>, Curtis Huttenhower
<chuttenh@hsph.harvard.edu>
Maintainer: Boyu Ren<bor158@mail.harvard.edu>, Emma Schwager
<emma.schwager@gmail.com>, George
Weingart<george.weingart@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sparseDOSSA
git_branch: RELEASE_3_13
git_last_commit: cf8e465
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sparseDOSSA_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sparseDOSSA_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sparseDOSSA_1.16.0.tgz
vignettes: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.html
vignetteTitles: Sparse Data Observations for the Simulation of
Synthetic Abundances (sparseDOSSA)
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.R
dependencyCount: 27
Package: sparseMatrixStats
Version: 1.4.2
Depends: MatrixGenerics (>= 1.4.2)
Imports: Rcpp, Matrix, matrixStats (>= 0.60.0), methods
LinkingTo: Rcpp
Suggests: testthat (>= 2.1.0), knitr, bench, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 34bb7ab45929c6d92deac396f794503b
NeedsCompilation: yes
Title: Summary Statistics for Rows and Columns of Sparse Matrices
Description: High performance functions for row and column operations
on sparse matrices. For example: col / rowMeans2, col /
rowMedians, col / rowVars etc. Currently, the optimizations are
limited to data in the column sparse format. This package is
inspired by the matrixStats package by Henrik Bengtsson.
biocViews: Infrastructure, Software, DataRepresentation
Author: Constantin Ahlmann-Eltze [aut, cre]
(<https://orcid.org/0000-0002-3762-068X>)
Maintainer: Constantin Ahlmann-Eltze <artjom31415@googlemail.com>
URL: https://github.com/const-ae/sparseMatrixStats
VignetteBuilder: knitr
BugReports: https://github.com/const-ae/sparseMatrixStats/issues
git_url: https://git.bioconductor.org/packages/sparseMatrixStats
git_branch: RELEASE_3_13
git_last_commit: 1ef80c7
git_last_commit_date: 2021-08-05
Date/Publication: 2021-08-08
source.ver: src/contrib/sparseMatrixStats_1.4.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sparseMatrixStats_1.4.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/sparseMatrixStats_1.4.2.tgz
vignettes: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.html
vignetteTitles: sparseMatrixStats
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.R
importsMe: DelayedMatrixStats, GSVA, adjclust
suggestsMe: MatrixGenerics, scPCA
dependencyCount: 11
Package: sparsenetgls
Version: 1.10.0
Depends: R (>= 4.0.0), Matrix, MASS
Imports: methods, glmnet, huge, stats, graphics, utils
Suggests: testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>=
5.0.0)
License: GPL-3
MD5sum: beaf248594ba7db298fd33e3a66b7de4
NeedsCompilation: no
Title: Using Gaussian graphical structue learning estimation in
generalized least squared regression for multivariate normal
regression
Description: The package provides methods of combining the graph
structure learning and generalized least squares regression to
improve the regression estimation. The main function
sparsenetgls() provides solutions for multivariate regression
with Gaussian distributed dependant variables and explanatory
variables utlizing multiple well-known graph structure learning
approaches to estimating the precision matrix, and uses a
penalized variance covariance matrix with a distance tuning
parameter of the graph structure in deriving the sandwich
estimators in generalized least squares (gls) regression. This
package also provides functions for assessing a Gaussian
graphical model which uses the penalized approach. It uses
Receiver Operative Characteristics curve as a visualization
tool in the assessment.
biocViews: ImmunoOncology,
GraphAndNetwork,Regression,Metabolomics,CopyNumberVariation,MassSpectrometry,Proteomics,Software,Visualization
Author: Irene Zeng [aut, cre], Thomas Lumley [ctb]
Maintainer: Irene Zeng <szen003@aucklanduni.ac.nz>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sparsenetgls
git_branch: RELEASE_3_13
git_last_commit: 3196fa4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sparsenetgls_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sparsenetgls_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sparsenetgls_1.10.0.tgz
vignettes: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.html
vignetteTitles: Introduction to sparsenetgls
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.R
dependencyCount: 22
Package: SparseSignatures
Version: 2.2.0
Depends: R (>= 4.0.0), NMF
Imports: nnlasso, nnls, parallel, data.table, Biostrings,
GenomicRanges, IRanges, BSgenome, GenomeInfoDb, ggplot2,
gridExtra, reshape2
Suggests: BiocGenerics, BSgenome.Hsapiens.1000genomes.hs37d5,
BiocStyle, testthat, knitr,
License: file LICENSE
MD5sum: 6660d105e52ff1f2f7c7229d22deef2a
NeedsCompilation: no
Title: SparseSignatures
Description: Point mutations occurring in a genome can be divided into
96 categories based on the base being mutated, the base it is
mutated into and its two flanking bases. Therefore, for any
patient, it is possible to represent all the point mutations
occurring in that patient's tumor as a vector of length 96,
where each element represents the count of mutations for a
given category in the patient. A mutational signature
represents the pattern of mutations produced by a mutagen or
mutagenic process inside the cell. Each signature can also be
represented by a vector of length 96, where each element
represents the probability that this particular mutagenic
process generates a mutation of the 96 above mentioned
categories. In this R package, we provide a set of functions to
extract and visualize the mutational signatures that best
explain the mutation counts of a large number of patients.
biocViews: BiomedicalInformatics, SomaticMutation
Author: Daniele Ramazzotti [cre, aut]
(<https://orcid.org/0000-0002-6087-2666>), Avantika Lal [aut],
Keli Liu [ctb], Luca De Sano [aut]
(<https://orcid.org/0000-0002-9618-3774>), Robert Tibshirani
[ctb], Arend Sidow [aut]
Maintainer: Luca De Sano <luca.desano@gmail.com>
URL: https://github.com/danro9685/SparseSignatures
VignetteBuilder: knitr
BugReports: https://github.com/danro9685/SparseSignatures
git_url: https://git.bioconductor.org/packages/SparseSignatures
git_branch: RELEASE_3_13
git_last_commit: fbc80a3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SparseSignatures_2.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SparseSignatures_2.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SparseSignatures_2.2.0.tgz
vignettes: vignettes/SparseSignatures/inst/doc/vignette.pdf
vignetteTitles: SparseSignatures
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SparseSignatures/inst/doc/vignette.R
dependencyCount: 96
Package: SpatialCPie
Version: 1.8.0
Depends: R (>= 3.6)
Imports: colorspace (>= 1.3-2), data.table (>= 1.12.2), digest (>=
0.6.21), dplyr (>= 0.7.6), ggforce (>= 0.3.0), ggiraph (>=
0.5.0), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), grid (>=
3.5.1), igraph (>= 1.2.2), lpSolve (>= 5.6.13), methods (>=
3.5.0), purrr (>= 0.2.5), readr (>= 1.1.1), rlang (>= 0.2.2),
shiny (>= 1.1.0), shinycssloaders (>= 0.2.0), shinyjs (>= 1.0),
shinyWidgets (>= 0.4.8), stats (>= 3.6.0), SummarizedExperiment
(>= 1.10.1), tibble (>= 1.4.2), tidyr (>= 0.8.1), tidyselect
(>= 0.2.4), tools (>= 3.6.0), utils (>= 3.5.0), zeallot (>=
0.1.0)
Suggests: BiocStyle (>= 2.8.2), jpeg (>= 0.1-8), knitr (>= 1.20),
rmarkdown (>= 1.10), testthat (>= 2.0.0)
License: MIT + file LICENSE
MD5sum: eff2bb4069f205de4326ecd62522b4ff
NeedsCompilation: no
Title: Cluster analysis of Spatial Transcriptomics data
Description: SpatialCPie is an R package designed to facilitate cluster
evaluation for spatial transcriptomics data by providing
intuitive visualizations that display the relationships between
clusters in order to guide the user during cluster
identification and other downstream applications. The package
is built around a shiny "gadget" to allow the exploration of
the data with multiple plots in parallel and an interactive UI.
The user can easily toggle between different cluster
resolutions in order to choose the most appropriate visual
cues.
biocViews: Transcriptomics, Clustering, RNASeq, Software
Author: Joseph Bergenstraahle [aut, cre]
Maintainer: Joseph Bergenstraahle <joseph.bergenstrahle@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SpatialCPie
git_branch: RELEASE_3_13
git_last_commit: 13b0098
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SpatialCPie_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SpatialCPie_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SpatialCPie_1.8.0.tgz
vignettes: vignettes/SpatialCPie/inst/doc/SpatialCPie.html
vignetteTitles: SpatialCPie
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SpatialCPie/inst/doc/SpatialCPie.R
dependencyCount: 108
Package: SpatialDecon
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: logNormReg, grDevices, stats, utils, graphics,
Suggests: testthat, knitr, rmarkdown
License: GPL-3 + file LICENSE
MD5sum: 4ed5430c8f02583346dd093455cc91b1
NeedsCompilation: no
Title: Deconvolution of mixed cells from spatial and/or bulk gene
expression data
Description: Using spatial or bulk gene expression data, estimates
abundance of mixed cell types within each observation. Based on
"Advances in mixed cell deconvolution enable quantification of
cell types in spatially-resolved gene expression data", Danaher
(2020). Designed for use with the NanoString GeoMx platform,
but applicable to any gene expression data.
biocViews: ImmunoOncology, FeatureExtraction, GeneExpression,
Transcriptomics
Author: Patrick Danaher [aut, cre]
Maintainer: Patrick Danaher <pdanaher@nanostring.com>
VignetteBuilder: knitr
BugReports: https://github.com/Nanostring-Biostats/SpatialDecon/issues
git_url: https://git.bioconductor.org/packages/SpatialDecon
git_branch: RELEASE_3_13
git_last_commit: df6b718
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SpatialDecon_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SpatialDecon_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SpatialDecon_1.2.0.tgz
vignettes: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.html
vignetteTitles: SpatialDecon_vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.R
dependencyCount: 5
Package: SpatialExperiment
Version: 1.2.1
Depends: methods, SingleCellExperiment
Imports: BiocFileCache, DropletUtils, rjson, magick, grDevices,
S4Vectors, SummarizedExperiment, BiocGenerics, utils
Suggests: knitr, rmarkdown, testthat, BiocStyle, BumpyMatrix
License: GPL-3
MD5sum: 6c8e46f6768c4dd7c337a1acd9db49df
NeedsCompilation: no
Title: S4 Class for Spatial Experiments handling
Description: Defines S4 classes for storing data for spatial
experiments. Main examples are reported by using seqFISH and
10x-Visium Spatial Gene Expression data. This includes
specialized methods for storing, retrieving spatial
coordinates, 10x dedicated parameters and their handling.
biocViews: DataRepresentation, DataImport, ImmunoOncology,
DataRepresentation, Infrastructure, SingleCell, GeneExpression
Author: Dario Righelli [aut, cre], Davide Risso [aut], Helena L.
Crowell [aut], Lukas M. Weber [aut]
Maintainer: Dario Righelli <dario.righelli@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/drighelli/SpatialExperiment/issues
git_url: https://git.bioconductor.org/packages/SpatialExperiment
git_branch: RELEASE_3_13
git_last_commit: 625ce87
git_last_commit_date: 2021-06-08
Date/Publication: 2021-06-10
source.ver: src/contrib/SpatialExperiment_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SpatialExperiment_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/SpatialExperiment_1.2.1.tgz
vignettes: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html
vignetteTitles: Building SpatialExperiment object
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.R
dependsOnMe: ExperimentSubset, MouseGastrulationData, spatialLIBD,
STexampleData, TENxVisiumData
importsMe: SingleCellMultiModal
suggestsMe: mistyR
dependencyCount: 94
Package: spatialHeatmap
Version: 1.2.0
Imports: av, BiocFileCache, data.table, DESeq2, edgeR, WGCNA,
flashClust, htmlwidgets, genefilter, ggplot2, ggdendro,
grImport, grid, gridExtra, gplots, igraph, HDF5Array, rsvg,
shiny, dynamicTreeCut, grDevices, graphics, ggplotify,
parallel, plotly, rols, rappdirs, stats, SummarizedExperiment,
shinydashboard, S4Vectors, utils, visNetwork, methods, xml2,
yaml
Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics,
ExpressionAtlas, DT, reshape2, Biobase, GEOquery, shinyWidgets,
shinyjs, htmltools, shinyBS, sortable
License: Artistic-2.0
Archs: i386, x64
MD5sum: c1eed099b95ad73b966cc1beea36a644
NeedsCompilation: no
Title: spatialHeatmap
Description: The spatialHeatmap package provides functionalities for
visualizing cell-, tissue- and organ-specific data of
biological assays by coloring the corresponding spatial
features defined in anatomical images according to a numeric
color key.
biocViews: Visualization, Microarray, Sequencing, GeneExpression,
DataRepresentation, Network, Clustering, GraphAndNetwork,
CellBasedAssays, ATACSeq, DNASeq, TissueMicroarray, SingleCell,
CellBiology, GeneTarget
Author: Jianhai Zhang [aut, trl, cre], Jordan Hayes [aut], Le Zhang
[aut], Bing Yang [aut], Wolf Frommer [aut], Julia Bailey-Serres
[aut], Thomas Girke [aut]
Maintainer: Jianhai Zhang <jianhai.zhang@email.ucr.edu>
URL: https://github.com/jianhaizhang/spatialHeatmap
VignetteBuilder: knitr
BugReports: https://github.com/jianhaizhang/spatialHeatmap/issues
git_url: https://git.bioconductor.org/packages/spatialHeatmap
git_branch: RELEASE_3_13
git_last_commit: a2af50d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/spatialHeatmap_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/spatialHeatmap_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/spatialHeatmap_1.2.0.tgz
vignettes: vignettes/spatialHeatmap/inst/doc/spatialHeatmap.html
vignetteTitles: spatialHeatmap: Visualizing Spatial Assays in
Anatomical Images and Network Graphs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/spatialHeatmap/inst/doc/spatialHeatmap.R
dependencyCount: 179
Package: specL
Version: 1.26.0
Depends: R (>= 3.6), DBI (>= 0.5), methods (>= 3.3), protViz (>= 0.5),
RSQLite (>= 1.1), seqinr (>= 3.3)
Suggests: BiocGenerics, BiocStyle (>= 2.2), knitr (>= 1.15), rmarkdown,
RUnit (>= 0.4)
License: GPL-3
MD5sum: da4b8d13e5cb0a51d1d467a619153189
NeedsCompilation: no
Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted
Proteomics
Description: provides a functions for generating spectra libraries that
can be used for MRM SRM MS workflows in proteomics. The package
provides a BiblioSpec reader, a function which can add the
protein information using a FASTA formatted amino acid file,
and an export method for using the created library in the
Spectronaut software. The package is developed, tested and used
at the Functional Genomics Center Zurich
<http://www.fgcz.ethz.ch>.
biocViews: MassSpectrometry, Proteomics
Author: Christian Panse [aut, cre]
(<https://orcid.org/0000-0003-1975-3064>), Jonas Grossmann
[aut] (<https://orcid.org/0000-0002-6899-9020>), Christian
Trachsel [aut], Witold E. Wolski [ctb]
Maintainer: Christian Panse <cp@fgcz.ethz.ch>
URL: http://bioconductor.org/packages/specL/
VignetteBuilder: knitr
BugReports: https://github.com/fgcz/specL/issues
git_url: https://git.bioconductor.org/packages/specL
git_branch: RELEASE_3_13
git_last_commit: bb4fa57
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/specL_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/specL_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/specL_1.26.0.tgz
vignettes: vignettes/specL/inst/doc/specL.pdf,
vignettes/specL/inst/doc/report.html
vignetteTitles: Introduction to specL, Automatic Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/specL/inst/doc/report.R,
vignettes/specL/inst/doc/specL.R
suggestsMe: msqc1, NestLink
dependencyCount: 29
Package: SpeCond
Version: 1.46.0
Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13),
fields, hwriter (>= 1.1), RColorBrewer, methods
License: LGPL (>=2)
MD5sum: c7fc36621515183dfa9b28ffe9e04e02
NeedsCompilation: no
Title: Condition specific detection from expression data
Description: This package performs a gene expression data analysis to
detect condition-specific genes. Such genes are significantly
up- or down-regulated in a small number of conditions. It does
so by fitting a mixture of normal distributions to the
expression values. Conditions can be environmental conditions,
different tissues, organs or any other sources that you wish to
compare in terms of gene expression.
biocViews: Microarray, DifferentialExpression, MultipleComparison,
Clustering, ReportWriting
Author: Florence Cavalli
Maintainer: Florence Cavalli <florence@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/SpeCond
git_branch: RELEASE_3_13
git_last_commit: 1c73337
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SpeCond_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SpeCond_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SpeCond_1.46.0.tgz
vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf
vignetteTitles: SpeCond
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SpeCond/inst/doc/SpeCond.R
dependencyCount: 49
Package: Spectra
Version: 1.2.2
Depends: R (>= 4.0.0), S4Vectors, BiocParallel, ProtGenerics (>=
1.23.8)
Imports: methods, IRanges, MsCoreUtils (>= 1.3.3), graphics, grDevices,
stats, tools, utils, fs, BiocGenerics
Suggests: testthat, knitr (>= 1.1.0), msdata (>= 0.19.3), roxygen2,
BiocStyle (>= 2.5.19), mzR (>= 2.19.6), rhdf5 (>= 2.32.0),
rmarkdown, vdiffr, magrittr
License: Artistic-2.0
MD5sum: 43203f4c3e8ad28f7c8a4bff0fd7e8c5
NeedsCompilation: no
Title: Spectra Infrastructure for Mass Spectrometry Data
Description: The Spectra package defines an efficient infrastructure
for storing and handling mass spectrometry spectra and
functionality to subset, process, visualize and compare spectra
data. It provides different implementations (backends) to store
mass spectrometry data. These comprise backends tuned for fast
data access and processing and backends for very large data
sets ensuring a small memory footprint.
biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics
Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto
[aut] (<https://orcid.org/0000-0002-1520-2268>), Johannes
Rainer [aut] (<https://orcid.org/0000-0002-6977-7147>),
Sebastian Gibb [aut] (<https://orcid.org/0000-0001-7406-4443>)
Maintainer: RforMassSpectrometry Package Maintainer
<maintainer@rformassspectrometry.org>
URL: https://github.com/RforMassSpectrometry/Spectra
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/Spectra/issues
git_url: https://git.bioconductor.org/packages/Spectra
git_branch: RELEASE_3_13
git_last_commit: ef88705
git_last_commit_date: 2021-10-05
Date/Publication: 2021-10-07
source.ver: src/contrib/Spectra_1.2.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Spectra_1.2.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/Spectra_1.2.2.tgz
vignettes: vignettes/Spectra/inst/doc/Spectra.html
vignetteTitles: Description and usage of Spectra object
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Spectra/inst/doc/Spectra.R
dependsOnMe: MsBackendMassbank, MsBackendMgf
suggestsMe: xcms
dependencyCount: 25
Package: SpectralTAD
Version: 1.8.0
Depends: R (>= 3.6)
Imports: dplyr, PRIMME, cluster, Matrix, parallel, BiocParallel,
magrittr, HiCcompare, GenomicRanges
Suggests: BiocCheck, BiocManager, BiocStyle, knitr, rmarkdown,
microbenchmark, testthat, covr
License: MIT + file LICENSE
MD5sum: fd86478e82813f0254670bd007ca3260
NeedsCompilation: no
Title: SpectralTAD: Hierarchical TAD detection using spectral
clustering
Description: SpectralTAD is an R package designed to identify
Topologically Associated Domains (TADs) from Hi-C contact
matrices. It uses a modified version of spectral clustering
that uses a sliding window to quickly detect TADs. The function
works on a range of different formats of contact matrices and
returns a bed file of TAD coordinates. The method does not
require users to adjust any parameters to work and gives them
control over the number of hierarchical levels to be returned.
biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering
Author: Kellen Cresswell <cresswellkg@vcu.edu>, John Stansfield
<stansfieldjc@vcu.edu>, Mikhail Dozmorov
<mikhail.dozmorov@vcuhealth.org>
Maintainer: Kellen Cresswell <cresswellkg@vcu.edu>
URL: https://github.com/dozmorovlab/SpectralTAD
VignetteBuilder: knitr
BugReports: https://github.com/dozmorovlab/SpectralTAD/issues
git_url: https://git.bioconductor.org/packages/SpectralTAD
git_branch: RELEASE_3_13
git_last_commit: 3c8bc95
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SpectralTAD_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SpectralTAD_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SpectralTAD_1.8.0.tgz
vignettes: vignettes/SpectralTAD/inst/doc/SpectralTAD.html
vignetteTitles: SpectralTAD
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SpectralTAD/inst/doc/SpectralTAD.R
suggestsMe: TADCompare
dependencyCount: 100
Package: SPEM
Version: 1.32.0
Depends: R (>= 2.15.1), Rsolnp, Biobase, methods
License: GPL-2
MD5sum: 5f34ab548c7bf45d9309510932d96834
NeedsCompilation: no
Title: S-system parameter estimation method
Description: This package can optimize the parameter in S-system models
given time series data
biocViews: Network, NetworkInference, Software
Author: Xinyi YANG Developer, Jennifer E. DENT Developer and Christine
NARDINI Supervisor
Maintainer: Xinyi YANG <yangxinyi@picb.ac.cn>
git_url: https://git.bioconductor.org/packages/SPEM
git_branch: RELEASE_3_13
git_last_commit: 0f2d973
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SPEM_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SPEM_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SPEM_1.32.0.tgz
vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf
vignetteTitles: Vignette for SPEM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SPEM/inst/doc/SPEM-package.R
importsMe: TMixClust
dependencyCount: 9
Package: SPIA
Version: 2.44.0
Depends: R (>= 2.14.0), graphics, KEGGgraph
Imports: graphics
Suggests: graph, Rgraphviz, hgu133plus2.db
License: file LICENSE
License_restricts_use: yes
MD5sum: 7972ed81e1634771ae479d31633e6966
NeedsCompilation: no
Title: Signaling Pathway Impact Analysis (SPIA) using combined evidence
of pathway over-representation and unusual signaling
perturbations
Description: This package implements the Signaling Pathway Impact
Analysis (SPIA) which uses the information form a list of
differentially expressed genes and their log fold changes
together with signaling pathways topology, in order to identify
the pathways most relevant to the condition under the study.
biocViews: Microarray, GraphAndNetwork
Author: Adi Laurentiu Tarca <atarca@med.wayne.edu>, Purvesh Kathri
<purvesh@cs.wayne.edu> and Sorin Draghici <sorin@wayne.edu>
Maintainer: Adi Laurentiu Tarca <atarca@med.wayne.edu>
URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1
git_url: https://git.bioconductor.org/packages/SPIA
git_branch: RELEASE_3_13
git_last_commit: 961f469
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SPIA_2.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SPIA_2.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SPIA_2.44.0.tgz
vignettes: vignettes/SPIA/inst/doc/SPIA.pdf
vignetteTitles: SPIA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SPIA/inst/doc/SPIA.R
importsMe: EnrichmentBrowser
suggestsMe: graphite, KEGGgraph
dependencyCount: 15
Package: spicyR
Version: 1.4.0
Depends: R (>= 4.0.0)
Imports: ggplot2, concaveman, BiocParallel, spatstat.core,
spatstat.geom, lmerTest, BiocGenerics, S4Vectors, lme4,
methods, mgcv, pheatmap, rlang, grDevices, IRanges, stats,
data.table, dplyr, tidyr
Suggests: BiocStyle, knitr, rmarkdown
License: GPL (>=2)
MD5sum: 6ac985f422bd22e57e036aa2489da432
NeedsCompilation: no
Title: Spatial analysis of in situ cytometry data
Description: spicyR provides a series of functions to aid in the
analysis of both immunofluorescence and mass cytometry imaging
data as well as other assays that can deeply phenotype
individual cells and their spatial location.
biocViews: SingleCell, CellBasedAssays
Author: Nicolas Canete [aut], Ellis Patrick [aut, cre]
Maintainer: Ellis Patrick <ellis.patrick@sydney.edu.au>
VignetteBuilder: knitr
BugReports: https://github.com/ellispatrick/spicyR/issues
git_url: https://git.bioconductor.org/packages/spicyR
git_branch: RELEASE_3_13
git_last_commit: d3143db
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/spicyR_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/spicyR_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/spicyR_1.4.0.tgz
vignettes: vignettes/spicyR/inst/doc/segmentedCells.html,
vignettes/spicyR/inst/doc/spicy.html
vignetteTitles: "Introduction to SegmentedCells", "Introduction to
spicy"
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/spicyR/inst/doc/segmentedCells.R,
vignettes/spicyR/inst/doc/spicy.R
importsMe: lisaClust
dependencyCount: 92
Package: SpidermiR
Version: 1.22.1
Depends: R (>= 3.0.0)
Imports: httr, igraph, utils, stats, miRNAtap, miRNAtap.db,
AnnotationDbi, org.Hs.eg.db, ggplot2, gridExtra, gplots,
grDevices, lattice, latticeExtra, visNetwork, TCGAbiolinks,
gdata, MAGeCKFlute,networkD3
Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2
License: GPL (>= 3)
MD5sum: 3125c67f94bf00627f456a07d1074201
NeedsCompilation: no
Title: SpidermiR: An R/Bioconductor package for integrative network
analysis with miRNA data
Description: The aims of SpidermiR are : i) facilitate the network
open-access data retrieval from GeneMania data, ii) prepare the
data using the appropriate gene nomenclature, iii) integration
of miRNA data in a specific network, iv) provide different
standard analyses and v) allow the user to visualize the
results. In more detail, the package provides multiple methods
for query, prepare and download network data (GeneMania), and
the integration with validated and predicted miRNA data
(mirWalk, miRTarBase, miRandola, Miranda, PicTar and
TargetScan). Furthermore, we also present a statistical test to
identify pharmaco-mir relationships using the gene-drug
interactions derived by DGIdb and MATADOR database.
biocViews: GeneRegulation, miRNA, Network
Author: Claudia Cava, Antonio Colaprico, Alex Graudenzi, Gloria
Bertoli, Tiago C. Silva, Catharina Olsen, Houtan Noushmehr,
Gianluca Bontempi, Giancarlo Mauri, Isabella Castiglioni
Maintainer: Claudia Cava <claudia.cava@ibfm.cnr.it>
URL: https://github.com/claudiacava/SpidermiR
VignetteBuilder: knitr
BugReports: https://github.com/claudiacava/SpidermiR/issues
git_url: https://git.bioconductor.org/packages/SpidermiR
git_branch: RELEASE_3_13
git_last_commit: 45680ec
git_last_commit_date: 2021-06-16
Date/Publication: 2021-06-17
source.ver: src/contrib/SpidermiR_1.22.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SpidermiR_1.22.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/SpidermiR_1.22.1.tgz
vignettes: vignettes/SpidermiR/inst/doc/SpidermiR.html
vignetteTitles: Working with SpidermiR package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SpidermiR/inst/doc/SpidermiR.R
importsMe: StarBioTrek
dependencyCount: 178
Package: spikeLI
Version: 2.52.0
Imports: graphics, grDevices, stats, utils
License: GPL-2
MD5sum: 2d2fb94ec4011d76205169c53a4e67cc
NeedsCompilation: no
Title: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool
Description: SpikeLI is a package that performs the analysis of the
Affymetrix spike-in data using the Langmuir Isotherm. The aim
of this package is to show the advantages of a
physical-chemistry based analysis of the Affymetrix microarray
data compared to the traditional methods. The spike-in (or
Latin square) data for the HGU95 and HGU133 chipsets have been
downloaded from the Affymetrix web site. The model used in the
spikeLI package is described in details in E. Carlon and T.
Heim, Physica A 362, 433 (2006).
biocViews: Microarray, QualityControl
Author: Delphine Baillon, Paul Leclercq <paulleclercq@hotmail.com>,
Sarah Ternisien, Thomas Heim, Enrico Carlon
<enrico.carlon@fys.kuleuven.be>
Maintainer: Enrico Carlon <enrico.carlon@fys.kuleuven.be>
git_url: https://git.bioconductor.org/packages/spikeLI
git_branch: RELEASE_3_13
git_last_commit: 5adc5e8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/spikeLI_2.52.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/spikeLI_2.52.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/spikeLI_2.52.0.tgz
vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf
vignetteTitles: spikeLI
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 4
Package: spkTools
Version: 1.48.0
Depends: R (>= 2.7.0), Biobase (>= 2.5.5)
Imports: Biobase (>= 2.5.5), graphics, grDevices, gtools, methods,
RColorBrewer, stats, utils
Suggests: xtable
License: GPL (>= 2)
MD5sum: 7936091fb2538cab6fca3d6065dee8a2
NeedsCompilation: no
Title: Methods for Spike-in Arrays
Description: The package contains functions that can be used to compare
expression measures on different array platforms.
biocViews: Software, Technology, Microarray
Author: Matthew N McCall <mccallm@gmail.com>, Rafael A Irizarry
<rafa@jhu.edu>
Maintainer: Matthew N McCall <mccallm@gmail.com>
URL: http://bioconductor.org
git_url: https://git.bioconductor.org/packages/spkTools
git_branch: RELEASE_3_13
git_last_commit: 71225ca
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/spkTools_1.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/spkTools_1.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/spkTools_1.48.0.tgz
vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf
vignetteTitles: spkTools: Spike-in Data Analysis and Visualization
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/spkTools/inst/doc/spkDoc.R
dependencyCount: 10
Package: splatter
Version: 1.16.1
Depends: R (>= 4.0), SingleCellExperiment
Imports: BiocGenerics, BiocParallel, checkmate (>= 2.0.0), edgeR,
fitdistrplus, ggplot2, locfit, matrixStats, methods, scales,
scater (>= 1.15.16), stats, SummarizedExperiment, utils,
crayon, S4Vectors, grDevices
Suggests: BiocStyle, covr, cowplot, magick, knitr, limSolve, lme4,
progress, pscl, testthat, preprocessCore, rmarkdown, scDD,
scran, mfa, phenopath, BASiCS (>= 1.7.10), zinbwave, SparseDC,
BiocManager, spelling, igraph, scuttle, BiocSingular,
VariantAnnotation, Biostrings, GenomeInfoDb, GenomicRanges,
IRanges
License: GPL-3 + file LICENSE
MD5sum: b99e91b551cc4dbb6a71d5850768e236
NeedsCompilation: no
Title: Simple Simulation of Single-cell RNA Sequencing Data
Description: Splatter is a package for the simulation of single-cell
RNA sequencing count data. It provides a simple interface for
creating complex simulations that are reproducible and
well-documented. Parameters can be estimated from real data and
functions are provided for comparing real and simulated
datasets.
biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression,
Sequencing, Software, ImmunoOncology
Author: Luke Zappia [aut, cre]
(<https://orcid.org/0000-0001-7744-8565>), Belinda Phipson
[aut] (<https://orcid.org/0000-0002-1711-7454>), Christina
Azodi [ctb] (<https://orcid.org/0000-0002-6097-606X>), Alicia
Oshlack [aut] (<https://orcid.org/0000-0001-9788-5690>)
Maintainer: Luke Zappia <luke@lazappi.id.au>
URL: https://github.com/Oshlack/splatter
VignetteBuilder: knitr
BugReports: https://github.com/Oshlack/splatter/issues
git_url: https://git.bioconductor.org/packages/splatter
git_branch: RELEASE_3_13
git_last_commit: 62da653
git_last_commit_date: 2021-05-20
Date/Publication: 2021-05-20
source.ver: src/contrib/splatter_1.16.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/splatter_1.16.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/splatter_1.16.1.tgz
vignettes: vignettes/splatter/inst/doc/splat_params.html,
vignettes/splatter/inst/doc/splatPop.html,
vignettes/splatter/inst/doc/splatter.html
vignetteTitles: Splat simulation parameters, splatPop simulation, An
introduction to the Splatter package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/splatter/inst/doc/splat_params.R,
vignettes/splatter/inst/doc/splatPop.R,
vignettes/splatter/inst/doc/splatter.R
importsMe: digitalDLSorteR
suggestsMe: NewWave, scone, scPCA, SummarizedBenchmark, bcTSNE
dependencyCount: 89
Package: SplicingFactory
Version: 1.0.3
Depends: R (>= 4.1)
Imports: SummarizedExperiment, methods, stats
Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr
License: GPL-3 + file LICENSE
MD5sum: 79ab8a80c0a6d9946747a9fa7a143515
NeedsCompilation: no
Title: Splicing Diversity Analysis for Transcriptome Data
Description: The SplicingFactory R package uses transcript-level
expression values to analyze splicing diversity based on
various statistical measures, like Shannon entropy or the Gini
index. These measures can quantify transcript isoform diversity
within samples or between conditions. Additionally, the package
analyzes the isoform diversity data, looking for significant
changes between conditions.
biocViews: Transcriptomics, RNASeq, DifferentialSplicing,
AlternativeSplicing, TranscriptomeVariant
Author: Peter A. Szikora [aut], Tamas Por [aut], Endre Sebestyen [aut,
cre] (<https://orcid.org/0000-0001-5470-2161>)
Maintainer: Endre Sebestyen <endre.sebestyen@gmail.com>
URL: https://github.com/SU-CompBio/SplicingFactory
VignetteBuilder: knitr
BugReports: https://github.com/SU-CompBio/SplicingFactory/issues
git_url: https://git.bioconductor.org/packages/SplicingFactory
git_branch: RELEASE_3_13
git_last_commit: 3e989bd
git_last_commit_date: 2021-06-22
Date/Publication: 2021-06-24
source.ver: src/contrib/SplicingFactory_1.0.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SplicingFactory_1.0.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/SplicingFactory_1.0.3.tgz
vignettes: vignettes/SplicingFactory/inst/doc/SplicingFactory.html
vignetteTitles: SplicingFactory
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SplicingFactory/inst/doc/SplicingFactory.R
dependencyCount: 26
Package: SplicingGraphs
Version: 1.32.0
Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22),
Rgraphviz (>= 2.3.7)
Imports: methods, utils, graphics, igraph, BiocGenerics, S4Vectors (>=
0.17.5), BiocParallel, IRanges (>= 2.21.2), GenomeInfoDb,
GenomicRanges (>= 1.23.21), GenomicFeatures, Rsamtools,
GenomicAlignments, graph, Rgraphviz
Suggests: igraph, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene,
RNAseqData.HNRNPC.bam.chr14, RUnit
License: Artistic-2.0
MD5sum: 1d8c19588396d7b359a8657afbeee3a1
NeedsCompilation: no
Title: Create, manipulate, visualize splicing graphs, and assign
RNA-seq reads to them
Description: This package allows the user to create, manipulate, and
visualize splicing graphs and their bubbles based on a gene
model for a given organism. Additionally it allows the user to
assign RNA-seq reads to the edges of a set of splicing graphs,
and to summarize them in different ways.
biocViews: Genetics, Annotation, DataRepresentation, Visualization,
Sequencing, RNASeq, GeneExpression, AlternativeSplicing,
Transcription, ImmunoOncology
Author: D. Bindreither, M. Carlson, M. Morgan, H. Pagès
Maintainer: H. Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/SplicingGraphs
BugReports: https://github.com/Bioconductor/SplicingGraphs/issues
git_url: https://git.bioconductor.org/packages/SplicingGraphs
git_branch: RELEASE_3_13
git_last_commit: 6066f74
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SplicingGraphs_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SplicingGraphs_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SplicingGraphs_1.32.0.tgz
vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf
vignetteTitles: Splicing graphs and RNA-seq data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.R
dependencyCount: 99
Package: splineTimeR
Version: 1.20.0
Depends: R (>= 3.3), Biobase, igraph, limma, GSEABase, gtools, splines,
GeneNet (>= 1.2.13), longitudinal (>= 1.1.12), FIs
Suggests: knitr
License: GPL-3
MD5sum: 7bb1b74dfe5fe9d4da4c6e7b937cc769
NeedsCompilation: no
Title: Time-course differential gene expression data analysis using
spline regression models followed by gene association network
reconstruction
Description: This package provides functions for differential gene
expression analysis of gene expression time-course data.
Natural cubic spline regression models are used. Identified
genes may further be used for pathway enrichment analysis
and/or the reconstruction of time dependent gene regulatory
association networks.
biocViews: GeneExpression, DifferentialExpression, TimeCourse,
Regression, GeneSetEnrichment, NetworkEnrichment,
NetworkInference, GraphAndNetwork
Author: Agata Michna
Maintainer: Herbert Braselmann <hbraselmann@online.de>, Martin
Selmansberger <martin.selmansberger@helmholtz-muenchen.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/splineTimeR
git_branch: RELEASE_3_13
git_last_commit: 8c36a77
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/splineTimeR_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/splineTimeR_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/splineTimeR_1.20.0.tgz
vignettes: vignettes/splineTimeR/inst/doc/splineTimeR.pdf
vignetteTitles: splineTimeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/splineTimeR/inst/doc/splineTimeR.R
dependencyCount: 64
Package: SPLINTER
Version: 1.18.0
Depends: R (>= 3.6.0), grDevices, stats
Imports: graphics, ggplot2, seqLogo, Biostrings, biomaRt,
GenomicAlignments, GenomicRanges, GenomicFeatures, Gviz,
IRanges, S4Vectors, GenomeInfoDb, utils, plyr,stringr, methods,
BSgenome.Mmusculus.UCSC.mm9, googleVis
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-2
MD5sum: fcdb2d46a368e6252f06584d1641c946
NeedsCompilation: no
Title: Splice Interpreter of Transcripts
Description: Provides tools to analyze alternative splicing sites,
interpret outcomes based on sequence information, select and
design primers for site validiation and give visual
representation of the event to guide downstream experiments.
biocViews: ImmunoOncology, GeneExpression, RNASeq, Visualization,
AlternativeSplicing
Author: Diana Low [aut, cre]
Maintainer: Diana Low <lowdiana@gmail.com>
URL: https://github.com/dianalow/SPLINTER/
VignetteBuilder: knitr
BugReports: https://github.com/dianalow/SPLINTER/issues
git_url: https://git.bioconductor.org/packages/SPLINTER
git_branch: RELEASE_3_13
git_last_commit: 9bb1aee
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SPLINTER_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SPLINTER_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SPLINTER_1.18.0.tgz
vignettes: vignettes/SPLINTER/inst/doc/vignette.pdf
vignetteTitles: SPLINTER
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SPLINTER/inst/doc/vignette.R
dependencyCount: 146
Package: splots
Version: 1.58.0
Imports: grid, RColorBrewer
Suggests: BiocStyle, knitr, rmarkdown, assertthat, HD2013SGI
License: LGPL
MD5sum: bdc3a3a788ff017916c1e8feacd6ef63
NeedsCompilation: no
Title: Visualization of high-throughput assays in microtitre plate or
slide format
Description: This package is provided to support legacy code and
reverse dependencies, but it should not be used as a dependency
for new code development. It provides a single function,
plotScreen, for visualising data in microtitre plate or slide
format. As a better alternative for such functionality, please
consider the platetools package on CRAN
(https://cran.r-project.org/package=platetools and
https://github.com/Swarchal/platetools), or generic ggplot2
graphics functionality.
biocViews: Visualization, Sequencing, MicrotitrePlateAssay
Author: Wolfgang Huber, Oleg Sklyar
Maintainer: Wolfgang Huber <wolfgang.huber@embl.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/splots
git_branch: RELEASE_3_13
git_last_commit: ed2585e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/splots_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/splots_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/splots_1.58.0.tgz
vignettes: vignettes/splots/inst/doc/splots.html
vignetteTitles: splots: visualization of data from assays in microtitre
plate or slide format
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/splots/inst/doc/splots.R
dependsOnMe: cellHTS2, HD2013SGI
importsMe: RNAinteract
dependencyCount: 2
Package: SPONGE
Version: 1.14.0
Depends: R (>= 3.4)
Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table,
MASS, expm, gRbase, glmnet, igraph, iterators,
Suggests: testthat, knitr, rmarkdown, visNetwork, ggplot2, ggrepel,
gridExtra, digest, doParallel, bigmemory
License: GPL (>=3)
MD5sum: 683ca85c876b673c4d303e8042a49b1b
NeedsCompilation: no
Title: Sparse Partial Correlations On Gene Expression
Description: This package provides methods to efficiently detect
competitive endogeneous RNA interactions between two genes.
Such interactions are mediated by one or several miRNAs such
that both gene and miRNA expression data for a larger number of
samples is needed as input.
biocViews: GeneExpression, Transcription, GeneRegulation,
NetworkInference, Transcriptomics, SystemsBiology, Regression
Author: Markus List, Azim Dehghani Amirabad, Dennis Kostka, Marcel H.
Schulz
Maintainer: Markus List <markus.list@wzw.tum.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SPONGE
git_branch: RELEASE_3_13
git_last_commit: 30cc6aa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SPONGE_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SPONGE_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SPONGE_1.14.0.tgz
vignettes: vignettes/SPONGE/inst/doc/SPONGE.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SPONGE/inst/doc/SPONGE.R
dependencyCount: 38
Package: spqn
Version: 1.4.0
Depends: R (>= 4.0), ggplot2, ggridges, SummarizedExperiment,
BiocGenerics
Imports: graphics, stats, utils, matrixStats
Suggests: BiocStyle, knitr, rmarkdown, tools, spqnData (>= 0.99.3),
RUnit
License: Artistic-2.0
MD5sum: a50f08c56ab3f724d233d19e4d4fdc9a
NeedsCompilation: no
Title: Spatial quantile normalization
Description: The spqn package implements spatial quantile normalization
(SpQN). This method was developed to remove a mean-correlation
relationship in correlation matrices built from gene expression
data. It can serve as pre-processing step prior to a
co-expression analysis.
biocViews: NetworkInference, GraphAndNetwork, Normalization
Author: Yi Wang [cre, aut], Kasper Daniel Hansen [aut]
Maintainer: Yi Wang <yiwangthu5@gmail.com>
URL: https://github.com/hansenlab/spqn
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/spqn/issues
git_url: https://git.bioconductor.org/packages/spqn
git_branch: RELEASE_3_13
git_last_commit: 9360415
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/spqn_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/spqn_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/spqn_1.4.0.tgz
vignettes: vignettes/spqn/inst/doc/spqn.html
vignetteTitles: spqn User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/spqn/inst/doc/spqn.R
dependencyCount: 59
Package: SPsimSeq
Version: 1.2.0
Depends: R (>= 4.0)
Imports: stats, methods, SingleCellExperiment, fitdistrplus, graphics,
edgeR, Hmisc, WGCNA, limma, mvtnorm, phyloseq, utils
Suggests: knitr, rmarkdown, LSD, testthat, BiocStyle
License: GPL-2
MD5sum: e35a1b8bccde92858f94dd0337dd222c
NeedsCompilation: no
Title: Semi-parametric simulation tool for bulk and single-cell RNA
sequencing data
Description: SPsimSeq uses a specially designed exponential family for
density estimation to constructs the distribution of gene
expression levels from a given real RNA sequencing data
(single-cell or bulk), and subsequently simulates a new dataset
from the estimated marginal distributions using
Gaussian-copulas to retain the dependence between genes. It
allows simulation of multiple groups and batches with any
required sample size and library size.
biocViews: GeneExpression, RNASeq, SingleCell, Sequencing, DNASeq
Author: Alemu Takele Assefa [aut], Olivier Thas [ths], Joris Meys
[cre], Stijn Hawinkel [aut]
Maintainer: Joris Meys <Joris.Meys@ugent.be>
URL: https://github.com/CenterForStatistics-UGent/SPsimSeq
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SPsimSeq
git_branch: RELEASE_3_13
git_last_commit: 069529a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SPsimSeq_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SPsimSeq_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SPsimSeq_1.2.0.tgz
vignettes: vignettes/SPsimSeq/inst/doc/SPsimSeq.html
vignetteTitles: Manual for the SPsimSeq package: semi-parametric
simulation for bulk and single cell RNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SPsimSeq/inst/doc/SPsimSeq.R
dependencyCount: 134
Package: SQLDataFrame
Version: 1.6.0
Depends: R (>= 3.6), dplyr (>= 0.8.0.1), dbplyr (>= 1.4.0), S4Vectors
Imports: DBI, lazyeval, methods, tools, stats, BiocGenerics, RSQLite,
tibble
Suggests: RMySQL, bigrquery, testthat, knitr, rmarkdown, DelayedArray
License: GPL-3
MD5sum: faf88a031df778aa27c6c74df1cdac06
NeedsCompilation: no
Title: Representation of SQL database in DataFrame metaphor
Description: SQLDataFrame is developed to lazily represent and
efficiently analyze SQL-based tables in _R_. SQLDataFrame
supports common and familiar 'DataFrame' operations such as '['
subsetting, rbind, cbind, etc.. The internal implementation is
based on the widely adopted dplyr grammar and SQL commands.
In-memory datasets or plain text files (.txt, .csv, etc.) could
also be easily converted into SQLDataFrames objects (which
generates a new database on-disk).
biocViews: Infrastructure, DataRepresentation
Author: Qian Liu [aut, cre] (<https://orcid.org/0000-0003-1456-5099>),
Martin Morgan [aut]
Maintainer: Qian Liu <qian.liu@roswellpark.org>
URL: https://github.com/Bioconductor/SQLDataFrame
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/SQLDataFrame/issues
git_url: https://git.bioconductor.org/packages/SQLDataFrame
git_branch: RELEASE_3_13
git_last_commit: f033f7b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SQLDataFrame_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SQLDataFrame_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SQLDataFrame_1.6.0.tgz
vignettes: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.html,
vignettes/SQLDataFrame/inst/doc/SQLDataFrame.html
vignetteTitles: SQLDataFrame Internal Implementation, SQLDataFrame:
Lazy representation of SQL database in DataFrame metaphor
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.R,
vignettes/SQLDataFrame/inst/doc/SQLDataFrame.R
dependencyCount: 42
Package: SQUADD
Version: 1.42.0
Depends: R (>= 2.11.0)
Imports: graphics, grDevices, methods, RColorBrewer, stats, utils
License: GPL (>=2)
MD5sum: 43c86c005b004ccda84f99812055c449
NeedsCompilation: no
Title: Add-on of the SQUAD Software
Description: This package SQUADD is a SQUAD add-on. It permits to
generate SQUAD simulation matrix, prediction Heat-Map and
Correlation Circle from PCA analysis.
biocViews: GraphAndNetwork, Network, Visualization
Author: Martial Sankar, supervised by Christian Hardtke and Ioannis
Xenarios
Maintainer: Martial Sankar <martial.sankar@sib.swiss>
URL: http://www.unil.ch/dbmv/page21142_en.html
git_url: https://git.bioconductor.org/packages/SQUADD
git_branch: RELEASE_3_13
git_last_commit: 4750b2f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SQUADD_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SQUADD_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SQUADD_1.42.0.tgz
vignettes: vignettes/SQUADD/inst/doc/SQUADD_ERK.pdf,
vignettes/SQUADD/inst/doc/SQUADD.pdf
vignetteTitles: SQUADD ERK exemple, SQUADD HOW-TO
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SQUADD/inst/doc/SQUADD_ERK.R,
vignettes/SQUADD/inst/doc/SQUADD.R
dependencyCount: 6
Package: sRACIPE
Version: 1.8.0
Depends: R (>= 3.6.0),SummarizedExperiment, methods, Rcpp
Imports: ggplot2, reshape2, MASS, RColorBrewer, gridExtra,visNetwork,
gplots, umap, htmlwidgets, S4Vectors, BiocGenerics, grDevices,
stats, utils, graphics
LinkingTo: Rcpp
Suggests: knitr, BiocStyle, rmarkdown, tinytest, doFuture
License: MIT + file LICENSE
MD5sum: 83ae8b8a50780210f0ab86e5142eb7e3
NeedsCompilation: yes
Title: Systems biology tool to simulate gene regulatory circuits
Description: sRACIPE implements a randomization-based method for gene
circuit modeling. It allows us to study the effect of both the
gene expression noise and the parametric variation on any gene
regulatory circuit (GRC) using only its topology, and simulates
an ensemble of models with random kinetic parameters at
multiple noise levels. Statistical analysis of the generated
gene expressions reveals the basin of attraction and stability
of various phenotypic states and their changes associated with
intrinsic and extrinsic noises. sRACIPE provides a holistic
picture to evaluate the effects of both the stochastic nature
of cellular processes and the parametric variation.
biocViews: ResearchField, SystemsBiology, MathematicalBiology,
GeneExpression, GeneRegulation, GeneTarget
Author: Vivek Kohar [aut, cre]
(<https://orcid.org/0000-0003-1813-1597>), Mingyang Lu [aut]
Maintainer: Vivek Kohar <vivek.kohar@gmail.com>
URL: https://vivekkohar.github.io/sRACIPE/,
https://github.com/vivekkohar/sRACIPE, https://geneex.jax.org/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sRACIPE
git_branch: RELEASE_3_13
git_last_commit: ba2d598
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sRACIPE_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sRACIPE_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sRACIPE_1.8.0.tgz
vignettes: vignettes/sRACIPE/inst/doc/sRACIPE.html
vignetteTitles: A systems biology tool for gene regulatory circuit
simulation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sRACIPE/inst/doc/sRACIPE.R
dependencyCount: 84
Package: SRAdb
Version: 1.54.0
Depends: RSQLite, graph, RCurl
Imports: GEOquery
Suggests: Rgraphviz
License: Artistic-2.0
MD5sum: 3d1bf04fbed86cdaab9bdcaeea8b784a
NeedsCompilation: no
Title: A compilation of metadata from NCBI SRA and tools
Description: The Sequence Read Archive (SRA) is the largest public
repository of sequencing data from the next generation of
sequencing platforms including Roche 454 GS System, Illumina
Genome Analyzer, Applied Biosystems SOLiD System, Helicos
Heliscope, and others. However, finding data of interest can be
challenging using current tools. SRAdb is an attempt to make
access to the metadata associated with submission, study,
sample, experiment and run much more feasible. This is
accomplished by parsing all the NCBI SRA metadata into a SQLite
database that can be stored and queried locally. Fulltext
search in the package make querying metadata very flexible and
powerful. fastq and sra files can be downloaded for doing
alignment locally. Beside ftp protocol, the SRAdb has funcitons
supporting fastp protocol (ascp from Aspera Connect) for faster
downloading large data files over long distance. The SQLite
database is updated regularly as new data is added to SRA and
can be downloaded at will for the most up-to-date metadata.
biocViews: Infrastructure, Sequencing, DataImport
Author: Jack Zhu and Sean Davis
Maintainer: Jack Zhu <zhujack@mail.nih.gov>
URL: http://gbnci.abcc.ncifcrf.gov/sra/
BugReports: https://github.com/seandavi/SRAdb/issues/new
git_url: https://git.bioconductor.org/packages/SRAdb
git_branch: RELEASE_3_13
git_last_commit: d4df032
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SRAdb_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SRAdb_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SRAdb_1.54.0.tgz
vignettes: vignettes/SRAdb/inst/doc/SRAdb.pdf
vignetteTitles: Using SRAdb to Query the Sequence Read Archive
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SRAdb/inst/doc/SRAdb.R
suggestsMe: parathyroidSE
dependencyCount: 61
Package: srnadiff
Version: 1.12.2
Depends: R (>= 3.6)
Imports: Rcpp (>= 0.12.8), methods, devtools, S4Vectors, GenomeInfoDb,
rtracklayer, SummarizedExperiment, IRanges, GenomicRanges,
DESeq2, edgeR, baySeq, Rsamtools, GenomicFeatures,
GenomicAlignments, grDevices, Gviz, BiocParallel, BiocStyle,
BiocManager
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat, BiocManager, BiocStyle
License: GPL-3
MD5sum: 16584d03cad15dd0b0cd83e000bee66f
NeedsCompilation: yes
Title: Finding differentially expressed unannotated genomic regions
from RNA-seq data
Description: srnadiff is a package that finds differently expressed
regions from RNA-seq data at base-resolution level without
relying on existing annotation. To do so, the package
implements the identify-then-annotate methodology that builds
on the idea of combining two pipelines approachs differential
expressed regions detection and differential expression
quantification. It reads BAM files as input, and outputs a list
differentially regions, together with the adjusted p-values.
biocViews: ImmunoOncology, GeneExpression, Coverage, SmallRNA,
Epigenetics, StatisticalMethod, Preprocessing,
DifferentialExpression
Author: Zytnicki Matthias [aut, cre], Gonzalez Ignacio [aut]
Maintainer: Zytnicki Matthias <matthias.zytnicki@inra.fr>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/srnadiff
git_branch: RELEASE_3_13
git_last_commit: 21bcbca
git_last_commit_date: 2021-06-02
Date/Publication: 2021-06-03
source.ver: src/contrib/srnadiff_1.12.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/srnadiff_1.11.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/srnadiff_1.12.2.tgz
vignettes: vignettes/srnadiff/inst/doc/srnadiff.html
vignetteTitles: The srnadiff package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/srnadiff/inst/doc/srnadiff.R
dependencyCount: 192
Package: sscore
Version: 1.64.0
Depends: R (>= 1.8.0), affy, affyio
Suggests: affydata
License: GPL (>= 2)
MD5sum: e407a4a3d1781c400532b5824988cc50
NeedsCompilation: no
Title: S-Score Algorithm for Affymetrix Oligonucleotide Microarrays
Description: This package contains an implementation of the S-Score
algorithm as described by Zhang et al (2002).
biocViews: DifferentialExpression
Author: Richard Kennedy <rkennedy@ms.soph.uab.edu>, based on C++ code
from Li Zhang <zhangli@odin.mdacc.tmc.edu> and Borland Delphi
code from Robnet Kerns <rtkerns@vcu.edu>.
Maintainer: Richard Kennedy <rkennedy@ms.soph.uab.edu>
URL: http://home.att.net/~richard-kennedy/professional.html
git_url: https://git.bioconductor.org/packages/sscore
git_branch: RELEASE_3_13
git_last_commit: cb449f5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sscore_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sscore_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sscore_1.64.0.tgz
vignettes: vignettes/sscore/inst/doc/sscore.pdf
vignetteTitles: SScore primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sscore/inst/doc/sscore.R
dependencyCount: 13
Package: sscu
Version: 2.22.0
Depends: R (>= 3.3)
Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>=
0.16.1)
Suggests: knitr, rmarkdown
License: GPL (>= 2)
MD5sum: 3acaf959952f008ef66d0c4d5b89f9dd
NeedsCompilation: no
Title: Strength of Selected Codon Usage
Description: The package calculates the indexes for selective stength
in codon usage in bacteria species. (1) The package can
calculate the strength of selected codon usage bias (sscu, also
named as s_index) based on Paul Sharp's method. The method take
into account of background mutation rate, and focus only on
four pairs of codons with universal translational advantages in
all bacterial species. Thus the sscu index is comparable among
different species. (2) The package can detect the strength of
translational accuracy selection by Akashi's test. The test
tabulating all codons into four categories with the feature as
conserved/variable amino acids and optimal/non-optimal codons.
(3) Optimal codon lists (selected codons) can be calculated by
either op_highly function (by using the highly expressed genes
compared with all genes to identify optimal codons), or
op_corre_CodonW/op_corre_NCprime function (by correlative
method developed by Hershberg & Petrov). Users will have a list
of optimal codons for further analysis, such as input to the
Akashi's test. (4) The detailed codon usage information, such
as RSCU value, number of optimal codons in the highly/all gene
set, as well as the genomic gc3 value, can be calculate by the
optimal_codon_statistics and genomic_gc3 function. (5)
Furthermore, we added one test function low_frequency_op in the
package. The function try to find the low frequency optimal
codons, among all the optimal codons identified by the
op_highly function.
biocViews: Genetics, GeneExpression, WholeGenome
Author: Yu Sun
Maintainer: Yu Sun <sunyu1357@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sscu
git_branch: RELEASE_3_13
git_last_commit: 932927e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sscu_2.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sscu_2.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sscu_2.22.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 27
Package: sSeq
Version: 1.30.0
Depends: R (>= 3.0), caTools, RColorBrewer
License: GPL (>= 3)
MD5sum: 5fca21dbb85c386db77380933ec87b75
NeedsCompilation: no
Title: Shrinkage estimation of dispersion in Negative Binomial models
for RNA-seq experiments with small sample size
Description: The purpose of this package is to discover the genes that
are differentially expressed between two conditions in RNA-seq
experiments. Gene expression is measured in counts of
transcripts and modeled with the Negative Binomial (NB)
distribution using a shrinkage approach for dispersion
estimation. The method of moment (MM) estimates for dispersion
are shrunk towards an estimated target, which minimizes the
average squared difference between the shrinkage estimates and
the initial estimates. The exact per-gene probability under the
NB model is calculated, and used to test the hypothesis that
the expected expression of a gene in two conditions identically
follow a NB distribution.
biocViews: ImmunoOncology, RNASeq
Author: Danni Yu <dyu@purdue.edu>, Wolfgang Huber <whuber@embl.de> and
Olga Vitek <ovitek@purdue.edu>
Maintainer: Danni Yu <dyu@purdue.edu>
git_url: https://git.bioconductor.org/packages/sSeq
git_branch: RELEASE_3_13
git_last_commit: 4770dd4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sSeq_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sSeq_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sSeq_1.30.0.tgz
vignettes: vignettes/sSeq/inst/doc/sSeq.pdf
vignetteTitles: sSeq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sSeq/inst/doc/sSeq.R
importsMe: MLSeq
dependencyCount: 3
Package: ssize
Version: 1.66.0
Depends: gdata, xtable
License: LGPL
Archs: i386, x64
MD5sum: 78d4331830d68690c64c6347279d649d
NeedsCompilation: no
Title: Estimate Microarray Sample Size
Description: Functions for computing and displaying sample size
information for gene expression arrays.
biocViews: Microarray, DifferentialExpression
Author: Gregory R. Warnes, Peng Liu, and Fasheng Li
Maintainer: Gregory R. Warnes <greg@random-technologies-llc.com>
git_url: https://git.bioconductor.org/packages/ssize
git_branch: RELEASE_3_13
git_last_commit: 5d7c39b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ssize_1.66.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ssize_1.66.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ssize_1.66.0.tgz
vignettes: vignettes/ssize/inst/doc/ssize.pdf
vignetteTitles: Sample Size Estimation for Microarray Experiments Using
the \code{ssize} package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ssize/inst/doc/ssize.R
importsMe: maGUI
dependencyCount: 6
Package: ssPATHS
Version: 1.6.0
Depends: R (>= 3.5.0), SummarizedExperiment
Imports: ROCR, dml, MESS
Suggests: ggplot2, testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: 2b73ccc7b3d598ecd4bde53e2b89b0a2
NeedsCompilation: no
Title: ssPATHS: Single Sample PATHway Score
Description: This package generates pathway scores from expression data
for single samples after training on a reference cohort. The
score is generated by taking the expression of a gene set
(pathway) from a reference cohort and performing linear
discriminant analysis to distinguish samples in the cohort that
have the pathway augmented and not. The separating hyperplane
is then used to score new samples.
biocViews: Software, GeneExpression, BiomedicalInformatics, RNASeq,
Pathways, Transcriptomics, DimensionReduction, Classification
Author: Natalie R. Davidson
Maintainer: Natalie R. Davidson <natalie.davidson@inf.ethz.ch>
git_url: https://git.bioconductor.org/packages/ssPATHS
git_branch: RELEASE_3_13
git_last_commit: 32ea581
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ssPATHS_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ssPATHS_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ssPATHS_1.6.0.tgz
vignettes: vignettes/ssPATHS/inst/doc/ssPATHS.pdf
vignetteTitles: Using ssPATHS
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ssPATHS/inst/doc/ssPATHS.R
dependencyCount: 110
Package: ssrch
Version: 1.8.1
Depends: R (>= 3.6), methods
Imports: shiny, DT, utils
Suggests: knitr, testthat, rmarkdown
License: Artistic-2.0
MD5sum: 5f16a58680dcf8805999cce85f006167
NeedsCompilation: no
Title: a simple search engine
Description: Demonstrate tokenization and a search gadget for
collections of CSV files.
biocViews: Infrastructure
Author: Vince Carey
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ssrch
git_branch: RELEASE_3_13
git_last_commit: 104d1ae
git_last_commit_date: 2021-07-28
Date/Publication: 2021-07-29
source.ver: src/contrib/ssrch_1.8.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ssrch_1.8.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ssrch_1.8.1.tgz
vignettes: vignettes/ssrch/inst/doc/ssrch.html
vignetteTitles: ssrch: small search engine
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ssrch/inst/doc/ssrch.R
importsMe: HumanTranscriptomeCompendium
dependencyCount: 40
Package: ssviz
Version: 1.26.0
Depends: R (>=
2.15.1),methods,Rsamtools,Biostrings,reshape,ggplot2,RColorBrewer,stats
Suggests: knitr
License: GPL-2
Archs: i386, x64
MD5sum: eb14c049b6909ee23867606da8f4061c
NeedsCompilation: no
Title: A small RNA-seq visualizer and analysis toolkit
Description: Small RNA sequencing viewer
biocViews: ImmunoOncology,
Sequencing,RNASeq,Visualization,MultipleComparison,Genetics
Author: Diana Low
Maintainer: Diana Low <lowdiana@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ssviz
git_branch: RELEASE_3_13
git_last_commit: 4bc179f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ssviz_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ssviz_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ssviz_1.26.0.tgz
vignettes: vignettes/ssviz/inst/doc/ssviz.pdf
vignetteTitles: ssviz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ssviz/inst/doc/ssviz.R
dependencyCount: 64
Package: stageR
Version: 1.14.0
Depends: R (>= 3.4), SummarizedExperiment
Imports: methods, stats
Suggests: knitr, rmarkdown, BiocStyle, methods, Biobase, edgeR, limma,
DEXSeq, testthat
License: GNU General Public License version 3
MD5sum: fafad71ca1b27f60f3b6e049a34c7c4b
NeedsCompilation: no
Title: stageR: stage-wise analysis of high throughput gene expression
data in R
Description: The stageR package allows automated stage-wise analysis of
high-throughput gene expression data. The method is published
in Genome Biology at
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0
biocViews: Software, StatisticalMethod
Author: Koen Van den Berge and Lieven Clement
Maintainer: Koen Van den Berge <koen.vdberge@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/stageR
git_branch: RELEASE_3_13
git_last_commit: f1df150
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/stageR_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/stageR_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/stageR_1.14.0.tgz
vignettes: vignettes/stageR/inst/doc/stageRVignette.html
vignetteTitles: stageR: stage-wise analysis of high-throughput gene
expression data in R
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/stageR/inst/doc/stageRVignette.R
dependsOnMe: rnaseqDTU
suggestsMe: MethReg, satuRn
dependencyCount: 26
Package: STAN
Version: 2.20.0
Depends: methods, poilog, parallel
Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb,
Gviz, Rsolnp
Suggests: BiocStyle, gplots, knitr
License: GPL (>= 2)
MD5sum: bb98dd9b49f43f6a475af9e928d8f47f
NeedsCompilation: yes
Title: The Genomic STate ANnotation Package
Description: Genome segmentation with hidden Markov models has become a
useful tool to annotate genomic elements, such as promoters and
enhancers. STAN (genomic STate ANnotation) implements
(bidirectional) hidden Markov models (HMMs) using a variety of
different probability distributions, which can model a wide
range of current genomic data (e.g. continuous, discrete,
binary). STAN de novo learns and annotates the genome into a
given number of 'genomic states'. The 'genomic states' may for
instance reflect distinct genome-associated protein complexes
(e.g. 'transcription states') or describe recurring patterns of
chromatin features (referred to as 'chromatin states'). Unlike
other tools, STAN also allows for the integration of
strand-specific (e.g. RNA) and non-strand-specific data (e.g.
ChIP).
biocViews: HiddenMarkovModel, GenomeAnnotation, Microarray, Sequencing,
ChIPSeq, RNASeq, ChipOnChip, Transcription, ImmunoOncology
Author: Benedikt Zacher, Julia Ertl, Rafael Campos-Martin, Julien
Gagneur, Achim Tresch
Maintainer: Rafael Campos-Martin <campos@mpipz.mpg.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/STAN
git_branch: RELEASE_3_13
git_last_commit: 9c7a3b4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/STAN_2.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/STAN_2.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/STAN_2.20.0.tgz
vignettes: vignettes/STAN/inst/doc/STAN-knitr.pdf
vignetteTitles: The genomic STate ANnotation package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/STAN/inst/doc/STAN-knitr.R
dependencyCount: 145
Package: staRank
Version: 1.34.0
Depends: methods, cellHTS2, R (>= 2.10)
License: GPL
MD5sum: d57c9c8d51937fc5fc0ab7122a0e538a
NeedsCompilation: no
Title: Stability Ranking
Description: Detecting all relevant variables from a data set is
challenging, especially when only few samples are available and
data is noisy. Stability ranking provides improved variable
rankings of increased robustness using resampling or
subsampling.
biocViews: ImmunoOncology, MultipleComparison, CellBiology,
CellBasedAssays, MicrotitrePlateAssay
Author: Juliane Siebourg, Niko Beerenwinkel
Maintainer: Juliane Siebourg <juliane.siebourg@bsse.ethz.ch>
git_url: https://git.bioconductor.org/packages/staRank
git_branch: RELEASE_3_13
git_last_commit: 7703706
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/staRank_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/staRank_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/staRank_1.34.0.tgz
vignettes: vignettes/staRank/inst/doc/staRank.pdf
vignetteTitles: Using staRank
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/staRank/inst/doc/staRank.R
dependencyCount: 92
Package: StarBioTrek
Version: 1.18.0
Depends: R (>= 3.3)
Imports: SpidermiR, graphite, AnnotationDbi, e1071, ROCR, MLmetrics,
grDevices, igraph, reshape2, ggplot2
Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2,
qgraph, png, grid
License: GPL (>= 3)
MD5sum: b7ff7a58c036d4d8e3be42416ccc7486
NeedsCompilation: no
Title: StarBioTrek
Description: This tool StarBioTrek presents some methodologies to
measure pathway activity and cross-talk among pathways
integrating also the information of network data.
biocViews: GeneRegulation, Network, Pathways, KEGG
Author: Claudia Cava, Isabella Castiglioni
Maintainer: Claudia Cava <claudia.cava@ibfm.cnr.it>
URL: https://github.com/claudiacava/StarBioTrek
VignetteBuilder: knitr
BugReports: https://github.com/claudiacava/StarBioTrek/issues
git_url: https://git.bioconductor.org/packages/StarBioTrek
git_branch: RELEASE_3_13
git_last_commit: 4e39230
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/StarBioTrek_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/StarBioTrek_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/StarBioTrek_1.18.0.tgz
vignettes: vignettes/StarBioTrek/inst/doc/StarBioTrek.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/StarBioTrek/inst/doc/StarBioTrek.R
dependencyCount: 188
Package: STATegRa
Version: 1.28.0
Depends: R (>= 2.10)
Imports: Biobase, gridExtra, ggplot2, methods, stats, grid, MASS,
calibrate, gplots, edgeR, limma, foreach, affy
Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>=
1.3), roxygen2, doSNOW
License: GPL-2
MD5sum: fa125ee1437d5c6ccdf721432fa0d201
NeedsCompilation: no
Title: Classes and methods for multi-omics data integration
Description: Classes and tools for multi-omics data integration.
biocViews: Software, StatisticalMethod, Clustering, DimensionReduction,
PrincipalComponent
Author: STATegra Consortia
Maintainer: David Gomez-Cabrero <david.gomezcabrero@ki.se>, Núria
Planell <nuria.planell.picola@navarra.es>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/STATegRa
git_branch: RELEASE_3_13
git_last_commit: 0992626
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/STATegRa_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/STATegRa_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/STATegRa_1.28.0.tgz
vignettes: vignettes/STATegRa/inst/doc/STATegRa.html
vignetteTitles: STATegRa User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R
dependencyCount: 60
Package: statTarget
Version: 1.22.0
Depends: R (>= 3.6.0)
Imports:
randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats,
pls,impute
Suggests: testthat, BiocStyle, knitr, rmarkdown,
gWidgets2,gWidgets2RGtk2,RGtk2
License: LGPL (>= 3)
MD5sum: 704a7e01fb26bc12c98547fde784ba42
NeedsCompilation: no
Title: Statistical Analysis of Molecular Profiles
Description: A streamlined tool provides a graphical user interface for
quality control based signal drift correction (QC-RFSC),
integration of data from multi-batch MS-based experiments, and
the comprehensive statistical analysis in metabolomics and
proteomics.
biocViews: ImmunoOncology, Metabolomics, Proteomics, Machine Learning,
Lipidomics, MassSpectrometry, QualityControl, Normalization,
QC-RFSC, QC-RLSC, ComBat, DifferentialExpression, BatchEffect,
Visualization, MultipleComparison,Preprocessing, GUI, Software
Author: Hemi Luan
Maintainer: Hemi Luan <hemi.luan@gmail.com>
URL: https://stattarget.github.io
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/statTarget
git_branch: RELEASE_3_13
git_last_commit: b41ec46
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/statTarget_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/statTarget_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/statTarget_1.22.0.tgz
vignettes: vignettes/statTarget/inst/doc/Combat.html,
vignettes/statTarget/inst/doc/pathway_analysis.html,
vignettes/statTarget/inst/doc/statTarget.html
vignetteTitles: QC_free approach with Combat method, statTarget2 for
pathway analysis, statTarget2 On using the Graphical User
Interface
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/statTarget/inst/doc/Combat.R,
vignettes/statTarget/inst/doc/pathway_analysis.R,
vignettes/statTarget/inst/doc/statTarget.R
dependencyCount: 30
Package: stepNorm
Version: 1.64.0
Depends: R (>= 1.8.0), marray, methods
Imports: marray, MASS, methods, stats
License: LGPL
MD5sum: 7936ca44e03ba4fd9f3a92d45ba993c2
NeedsCompilation: no
Title: Stepwise normalization functions for cDNA microarrays
Description: Stepwise normalization functions for cDNA microarray data.
biocViews: Microarray, TwoChannel, Preprocessing
Author: Yuanyuan Xiao <yxiao@itsa.ucsf.edu>, Yee Hwa (Jean) Yang
<jean@biostat.ucsf.edu>
Maintainer: Yuanyuan Xiao <yxiao@itsa.ucsf.edu>
URL: http://www.biostat.ucsf.edu/jean/
git_url: https://git.bioconductor.org/packages/stepNorm
git_branch: RELEASE_3_13
git_last_commit: 5cd3b38
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/stepNorm_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/stepNorm_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/stepNorm_1.64.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 8
Package: strandCheckR
Version: 1.10.0
Imports: dplyr, magrittr, GenomeInfoDb, GenomicAlignments,
GenomicRanges, IRanges, Rsamtools, S4Vectors, grid,
BiocGenerics, ggplot2, reshape2, stats, gridExtra,
TxDb.Hsapiens.UCSC.hg38.knownGene, methods, stringr
Suggests: BiocStyle, knitr, testthat
License: GPL (>= 2)
MD5sum: 7b8536c4046860f71511e9ff3b2ec930
NeedsCompilation: no
Title: Calculate strandness information of a bam file
Description: This package aims to quantify and remove putative double
strand DNA from a strand-specific RNA sample. There are also
options and methods to plot the positive/negative proportions
of all sliding windows, which allow users to have an idea of
how much the sample was contaminated and the appropriate
threshold to be used for filtering.
biocViews: RNASeq, Alignment, QualityControl, Coverage, ImmunoOncology
Author: Thu-Hien To [aut, cre], Steve Pederson [aut]
Maintainer: Thu-Hien To <tothuhien@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/strandCheckR
git_branch: RELEASE_3_13
git_last_commit: c7a42a9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/strandCheckR_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/strandCheckR_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/strandCheckR_1.10.0.tgz
vignettes: vignettes/strandCheckR/inst/doc/strandCheckR.html
vignetteTitles: An Introduction To strandCheckR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/strandCheckR/inst/doc/strandCheckR.R
dependencyCount: 114
Package: Streamer
Version: 1.38.0
Imports: methods, graph, RBGL, parallel, BiocGenerics
Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz
License: Artistic-2.0
Archs: i386, x64
MD5sum: 4d31ee9b82b9a24017ea422d84d327ed
NeedsCompilation: yes
Title: Enabling stream processing of large files
Description: Large data files can be difficult to work with in R, where
data generally resides in memory. This package encourages a
style of programming where data is 'streamed' from disk into R
via a `producer' and through a series of `consumers' that,
typically reduce the original data to a manageable size. The
package provides useful Producer and Consumer stream components
for operations such as data input, sampling, indexing, and
transformation; see package?Streamer for details.
biocViews: Infrastructure, DataImport
Author: Martin Morgan, Nishant Gopalakrishnan
Maintainer: Martin Morgan <martin.morgan@roswellpark.org>
git_url: https://git.bioconductor.org/packages/Streamer
git_branch: RELEASE_3_13
git_last_commit: c079c02
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Streamer_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Streamer_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Streamer_1.38.0.tgz
vignettes: vignettes/Streamer/inst/doc/Streamer.pdf
vignetteTitles: Streamer: A simple example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Streamer/inst/doc/Streamer.R
importsMe: plethy
dependencyCount: 10
Package: STRINGdb
Version: 2.4.2
Depends: R (>= 2.14.0)
Imports: png, sqldf, plyr, igraph, RCurl, methods, RColorBrewer,
gplots, hash, plotrix
Suggests: RUnit, BiocGenerics
License: GPL-2
MD5sum: 1646a16a5265db675c1ed168672df068
NeedsCompilation: no
Title: STRINGdb (Search Tool for the Retrieval of Interacting proteins
database)
Description: The STRINGdb package provides a R interface to the STRING
protein-protein interactions database
(https://www.string-db.org).
biocViews: Network
Author: Andrea Franceschini <andrea.franceschini@isb-sib.ch>
Maintainer: Damian Szklarczyk <damian.szklarczyk@imls.uzh.ch>
git_url: https://git.bioconductor.org/packages/STRINGdb
git_branch: RELEASE_3_13
git_last_commit: 38baec2
git_last_commit_date: 2021-09-17
Date/Publication: 2021-09-19
source.ver: src/contrib/STRINGdb_2.4.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/STRINGdb_2.4.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/STRINGdb_2.4.2.tgz
vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf
vignetteTitles: STRINGdb Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/STRINGdb/inst/doc/STRINGdb.R
dependsOnMe: PPInfer
importsMe: coexnet, IMMAN, pwOmics, RITAN, XINA
suggestsMe: epiNEM, GeneNetworkBuilder, martini, netSmooth, PCAN,
protti
dependencyCount: 40
Package: STROMA4
Version: 1.16.0
Depends: R (>= 3.4), Biobase, BiocParallel, cluster, matrixStats,
stats, graphics, utils
Suggests: breastCancerMAINZ
License: GPL-3
MD5sum: d73a9a5c07b61dc5c55823db1f1d0365
NeedsCompilation: no
Title: Assign Properties to TNBC Patients
Description: This package estimates four stromal properties identified
in TNBC patients in each patient of a gene expression datasets.
These stromal property assignments can be combined to subtype
patients. These four stromal properties were identified in
Triple negative breast cancer (TNBC) patients and represent the
presence of different cells in the stroma: T-cells (T), B-cells
(B), stromal infiltrating epithelial cells (E), and desmoplasia
(D). Additionally this package can also be used to estimate
generative properties for the Lehmann subtypes, an alternative
TNBC subtyping scheme (PMID: 21633166).
biocViews: ImmunoOncology, GeneExpression, BiomedicalInformatics,
Classification, Microarray, RNASeq, Software
Author: Sadiq Saleh [aut, cre], Michael Hallett [aut]
Maintainer: Sadiq Saleh <sadiq.mehdiismailsaleh@mail.mcgill.ca>
git_url: https://git.bioconductor.org/packages/STROMA4
git_branch: RELEASE_3_13
git_last_commit: 2107780
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/STROMA4_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/STROMA4_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/STROMA4_1.16.0.tgz
vignettes: vignettes/STROMA4/inst/doc/STROMA4-vignette.pdf
vignetteTitles: Using the STROMA4 package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/STROMA4/inst/doc/STROMA4-vignette.R
dependencyCount: 17
Package: struct
Version: 1.4.0
Depends: R (>= 4.0)
Imports: methods, ontologyIndex, datasets, graphics, stats, utils,
knitr, SummarizedExperiment, S4Vectors
Suggests: testthat, rstudioapi, rmarkdown, covr, BiocStyle, openxlsx,
ggplot2, magick
License: GPL-3
MD5sum: 308eea3f9181f76400bc4af256d0a496
NeedsCompilation: no
Title: Statistics in R Using Class-based Templates
Description: Defines and includes a set of class-based templates for
developing and implementing data processing and analysis
workflows, with a strong emphasis on statistics and machine
learning. The templates can be used and where needed extended
to 'wrap' tools and methods from other packages into a common
standardised structure to allow for effective and fast
integration. Model objects can be combined into sequences, and
sequences nested in iterators using overloaded operators to
simplify and improve readability of the code. STATistics
Ontology (STATO) has been integrated and implemented to provide
standardised definitions for methods, inputs and outputs
wrapped using the class-based templates.
biocViews: WorkflowStep
Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut]
Maintainer: Gavin Rhys Lloyd <g.r.lloyd@bham.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/struct
git_branch: RELEASE_3_13
git_last_commit: f884f6a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/struct_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/struct_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/struct_1.4.0.tgz
vignettes:
vignettes/struct/inst/doc/struct_templates_and_helper_functions.html
vignetteTitles: Introduction to STRUCT - STatistics in R using
Class-based Templates
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/struct/inst/doc/struct_templates_and_helper_functions.R
dependsOnMe: structToolbox
importsMe: metabolomicsWorkbenchR
dependencyCount: 37
Package: Structstrings
Version: 1.8.0
Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9),
Biostrings (>= 2.57.2)
Imports: methods, BiocGenerics, XVector, stringr, stringi, crayon,
grDevices
LinkingTo: IRanges, S4Vectors
Suggests: testthat, knitr, rmarkdown, tRNAscanImport, BiocStyle
License: Artistic-2.0
MD5sum: fe9b9b5985b37749489a1c16f312ee4f
NeedsCompilation: yes
Title: Implementation of the dot bracket annotations with Biostrings
Description: The Structstrings package implements the widely used dot
bracket annotation for storing base pairing information in
structured RNA. Structstrings uses the infrastructure provided
by the Biostrings package and derives the DotBracketString and
related classes from the BString class. From these, base pair
tables can be produced for in depth analysis. In addition, the
loop indices of the base pairs can be retrieved as well. For
better efficiency, information conversion is implemented in C,
inspired to a large extend by the ViennaRNA package.
biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing,
Software, Alignment, SequenceMatching
Author: Felix G.M. Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/Structstrings
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/Structstrings/issues
git_url: https://git.bioconductor.org/packages/Structstrings
git_branch: RELEASE_3_13
git_last_commit: 77bcc49
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Structstrings_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Structstrings_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Structstrings_1.8.0.tgz
vignettes: vignettes/Structstrings/inst/doc/Structstrings.html
vignetteTitles: Structstrings
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Structstrings/inst/doc/Structstrings.R
dependsOnMe: tRNA, tRNAdbImport
importsMe: tRNAscanImport
dependencyCount: 23
Package: structToolbox
Version: 1.4.3
Depends: R (>= 4.0), struct (>= 1.2.0)
Imports: ggplot2, ggthemes, grid, gridExtra, methods, scales, sp,
stats, utils
Suggests: agricolae, BiocFileCache, BiocStyle, car, covr, cowplot,
e1071, emmeans, ggdendro, knitr, magick, nlme, openxlsx, pls,
pmp, reshape2, ropls, rmarkdown, Rtsne, testthat
License: GPL-3
MD5sum: 2c4ddd9be6d40de23e4d6df96217cf77
NeedsCompilation: no
Title: Data processing & analysis tools for Metabolomics and other
omics
Description: An extensive set of data (pre-)processing and analysis
methods and tools for metabolomics and other omics, with a
strong emphasis on statistics and machine learning. This
toolbox allows the user to build extensive and standardised
workflows for data analysis. The methods and tools have been
implemented using class-based templates provided by the struct
(Statistics in R Using Class-based Templates) package. The
toolbox includes pre-processing methods (e.g. signal drift and
batch correction, normalisation, missing value imputation and
scaling), univariate (e.g. ttest, various forms of ANOVA,
Kruskal–Wallis test and more) and multivariate statistical
methods (e.g. PCA and PLS, including cross-validation and
permutation testing) as well as machine learning methods (e.g.
Support Vector Machines). The STATistics Ontology (STATO) has
been integrated and implemented to provide standardised
definitions for the different methods, inputs and outputs.
biocViews: WorkflowStep, Metabolomics
Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut]
Maintainer: Gavin Rhys Lloyd <g.r.lloyd@bham.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/structToolbox
git_branch: RELEASE_3_13
git_last_commit: 1af2d87
git_last_commit_date: 2021-09-17
Date/Publication: 2021-09-21
source.ver: src/contrib/structToolbox_1.4.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/structToolbox_1.4.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/structToolbox_1.4.3.tgz
vignettes:
vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.html
vignetteTitles: Data analysis of metabolomics and other omics datasets
using the structToolbox
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.R
suggestsMe: metabolomicsWorkbenchR
dependencyCount: 70
Package: StructuralVariantAnnotation
Version: 1.8.2
Depends: GenomicRanges, rtracklayer, VariantAnnotation, BiocGenerics, R
(>= 4.1.0)
Imports: assertthat, Biostrings, stringr, dplyr, methods, rlang,
GenomicFeatures, IRanges, S4Vectors, SummarizedExperiment,
GenomeInfoDb,
Suggests: ggplot2, devtools, testthat (>= 2.1.0), roxygen2, rmarkdown,
tidyverse, knitr, ggbio, biovizBase,
TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19,
License: GPL-3 + file LICENSE
MD5sum: 4446a01a271f1138a6a34f47c2e15bcb
NeedsCompilation: no
Title: Variant annotations for structural variants
Description: StructuralVariantAnnotation provides a framework for
analysis of structural variants within the Bioconductor
ecosystem. This package contains contains useful helper
functions for dealing with structural variants in VCF format.
The packages contains functions for parsing VCFs from a number
of popular callers as well as functions for dealing with
breakpoints involving two separate genomic loci encoded as
GRanges objects.
biocViews: DataImport, Sequencing, Annotation, Genetics,
VariantAnnotation
Author: Daniel Cameron [aut, cre]
(<https://orcid.org/0000-0002-0951-7116>), Ruining Dong [aut]
(<https://orcid.org/0000-0003-1433-0484>)
Maintainer: Daniel Cameron <daniel.l.cameron@gmail.com>
VignetteBuilder: knitr
git_url:
https://git.bioconductor.org/packages/StructuralVariantAnnotation
git_branch: RELEASE_3_13
git_last_commit: 325f68f
git_last_commit_date: 2021-08-05
Date/Publication: 2021-08-05
source.ver: src/contrib/StructuralVariantAnnotation_1.8.2.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/StructuralVariantAnnotation_1.8.2.zip
mac.binary.ver:
bin/macosx/contrib/4.1/StructuralVariantAnnotation_1.8.2.tgz
vignettes:
vignettes/StructuralVariantAnnotation/inst/doc/vignettes.html
vignetteTitles: Structural Variant Annotation Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.R
dependencyCount: 98
Package: SubCellBarCode
Version: 1.8.0
Depends: R (>= 3.6)
Imports: Rtsne, scatterplot3d, caret, e1071, ggplot2, gridExtra,
networkD3, ggrepel, graphics, stats, org.Hs.eg.db,
AnnotationDbi
Suggests: knitr, rmarkdown, BiocStyle
License: GPL-2
Archs: i386, x64
MD5sum: 89bb68fd0c8c68e46bea13c2465c111e
NeedsCompilation: no
Title: SubCellBarCode: Integrated workflow for robust mapping and
visualizing whole human spatial proteome
Description: Mass-Spectrometry based spatial proteomics have enabled
the proteome-wide mapping of protein subcellular localization
(Orre et al. 2019, Molecular Cell). SubCellBarCode R package
robustly classifies proteins into corresponding subcellular
localization.
biocViews: Proteomics, MassSpectrometry, Classification
Author: Taner Arslan
Maintainer: Taner Arslan <taner.arslan@ki.se>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SubCellBarCode
git_branch: RELEASE_3_13
git_last_commit: b5578c6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SubCellBarCode_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SubCellBarCode_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SubCellBarCode_1.8.0.tgz
vignettes: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.html
vignetteTitles: SubCellBarCode R Markdown vignettes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.R
dependencyCount: 122
Package: subSeq
Version: 1.22.0
Depends: R (>= 3.2)
Imports: data.table, dplyr, tidyr, ggplot2, magrittr, qvalue (>= 1.99),
digest, Biobase
Suggests: limma, edgeR, DESeq2, DEXSeq (>= 1.9.7), testthat, knitr
License: MIT + file LICENSE
MD5sum: 1baa055fc4b5d7f5981ca392ac4b2f0a
NeedsCompilation: no
Title: Subsampling of high-throughput sequencing count data
Description: Subsampling of high throughput sequencing count data for
use in experiment design and analysis.
biocViews: ImmunoOncology, Sequencing, Transcription, RNASeq,
GeneExpression, DifferentialExpression
Author: David Robinson, John D. Storey, with contributions from Andrew
J. Bass
Maintainer: Andrew J. Bass <ajbass@princeton.edu>, John D. Storey
<jstorey@princeton.edu>
URL: http://github.com/StoreyLab/subSeq
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/subSeq
git_branch: RELEASE_3_13
git_last_commit: 1c1de44
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/subSeq_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/subSeq_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/subSeq_1.22.0.tgz
vignettes: vignettes/subSeq/inst/doc/subSeq.pdf
vignetteTitles: subSeq Example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/subSeq/inst/doc/subSeq.R
dependencyCount: 55
Package: SummarizedBenchmark
Version: 2.10.0
Depends: R (>= 3.6), tidyr, SummarizedExperiment, S4Vectors,
BiocGenerics, methods, UpSetR, rlang, stringr, utils,
BiocParallel, ggplot2, mclust, dplyr, digest, sessioninfo,
crayon, tibble
Suggests: iCOBRA, BiocStyle, knitr, magrittr, IHW, qvalue, testthat,
DESeq2, edgeR, limma, tximport, readr, scRNAseq, splatter,
scater, rnaseqcomp, biomaRt
License: GPL (>= 3)
MD5sum: 9d333a7cd6a7e6e376a309cba58de762
NeedsCompilation: no
Title: Classes and methods for performing benchmark comparisons
Description: This package defines the BenchDesign and
SummarizedBenchmark classes for building, executing, and
evaluating benchmark experiments of computational methods. The
SummarizedBenchmark class extends the
RangedSummarizedExperiment object, and is designed to provide
infrastructure to store and compare the results of applying
different methods to a shared data set. This class provides an
integrated interface to store metadata such as method
parameters and software versions as well as ground truths (when
these are available) and evaluation metrics.
biocViews: Software, Infrastructure
Author: Alejandro Reyes [aut]
(<https://orcid.org/0000-0001-8717-6612>), Patrick Kimes [aut,
cre] (<https://orcid.org/0000-0001-6819-9077>)
Maintainer: Patrick Kimes <patrick.kimes@gmail.com>
URL: https://github.com/areyesq89/SummarizedBenchmark,
http://bioconductor.org/packages/SummarizedBenchmark/
VignetteBuilder: knitr
BugReports: https://github.com/areyesq89/SummarizedBenchmark/issues
git_url: https://git.bioconductor.org/packages/SummarizedBenchmark
git_branch: RELEASE_3_13
git_last_commit: 4d7e78f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SummarizedBenchmark_2.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SummarizedBenchmark_2.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SummarizedBenchmark_2.10.0.tgz
vignettes:
vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.html,
vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.html,
vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.html,
vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.html,
vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.html,
vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.html,
vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.html,
vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.html
vignetteTitles: Case Study: Benchmarking non-R Methods, Case Study:
Single-Cell RNA-Seq Simulation, Feature: Error Handling,
Feature: Iterative Benchmarking, Feature: Parallelization,
SummarizedBenchmark: Class Details, SummarizedBenchmark: Full
Case Study, SummarizedBenchmark: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.R,
vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.R,
vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.R,
vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.R,
vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.R,
vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.R,
vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.R,
vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.R
suggestsMe: benchmarkfdrData2019
dependencyCount: 77
Package: SummarizedExperiment
Version: 1.22.0
Depends: R (>= 4.0.0), methods, MatrixGenerics (>= 1.1.3),
GenomicRanges (>= 1.41.5), Biobase
Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.37.0),
S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>=
1.13.1), DelayedArray (>= 0.15.10)
Suggests: HDF5Array (>= 1.7.5), annotate, AnnotationDbi, hgu95av2.db,
GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, jsonlite,
rhdf5, airway, BiocStyle, knitr, rmarkdown, RUnit, testthat,
digest
License: Artistic-2.0
MD5sum: 65663a5b4371aa6a1b4bcfb1cdce60ed
NeedsCompilation: no
Title: SummarizedExperiment container
Description: The SummarizedExperiment container contains one or more
assays, each represented by a matrix-like object of numeric or
other mode. The rows typically represent genomic ranges of
interest and the columns represent samples.
biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage,
GenomeAnnotation
Author: Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/SummarizedExperiment
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/SummarizedExperiment/issues
git_url: https://git.bioconductor.org/packages/SummarizedExperiment
git_branch: RELEASE_3_13
git_last_commit: 7d1110e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SummarizedExperiment_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SummarizedExperiment_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SummarizedExperiment_1.22.0.tgz
vignettes: vignettes/SummarizedExperiment/inst/doc/Extensions.html,
vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html
vignetteTitles: 2. Extending the SummarizedExperiment class, 1.
SummarizedExperiment for Coordinating Experimental Assays,,
Samples,, and Regions of Interest
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SummarizedExperiment/inst/doc/Extensions.R,
vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.R
dependsOnMe: AffiXcan, AllelicImbalance, ASpediaFI, bambu,
BDMMAcorrect, BiocSklearn, BiSeq, bnbc, BrainSABER, bsseq,
CAGEfightR, celaref, clusterExperiment, compartmap, CoreGx,
coseq, csaw, CSSQ, DaMiRseq, deco, deepSNV, DeMixT, DESeq2,
DEXSeq, DiffBind, diffcoexp, diffHic, divergence, DMCFB,
DMCHMM, DMRcate, EnrichmentBrowser, epigenomix, evaluomeR,
EventPointer, ExperimentSubset, ExpressionAtlas, FEAST, FRASER,
GenoGAM, GenomicAlignments, GenomicFiles,
GenomicSuperSignature, GRmetrics, GSEABenchmarkeR, HelloRanges,
hipathia, IgGeneUsage, InteractionSet, IntEREst, iSEE, isomiRs,
ivygapSE, lefser, lipidr, LoomExperiment, made4, MatrixQCvis,
MBASED, methrix, methylPipe, MetNet, mia, miaViz, minfi,
miRmine, moanin, mpra, MultiAssayExperiment, NADfinder,
NBAMSeq, NewWave, OUTRIDER, padma, PDATK, PhIPData,
profileplyr, recount, recount3, RegEnrich, REMP, ROCpAI, rqt,
runibic, Scale4C, scClassifR, scGPS, scone, SDAMS, SeqGate,
SGSeq, signatureSearch, SingleCellExperiment, singleCellTK,
SingleR, soGGi, spqn, ssPATHS, stageR, SummarizedBenchmark,
survtype, tidySummarizedExperiment, TimeSeriesExperiment,
TissueEnrich, TNBC.CMS, UMI4Cats, VanillaICE,
VariantAnnotation, VariantExperiment, velociraptor, weitrix,
yamss, zinbwave, airway, benchmarkfdrData2019, bodymapRat,
celldex, curatedAdipoChIP, curatedAdipoRNA,
curatedMetagenomicData, DREAM4, fission, FlowSorted.Blood.EPIC,
FlowSorted.CordBloodCombined.450k, HDCytoData,
HighlyReplicatedRNASeq, HMP16SData, MetaGxOvarian,
MetaGxPancreas, MethylSeqData, microbiomeDataSets, microRNAome,
MouseGastrulationData, MouseThymusAgeing, ObMiTi,
parathyroidSE, restfulSEData, sampleClassifierData, spqnData,
timecoursedata, DRomics
importsMe: ADAM, ADImpute, aggregateBioVar, airpart, ALDEx2, alpine,
AlpsNMR, animalcules, anota2seq, APAlyzer, apeglm, appreci8R,
ASICS, AUCell, autonomics, awst, barcodetrackR, BASiCS,
batchelor, BayesSpace, bayNorm, BBCAnalyzer, bigPint,
BiocOncoTK, BioNERO, biotmle, biovizBase, biscuiteer, BiSeq,
blacksheepr, BloodGen3Module, BRGenomics, BUMHMM, BUScorrect,
CAGEr, CATALYST, cBioPortalData, ccfindR, celda, CelliD,
CellMixS, CellTrails, censcyt, CeTF, CHETAH, ChIPpeakAnno,
ChromSCape, chromVAR, CiteFuse, clustifyr, cmapR, CNVfilteR,
CNVRanger, coexnet, CoGAPS, combi, conclus, condiments,
consensusDE, CopyNumberPlots, corral, countsimQC, crossmeta,
cydar, cytomapper, DAMEfinder, dasper, debCAM, debrowser,
DEComplexDisease, decompTumor2Sig, DEFormats, DEGreport,
deltaCaptureC, DEP, DEScan2, DEWSeq, diffcyt, diffUTR,
DiscoRhythm, distinct, dittoSeq, DominoEffect, doppelgangR,
doseR, DropletUtils, Dune, easyRNASeq, eisaR, ELMER,
ensemblVEP, epialleleR, epigraHMM, epivizrData, erma, EWCE,
FCBF, fcScan, fishpond, GARS, gCrisprTools, GeneTonic,
GenomicDataCommons, getDEE2, ggbio, Glimma, glmGamPoi,
glmSparseNet, GreyListChIP, gscreend, GSVA, gwasurvivr, GWENA,
HTSeqGenie, HumanTranscriptomeCompendium, hummingbird, iasva,
icetea, ideal, ILoReg, infercnv, INSPEcT, InterMineR, iSEEu,
iteremoval, LACE, LineagePulse, lionessR, MADSEQ, marr, MAST,
mbkmeans, MBQN, mCSEA, MEAL, MEAT, MEB, metabolomicsWorkbenchR,
MetaNeighbor, metaseqR2, MethReg, methyAnalysis, MethylAid,
methylscaper, methylumi, MicrobiotaProcess, midasHLA, miloR,
MinimumDistance, miRSM, missMethyl, MLSeq, MoonlightR,
motifbreakR, motifmatchr, MPRAnalyze, MsFeatures, msgbsR,
MSPrep, msqrob2, MultiDataSet, multiOmicsViz, mumosa, muscat,
musicatk, MWASTools, NanoMethViz, Nebulosa, netSmooth,
NormalyzerDE, oligoClasses, omicRexposome, OmicsLonDA,
omicsPrint, oncomix, ORFik, OVESEG, PAIRADISE, pcaExplorer,
peco, PharmacoGx, phemd, phenopath, PhosR, pipeComp, pmp,
POWSC, proActiv, proDA, psichomics, pulsedSilac, PureCN,
QFeatures, qsmooth, quantiseqr, R453Plus1Toolbox, RadioGx,
RaggedExperiment, RareVariantVis, RcisTarget, receptLoss,
regionReport, regsplice, rgsepd, Rmmquant, RNAAgeCalc,
RNAsense, roar, rScudo, RTCGAToolbox, RTN, satuRn, SBGNview,
SC3, SCArray, scater, scBFA, scCB2, scDblFinder, scDD, scds,
scHOT, scmap, scMerge, scmeth, SCnorm, scoreInvHap, scp,
scPipe, scran, scRepertoire, scruff, scry, scTensor, scTGIF,
scuttle, sechm, seqCAT, sesame, SEtools, sigFeature, SigsPack,
singscore, slalom, slingshot, slinky, snapcount, SNPhood,
Spaniel, SpatialCPie, SpatialExperiment, spatialHeatmap,
splatter, SplicingFactory, srnadiff, struct,
StructuralVariantAnnotation, supersigs, switchde, systemPipeR,
systemPipeTools, TBSignatureProfiler, TCGAbiolinks,
TCGAbiolinksGUI, TCGAutils, TCseq, tenXplore, tidybulk,
tidySingleCellExperiment, TOAST, tomoda, ToxicoGx, tradeSeq,
TrajectoryUtils, TraRe, TreeSummarizedExperiment, Trendy,
tricycle, TSCAN, tscR, TSRchitect, TTMap, TVTB, tximeta,
VariantFiltering, vidger, wpm, xcms, zellkonverter, zFPKM,
BloodCancerMultiOmics2017, brgedata, CLLmethylation, COSMIC.67,
curatedTCGAData, emtdata, FieldEffectCrc, GSE13015, HMP2Data,
IHWpaper, MetaGxBreast, scRNAseq, SingleCellMultiModal,
spatialLIBD, TCGAWorkflowData, ExpHunterSuite, fluentGenomics,
SingscoreAMLMutations, TCGAWorkflow, BinQuasi, digitalDLSorteR,
HeritSeq, imcExperiment, microbial, PlasmaMutationDetector,
pulseTD, SC.MEB
suggestsMe: AnnotationHub, biobroom, BiocPkgTools, dcanr, dce, dearseq,
DelayedArray, edgeR, EnMCB, epivizr, epivizrChart, esetVis,
fobitools, GENIE3, GenomicRanges, globalSeq, gsean, HDF5Array,
Informeasure, InteractiveComplexHeatmap, interactiveDisplay,
MatrixGenerics, mistyR, MOFA2, MSnbase, pathwayPCA, podkat,
PubScore, RiboProfiling, S4Vectors, scFeatureFilter, semisup,
systemPipeShiny, TFutils, biotmleData, curatedAdipoArray,
dorothea, DuoClustering2018, RforProteomics, SBGNview.data,
tissueTreg, CAGEWorkflow, clustree, conos, dyngen, polyRAD,
RaceID, seqgendiff, Seurat, Signac, singleCellHaystack
dependencyCount: 25
Package: Summix
Version: 1.0.3
Depends: R (>= 4.1)
Imports: nloptr, methods
Suggests: rmarkdown, markdown, knitr
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: a553015627d5bf8ce6aef0aa69e15a84
NeedsCompilation: no
Title: Summix: A method to estimate and adjust for population structure
in genetic summary data
Description: This package contains the Summix method for estimating and
adjusting for ancestry in genetic summary allele frequency
data. The function summix estimates reference ancestry
proportions using a mixture model. The adjAF function produces
ancestry adjusted allele frequencies for an observed sample
with ancestry proportions matching a target person or sample.
biocViews: StatisticalMethod, WholeGenome, Genetics
Author: Audrey Hendricks [cre], Stoneman Haley [aut]
Maintainer: Audrey Hendricks <audrey.hendricks@ucdenver.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/Summix/issues
git_url: https://git.bioconductor.org/packages/Summix
git_branch: RELEASE_3_13
git_last_commit: 1eb1cf6
git_last_commit_date: 2021-10-04
Date/Publication: 2021-10-07
source.ver: src/contrib/Summix_1.0.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Summix_1.0.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/Summix_1.0.3.tgz
vignettes: vignettes/Summix/inst/doc/Summix.html
vignetteTitles: Summix.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Summix/inst/doc/Summix.R
dependencyCount: 2
Package: supersigs
Version: 1.0.0
Depends: R (>= 4.1)
Imports: assertthat, caret, dplyr, tidyr, rsample, methods, rlang,
utils, Biostrings, stats, SummarizedExperiment
Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38,
knitr, rmarkdown, ggplot2, testthat, VariantAnnotation
License: GPL-3
MD5sum: f73998c7f13911cff20d71efaff3a9b1
NeedsCompilation: no
Title: Supervised mutational signatures
Description: Generate SuperSigs (supervised mutational signatures) from
single nucleotide variants in the cancer genome. Functions
included in the package allow the user to learn supervised
mutational signatures from their data and apply them to new
data. The methodology is based on the one described in Afsari
(2021, ELife).
biocViews: FeatureExtraction, Classification, Regression, Sequencing,
WholeGenome, SomaticMutation
Author: Albert Kuo [aut, cre]
(<https://orcid.org/0000-0001-5155-0748>), Yifan Zhang [aut],
Bahman Afsari [aut], Cristian Tomasetti [aut]
Maintainer: Albert Kuo <albertkuo@jhu.edu>
URL: https://tomasettilab.github.io/supersigs/
VignetteBuilder: knitr
BugReports: https://github.com/TomasettiLab/supersigs/issues
git_url: https://git.bioconductor.org/packages/supersigs
git_branch: RELEASE_3_13
git_last_commit: 8ddfdfe
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/supersigs_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/supersigs_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/supersigs_1.0.0.tgz
vignettes: vignettes/supersigs/inst/doc/supersigs.html
vignetteTitles: Using supersigs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/supersigs/inst/doc/supersigs.R
dependencyCount: 102
Package: supraHex
Version: 1.30.0
Depends: R (>= 3.6), hexbin
Imports: ape, MASS, grDevices, graphics, stats, readr, tibble, tidyr,
dplyr, stringr, purrr, magrittr, igraph, methods
License: GPL-2
MD5sum: 96e2c826e6fdde0826d446df22ac9ca2
NeedsCompilation: no
Title: supraHex: a supra-hexagonal map for analysing tabular omics data
Description: A supra-hexagonal map is a giant hexagon on a
2-dimensional grid seamlessly consisting of smaller hexagons.
It is supposed to train, analyse and visualise a
high-dimensional omics input data. The supraHex is able to
carry out gene clustering/meta-clustering and sample
correlation, plus intuitive visualisations to facilitate
exploratory analysis. More importantly, it allows for
overlaying additional data onto the trained map to explore
relations between input and additional data. So with supraHex,
it is also possible to carry out multilayer omics data
comparisons. Newly added utilities are advanced heatmap
visualisation and tree-based analysis of sample relationships.
Uniquely to this package, users can ultrafastly understand any
tabular omics data, both scientifically and artistically,
especially in a sample-specific fashion but without loss of
information on large genes.
biocViews: Software, Clustering, Visualization, GeneExpression
Author: Hai Fang and Julian Gough
Maintainer: Hai Fang <hfang@well.ox.ac.uk>
URL: http://suprahex.r-forge.r-project.org
git_url: https://git.bioconductor.org/packages/supraHex
git_branch: RELEASE_3_13
git_last_commit: 67ac896
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/supraHex_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/supraHex_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/supraHex_1.30.0.tgz
vignettes: vignettes/supraHex/inst/doc/supraHex_vignettes.pdf
vignetteTitles: supraHex User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/supraHex/inst/doc/supraHex_vignettes.R
dependsOnMe: dnet
importsMe: Pi
suggestsMe: TCGAbiolinks
dependencyCount: 48
Package: survcomp
Version: 1.42.0
Depends: survival, prodlim, R (>= 3.4)
Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid,
rmeta, stats, graphics
Suggests: Hmisc, CPE, clinfun, xtable, Biobase, BiocManager
License: Artistic-2.0
MD5sum: 9ac61aa20b66250ddb0508d92f1fd685
NeedsCompilation: yes
Title: Performance Assessment and Comparison for Survival Analysis
Description: Assessment and Comparison for Performance of Risk
Prediction (Survival) Models.
biocViews: GeneExpression, DifferentialExpression, Visualization
Author: Benjamin Haibe-Kains, Markus Schroeder, Catharina Olsen,
Christos Sotiriou, Gianluca Bontempi, John Quackenbush, Samuel
Branders, Zhaleh Safikhani
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>,
Markus Schroeder <markus.schroeder@ucdconnect.ie>, Catharina
Olsen <colsen@ulb.ac.be>
URL: http://www.pmgenomics.ca/bhklab/
git_url: https://git.bioconductor.org/packages/survcomp
git_branch: RELEASE_3_13
git_last_commit: 2b6bd69
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/survcomp_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/survcomp_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/survcomp_1.42.0.tgz
vignettes: vignettes/survcomp/inst/doc/survcomp.pdf
vignetteTitles: SurvComp: a package for performance assessment and
comparison for survival analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/survcomp/inst/doc/survcomp.R
dependsOnMe: genefu
importsMe: metaseqR2, PDATK, pencal, plsRcox, SIGN
suggestsMe: glmSparseNet, GSgalgoR, breastCancerMAINZ, breastCancerNKI,
breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP,
breastCancerVDX
dependencyCount: 35
Package: survtype
Version: 1.8.0
Depends: SummarizedExperiment, pheatmap, survival, survminer,
clustvarsel, stats, utils
Suggests: maftools, scales, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 705baac0652cb4144a2dc4e49504f4d2
NeedsCompilation: no
Title: Subtype Identification with Survival Data
Description: Subtypes are defined as groups of samples that have
distinct molecular and clinical features. Genomic data can be
analyzed for discovering patient subtypes, associated with
clinical data, especially for survival information. This
package is aimed to identify subtypes that are both clinically
relevant and biologically meaningful.
biocViews: Software, StatisticalMethod, GeneExpression, Survival,
Clustering, Sequencing, Coverage
Author: Dongmin Jung
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/survtype
git_branch: RELEASE_3_13
git_last_commit: 3deeff9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/survtype_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/survtype_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/survtype_1.8.0.tgz
vignettes: vignettes/survtype/inst/doc/survtype.html
vignetteTitles: survtype
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/survtype/inst/doc/survtype.R
dependencyCount: 149
Package: Sushi
Version: 1.30.0
Depends: R (>= 2.10), zoo,biomaRt
Imports: graphics, grDevices
License: GPL (>= 2)
MD5sum: 9840a3797be862b490e7070e54bc68ec
NeedsCompilation: no
Title: Tools for visualizing genomics data
Description: Flexible, quantitative, and integrative genomic
visualizations for publication-quality multi-panel figures
biocViews: DataRepresentation, Visualization, Genetics, Sequencing,
Infrastructure, HiC
Author: Douglas H Phanstiel <dphansti@stanford.edu>
Maintainer: Douglas H Phanstiel <dphansti@stanford.edu>
git_url: https://git.bioconductor.org/packages/Sushi
git_branch: RELEASE_3_13
git_last_commit: 817131b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Sushi_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Sushi_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Sushi_1.30.0.tgz
vignettes: vignettes/Sushi/inst/doc/Sushi.pdf
vignetteTitles: Sushi
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Sushi/inst/doc/Sushi.R
importsMe: ChromSCape, diffloop, Ularcirc, VaSP
dependencyCount: 75
Package: sva
Version: 3.40.0
Depends: R (>= 3.2), mgcv, genefilter, BiocParallel
Imports: matrixStats, stats, graphics, utils, limma, edgeR
Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat
License: Artistic-2.0
MD5sum: 61356712b8764b92a8d0a63067c10b01
NeedsCompilation: yes
Title: Surrogate Variable Analysis
Description: The sva package contains functions for removing batch
effects and other unwanted variation in high-throughput
experiment. Specifically, the sva package contains functions
for the identifying and building surrogate variables for
high-dimensional data sets. Surrogate variables are covariates
constructed directly from high-dimensional data (like gene
expression/RNA sequencing/methylation/brain imaging data) that
can be used in subsequent analyses to adjust for unknown,
unmodeled, or latent sources of noise. The sva package can be
used to remove artifacts in three ways: (1) identifying and
estimating surrogate variables for unknown sources of variation
in high-throughput experiments (Leek and Storey 2007 PLoS
Genetics,2008 PNAS), (2) directly removing known batch effects
using ComBat (Johnson et al. 2007 Biostatistics) and (3)
removing batch effects with known control probes (Leek 2014
biorXiv). Removing batch effects and using surrogate variables
in differential expression analysis have been shown to reduce
dependence, stabilize error rate estimates, and improve
reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008
PNAS or Leek et al. 2011 Nat. Reviews Genetics).
biocViews: ImmunoOncology, Microarray, StatisticalMethod,
Preprocessing, MultipleComparison, Sequencing, RNASeq,
BatchEffect, Normalization
Author: Jeffrey T. Leek <jtleek@gmail.com>, W. Evan Johnson
<wej@bu.edu>, Hilary S. Parker <hiparker@jhsph.edu>, Elana J.
Fertig <ejfertig@jhmi.edu>, Andrew E. Jaffe <ajaffe@jhsph.edu>,
Yuqing Zhang <zhangyuqing.pkusms@gmail.com>, John D. Storey
<jstorey@princeton.edu>, Leonardo Collado Torres
<lcolladotor@gmail.com>
Maintainer: Jeffrey T. Leek <jtleek@gmail.com>, John D. Storey
<jstorey@princeton.edu>, W. Evan Johnson <wej@bu.edu>
git_url: https://git.bioconductor.org/packages/sva
git_branch: RELEASE_3_13
git_last_commit: 3165ab9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/sva_3.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/sva_3.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/sva_3.40.0.tgz
vignettes: vignettes/sva/inst/doc/sva.pdf
vignetteTitles: sva tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sva/inst/doc/sva.R
dependsOnMe: SCAN.UPC, rnaseqGene, bapred, leapp, SmartSVA
importsMe: ASSIGN, ballgown, BatchQC, BioNERO, bnbc, bnem, crossmeta,
CytoTree, DaMiRseq, debrowser, DExMA, doppelgangR, edge,
KnowSeq, MSPrep, omicRexposome, PAA, proBatch, PROPS, qsmooth,
SEtools, singleCellTK, trigger, DeSousa2013,
ExpressionNormalizationWorkflow, cate, cinaR, DGEobj.utils,
dSVA, oncoPredict, seqgendiff
suggestsMe: Harman, iasva, MAGeCKFlute, randRotation, RnBeads, scp,
SomaticSignatures, TBSignatureProfiler, TCGAbiolinks, tidybulk,
curatedBladderData, curatedCRCData, curatedOvarianData,
FieldEffectCrc, CAGEWorkflow, SuperLearner
dependencyCount: 68
Package: SWATH2stats
Version: 1.22.0
Depends: R(>= 2.10.0)
Imports: data.table, reshape2, ggplot2, stats, grDevices, graphics,
utils, biomaRt, methods
Suggests: testthat, knitr, rmarkdown
Enhances: MSstats, PECA, aLFQ
License: GPL-3
MD5sum: 735cf70e3497bb6fd6b917b5117faa72
NeedsCompilation: no
Title: Transform and Filter SWATH Data for Statistical Packages
Description: This package is intended to transform SWATH data from the
OpenSWATH software into a format readable by other statistics
packages while performing filtering, annotation and FDR
estimation.
biocViews: Proteomics, Annotation, ExperimentalDesign, Preprocessing,
MassSpectrometry, ImmunoOncology
Author: Peter Blattmann [aut, cre] Moritz Heusel [aut] Ruedi Aebersold
[aut]
Maintainer: Peter Blattmann <blattmann@imsb.biol.ethz.ch>
URL: https://peterblattmann.github.io/SWATH2stats/
VignetteBuilder: knitr
BugReports: https://github.com/peterblattmann/SWATH2stats
git_url: https://git.bioconductor.org/packages/SWATH2stats
git_branch: RELEASE_3_13
git_last_commit: 9dd20d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SWATH2stats_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SWATH2stats_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SWATH2stats_1.22.0.tgz
vignettes:
vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.pdf,
vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.pdf
vignetteTitles: SWATH2stats example script, SWATH2stats package
Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.R,
vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.R
dependencyCount: 92
Package: SwathXtend
Version: 2.14.0
Depends: e1071, openxlsx, VennDiagram, lattice
License: GPL-2
MD5sum: f031129eba2860feea7dd69c74f332a3
NeedsCompilation: no
Title: SWATH extended library generation and statistical data analysis
Description: Contains utility functions for integrating spectral
libraries for SWATH and statistical data analysis for SWATH
generated data.
biocViews: Software
Author: J WU and D Pascovici
Maintainer: Jemma Wu <jwu@proteome.org.au>
git_url: https://git.bioconductor.org/packages/SwathXtend
git_branch: RELEASE_3_13
git_last_commit: 32e1647
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SwathXtend_2.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SwathXtend_2.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SwathXtend_2.14.0.tgz
vignettes: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.pdf
vignetteTitles: SwathXtend
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.R
dependencyCount: 21
Package: swfdr
Version: 1.18.0
Depends: R (>= 3.4)
Imports: methods, splines, stats4, stats
Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2,
rmarkdown, testthat
License: GPL (>= 3)
MD5sum: 5134ba7f6a3ea7b9155b713999394d1f
NeedsCompilation: no
Title: Estimation of the science-wise false discovery rate and the
false discovery rate conditional on covariates
Description: This package allows users to estimate the science-wise
false discovery rate from Jager and Leek, "Empirical estimates
suggest most published medical research is true," 2013,
Biostatistics, using an EM approach due to the presence of
rounding and censoring. It also allows users to estimate the
false discovery rate conditional on covariates, using a
regression framework, as per Boca and Leek, "A direct approach
to estimating false discovery rates conditional on covariates,"
2018, PeerJ.
biocViews: MultipleComparison, StatisticalMethod, Software
Author: Jeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka
Maintainer: Simina M. Boca <smb310@georgetown.edu>, Jeffrey T. Leek
<jtleek@gmail.com>
URL: https://github.com/leekgroup/swfdr
VignetteBuilder: knitr
BugReports: https://github.com/leekgroup/swfdr/issues
git_url: https://git.bioconductor.org/packages/swfdr
git_branch: RELEASE_3_13
git_last_commit: 3011931
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/swfdr_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/swfdr_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/swfdr_1.18.0.tgz
vignettes: vignettes/swfdr/inst/doc/swfdrQ.pdf,
vignettes/swfdr/inst/doc/swfdrTutorial.pdf
vignetteTitles: Computing covariate-adjusted q-values, Tutorial for
swfdr package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/swfdr/inst/doc/swfdrQ.R,
vignettes/swfdr/inst/doc/swfdrTutorial.R
dependencyCount: 4
Package: SwimR
Version: 1.29.0
Depends: R (>= 3.0.0), methods, gplots (>= 2.10.1), heatmap.plus (>=
1.3), signal (>= 0.7), R2HTML (>= 2.2.1)
Imports: methods
License: LGPL-2
Archs: i386, x64
MD5sum: 65e1d5ce3394de3c0c8dadc82734f2af
NeedsCompilation: no
Title: SwimR: A Suite of Analytical Tools for Quantification of C.
elegans Swimming Behavior
Description: SwimR is an R-based suite that calculates, analyses, and
plots the frequency of C. elegans swimming behavior over time.
It places a particular emphasis on identifying paralysis and
quantifying the kinetic elements of paralysis during swimming.
Data is input to SwipR from a custom built program that fits a
5 point morphometric spine to videos of single worms swimming
in a buffer called Worm Tracker.
biocViews: Visualization
Author: Jing Wang <jing.wang.2@vanderbilt.edu>, Andrew Hardaway
<hardawayja@gmail.com> and Bing Zhang
<bing.zhang@vanderbilt.edu>
Maintainer: Randy Blakely <Randy.Blakely@vanderbilt.edu>
git_url: https://git.bioconductor.org/packages/SwimR
git_branch: master
git_last_commit: fd53d6c
git_last_commit_date: 2020-10-27
Date/Publication: 2021-03-19
source.ver: src/contrib/SwimR_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SwimR_1.29.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SwimR_1.29.0.tgz
vignettes: vignettes/SwimR/inst/doc/SwimR.pdf
vignetteTitles: SwimR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SwimR/inst/doc/SwimR.R
dependencyCount: 14
Package: switchBox
Version: 1.28.0
Depends: R (>= 2.13.1), pROC, gplots
License: GPL-2
MD5sum: 52bf8e1f62c9a08a7bfaac0034aeeea6
NeedsCompilation: yes
Title: Utilities to train and validate classifiers based on pair
switching using the K-Top-Scoring-Pair (KTSP) algorithm
Description: The package offer different classifiers based on
comparisons of pair of features (TSP), using various decision
rules (e.g., majority wins principle).
biocViews: Software, StatisticalMethod, Classification
Author: Bahman Afsari <bahman@jhu.edu>, Luigi Marchionni
<marchion@jhu.edu>, Wikum Dinalankara <wdinala1@jhmi.edu>
Maintainer: Bahman Afsari <bahman@jhu.edu>, Luigi Marchionni
<marchion@jhu.edu>, Wikum Dinalankara <wdinala1@jhmi.edu>
git_url: https://git.bioconductor.org/packages/switchBox
git_branch: RELEASE_3_13
git_last_commit: bc4ba73
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/switchBox_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/switchBox_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/switchBox_1.28.0.tgz
vignettes: vignettes/switchBox/inst/doc/switchBox.pdf
vignetteTitles: Working with the switchBox package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/switchBox/inst/doc/switchBox.R
importsMe: PDATK
suggestsMe: multiclassPairs
dependencyCount: 11
Package: switchde
Version: 1.18.0
Depends: R (>= 3.4), SingleCellExperiment
Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats
Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr
License: GPL (>= 2)
MD5sum: d66ec434655528af5813e453f0262755
NeedsCompilation: no
Title: Switch-like differential expression across single-cell
trajectories
Description: Inference and detection of switch-like differential
expression across single-cell RNA-seq trajectories.
biocViews: ImmunoOncology, Software, Transcriptomics, GeneExpression,
RNASeq, Regression, DifferentialExpression, SingleCell
Author: Kieran Campbell [aut, cre]
Maintainer: Kieran Campbell <kieranrcampbell@gmail.com>
URL: https://github.com/kieranrcampbell/switchde
VignetteBuilder: knitr
BugReports: https://github.com/kieranrcampbell/switchde
git_url: https://git.bioconductor.org/packages/switchde
git_branch: RELEASE_3_13
git_last_commit: a66a9eb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/switchde_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/switchde_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/switchde_1.18.0.tgz
vignettes: vignettes/switchde/inst/doc/switchde_vignette.html
vignetteTitles: An overview of the switchde package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/switchde/inst/doc/switchde_vignette.R
dependencyCount: 61
Package: synergyfinder
Version: 3.0.14
Depends: R (>= 4.0.0)
Imports: drc (>= 3.0-1), reshape2 (>= 1.4.4), tidyverse (>= 1.3.0),
dplyr (>= 1.0.3), tidyr (>= 1.1.2), purrr (>= 0.3.4), furrr (>=
0.2.2), ggplot2 (>= 3.3.3), ggforce (>= 0.3.2), grid (>=
4.0.2), vegan (>= 2.5-7), gstat (>= 2.0-6), sp (>= 1.4-5),
methods (>= 4.0.2), SpatialExtremes (>= 2.0-9), ggrepel (>=
0.9.1), kriging (>= 1.1), plotly (>= 4.9.3), stringr (>=
1.4.0), future (>= 1.21.0), mice (>= 3.13.0), lattice (>=
0.20-41), nleqslv (>= 3.3.2), stats (>= 4.0.2), graphics (>=
4.0.2), grDevices (>= 4.0.2), magrittr (>= 2.0.1), pbapply (>=
1.4-3), metR (>= 0.9.1)
Suggests: knitr, rmarkdown
License: Mozilla Public License 2.0
Archs: i386, x64
MD5sum: 8ec86900ffaf2ba8abf49ff8cc1fdec1
NeedsCompilation: no
Title: Calculate and Visualize Synergy Scores for Drug Combinations
Description: Efficient implementations for analyzing pre-clinical
multiple drug combination datasets. 1. Synergy scores
valuculation via all the popular models, including HSA, Loewe,
Bliss and ZIP; 2. Drug Sensitivity Score (CSS) and Relitave
Inhibition (RI) for drug sensitivity evaluation; 3.
Visualization for drug combination matrices and scores. Based
on this package, we also provide a web application
(https://synergyfinderplus.org/) for users who prefer more
friendly user interface.
biocViews: Software, StatisticalMethod
Author: Shuyu Zheng [aut, cre], Jing Tang [aut]
Maintainer: Shuyu Zheng <shuyu.zheng@helsinki.fi>
URL: https://synergyfinderplus.org/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/synergyfinder
git_branch: RELEASE_3_13
git_last_commit: 11d2c59
git_last_commit_date: 2021-10-13
Date/Publication: 2021-10-14
source.ver: src/contrib/synergyfinder_3.0.14.tar.gz
win.binary.ver: bin/windows/contrib/4.1/synergyfinder_3.0.14.zip
mac.binary.ver: bin/macosx/contrib/4.1/synergyfinder_3.0.14.tgz
vignettes:
vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.html
vignetteTitles: User tutorial of the SynergyFinder Plus
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.R
dependencyCount: 186
Package: SynExtend
Version: 1.4.1
Depends: R (>= 4.0.0), DECIPHER (>= 2.18.0)
Imports: methods, Biostrings, S4Vectors, IRanges, utils, stats
Suggests: BiocStyle, knitr, markdown, rtracklayer, igraph, rmarkdown
License: GPL-3
MD5sum: 6240e1d4a5d995682239bae7210133d4
NeedsCompilation: no
Title: Tools for Working With Synteny Objects
Description: Shared order between genomic sequences provide a great
deal of information. Synteny objects produced by the R package
DECIPHER provides quantitative information about that shared
order. SynExtend provides tools for extracting information from
Synteny objects.
biocViews: Genetics, Clustering, ComparativeGenomics, DataImport
Author: Nicholas Cooley [aut, cre]
(<https://orcid.org/0000-0002-6029-304X>), Adelle Fernando
[ctb], Erik Wright [aut]
Maintainer: Nicholas Cooley <npc19@pitt.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SynExtend
git_branch: RELEASE_3_13
git_last_commit: 47bd51d
git_last_commit_date: 2021-05-27
Date/Publication: 2021-05-30
source.ver: src/contrib/SynExtend_1.4.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SynExtend_1.4.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/SynExtend_1.4.1.tgz
vignettes: vignettes/SynExtend/inst/doc/UsingSynExtend.pdf
vignetteTitles: UsingSynExtend
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SynExtend/inst/doc/UsingSynExtend.R
dependencyCount: 35
Package: synlet
Version: 1.22.0
Depends: R (>= 3.2.0), ggplot2
Imports: doBy, dplyr, grid, magrittr, RColorBrewer, RankProd, reshape2
Suggests: knitr, testthat
License: GPL-3
MD5sum: 88ddeaa10d28c654a7e18dfa9c52d001
NeedsCompilation: no
Title: Hits Selection for Synthetic Lethal RNAi Screen Data
Description: Select hits from synthetic lethal RNAi screen data. For
example, there are two identical celllines except one gene is
knocked-down in one cellline. The interest is to find genes
that lead to stronger lethal effect when they are knocked-down
further by siRNA. Quality control and various visualisation
tools are implemented. Four different algorithms could be used
to pick up the interesting hits. This package is designed based
on 384 wells plates, but may apply to other platforms with
proper configuration.
biocViews: ImmunoOncology, CellBasedAssays, QualityControl,
Preprocessing, Visualization, FeatureExtraction
Author: Chunxuan Shao <c.shao@dkfz.de>
Maintainer: Chunxuan Shao <c.shao@dkfz.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/synlet
git_branch: RELEASE_3_13
git_last_commit: c97772b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/synlet_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/synlet_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/synlet_1.22.0.tgz
vignettes: vignettes/synlet/inst/doc/synlet-vignette.html
vignetteTitles: A working Demo for synlet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R
dependencyCount: 72
Package: SynMut
Version: 1.8.0
Imports: seqinr, methods, Biostrings, stringr, BiocGenerics
Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc,
glue
License: GPL-2
MD5sum: 94aefa02efa87ba3ef00271d526bc995
NeedsCompilation: no
Title: SynMut: Designing Synonymously Mutated Sequences with Different
Genomic Signatures
Description: There are increasing demands on designing virus mutants
with specific dinucleotide or codon composition. This tool can
take both dinucleotide preference and/or codon usage bias into
account while designing mutants. It is a powerful tool for in
silico designs of DNA sequence mutants.
biocViews: SequenceMatching, ExperimentalDesign, Preprocessing
Author: Haogao Gu [aut, cre], Leo L.M. Poon [led]
Maintainer: Haogao Gu <hggu@connect.hku.hk>
URL: https://github.com/Koohoko/SynMut
VignetteBuilder: knitr
BugReports: https://github.com/Koohoko/SynMut/issues
git_url: https://git.bioconductor.org/packages/SynMut
git_branch: RELEASE_3_13
git_last_commit: ea7c78c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/SynMut_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/SynMut_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SynMut_1.8.0.tgz
vignettes: vignettes/SynMut/inst/doc/SynMut.html
vignetteTitles: SynMut: Designing Synonymous Mutants for DNA Sequences
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SynMut/inst/doc/SynMut.R
dependencyCount: 31
Package: systemPipeR
Version: 1.26.3
Depends: Rsamtools (>= 1.31.2), Biostrings, ShortRead (>= 1.37.1),
methods
Imports: GenomicRanges, GenomicFeatures (>= 1.31.3),
SummarizedExperiment, VariantAnnotation (>= 1.25.11), rjson,
ggplot2, limma, edgeR, DESeq2, GOstats, GO.db, annotate,
pheatmap, batchtools, yaml, stringr, assertthat, magrittr, DOT,
rsvg, IRanges, testthat, S4Vectors, crayon
Suggests: BiocGenerics, ape, BiocStyle, knitr, rmarkdown, biomaRt,
BiocParallel, BiocManager, systemPipeRdata, GenomicAlignments,
grid, DT, dplyr, kableExtra
License: Artistic-2.0
MD5sum: 5b496836f16641afc7d7a24cf69494e3
NeedsCompilation: no
Title: systemPipeR: NGS workflow and report generation environment
Description: R package for building and running automated end-to-end
analysis workflows for a wide range of next generation sequence
(NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and
Ribo-Seq. Important features include a uniform workflow
interface across different NGS applications, automated report
generation, and support for running both R and command-line
software, such as NGS aligners or peak/variant callers, on
local computers or compute clusters. Efficient handling of
complex sample sets and experimental designs is facilitated by
a consistently implemented sample annotation infrastructure.
Instructions for using systemPipeR are given in the Overview
Vignette (HTML). The remaining Vignettes, linked below, are
workflow templates for common NGS use cases.
biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq,
RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage,
GeneSetEnrichment, Alignment, QualityControl, ImmunoOncology,
ReportWriting, Workflow
Author: Thomas Girke
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://systempipe.org/
SystemRequirements: systemPipeR can be used to run external
command-line software (e.g. short read aligners), but the
corresponding tool needs to be installed on a system.
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/systemPipeR
git_branch: RELEASE_3_13
git_last_commit: 08593cd
git_last_commit_date: 2021-06-26
Date/Publication: 2021-06-27
source.ver: src/contrib/systemPipeR_1.26.3.tar.gz
win.binary.ver: bin/windows/contrib/4.1/systemPipeR_1.26.3.zip
mac.binary.ver: bin/macosx/contrib/4.1/systemPipeR_1.26.3.tgz
vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_CWL.html,
vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html,
vignettes/systemPipeR/inst/doc/systemPipeR.html
vignetteTitles: systemPipeR and CWL, systemPipeR: Workflows collection,
systemPipeR: Workflow design and reporting generation
environment
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/systemPipeR/inst/doc/systemPipeR_CWL.R,
vignettes/systemPipeR/inst/doc/systemPipeR_workflows.R,
vignettes/systemPipeR/inst/doc/systemPipeR.R
importsMe: DiffBind, RNASeqR
suggestsMe: systemPipeShiny, systemPipeTools, systemPipeRdata
dependencyCount: 159
Package: systemPipeShiny
Version: 1.2.0
Depends: R (>= 4.0.0), shiny (>= 1.5.0), spsUtil (>= 0.1.2), spsComps
(>= 0.3.0), drawer
Imports: DT, assertthat, bsplus, crayon, dplyr, ggplot2, htmltools,
glue, magrittr, methods, plotly, rlang, rstudioapi, shinyAce,
shinyFiles, shinyWidgets, shinydashboard, shinydashboardPlus
(>= 2.0.0), shinyjqui, shinyjs, shinytoastr, stringr, stats,
styler, tibble, utils, vroom (>= 1.3.1), yaml, R6, RSQLite,
openssl
Suggests: testthat, BiocStyle, knitr, rmarkdown, systemPipeR,
systemPipeRdata, networkD3, rhandsontable, zip, callr, pushbar,
fs, readr, R.utils, DOT, shinyTree, DESeq2,
SummarizedExperiment, glmpca, pheatmap, grid, ape, ggtree,
Rtsne, UpSetR, tidyr, esquisse (>= 1.0.0), cicerone
License: GPL (>= 3)
Archs: i386, x64
MD5sum: 385f7fe06c286d13f24f28582f2c2031
NeedsCompilation: no
Title: systemPipeShiny: An Interactive Framework for Workflow
Management and Visualization
Description: systemPipeShiny (SPS) extends the widely used systemPipeR
(SPR) workflow environment with a versatile graphical user
interface provided by a Shiny App. This allows non-R users,
such as experimentalists, to run many systemPipeR’s workflow
designs, control, and visualization functionalities
interactively without requiring knowledge of R. Most
importantly, SPS has been designed as a general purpose
framework for interacting with other R packages in an intuitive
manner. Like most Shiny Apps, SPS can be used on both local
computers as well as centralized server-based deployments that
can be accessed remotely as a public web service for using
SPR’s functionalities with community and/or private data. The
framework can integrate many core packages from the
R/Bioconductor ecosystem. Examples of SPS’ current
functionalities include: (a) interactive creation of
experimental designs and metadata using an easy to use tabular
editor or file uploader; (b) visualization of workflow
topologies combined with auto-generation of R Markdown preview
for interactively designed workflows; (d) access to a wide
range of data processing routines; (e) and an extendable set of
visualization functionalities. Complex visual results can be
managed on a 'Canvas Workbench’ allowing users to organize and
to compare plots in an efficient manner combined with a session
snapshot feature to continue work at a later time. The present
suite of pre-configured visualization examples. The modular
design of SPR makes it easy to design custom functions without
any knowledge of Shiny, as well as extending the environment in
the future with contributions from the community.
biocViews: Infrastructure, DataImport, Sequencing, QualityControl,
ReportWriting, ExperimentalDesign, Clustering
Author: Le Zhang [aut, cre], Daniela Cassol [aut], Ponmathi Ramasamy
[aut], Jianhai Zhang [aut], Gordon Mosher [aut], Thomas Girke
[aut]
Maintainer: Le Zhang <le.zhang001@email.ucr.edu>
URL: https://systempipe.org/sps,
https://github.com/systemPipeR/systemPipeShiny
VignetteBuilder: knitr
BugReports: https://github.com/systemPipeR/systemPipeShiny/issues
git_url: https://git.bioconductor.org/packages/systemPipeShiny
git_branch: RELEASE_3_13
git_last_commit: 6765e8d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/systemPipeShiny_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/systemPipeShiny_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/systemPipeShiny_1.2.0.tgz
vignettes: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.html
vignetteTitles: systemPipeShiny
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.R
dependencyCount: 122
Package: systemPipeTools
Version: 1.0.0
Imports: DESeq2, GGally, Rtsne, SummarizedExperiment, ape, dplyr,
ggplot2, ggrepel, ggtree, glmpca, pheatmap, plotly, tibble,
magrittr, DT, stats
Suggests: systemPipeR, knitr, BiocStyle, rmarkdown, testthat (>=
3.0.0), BiocGenerics, Biostrings, methods
License: Artistic-2.0
MD5sum: b00418e13af36f1a213bb106f3517b3a
NeedsCompilation: no
Title: Tools for data visualization
Description: systemPipeTools package extends the widely used
systemPipeR (SPR) workflow environment with an enhanced toolkit
for data visualization, including utilities to automate the
data visualizaton for analysis of differentially expressed
genes (DEGs). systemPipeTools provides data transformation and
data exploration functions via scatterplots, hierarchical
clustering heatMaps, principal component analysis,
multidimensional scaling, generalized principal components,
t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and
volcano plots. All these utilities can be integrated with the
modular design of the systemPipeR environment that allows users
to easily substitute any of these features and/or custom with
alternatives.
biocViews: Infrastructure, DataImport, Sequencing, QualityControl,
ReportWriting, ExperimentalDesign, Clustering,
DifferentialExpression, MultidimensionalScaling,
PrincipalComponent
Author: Daniela Cassol [aut, cre], Ponmathi Ramasamy [aut], Le Zhang
[aut], Thomas Girke [aut]
Maintainer: Daniela Cassol <danicassol@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/systemPipeTools
git_branch: RELEASE_3_13
git_last_commit: a3bb4cd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/systemPipeTools_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/systemPipeTools_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/systemPipeTools_1.0.0.tgz
vignettes: vignettes/systemPipeTools/inst/doc/systemPipeTools.html
vignetteTitles: systemPipeTools: Data Visualizations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/systemPipeTools/inst/doc/systemPipeTools.R
dependencyCount: 132
Package: TADCompare
Version: 1.2.0
Depends: R (>= 4.0)
Imports: dplyr, PRIMME, cluster, Matrix, magrittr, HiCcompare, ggplot2,
tidyr, ggpubr, RColorBrewer, reshape2, cowplot
Suggests: BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr,
pheatmap, rGREAT, SpectralTAD
License: MIT + file LICENSE
MD5sum: 6eea1a0b87f8e11c91f5d710b4b695db
NeedsCompilation: no
Title: TADCompare: Identification and characterization of differential
TADs
Description: TADCompare is an R package designed to identify and
characterize differential Topologically Associated Domains
(TADs) between multiple Hi-C contact matrices. It contains
functions for finding differential TADs between two datasets,
finding differential TADs over time and identifying consensus
TADs across multiple matrices. It takes all of the main types
of HiC input and returns simple, comprehensive, easy to analyze
results.
biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering
Author: Kellen Cresswell <cresswellkg@vcu.edu>, Mikhail Dozmorov
<mikhail.dozmorov@vcuhealth.org>
Maintainer: Kellen Cresswell <cresswellkg@vcu.edu>
URL: https://github.com/dozmorovlab/TADCompare
VignetteBuilder: knitr
BugReports: https://github.com/dozmorovlab/TADCompare/issues
git_url: https://git.bioconductor.org/packages/TADCompare
git_branch: RELEASE_3_13
git_last_commit: 1d1f391
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TADCompare_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TADCompare_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TADCompare_1.2.0.tgz
vignettes: vignettes/TADCompare/inst/doc/Input_Data.html,
vignettes/TADCompare/inst/doc/Ontology_Analysis.html,
vignettes/TADCompare/inst/doc/TADCompare.html
vignetteTitles: Input data formats, Gene Ontology Enrichment Analysis,
TAD comparison between two conditions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TADCompare/inst/doc/Input_Data.R,
vignettes/TADCompare/inst/doc/Ontology_Analysis.R,
vignettes/TADCompare/inst/doc/TADCompare.R
dependencyCount: 158
Package: TAPseq
Version: 1.4.0
Depends: R (>= 4.0)
Imports: methods, GenomicAlignments, GenomicRanges, IRanges,
BiocGenerics, S4Vectors (>= 0.20.1), GenomeInfoDb, BSgenome,
GenomicFeatures, Biostrings, dplyr, tidyr, BiocParallel
Suggests: testthat, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown,
ggplot2, Seurat, glmnet, cowplot, Matrix, rtracklayer
License: MIT + file LICENSE
MD5sum: d3c4e3365fc09cd2efff7d0711f8936c
NeedsCompilation: no
Title: Targeted scRNA-seq primer design for TAP-seq
Description: Design primers for targeted single-cell RNA-seq used by
TAP-seq. Create sequence templates for target gene panels and
design gene-specific primers using Primer3. Potential
off-targets can be estimated with BLAST. Requires working
installations of Primer3 and BLASTn.
biocViews: SingleCell, Sequencing, Technology, CRISPR, PooledScreens
Author: Andreas Gschwind [aut, cre]
(<https://orcid.org/0000-0002-0769-6907>), Lars Velten [aut]
(<https://orcid.org/0000-0002-1233-5874>), Lars Steinmetz [aut]
Maintainer: Andreas Gschwind <andreas.gschwind@stanford.edu>
URL: https://github.com/argschwind/TAPseq
SystemRequirements: Primer3 (>= 2.5.0), BLAST+ (>=2.6.0)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TAPseq
git_branch: RELEASE_3_13
git_last_commit: cd5c228
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TAPseq_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TAPseq_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TAPseq_1.4.0.tgz
vignettes: vignettes/TAPseq/inst/doc/tapseq_primer_design.html,
vignettes/TAPseq/inst/doc/tapseq_target_genes.html
vignetteTitles: TAP-seq primer design workflow, Select target genes for
TAP-seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TAPseq/inst/doc/tapseq_primer_design.R,
vignettes/TAPseq/inst/doc/tapseq_target_genes.R
dependencyCount: 99
Package: target
Version: 1.6.0
Depends: R (>= 3.6)
Imports: BiocGenerics, GenomicRanges, IRanges, matrixStats, methods,
stats, graphics, shiny
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, shinytest, shinyBS,
covr
License: GPL-3
MD5sum: b93ef0fde65cc44770eb0016ab3a7f41
NeedsCompilation: no
Title: Predict Combined Function of Transcription Factors
Description: Implement the BETA algorithm for infering direct target
genes from DNA-binding and perturbation expression data Wang et
al. (2013) <doi: 10.1038/nprot.2013.150>. Extend the algorithm
to predict the combined function of two DNA-binding elements
from comprable binding and expression data.
biocViews: Software, StatisticalMethod, Transcription
Author: Mahmoud Ahmed [aut, cre]
Maintainer: Mahmoud Ahmed <mahmoud.s.fahmy@students.kasralainy.edu.eg>
URL: https://github.com/MahShaaban/target
VignetteBuilder: knitr
BugReports: https://github.com/MahShaaban/target/issues
git_url: https://git.bioconductor.org/packages/target
git_branch: RELEASE_3_13
git_last_commit: e8a5075
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/target_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/target_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/target_1.6.0.tgz
vignettes: vignettes/target/inst/doc/extend-target.html,
vignettes/target/inst/doc/target.html
vignetteTitles: Using target to predict combined binding, Using the
target package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/target/inst/doc/extend-target.R,
vignettes/target/inst/doc/target.R
dependencyCount: 48
Package: TargetScore
Version: 1.30.0
Depends: pracma, Matrix
Suggests: TargetScoreData, gplots, Biobase, GEOquery
License: GPL-2
MD5sum: 2c092377935b1f27a2f9153333134d0f
NeedsCompilation: no
Title: TargetScore: Infer microRNA targets using
microRNA-overexpression data and sequence information
Description: Infer the posterior distributions of microRNA targets by
probabilistically modelling the likelihood
microRNA-overexpression fold-changes and sequence-based scores.
Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied
to log fold-changes and sequence scores to obtain the
posteriors of latent variable being the miRNA targets. The
final targetScore is computed as the sigmoid-transformed
fold-change weighted by the averaged posteriors of target
components over all of the features.
biocViews: miRNA
Author: Yue Li
Maintainer: Yue Li <yueli@cs.toronto.edu>
URL: http://www.cs.utoronto.ca/~yueli/software.html
git_url: https://git.bioconductor.org/packages/TargetScore
git_branch: RELEASE_3_13
git_last_commit: c61d77c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TargetScore_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TargetScore_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TargetScore_1.30.0.tgz
vignettes: vignettes/TargetScore/inst/doc/TargetScore.pdf
vignetteTitles: TargetScore: Infer microRNA targets using
microRNA-overexpression data and sequence information
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TargetScore/inst/doc/TargetScore.R
suggestsMe: TargetScoreData
dependencyCount: 9
Package: TargetSearch
Version: 1.48.0
Imports: graphics, grDevices, methods, ncdf4, stats, utils, assertthat
Suggests: TargetSearchData, BiocStyle, knitr, tinytest
License: GPL (>= 2)
MD5sum: 42d1737a68bb27604fe4cb62c7497437
NeedsCompilation: yes
Title: A package for the analysis of GC-MS metabolite profiling data
Description: This packages provides a targeted pre-processing method
for GC-MS data.
biocViews: MassSpectrometry, Preprocessing, DecisionTree,
ImmunoOncology
Author: Alvaro Cuadros-Inostroza <acuadros+bioc@gmail.com>, Jan Lisec,
Henning Redestig, Matt Hannah
Maintainer: Alvaro Cuadros-Inostroza <acuadros+bioc@gmail.com>
URL: https://github.com/acinostroza/TargetSearch
VignetteBuilder: knitr
BugReports: https://github.com/acinostroza/TargetSearch/issues
git_url: https://git.bioconductor.org/packages/TargetSearch
git_branch: RELEASE_3_13
git_last_commit: d59a715
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TargetSearch_1.48.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TargetSearch_1.48.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TargetSearch_1.48.0.tgz
vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf,
vignettes/TargetSearch/inst/doc/TargetSearch.pdf
vignetteTitles: RI correction extra, The TargetSearch Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TargetSearch/inst/doc/RetentionIndexCorrection.R,
vignettes/TargetSearch/inst/doc/RICorrection.R,
vignettes/TargetSearch/inst/doc/TargetSearch.R
dependencyCount: 8
Package: TarSeqQC
Version: 1.22.0
Depends: R (>= 3.5.1), methods, GenomicRanges, Rsamtools (>= 1.9.2),
ggplot2, plyr, openxlsx
Imports: grDevices, stats, utils, S4Vectors, IRanges, BiocGenerics,
reshape2, GenomeInfoDb, BiocParallel, Biostrings, cowplot,
graphics, GenomicAlignments, Hmisc
Suggests: BiocManager, RUnit
License: GPL (>=2)
MD5sum: 6d7b69223c1420f24082260b90c96a7f
NeedsCompilation: no
Title: TARgeted SEQuencing Experiment Quality Control
Description: The package allows the representation of targeted
experiment in R. This is based on current packages and
incorporates functions to do a quality control over this kind
of experiments and a fast exploration of the sequenced regions.
An xlsx file is generated as output.
biocViews: Software, Sequencing, TargetedResequencing, QualityControl,
Visualization, Coverage, Alignment, DataImport
Author: Gabriela A. Merino, Cristobal Fresno, Yanina Murua, Andrea S.
Llera and Elmer A. Fernandez
Maintainer: Gabriela Merino <merino.gabriela33@gmail.com>
URL: http://www.bdmg.com.ar
git_url: https://git.bioconductor.org/packages/TarSeqQC
git_branch: RELEASE_3_13
git_last_commit: 2f87a51
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TarSeqQC_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TarSeqQC_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TarSeqQC_1.22.0.tgz
vignettes: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.pdf
vignetteTitles: TarSeqQC: Targeted Sequencing Experiment Quality
Control
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.R
dependencyCount: 102
Package: TBSignatureProfiler
Version: 1.4.11
Depends: R (>= 4.1)
Imports: ASSIGN (>= 1.23.1), GSVA, singscore, methods, ComplexHeatmap,
RColorBrewer, ggplot2, S4Vectors, reshape2, ROCit, DESeq2, DT,
edgeR, gdata, SummarizedExperiment, magrittr, stats, rlang,
BiocParallel, BiocGenerics
Suggests: testthat, spelling, lintr, covr, knitr, rmarkdown, BiocStyle,
shiny, circlize, caret, dplyr, plyr, impute, sva, glmnet,
randomForest, MASS, class, e1071, pROC, HGNChelper
License: MIT + file LICENSE
MD5sum: 7a19ac0f58e471c088b6ea56900f065b
NeedsCompilation: no
Title: Profile RNA-Seq Data Using TB Pathway Signatures
Description: Gene signatures of TB progression, TB disease, and other
TB disease states have been validated and published previously.
This package aggregates known signatures and provides
computational tools to enlist their usage on other datasets.
The TBSignatureProfiler makes it easy to profile RNA-Seq data
using these signatures and includes common signature profiling
tools including ASSIGN, GSVA, and ssGSEA.
biocViews: GeneExpression, DifferentialExpression
Author: David Jenkins [aut], Aubrey Odom [aut, cre], Xutao Wang [aut],
Yue Zhao [aut], Christian Love [aut], W. Evan Johnson [aut]
Maintainer: Aubrey Odom <aodom@bu.edu>
URL: https://github.com/compbiomed/TBSignatureProfiler
https://compbiomed.github.io/TBSignatureProfiler-docs/
VignetteBuilder: knitr
BugReports: https://github.com/compbiomed/TBSignatureProfiler/issues
git_url: https://git.bioconductor.org/packages/TBSignatureProfiler
git_branch: RELEASE_3_13
git_last_commit: 2730a24
git_last_commit_date: 2021-10-13
Date/Publication: 2021-10-14
source.ver: src/contrib/TBSignatureProfiler_1.4.11.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TBSignatureProfiler_1.4.11.zip
mac.binary.ver: bin/macosx/contrib/4.1/TBSignatureProfiler_1.4.11.tgz
vignettes: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.html
vignetteTitles: "Introduction to the TBSignatureProfiler"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.R
dependencyCount: 160
Package: TCC
Version: 1.32.0
Depends: R (>= 3.0), methods, DESeq2, edgeR, baySeq, ROC
Suggests: RUnit, BiocGenerics
Enhances: snow
License: GPL-2
MD5sum: 1a33afbaa0c915cf1cb5077af6bea337
NeedsCompilation: no
Title: TCC: Differential expression analysis for tag count data with
robust normalization strategies
Description: This package provides a series of functions for performing
differential expression analysis from RNA-seq count data using
robust normalization strategy (called DEGES). The basic idea of
DEGES is that potential differentially expressed genes or
transcripts (DEGs) among compared samples should be removed
before data normalization to obtain a well-ranked gene list
where true DEGs are top-ranked and non-DEGs are bottom ranked.
This can be done by performing a multi-step normalization
strategy (called DEGES for DEG elimination strategy). A major
characteristic of TCC is to provide the robust normalization
methods for several kinds of count data (two-group with or
without replicates, multi-group/multi-factor, and so on) by
virtue of the use of combinations of functions in depended
packages.
biocViews: ImmunoOncology, Sequencing, DifferentialExpression, RNASeq
Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji
Kadota
Maintainer: Jianqiang Sun <wukong@bi.a.u-tokyo.ac.jp>, Tomoaki
Nishiyama <tomoakin@staff.kanazawa-u.ac.jp>
git_url: https://git.bioconductor.org/packages/TCC
git_branch: RELEASE_3_13
git_last_commit: c9bfbb9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TCC_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TCC_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TCC_1.32.0.tgz
vignettes: vignettes/TCC/inst/doc/TCC.pdf
vignetteTitles: TCC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TCC/inst/doc/TCC.R
suggestsMe: compcodeR, ExpHunterSuite
dependencyCount: 105
Package: TCGAbiolinks
Version: 2.20.1
Depends: R (>= 4.0)
Imports: downloader (>= 0.4), grDevices, biomaRt, dplyr, graphics,
tibble, GenomicRanges, XML (>= 3.98.0), data.table, jsonlite
(>= 1.0.0), plyr, knitr, methods, ggplot2, stringr (>= 1.0.0),
IRanges, rvest (>= 0.3.0), stats, utils, S4Vectors, R.utils,
SummarizedExperiment (>= 1.4.0), TCGAbiolinksGUI.data, readr,
tools, tidyr, purrr, xml2, httr (>= 1.2.1)
Suggests: jpeg, png, BiocStyle, rmarkdown, devtools, maftools,
parmigene, c3net, minet, dnet, Biobase, affy, testthat, sesame,
pathview, clusterProfiler, ComplexHeatmap, circlize,
ConsensusClusterPlus, igraph, supraHex, limma, edgeR, sva,
EDASeq, survminer, genefilter, gridExtra, survival, doParallel,
parallel, ggrepel (>= 0.6.3), scales, grid
License: GPL (>= 3)
MD5sum: 6e9004f115b1b3c48a113d6ef08cfe52
NeedsCompilation: no
Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis
with GDC data
Description: The aim of TCGAbiolinks is : i) facilitate the GDC
open-access data retrieval, ii) prepare the data using the
appropriate pre-processing strategies, iii) provide the means
to carry out different standard analyses and iv) to easily
reproduce earlier research results. In more detail, the package
provides multiple methods for analysis (e.g., differential
expression analysis, identifying differentially methylated
regions) and methods for visualization (e.g., survival plots,
volcano plots, starburst plots) in order to easily develop
complete analysis pipelines.
biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation,
GeneExpression, MethylationArray, DifferentialExpression,
Pathways, Network, Sequencing, Survival, Software
Author: Antonio Colaprico, Tiago Chedraoui Silva, Catharina Olsen,
Luciano Garofano, Davide Garolini, Claudia Cava, Thais Sabedot,
Tathiane Malta, Stefano M. Pagnotta, Isabella Castiglioni,
Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr
Maintainer: Tiago Chedraoui Silva <tiagochst@gmail.com>, Antonio
Colaprico <axc1833@med.miami.edu>
URL: https://github.com/BioinformaticsFMRP/TCGAbiolinks
VignetteBuilder: knitr
BugReports: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues
git_url: https://git.bioconductor.org/packages/TCGAbiolinks
git_branch: RELEASE_3_13
git_last_commit: d7830c91
git_last_commit_date: 2021-10-04
Date/Publication: 2021-10-07
source.ver: src/contrib/TCGAbiolinks_2.20.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TCGAbiolinks_2.20.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/TCGAbiolinks_2.20.1.tgz
vignettes: vignettes/TCGAbiolinks/inst/doc/analysis.html,
vignettes/TCGAbiolinks/inst/doc/casestudy.html,
vignettes/TCGAbiolinks/inst/doc/classifiers.html,
vignettes/TCGAbiolinks/inst/doc/clinical.html,
vignettes/TCGAbiolinks/inst/doc/download_prepare.html,
vignettes/TCGAbiolinks/inst/doc/extension.html,
vignettes/TCGAbiolinks/inst/doc/gui.html,
vignettes/TCGAbiolinks/inst/doc/index.html,
vignettes/TCGAbiolinks/inst/doc/mutation.html,
vignettes/TCGAbiolinks/inst/doc/query.html,
vignettes/TCGAbiolinks/inst/doc/subtypes.html
vignetteTitles: 7. Analyzing and visualizing TCGA data, 8. Case
Studies, 10. Classifiers, "4. Clinical data", "3. Downloading
and preparing files for analysis", "10.
TCGAbiolinks_Extension", "9. Graphical User Interface (GUI)",
"1. Introduction", "5. Mutation data", "2. Searching GDC
database", 6. Compilation of TCGA molecular subtypes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TCGAbiolinks/inst/doc/analysis.R,
vignettes/TCGAbiolinks/inst/doc/casestudy.R,
vignettes/TCGAbiolinks/inst/doc/classifiers.R,
vignettes/TCGAbiolinks/inst/doc/clinical.R,
vignettes/TCGAbiolinks/inst/doc/download_prepare.R,
vignettes/TCGAbiolinks/inst/doc/extension.R,
vignettes/TCGAbiolinks/inst/doc/gui.R,
vignettes/TCGAbiolinks/inst/doc/index.R,
vignettes/TCGAbiolinks/inst/doc/mutation.R,
vignettes/TCGAbiolinks/inst/doc/query.R,
vignettes/TCGAbiolinks/inst/doc/subtypes.R
importsMe: ELMER, MoonlightR, SpidermiR, TCGAbiolinksGUI,
SingscoreAMLMutations, TCGAWorkflow
suggestsMe: Rediscover
dependencyCount: 114
Package: TCGAbiolinksGUI
Version: 1.18.0
Depends: R (>= 3.3.1), shinydashboard (>= 0.5.3), TCGAbiolinksGUI.data
Imports: shiny (>= 0.14.1), downloader (>= 0.4), grid, DT, plotly,
readr, maftools, stringr (>= 1.1.0), SummarizedExperiment,
ggrepel, data.table, caret, shinyFiles (>= 0.6.2), ggplot2 (>=
2.1.0), pathview, ELMER (>= 2.0.0), clusterProfiler, parallel,
TCGAbiolinks (>= 2.5.5), shinyjs (>= 0.7), colourpicker,
sesame, shinyBS (>= 0.61)
Suggests: testthat, dplyr, knitr, roxygen2, devtools, rvest, xml2,
BiocStyle, animation, pander
License: GPL (>= 3)
MD5sum: ba22715a8e237e91b844afc7fea7d8b7
NeedsCompilation: no
Title: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer
molecular and clinical data"
Description: "TCGAbiolinksGUI: A Graphical User Interface to analyze
cancer molecular and clinical data. A demo version of GUI is
found in https://tcgabiolinksgui.shinyapps.io/tcgabiolinks/"
biocViews: Genetics, GUI, DNAMethylation, StatisticalMethod,
DifferentialMethylation, GeneRegulation, GeneExpression,
MethylationArray, DifferentialExpression, Sequencing, Pathways,
Network, DNASeq
Author: Tiago Chedraoui Silva <tiagochst@gmail.com>, Antonio Colaprico
<antonio.colaprico@ulb.ac.be>, Catharina Olsen
<colsen@ulb.ac.be>, Michele Ceccarelli, Gianluca Bontempi
<gbonte@ulb.ac.be>, Benjamin P. Berman
<Benjamin.Berman@cshs.org>, Houtan Noushmehr
<houtana@gmail.com>
Maintainer: Tiago C. Silva <tiagochst@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TCGAbiolinksGUI
git_branch: RELEASE_3_13
git_last_commit: 8315ea1
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TCGAbiolinksGUI_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TCGAbiolinksGUI_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TCGAbiolinksGUI_1.18.0.tgz
vignettes: vignettes/TCGAbiolinksGUI/inst/doc/analysis.html,
vignettes/TCGAbiolinksGUI/inst/doc/Cases.html,
vignettes/TCGAbiolinksGUI/inst/doc/data.html,
vignettes/TCGAbiolinksGUI/inst/doc/index.html,
vignettes/TCGAbiolinksGUI/inst/doc/integrative.html
vignetteTitles: "3. Analysis menu", "5. Cases study", "2. Data menu",
"1. Introduction", "4. Integrative analysis menu"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TCGAbiolinksGUI/inst/doc/data.R,
vignettes/TCGAbiolinksGUI/inst/doc/index.R
dependencyCount: 298
Package: TCGAutils
Version: 1.12.0
Depends: R (>= 4.0.0)
Imports: AnnotationDbi, BiocGenerics, GenomeInfoDb, GenomicFeatures,
GenomicRanges, GenomicDataCommons, IRanges, methods,
MultiAssayExperiment, RaggedExperiment (>= 1.5.7), rvest,
S4Vectors, stats, stringr, SummarizedExperiment, utils, xml2
Suggests: BiocFileCache, BiocStyle, curatedTCGAData, ComplexHeatmap,
devtools, dplyr, IlluminaHumanMethylation450kanno.ilmn12.hg19,
impute, knitr, magrittr, mirbase.db, org.Hs.eg.db,
RColorBrewer, readr, rmarkdown, RTCGAToolbox (>= 2.17.4),
rtracklayer, R.utils, testthat,
TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: Artistic-2.0
MD5sum: 952ca0341b0cfee2843b3aaa3736b51c
NeedsCompilation: no
Title: TCGA utility functions for data management
Description: A suite of helper functions for checking and manipulating
TCGA data including data obtained from the curatedTCGAData
experiment package. These functions aim to simplify and make
working with TCGA data more manageable.
biocViews: Software, WorkflowStep, Preprocessing
Author: Marcel Ramos [aut, cre], Lucas Schiffer [aut], Sean Davis
[ctb], Levi Waldron [aut]
Maintainer: Marcel Ramos <marcel.ramos@roswellpark.org>
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/TCGAutils/issues
git_url: https://git.bioconductor.org/packages/TCGAutils
git_branch: RELEASE_3_13
git_last_commit: d0b9159
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TCGAutils_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TCGAutils_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TCGAutils_1.12.0.tgz
vignettes: vignettes/TCGAutils/inst/doc/TCGAutils.html
vignetteTitles: TCGAutils Essentials
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TCGAutils/inst/doc/TCGAutils.R
importsMe: cBioPortalData, RTCGAToolbox
suggestsMe: CNVRanger, dce, glmSparseNet, netDx, curatedTCGAData
dependencyCount: 107
Package: TCseq
Version: 1.16.0
Depends: R (>= 3.4)
Imports: edgeR, BiocGenerics, reshape2, GenomicRanges, IRanges,
SummarizedExperiment, GenomicAlignments, Rsamtools, e1071,
cluster, ggplot2, grid, grDevices, stats, utils, methods,
locfit
Suggests: testthat
License: GPL (>= 2)
MD5sum: 6011df00b02a6f375ef21b459ca67458
NeedsCompilation: no
Title: Time course sequencing data analysis
Description: Quantitative and differential analysis of epigenomic and
transcriptomic time course sequencing data, clustering analysis
and visualization of temporal patterns of time course data.
biocViews: Epigenetics, TimeCourse, Sequencing, ChIPSeq, RNASeq,
DifferentialExpression, Clustering, Visualization
Author: Mengjun Wu <minervajunjun@gmail.com>, Lei Gu
<leigu@broadinstitute.org>
Maintainer: Mengjun Wu <minervajunjun@gmail.com>
git_url: https://git.bioconductor.org/packages/TCseq
git_branch: RELEASE_3_13
git_last_commit: fa7ce98
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TCseq_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TCseq_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TCseq_1.16.0.tgz
vignettes: vignettes/TCseq/inst/doc/TCseq.pdf
vignetteTitles: TCseq Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TCseq/inst/doc/TCseq.R
dependencyCount: 79
Package: TDARACNE
Version: 1.42.0
Depends: GenKern, Rgraphviz, Biobase
License: GPL-2
Archs: i386, x64
MD5sum: 0b64d21387b03e942e3a37f06b22b8c0
NeedsCompilation: no
Title: Network reverse engineering from time course data.
Description: To infer gene networks from time-series measurements is a
current challenge into bioinformatics research area. In order
to detect dependencies between genes at different time delays,
we propose an approach to infer gene regulatory networks from
time-series measurements starting from a well known algorithm
based on information theory. The proposed algorithm is expected
to be useful in reconstruction of small biological directed
networks from time course data.
biocViews: Microarray, TimeCourse
Author: Zoppoli P.,Morganella S., Ceccarelli M.
Maintainer: Zoppoli Pietro <zoppoli.pietro@gmail.com>
git_url: https://git.bioconductor.org/packages/TDARACNE
git_branch: RELEASE_3_13
git_last_commit: 2e91540
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TDARACNE_1.42.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TDARACNE_1.42.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TDARACNE_1.42.0.tgz
vignettes: vignettes/TDARACNE/inst/doc/TDARACNE.pdf
vignetteTitles: TDARACNE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TDARACNE/inst/doc/TDARACNE.R
dependencyCount: 14
Package: tenXplore
Version: 1.14.2
Depends: R (>= 3.4), shiny, restfulSE (>= 0.99.12)
Imports: methods, ontoProc (>= 0.99.7), SummarizedExperiment,
AnnotationDbi, matrixStats, org.Mm.eg.db, stats, utils
Suggests: org.Hs.eg.db, testthat, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 13c43f428c6a640a24ef4d164a5fc59f
NeedsCompilation: no
Title: ontological exploration of scRNA-seq of 1.3 million mouse
neurons from 10x genomics
Description: Perform ontological exploration of scRNA-seq of 1.3
million mouse neurons from 10x genomics.
biocViews: ImmunoOncology, DimensionReduction, PrincipalComponent,
Transcriptomics, SingleCell
Author: Vince Carey
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tenXplore
git_branch: RELEASE_3_13
git_last_commit: bd72b38
git_last_commit_date: 2021-09-11
Date/Publication: 2021-09-12
source.ver: src/contrib/tenXplore_1.14.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tenXplore_1.14.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/tenXplore_1.14.2.tgz
vignettes: vignettes/tenXplore/inst/doc/tenXplore.html
vignetteTitles: tenXplore: ontology for scRNA-seq,, applied to 10x 1.3
million neurons
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tenXplore/inst/doc/tenXplore.R
dependencyCount: 119
Package: TEQC
Version: 4.14.0
Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5),
Rsamtools, hwriter
Imports: Biobase (>= 2.15.1)
License: GPL (>= 2)
MD5sum: 044792a752ca178d0af1d6ad408f820f
NeedsCompilation: no
Title: Quality control for target capture experiments
Description: Target capture experiments combine hybridization-based (in
solution or on microarrays) capture and enrichment of genomic
regions of interest (e.g. the exome) with high throughput
sequencing of the captured DNA fragments. This package provides
functionalities for assessing and visualizing the quality of
the target enrichment process, like specificity and sensitivity
of the capture, per-target read coverage and so on.
biocViews: QualityControl, Microarray, Sequencing, Genetics
Author: M. Hummel, S. Bonnin, E. Lowy, G. Roma
Maintainer: Sarah Bonnin <Sarah.Bonnin@crg.eu>
git_url: https://git.bioconductor.org/packages/TEQC
git_branch: RELEASE_3_13
git_last_commit: a05076b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TEQC_4.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TEQC_4.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TEQC_4.14.0.tgz
vignettes: vignettes/TEQC/inst/doc/TEQC.pdf
vignetteTitles: TEQC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TEQC/inst/doc/TEQC.R
dependencyCount: 31
Package: ternarynet
Version: 1.36.0
Depends: R (>= 4.0)
Imports: utils, igraph, methods, graphics, stats, BiocParallel
Suggests: testthat
Enhances: Rmpi, snow
License: GPL (>= 2)
MD5sum: 5f1514b50dd48633417e9944fcde0c2f
NeedsCompilation: yes
Title: Ternary Network Estimation
Description: Gene-regulatory network (GRN) modeling seeks to infer
dependencies between genes and thereby provide insight into the
regulatory relationships that exist within a cell. This package
provides a computational Bayesian approach to GRN estimation
from perturbation experiments using a ternary network model, in
which gene expression is discretized into one of 3 states: up,
unchanged, or down). The ternarynet package includes a parallel
implementation of the replica exchange Monte Carlo algorithm
for fitting network models, using MPI.
biocViews: Software, CellBiology, GraphAndNetwork, Network, Bayesian
Author: Matthew N. McCall <mccallm@gmail.com>, Anthony Almudevar
<Anthony_Alumudevar@urmc.rochester.edu>, David Burton
<David_Burton@urmc.rochester.edu>, Harry Stern
<harry.stern@rochester.edu>
Maintainer: McCall N. Matthew <mccallm@gmail.com>
git_url: https://git.bioconductor.org/packages/ternarynet
git_branch: RELEASE_3_13
git_last_commit: cb404d2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ternarynet_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ternarynet_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ternarynet_1.36.0.tgz
vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf
vignetteTitles: ternarynet: A Computational Bayesian Approach to
Ternary Network Estimation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R
dependencyCount: 19
Package: TFARM
Version: 1.14.0
Depends: R (>= 3.4)
Imports: arules, fields, GenomicRanges, graphics, stringr, methods,
stats, gplots
Suggests: BiocStyle, knitr, plyr
License: Artistic-2.0
MD5sum: a8d292b41bc27b2847135e3d90be2340
NeedsCompilation: no
Title: Transcription Factors Association Rules Miner
Description: It searches for relevant associations of transcription
factors with a transcription factor target, in specific genomic
regions. It also allows to evaluate the Importance Index
distribution of transcription factors (and combinations of
transcription factors) in association rules.
biocViews: BiologicalQuestion, Infrastructure, StatisticalMethod,
Transcription
Author: Liuba Nausicaa Martino, Alice Parodi, Gaia Ceddia, Piercesare
Secchi, Stefano Campaner, Marco Masseroli
Maintainer: Liuba Nausicaa Martino <liuban.martino@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TFARM
git_branch: RELEASE_3_13
git_last_commit: 8557630
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TFARM_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TFARM_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TFARM_1.14.0.tgz
vignettes: vignettes/TFARM/inst/doc/TFARM.pdf
vignetteTitles: Transcription Factor Association Rule Miner
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TFARM/inst/doc/TFARM.R
dependencyCount: 64
Package: TFBSTools
Version: 1.30.0
Depends: R (>= 3.2.2)
Imports: Biobase(>= 2.28), Biostrings(>= 2.36.4), BiocGenerics(>=
0.14.0), BiocParallel(>= 1.2.21), BSgenome(>= 1.36.3),
caTools(>= 1.17.1), CNEr(>= 1.4.0), DirichletMultinomial(>=
1.10.0), GenomeInfoDb(>= 1.6.1), GenomicRanges(>= 1.20.6),
gtools(>= 3.5.0), grid, IRanges(>= 2.2.7), methods, DBI (>=
0.6), RSQLite(>= 1.0.0), rtracklayer(>= 1.28.10), seqLogo(>=
1.34.0), S4Vectors(>= 0.9.25), TFMPvalue(>= 0.0.5), XML(>=
3.98-1.3), XVector(>= 0.8.0), parallel
Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11),
testthat, JASPAR2016(>= 1.0.0), JASPAR2018(>= 1.0.0)
License: GPL-2
MD5sum: f57defaa5dac8b96286881e0cfd13c35
NeedsCompilation: yes
Title: Software Package for Transcription Factor Binding Site (TFBS)
Analysis
Description: TFBSTools is a package for the analysis and manipulation
of transcription factor binding sites. It includes matrices
conversion between Position Frequency Matirx (PFM), Position
Weight Matirx (PWM) and Information Content Matrix (ICM). It
can also scan putative TFBS from sequence/alignment, query
JASPAR database and provides a wrapper of de novo motif
discovery software.
biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery,
Transcription, Alignment
Author: Ge Tan [aut, cre]
Maintainer: Ge Tan <ge_tan@live.com>
URL: https://github.com/ge11232002/TFBSTools
VignetteBuilder: knitr
BugReports: https://github.com/ge11232002/TFBSTools/issues
git_url: https://git.bioconductor.org/packages/TFBSTools
git_branch: RELEASE_3_13
git_last_commit: a8d5eba
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TFBSTools_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TFBSTools_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TFBSTools_1.30.0.tgz
vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.html
vignetteTitles: Transcription factor binding site (TFBS) analysis with
the "TFBSTools" package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R
importsMe: chromVAR, enrichTF, esATAC, MatrixRider, motifmatchr,
primirTSS
suggestsMe: MAGAR, MethReg, pageRank, universalmotif, JASPAR2018,
JASPAR2020, CAGEWorkflow, Signac
dependencyCount: 122
Package: TFEA.ChIP
Version: 1.12.0
Depends: R (>= 3.3)
Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, grDevices,
dplyr, stats, utils, R.utils, methods, org.Hs.eg.db
Suggests: knitr, rmarkdown, S4Vectors, plotly, scales, tidyr, ggplot2,
GSEABase, DESeq2, BiocGenerics, ggrepel, rcompanion,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: Artistic-2.0
MD5sum: ef8178cfa520b80d703ac561b5c05bb7
NeedsCompilation: no
Title: Analyze Transcription Factor Enrichment
Description: Package to analize transcription factor enrichment in a
gene set using data from ChIP-Seq experiments.
biocViews: Transcription, GeneRegulation, GeneSetEnrichment,
Transcriptomics, Sequencing, ChIPSeq, RNASeq, ImmunoOncology
Author: Laura Puente SantamarÃa, Luis del Peso
Maintainer: Laura Puente SantamarÃa <lpsantamaria@iib.uam.es>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TFEA.ChIP
git_branch: RELEASE_3_13
git_last_commit: ef83d9d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TFEA.ChIP_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TFEA.ChIP_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TFEA.ChIP_1.12.0.tgz
vignettes: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.html
vignetteTitles: TFEA.ChIP: a tool kit for transcription factor
enrichment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.R
dependencyCount: 100
Package: TFHAZ
Version: 1.14.0
Depends: R(>= 3.4)
Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils,
IRanges, methods
Suggests: BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 045208b9138fb46a5fdab5bd4d8756e1
NeedsCompilation: no
Title: Transcription Factor High Accumulation Zones
Description: It finds trascription factor (TF) high accumulation DNA
zones, i.e., regions along the genome where there is a high
presence of different transcription factors. Starting from a
dataset containing the genomic positions of TF binding regions,
for each base of the selected chromosome the accumulation of
TFs is computed. Three different types of accumulation (TF,
region and base accumulation) are available, together with the
possibility of considering, in the single base accumulation
computing, the TFs present not only in that single base, but
also in its neighborhood, within a window of a given width. Two
different methods for the search of TF high accumulation DNA
zones, called "binding regions" and "overlaps", are available.
In addition, some functions are provided in order to analyze,
visualize and compare results obtained with different input
parameters.
biocViews: Software, BiologicalQuestion, Transcription, ChIPSeq,
Coverage
Author: Alberto Marchesi, Marco Masseroli
Maintainer: Alberto Marchesi <alberto.march91@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TFHAZ
git_branch: RELEASE_3_13
git_last_commit: cee2ddc
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TFHAZ_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TFHAZ_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TFHAZ_1.14.0.tgz
vignettes: vignettes/TFHAZ/inst/doc/TFHAZ.html
vignetteTitles: TFHAZ
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TFHAZ/inst/doc/TFHAZ.R
dependencyCount: 18
Package: TFutils
Version: 1.12.2
Depends: R (>= 3.5.0)
Imports: methods, dplyr, magrittr, miniUI, shiny, Rsamtools, GSEABase,
rjson, BiocFileCache, DT, httr, readxl
Suggests: knitr, data.table, testthat, AnnotationDbi, AnnotationFilter,
Biobase, GenomicFeatures, GenomicRanges, Gviz, IRanges,
S4Vectors, org.Hs.eg.db, EnsDb.Hsapiens.v75, BiocParallel,
BiocStyle, GO.db, GenomicFiles, GenomeInfoDb,
SummarizedExperiment, UpSetR, ggplot2, png, gwascat, MotifDb,
motifStack, RColorBrewer, rmarkdown
License: Artistic-2.0
MD5sum: 93fcf6da2b85c47c9e175c2d342bc94f
NeedsCompilation: no
Title: TFutils
Description: Package to work with TF metadata from various sources.
biocViews: Transcriptomics
Author: Vincent Carey [aut, cre], Shweta Gopaulakrishnan [aut]
Maintainer: Vincent Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TFutils
git_branch: RELEASE_3_13
git_last_commit: df013ae
git_last_commit_date: 2021-08-03
Date/Publication: 2021-08-05
source.ver: src/contrib/TFutils_1.12.2.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/TFutils_1.12.2.tgz
vignettes: vignettes/TFutils/inst/doc/fimo16.html,
vignettes/TFutils/inst/doc/TFutils.html
vignetteTitles: A note on fimo16, TFutils -- representing TFBS and TF
target sets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TFutils/inst/doc/fimo16.R,
vignettes/TFutils/inst/doc/TFutils.R
dependencyCount: 105
Package: tidybulk
Version: 1.4.0
Depends: R (>= 4.1.0)
Imports: tibble, readr, dplyr, magrittr, tidyr, stringr, rlang, purrr,
preprocessCore, stats, parallel, utils, lifecycle, scales,
SummarizedExperiment, methods
Suggests: BiocStyle, testthat, vctrs, AnnotationDbi, BiocManager,
Rsubread, e1071, edgeR, limma, org.Hs.eg.db, org.Mm.eg.db, sva,
GGally, knitr, qpdf, covr, Seurat, KernSmooth, Rtsne,
S4Vectors, ggplot2, widyr, clusterProfiler, msigdbr, DESeq2,
broom, survival, boot, betareg, tidyHeatmap, pasilla, ggrepel,
devtools, functional, survminer, tidySummarizedExperiment,
markdown
License: GPL-3
MD5sum: 3906f7e8f13c3265494221277511a70c
NeedsCompilation: no
Title: Brings transcriptomics to the tidyverse
Description: This is a collection of utility functions that allow to
perform exploration of and calculations to RNA sequencing data,
in a modular, pipe-friendly and tidy fashion.
biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression,
GeneExpression, Normalization, Clustering, QualityControl,
Sequencing, Transcription, Transcriptomics
Author: Stefano Mangiola [aut, cre], Maria Doyle [ctb]
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tidybulk
git_branch: RELEASE_3_13
git_last_commit: 945a727
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tidybulk_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tidybulk_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tidybulk_1.4.0.tgz
vignettes: vignettes/tidybulk/inst/doc/comparison_with_base_R.html,
vignettes/tidybulk/inst/doc/introduction.html,
vignettes/tidybulk/inst/doc/manuscript_differential_transcript_abundance.html,
vignettes/tidybulk/inst/doc/manuscript_transcriptional_signatures.html
vignetteTitles: Comparison with base R, Overview of the tidybulk
package, Manuscript code - differential feature abundance,
Manuscript code - transcriptional signature identification
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tidybulk/inst/doc/comparison_with_base_R.R,
vignettes/tidybulk/inst/doc/introduction.R,
vignettes/tidybulk/inst/doc/manuscript_differential_transcript_abundance.R,
vignettes/tidybulk/inst/doc/manuscript_transcriptional_signatures.R
dependencyCount: 66
Package: tidySingleCellExperiment
Version: 1.2.1
Depends: R (>= 4.0.0), SingleCellExperiment
Imports: SummarizedExperiment, dplyr, tibble, tidyr, ggplot2, plotly,
magrittr, rlang, purrr, lifecycle, methods, utils, S4Vectors,
tidyselect, ellipsis, pillar, stringr, cli, fansi
Suggests: BiocStyle, testthat, knitr, markdown, SingleCellSignalR,
SingleR, scater, scran, tidyHeatmap, igraph, GGally, Matrix,
uwot, celldex, dittoSeq, EnsDb.Hsapiens.v86
License: GPL-3
MD5sum: b571c190a6502d7cc2de95432a850d96
NeedsCompilation: no
Title: Brings SingleCellExperiment to the Tidyverse
Description: tidySingleCellExperiment is an adapter that abstracts the
'SingleCellExperiment' container in the form of tibble and
allows the data manipulation, plotting and nesting using
'tidyverse'
biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression,
GeneExpression, Normalization, Clustering, QualityControl,
Sequencing
Author: Stefano Mangiola [aut, cre]
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
URL: https://github.com/stemangiola/tidySingleCellExperiment
VignetteBuilder: knitr
BugReports:
https://github.com/stemangiola/tidySingleCellExperiment/issues
git_url: https://git.bioconductor.org/packages/tidySingleCellExperiment
git_branch: RELEASE_3_13
git_last_commit: 775f980
git_last_commit_date: 2021-08-17
Date/Publication: 2021-08-19
source.ver: src/contrib/tidySingleCellExperiment_1.2.1.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/tidySingleCellExperiment_1.2.1.zip
mac.binary.ver:
bin/macosx/contrib/4.1/tidySingleCellExperiment_1.2.1.tgz
vignettes:
vignettes/tidySingleCellExperiment/inst/doc/introduction.html
vignetteTitles: Overview of the tidySingleCellExperiment package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tidySingleCellExperiment/inst/doc/introduction.R
dependencyCount: 84
Package: tidySummarizedExperiment
Version: 1.2.0
Depends: R (>= 4.0.0), SummarizedExperiment
Imports: tibble (>= 3.0.4), dplyr, magrittr, tidyr, ggplot2, rlang,
purrr, lifecycle, methods, plotly, utils, S4Vectors,
tidyselect, ellipsis, pillar, stringr, cli, fansi
Suggests: BiocStyle, testthat, knitr, markdown
License: GPL-3
MD5sum: 895eef3b22dd806cf1c4ca437e4725d3
NeedsCompilation: no
Title: Brings SummarizedExperiment to the Tidyverse
Description: tidySummarizedExperiment is an adapter that abstracts the
'SummarizedExperiment' container in the form of tibble and
allows the data manipulation, plotting and nesting using
'tidyverse'
biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression,
GeneExpression, Normalization, Clustering, QualityControl,
Sequencing, Transcription, Transcriptomics
Author: Stefano Mangiola [aut, cre]
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tidySummarizedExperiment
git_branch: RELEASE_3_13
git_last_commit: bd649f2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tidySummarizedExperiment_1.2.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/tidySummarizedExperiment_1.2.0.zip
mac.binary.ver:
bin/macosx/contrib/4.1/tidySummarizedExperiment_1.2.0.tgz
vignettes:
vignettes/tidySummarizedExperiment/inst/doc/introduction.html
vignetteTitles: Overview of the tidySummarizedExperiment package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tidySummarizedExperiment/inst/doc/introduction.R
suggestsMe: tidybulk
dependencyCount: 83
Package: tigre
Version: 1.46.0
Depends: R (>= 2.11.0), BiocGenerics, Biobase
Imports: methods, AnnotationDbi, gplots, graphics, grDevices, stats,
utils, annotate, DBI, RSQLite
Suggests: drosgenome1.db, puma, lumi, BiocStyle, BiocManager
License: AGPL-3
Archs: i386, x64
MD5sum: a654971a7f687ca64bcda45e0185e3ee
NeedsCompilation: yes
Title: Transcription factor Inference through Gaussian process
Reconstruction of Expression
Description: The tigre package implements our methodology of Gaussian
process differential equation models for analysis of gene
expression time series from single input motif networks. The
package can be used for inferring unobserved transcription
factor (TF) protein concentrations from expression measurements
of known target genes, or for ranking candidate targets of a
TF.
biocViews: Microarray, TimeCourse, GeneExpression, Transcription,
GeneRegulation, NetworkInference, Bayesian
Author: Antti Honkela, Pei Gao, Jonatan Ropponen, Miika-Petteri
Matikainen, Magnus Rattray, Neil D. Lawrence
Maintainer: Antti Honkela <antti.honkela@helsinki.fi>
URL: https://github.com/ahonkela/tigre
BugReports: https://github.com/ahonkela/tigre/issues
git_url: https://git.bioconductor.org/packages/tigre
git_branch: RELEASE_3_13
git_last_commit: 95c5a86
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tigre_1.46.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tigre_1.46.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tigre_1.46.0.tgz
vignettes: vignettes/tigre/inst/doc/tigre.pdf
vignetteTitles: tigre User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tigre/inst/doc/tigre.R
dependencyCount: 53
Package: TileDBArray
Version: 1.2.1
Depends: DelayedArray (>= 0.15.16)
Imports: methods, Rcpp, tiledb, S4Vectors
LinkingTo: Rcpp
Suggests: knitr, Matrix, rmarkdown, BiocStyle, BiocParallel, testthat
License: MIT + file LICENSE
MD5sum: 90de61a5f27afcc08ec723ba82ff248d
NeedsCompilation: yes
Title: Using TileDB as a DelayedArray Backend
Description: Implements a DelayedArray backend for reading and writing
dense or sparse arrays in the TileDB format. The resulting
TileDBArrays are compatible with all Bioconductor pipelines
that can accept DelayedArray instances.
biocViews: DataRepresentation, Infrastructure, Software
Author: Aaron Lun [aut, cre], Genentech, Inc. [cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/TileDBArray
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/TileDBArray
git_url: https://git.bioconductor.org/packages/TileDBArray
git_branch: RELEASE_3_13
git_last_commit: 5c323a8
git_last_commit_date: 2021-05-20
Date/Publication: 2021-05-20
source.ver: src/contrib/TileDBArray_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TileDBArray_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/TileDBArray_1.2.1.tgz
vignettes: vignettes/TileDBArray/inst/doc/userguide.html
vignetteTitles: User guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TileDBArray/inst/doc/userguide.R
dependencyCount: 24
Package: tilingArray
Version: 1.70.0
Depends: R (>= 2.11.0), Biobase, methods, pixmap
Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4
License: Artistic-2.0
Archs: i386, x64
MD5sum: 6b4deb38ecc0e78071f9dc8c252e1b00
NeedsCompilation: yes
Title: Transcript mapping with high-density oligonucleotide tiling
arrays
Description: The package provides functionality that can be useful for
the analysis of high-density tiling microarray data (such as
from Affymetrix genechips) for measuring transcript abundance
and architecture. The main functionalities of the package are:
1. the class 'segmentation' for representing partitionings of a
linear series of data; 2. the function 'segment' for fitting
piecewise constant models using a dynamic programming algorithm
that is both fast and exact; 3. the function 'confint' for
calculating confidence intervals using the strucchange package;
4. the function 'plotAlongChrom' for generating pretty plots;
5. the function 'normalizeByReference' for probe-sequence
dependent response adjustment from a (set of) reference
hybridizations.
biocViews: Microarray, OneChannel, Preprocessing, Visualization
Author: Wolfgang Huber, Zhenyu Xu, Joern Toedling with contributions
from Matt Ritchie
Maintainer: Zhenyu Xu <zxu@embl.de>
git_url: https://git.bioconductor.org/packages/tilingArray
git_branch: RELEASE_3_13
git_last_commit: 74c74b4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tilingArray_1.70.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tilingArray_1.70.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tilingArray_1.70.0.tgz
vignettes: vignettes/tilingArray/inst/doc/assessNorm.pdf,
vignettes/tilingArray/inst/doc/costMatrix.pdf,
vignettes/tilingArray/inst/doc/findsegments.pdf,
vignettes/tilingArray/inst/doc/plotAlongChrom.pdf,
vignettes/tilingArray/inst/doc/segmentation.pdf
vignetteTitles: Normalisation with the normalizeByReference function in
the tilingArray package, Supplement. Calculation of the cost
matrix, Introduction to using the segment function to fit a
piecewise constant curve, Introduction to the plotAlongChrom
function, Segmentation demo
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tilingArray/inst/doc/findsegments.R,
vignettes/tilingArray/inst/doc/plotAlongChrom.R
dependsOnMe: davidTiling
importsMe: ADaCGH2, snapCGH
dependencyCount: 87
Package: timecourse
Version: 1.64.0
Depends: R (>= 2.1.1), MASS, methods
Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods,
stats
License: LGPL
MD5sum: 7e50243c160ee0f480294698f8a12606
NeedsCompilation: no
Title: Statistical Analysis for Developmental Microarray Time Course
Data
Description: Functions for data analysis and graphical displays for
developmental microarray time course data.
biocViews: Microarray, TimeCourse, DifferentialExpression
Author: Yu Chuan Tai
Maintainer: Yu Chuan Tai <yuchuan@stat.berkeley.edu>
URL: http://www.bioconductor.org
git_url: https://git.bioconductor.org/packages/timecourse
git_branch: RELEASE_3_13
git_last_commit: c91dd66
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/timecourse_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/timecourse_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/timecourse_1.64.0.tgz
vignettes: vignettes/timecourse/inst/doc/timecourse.pdf
vignetteTitles: timecourse manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/timecourse/inst/doc/timecourse.R
dependencyCount: 11
Package: timeOmics
Version: 1.4.0
Depends: mixOmics, R (>= 4.0)
Imports: dplyr, tidyr, tibble, purrr, magrittr, ggplot2, stringr,
ggrepel, propr, lmtest, plyr
Suggests: BiocStyle, knitr, rmarkdown, testthat, snow, tidyverse,
igraph, gplots
License: GPL-3
MD5sum: adf11988086fee980925ef6f4a7c7337
NeedsCompilation: no
Title: Time-Course Multi-Omics data integration
Description: timeOmics is a generic data-driven framework to integrate
multi-Omics longitudinal data measured on the same biological
samples and select key temporal features with strong
associations within the same sample group. The main steps of
timeOmics are: 1. Plaform and time-specific normalization and
filtering steps; 2. Modelling each biological into one time
expression profile; 3. Clustering features with the same
expression profile over time; 4. Post-hoc validation step.
biocViews:
Clustering,FeatureExtraction,TimeCourse,DimensionReduction,Software,
Sequencing, Microarray, Metabolomics, Metagenomics, Proteomics,
Classification, Regression, ImmunoOncology, GenePrediction,
MultipleComparison
Author: Antoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le
Cao [aut], Arnaud Droit [aut]
Maintainer: Antoine Bodein <antoine.bodein.1@ulaval.ca>
VignetteBuilder: knitr
BugReports: https://github.com/abodein/timeOmics/issues
git_url: https://git.bioconductor.org/packages/timeOmics
git_branch: RELEASE_3_13
git_last_commit: 65fbb16
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/timeOmics_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/timeOmics_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/timeOmics_1.4.0.tgz
vignettes: vignettes/timeOmics/inst/doc/vignette.html
vignetteTitles: timeOmics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/timeOmics/inst/doc/vignette.R
dependencyCount: 72
Package: timescape
Version: 1.16.0
Depends: R (>= 3.3)
Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), stringr (>=
1.0.0), dplyr (>= 0.4.3), gtools (>= 3.5.0)
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 5709b478a87c9418dbcf6492cadd4888
NeedsCompilation: no
Title: Patient Clonal Timescapes
Description: TimeScape is an automated tool for navigating temporal
clonal evolution data. The key attributes of this
implementation involve the enumeration of clones, their
evolutionary relationships and their shifting dynamics over
time. TimeScape requires two inputs: (i) the clonal phylogeny
and (ii) the clonal prevalences. Optionally, TimeScape accepts
a data table of targeted mutations observed in each clone and
their allele prevalences over time. The output is the TimeScape
plot showing clonal prevalence vertically, time horizontally,
and the plot height optionally encoding tumour volume during
tumour-shrinking events. At each sampling time point (denoted
by a faint white line), the height of each clone accurately
reflects its proportionate prevalence. These prevalences form
the anchors for bezier curves that visually represent the
dynamic transitions between time points.
biocViews: Visualization, BiomedicalInformatics
Author: Maia Smith [aut, cre]
Maintainer: Maia Smith <maiaannesmith@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/timescape
git_branch: RELEASE_3_13
git_last_commit: 9a1760d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/timescape_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/timescape_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/timescape_1.16.0.tgz
vignettes: vignettes/timescape/inst/doc/timescape_vignette.html
vignetteTitles: TimeScape vignette
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/timescape/inst/doc/timescape_vignette.R
dependencyCount: 33
Package: TimeSeriesExperiment
Version: 1.10.1
Depends: R (>= 4.1), S4Vectors (>= 0.19.23), SummarizedExperiment (>=
1.11.6)
Imports: dynamicTreeCut, dplyr, edgeR, DESeq2, ggplot2 (>= 3.0.0),
graphics, Hmisc, limma, methods, magrittr, proxy, stats,
tibble, tidyr, vegan, viridis, utils
Suggests: Biobase, BiocFileCache (>= 1.5.8), circlize, ComplexHeatmap,
GO.db, grDevices, grid, knitr, org.Mm.eg.db, org.Hs.eg.db,
MASS, RColorBrewer, rmarkdown, UpSetR,
License: MIT + file LICENSE
MD5sum: 82e3d869096ef9190c95d3c1f3d2f88e
NeedsCompilation: no
Title: Analysis for short time-series data
Description: TimeSeriesExperiment is a visualization and analysis
toolbox for short time course data. The package includes
dimensionality reduction, clustering, two-sample differential
expression testing and gene ranking techniques. Additionally,
it also provides methods for retrieving enriched pathways.
biocViews: TimeCourse, Sequencing, RNASeq, Microbiome, GeneExpression,
ImmunoOncology, Transcription, Normalization,
DifferentialExpression, PrincipalComponent, Clustering,
Visualization, Pathways
Author: Lan Huong Nguyen [cre, aut]
(<https://orcid.org/0000-0003-3397-0380>)
Maintainer: Lan Huong Nguyen <nlhuong90@gmail.com>
URL: https://github.com/nlhuong/TimeSeriesExperiment
VignetteBuilder: knitr
BugReports: https://github.com/nlhuong/TimeSeriesExperiment/issues
git_url: https://git.bioconductor.org/packages/TimeSeriesExperiment
git_branch: RELEASE_3_13
git_last_commit: 543ffaf
git_last_commit_date: 2021-08-31
Date/Publication: 2021-09-02
source.ver: src/contrib/TimeSeriesExperiment_1.10.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TimeSeriesExperiment_1.10.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/TimeSeriesExperiment_1.10.1.tgz
vignettes:
vignettes/TimeSeriesExperiment/inst/doc/cop1_knockout_timecourse.html
vignetteTitles: Gene expression time course data analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/TimeSeriesExperiment/inst/doc/cop1_knockout_timecourse.R
dependencyCount: 130
Package: TimiRGeN
Version: 1.2.0
Depends: R (>= 4.0), Mfuzz, MultiAssayExperiment
Imports: biomaRt, clusterProfiler, dplyr (>= 0.8.4), FreqProf, gtools
(>= 3.8.1), gplots, ggdendro, gghighlight, ggplot2, graphics,
grDevices, igraph (>= 1.2.4.2), RCy3, readxl, reshape2,
rWikiPathways, scales, stats, tidyr (>= 1.0.2), stringr (>=
1.4.0)
Suggests: BiocManager, kableExtra, knitr (>= 1.27), org.Hs.eg.db,
org.Mm.eg.db, testthat, rmarkdown
License: GPL-3
MD5sum: 7f73b29b0e774d0d5aaf19088098e0c2
NeedsCompilation: no
Title: Time sensitive microRNA-mRNA integration, analysis and network
generation tool
Description: TimiRGeN (Time Incorporated miR-mRNA Generation of
Networks) is a novel R package which functionally analyses and
integrates time course miRNA-mRNA differential expression data.
This tool can generate small networks within R or export
results into cytoscape or pathvisio for more detailed network
construction and hypothesis generation. This tool is created
for researchers that wish to dive deep into time series
multi-omic datasets. TimiRGeN goes further than many other
tools in terms of data reduction. Here, potentially hundreds of
thousands of potential miRNA-mRNA interactions can be whittled
down into a handful of high confidence miRNA-mRNA interactions
effecting a signalling pathway, across a time course.
biocViews: Clustering, miRNA, Network, Pathways, Software, TimeCourse,
Visualization
Author: Krutik Patel [aut, cre]
Maintainer: Krutik Patel <K.Patel5@newcastle.ac.uk>
URL: https://github.com/Krutik6/TimiRGeN/
VignetteBuilder: knitr
BugReports: https://github.com/Krutik6/TimiRGeN/issues
git_url: https://git.bioconductor.org/packages/TimiRGeN
git_branch: RELEASE_3_13
git_last_commit: b07ea29
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-20
source.ver: src/contrib/TimiRGeN_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TimiRGeN_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TimiRGeN_1.2.0.tgz
vignettes: vignettes/TimiRGeN/inst/doc/TimiRGeN_tutorial.html
vignetteTitles: TimiRGeN
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TimiRGeN/inst/doc/TimiRGeN_tutorial.R
dependencyCount: 194
Package: TIN
Version: 1.24.0
Depends: R (>= 2.12.0), data.table, impute, aroma.affymetrix
Imports: WGCNA, squash, stringr
Suggests: knitr, aroma.light, affxparser, RUnit, BiocGenerics
License: Artistic-2.0
Archs: i386, x64
MD5sum: b26da7a2e19f76170c54670dfcdaaa20
NeedsCompilation: no
Title: Transcriptome instability analysis
Description: The TIN package implements a set of tools for
transcriptome instability analysis based on exon expression
profiles. Deviating exon usage is studied in the context of
splicing factors to analyse to what degree transcriptome
instability is correlated to splicing factor expression. In the
transcriptome instability correlation analysis, the data is
compared to both random permutations of alternative splicing
scores and expression of random gene sets.
biocViews: ExonArray, Microarray, GeneExpression, AlternativeSplicing,
Genetics, DifferentialSplicing
Author: Bjarne Johannessen, Anita Sveen and Rolf I. Skotheim
Maintainer: Bjarne Johannessen <bjajoh@rr-research.no>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TIN
git_branch: RELEASE_3_13
git_last_commit: 4ee1fa2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TIN_1.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TIN_1.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TIN_1.24.0.tgz
vignettes: vignettes/TIN/inst/doc/TIN.pdf
vignetteTitles: Introduction to the TIN package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TIN/inst/doc/TIN.R
dependencyCount: 129
Package: TissueEnrich
Version: 1.12.0
Depends: R (>= 3.5), ensurer (>= 1.1.0), ggplot2 (>= 2.2.1),
SummarizedExperiment (>= 1.6.5), GSEABase (>= 1.38.2)
Imports: dplyr (>= 0.7.3), tidyr (>= 0.8.0), stats
Suggests: knitr, rmarkdown, testthat
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: fc3b1232ef676afdda4032e536df55cb
NeedsCompilation: no
Title: Tissue-specific gene enrichment analysis
Description: The TissueEnrich package is used to calculate enrichment
of tissue-specific genes in a set of input genes. For example,
the user can input the most highly expressed genes from RNA-Seq
data, or gene co-expression modules to determine which
tissue-specific genes are enriched in those datasets.
Tissue-specific genes were defined by processing RNA-Seq data
from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx
(Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using
the algorithm from the HPA (Uhlén et al. 2015).The
hypergeometric test is being used to determine if the
tissue-specific genes are enriched among the input genes. Along
with tissue-specific gene enrichment, the TissueEnrich package
can also be used to define tissue-specific genes from
expression datasets provided by the user, which can then be
used to calculate tissue-specific gene enrichments.
biocViews: GeneSetEnrichment, GeneExpression, Sequencing
Author: Ashish Jain [aut, cre], Geetu Tuteja [aut]
Maintainer: Ashish Jain <jain@iastate.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TissueEnrich
git_branch: RELEASE_3_13
git_last_commit: 27c5ae2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TissueEnrich_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TissueEnrich_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TissueEnrich_1.12.0.tgz
vignettes: vignettes/TissueEnrich/inst/doc/TissueEnrich.html
vignetteTitles: TissueEnrich
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TissueEnrich/inst/doc/TissueEnrich.R
dependencyCount: 89
Package: TitanCNA
Version: 1.30.0
Depends: R (>= 3.5.1)
Imports: BiocGenerics (>= 0.31.6), IRanges (>= 2.6.1), GenomicRanges
(>= 1.24.3), VariantAnnotation (>= 1.18.7), foreach (>= 1.4.3),
GenomeInfoDb (>= 1.8.7), data.table (>= 1.10.4), dplyr (>=
0.5.0),
License: GPL-3
Archs: i386, x64
MD5sum: f9526f1532050191e426c3b5c39acc0b
NeedsCompilation: yes
Title: Subclonal copy number and LOH prediction from whole genome
sequencing of tumours
Description: Hidden Markov model to segment and predict regions of
subclonal copy number alterations (CNA) and loss of
heterozygosity (LOH), and estimate cellular prevalence of
clonal clusters in tumour whole genome sequencing data.
biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq,
StatisticalMethod, CopyNumberVariation, HiddenMarkovModel,
Genetics, GenomicVariation, ImmunoOncology
Author: Gavin Ha
Maintainer: Gavin Ha <gha@fredhutch.org>
URL: https://github.com/gavinha/TitanCNA
git_url: https://git.bioconductor.org/packages/TitanCNA
git_branch: RELEASE_3_13
git_last_commit: 4694694
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TitanCNA_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TitanCNA_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TitanCNA_1.30.0.tgz
vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R
dependencyCount: 102
Package: tkWidgets
Version: 1.70.0
Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>=
1.3.0), tools
Suggests: Biobase, hgu95av2
License: Artistic-2.0
MD5sum: 5ab5977b3f50ec45eb240e0418d7d7e6
NeedsCompilation: no
Title: R based tk widgets
Description: Widgets to provide user interfaces. tcltk should have been
installed for the widgets to run.
biocViews: Infrastructure
Author: J. Zhang <jzhang@jimmy.harvard.edu>
Maintainer: J. Zhang <jzhang@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/tkWidgets
git_branch: RELEASE_3_13
git_last_commit: 0c61420
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tkWidgets_1.70.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tkWidgets_1.70.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tkWidgets_1.70.0.tgz
vignettes: vignettes/tkWidgets/inst/doc/importWizard.pdf,
vignettes/tkWidgets/inst/doc/tkWidgets.pdf
vignetteTitles: tkWidgets importWizard, tkWidgets contents
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tkWidgets/inst/doc/importWizard.R,
vignettes/tkWidgets/inst/doc/tkWidgets.R
importsMe: Mfuzz, OLINgui
suggestsMe: affy, annotate, Biobase, genefilter, marray
dependencyCount: 6
Package: tLOH
Version: 1.0.0
Depends: R (>= 4.0)
Imports: scales, stats, utils, ggplot2, data.table, purrr, dplyr,
VariantAnnotation, GenomicRanges, MatrixGenerics
Suggests: knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: a009b2743ab766cf5311c42c664da9f5
NeedsCompilation: no
Title: Assessment of evidence for LOH in spatial transcriptomics
pre-processed data using Bayes factor calculations
Description: tLOH, or transcriptomicsLOH, assesses evidence for loss of
heterozygosity (LOH) in pre-processed spatial transcriptomics
data. This tool requires spatial transcriptomics cluster and
allele count information at likely heterozygous
single-nucleotide polymorphism (SNP) positions in VCF format.
Bayes factors are calculated at each SNP to determine
likelihood of potential loss of heterozygosity event. Two
plotting functions are included to visualize allele fraction
and aggregated Bayes factor per chromosome. Data generated with
the 10X Genomics Visium Spatial Gene Expression platform must
be pre-processed to obtain an individual sample VCF with
columns for each cluster. Required fields are allele depth (AD)
with counts for reference/alternative alleles and read depth
(DP).
biocViews: CopyNumberVariation, Transcription, SNP, GeneExpression,
Transcriptomics
Author: Michelle Webb [cre, aut], David Craig [aut]
Maintainer: Michelle Webb <michelgw@usc.edu>
URL: https://github.com/USCDTG/tLOH
VignetteBuilder: knitr
BugReports: https://github.com/USCDTG/tLOH/issues
git_url: https://git.bioconductor.org/packages/tLOH
git_branch: RELEASE_3_13
git_last_commit: dc3ee89
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tLOH_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tLOH_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tLOH_1.0.0.tgz
vignettes: vignettes/tLOH/inst/doc/tLOH_vignette.html
vignetteTitles: tLOH
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tLOH/inst/doc/tLOH_vignette.R
dependencyCount: 113
Package: TMixClust
Version: 1.14.0
Depends: R (>= 3.4)
Imports: gss, mvtnorm, stats, zoo, cluster, utils, BiocParallel,
flexclust, grDevices, graphics, Biobase, SPEM
Suggests: rmarkdown, knitr, BiocStyle, testthat
License: GPL (>=2)
MD5sum: 2fb70166b3e562c0c510446af2de6df5
NeedsCompilation: no
Title: Time Series Clustering of Gene Expression with Gaussian
Mixed-Effects Models and Smoothing Splines
Description: Implementation of a clustering method for time series gene
expression data based on mixed-effects models with Gaussian
variables and non-parametric cubic splines estimation. The
method can robustly account for the high levels of noise
present in typical gene expression time series datasets.
biocViews: Software, StatisticalMethod, Clustering, TimeCourse,
GeneExpression
Author: Monica Golumbeanu <golumbeanu.monica@gmail.com>
Maintainer: Monica Golumbeanu <golumbeanu.monica@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TMixClust
git_branch: RELEASE_3_13
git_last_commit: 634a52d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TMixClust_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TMixClust_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TMixClust_1.14.0.tgz
vignettes: vignettes/TMixClust/inst/doc/TMixClust.pdf
vignetteTitles: Clustering time series gene expression data with
TMixClust
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TMixClust/inst/doc/TMixClust.R
dependencyCount: 29
Package: TNBC.CMS
Version: 1.8.0
Depends: R (>= 3.6.0), e1071, quadprog, SummarizedExperiment
Imports: GSVA (>= 1.26.0), pheatmap, grDevices, RColorBrewer, pracma,
GGally, R.utils, forestplot, ggplot2, ggpubr, survival, grid,
stats, methods
Suggests: knitr
License: GPL-3
MD5sum: f5d5e6de69bd63c63e77f4edb9a8a1ee
NeedsCompilation: no
Title: TNBC.CMS: Prediction of TNBC Consensus Molecular Subtypes
Description: This package implements a machine learning-based
classifier for the assignment of consensus molecular subtypes
to TNBC samples. It also provides functions to summarize
genomic and clinical characteristics.
biocViews: Classification, Clustering, GeneExpression, GenePrediction,
SupportVectorMachine
Author: Doyeong Yu, Jihyun Kim, In Hae Park, Charny Park
Maintainer: Doyeong Yu <parklab.bi@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TNBC.CMS
git_branch: RELEASE_3_13
git_last_commit: 8beeb75
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TNBC.CMS_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TNBC.CMS_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TNBC.CMS_1.8.0.tgz
vignettes: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.pdf
vignetteTitles: TNBC.CMS.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.R
dependencyCount: 176
Package: TnT
Version: 1.14.0
Depends: R (>= 3.4), GenomicRanges
Imports: methods, stats, utils, grDevices, htmlwidgets, jsonlite,
data.table, Biobase, GenomeInfoDb, IRanges, S4Vectors, knitr
Suggests: GenomicFeatures, shiny, BiocManager, rmarkdown, testthat
License: AGPL-3
MD5sum: 27b57c154617036ca03100b0cb01af2d
NeedsCompilation: no
Title: Interactive Visualization for Genomic Features
Description: A R interface to the TnT javascript library
(https://github.com/ tntvis) to provide interactive and
flexible visualization of track-based genomic data.
biocViews: Infrastructure, Visualization
Author: Jialin Ma [cre, aut], Miguel Pignatelli [aut], Toby Hocking
[aut]
Maintainer: Jialin Ma <marlin-@gmx.cn>
URL: https://github.com/Marlin-Na/TnT
VignetteBuilder: knitr
BugReports: https://github.com/Marlin-Na/TnT/issues
git_url: https://git.bioconductor.org/packages/TnT
git_branch: RELEASE_3_13
git_last_commit: 87de662
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TnT_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TnT_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TnT_1.14.0.tgz
vignettes: vignettes/TnT/inst/doc/introduction.html
vignetteTitles: Introduction to TnT
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TnT/inst/doc/introduction.R
dependencyCount: 36
Package: TOAST
Version: 1.6.0
Depends: R (>= 3.6), RefFreeEWAS, EpiDISH, limma, nnls
Imports: stats, methods, SummarizedExperiment, corpcor
Suggests: BiocStyle, knitr, rmarkdown, csSAM, gplots, matrixStats,
Matrix
License: GPL-2
MD5sum: d30deeef3d4cde48ad37ad1539a9143d
NeedsCompilation: no
Title: Tools for the analysis of heterogeneous tissues
Description: This package is devoted to analyzing high-throughput data
(e.g. gene expression microarray, DNA methylation microarray,
RNA-seq) from complex tissues. Current functionalities include
1. detect cell-type specific or cross-cell type differential
signals 2. improve variable selection in reference-free
deconvolution 3. partial reference-free deconvolution with
prior knowledge.
biocViews: DNAMethylation, GeneExpression, DifferentialExpression,
DifferentialMethylation, Microarray, GeneTarget, Epigenetics,
MethylationArray
Author: Ziyi Li and Hao Wu
Maintainer: Ziyi Li <ziyi.li@emory.edu>
VignetteBuilder: knitr
BugReports: https://github.com/ziyili20/TOAST/issues
git_url: https://git.bioconductor.org/packages/TOAST
git_branch: RELEASE_3_13
git_last_commit: 68b0442
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TOAST_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TOAST_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TOAST_1.6.0.tgz
vignettes: vignettes/TOAST/inst/doc/TOAST.html
vignetteTitles: The TOAST User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TOAST/inst/doc/TOAST.R
dependencyCount: 42
Package: tofsims
Version: 1.20.0
Depends: R (>= 3.3.0), methods, utils, ProtGenerics
Imports: Rcpp (>= 0.11.2), ALS, alsace, signal, KernSmooth, graphics,
grDevices, stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: EBImage, knitr, rmarkdown, testthat, tofsimsData,
BiocParallel, RColorBrewer
Enhances: parallel
License: GPL-3
MD5sum: dac5083f3e8ff783fe5a715bd339e8c8
NeedsCompilation: yes
Title: Import, process and analysis of Time-of-Flight Secondary Ion
Mass Spectrometry (ToF-SIMS) imaging data
Description: This packages offers a pipeline for import, processing and
analysis of ToF-SIMS 2D image data. Import of Iontof and
Ulvac-Phi raw or preprocessed data is supported. For rawdata,
mass calibration, peak picking and peak integration exist.
General funcionality includes data binning, scaling, image
subsetting and visualization. A range of multivariate tools
common in the ToF-SIMS community are implemented (PCA, MCR,
MAF, MNF). An interface to the bioconductor image processing
package EBImage offers image segmentation functionality.
biocViews: ImmunoOncology, Infrastructure, DataImport,
MassSpectrometry, ImagingMassSpectrometry, Proteomics,
Metabolomics
Author: Lorenz Gerber, Viet Mai Hoang
Maintainer: Lorenz Gerber <genfys@gmail.com>
URL: https://github.com/lorenzgerber/tofsims
VignetteBuilder: knitr
BugReports: https://github.com/lorenzgerber/tofsims/issues
git_url: https://git.bioconductor.org/packages/tofsims
git_branch: RELEASE_3_13
git_last_commit: 681d0c3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tofsims_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tofsims_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tofsims_1.20.0.tgz
vignettes: vignettes/tofsims/inst/doc/workflow.html
vignetteTitles: Workflow with the `tofsims` package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tofsims/inst/doc/workflow.R
dependencyCount: 17
Package: tomoda
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: methods, stats, grDevices, reshape2, Rtsne, umap,
RColorBrewer, ggplot2, ggrepel, SummarizedExperiment
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 62633ef79b9d862a7c8fe9c6331df92a
NeedsCompilation: no
Title: Tomo-seq data analysis
Description: This package provides many easy-to-use methods to analyze
and visualize tomo-seq data. The tomo-seq technique is based on
cryosectioning of tissue and performing RNA-seq on consecutive
sections. (Reference: Kruse F, Junker JP, van Oudenaarden A,
Bakkers J. Tomo-seq: A method to obtain genome-wide expression
data with spatial resolution. Methods Cell Biol.
2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main
purpose of the package is to find zones with similar
transcriptional profiles and spatially expressed genes in a
tomo-seq sample. Several visulization functions are available
to create easy-to-modify plots.
biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics,
Clustering, Visualization
Author: Wendao Liu [aut, cre] (<https://orcid.org/0000-0002-5124-9338>)
Maintainer: Wendao Liu <liuwd15@tsinghua.org.cn>
URL: https://github.com/liuwd15/tomoda
VignetteBuilder: knitr
BugReports: https://github.com/liuwd15/tomoda/issues
git_url: https://git.bioconductor.org/packages/tomoda
git_branch: RELEASE_3_13
git_last_commit: fa482a2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tomoda_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tomoda_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tomoda_1.2.0.tgz
vignettes: vignettes/tomoda/inst/doc/tomoda.html
vignetteTitles: tomoda
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tomoda/inst/doc/tomoda.R
dependencyCount: 75
Package: topconfects
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: methods, utils, stats, assertthat, ggplot2
Suggests: limma, edgeR, statmod, DESeq2, ashr, NBPSeq, dplyr, testthat,
reshape2, tidyr, readr, org.At.tair.db, AnnotationDbi, knitr,
rmarkdown, BiocStyle
License: LGPL-2.1 | file LICENSE
MD5sum: 89f17c16a64ea94156d8c9c3f617f7aa
NeedsCompilation: no
Title: Top Confident Effect Sizes
Description: Rank results by confident effect sizes, while maintaining
False Discovery Rate and False Coverage-statement Rate control.
Topconfects is an alternative presentation of TREAT results
with improved usability, eliminating p-values and instead
providing confidence bounds. The main application is
differential gene expression analysis, providing genes ranked
in order of confident log2 fold change, but it can be applied
to any collection of effect sizes with associated standard
errors.
biocViews: GeneExpression, DifferentialExpression, Transcriptomics,
RNASeq, mRNAMicroarray, Regression, MultipleComparison
Author: Paul Harrison [aut, cre]
(<https://orcid.org/0000-0002-3980-268X>)
Maintainer: Paul Harrison <paul.harrison@monash.edu>
URL: https://github.com/pfh/topconfects
VignetteBuilder: knitr
BugReports: https://github.com/pfh/topconfects/issues
git_url: https://git.bioconductor.org/packages/topconfects
git_branch: RELEASE_3_13
git_last_commit: 52bb4b8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/topconfects_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/topconfects_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/topconfects_1.8.0.tgz
vignettes: vignettes/topconfects/inst/doc/an_overview.html,
vignettes/topconfects/inst/doc/fold_change.html
vignetteTitles: An overview of topconfects, Confident fold change
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/topconfects/inst/doc/an_overview.R,
vignettes/topconfects/inst/doc/fold_change.R
importsMe: MetaVolcanoR, weitrix
dependencyCount: 40
Package: topdownr
Version: 1.14.0
Depends: R (>= 3.5), methods, BiocGenerics (>= 0.20.0), ProtGenerics
(>= 1.10.0), Biostrings (>= 2.42.1), S4Vectors (>= 0.12.2)
Imports: grDevices, stats, tools, utils, Biobase, Matrix (>= 1.2.10),
MSnbase (>= 2.3.10), ggplot2 (>= 2.2.1), mzR (>= 2.11.4)
Suggests: topdownrdata (>= 0.2), knitr, ranger, testthat, BiocStyle,
xml2
License: GPL (>= 3)
MD5sum: 26f545730bfc08f6b6490b15fb6cf6ea
NeedsCompilation: no
Title: Investigation of Fragmentation Conditions in Top-Down Proteomics
Description: The topdownr package allows automatic and systemic
investigation of fragment conditions. It creates Thermo
Orbitrap Fusion Lumos method files to test hundreds of
fragmentation conditions. Additionally it provides functions to
analyse and process the generated MS data and determine the
best conditions to maximise overall fragment coverage.
biocViews: ImmunoOncology, Infrastructure, Proteomics,
MassSpectrometry, Coverage
Author: Sebastian Gibb [aut, cre]
(<https://orcid.org/0000-0001-7406-4443>), Pavel Shliaha [aut]
(<https://orcid.org/0000-0003-3092-0724>), Ole Nørregaard
Jensen [aut] (<https://orcid.org/0000-0003-1862-8528>)
Maintainer: Sebastian Gibb <mail@sebastiangibb.de>
URL: https://github.com/sgibb/topdownr/
VignetteBuilder: knitr
BugReports: https://github.com/sgibb/topdownr/issues/
git_url: https://git.bioconductor.org/packages/topdownr
git_branch: RELEASE_3_13
git_last_commit: 8a127d3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/topdownr_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/topdownr_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/topdownr_1.14.0.tgz
vignettes: vignettes/topdownr/inst/doc/analysis.html,
vignettes/topdownr/inst/doc/data-generation.html
vignetteTitles: Fragmentation Analysis with topdownr, Data Generation
for topdownr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/topdownr/inst/doc/analysis.R,
vignettes/topdownr/inst/doc/data-generation.R
dependsOnMe: topdownrdata
dependencyCount: 84
Package: topGO
Version: 2.44.0
Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.6), graph (>=
1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi
(>= 1.7.19), SparseM (>= 0.73)
Imports: lattice, matrixStats, DBI
Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest,
Rgraphviz, globaltest
License: LGPL
MD5sum: 3e3235cb32fff58fa9a5ed0d8903055d
NeedsCompilation: no
Title: Enrichment Analysis for Gene Ontology
Description: topGO package provides tools for testing GO terms while
accounting for the topology of the GO graph. Different test
statistics and different methods for eliminating local
similarities and dependencies between GO terms can be
implemented and applied.
biocViews: Microarray, Visualization
Author: Adrian Alexa, Jorg Rahnenfuhrer
Maintainer: Adrian Alexa <adrian.alexa@gmail.com>
git_url: https://git.bioconductor.org/packages/topGO
git_branch: RELEASE_3_13
git_last_commit: d907c12
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/topGO_2.44.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/topGO_2.44.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/topGO_2.44.0.tgz
vignettes: vignettes/topGO/inst/doc/topGO.pdf
vignetteTitles: topGO
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/topGO/inst/doc/topGO.R
dependsOnMe: BgeeDB, cellTree, compEpiTools, EGSEA, ideal, moanin,
tRanslatome, ccTutorial, maEndToEnd
importsMe: BioMM, cellity, FoldGO, GOSim, OmaDB, pcaExplorer,
psygenet2r, transcriptogramer, ViSEAGO, ExpHunterSuite
suggestsMe: FGNet, geva, IntramiRExploreR, miRNAtap, Ringo
dependencyCount: 52
Package: ToxicoGx
Version: 1.2.1
Depends: R (>= 4.1), CoreGx
Imports: SummarizedExperiment, S4Vectors, Biobase, BiocParallel,
ggplot2, tibble, dplyr, caTools, downloader, magrittr, methods,
reshape2, tidyr, data.table, assertthat, scales, graphics,
grDevices, parallel, stats, utils, limma, jsonlite
Suggests: rmarkdown, testthat, BiocStyle, knitr, tinytex, devtools,
PharmacoGx, xtable, markdown
License: MIT + file LICENSE
MD5sum: 2ee0635486ebd97c3aa9ed9feaf8f061
NeedsCompilation: no
Title: Analysis of Large-Scale Toxico-Genomic Data
Description: Contains a set of functions to perform large-scale
analysis of toxicogenomic data, providing a standardized data
structure to hold information relevant to annotation,
visualization and statistical analysis of toxicogenomic data.
biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software
Author: Sisira Nair [aut], Esther Yoo [aut], Christopher Eeles [aut],
Amy Tang [aut], Nehme El-Hachem [aut], Petr Smirnov [aut],
Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ToxicoGx
git_branch: RELEASE_3_13
git_last_commit: 04482fe
git_last_commit_date: 2021-06-17
Date/Publication: 2021-06-20
source.ver: src/contrib/ToxicoGx_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ToxicoGx_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/ToxicoGx_1.2.1.tgz
vignettes: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.html
vignetteTitles: ToxicoGx: An R Platform for Integrated Toxicogenomics
Data Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.R
dependencyCount: 123
Package: TPP
Version: 3.20.1
Depends: R (>= 3.4), Biobase, dplyr, magrittr, tidyr
Imports: biobroom, broom, data.table, doParallel, foreach,
futile.logger, ggplot2, grDevices, gridExtra, grid, knitr,
limma, MASS, mefa, nls2, openxlsx (>= 2.4.0), parallel, plyr,
purrr, RColorBrewer, RCurl, reshape2, rmarkdown, splines,
stats, stringr, tibble, utils, VennDiagram, VGAM
Suggests: BiocStyle, testthat
License: Artistic-2.0
MD5sum: c61595234dedf38e60f3157d44beb040
NeedsCompilation: no
Title: Analyze thermal proteome profiling (TPP) experiments
Description: Analyze thermal proteome profiling (TPP) experiments with
varying temperatures (TR) or compound concentrations (CCR).
biocViews: ImmunoOncology, Proteomics, MassSpectrometry
Author: Dorothee Childs, Nils Kurzawa, Holger Franken, Carola Doce,
Mikhail Savitski and Wolfgang Huber
Maintainer: Dorothee Childs <dorothee.childs@embl.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TPP
git_branch: RELEASE_3_13
git_last_commit: 97ad451
git_last_commit_date: 2021-07-27
Date/Publication: 2021-07-27
source.ver: src/contrib/TPP_3.20.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TPP_3.20.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/TPP_3.20.1.tgz
vignettes: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.pdf,
vignettes/TPP/inst/doc/TPP_introduction_1D.pdf,
vignettes/TPP/inst/doc/TPP_introduction_2D.pdf
vignetteTitles: TPP_introduction_NPARC, TPP_introduction_1D,
TPP_introduction_2D
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.R,
vignettes/TPP/inst/doc/TPP_introduction_1D.R,
vignettes/TPP/inst/doc/TPP_introduction_2D.R
suggestsMe: Rtpca
dependencyCount: 89
Package: TPP2D
Version: 1.8.0
Depends: R (>= 3.6.0), stats, utils, dplyr, methods
Imports: ggplot2, tidyr, foreach, doParallel, openxlsx, stringr, RCurl,
parallel, MASS, BiocParallel, limma
Suggests: knitr, testthat, rmarkdown
License: GPL-3
MD5sum: 11ee1dbc8413fb3d1a7d28702922fe97
NeedsCompilation: no
Title: Detection of ligand-protein interactions from 2D thermal
profiles (DLPTP)
Description: Detection of ligand-protein interactions from 2D thermal
profiles (DLPTP), Performs an FDR-controlled analysis of 2D-TPP
experiments by functional analysis of dose-response curves
across temperatures.
biocViews: Software, Proteomics, DataImport
Author: Nils Kurzawa [aut, cre], Holger Franken [aut], Simon Anders
[aut], Wolfgang Huber [aut], Mikhail M. Savitski [aut]
Maintainer: Nils Kurzawa <nils.kurzawa@embl.de>
URL: http://bioconductor.org/packages/TPP2D
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/TPP2D
git_branch: RELEASE_3_13
git_last_commit: 432c4fa
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TPP2D_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TPP2D_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TPP2D_1.8.0.tgz
vignettes: vignettes/TPP2D/inst/doc/TPP2D.html
vignetteTitles: Introduction to TPP2D for 2D-TPP analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TPP2D/inst/doc/TPP2D.R
dependencyCount: 65
Package: tracktables
Version: 1.26.0
Depends: R (>= 3.0.0)
Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base,
stringr, RColorBrewer, methods
Suggests: knitr, BiocStyle
License: GPL (>= 3)
Archs: i386, x64
MD5sum: cbc6ea115396ce138a0a8d84a5faa820
NeedsCompilation: no
Title: Build IGV tracks and HTML reports
Description: Methods to create complex IGV genome browser sessions and
dynamic IGV reports in HTML pages.
biocViews: Sequencing, ReportWriting
Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang
Maintainer: Tom Carroll <tc.infomatics@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tracktables
git_branch: RELEASE_3_13
git_last_commit: bccca31
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tracktables_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tracktables_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tracktables_1.26.0.tgz
vignettes: vignettes/tracktables/inst/doc/tracktables.pdf
vignetteTitles: Creating IGV HTML reports with tracktables
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tracktables/inst/doc/tracktables.R
dependencyCount: 41
Package: trackViewer
Version: 1.28.1
Depends: R (>= 3.5.0), grDevices, methods, GenomicRanges, grid, Rcpp
Imports: GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz,
Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools,
IRanges, AnnotationDbi, grImport, htmlwidgets, plotrix,
Rgraphviz, InteractionSet, graph, utils, rhdf5
LinkingTo: Rcpp
Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit,
org.Hs.eg.db, BiocStyle, knitr, VariantAnnotation, httr,
htmltools, rmarkdown
License: GPL (>= 2)
MD5sum: 406152a2ef62139e7839963e6b9fa736
NeedsCompilation: yes
Title: A R/Bioconductor package with web interface for drawing elegant
interactive tracks or lollipop plot to facilitate integrated
analysis of multi-omics data
Description: Visualize mapped reads along with annotation as track
layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq,
DNA-seq, SNPs and methylation data.
biocViews: Visualization
Author: Jianhong Ou [aut, cre]
(<https://orcid.org/0000-0002-8652-2488>), Julie Lihua Zhu
[aut]
Maintainer: Jianhong Ou <jianhong.ou@duke.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/trackViewer
git_branch: RELEASE_3_13
git_last_commit: 84fca6e
git_last_commit_date: 2021-07-06
Date/Publication: 2021-07-08
source.ver: src/contrib/trackViewer_1.28.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/trackViewer_1.28.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/trackViewer_1.28.1.tgz
vignettes: vignettes/trackViewer/inst/doc/changeTracksStyles.html,
vignettes/trackViewer/inst/doc/dandelionPlot.html,
vignettes/trackViewer/inst/doc/lollipopPlot.html,
vignettes/trackViewer/inst/doc/plotInteractionData.html,
vignettes/trackViewer/inst/doc/trackViewer.html
vignetteTitles: trackViewer Vignette: change the track styles,
trackViewer Vignette: dandelionPlot, trackViewer Vignette:
lollipopPlot, trackViewer Vignette: plot interaction data,
trackViewer Vignette: overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/trackViewer/inst/doc/changeTracksStyles.R,
vignettes/trackViewer/inst/doc/dandelionPlot.R,
vignettes/trackViewer/inst/doc/lollipopPlot.R,
vignettes/trackViewer/inst/doc/plotInteractionData.R,
vignettes/trackViewer/inst/doc/trackViewer.R
importsMe: NADfinder
suggestsMe: ATACseqQC, ChIPpeakAnno
dependencyCount: 150
Package: tradeSeq
Version: 1.6.0
Depends: R (>= 3.6)
Imports: mgcv, edgeR, SingleCellExperiment, SummarizedExperiment,
slingshot, magrittr, RColorBrewer, BiocParallel, Biobase,
pbapply, ggplot2, princurve, methods, monocle, igraph,
S4Vectors, tibble, Matrix, viridis, matrixStats
Suggests: knitr, rmarkdown, testthat, covr, clusterExperiment
License: MIT + file LICENSE
MD5sum: 5c4f6d2c6e9afef1f0ba6ef0bf6f06da
NeedsCompilation: no
Title: trajectory-based differential expression analysis for sequencing
data
Description: tradeSeq provides a flexible method for fitting regression
models that can be used to find genes that are differentially
expressed along one or multiple lineages in a trajectory. Based
on the fitted models, it uses a variety of tests suited to
answer different questions of interest, e.g. the discovery of
genes for which expression is associated with pseudotime, or
which are differentially expressed (in a specific region) along
the trajectory. It fits a negative binomial generalized
additive model (GAM) for each gene, and performs inference on
the parameters of the GAM.
biocViews: Clustering, Regression, TimeCourse, DifferentialExpression,
GeneExpression, RNASeq, Sequencing, Software, SingleCell,
Transcriptomics, MultipleComparison, Visualization
Author: Koen Van den Berge [aut], Hector Roux de Bezieux [aut, cre]
(<https://orcid.org/0000-0002-1489-8339>), Kelly Street [ctb],
Lieven Clement [aut, ctb], Sandrine Dudoit [ctb]
Maintainer: Hector Roux de Bezieux <hector.rouxdebezieux@berkeley.edu>
URL: https://statomics.github.io/tradeSeq/index.html
VignetteBuilder: knitr
BugReports: https://github.com/statOmics/tradeSeq/issues
git_url: https://git.bioconductor.org/packages/tradeSeq
git_branch: RELEASE_3_13
git_last_commit: 6710190
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tradeSeq_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tradeSeq_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tradeSeq_1.6.0.tgz
vignettes: vignettes/tradeSeq/inst/doc/fitGAM.html,
vignettes/tradeSeq/inst/doc/Monocle.html,
vignettes/tradeSeq/inst/doc/multipleConditions.html,
vignettes/tradeSeq/inst/doc/tradeSeq.html
vignetteTitles: More details on working with fitGAM, Monocle +
tradeSeq, Differential expression across conditions, The
tradeSeq workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tradeSeq/inst/doc/fitGAM.R,
vignettes/tradeSeq/inst/doc/Monocle.R,
vignettes/tradeSeq/inst/doc/tradeSeq.R
dependsOnMe: OSCA.advanced
dependencyCount: 109
Package: TrajectoryGeometry
Version: 1.0.0
Depends: R (>= 4.1)
Imports: pracma, rgl, ggplot2, stats, methods
Suggests: dplyr, knitr, RColorBrewer, rmarkdown
License: MIT + file LICENSE
MD5sum: 43de8e9adabe30b16f29817945d43c7d
NeedsCompilation: no
Title: This Package Discovers Directionality in Time and Pseudo-times
Series of Gene Expression Patterns
Description: Given a time series or pseudo-times series of gene
expression data, we might wish to know: Do the changes in gene
expression in these data exhibit directionality? Are there
turning points in this directionality. Do different subsets of
the data move in different directions? This package uses
spherical geometry to probe these sorts of questions. In
particular, if we are looking at (say) the first n dimensions
of the PCA of gene expression, directionality can be detected
as the clustering of points on the (n-1)-dimensional sphere.
biocViews: BiologicalQuestion, StatisticalMethod, GeneExpression,
SingleCell
Author: Michael Shapiro [aut, cre]
(<https://orcid.org/0000-0002-2769-9320>)
Maintainer: Michael Shapiro <michael.shapiro@crick.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TrajectoryGeometry
git_branch: RELEASE_3_13
git_last_commit: 2e2323e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TrajectoryGeometry_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TrajectoryGeometry_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TrajectoryGeometry_1.0.0.tgz
vignettes:
vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.html,
vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.html
vignetteTitles: SingleCellTrajectoryAnalysis, TrajectoryGeometry
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.R,
vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.R
dependencyCount: 55
Package: TrajectoryUtils
Version: 1.0.0
Depends: SingleCellExperiment
Imports: methods, stats, Matrix, igraph, S4Vectors,
SummarizedExperiment
Suggests: BiocNeighbors, DelayedArray, DelayedMatrixStats,
BiocParallel, testthat, knitr, BiocStyle, rmarkdown
License: GPL-3
MD5sum: 05051a749946bb402c031371597c6bb7
NeedsCompilation: no
Title: Single-Cell Trajectory Analysis Utilities
Description: Implements low-level utilities for single-cell trajectory
analysis, primarily intended for re-use inside higher-level
packages. Include a function to create a cluster-level minimum
spanning tree and data structures to hold pseudotime inference
results.
biocViews: GeneExpression, SingleCell
Author: Aaron Lun [aut, cre], Kelly Street [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://bioconductor.org/packages/TrajectoryUtils
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/TrajectoryUtils/issues
git_url: https://git.bioconductor.org/packages/TrajectoryUtils
git_branch: RELEASE_3_13
git_last_commit: f78814d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TrajectoryUtils_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TrajectoryUtils_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TrajectoryUtils_1.0.0.tgz
vignettes: vignettes/TrajectoryUtils/inst/doc/overview.html
vignetteTitles: Trajectory utilities
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TrajectoryUtils/inst/doc/overview.R
dependsOnMe: slingshot, TSCAN
importsMe: condiments
dependencyCount: 30
Package: transcriptogramer
Version: 1.14.0
Depends: R (>= 3.4), methods
Imports: biomaRt, data.table, doSNOW, foreach, ggplot2, graphics,
grDevices, igraph, limma, parallel, progress, RedeR, snow,
stats, tidyr, topGO
Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics
License: GPL (>= 2)
MD5sum: a652f875db1d0227fdf71f535cf773ae
NeedsCompilation: no
Title: Transcriptional analysis based on transcriptograms
Description: R package for transcriptional analysis based on
transcriptograms, a method to analyze transcriptomes that
projects expression values on a set of ordered proteins,
arranged such that the probability that gene products
participate in the same metabolic pathway exponentially
decreases with the increase of the distance between two
proteins of the ordering. Transcriptograms are, hence, genome
wide gene expression profiles that provide a global view for
the cellular metabolism, while indicating gene sets whose
expressions are altered.
biocViews: Software, Network, Visualization, SystemsBiology,
GeneExpression, GeneSetEnrichment, GraphAndNetwork, Clustering,
DifferentialExpression, Microarray, RNASeq, Transcription,
ImmunoOncology
Author: Diego Morais [aut, cre], Rodrigo Dalmolin [aut]
Maintainer: Diego Morais <vinx@ufrn.edu.br>
URL: https://github.com/arthurvinx/transcriptogramer
SystemRequirements: Java Runtime Environment (>= 6)
VignetteBuilder: knitr
BugReports: https://github.com/arthurvinx/transcriptogramer/issues
git_url: https://git.bioconductor.org/packages/transcriptogramer
git_branch: RELEASE_3_13
git_last_commit: 33f0ca5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/transcriptogramer_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/transcriptogramer_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/transcriptogramer_1.14.0.tgz
vignettes: vignettes/transcriptogramer/inst/doc/transcriptogramer.html
vignetteTitles: The transcriptogramer user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/transcriptogramer/inst/doc/transcriptogramer.R
dependencyCount: 105
Package: transcriptR
Version: 1.20.0
Depends: methods, R (>= 3.3)
Imports: BiocGenerics, caret, chipseq, e1071, GenomicAlignments,
GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2,
graphics, grDevices, IRanges (>= 2.11.15), pROC, reshape2,
Rsamtools, rtracklayer, S4Vectors, stats, utils
Suggests: BiocStyle, knitr, rmarkdown,
TxDb.Hsapiens.UCSC.hg19.knownGene, testthat
License: GPL-3
MD5sum: b2152dc22685cd063ff9b7ed2f5c15a2
NeedsCompilation: no
Title: An Integrative Tool for ChIP- And RNA-Seq Based Primary
Transcripts Detection and Quantification
Description: The differences in the RNA types being sequenced have an
impact on the resulting sequencing profiles. mRNA-seq data is
enriched with reads derived from exons, while GRO-, nucRNA- and
chrRNA-seq demonstrate a substantial broader coverage of both
exonic and intronic regions. The presence of intronic reads in
GRO-seq type of data makes it possible to use it to
computationally identify and quantify all de novo continuous
regions of transcription distributed across the genome. This
type of data, however, is more challenging to interpret and
less common practice compared to mRNA-seq. One of the
challenges for primary transcript detection concerns the
simultaneous transcription of closely spaced genes, which needs
to be properly divided into individually transcribed units. The
R package transcriptR combines RNA-seq data with ChIP-seq data
of histone modifications that mark active Transcription Start
Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this
challenge. The advantage of this approach over the use of, for
example, gene annotations is that this approach is data driven
and therefore able to deal also with novel and case specific
events. Furthermore, the integration of ChIP- and RNA-seq data
allows the identification all known and novel active
transcription start sites within a given sample.
biocViews: ImmunoOncology, Transcription, Software, Sequencing, RNASeq,
Coverage
Author: Armen R. Karapetyan <armen.karapetyan87@gmail.com>
Maintainer: Armen R. Karapetyan <armen.karapetyan87@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/transcriptR
git_branch: RELEASE_3_13
git_last_commit: 812d92c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/transcriptR_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/transcriptR_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/transcriptR_1.20.0.tgz
vignettes: vignettes/transcriptR/inst/doc/transcriptR.html
vignetteTitles: transcriptR: an integrative tool for ChIP- and RNA-seq
based primary transcripts detection and quantification
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/transcriptR/inst/doc/transcriptR.R
dependencyCount: 148
Package: transite
Version: 1.10.0
Depends: R (>= 3.5)
Imports: BiocGenerics (>= 0.26.0), Biostrings (>= 2.48.0), dplyr (>=
0.7.6), GenomicRanges (>= 1.32.6), ggplot2 (>= 3.0.0),
ggseqlogo (>= 0.1), grDevices, gridExtra (>= 2.3), methods,
parallel, Rcpp (>= 1.0.4.8), scales (>= 1.0.0), stats,
TFMPvalue (>= 0.0.8), utils
LinkingTo: Rcpp (>= 1.0.4.8)
Suggests: knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0),
testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: ea23280426470cb1bcfbea8c6b93c785
NeedsCompilation: yes
Title: RNA-binding protein motif analysis
Description: transite is a computational method that allows
comprehensive analysis of the regulatory role of RNA-binding
proteins in various cellular processes by leveraging
preexisting gene expression data and current knowledge of
binding preferences of RNA-binding proteins.
biocViews: GeneExpression, Transcription, DifferentialExpression,
Microarray, mRNAMicroarray, Genetics, GeneSetEnrichment
Author: Konstantin Krismer [aut, cre, cph]
(<https://orcid.org/0000-0001-8994-3416>), Anna Gattinger [aut]
(<https://orcid.org/0000-0001-7094-9279>), Michael Yaffe [ths,
cph] (<https://orcid.org/0000-0002-9547-3251>), Ian Cannell
[ths] (<https://orcid.org/0000-0001-5832-9210>)
Maintainer: Konstantin Krismer <krismer@mit.edu>
URL: https://transite.mit.edu
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/transite
git_branch: RELEASE_3_13
git_last_commit: 84f24a9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/transite_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/transite_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/transite_1.10.0.tgz
vignettes: vignettes/transite/inst/doc/spma.html
vignetteTitles: Spectrum Motif Analysis (SPMA)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/transite/inst/doc/spma.R
dependencyCount: 60
Package: tRanslatome
Version: 1.30.0
Depends: R (>= 2.15.0), methods, limma, sigPathway, anota, DESeq2,
edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus,
gplots, plotrix, Biobase
License: GPL-3
MD5sum: 1dad048b4c6ea07161c2f35af39f1d25
NeedsCompilation: no
Title: Comparison between multiple levels of gene expression
Description: Detection of differentially expressed genes (DEGs) from
the comparison of two biological conditions (treated vs.
untreated, diseased vs. normal, mutant vs. wild-type) among
different levels of gene expression (transcriptome
,translatome, proteome), using several statistical methods:
Rank Product, Translational Efficiency, t-test, Limma, ANOTA,
DESeq, edgeR. Possibility to plot the results with
scatterplots, histograms, MA plots, standard deviation (SD)
plots, coefficient of variation (CV) plots. Detection of
significantly enriched post-transcriptional regulatory factors
(RBPs, miRNAs, etc) and Gene Ontology terms in the lists of
DEGs previously identified for the two expression levels.
Comparison of GO terms enriched only in one of the levels or in
both. Calculation of the semantic similarity score between the
lists of enriched GO terms coming from the two expression
levels. Visual examination and comparison of the enriched terms
with heatmaps, radar plots and barplots.
biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression,
DifferentialExpression, Microarray, HighThroughputSequencing,
QualityControl, GO, MultipleComparisons, Bioinformatics
Author: Toma Tebaldi, Erik Dassi, Galena Kostoska
Maintainer: Toma Tebaldi <tebaldi@science.unitn.it>, Erik Dassi
<erik.dassi@unitn.it>
git_url: https://git.bioconductor.org/packages/tRanslatome
git_branch: RELEASE_3_13
git_last_commit: 256a394
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tRanslatome_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tRanslatome_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tRanslatome_1.30.0.tgz
vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf
vignetteTitles: tRanslatome
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tRanslatome/inst/doc/tRanslatome_package.R
dependencyCount: 118
Package: transomics2cytoscape
Version: 1.2.2
Imports: RCy3, KEGGREST, dplyr
Suggests: testthat, roxygen2, knitr, BiocStyle, rmarkdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: a8519f00bd7341cd237916d8e7971b73
NeedsCompilation: no
Title: A tool set for 3D Trans-Omic network visualization with
Cytoscape
Description: transomics2cytoscape generates a file for 3D transomics
visualization by providing input that specifies the IDs of
multiple KEGG pathway layers, their corresponding Z-axis
heights, and an input that represents the edges between the
pathway layers. The edges are used, for example, to describe
the relationships between kinase on a pathway and enzyme on
another pathway. This package automates creation of a
transomics network as shown in the figure in Yugi.2014
(https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape
automation (https://doi.org/10.1186/s13059-019-1758-4).
biocViews: Network, Software, Pathways, DataImport, KEGG
Author: Kozo Nishida [aut, cre]
(<https://orcid.org/0000-0001-8501-7319>), Katsuyuki Yugi [aut]
(<https://orcid.org/0000-0002-2046-4289>)
Maintainer: Kozo Nishida <kozo.nishida@gmail.com>
SystemRequirements: Java 11, Cytoscape 3.8.2, Cy3D >= 1.1.3
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/transomics2cytoscape
git_branch: RELEASE_3_13
git_last_commit: 724a2d6
git_last_commit_date: 2021-10-08
Date/Publication: 2021-10-10
source.ver: src/contrib/transomics2cytoscape_1.2.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/transomics2cytoscape_1.2.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/transomics2cytoscape_1.2.2.tgz
vignettes:
vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.html
vignetteTitles: transomics2cytoscape
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.R
dependencyCount: 78
Package: TransView
Version: 1.36.0
Depends: methods, GenomicRanges
Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, zlibbioc, gplots
LinkingTo: Rhtslib (>= 1.15.3)
Suggests: RUnit, pasillaBamSubset, BiocManager
License: GPL-3
MD5sum: fc3723b2e4ccf1275eda745fb3d76d6f
NeedsCompilation: yes
Title: Read density map construction and accession. Visualization of
ChIPSeq and RNASeq data sets
Description: This package provides efficient tools to generate, access
and display read densities of sequencing based data sets such
as from RNA-Seq and ChIP-Seq.
biocViews: ImmunoOncology, DNAMethylation, GeneExpression,
Transcription, Microarray, Sequencing, Sequencing, ChIPSeq,
RNASeq, MethylSeq, DataImport, Visualization, Clustering,
MultipleComparison
Author: Julius Muller
Maintainer: Julius Muller <ju-mu@alumni.ethz.ch>
URL: http://bioconductor.org/packages/release/bioc/html/TransView.html
SystemRequirements: GNU make
git_url: https://git.bioconductor.org/packages/TransView
git_branch: RELEASE_3_13
git_last_commit: f763b52
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TransView_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TransView_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TransView_1.36.0.tgz
vignettes: vignettes/TransView/inst/doc/TransView.pdf
vignetteTitles: An introduction to TransView
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TransView/inst/doc/TransView.R
dependencyCount: 22
Package: TraRe
Version: 1.0.0
Depends: R (>= 4.1)
Imports: hash, ggplot2, stats, methods, igraph, utils, glmnet, vbsr,
grDevices, gplots, gtools, pvclust, R.utils, dqrng,
SummarizedExperiment, BiocParallel, matrixStats
Suggests: knitr, rmarkdown, BiocGenerics, RUnit, BiocStyle
License: MIT + file LICENSE
MD5sum: c8060582c661d72d946b975abb477132
NeedsCompilation: no
Title: Transcriptional Rewiring
Description: TraRe (Transcriptional Rewiring) is an R package which
contains the necessary tools to carry out several functions.
Identification of module-based gene regulatory networks (GRN);
score-based classification of these modules via a rewiring
test; visualization of rewired modules to analyze
condition-based GRN deregulation and drop out genes recovering
via cliques methodology. For each tool, an html report can be
generated containing useful information about the generated GRN
and statistical data about the performed tests. These tools
have been developed considering sequenced data (RNA-Seq).
biocViews: GeneRegulation, RNASeq, GraphAndNetwork, Bayesian,
GeneTarget, Classification
Author: Jesus De La Fuente Cedeño [aut, cre, cph]
(<https://orcid.org/0000-0003-1856-2469>), Mikel Hernaez [aut,
cph, ths] (<https://orcid.org/0000-0003-0443-2305>), Charles
Blatti [aut, cph] (<https://orcid.org/0000-0002-4683-6271>)
Maintainer: Jesus De La Fuente Cedeño <jdelafuentec@unav.es>
URL: https://github.com/ubioinformat/TraRe/tree/master
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TraRe
git_branch: RELEASE_3_13
git_last_commit: 8a2e150
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TraRe_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TraRe_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TraRe_1.0.0.tgz
vignettes: vignettes/TraRe/inst/doc/TraRe.html
vignetteTitles: TraRe: Identification of conditions dependant Gene
Regulatory Networks
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TraRe/inst/doc/TraRe.R
dependencyCount: 83
Package: traseR
Version: 1.22.0
Depends: R (>= 3.2.0),GenomicRanges,IRanges,BSgenome.Hsapiens.UCSC.hg19
Suggests: BiocStyle,RUnit, BiocGenerics
License: GPL
MD5sum: ae7785ca8c1a23fccead6d36b4f6667a
NeedsCompilation: no
Title: GWAS trait-associated SNP enrichment analyses in genomic
intervals
Description: traseR performs GWAS trait-associated SNP enrichment
analyses in genomic intervals using different hypothesis
testing approaches, also provides various functionalities to
explore and visualize the results.
biocViews: Genetics,Sequencing, Coverage, Alignment, QualityControl,
DataImport
Author: Li Chen, Zhaohui S.Qin
Maintainer: li chen<li.chen@emory.edu>
git_url: https://git.bioconductor.org/packages/traseR
git_branch: RELEASE_3_13
git_last_commit: c0cd9a7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/traseR_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/traseR_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/traseR_1.22.0.tgz
vignettes: vignettes/traseR/inst/doc/traseR.pdf
vignetteTitles: Perform GWAS trait-associated SNP enrichment analyses
in genomic intervals
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/traseR/inst/doc/traseR.R
dependencyCount: 46
Package: Travel
Version: 1.0.0
Imports: Rcpp
LinkingTo: Rcpp
Suggests: testthat, BiocStyle, knitr, rmarkdown, inline, parallel
License: GPL-3
MD5sum: d276ed97abdb123ab96ad68b6b24f38b
NeedsCompilation: yes
Title: An utility to create an ALTREP object with a virtual pointer
Description: Creates a virtual pointer for R's ALTREP object which does
not have the data allocates in memory. The pointer is made by
the file mapping of a virtual file so it behaves exactly the
same as a regular pointer. All the requests to access the
pointer will be sent to the underlying file system and
eventually handled by a customized data-reading function. The
main purpose of the package is to reduce the memory consumption
when using R's vector to represent a large data. The use cases
of the package include on-disk data representation, compressed
vector(e.g. RLE) and etc.
biocViews: Infrastructure
Author: Jiefei Wang [aut, cre]
Maintainer: Jiefei Wang <szwjf08@gmail.com>
URL: https://github.com/Jiefei-Wang/Travel
SystemRequirements: C++11 Windows: Dokan Linux&Mac: fuse, pkg-config
VignetteBuilder: knitr
BugReports: https://github.com/Jiefei-Wang/Travel/issues
git_url: https://git.bioconductor.org/packages/Travel
git_branch: RELEASE_3_13
git_last_commit: 8b0cf33
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Travel_1.0.0.tar.gz
vignettes: vignettes/Travel/inst/doc/vignette.html
vignetteTitles: vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Travel/inst/doc/vignette.R
dependencyCount: 3
Package: TreeAndLeaf
Version: 1.4.2
Depends: R(>= 4.0)
Imports: RedeR(>= 1.40.4), igraph, ape
Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, stringr,
geneplast, ggtree, ggplot2, dplyr, dendextend, RColorBrewer
License: Artistic-2.0
MD5sum: cab5c0568fd07935a2d8eb5d56d057f6
NeedsCompilation: no
Title: Displaying binary trees with focus on dendrogram leaves
Description: The TreeAndLeaf package combines unrooted and
force-directed graph algorithms in order to layout binary
trees, aiming to represent multiple layers of information onto
dendrogram leaves.
biocViews: Infrastructure, GraphAndNetwork, Software, Network,
Visualization, DataRepresentation
Author: Leonardo W. Kume, Luis E. A. Rizzardi, Milena A. Cardoso, Mauro
A. A. Castro
Maintainer: Milena A. Cardoso <milenandreuzo@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TreeAndLeaf
git_branch: RELEASE_3_13
git_last_commit: af156ef
git_last_commit_date: 2021-08-28
Date/Publication: 2021-09-02
source.ver: src/contrib/TreeAndLeaf_1.4.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TreeAndLeaf_1.4.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/TreeAndLeaf_1.4.2.tgz
vignettes: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.html
vignetteTitles: TreeAndLeaf: an graph layout to dendrograms.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.R
dependencyCount: 17
Package: treeio
Version: 1.16.2
Depends: R (>= 3.6.0)
Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, tibble,
tidytree (>= 0.3.0), utils
Suggests: Biostrings, ggplot2, ggtree, igraph, knitr, rmarkdown,
phangorn, prettydoc, testthat, tidyr, vroom, xml2, yaml
License: Artistic-2.0
MD5sum: 99e9cec901728976da8b52b8e0d9bf7d
NeedsCompilation: no
Title: Base Classes and Functions for Phylogenetic Tree Input and
Output
Description: 'treeio' is an R package to make it easier to import and
store phylogenetic tree with associated data; and to link
external data from different sources to phylogeny. It also
supports exporting phylogenetic tree with heterogeneous
associated data to a single tree file and can be served as a
platform for merging tree with associated data and converting
file formats.
biocViews: Software, Annotation, Clustering, DataImport,
DataRepresentation, Alignment, MultipleSequenceAlignment,
Phylogenetics
Author: Guangchuang Yu [aut, cre]
(<https://orcid.org/0000-0002-6485-8781>), Tommy Tsan-Yuk Lam
[ctb, ths], Shuangbin Xu [ctb]
(<https://orcid.org/0000-0003-3513-5362>), Bradley Jones [ctb],
Casey Dunn [ctb], Tyler Bradley [ctb], Konstantinos Geles [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://github.com/YuLab-SMU/treeio (devel),
https://docs.ropensci.org/treeio/ (docs),
https://yulab-smu.top/treedata-book/ (book)
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/treeio/issues
git_url: https://git.bioconductor.org/packages/treeio
git_branch: RELEASE_3_13
git_last_commit: 5d5bfb8
git_last_commit_date: 2021-08-17
Date/Publication: 2021-08-17
source.ver: src/contrib/treeio_1.16.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/treeio_1.16.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/treeio_1.16.2.tgz
vignettes: vignettes/treeio/inst/doc/treeio.html
vignetteTitles: treeio
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/treeio/inst/doc/treeio.R
importsMe: ggtree, MicrobiotaProcess, TreeSummarizedExperiment,
RevGadgets
suggestsMe: enrichplot, ggtreeExtra, rfaRm, idiogramFISH, nosoi,
tidytree
dependencyCount: 35
Package: treekoR
Version: 1.0.0
Depends: R (>= 4.1)
Imports: stats, utils, tidyr, dplyr, magrittr, data.table, ggiraph,
ggplot2, hopach, ape, ggtree, patchwork, SingleCellExperiment
Suggests: knitr, rmarkdown, BiocStyle, CATALYST, testthat (>= 3.0.0)
License: GPL-3
Archs: i386, x64
MD5sum: e345e2be8cb2d46adf35c18c510ebf15
NeedsCompilation: no
Title: Cytometry Cluster Hierarchy and Proportions to Parent
Description: treekoR is a novel framework that aims to utilise the
hierarchical nature of single cell cytometry data to find
robust and interpretable associations between cell subsets and
patient clinical end points. These associations are aimed to
recapitulate the nested proportions prevalent in workflows
inovlving manual gating, which are often overlooked in
workflows using automatic clustering to identify cell
populations. We developed treekoR to: Derive a hierarchical
tree structure of cell clusters; measure the proportions to
parent (proportions of cells each node in the tree relative to
the number of cells belonging its parent node), in addition to
the proportions to all (proportion of cells in each node
relative to all cells); perform significance testing using the
calculated proportions; and provide an interactive html
visualisation to help highlight key results.
biocViews: Clustering, DifferentialExpression, FlowCytometry,
ImmunoOncology, MassSpectrometry, SingleCell, Software,
StatisticalMethod, Visualization
Author: Adam Chan [aut, cre], Ellis Patrick [ctb]
Maintainer: Adam Chan <adam.s.chan@sydney.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/treekoR
git_branch: RELEASE_3_13
git_last_commit: 2b645b7
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/treekoR_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/treekoR_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/treekoR_1.0.0.tgz
vignettes: vignettes/treekoR/inst/doc/vignette.html
vignetteTitles: vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/treekoR/inst/doc/vignette.R
dependencyCount: 87
Package: TreeSummarizedExperiment
Version: 2.0.3
Depends: R(>= 3.6.0), SingleCellExperiment, S4Vectors (>= 0.23.18),
Biostrings
Imports: methods, BiocGenerics, utils, ape, rlang, dplyr,
SummarizedExperiment, BiocParallel, IRanges, treeio
Suggests: ggtree, ggplot2, BiocStyle, knitr, rmarkdown, testthat
License: GPL (>=2)
MD5sum: e78d84b15714144d6957183e8f124911
NeedsCompilation: no
Title: TreeSummarizedExperiment: a S4 Class for Data with Tree
Structures
Description: TreeSummarizedExperiment has extended SingleCellExperiment
to include hierarchical information on the rows or columns of
the rectangular data.
biocViews: DataRepresentation, Infrastructure
Author: Ruizhu Huang [aut, cre]
(<https://orcid.org/0000-0003-3285-1945>), Felix G.M. Ernst
[ctb] (<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Ruizhu Huang <ruizhuRH@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TreeSummarizedExperiment
git_branch: RELEASE_3_13
git_last_commit: 0874f51
git_last_commit_date: 2021-08-15
Date/Publication: 2021-08-17
source.ver: src/contrib/TreeSummarizedExperiment_2.0.3.tar.gz
win.binary.ver:
bin/windows/contrib/4.1/TreeSummarizedExperiment_2.0.3.zip
mac.binary.ver:
bin/macosx/contrib/4.1/TreeSummarizedExperiment_2.0.3.tgz
vignettes:
vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.html,
vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.html
vignetteTitles: 1. Introduction to TreeSE, 2. Combine TSEs
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.R,
vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.R
dependsOnMe: ExperimentSubset, mia, miaViz, curatedMetagenomicData,
microbiomeDataSets
dependencyCount: 62
Package: trena
Version: 1.14.0
Depends: R (>= 3.5.0), utils, glmnet (>= 2.0.3), MotifDb (>= 1.19.17)
Imports: RSQLite, RMySQL, lassopv, randomForest, vbsr, xgboost,
BiocParallel, RPostgreSQL, methods, DBI, BSgenome,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm10, SNPlocs.Hsapiens.dbSNP150.GRCh38,
org.Hs.eg.db, Biostrings, GenomicRanges, biomaRt, AnnotationDbi
Suggests: RUnit, plyr, knitr, BiocGenerics, rmarkdown,
BSgenome.Scerevisiae.UCSC.sacCer3,
BSgenome.Athaliana.TAIR.TAIR9
License: GPL-3
MD5sum: bdb8b28746c6f9f5f38cbbb58ca1566e
NeedsCompilation: no
Title: Fit transcriptional regulatory networks using gene expression,
priors, machine learning
Description: Methods for reconstructing transcriptional regulatory
networks, especially in species for which genome-wide TF
binding site information is available.
biocViews: Transcription, GeneRegulation, NetworkInference,
FeatureExtraction, Regression, SystemsBiology, GeneExpression
Author: Seth Ament <seth.ament@systemsbiology.org>, Paul Shannon
<pshannon@systemsbioloyg.org>, Matthew Richards
<mrichard@systemsbiology.org>
Maintainer: Paul Shannon <paul.thurmond.shannon@gmail.com>
URL: https://pricelab.github.io/trena/
VignetteBuilder: knitr
BugReports: https://github.com/PriceLab/trena/issues
git_url: https://git.bioconductor.org/packages/trena
git_branch: RELEASE_3_13
git_last_commit: c848203
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/trena_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/trena_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/trena_1.14.0.tgz
vignettes: vignettes/trena/inst/doc/caseStudyFour.html,
vignettes/trena/inst/doc/caseStudyOne.html,
vignettes/trena/inst/doc/caseStudyThree.html,
vignettes/trena/inst/doc/caseStudyTwo.html,
vignettes/trena/inst/doc/overview.html,
vignettes/trena/inst/doc/simple.html,
vignettes/trena/inst/doc/tiny.html,
vignettes/trena/inst/doc/TReNA_Vignette.html
vignetteTitles: "Case Study Four: a novel regulator of GATA2 in
erythropoieis?", "Case Study One: reproduce known regulation of
NFE2 by GATA1 in bulk RNA-seq", "Case Study Three: reproduce
known regulation of NFE2 by GATA1 in bulk RNA-seq", "Case Study
Two reproduces known regulation of NFE2 by GATA1 in erytrhop
RNA-seq", "TRENA: computational prediction of gene regulation",
"Explore output controls", "Tiny Vignette Example", A Brief
Introduction to TReNA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/trena/inst/doc/overview.R,
vignettes/trena/inst/doc/simple.R,
vignettes/trena/inst/doc/tiny.R,
vignettes/trena/inst/doc/TReNA_Vignette.R
dependencyCount: 118
Package: Trendy
Version: 1.14.0
Depends: R (>= 3.4)
Imports: stats, utils, graphics, grDevices, segmented, gplots,
parallel, magrittr, BiocParallel, DT, S4Vectors,
SummarizedExperiment, methods, shiny, shinyFiles
Suggests: BiocStyle, knitr, rmarkdown, devtools
License: GPL-3
Archs: i386, x64
MD5sum: 501ee72b868fe74939983c8fef664c03
NeedsCompilation: no
Title: Breakpoint analysis of time-course expression data
Description: Trendy implements segmented (or breakpoint) regression
models to estimate breakpoints which represent changes in
expression for each feature/gene in high throughput data with
ordered conditions.
biocViews: TimeCourse, RNASeq, Regression, ImmunoOncology
Author: Rhonda Bacher and Ning Leng
Maintainer: Rhonda Bacher <rbacher@ufl.edu>
URL: https://github.com/rhondabacher/Trendy
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Trendy
git_branch: RELEASE_3_13
git_last_commit: 64504c2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Trendy_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Trendy_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Trendy_1.14.0.tgz
vignettes: vignettes/Trendy/inst/doc/Trendy_vignette.pdf
vignetteTitles: Trendy Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Trendy/inst/doc/Trendy_vignette.R
dependencyCount: 80
Package: tricycle
Version: 1.0.0
Depends: R (>= 4.1), SingleCellExperiment
Imports: methods, circular, ggplot2, AnnotationDbi, scater,
GenomicRanges, IRanges, S4Vectors, scattermore, dplyr,
RColorBrewer, grDevices, stats, SummarizedExperiment, utils
Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, CircStats,
cowplot, htmltools, Seurat, org.Hs.eg.db, org.Mm.eg.db
License: GPL-3
MD5sum: b886c6facfd3b27523543455be9196ab
NeedsCompilation: no
Title: tricycle: Transferable Representation and Inference of cell
cycle
Description: The package contains functions to infer and visualize cell
cycle process using Single Cell RNASeq data. It exploits the
idea of transfer learning, projecting new data to the previous
learned biologically interpretable space. We provide a
pre-learned cell cycle space, which could be used to infer cell
cycle time of human and mouse single cell samples. In addition,
we also offer functions to visualize cell cycle time on
different embeddings and functions to build new reference.
biocViews: SingleCell, Software, Transcriptomics, RNASeq,
Transcription, BiologicalQuestion, DimensionReduction,
ImmunoOncology
Author: Shijie Zheng [aut, cre]
Maintainer: Shijie Zheng <shijieczheng@gmail.com>
URL: https://github.com/hansenlab/tricycle
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/tricycle/issues
git_url: https://git.bioconductor.org/packages/tricycle
git_branch: RELEASE_3_13
git_last_commit: 0586b67
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tricycle_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tricycle_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tricycle_1.0.0.tgz
vignettes: vignettes/tricycle/inst/doc/tricycle.html
vignetteTitles: tricycle: Transferable Representation and Inference of
Cell Cycle
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tricycle/inst/doc/tricycle.R
dependencyCount: 110
Package: trigger
Version: 1.38.0
Depends: R (>= 2.14.0), corpcor, qtl
Imports: qvalue, methods, graphics, sva
License: GPL-3
MD5sum: 1b2e6d18713c70c29d4e61c58d451a90
NeedsCompilation: yes
Title: Transcriptional Regulatory Inference from Genetics of Gene
ExpRession
Description: This R package provides tools for the statistical analysis
of integrative genomic data that involve some combination of:
genotypes, high-dimensional intermediate traits (e.g., gene
expression, protein abundance), and higher-order traits
(phenotypes). The package includes functions to: (1) construct
global linkage maps between genetic markers and gene
expression; (2) analyze multiple-locus linkage (epistasis) for
gene expression; (3) quantify the proportion of genome-wide
variation explained by each locus and identify eQTL hotspots;
(4) estimate pair-wise causal gene regulatory probabilities and
construct gene regulatory networks; and (5) identify causal
genes for a quantitative trait of interest.
biocViews: GeneExpression, SNP, GeneticVariability, Microarray,
Genetics
Author: Lin S. Chen <lchen@health.bsd.uchicago.edu>, Dipen P.
Sangurdekar <dps@genomics.princeton.edu> and John D. Storey
<jstorey@princeton.edu>
Maintainer: John D. Storey <jstorey@princeton.edu>
git_url: https://git.bioconductor.org/packages/trigger
git_branch: RELEASE_3_13
git_last_commit: a0ef31f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/trigger_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/trigger_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/trigger_1.38.0.tgz
vignettes: vignettes/trigger/inst/doc/trigger.pdf
vignetteTitles: Trigger Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/trigger/inst/doc/trigger.R
dependencyCount: 96
Package: trio
Version: 3.30.0
Depends: R (>= 3.0.1)
Imports: grDevices, graphics, methods, stats, survival, utils,
siggenes, LogicReg (>= 1.6.1)
Suggests: haplo.stats, mcbiopi, splines, logicFS (>= 1.28.1),
KernSmooth, VariantAnnotation
License: LGPL-2
MD5sum: 074e8d4b3438f7abf25125cdf36a56d7
NeedsCompilation: no
Title: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies
Description: Testing SNPs and SNP interactions with a genotypic TDT.
This package furthermore contains functions for computing
pairwise values of LD measures and for identifying LD blocks,
as well as functions for setting up matched case pseudo-control
genotype data for case-parent trios in order to run trio logic
regression, for imputing missing genotypes in trios, for
simulating case-parent trios with disease risk dependent on SNP
interaction, and for power and sample size calculation in trio
data.
biocViews: SNP, GeneticVariability, Microarray, Genetics
Author: Holger Schwender, Qing Li, Philipp Berger, Christoph Neumann,
Margaret Taub, Ingo Ruczinski
Maintainer: Holger Schwender <holger.schw@gmx.de>
git_url: https://git.bioconductor.org/packages/trio
git_branch: RELEASE_3_13
git_last_commit: 0ed315f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/trio_3.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/trio_3.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/trio_3.30.0.tgz
vignettes: vignettes/trio/inst/doc/trio.pdf
vignetteTitles: Trio Logic Regression and genotypic TDT
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/trio/inst/doc/trio.R
dependencyCount: 19
Package: triplex
Version: 1.32.0
Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 2.5.27),
XVector (>= 0.11.6), Biostrings (>= 2.39.10)
Imports: methods, grid, GenomicRanges
LinkingTo: S4Vectors, IRanges, XVector, Biostrings
Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer
License: BSD_2_clause + file LICENSE
MD5sum: b96754f1a8c25004cfc53a4e290127f8
NeedsCompilation: yes
Title: Search and visualize intramolecular triplex-forming sequences in
DNA
Description: This package provides functions for identification and
visualization of potential intramolecular triplex patterns in
DNA sequence. The main functionality is to detect the positions
of subsequences capable of folding into an intramolecular
triplex (H-DNA) in a much larger sequence. The potential H-DNA
(triplexes) should be made of as many cannonical nucleotide
triplets as possible. The package includes visualization
showing the exact base-pairing in 1D, 2D or 3D.
biocViews: SequenceMatching, GeneRegulation
Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with
contributions from Daniel Kopecek
Maintainer: Jiri Hon <jiri.hon@gmail.com>
URL: http://www.fi.muni.cz/~lexa/triplex/
git_url: https://git.bioconductor.org/packages/triplex
git_branch: RELEASE_3_13
git_last_commit: 70fc004
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/triplex_1.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/triplex_1.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/triplex_1.32.0.tgz
vignettes: vignettes/triplex/inst/doc/triplex.pdf
vignetteTitles: Triplex User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/triplex/inst/doc/triplex.R
dependencyCount: 21
Package: tRNA
Version: 1.10.0
Depends: R (>= 3.5), GenomicRanges, Structstrings
Imports: stringr, S4Vectors, methods, BiocGenerics, IRanges, XVector,
Biostrings, Modstrings, ggplot2, scales
Suggests: knitr, rmarkdown, testthat, BiocStyle, tRNAscanImport
License: GPL-3 + file LICENSE
MD5sum: 61a09c32ff15b8ffd22ed779f632c398
NeedsCompilation: no
Title: Analyzing tRNA sequences and structures
Description: The tRNA package allows tRNA sequences and structures to
be accessed and used for subsetting. In addition, it provides
visualization tools to compare feature parameters of multiple
tRNA sets and correlate them to additional data. The tRNA
package uses GRanges objects as inputs requiring only few
additional column data sets.
biocViews: Software, Visualization
Author: Felix GM Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Felix GM Ernst <felix.gm.ernst@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/tRNA/issues
git_url: https://git.bioconductor.org/packages/tRNA
git_branch: RELEASE_3_13
git_last_commit: 5338871
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tRNA_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tRNA_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tRNA_1.10.0.tgz
vignettes: vignettes/tRNA/inst/doc/tRNA.html
vignetteTitles: tRNA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tRNA/inst/doc/tRNA.R
dependsOnMe: tRNAdbImport, tRNAscanImport
dependencyCount: 56
Package: tRNAdbImport
Version: 1.10.0
Depends: R (>= 3.5), GenomicRanges, Modstrings, Structstrings, tRNA
Imports: Biostrings, BiocGenerics, stringr, xml2, S4Vectors, methods,
httr, IRanges, utils
Suggests: knitr, rmarkdown, testthat, httptest, BiocStyle, rtracklayer
License: GPL-3 + file LICENSE
MD5sum: 7e7286c76a76939ca16203df5d3f4c9e
NeedsCompilation: no
Title: Importing from tRNAdb and mitotRNAdb as GRanges objects
Description: tRNAdbImport imports the entries of the tRNAdb and mtRNAdb
(http://trna.bioinf.uni-leipzig.de) as GRanges object.
biocViews: Software, Visualization, DataImport
Author: Felix G.M. Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/tRNAdbImport/issues
git_url: https://git.bioconductor.org/packages/tRNAdbImport
git_branch: RELEASE_3_13
git_last_commit: 82fa20a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tRNAdbImport_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tRNAdbImport_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tRNAdbImport_1.10.0.tgz
vignettes: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.html
vignetteTitles: tRNAdbImport
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.R
importsMe: EpiTxDb
dependencyCount: 65
Package: tRNAscanImport
Version: 1.12.0
Depends: R (>= 3.5), GenomicRanges, tRNA
Imports: methods, stringr, BiocGenerics, Biostrings, Structstrings,
S4Vectors, IRanges, XVector, GenomeInfoDb, rtracklayer,
BSgenome, Rsamtools
Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2,
BSgenome.Scerevisiae.UCSC.sacCer3
License: GPL-3 + file LICENSE
MD5sum: 30cb7f667019a47bda14e0ce9b8e3999
NeedsCompilation: no
Title: Importing a tRNAscan-SE result file as GRanges object
Description: The package imports the result of tRNAscan-SE as a GRanges
object.
biocViews: Software, DataImport, WorkflowStep, Preprocessing,
Visualization
Author: Felix G.M. Ernst [aut, cre]
(<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/tRNAscanImport
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/tRNAscanImport/issues
git_url: https://git.bioconductor.org/packages/tRNAscanImport
git_branch: RELEASE_3_13
git_last_commit: cb59bfe
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tRNAscanImport_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tRNAscanImport_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tRNAscanImport_1.12.0.tgz
vignettes: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.html
vignetteTitles: tRNAscanImport
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.R
suggestsMe: Structstrings, tRNA
dependencyCount: 79
Package: TRONCO
Version: 2.24.0
Depends: R (>= 4.0.0),
Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel,
iterators, RColorBrewer, circlize, cgdsr, igraph, grid,
gridExtra, xtable, gtable, scales, R.matlab, grDevices,
graphics, stats, utils, methods
Suggests: BiocGenerics, BiocStyle, testthat, knitr, rWikiPathways
License: GPL-3
MD5sum: 43f1b076e0a49013f1d5a1da7de7464b
NeedsCompilation: no
Title: TRONCO, an R package for TRanslational ONCOlogy
Description: The TRONCO (TRanslational ONCOlogy) R package collects
algorithms to infer progression models via the approach of
Suppes-Bayes Causal Network, both from an ensemble of tumors
(cross-sectional samples) and within an individual patient
(multi-region or single-cell samples). The package provides
parallel implementation of algorithms that process binary
matrices where each row represents a tumor sample and each
column a single-nucleotide or a structural variant driving the
progression; a 0/1 value models the absence/presence of that
alteration in the sample. The tool can import data from plain,
MAF or GISTIC format files, and can fetch it from the
cBioPortal for cancer genomics. Functions for data manipulation
and visualization are provided, as well as functions to
import/export such data to other bioinformatics tools for, e.g,
clustering or detection of mutually exclusive alterations.
Inferred models can be visualized and tested for their
confidence via bootstrap and cross-validation. TRONCO is used
for the implementation of the Pipeline for Cancer Inference
(PICNIC).
biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork,
SomaticMutation, NetworkInference, Network, Clustering,
DataImport, SingleCell, ImmunoOncology
Author: Marco Antoniotti [ctb], Giulio Caravagna [aut, cre], Luca De
Sano [aut], Alex Graudenzi [aut], Giancarlo Mauri [ctb], Bud
Mishra [ctb], Daniele Ramazzotti [aut]
(<https://orcid.org/0000-0002-6087-2666>)
Maintainer: Luca De Sano <luca.desano@gmail.com>
URL: https://sites.google.com/site/troncopackage/
VignetteBuilder: knitr
BugReports: https://github.com/BIMIB-DISCo/TRONCO
git_url: https://git.bioconductor.org/packages/TRONCO
git_branch: RELEASE_3_13
git_last_commit: 327bbb9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TRONCO_2.24.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TRONCO_2.24.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TRONCO_2.24.0.tgz
vignettes: vignettes/TRONCO/inst/doc/vignette.pdf
vignetteTitles: An R Package for TRanslational ONCOlogy
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TRONCO/inst/doc/vignette.R
dependencyCount: 52
Package: TSCAN
Version: 1.30.0
Depends: SingleCellExperiment, TrajectoryUtils
Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv,
mclust, gplots, methods, stats, Matrix, SummarizedExperiment,
DelayedArray, S4Vectors
Suggests: knitr, testthat, scuttle, scran, metapod, BiocParallel,
BiocNeighbors, batchelor
License: GPL(>=2)
MD5sum: 9ae81dcacbe8bb84d79f8d9ba646584a
NeedsCompilation: no
Title: Tools for Single-Cell Analysis
Description: Provides methods to perform trajectory analysis based on a
minimum spanning tree constructed from cluster centroids.
Computes pseudotemporal cell orderings by mapping cells in each
cluster (or new cells) to the closest edge in the tree. Uses
linear modelling to identify differentially expressed genes
along each path through the tree. Several plotting and
interactive visualization functions are also implemented.
biocViews: GeneExpression, Visualization, GUI
Author: Zhicheng Ji [aut, cre], Hongkai Ji [aut], Aaron Lun [ctb]
Maintainer: Zhicheng Ji <zji4@jhu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TSCAN
git_branch: RELEASE_3_13
git_last_commit: ff908e9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TSCAN_1.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TSCAN_1.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TSCAN_1.30.0.tgz
vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf
vignetteTitles: TSCAN: Tools for Single-Cell ANalysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R
dependsOnMe: OSCA.advanced, OSCA.multisample
importsMe: ctgGEM, FEAST, DIscBIO
suggestsMe: condiments
dependencyCount: 87
Package: tscR
Version: 1.4.0
Depends: R (>= 4.0), dplyr
Imports: gridExtra, methods, dtw, class, kmlShape, graphics, cluster,
RColorBrewer, grDevices, knitr, rmarkdown, prettydoc, grid,
ggplot2, latex2exp, stats, SummarizedExperiment, GenomicRanges,
IRanges, S4Vectors
Suggests: testthat
License: GPL (>=2)
Archs: i386, x64
MD5sum: c74dbe0c7d3cf064c1a92a236d862e95
NeedsCompilation: yes
Title: A time series clustering package combining slope and Frechet
distances
Description: Clustering for time series data using slope distance
and/or shape distance.
biocViews: GeneExpression, Clustering, DNAMethylation, Microarray
Author: Miriam Riquelme-Pérez and Fernando Pérez-Sanz
Maintainer: Pérez-Sanz, Fernando <fernando.perez8@um.es>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tscR
git_branch: RELEASE_3_13
git_last_commit: b8f82b0
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tscR_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tscR_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tscR_1.4.0.tgz
vignettes: vignettes/tscR/inst/doc/tscR.html
vignetteTitles: tscR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tscR/inst/doc/tscR.R
dependencyCount: 91
Package: tspair
Version: 1.50.0
Depends: R (>= 2.10), Biobase (>= 2.4.0)
License: GPL-2
MD5sum: c7bc7631f9aabf7e2e5b65ca5d5f6fd5
NeedsCompilation: yes
Title: Top Scoring Pairs for Microarray Classification
Description: These functions calculate the pair of genes that show the
maximum difference in ranking between two user specified
groups. This "top scoring pair" maximizes the average of
sensitivity and specificity over all rank based classifiers
using a pair of genes in the data set. The advantage of
classifying samples based on only the relative rank of a pair
of genes is (a) the classifiers are much simpler and often more
interpretable than more complicated classification schemes and
(b) if arrays can be classified using only a pair of genes, PCR
based tests could be used for classification of samples. See
the references for the tspcalc() function for references
regarding TSP classifiers.
biocViews: Microarray
Author: Jeffrey T. Leek <jtleek@jhu.edu>
Maintainer: Jeffrey T. Leek <jtleek@jhu.edu>
git_url: https://git.bioconductor.org/packages/tspair
git_branch: RELEASE_3_13
git_last_commit: bf4fcb4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tspair_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tspair_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tspair_1.50.0.tgz
vignettes: vignettes/tspair/inst/doc/tsp.pdf
vignetteTitles: tspTutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tspair/inst/doc/tsp.R
dependencyCount: 7
Package: TSRchitect
Version: 1.18.0
Depends: R (>= 3.5)
Imports: AnnotationHub, BiocGenerics, BiocParallel, dplyr,
GenomicAlignments, GenomeInfoDb, GenomicRanges, gtools,
IRanges, methods, readxl, Rsamtools (>= 1.14.3), rtracklayer,
S4Vectors, SummarizedExperiment, tools, utils
Suggests: ENCODExplorer, ggplot2, knitr, rmarkdown
License: GPL-3
Archs: i386, x64
MD5sum: 7809a04854da19076aa9f02033f201de
NeedsCompilation: no
Title: Promoter identification from large-scale TSS profiling data
Description: In recent years, large-scale transcriptional sequence data
has yielded considerable insights into the nature of gene
expression and regulation in eukaryotes. Techniques that
identify the 5' end of mRNAs, most notably CAGE, have mapped
the promoter landscape across a number of model organisms. Due
to the variability of TSS distributions and the transcriptional
noise present in datasets, precisely identifying the active
promoter(s) for genes from these datasets is not
straightforward. TSRchitect allows the user to efficiently
identify the putative promoter (the transcription start region,
or TSR) from a variety of TSS profiling data types, including
both single-end (e.g. CAGE) as well as paired-end (RAMPAGE,
PEAT, STRIPE-seq). In addition, (new with version 1.3.0)
TSRchitect provides the ability to import aligned EST and cDNA
data. Along with the coordiantes of identified TSRs, TSRchitect
also calculates the width, abundance and two forms of the Shape
Index, and handles biological replicates for expression
profiling. Finally, TSRchitect imports annotation files,
allowing the user to associate identified promoters with genes
and other genomic features. Three detailed examples of
TSRchitect's utility are provided in the User's Guide, included
with this package.
biocViews: Clustering, FunctionalGenomics, GeneExpression,
GeneRegulation, GenomeAnnotation, Sequencing, Transcription
Author: R. Taylor Raborn [aut, cre, cph] Volker P. Brendel [aut, cph]
Krishnakumar Sridharan [ctb]
Maintainer: R. Taylor Raborn <rtraborn@indiana.edu>
URL: https://github.com/brendelgroup/tsrchitect
VignetteBuilder: knitr
BugReports: https://github.com/brendelgroup/tsrchitect/issues
git_url: https://git.bioconductor.org/packages/TSRchitect
git_branch: RELEASE_3_13
git_last_commit: daeccc8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TSRchitect_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TSRchitect_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TSRchitect_1.18.0.tgz
vignettes: vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.pdf,
vignettes/TSRchitect/inst/doc/TSRchitect.html,
vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.html
vignetteTitles: TSRchitect User's Guide, TSRchitect vignette,
TSRchitect User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TSRchitect/inst/doc/TSRchitect.R
dependencyCount: 117
Package: ttgsea
Version: 1.0.0
Depends: keras
Imports: tm, text2vec, tokenizers, textstem, stopwords, data.table,
purrr, DiagrammeR, stats
Suggests: fgsea, knitr, testthat, reticulate, rmarkdown
License: Artistic-2.0
MD5sum: 7bf4a9d487b77b6e51ff1d9b4089b039
NeedsCompilation: no
Title: Tokenizing Text of Gene Set Enrichment Analysis
Description: Functional enrichment analysis methods such as gene set
enrichment analysis (GSEA) have been widely used for analyzing
gene expression data. GSEA is a powerful method to infer
results of gene expression data at a level of gene sets by
calculating enrichment scores for predefined sets of genes.
GSEA depends on the availability and accuracy of gene sets.
There are overlaps between terms of gene sets or categories
because multiple terms may exist for a single biological
process, and it can thus lead to redundancy within enriched
terms. In other words, the sets of related terms are
overlapping. Using deep learning, this pakage is aimed to
predict enrichment scores for unique tokens or words from text
in names of gene sets to resolve this overlapping set issue.
Furthermore, we can coin a new term by combining tokens and
find its enrichment score by predicting such a combined tokens.
biocViews: Software, GeneExpression, GeneSetEnrichment
Author: Dongmin Jung [cre, aut]
(<https://orcid.org/0000-0001-7499-8422>)
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ttgsea
git_branch: RELEASE_3_13
git_last_commit: 9a72aa9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ttgsea_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ttgsea_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ttgsea_1.0.0.tgz
vignettes: vignettes/ttgsea/inst/doc/ttgsea.html
vignetteTitles: ttgsea
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ttgsea/inst/doc/ttgsea.R
importsMe: DeepPINCS
dependencyCount: 122
Package: TTMap
Version: 1.14.0
Depends: rgl, colorRamps
Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment,
Biobase
Suggests: BiocStyle, airway
License: GPL-2
MD5sum: 3d04397b3b4c7ffc7d419c8fcf1eabaf
NeedsCompilation: no
Title: Two-Tier Mapper: a clustering tool based on topological data
analysis
Description: TTMap is a clustering method that groups together samples
with the same deviation in comparison to a control group. It is
specially useful when the data is small. It is parameter free.
biocViews: Software, Microarray, DifferentialExpression,
MultipleComparison, Clustering, Classification
Author: Rachel Jeitziner
Maintainer: Rachel Jeitziner <rachel.jeitziner@epfl.ch>
git_url: https://git.bioconductor.org/packages/TTMap
git_branch: RELEASE_3_13
git_last_commit: b8057a5
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TTMap_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TTMap_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TTMap_1.14.0.tgz
vignettes: vignettes/TTMap/inst/doc/TTMap.pdf
vignetteTitles: Manual for the TTMap library
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TTMap/inst/doc/TTMap.R
dependencyCount: 47
Package: TurboNorm
Version: 1.40.0
Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray
Imports: stats, grDevices, affy, lattice
Suggests: BiocStyle, affydata
License: LGPL
MD5sum: ac182ea71fc7fc16c7b9d4a2b32299ed
NeedsCompilation: yes
Title: A fast scatterplot smoother suitable for microarray
normalization
Description: A fast scatterplot smoother based on B-splines with
second-order difference penalty. Functions for microarray
normalization of single-colour data i.e. Affymetrix/Illumina
and two-colour data supplied as marray MarrayRaw-objects or
limma RGList-objects are available.
biocViews: Microarray, OneChannel, TwoChannel, Preprocessing,
DNAMethylation, CpGIsland, MethylationArray, Normalization
Author: Maarten van Iterson and Chantal van Leeuwen
Maintainer: Maarten van Iterson <mviterson@gmail.com>
URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html
git_url: https://git.bioconductor.org/packages/TurboNorm
git_branch: RELEASE_3_13
git_last_commit: 707184a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TurboNorm_1.40.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TurboNorm_1.40.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TurboNorm_1.40.0.tgz
vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf
vignetteTitles: TurboNorm Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TurboNorm/inst/doc/turbonorm.R
dependencyCount: 18
Package: TVTB
Version: 1.18.0
Depends: R (>= 3.4), methods, utils, stats
Imports: AnnotationFilter, BiocGenerics (>= 0.25.1), BiocParallel,
Biostrings, ensembldb, ensemblVEP, GenomeInfoDb, GenomicRanges,
GGally, ggplot2, Gviz, limma, IRanges (>= 2.21.6), reshape2,
Rsamtools, S4Vectors (>= 0.25.14), SummarizedExperiment,
VariantAnnotation (>= 1.19.9)
Suggests: EnsDb.Hsapiens.v75 (>= 0.99.7), shiny (>= 0.13.2.9005), DT
(>= 0.1.67), rtracklayer, BiocStyle (>= 2.5.19), knitr (>=
1.12), rmarkdown, testthat, covr, pander
License: Artistic-2.0
MD5sum: c4b2e05b56f106ec583d51c63bbd7d45
NeedsCompilation: no
Title: TVTB: The VCF Tool Box
Description: The package provides S4 classes and methods to filter,
summarise and visualise genetic variation data stored in VCF
files. In particular, the package extends the FilterRules class
(S4Vectors package) to define news classes of filter rules
applicable to the various slots of VCF objects. Functionalities
are integrated and demonstrated in a Shiny web-application, the
Shiny Variant Explorer (tSVE).
biocViews: Software, Genetics, GeneticVariability, GenomicVariation,
DataRepresentation, GUI, Genetics, DNASeq, WholeGenome,
Visualization, MultipleComparison, DataImport,
VariantAnnotation, Sequencing, Coverage, Alignment,
SequenceMatching
Author: Kevin Rue-Albrecht [aut, cre]
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/kevinrue/TVTB
VignetteBuilder: knitr
BugReports: https://github.com/kevinrue/TVTB/issues
git_url: https://git.bioconductor.org/packages/TVTB
git_branch: RELEASE_3_13
git_last_commit: 954f99a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TVTB_1.18.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/TVTB_1.18.0.tgz
vignettes: vignettes/TVTB/inst/doc/Introduction.html,
vignettes/TVTB/inst/doc/tSVE.html,
vignettes/TVTB/inst/doc/VcfFilterRules.html
vignetteTitles: Introduction to TVTB, The Shiny Variant Explorer, VCF
filter rules
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TVTB/inst/doc/Introduction.R,
vignettes/TVTB/inst/doc/tSVE.R,
vignettes/TVTB/inst/doc/VcfFilterRules.R
dependencyCount: 151
Package: tweeDEseq
Version: 1.38.0
Depends: R (>= 2.12.0)
Imports: MASS, limma, edgeR, parallel, cqn
Suggests: tweeDEseqCountData, xtable
License: GPL (>= 2)
MD5sum: 2afd86b9c7cfc6579a95fe17b0f3d0ea
NeedsCompilation: yes
Title: RNA-seq data analysis using the Poisson-Tweedie family of
distributions
Description: Differential expression analysis of RNA-seq using the
Poisson-Tweedie family of distributions.
biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression,
Sequencing, RNASeq
Author: Juan R Gonzalez <jrgonzalez@creal.cat> and Mikel Esnaola
<mesnaola@creal.cat> (with contributions from Robert Castelo
<robert.castelo@upf.edu>)
Maintainer: Juan R Gonzalez <jrgonzalez@creal.cat>
URL: http://www.creal.cat/jrgonzalez/software.htm
git_url: https://git.bioconductor.org/packages/tweeDEseq
git_branch: RELEASE_3_13
git_last_commit: 375f48a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tweeDEseq_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tweeDEseq_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tweeDEseq_1.38.0.tgz
vignettes: vignettes/tweeDEseq/inst/doc/tweeDEseq.pdf
vignetteTitles: tweeDEseq: analysis of RNA-seq data using the
Poisson-Tweedie family of distributions
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tweeDEseq/inst/doc/tweeDEseq.R
importsMe: ptmixed
dependencyCount: 25
Package: twilight
Version: 1.68.0
Depends: R (>= 2.10), splines (>= 2.2.0), stats (>= 2.2.0), Biobase(>=
1.12.0)
Imports: Biobase, graphics, grDevices, stats
Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2)
License: GPL (>= 2)
MD5sum: 77c3577f871832c3c31ff250d2cb561a
NeedsCompilation: yes
Title: Estimation of local false discovery rate
Description: In a typical microarray setting with gene expression data
observed under two conditions, the local false discovery rate
describes the probability that a gene is not differentially
expressed between the two conditions given its corrresponding
observed score or p-value level. The resulting curve of
p-values versus local false discovery rate offers an insight
into the twilight zone between clear differential and clear
non-differential gene expression. Package 'twilight' contains
two main functions: Function twilight.pval performs a
two-condition test on differences in means for a given input
matrix or expression set and computes permutation based
p-values. Function twilight performs a stochastic downhill
search to estimate local false discovery rates and effect size
distributions. The package further provides means to filter for
permutations that describe the null distribution correctly.
Using filtered permutations, the influence of hidden
confounders could be diminished.
biocViews: Microarray, DifferentialExpression, MultipleComparison
Author: Stefanie Scheid <stefanie.scheid@gmx.de>
Maintainer: Stefanie Scheid <stefanie.scheid@gmx.de>
URL: http://compdiag.molgen.mpg.de/software/twilight.shtml
git_url: https://git.bioconductor.org/packages/twilight
git_branch: RELEASE_3_13
git_last_commit: d6432d4
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/twilight_1.68.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/twilight_1.68.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/twilight_1.68.0.tgz
vignettes: vignettes/twilight/inst/doc/tr_2004_01.pdf
vignetteTitles: Estimation of Local False Discovery Rates
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/twilight/inst/doc/tr_2004_01.R
dependsOnMe: OrderedList
dependencyCount: 9
Package: twoddpcr
Version: 1.16.0
Depends: R (>= 3.4)
Imports: class, ggplot2, hexbin, methods, scales, shiny, stats, utils,
RColorBrewer, S4Vectors
Suggests: devtools, knitr, reshape2, rmarkdown, testthat, BiocStyle
License: GPL-3
Archs: i386, x64
MD5sum: 52047bbdc8854425cd55674b8dd10481
NeedsCompilation: no
Title: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the
number of starting molecules
Description: The twoddpcr package takes Droplet Digital PCR (ddPCR)
droplet amplitude data from Bio-Rad's QuantaSoft and can
classify the droplets. A summary of the positive/negative
droplet counts can be generated, which can then be used to
estimate the number of molecules using the Poisson
distribution. This is the first open source package that
facilitates the automatic classification of general two channel
ddPCR data. Previous work includes 'definetherain' (Jones et
al., 2014) and 'ddpcRquant' (Trypsteen et al., 2015) which both
handle one channel ddPCR experiments only. The 'ddpcr' package
available on CRAN (Attali et al., 2016) supports automatic
gating of a specific class of two channel ddPCR experiments
only.
biocViews: ddPCR, Software, Classification
Author: Anthony Chiu [aut, cre]
Maintainer: Anthony Chiu <anthony@achiu.me>
URL: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/
VignetteBuilder: knitr
BugReports:
http://github.com/CRUKMI-ComputationalBiology/twoddpcr/issues/
git_url: https://git.bioconductor.org/packages/twoddpcr
git_branch: RELEASE_3_13
git_last_commit: c997928
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/twoddpcr_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/twoddpcr_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/twoddpcr_1.16.0.tgz
vignettes: vignettes/twoddpcr/inst/doc/twoddpcr.html
vignetteTitles: twoddpcr: A package for Droplet Digital PCR analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/twoddpcr/inst/doc/twoddpcr.R
dependencyCount: 65
Package: tximeta
Version: 1.10.0
Imports: SummarizedExperiment, tximport, jsonlite, S4Vectors, IRanges,
GenomicRanges, AnnotationDbi, GenomicFeatures, ensembldb,
BiocFileCache, AnnotationHub, Biostrings, tibble, GenomeInfoDb,
tools, utils, methods, Matrix
Suggests: knitr, rmarkdown, testthat, tximportData, org.Dm.eg.db,
DESeq2, edgeR, limma, devtools
License: GPL-2
Archs: i386, x64
MD5sum: 346f9cae00b58bab74df29381fbfcad9
NeedsCompilation: no
Title: Transcript Quantification Import with Automatic Metadata
Description: Transcript quantification import from Salmon and alevin
with automatic attachment of transcript ranges and release
information, and other associated metadata. De novo
transcriptomes can be linked to the appropriate sources with
linkedTxomes and shared for computational reproducibility.
biocViews: Annotation, GenomeAnnotation, DataImport, Preprocessing,
RNASeq, SingleCell, Transcriptomics, Transcription,
GeneExpression, FunctionalGenomics, ReproducibleResearch,
ReportWriting, ImmunoOncology
Author: Michael Love [aut, cre], Charlotte Soneson [aut, ctb], Peter
Hickey [aut, ctb], Rob Patro [aut, ctb], NIH NHGRI [fnd], CZI
[fnd]
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
URL: https://github.com/mikelove/tximeta
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tximeta
git_branch: RELEASE_3_13
git_last_commit: da94a30
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tximeta_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tximeta_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tximeta_1.10.0.tgz
vignettes: vignettes/tximeta/inst/doc/tximeta.html
vignetteTitles: Transcript quantification import with automatic
metadata
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tximeta/inst/doc/tximeta.R
dependsOnMe: rnaseqGene
importsMe: IsoformSwitchAnalyzeR
suggestsMe: DESeq2, fishpond, fluentGenomics
dependencyCount: 122
Package: tximport
Version: 1.20.0
Imports: utils, stats, methods
Suggests: knitr, rmarkdown, testthat, tximportData,
TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), limma,
edgeR, DESeq2 (>= 1.11.6), rhdf5, jsonlite, matrixStats,
Matrix, fishpond
License: GPL (>=2)
MD5sum: 88df7ee17bf3a870f7910eb2ac07fd40
NeedsCompilation: no
Title: Import and summarize transcript-level estimates for transcript-
and gene-level analysis
Description: Imports transcript-level abundance, estimated counts and
transcript lengths, and summarizes into matrices for use with
downstream gene-level analysis packages. Average transcript
length, weighted by sample-specific transcript abundance
estimates, is provided as a matrix which can be used as an
offset for different expression of gene-level counts.
biocViews: DataImport, Preprocessing, RNASeq, Transcriptomics,
Transcription, GeneExpression, ImmunoOncology
Author: Michael Love [cre,aut], Charlotte Soneson [aut], Mark Robinson
[aut], Rob Patro [ctb], Andrew Parker Morgan [ctb], Ryan C.
Thompson [ctb], Matt Shirley [ctb], Avi Srivastava [ctb]
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
URL: https://github.com/mikelove/tximport
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tximport
git_branch: RELEASE_3_13
git_last_commit: 5215e43
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/tximport_1.20.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/tximport_1.20.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/tximport_1.20.0.tgz
vignettes: vignettes/tximport/inst/doc/tximport.html
vignetteTitles: Importing transcript abundance datasets with tximport
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tximport/inst/doc/tximport.R
importsMe: alevinQC, BgeeCall, EventPointer, IsoformSwitchAnalyzeR,
singleCellTK, tximeta
suggestsMe: BANDITS, DESeq2, HumanTranscriptomeCompendium,
SummarizedBenchmark, variancePartition
dependencyCount: 3
Package: TypeInfo
Version: 1.58.0
Depends: methods
Suggests: Biobase
License: BSD
MD5sum: ddddbb0fd2866329844356d1e34725ab
NeedsCompilation: no
Title: Optional Type Specification Prototype
Description: A prototype for a mechanism for specifying the types of
parameters and the return value for an R function. This is
meta-information that can be used to generate stubs for servers
and various interfaces to these functions. Additionally, the
arguments in a call to a typed function can be validated using
the type specifications. We allow types to be specified as
either i) by class name using either inheritance - is(x,
className), or strict instance of - class(x) %in% className, or
ii) a dynamic test given as an R expression which is evaluated
at run-time. More precise information and interesting tests can
be done via ii), but it is harder to use this information as
meta-data as it requires more effort to interpret it and it is
of course run-time information. It is typically more
meaningful.
biocViews: Infrastructure
Author: Duncan Temple Lang Robert Gentleman (<rgentlem@fhcrc.org>)
Maintainer: Duncan Temple Lang <duncan@wald.ucdavis.edu>
git_url: https://git.bioconductor.org/packages/TypeInfo
git_branch: RELEASE_3_13
git_last_commit: 11a4340
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/TypeInfo_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/TypeInfo_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/TypeInfo_1.58.0.tgz
vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf
vignetteTitles: TypeInfo R News
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TypeInfo/inst/doc/TypeInfoNews.R
dependencyCount: 1
Package: Ularcirc
Version: 1.10.0
Depends: R (>= 3.4.0)
Imports: AnnotationHub, AnnotationDbi, BiocGenerics, Biostrings,
BSgenome, data.table (>= 1.9.4), DT, GenomicFeatures,
GenomeInfoDb, GenomeInfoDbData, GenomicAlignments,
GenomicRanges, ggplot2, ggrepel, gsubfn, mirbase.db, moments,
Organism.dplyr, S4Vectors, shiny, shinydashboard, shinyFiles,
shinyjs, Sushi, yaml
Suggests: BSgenome.Hsapiens.UCSC.hg38, BiocStyle, httpuv, knitr,
org.Hs.eg.db, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene
License: file LICENSE
MD5sum: a057dcd3ee9ded7aee8bf240816e780e
NeedsCompilation: no
Title: Shiny app for canonical and back splicing analysis (i.e.
circular and mRNA analysis)
Description: Ularcirc reads in STAR aligned splice junction files and
provides visualisation and analysis tools for splicing
analysis. Users can assess backsplice junctions and forward
canonical junctions.
biocViews: DataRepresentation,Visualization, Genetics, Sequencing,
Annotation, Coverage, AlternativeSplicing, DifferentialSplicing
Author: David Humphreys [aut, cre]
Maintainer: David Humphreys <d.humphreys@victorchang.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Ularcirc
git_branch: RELEASE_3_13
git_last_commit: 5661ddd
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Ularcirc_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Ularcirc_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Ularcirc_1.10.0.tgz
vignettes: vignettes/Ularcirc/inst/doc/Ularcirc.html
vignetteTitles: Ularcirc
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Ularcirc/inst/doc/Ularcirc.R
dependencyCount: 146
Package: UMI4Cats
Version: 1.2.1
Depends: R (>= 4.0.0), SummarizedExperiment
Imports: magick, cowplot, scales, GenomicRanges, ShortRead, zoo,
ggplot2, reshape2, regioneR, IRanges, S4Vectors, magrittr,
dplyr, BSgenome, Biostrings, DESeq2, R.utils, Rsamtools,
stringr, Rbowtie2, methods, GenomeInfoDb, GenomicAlignments,
RColorBrewer, utils, grDevices, stats, org.Hs.eg.db, annotate,
TxDb.Hsapiens.UCSC.hg19.knownGene, rlang, GenomicFeatures,
BiocFileCache, rappdirs, fda, BiocGenerics
Suggests: knitr, rmarkdown, BiocStyle, BSgenome.Hsapiens.UCSC.hg19,
tidyr, testthat
License: Artistic-2.0
Archs: i386, x64
MD5sum: 26aa8c36df43a0f1949badc8ea775c68
NeedsCompilation: no
Title: UMI4Cats: Processing, analysis and visualization of UMI-4C
chromatin contact data
Description: UMI-4C is a technique that allows characterization of 3D
chromatin interactions with a bait of interest, taking
advantage of a sonication step to produce unique molecular
identifiers (UMIs) that help remove duplication bias, thus
allowing a better differential comparsion of chromatin
interactions between conditions. This package allows processing
of UMI-4C data, starting from FastQ files provided by the
sequencing facility. It provides two statistical methods for
detecting differential contacts and includes a visualization
function to plot integrated information from a UMI-4C assay.
biocViews: QualityControl, Preprocessing, Alignment, Normalization,
Visualization, Sequencing, Coverage
Author: Mireia Ramos-Rodriguez [aut, cre]
(<https://orcid.org/0000-0001-8083-2445>), Marc Subirana-Granes
[aut], Lorenzo Pasquali [aut]
Maintainer: Mireia Ramos-Rodriguez <mireiarr9@gmail.com>
URL: https://github.com/Pasquali-lab/UMI4Cats
VignetteBuilder: knitr
BugReports: https://github.com/Pasquali-lab/UMI4Cats/issues
git_url: https://git.bioconductor.org/packages/UMI4Cats
git_branch: RELEASE_3_13
git_last_commit: a8ed2ca
git_last_commit_date: 2021-06-11
Date/Publication: 2021-06-13
source.ver: src/contrib/UMI4Cats_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/UMI4Cats_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/UMI4Cats_1.2.1.tgz
vignettes: vignettes/UMI4Cats/inst/doc/UMI4Cats.html
vignetteTitles: Analyzing UMI-4C data with UMI4Cats
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/UMI4Cats/inst/doc/UMI4Cats.R
dependencyCount: 154
Package: uncoverappLib
Version: 1.2.0
Imports: markdown, shiny, shinyjs, shinyBS,
shinyWidgets,shinycssloaders, DT, Gviz, Homo.sapiens, openxlsx,
condformat, stringr, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg38.knownGene, BiocFileCache,rappdirs,
TxDb.Hsapiens.UCSC.hg19.knownGene, rlist, utils,
EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v86, OrganismDbi,
BSgenome.Hsapiens.UCSC.hg19, processx, Rsamtools, GenomicRanges
Suggests: BiocStyle, knitr, testthat, rmarkdown, dplyr
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 89027c5c318d990998c8165991a0d810
NeedsCompilation: no
Title: Interactive graphical application for clinical assessment of
sequence coverage at the base-pair level
Description: a Shiny application containing a suite of graphical and
statistical tools to support clinical assessment of low
coverage regions.It displays three web pages each providing a
different analysis module: Coverage analysis, calculate AF by
allele frequency app and binomial distribution.
biocViews: Software, Visualization, Annotation, Coverage
Author: Emanuela Iovino [cre, aut], Tommaso Pippucci [aut]
Maintainer: Emanuela Iovino <emanuela.iovino@unibo.it>
URL: https://github.com/Manuelaio/uncoverappLib
VignetteBuilder: knitr
BugReports: https://github.com/Manuelaio/uncoverappLib/issues
git_url: https://git.bioconductor.org/packages/uncoverappLib
git_branch: RELEASE_3_13
git_last_commit: eac9828
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/uncoverappLib_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/uncoverappLib_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/uncoverappLib_1.2.0.tgz
vignettes: vignettes/uncoverappLib/inst/doc/uncoverappLib.html
vignetteTitles: uncoverappLib
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/uncoverappLib/inst/doc/uncoverappLib.R
dependencyCount: 181
Package: UNDO
Version: 1.34.0
Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase
Imports: MASS, boot, nnls, stats, utils
License: GPL-2
Archs: i386, x64
MD5sum: 226ed69e2f5c2a19c49fb6dd399faf35
NeedsCompilation: no
Title: Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions
Description: UNDO is an R package for unsupervised deconvolution of
tumor and stromal mixed expression data. It detects marker
genes and deconvolutes the mixing expression data without any
prior knowledge.
biocViews: Software
Author: Niya Wang <wangny@vt.edu>
Maintainer: Niya Wang <wangny@vt.edu>
git_url: https://git.bioconductor.org/packages/UNDO
git_branch: RELEASE_3_13
git_last_commit: 93cb5fb
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/UNDO_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/UNDO_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/UNDO_1.34.0.tgz
vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf
vignetteTitles: UNDO Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R
dependencyCount: 11
Package: unifiedWMWqPCR
Version: 1.28.0
Depends: methods
Imports: BiocGenerics, stats, graphics, HTqPCR
License: GPL (>=2)
MD5sum: 2632782871c32d444a83a72a0752f507
NeedsCompilation: no
Title: Unified Wilcoxon-Mann Whitney Test for testing differential
expression in qPCR data
Description: This packages implements the unified Wilcoxon-Mann-Whitney
Test for qPCR data. This modified test allows for testing
differential expression in qPCR data.
biocViews: DifferentialExpression, GeneExpression,
MicrotitrePlateAssay, MultipleComparison, QualityControl,
Software, Visualization, qPCR
Author: Jan R. De Neve & Joris Meys
Maintainer: Joris Meys <Joris.Meys@UGent.be>
git_url: https://git.bioconductor.org/packages/unifiedWMWqPCR
git_branch: RELEASE_3_13
git_last_commit: cce7e4c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/unifiedWMWqPCR_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/unifiedWMWqPCR_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/unifiedWMWqPCR_1.28.0.tgz
vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf
vignetteTitles: Using unifiedWMWqPCR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R
dependencyCount: 22
Package: UniProt.ws
Version: 2.32.0
Depends: methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8)
Imports: AnnotationDbi, BiocFileCache, rappdirs
Suggests: RUnit, BiocStyle, knitr
License: Artistic License 2.0
MD5sum: 6ce699dc31ac30b0d9ab75a99080f72b
NeedsCompilation: no
Title: R Interface to UniProt Web Services
Description: A collection of functions for retrieving, processing and
repackaging the UniProt web services.
biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta
Author: Marc Carlson [aut], Csaba Ortutay [ctb], Bioconductor Package
Maintainer [aut, cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/UniProt.ws
git_branch: RELEASE_3_13
git_last_commit: f289fbf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/UniProt.ws_2.32.0.tar.gz
mac.binary.ver: bin/macosx/contrib/4.1/UniProt.ws_2.32.0.tgz
vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.pdf
vignetteTitles: UniProt.ws: A package for retrieving data from the
UniProt web service
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/UniProt.ws/inst/doc/UniProt.ws.R
importsMe: dagLogo, drugTargetInteractions
suggestsMe: cleaver, qPLEXanalyzer
dependencyCount: 63
Package: Uniquorn
Version: 2.12.0
Depends: R (>= 3.5)
Imports: stringr, R.utils, WriteXLS, stats, doParallel, foreach,
GenomicRanges, IRanges, VariantAnnotation
Suggests: testthat, knitr, rmarkdown, BiocGenerics, RUnit
License: Artistic-2.0
MD5sum: 20300ca4e5a1d8ef0ae69c5026cb391a
NeedsCompilation: no
Title: Identification of cancer cell lines based on their weighted
mutational/ variational fingerprint
Description: This packages enables users to identify cancer cell lines.
Cancer cell line misidentification and cross-contamination
reprents a significant challenge for cancer researchers. The
identification is vital and in the frame of this package based
on the locations/ loci of somatic and germline mutations/
variations. The input format is vcf/ vcf.gz and the files have
to contain a single cancer cell line sample (i.e. a single
member/genotype/gt column in the vcf file). The implemented
method is optimized for the Next-generation whole exome and
whole genome DNA-sequencing technology. RNA-seq data is very
likely to work as well but hasn't been rigiously tested yet.
Panel-seq will require manual adjustment of thresholds
biocViews: ImmunoOncology, StatisticalMethod, WholeGenome, ExomeSeq
Author: Raik Otto
Maintainer: 'Raik Otto' <raik.otto@hu-berlin.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Uniquorn
git_branch: RELEASE_3_13
git_last_commit: 689be91
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Uniquorn_2.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Uniquorn_2.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Uniquorn_2.12.0.tgz
vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html
vignetteTitles: Uniquorn vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Uniquorn/inst/doc/Uniquorn.R
dependencyCount: 106
Package: universalmotif
Version: 1.10.2
Depends: R (>= 3.5.0)
Imports: methods, stats, utils, MASS, ggplot2, yaml, IRanges, Rcpp,
Biostrings, BiocGenerics, S4Vectors, rlang, grid
LinkingTo: Rcpp, RcppThread
Suggests: spelling, knitr, bookdown, TFBSTools, rmarkdown, MotifDb,
testthat, BiocParallel, seqLogo, motifStack, dplyr, ape,
ggtree, processx, ggseqlogo, cowplot, GenomicRanges, ggbio
Enhances: PWMEnrich, rGADEM
License: GPL-3
MD5sum: 141e459464905b21a47ed7a2e3bcf28b
NeedsCompilation: yes
Title: Import, Modify, and Export Motifs with R
Description: Allows for importing most common motif types into R for
use by functions provided by other Bioconductor motif-related
packages. Motifs can be exported into most major motif formats
from various classes as defined by other Bioconductor packages.
A suite of motif and sequence manipulation and analysis
functions are included, including enrichment, comparison,
P-value calculation, shuffling, trimming, higher-order motifs,
and others.
biocViews: MotifAnnotation, MotifDiscovery, DataImport, GeneRegulation
Author: Benjamin Jean-Marie Tremblay [aut, cre]
(<https://orcid.org/0000-0002-7441-2951>), Spencer Nystrom
[ctb] (<https://orcid.org/0000-0003-1000-1579>)
Maintainer: Benjamin Jean-Marie Tremblay
<benjamin.tremblay@uwaterloo.ca>
URL: https://bioconductor.org/packages/universalmotif/
VignetteBuilder: knitr
BugReports: https://github.com/bjmt/universalmotif/issues
git_url: https://git.bioconductor.org/packages/universalmotif
git_branch: RELEASE_3_13
git_last_commit: 556bc8f
git_last_commit_date: 2021-08-03
Date/Publication: 2021-08-05
source.ver: src/contrib/universalmotif_1.10.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/universalmotif_1.10.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/universalmotif_1.10.2.tgz
vignettes: vignettes/universalmotif/inst/doc/Introduction.pdf,
vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.pdf,
vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.pdf,
vignettes/universalmotif/inst/doc/MotifManipulation.pdf,
vignettes/universalmotif/inst/doc/SequenceSearches.pdf
vignetteTitles: Introduction to "universalmotif", Introduction to
sequence motifs, Motif comparisons and P-values, Motif import,,
export,, and manipulation, Sequence manipulation and scanning
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/universalmotif/inst/doc/Introduction.R,
vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.R,
vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.R,
vignettes/universalmotif/inst/doc/MotifManipulation.R,
vignettes/universalmotif/inst/doc/SequenceSearches.R
importsMe: circRNAprofiler, memes
dependencyCount: 54
Package: uSORT
Version: 1.18.0
Depends: R (>= 3.3.0), tcltk
Imports: igraph, Matrix, RANN, RSpectra, VGAM, gplots, parallel, plyr,
methods, cluster, Biobase, fpc, BiocGenerics, monocle,
grDevices, graphics, stats, utils
Suggests: knitr, RUnit, testthat, ggplot2
License: Artistic-2.0
MD5sum: 2a584f7452b5139a982f6d041c0a75f1
NeedsCompilation: no
Title: uSORT: A self-refining ordering pipeline for gene selection
Description: This package is designed to uncover the intrinsic cell
progression path from single-cell RNA-seq data. It incorporates
data pre-processing, preliminary PCA gene selection,
preliminary cell ordering, feature selection, refined cell
ordering, and post-analysis interpretation and visualization.
biocViews: ImmunoOncology, RNASeq, GUI, CellBiology, DNASeq
Author: Mai Chan Lau, Hao Chen, Jinmiao Chen
Maintainer: Hao Chen <chen_hao@immunol.a-star.edu.sg>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/uSORT
git_branch: RELEASE_3_13
git_last_commit: d2b1154
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/uSORT_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/uSORT_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/uSORT_1.18.0.tgz
vignettes: vignettes/uSORT/inst/doc/uSORT_quick_start.html
vignetteTitles: Quick Start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/uSORT/inst/doc/uSORT_quick_start.R
dependencyCount: 103
Package: VanillaICE
Version: 1.54.0
Depends: R (>= 3.5.0), BiocGenerics (>= 0.13.6), GenomicRanges (>=
1.27.6), SummarizedExperiment (>= 1.5.3)
Imports: MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges (>=
1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats,
data.table, grid, lattice, methods, GenomeInfoDb (>= 1.11.4),
crlmm, tools, stats, utils, BSgenome.Hsapiens.UCSC.hg18
Suggests: RUnit, human610quadv1bCrlmm
Enhances: doMC, doMPI, doSNOW, doParallel, doRedis
License: LGPL-2
MD5sum: 07d9c81cc6b6105092233aceb8dc98c1
NeedsCompilation: yes
Title: A Hidden Markov Model for high throughput genotyping arrays
Description: Hidden Markov Models for characterizing chromosomal
alteration in high throughput SNP arrays.
biocViews: CopyNumberVariation
Author: Robert Scharpf [aut, cre]
Maintainer: Robert Scharpf <rscharpf@jhu.edu>
git_url: https://git.bioconductor.org/packages/VanillaICE
git_branch: RELEASE_3_13
git_last_commit: 5c4c822
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/VanillaICE_1.54.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VanillaICE_1.54.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VanillaICE_1.54.0.tgz
vignettes: vignettes/VanillaICE/inst/doc/crlmmDownstream.pdf,
vignettes/VanillaICE/inst/doc/VanillaICE.pdf
vignetteTitles: crlmmDownstream, VanillaICE Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VanillaICE/inst/doc/crlmmDownstream.R,
vignettes/VanillaICE/inst/doc/VanillaICE.R
dependsOnMe: MinimumDistance
suggestsMe: oligoClasses
dependencyCount: 83
Package: VarCon
Version: 1.0.0
Depends: Biostrings, BSgenome, GenomicRanges, R (>= 4.1)
Imports: methods, stats, IRanges, shiny, shinycssloaders, shinyFiles,
ggplot2
Suggests: testthat, knitr, rmarkdown
License: GPL-3
MD5sum: 3b3ea890ff17e951ebad0ef9e5fb4e45
NeedsCompilation: no
Title: VarCon: an R package for retrieving neighboring nucleotides of
an SNV
Description: VarCon is an R package which converts the positional
information from the annotation of an single nucleotide
variation (SNV) (either referring to the coding sequence or the
reference genomic sequence). It retrieves the genomic reference
sequence around the position of the single nucleotide
variation. To asses, whether the SNV could potentially
influence binding of splicing regulatory proteins VarCon
calcualtes the HEXplorer score as an estimation. Besides,
VarCon additionally reports splice site strengths of splice
sites within the retrieved genomic sequence and any changes due
to the SNV.
biocViews: FunctionalGenomics, AlternativeSplicing
Author: Johannes Ptok [aut, cre]
Maintainer: Johannes Ptok <Johannes.Ptok@posteo.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/VarCon
git_branch: RELEASE_3_13
git_last_commit: aeff27c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/VarCon_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VarCon_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VarCon_1.0.0.tgz
vignettes: vignettes/VarCon/inst/doc/VarCon.html
vignetteTitles: Analysing SNVs with VarCon
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/VarCon/inst/doc/VarCon.R
dependencyCount: 96
Package: variancePartition
Version: 1.22.0
Depends: R (>= 3.6.0), ggplot2, limma, BiocParallel, scales, Biobase,
methods
Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, iterators, splines,
foreach, doParallel, colorRamps, gplots, progress, reshape2,
lme4 (>= 1.1-10), grDevices, graphics, utils, stats
Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend,
tximport, tximportData, ballgown, DESeq2, RUnit, BiocGenerics,
r2glmm, readr
License: GPL (>= 2)
MD5sum: bad1236e782a3f1b02d7741284c8b1f3
NeedsCompilation: no
Title: Quantify and interpret divers of variation in multilevel gene
expression experiments
Description: Quantify and interpret multiple sources of biological and
technical variation in gene expression experiments. Uses a
linear mixed model to quantify variation in gene expression
attributable to individual, tissue, time point, or technical
variables. Includes dream differential expression analysis for
repeated measures.
biocViews: RNASeq, GeneExpression, GeneSetEnrichment,
DifferentialExpression, BatchEffect, QualityControl,
Regression, Epigenetics, FunctionalGenomics, Transcriptomics,
Normalization, Preprocessing, Microarray, ImmunoOncology,
Software
Author: Gabriel E. Hoffman
Maintainer: Gabriel E. Hoffman <gabriel.hoffman@mssm.edu>
VignetteBuilder: knitr
BugReports: https://github.com/GabrielHoffman/variancePartition/issues
git_url: https://git.bioconductor.org/packages/variancePartition
git_branch: RELEASE_3_13
git_last_commit: 25d1f1e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/variancePartition_1.22.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/variancePartition_1.22.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/variancePartition_1.22.0.tgz
vignettes:
vignettes/variancePartition/inst/doc/theory_practice_random_effects.pdf,
vignettes/variancePartition/inst/doc/variancePartition.pdf,
vignettes/variancePartition/inst/doc/additional_visualization.html,
vignettes/variancePartition/inst/doc/dream.html,
vignettes/variancePartition/inst/doc/FAQ.html
vignetteTitles: 3) Theory and practice of random effects and REML, 1)
Tutorial on using variancePartition, 2) Additional
visualizations, 4) dream: differential expression testing with
repeated measures designs, 5) Frequently asked questions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/variancePartition/inst/doc/additional_visualization.R,
vignettes/variancePartition/inst/doc/dream.R,
vignettes/variancePartition/inst/doc/FAQ.R,
vignettes/variancePartition/inst/doc/theory_practice_random_effects.R,
vignettes/variancePartition/inst/doc/variancePartition.R
importsMe: muscat
dependencyCount: 89
Package: VariantAnnotation
Version: 1.38.0
Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0),
MatrixGenerics, GenomeInfoDb (>= 1.15.2), GenomicRanges (>=
1.41.5), SummarizedExperiment (>= 1.19.5), Rsamtools (>=
1.99.0)
Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.27.12), IRanges
(>= 2.23.9), XVector (>= 0.29.2), Biostrings (>= 2.57.2),
AnnotationDbi (>= 1.27.9), rtracklayer (>= 1.39.7), BSgenome
(>= 1.47.3), GenomicFeatures (>= 1.31.3)
LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib
Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene,
SNPlocs.Hsapiens.dbSNP.20101109, SIFT.Hsapiens.dbSNP132,
SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats,
ggplot2, BiocStyle
License: Artistic-2.0
MD5sum: 5edc290f5e26ae801388b6fd409c1981
NeedsCompilation: yes
Title: Annotation of Genetic Variants
Description: Annotate variants, compute amino acid coding changes,
predict coding outcomes.
biocViews: DataImport, Sequencing, SNP, Annotation, Genetics,
VariantAnnotation
Author: Bioconductor Package Maintainer [aut, cre], Valerie Oberchain
[aut], Martin Morgan [aut], Michael Lawrence [aut], Stephanie
Gogarten [ctb]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
SystemRequirements: GNU make
Video:
https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q
git_url: https://git.bioconductor.org/packages/VariantAnnotation
git_branch: RELEASE_3_13
git_last_commit: 1deefec
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/VariantAnnotation_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VariantAnnotation_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VariantAnnotation_1.38.0.tgz
vignettes: vignettes/VariantAnnotation/inst/doc/filterVcf.pdf,
vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf
vignetteTitles: 2. Using filterVcf to Select Variants from VCF Files,
1. Introduction to VariantAnnotation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VariantAnnotation/inst/doc/filterVcf.R,
vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R
dependsOnMe: CNVrd2, deepSNV, ensemblVEP, genotypeeval, HelloRanges,
HTSeqGenie, myvariant, PureCN, R453Plus1Toolbox,
RareVariantVis, seqCAT, signeR, SomaticSignatures,
StructuralVariantAnnotation, VariantFiltering, VariantTools,
PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP132,
SIFT.Hsapiens.dbSNP137, VariantToolsData, annotation,
sequencing, variants, PlasmaMutationDetector
importsMe: AllelicImbalance, APAlyzer, appreci8R, BadRegionFinder,
BBCAnalyzer, biovizBase, biscuiteer, CNVfilteR,
CopyNumberPlots, customProDB, DAMEfinder, decompTumor2Sig,
DominoEffect, epialleleR, fcScan, GA4GHclient, genbankr,
GenomicFiles, GenVisR, ggbio, gmapR, gwascat, gwasurvivr,
icetea, igvR, karyoploteR, ldblock, MADSEQ, methyAnalysis,
MMAPPR2, motifbreakR, musicatk, MutationalPatterns,
scoreInvHap, SigsPack, SNPhood, systemPipeR, TitanCNA, tLOH,
TVTB, Uniquorn, VCFArray, XCIR, YAPSA, COSMIC.67
suggestsMe: AnnotationHub, BiocParallel, cellbaseR, CNVgears,
CrispRVariants, GenomicRanges, GenomicScores, GWASTools,
omicsPrint, podkat, RVS, SeqArray, splatter, supersigs,
trackViewer, trio, vtpnet, AshkenazimSonChr21,
GeuvadisTranscriptExpr, deconstructSigs, ldsep, polyRAD
dependencyCount: 97
Package: VariantExperiment
Version: 1.6.0
Depends: R (>= 3.6.0), S4Vectors (>= 0.21.24), SummarizedExperiment (>=
1.13.0), GenomicRanges, GDSArray (>= 1.3.0), DelayedDataFrame
(>= 1.0.0)
Imports: tools, utils, stats, methods, gdsfmt, SNPRelate, SeqArray,
SeqVarTools, DelayedArray, Biostrings, IRanges
Suggests: testthat, knitr
License: GPL-3
MD5sum: 55b103e7b65ce4414cdb7df423eadded
NeedsCompilation: no
Title: A RangedSummarizedExperiment Container for VCF/GDS Data with GDS
Backend
Description: VariantExperiment is a Bioconductor package for saving
data in VCF/GDS format into RangedSummarizedExperiment object.
The high-throughput genetic/genomic data are saved in GDSArray
objects. The annotation data for features/samples are saved in
DelayedDataFrame format with mono-dimensional GDSArray in each
column. The on-disk representation of both assay data and
annotation data achieves on-disk reading and processing and
saves memory space significantly. The interface of
RangedSummarizedExperiment data format enables easy and common
manipulations for high-throughput genetic/genomic data with
common SummarizedExperiment metaphor in R and Bioconductor.
biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation,
GenomeAnnotation, GenotypingArray
Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut]
Maintainer: Qian Liu <Qian.Liu@roswellpark.org>
URL: https://github.com/Bioconductor/VariantExperiment
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/VariantExperiment/issues
git_url: https://git.bioconductor.org/packages/VariantExperiment
git_branch: RELEASE_3_13
git_last_commit: 41711c2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/VariantExperiment_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VariantExperiment_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VariantExperiment_1.6.0.tgz
vignettes:
vignettes/VariantExperiment/inst/doc/VariantExperiment-class.html,
vignettes/VariantExperiment/inst/doc/VariantExperiment-methods.html
vignetteTitles: VariantExperiment-class, VariantExperiment-methods
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.R,
vignettes/VariantExperiment/inst/doc/VariantExperiment-methods.R
dependencyCount: 67
Package: VariantFiltering
Version: 1.28.0
Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.25.1),
VariantAnnotation (>= 1.13.29)
Imports: utils, stats, Biobase, S4Vectors (>= 0.9.25), IRanges (>=
2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Biostrings
(>= 2.33.11), GenomeInfoDb (>= 1.3.6), GenomicRanges (>=
1.19.13), SummarizedExperiment, GenomicFeatures, Rsamtools (>=
1.17.8), BSgenome, GenomicScores (>= 1.0.0), Gviz, shiny,
shinythemes, shinyjs, DT, shinyTree
LinkingTo: S4Vectors, IRanges, XVector, Biostrings
Suggests: RUnit, BiocStyle, org.Hs.eg.db,
BSgenome.Hsapiens.1000genomes.hs37d5,
TxDb.Hsapiens.UCSC.hg19.knownGene,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
MafDb.1Kgenomes.phase1.hs37d5, phastCons100way.UCSC.hg19,
PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137
License: Artistic-2.0
MD5sum: e73b363d0af57ee2b1a8ecbabf548a58
NeedsCompilation: yes
Title: Filtering of coding and non-coding genetic variants
Description: Filter genetic variants using different criteria such as
inheritance model, amino acid change consequence, minor allele
frequencies across human populations, splice site strength,
conservation, etc.
biocViews: Genetics, Homo_sapiens, Annotation, SNP, Sequencing,
HighThroughputSequencing
Author: Robert Castelo [aut, cre], Dei Martinez Elurbe [ctb], Pau
Puigdevall [ctb], Joan Fernandez [ctb]
Maintainer: Robert Castelo <robert.castelo@upf.edu>
URL: https://github.com/rcastelo/VariantFiltering
BugReports: https://github.com/rcastelo/VariantFiltering/issues
git_url: https://git.bioconductor.org/packages/VariantFiltering
git_branch: RELEASE_3_13
git_last_commit: a7b2d16
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/VariantFiltering_1.28.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VariantFiltering_1.28.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VariantFiltering_1.28.0.tgz
vignettes:
vignettes/VariantFiltering/inst/doc/usingVariantFiltering.pdf
vignetteTitles: VariantFiltering: filter coding and non-coding genetic
variants
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.R
dependencyCount: 170
Package: VariantTools
Version: 1.34.0
Depends: S4Vectors (>= 0.17.33), IRanges (>= 2.13.12), GenomicRanges
(>= 1.31.8), VariantAnnotation (>= 1.11.16), methods
Imports: Rsamtools (>= 1.31.2), BiocGenerics, Biostrings, parallel,
GenomicFeatures (>= 1.31.3), Matrix, rtracklayer (>= 1.39.7),
BiocParallel, GenomeInfoDb, BSgenome, Biobase
Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph, gmapR (>=
1.21.3)
License: Artistic-2.0
Archs: i386, x64
MD5sum: 300357f19936789ef0d3d87ffb500ecc
NeedsCompilation: no
Title: Tools for Exploratory Analysis of Variant Calls
Description: Explore, diagnose, and compare variant calls using
filters.
biocViews: Genetics, GeneticVariability, Sequencing
Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman
Maintainer: Michael Lawrence <michafla@gene.com>
git_url: https://git.bioconductor.org/packages/VariantTools
git_branch: RELEASE_3_13
git_last_commit: f5b011f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/VariantTools_1.34.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VariantTools_1.34.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VariantTools_1.34.0.tgz
vignettes: vignettes/VariantTools/inst/doc/tutorial.pdf,
vignettes/VariantTools/inst/doc/VariantTools.pdf
vignetteTitles: tutorial.pdf, Introduction to VariantTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R
importsMe: HTSeqGenie, MMAPPR2
suggestsMe: VariantToolsData
dependencyCount: 98
Package: VaSP
Version: 1.4.0
Depends: R (>= 4.0), ballgown
Imports: IRanges, GenomicRanges, S4Vectors, Sushi, parallel,
matrixStats, GenomicAlignments, GenomeInfoDb, Rsamtools,
cluster, stats, graphics, methods
Suggests: knitr, rmarkdown
License: GPL (>= 2.0)
MD5sum: 7e056e5c5b09c42c9a4943b63c255df1
NeedsCompilation: no
Title: Quantification and Visualization of Variations of Splicing in
Population
Description: Discovery of genome-wide variable alternative splicing
events from short-read RNA-seq data and visualizations of gene
splicing information for publication-quality multi-panel
figures in a population.
biocViews: RNASeq, AlternativeSplicing, DifferentialSplicing,
StatisticalMethod, Visualization, Preprocessing, Clustering,
DifferentialExpression, KEGG, ImmunoOncology
Author: Huihui Yu [aut, cre] (<https://orcid.org/0000-0003-2725-1937>),
Qian Du [aut] (<https://orcid.org/0000-0003-3864-8745>), Chi
Zhang [aut] (<https://orcid.org/0000-0002-1827-8137>)
Maintainer: Huihui Yu <yuhuihui2011@foxmail.com>
URL: https://github.com/yuhuihui2011/VaSP
VignetteBuilder: knitr
BugReports: https://github.com/yuhuihui2011/VaSP/issues
git_url: https://git.bioconductor.org/packages/VaSP
git_branch: RELEASE_3_13
git_last_commit: 634a71f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/VaSP_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VaSP_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VaSP_1.4.0.tgz
vignettes: vignettes/VaSP/inst/doc/VaSP.html
vignetteTitles: user guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VaSP/inst/doc/VaSP.R
dependencyCount: 111
Package: vbmp
Version: 1.60.0
Depends: R (>= 2.10)
Suggests: Biobase (>= 2.5.5), statmod
License: GPL (>= 2)
Archs: i386, x64
MD5sum: 3f5f05e7e127e6da065db6d17302abf9
NeedsCompilation: no
Title: Variational Bayesian Multinomial Probit Regression
Description: Variational Bayesian Multinomial Probit Regression with
Gaussian Process Priors. It estimates class membership
posterior probability employing variational and sparse
approximation to the full posterior. This software also
incorporates feature weighting by means of Automatic Relevance
Determination.
biocViews: Classification
Author: Nicola Lama <nicola.lama@unina2.it>, Mark Girolami
<girolami@dcs.gla.ac.uk>
Maintainer: Nicola Lama <nicola.lama@unina2.it>
URL:
http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1
git_url: https://git.bioconductor.org/packages/vbmp
git_branch: RELEASE_3_13
git_last_commit: 2eddf66
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/vbmp_1.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/vbmp_1.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/vbmp_1.60.0.tgz
vignettes: vignettes/vbmp/inst/doc/vbmp.pdf
vignetteTitles: vbmp Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/vbmp/inst/doc/vbmp.R
dependencyCount: 0
Package: VCFArray
Version: 1.8.0
Depends: R (>= 3.6), methods, BiocGenerics, DelayedArray (>= 0.7.28)
Imports: tools, GenomicRanges, VariantAnnotation (>= 1.29.3),
GenomicFiles (>= 1.17.3), S4Vectors (>= 0.19.19), Rsamtools
Suggests: SeqArray, BiocStyle, BiocManager, testthat, knitr, rmarkdown
License: GPL-3
MD5sum: 5412aad0db0e0f77fe300ffe1225e238
NeedsCompilation: no
Title: Representing on-disk / remote VCF files as array-like objects
Description: VCFArray extends the DelayedArray to represent VCF data
entries as array-like objects with on-disk / remote VCF file as
backend. Data entries from VCF files, including info fields,
FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER)
could be converted into VCFArray instances with different
dimensions.
biocViews: Infrastructure, DataRepresentation, Sequencing,
VariantAnnotation
Author: Qian Liu [aut, cre], Martin Morgan [aut]
Maintainer: Qian Liu <qliu7@buffalo.edu>
URL: https://github.com/Liubuntu/VCFArray
VignetteBuilder: knitr
BugReports: https://github.com/Liubuntu/VCFArray/issues
git_url: https://git.bioconductor.org/packages/VCFArray
git_branch: RELEASE_3_13
git_last_commit: 5de9e30
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/VCFArray_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VCFArray_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VCFArray_1.8.0.tgz
vignettes: vignettes/VCFArray/inst/doc/VCFArray.html
vignetteTitles: VCFArray: DelayedArray objects with on-disk/remote VCF
backend
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VCFArray/inst/doc/VCFArray.R
dependencyCount: 99
Package: VegaMC
Version: 3.30.0
Depends: R (>= 2.10.0), biomaRt, Biobase
Imports: methods
License: GPL-2
MD5sum: 0047a3fb7f831aafed61131e49fc7f94
NeedsCompilation: yes
Title: VegaMC: A Package Implementing a Variational Piecewise Smooth
Model for Identification of Driver Chromosomal Imbalances in
Cancer
Description: This package enables the detection of driver chromosomal
imbalances including loss of heterozygosity (LOH) from array
comparative genomic hybridization (aCGH) data. VegaMC performs
a joint segmentation of a dataset and uses a statistical
framework to distinguish between driver and passenger mutation.
VegaMC has been implemented so that it can be immediately
integrated with the output produced by PennCNV tool. In
addition, VegaMC produces in output two web pages that allows a
rapid navigation between both the detected regions and the
altered genes. In the web page that summarizes the altered
genes, the link to the respective Ensembl gene web page is
reported.
biocViews: aCGH, CopyNumberVariation
Author: S. Morganella and M. Ceccarelli
Maintainer: Sandro Morganella <morganellaalx@gmail.com>
git_url: https://git.bioconductor.org/packages/VegaMC
git_branch: RELEASE_3_13
git_last_commit: 37e7f4f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/VegaMC_3.30.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VegaMC_3.30.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VegaMC_3.30.0.tgz
vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf
vignetteTitles: VegaMC
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VegaMC/inst/doc/VegaMC.R
dependencyCount: 72
Package: velociraptor
Version: 1.2.0
Depends: SummarizedExperiment
Imports: methods, stats, Matrix, BiocGenerics, reticulate, S4Vectors,
DelayedArray, basilisk, zellkonverter, scuttle,
SingleCellExperiment, BiocParallel, BiocSingular
Suggests: BiocStyle, testthat, knitr, rmarkdown, pkgdown, scran,
scater, scRNAseq, Rtsne, graphics, grDevices, ggplot2, cowplot,
GGally, patchwork, metR
License: MIT + file LICENSE
Archs: i386, x64
MD5sum: 45d17204b63aafcaccd58a8c9dbfb762
NeedsCompilation: no
Title: Toolkit for Single-Cell Velocity
Description: This package provides Bioconductor-friendly wrappers for
RNA velocity calculations in single-cell RNA-seq data. We use
the basilisk package to manage Conda environments, and the
zellkonverter package to convert data structures between
SingleCellExperiment (R) and AnnData (Python). The information
produced by the velocity methods is stored in the various
components of the SingleCellExperiment class.
biocViews: SingleCell, GeneExpression, Sequencing, Coverage
Author: Kevin Rue-Albrecht [aut, cre]
(<https://orcid.org/0000-0003-3899-3872>), Aaron Lun [aut]
(<https://orcid.org/0000-0002-3564-4813>), Charlotte Soneson
[aut] (<https://orcid.org/0000-0003-3833-2169>), Michael
Stadler [aut] (<https://orcid.org/0000-0002-2269-4934>)
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/kevinrue/velociraptor
VignetteBuilder: knitr
BugReports: https://github.com/kevinrue/velociraptor/issues
git_url: https://git.bioconductor.org/packages/velociraptor
git_branch: RELEASE_3_13
git_last_commit: 3e78025
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/velociraptor_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/velociraptor_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/velociraptor_1.2.0.tgz
vignettes: vignettes/velociraptor/inst/doc/velociraptor.html
vignetteTitles: User's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/velociraptor/inst/doc/velociraptor.R
dependsOnMe: OSCA.advanced
dependencyCount: 55
Package: VennDetail
Version: 1.8.0
Imports: utils, grDevices, stats, methods, dplyr, purrr, tibble,
magrittr, ggplot2, UpSetR, VennDiagram, grid, futile.logger
Suggests: knitr, rmarkdown, testthat
License: GPL-2
MD5sum: 694403802e46de7374d64ad1c4e0c6ae
NeedsCompilation: no
Title: A package for visualization and extract details
Description: A set of functions to generate high-resolution
Venn,Vennpie plot,extract and combine details of these subsets
with user datasets in data frame is available.
biocViews: DataRepresentation,GraphAndNetwork
Author: Kai Guo, Brett McGregor
Maintainer: Kai Guo <guokai8@gmail.com>
URL: https://github.com/guokai8/VennDetail
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/VennDetail
git_branch: RELEASE_3_13
git_last_commit: 00c2f83
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/VennDetail_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VennDetail_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VennDetail_1.8.0.tgz
vignettes: vignettes/VennDetail/inst/doc/VennDetail.html
vignetteTitles: VennDetail
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VennDetail/inst/doc/VennDetail.R
dependencyCount: 51
Package: VERSO
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: ape, parallel, Rfast, stats
Suggests: BiocGenerics, BiocStyle, testthat, knitr
License: file LICENSE
Archs: i386, x64
MD5sum: 6edd27553671e141b7ff4be10e4f0a26
NeedsCompilation: no
Title: Viral Evolution ReconStructiOn (VERSO)
Description: Mutations that rapidly accumulate in viral genomes during
a pandemic can be used to track the evolution of the virus and,
accordingly, unravel the viral infection network. To this
extent, sequencing samples of the virus can be employed to
estimate models from genomic epidemiology and may serve, for
instance, to estimate the proportion of undetected infected
people by uncovering cryptic transmissions, as well as to
predict likely trends in the number of infected, hospitalized,
dead and recovered people. VERSO is an algorithmic framework
that processes variants profiles from viral samples to produce
phylogenetic models of viral evolution. The approach solves a
Boolean Matrix Factorization problem with phylogenetic
constraints, by maximizing a log-likelihood function. VERSO
includes two separate and subsequent steps; in this package we
provide an R implementation of VERSO STEP 1.
biocViews: BiomedicalInformatics, Sequencing, SomaticMutation
Author: Daniele Ramazzotti [aut]
(<https://orcid.org/0000-0002-6087-2666>), Fabrizio Angaroni
[aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De
Sano [ctb]
Maintainer: Davide Maspero <d.maspero@campus.unimib.it>
URL: https://github.com/BIMIB-DISCo/VERSO
VignetteBuilder: knitr
BugReports: https://github.com/BIMIB-DISCo/VERSO
git_url: https://git.bioconductor.org/packages/VERSO
git_branch: RELEASE_3_13
git_last_commit: db11dc9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/VERSO_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VERSO_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VERSO_1.2.0.tgz
vignettes: vignettes/VERSO/inst/doc/vignette.pdf
vignetteTitles: VERSO
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/VERSO/inst/doc/vignette.R
dependencyCount: 16
Package: vidger
Version: 1.12.0
Depends: R (>= 3.5)
Imports: Biobase, DESeq2, edgeR, GGally, ggplot2, ggrepel, knitr,
RColorBrewer, rmarkdown, scales, stats, SummarizedExperiment,
tidyr, utils
Suggests: BiocStyle, testthat
License: GPL-3 | file LICENSE
MD5sum: b8fa2bed3631388b6f1edd42f7d3492d
NeedsCompilation: no
Title: Create rapid visualizations of RNAseq data in R
Description: The aim of vidger is to rapidly generate information-rich
visualizations for the interpretation of differential gene
expression results from three widely-used tools: Cuffdiff,
DESeq2, and edgeR.
biocViews: ImmunoOncology, Visualization, RNASeq,
DifferentialExpression, GeneExpression, ImmunoOncology
Author: Brandon Monier [aut, cre], Adam McDermaid [aut], Jing Zhao
[aut], Qin Ma [aut, fnd]
Maintainer: Brandon Monier <brandon.monier@gmail.com>
URL: https://github.com/btmonier/vidger,
https://bioconductor.org/packages/release/bioc/html/vidger.html
VignetteBuilder: knitr
BugReports: https://github.com/btmonier/vidger/issues
git_url: https://git.bioconductor.org/packages/vidger
git_branch: RELEASE_3_13
git_last_commit: 8ce5d28
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/vidger_1.12.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/vidger_1.12.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/vidger_1.12.0.tgz
vignettes: vignettes/vidger/inst/doc/vidger.html
vignetteTitles: Visualizing RNA-seq data with ViDGER
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/vidger/inst/doc/vidger.R
dependencyCount: 121
Package: viper
Version: 1.26.0
Depends: R (>= 2.14.0), Biobase, methods
Imports: mixtools, stats, parallel, e1071, KernSmooth
Suggests: bcellViper
License: file LICENSE
MD5sum: 7349a637881d471c2d4dd0990454900b
NeedsCompilation: no
Title: Virtual Inference of Protein-activity by Enriched Regulon
analysis
Description: Inference of protein activity from gene expression data,
including the VIPER and msVIPER algorithms
biocViews: SystemsBiology, NetworkEnrichment, GeneExpression,
FunctionalPrediction, GeneRegulation
Author: Mariano J Alvarez <reef103@gmail.com>
Maintainer: Mariano J Alvarez <reef103@gmail.com>
git_url: https://git.bioconductor.org/packages/viper
git_branch: RELEASE_3_13
git_last_commit: 1c52a94
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/viper_1.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/viper_1.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/viper_1.26.0.tgz
vignettes: vignettes/viper/inst/doc/viper.pdf
vignetteTitles: Using VIPER
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/viper/inst/doc/viper.R
dependsOnMe: vulcan, aracne.networks
importsMe: decoupleR, diggit, RTN, diggitdata, dorothea
suggestsMe: MethReg, MOMA
dependencyCount: 21
Package: ViSEAGO
Version: 1.6.0
Depends: R (>= 3.6)
Imports: data.table, AnnotationDbi, AnnotationForge, biomaRt,
dendextend, DiagrammeR, DT, dynamicTreeCut, fgsea, GOSemSim,
ggplot2, GO.db, grDevices, heatmaply, htmlwidgets, igraph,
methods, plotly, processx, topGO, RColorBrewer, R.utils,
scales, stats, UpSetR, utils
Suggests: htmltools, org.Mm.eg.db, limma, Rgraphviz, BiocStyle, knitr,
rmarkdown, corrplot, remotes, BiocManager
License: GPL-3
MD5sum: 36d95fdbf0d74fedd51734da1867eb57
NeedsCompilation: no
Title: ViSEAGO: a Bioconductor package for clustering biological
functions using Gene Ontology and semantic similarity
Description: The main objective of ViSEAGO package is to carry out a
data mining of biological functions and establish links between
genes involved in the study. We developed ViSEAGO in R to
facilitate functional Gene Ontology (GO) analysis of complex
experimental design with multiple comparisons of interest. It
allows to study large-scale datasets together and visualize GO
profiles to capture biological knowledge. The acronym stands
for three major concepts of the analysis: Visualization,
Semantic similarity and Enrichment Analysis of Gene Ontology.
It provides access to the last current GO annotations, which
are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot
databases for several species. Using available R packages and
novel developments, ViSEAGO extends classical functional GO
analysis to focus on functional coherence by aggregating
closely related biological themes while studying multiple
datasets at once. It provides both a synthetic and detailed
view using interactive functionalities respecting the GO graph
structure and ensuring functional coherence supplied by
semantic similarity. ViSEAGO has been successfully applied on
several datasets from different species with a variety of
biological questions. Results can be easily shared between
bioinformaticians and biologists, enhancing reporting
capabilities while maintaining reproducibility.
biocViews: Software, Annotation, GO, GeneSetEnrichment,
MultipleComparison, Clustering, Visualization
Author: Aurelien Brionne [aut, cre], Amelie Juanchich [aut], Christelle
hennequet-antier [aut]
Maintainer: Aurelien Brionne <aurelien.brionne@inrae.fr>
URL:
https://www.bioconductor.org/packages/release/bioc/html/ViSEAGO.html,
https://forgemia.inra.fr/UMR-BOA/ViSEAGO
VignetteBuilder: knitr
BugReports: https://forgemia.inra.fr/UMR-BOA/ViSEAGO/issues
git_url: https://git.bioconductor.org/packages/ViSEAGO
git_branch: RELEASE_3_13
git_last_commit: 320ba38
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/ViSEAGO_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/ViSEAGO_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/ViSEAGO_1.6.0.tgz
vignettes: vignettes/ViSEAGO/inst/doc/fgsea_alternative.html,
vignettes/ViSEAGO/inst/doc/mouse_bioconductor.html,
vignettes/ViSEAGO/inst/doc/SS_choice.html,
vignettes/ViSEAGO/inst/doc/ViSEAGO.html
vignetteTitles: 3: fgsea_alternative, 2: mouse_bionconductor, 4:
SS_choice, 1: ViSEAGO
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ViSEAGO/inst/doc/fgsea_alternative.R,
vignettes/ViSEAGO/inst/doc/mouse_bioconductor.R,
vignettes/ViSEAGO/inst/doc/SS_choice.R,
vignettes/ViSEAGO/inst/doc/ViSEAGO.R
dependencyCount: 155
Package: vissE
Version: 1.0.0
Depends: R (>= 4.1)
Imports: igraph, methods, plyr, ggplot2, ggnewscale, scico,
RColorBrewer, tm, ggwordcloud, GSEABase, reshape2, grDevices,
ggforce, msigdb, Matrix, ggrepel, textstem
Suggests: testthat, org.Hs.eg.db, org.Mm.eg.db, ggpubr, singscore,
knitr, rmarkdown, prettydoc, BiocStyle
License: GPL-3
MD5sum: 947dac3c5e624576ea7e5dec1d273366
NeedsCompilation: no
Title: Visualising Set Enrichment Analysis Results
Description: This package enables the interpretation and analysis of
results from a gene set enrichment analysis using network-based
and text-mining approaches. Most enrichment analyses result in
large lists of significant gene sets that are difficult to
interpret. Tools in this package help build a similarity-based
network of significant gene sets from a gene set enrichment
analysis that can then be investigated for their biological
function using text-mining approaches.
biocViews: Software, GeneExpression, GeneSetEnrichment,
NetworkEnrichment, Network
Author: Dharmesh D. Bhuva [aut, cre]
(<https://orcid.org/0000-0002-6398-9157>)
Maintainer: Dharmesh D. Bhuva <bhuva.d@wehi.edu.au>
URL: https://davislaboratory.github.io/vissE
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/vissE/issues
git_url: https://git.bioconductor.org/packages/vissE
git_branch: RELEASE_3_13
git_last_commit: 1a90b74
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/vissE_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/vissE_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/vissE_1.0.0.tgz
vignettes: vignettes/vissE/inst/doc/vissE.html
vignetteTitles: vissE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/vissE/inst/doc/vissE.R
suggestsMe: msigdb
dependencyCount: 155
Package: VplotR
Version: 1.2.0
Depends: R (>= 4.0), GenomicRanges, IRanges, ggplot2
Imports: cowplot, magrittr, GenomeInfoDb, GenomicAlignments,
RColorBrewer, zoo, Rsamtools, S4Vectors, parallel, reshape2,
methods, graphics, stats
Suggests: GenomicFeatures, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene,
testthat, covr, knitr, rmarkdown, pkgdown
License: GPL-3
Archs: i386, x64
MD5sum: caef57339da8e6c34b0e316773f86165
NeedsCompilation: no
Title: Set of tools to make V-plots and compute footprint profiles
Description: The pattern of digestion and protection from DNA nucleases
such as DNAse I, micrococcal nuclease, and Tn5 transposase can
be used to infer the location of associated proteins. This
package contains useful functions to analyze patterns of
paired-end sequencing fragment density. VplotR facilitates the
generation of V-plots and footprint profiles over single or
aggregated genomic loci of interest.
biocViews: NucleosomePositioning, Coverage, Sequencing,
BiologicalQuestion, ATACSeq, Alignment
Author: Jacques Serizay [aut, cre]
(<https://orcid.org/0000-0002-4295-0624>)
Maintainer: Jacques Serizay <jacquesserizay@gmail.com>
URL: https://github.com/js2264/VplotR
VignetteBuilder: knitr
BugReports: https://github.com/js2264/VplotR/issues
git_url: https://git.bioconductor.org/packages/VplotR
git_branch: RELEASE_3_13
git_last_commit: 8077df2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-21
source.ver: src/contrib/VplotR_1.2.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/VplotR_1.2.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/VplotR_1.2.0.tgz
vignettes: vignettes/VplotR/inst/doc/VplotR.html
vignetteTitles: VplotR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VplotR/inst/doc/VplotR.R
dependencyCount: 74
Package: vsn
Version: 3.60.0
Depends: R (>= 3.4.0), Biobase
Imports: methods, affy, limma, lattice, ggplot2
Suggests: affydata, hgu95av2cdf, BiocStyle, knitr, dplyr, testthat
License: Artistic-2.0
MD5sum: b23b7e61916cf5e798e2f04ab3a2198d
NeedsCompilation: yes
Title: Variance stabilization and calibration for microarray data
Description: The package implements a method for normalising microarray
intensities, and works for single- and multiple-color arrays.
It can also be used for data from other technologies, as long
as they have similar format. The method uses a robust variant
of the maximum-likelihood estimator for an
additive-multiplicative error model and affine calibration. The
model incorporates data calibration step (a.k.a.
normalization), a model for the dependence of the variance on
the mean intensity and a variance stabilizing data
transformation. Differences between transformed intensities are
analogous to "normalized log-ratios". However, in contrast to
the latter, their variance is independent of the mean, and they
are usually more sensitive and specific in detecting
differential transcription.
biocViews: Microarray, OneChannel, TwoChannel, Preprocessing
Author: Wolfgang Huber, with contributions from Anja von Heydebreck.
Many comments and suggestions by users are acknowledged, among
them Dennis Kostka, David Kreil, Hans-Ulrich Klein, Robert
Gentleman, Deepayan Sarkar and Gordon Smyth
Maintainer: Wolfgang Huber <wolfgang.huber@embl.de>
URL: http://www.r-project.org, http://www.ebi.ac.uk/huber
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/vsn
git_branch: RELEASE_3_13
git_last_commit: 942f366
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/vsn_3.60.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/vsn_3.60.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/vsn_3.60.0.tgz
vignettes: vignettes/vsn/inst/doc/C-likelihoodcomputations.pdf,
vignettes/vsn/inst/doc/D-convergence.pdf,
vignettes/vsn/inst/doc/A-vsn.html
vignetteTitles: Likelihood Calculations for vsn, Verifying and
assessing the performance with simulated data, Introduction to
vsn (HTML version)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/vsn/inst/doc/A-vsn.R,
vignettes/vsn/inst/doc/C-likelihoodcomputations.R
dependsOnMe: affyPara, cellHTS2, webbioc, rnaseqGene
importsMe: arrayQualityMetrics, bnem, coexnet, DAPAR, DEP, Doscheda,
imageHTS, MatrixQCvis, metaseqR2, MSnbase, NormalyzerDE, pvca,
Ringo, tilingArray, ExpressionNormalizationWorkflow
suggestsMe: adSplit, beadarray, DESeq2, ggbio, GlobalAncova,
globaltest, limma, lumi, MsCoreUtils, PAA, QFeatures, scp,
twilight, estrogen, wrMisc
dependencyCount: 47
Package: vtpnet
Version: 0.32.0
Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel,
foreach
Suggests: MotifDb, VariantAnnotation, Rgraphviz
License: Artistic-2.0
MD5sum: 193a32fd157326da32460b10b670e586
NeedsCompilation: no
Title: variant-transcription factor-phenotype networks
Description: variant-transcription factor-phenotype networks, inspired
by Maurano et al., Science (2012), PMID 22955828
biocViews: Network
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
git_url: https://git.bioconductor.org/packages/vtpnet
git_branch: RELEASE_3_13
git_last_commit: adfd187
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/vtpnet_0.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/vtpnet_0.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/vtpnet_0.32.0.tgz
vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf
vignetteTitles: vtpnet: variant-transcription factor-network tools
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/vtpnet/inst/doc/vtpnet.R
dependencyCount: 134
Package: vulcan
Version: 1.14.0
Depends: R (>= 4.0), ChIPpeakAnno,TxDb.Hsapiens.UCSC.hg19.knownGene,
zoo, GenomicRanges, S4Vectors, viper, DiffBind, locfit
Imports: wordcloud, csaw, gplots, stats, utils, caTools, graphics,
DESeq2, Biobase
Suggests: vulcandata
License: LGPL-3
MD5sum: 794c1da0188b629a46964c10cc5be973
NeedsCompilation: no
Title: VirtUaL ChIP-Seq data Analysis using Networks
Description: Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a
package that interrogates gene regulatory networks to infer
cofactors significantly enriched in a differential binding
signature coming from ChIP-Seq data. In order to do so, our
package combines strategies from different BioConductor
packages: DESeq for data normalization, ChIPpeakAnno and
DiffBind for annotation and definition of ChIP-Seq genomic
peaks, csaw to define optimal peak width and viper for applying
a regulatory network over a differential binding signature.
biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, ChIPSeq
Author: Federico M. Giorgi, Andrew N. Holding, Florian Markowetz
Maintainer: Federico M. Giorgi <federico.giorgi@gmail.com>
git_url: https://git.bioconductor.org/packages/vulcan
git_branch: RELEASE_3_13
git_last_commit: e43bb90
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/vulcan_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/vulcan_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/vulcan_1.14.0.tgz
vignettes: vignettes/vulcan/inst/doc/vulcan.pdf
vignetteTitles: Vulcan: VirtUaL ChIP-Seq Analysis through Networks
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/vulcan/inst/doc/vulcan.R
dependencyCount: 206
Package: waddR
Version: 1.6.1
Depends: R (>= 3.6.0)
Imports: Rcpp (>= 1.0.1), arm (>= 1.10-1), eva, BiocFileCache,
BiocParallel, SingleCellExperiment, parallel, methods, stats
LinkingTo: Rcpp, RcppArmadillo,
Suggests: knitr, devtools, testthat, roxygen2, rprojroot, rmarkdown,
scater
License: MIT + file LICENSE
MD5sum: 7279870445a17ff7030829682e0ba941
NeedsCompilation: yes
Title: Statistical tests for detecting differential distributions based
on the 2-Wasserstein distance
Description: The package offers statistical tests based on the
2-Wasserstein distance for detecting and characterizing
differences between two distributions given in the form of
samples. Functions for calculating the 2-Wasserstein distance
and testing for differential distributions are provided, as
well as a specifically tailored test for differential
expression in single-cell RNA sequencing data.
biocViews: Software, StatisticalMethod, SingleCell,
DifferentialExpression
Author: Roman Schefzik [aut], Julian Flesch [cre]
Maintainer: Julian Flesch <julianflesch@gmail.com>
URL: https://github.com/goncalves-lab/waddR.git
VignetteBuilder: knitr
BugReports: https://github.com/goncalves-lab/waddR/issues
git_url: https://git.bioconductor.org/packages/waddR
git_branch: RELEASE_3_13
git_last_commit: 90d967f
git_last_commit_date: 2021-05-28
Date/Publication: 2021-05-30
source.ver: src/contrib/waddR_1.6.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/waddR_1.6.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/waddR_1.6.1.tgz
vignettes: vignettes/waddR/inst/doc/waddR.html,
vignettes/waddR/inst/doc/wasserstein_metric.html,
vignettes/waddR/inst/doc/wasserstein_singlecell.html,
vignettes/waddR/inst/doc/wasserstein_test.html
vignetteTitles: waddR, wasserstein_metric, wasserstein_singlecell,
wasserstein_test
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/waddR/inst/doc/waddR.R,
vignettes/waddR/inst/doc/wasserstein_metric.R,
vignettes/waddR/inst/doc/wasserstein_singlecell.R,
vignettes/waddR/inst/doc/wasserstein_test.R
dependencyCount: 102
Package: wateRmelon
Version: 1.36.0
Depends: R (>= 2.10), Biobase, limma, methods, matrixStats, methylumi,
lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19,
illuminaio
Imports: Biobase
Suggests: RPMM, IlluminaHumanMethylationEPICanno.ilm10b2.hg19,
IlluminaHumanMethylationEPICmanifest, irlba
Enhances: minfi
License: GPL-3
MD5sum: 8f9092141a5de3ebdaed06833a931fed
NeedsCompilation: no
Title: Illumina 450 methylation array normalization and metrics
Description: 15 flavours of betas and three performance metrics, with
methods for objects produced by methylumi and minfi packages.
biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing,
QualityControl
Author: Leonard C Schalkwyk, Ruth Pidsley, Chloe CY Wong, with
functions contributed by Nizar Touleimat, Matthieu Defrance,
Andrew Teschendorff, Jovana Maksimovic, Tyler Gorrie-Stone,
Louis El Khoury
Maintainer: Leo <lschal@essex.ac.uk>
git_url: https://git.bioconductor.org/packages/wateRmelon
git_branch: RELEASE_3_13
git_last_commit: 2ff511c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/wateRmelon_1.36.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/wateRmelon_1.36.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/wateRmelon_1.36.0.tgz
vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.pdf
vignetteTitles: The \Rpackage{wateRmelon} Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R
dependsOnMe: bigmelon, skewr
importsMe: ChAMP, MEAT
suggestsMe: RnBeads
dependencyCount: 166
Package: wavClusteR
Version: 2.26.0
Depends: R (>= 3.2), GenomicRanges (>= 1.31.8), Rsamtools
Imports: methods, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>=
2.13.12), Biostrings (>= 2.47.6), foreach, GenomicFeatures (>=
1.31.3), ggplot2, Hmisc, mclust, rtracklayer (>= 1.39.7),
seqinr, stringr
Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19
Enhances: doMC
License: GPL-2
MD5sum: 1fa57fc721162313ff65ba6cb4e4675e
NeedsCompilation: no
Title: Sensitive and highly resolved identification of RNA-protein
interaction sites in PAR-CLIP data
Description: The package provides an integrated pipeline for the
analysis of PAR-CLIP data. PAR-CLIP-induced transitions are
first discriminated from sequencing errors, SNPs and additional
non-experimental sources by a non- parametric mixture model.
The protein binding sites (clusters) are then resolved at high
resolution and cluster statistics are estimated using a
rigorous Bayesian framework. Post-processing of the results,
data export for UCSC genome browser visualization and motif
search analysis are provided. In addition, the package allows
to integrate RNA-Seq data to estimate the False Discovery Rate
of cluster detection. Key functions support parallel multicore
computing. Note: while wavClusteR was designed for PAR-CLIP
data analysis, it can be applied to the analysis of other NGS
data obtained from experimental procedures that induce
nucleotide substitutions (e.g. BisSeq).
biocViews: ImmunoOncology, Sequencing, Technology, RIPSeq, RNASeq,
Bayesian
Author: Federico Comoglio and Cem Sievers
Maintainer: Federico Comoglio <federico.comoglio@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/wavClusteR
git_branch: RELEASE_3_13
git_last_commit: 14989df
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/wavClusteR_2.26.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/wavClusteR_2.26.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/wavClusteR_2.26.0.tgz
vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.html
vignetteTitles: wavClusteR: a workflow for PAR-CLIP data analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R
dependencyCount: 142
Package: weaver
Version: 1.58.0
Depends: R (>= 2.5.0), digest, tools, utils, codetools
Suggests: codetools
License: GPL-2
MD5sum: 607b39d5db4270feaed5ddd7491df345
NeedsCompilation: no
Title: Tools and extensions for processing Sweave documents
Description: This package provides enhancements on the Sweave()
function in the base package. In particular a facility for
caching code chunk results is included.
biocViews: Infrastructure
Author: Seth Falcon
Maintainer: Seth Falcon <seth@userprimary.net>
git_url: https://git.bioconductor.org/packages/weaver
git_branch: RELEASE_3_13
git_last_commit: 7c31039
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/weaver_1.58.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/weaver_1.58.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/weaver_1.58.0.tgz
vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf
vignetteTitles: Using weaver to process Sweave documents
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/weaver/inst/doc/weaver_howTo.R
dependencyCount: 4
Package: webbioc
Version: 1.64.0
Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma,
qvalue
Imports: multtest, qvalue, stats, utils, BiocManager
License: GPL (>= 2)
MD5sum: 09a76dc3e2aa948aea1a349e8f4e6560
NeedsCompilation: no
Title: Bioconductor Web Interface
Description: An integrated web interface for doing microarray analysis
using several of the Bioconductor packages. It is intended to
be deployed as a centralized bioinformatics resource for use by
many users. (Currently only Affymetrix oligonucleotide analysis
is supported.)
biocViews: Infrastructure, Microarray, OneChannel,
DifferentialExpression
Author: Colin A. Smith <colin@colinsmith.org>
Maintainer: Colin A. Smith <colin@colinsmith.org>
URL: http://www.bioconductor.org/
SystemRequirements: Unix, Perl (>= 5.6.0), Netpbm
git_url: https://git.bioconductor.org/packages/webbioc
git_branch: RELEASE_3_13
git_last_commit: 69f5af6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/webbioc_1.64.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/webbioc_1.64.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/webbioc_1.64.0.tgz
vignettes: vignettes/webbioc/inst/doc/demoscript.pdf,
vignettes/webbioc/inst/doc/webbioc.pdf
vignetteTitles: webbioc Demo Script, webbioc Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 88
Package: weitrix
Version: 1.4.0
Depends: R (>= 3.6), SummarizedExperiment
Imports: methods, utils, stats, grDevices, assertthat, S4Vectors,
DelayedArray, DelayedMatrixStats, BiocParallel, BiocGenerics,
limma, topconfects, dplyr, purrr, ggplot2, rlang, scales,
reshape2, splines, Ckmeans.1d.dp, glm2, RhpcBLASctl
Suggests: knitr, rmarkdown, BiocStyle, tidyverse, airway, edgeR,
EnsDb.Hsapiens.v86, org.Sc.sgd.db, AnnotationDbi,
ComplexHeatmap, patchwork, testthat (>= 2.1.0)
License: LGPL-2.1 | file LICENSE
Archs: i386, x64
MD5sum: 3705717e12b131ebb3c9b45de8c20e2d
NeedsCompilation: no
Title: Tools for matrices with precision weights, test and explore
weighted or sparse data
Description: Data type and tools for working with matrices having
precision weights and missing data. This package provides a
common representation and tools that can be used with many
types of high-throughput data. The meaning of the weights is
compatible with usage in the base R function "lm" and the
package "limma". Calibrate weights to account for known
predictors of precision. Find rows with excess variability.
Perform differential testing and find rows with the largest
confident differences. Find PCA-like components of variation
even with many missing values, rotated so that individual
components may be meaningfully interpreted. DelayedArray
matrices and BiocParallel are supported.
biocViews: Software, DataRepresentation, DimensionReduction,
GeneExpression, Transcriptomics, RNASeq, SingleCell, Regression
Author: Paul Harrison [aut, cre]
(<https://orcid.org/0000-0002-3980-268X>)
Maintainer: Paul Harrison <paul.harrison@monash.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/weitrix
git_branch: RELEASE_3_13
git_last_commit: 1fe8dc8
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/weitrix_1.4.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/weitrix_1.4.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/weitrix_1.4.0.tgz
vignettes: vignettes/weitrix/inst/doc/V1_overview.html,
vignettes/weitrix/inst/doc/V2_tail_length.html,
vignettes/weitrix/inst/doc/V3_shift.html,
vignettes/weitrix/inst/doc/V4_airway.html,
vignettes/weitrix/inst/doc/V5_slam_seq.html
vignetteTitles: 1. Concepts and practical details, 2. poly(A) tail
length example, 3. Alternative polyadenylation, 4. RNA-Seq
expression example, 5. Proportions data example with SLAM-Seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/weitrix/inst/doc/V2_tail_length.R,
vignettes/weitrix/inst/doc/V3_shift.R,
vignettes/weitrix/inst/doc/V4_airway.R,
vignettes/weitrix/inst/doc/V5_slam_seq.R
dependencyCount: 82
Package: widgetTools
Version: 1.70.0
Depends: R (>= 2.4.0), methods, utils, tcltk
Suggests: Biobase
License: LGPL
MD5sum: d4df55be793cc71f451a876717d6da3e
NeedsCompilation: no
Title: Creates an interactive tcltk widget
Description: This packages contains tools to support the construction
of tcltk widgets
biocViews: Infrastructure
Author: Jianhua Zhang
Maintainer: Jianhua Zhang <jzhang@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/widgetTools
git_branch: RELEASE_3_13
git_last_commit: f09a56a
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/widgetTools_1.70.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/widgetTools_1.70.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/widgetTools_1.70.0.tgz
vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf
vignetteTitles: widgetTools Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/widgetTools/inst/doc/widgetTools.R
dependsOnMe: tkWidgets
importsMe: OLINgui, SeqFeatR
suggestsMe: affy
dependencyCount: 3
Package: wiggleplotr
Version: 1.16.0
Depends: R (>= 3.6)
Imports: dplyr, ggplot2 (>= 2.2.0), GenomicRanges, rtracklayer,
cowplot, assertthat, purrr, S4Vectors, IRanges, GenomeInfoDb
Suggests: knitr, rmarkdown, biomaRt, GenomicFeatures, testthat,
ensembldb, EnsDb.Hsapiens.v86, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi,
AnnotationFilter
License: Apache License 2.0
MD5sum: a5075a419ae605f928ccc6e3e12e16ae
NeedsCompilation: no
Title: Make read coverage plots from BigWig files
Description: Tools to visualise read coverage from sequencing
experiments together with genomic annotations (genes,
transcripts, peaks). Introns of long transcripts can be
rescaled to a fixed length for better visualisation of exonic
read coverage.
biocViews: ImmunoOncology, Coverage, RNASeq, ChIPSeq, Sequencing,
Visualization, GeneExpression, Transcription,
AlternativeSplicing
Author: Kaur Alasoo [aut, cre]
Maintainer: Kaur Alasoo <kaur.alasoo@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/wiggleplotr
git_branch: RELEASE_3_13
git_last_commit: e02f0d6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/wiggleplotr_1.16.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/wiggleplotr_1.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/wiggleplotr_1.16.0.tgz
vignettes: vignettes/wiggleplotr/inst/doc/wiggleplotr.html
vignetteTitles: Introduction to wiggleplotr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/wiggleplotr/inst/doc/wiggleplotr.R
dependencyCount: 79
Package: wpm
Version: 1.2.1
Depends: R (>= 4.0.0)
Imports: utils, methods, cli, Biobase, SummarizedExperiment, config,
golem, shiny, DT, ggplot2, dplyr, rlang, stringr,
shinydashboard, shinyWidgets, shinycustomloader, RColorBrewer,
logging
Suggests: MSnbase, testthat, BiocStyle, knitr, rmarkdown
License: Artistic-2.0
Archs: i386, x64
MD5sum: f459c74d146c10edb24bd44369132623
NeedsCompilation: no
Title: Well Plate Maker
Description: The Well-Plate Maker (WPM) is a shiny application deployed
as an R package. Functions for a command-line/script use are
also available. The WPM allows users to generate well plate
maps to carry out their experiments while improving the
handling of batch effects. In particular, it helps controlling
the "plate effect" thanks to its ability to randomize samples
over multiple well plates. The algorithm for placing the
samples is inspired by the backtracking algorithm: the samples
are placed at random while respecting specific spatial
constraints.
biocViews: GUI, Proteomics, MassSpectrometry, BatchEffect,
ExperimentalDesign
Author: Helene Borges [aut, cre], Thomas Burger [aut]
Maintainer: Helene Borges <borges.helene.sophie@gmail.com>
URL: https://github.com/HelBor/wpm,
https://bioconductor.org/packages/release/bioc/html/wpm.html
VignetteBuilder: knitr
BugReports: https://github.com/HelBor/wpm/issues
git_url: https://git.bioconductor.org/packages/wpm
git_branch: RELEASE_3_13
git_last_commit: 4ce33a4
git_last_commit_date: 2021-06-15
Date/Publication: 2021-06-17
source.ver: src/contrib/wpm_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/wpm_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/wpm_1.2.1.tgz
vignettes: vignettes/wpm/inst/doc/wpm_vignette.html
vignetteTitles: How to use Well Plate Maker
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/wpm/inst/doc/wpm_vignette.R
dependencyCount: 134
Package: wppi
Version: 1.0.0
Depends: R(>= 4.1)
Imports: dplyr, igraph, logger, methods, magrittr, Matrix, OmnipathR(>=
2.99.8), progress, purrr, rlang, RCurl, stats, tibble, tidyr
Suggests: knitr, testthat
License: MIT + file LICENSE
MD5sum: 771fdabb212a131def687b5d4973ff56
NeedsCompilation: no
Title: Weighting protein-protein interactions
Description: Protein-protein interaction data is essential for omics
data analysis and modeling. Database knowledge is general, not
specific for cell type, physiological condition or any other
context determining which connections are functional and
contribute to the signaling. Functional annotations such as
Gene Ontology and Human Phenotype Ontology might help to
evaluate the relevance of interactions. This package predicts
functional relevance of protein-protein interactions based on
functional annotations such as Human Protein Ontology and Gene
Ontology, and prioritizes genes based on network topology,
functional scores and a path search algorithm.
biocViews: GraphAndNetwork, Network, Pathways, Software, GeneSignaling,
GeneTarget, SystemsBiology, Transcriptomics, Annotation
Author: Ana Galhoz [cre, aut]
(<https://orcid.org/0000-0001-7402-5292>), Denes Turei [aut]
(<https://orcid.org/0000-0002-7249-9379>), Albert Krewinkel
[ctb, cph] (pagebreak Lua filter)
Maintainer: Ana Galhoz <ana.galhoz@helmholtz-muenchen.de>
URL: https://github.com/AnaGalhoz37/wppi
VignetteBuilder: knitr
BugReports: https://github.com/AnaGalhoz37/wppi/issues
git_url: https://git.bioconductor.org/packages/wppi
git_branch: RELEASE_3_13
git_last_commit: 0d3ed1c
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/wppi_1.0.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/wppi_1.0.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/wppi_1.0.0.tgz
vignettes: vignettes/wppi/inst/doc/wppi_workflow.html
vignetteTitles: WPPI workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/wppi/inst/doc/wppi_workflow.R
dependencyCount: 64
Package: Wrench
Version: 1.10.0
Depends: R (>= 3.5.0)
Imports: limma, matrixStats, locfit, stats, graphics
Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR
License: Artistic-2.0
MD5sum: df5da273576d9eb1590e262c2fba900b
NeedsCompilation: no
Title: Wrench normalization for sparse count data
Description: Wrench is a package for normalization sparse genomic count
data, like that arising from 16s metagenomic surveys.
biocViews: Normalization, Sequencing, Software
Author: Senthil Kumar Muthiah [aut], Hector Corrada Bravo [aut, cre]
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
URL: https://github.com/HCBravoLab/Wrench
VignetteBuilder: knitr
BugReports: https://github.com/HCBravoLab/Wrench/issues
git_url: https://git.bioconductor.org/packages/Wrench
git_branch: RELEASE_3_13
git_last_commit: 58313f9
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Wrench_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Wrench_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Wrench_1.10.0.tgz
vignettes: vignettes/Wrench/inst/doc/vignette.html
vignetteTitles: Wrench
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Wrench/inst/doc/vignette.R
importsMe: metagenomeSeq
suggestsMe: PLNmodels
dependencyCount: 10
Package: XCIR
Version: 1.6.0
Depends: methods
Imports: stats, utils, data.table, IRanges, VariantAnnotation,
seqminer, ggplot2, biomaRt, readxl, S4Vectors
Suggests: knitr, rmarkdown
License: GPL-2
MD5sum: c05c468697dd11078bf6b8fb8c4486b2
NeedsCompilation: no
Title: XCI-inference
Description: Models and tools for subject level analysis of X
chromosome inactivation (XCI) and XCI-escape inference.
biocViews: StatisticalMethod, RNASeq, Sequencing, Coverage
Author: Renan Sauteraud, Dajiang Liu
Maintainer: Renan Sauteraud <rxs575@psu.edu>
URL: https://github.com/SRenan/XCIR
VignetteBuilder: knitr
BugReports: https://github.com/SRenan/XCIR/issues
git_url: https://git.bioconductor.org/packages/XCIR
git_branch: RELEASE_3_13
git_last_commit: a1c5af3
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/XCIR_1.6.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/XCIR_1.6.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/XCIR_1.6.0.tgz
vignettes: vignettes/XCIR/inst/doc/xcir_intro.html
vignetteTitles: Introduction to XCIR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/XCIR/inst/doc/xcir_intro.R
dependencyCount: 117
Package: xcms
Version: 3.14.1
Depends: R (>= 4.0.0), BiocParallel (>= 1.8.0), MSnbase (>= 2.17.7)
Imports: mzR (>= 2.25.3), methods, Biobase, BiocGenerics, ProtGenerics
(>= 1.23.7), lattice, RColorBrewer, plyr, RANN, MassSpecWavelet
(>= 1.5.2), S4Vectors, robustbase, IRanges,
SummarizedExperiment, MsCoreUtils
Suggests: BiocStyle, caTools, knitr (>= 1.1.0), faahKO, msdata (>=
0.25.1), ncdf4, testthat, pander, magrittr, rmarkdown,
multtest, MALDIquant, pheatmap, Spectra (>= 1.1.17),
MsBackendMgf
Enhances: Rgraphviz, rgl, XML
License: GPL (>= 2) + file LICENSE
MD5sum: 5600d074aed8cb25632f27e2c229eff9
NeedsCompilation: yes
Title: LC-MS and GC-MS Data Analysis
Description: Framework for processing and visualization of
chromatographically separated and single-spectra mass spectral
data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML
files. Preprocesses data for high-throughput, untargeted
analyte profiling.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics
Author: Colin A. Smith [ctb], Ralf Tautenhahn [ctb], Steffen Neumann
[aut, cre] (<https://orcid.org/0000-0002-7899-7192>), Paul
Benton [ctb], Christopher Conley [ctb], Johannes Rainer [ctb]
(<https://orcid.org/0000-0002-6977-7147>), Michael Witting
[ctb], William Kumler [ctb]
(<https://orcid.org/0000-0002-5022-8009>)
Maintainer: Steffen Neumann <sneumann@ipb-halle.de>
URL: https://github.com/sneumann/xcms
VignetteBuilder: knitr
BugReports: https://github.com/sneumann/xcms/issues/new
git_url: https://git.bioconductor.org/packages/xcms
git_branch: RELEASE_3_13
git_last_commit: 71f6b4f
git_last_commit_date: 2021-07-23
Date/Publication: 2021-07-27
source.ver: src/contrib/xcms_3.14.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/xcms_3.14.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/xcms_3.14.1.tgz
vignettes: vignettes/xcms/inst/doc/xcms-direct-injection.html,
vignettes/xcms/inst/doc/xcms-lcms-ms.html,
vignettes/xcms/inst/doc/xcms.html
vignetteTitles: Grouping FTICR-MS data with xcms, LC-MS/MS data
analysis with xcms, LCMS data preprocessing and analysis with
xcms
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/xcms/inst/doc/xcms-direct-injection.R,
vignettes/xcms/inst/doc/xcms-lcms-ms.R,
vignettes/xcms/inst/doc/xcms.R
dependsOnMe: CAMERA, flagme, IPO, LOBSTAHS, Metab, metaMS, ncGTW,
proFIA, faahKO, PtH2O2lipids, MetaClean
importsMe: CAMERA, cliqueMS, cosmiq, Risa, specmine.datasets
suggestsMe: CluMSID, MassSpecWavelet, msPurity, RMassBank, msdata,
mtbls2, RforProteomics, CorrectOverloadedPeaks, enviGCMS,
isatabr, RAMClustR, specmine
dependencyCount: 93
Package: XDE
Version: 2.38.0
Depends: R (>= 2.10.0), Biobase (>= 2.5.5)
Imports: BiocGenerics, genefilter, graphics, grDevices, gtools,
methods, stats, utils, mvtnorm, RColorBrewer, GeneMeta,
siggenes
Suggests: MASS, RUnit
Enhances: coda
License: LGPL-2
MD5sum: e49bf080728b6793d8dc608732b0ef85
NeedsCompilation: yes
Title: XDE: a Bayesian hierarchical model for cross-study analysis of
differential gene expression
Description: Multi-level model for cross-study detection of
differential gene expression.
biocViews: Microarray, DifferentialExpression
Author: R.B. Scharpf, G. Parmigiani, A.B. Nobel, and H. Tjelmeland
Maintainer: Robert Scharpf <rscharpf@jhsph.edu>
git_url: https://git.bioconductor.org/packages/XDE
git_branch: RELEASE_3_13
git_last_commit: 3e89cb6
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/XDE_2.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/XDE_2.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/XDE_2.38.0.tgz
vignettes: vignettes/XDE/inst/doc/XDE.pdf,
vignettes/XDE/inst/doc/XdeParameterClass.pdf
vignetteTitles: XDE Vignette, XdeParameterClass Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/XDE/inst/doc/XDE.R,
vignettes/XDE/inst/doc/XdeParameterClass.R
dependencyCount: 63
Package: Xeva
Version: 1.8.0
Depends: R (>= 3.6)
Imports: methods, stats, utils, BBmisc, Biobase, grDevices, ggplot2,
scales, ComplexHeatmap, parallel, doParallel, Rmisc, grid,
nlme, PharmacoGx, downloader
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 0196021da3c23912102ad14e8178d8ae
NeedsCompilation: no
Title: Analysis of patient-derived xenograft (PDX) data
Description: Contains set of functions to perform analysis of
patient-derived xenograft (PDX) data.
biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics,
Software, Classification
Author: Arvind Mer, Benjamin Haibe-Kains
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
BugReports: https://github.com/bhklab/Xeva/issues
git_url: https://git.bioconductor.org/packages/Xeva
git_branch: RELEASE_3_13
git_last_commit: f71d6a2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/Xeva_1.8.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/Xeva_1.8.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Xeva_1.8.0.tgz
vignettes: vignettes/Xeva/inst/doc/Xeva.pdf
vignetteTitles: The Xeva User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Xeva/inst/doc/Xeva.R
dependencyCount: 149
Package: XINA
Version: 1.10.0
Depends: R (>= 3.5)
Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools,
grDevices, graphics, utils, STRINGdb
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 5c35ea3ee7acd8629014db641b82223d
NeedsCompilation: no
Title: Multiplexes Isobaric Mass Tagged-based Kinetics Data for Network
Analysis
Description: The aim of XINA is to determine which proteins exhibit
similar patterns within and across experimental conditions,
since proteins with co-abundance patterns may have common
molecular functions. XINA imports multiple datasets, tags
dataset in silico, and combines the data for subsequent
subgrouping into multiple clusters. The result is a single
output depicting the variation across all conditions. XINA, not
only extracts coabundance profiles within and across
experiments, but also incorporates protein-protein interaction
databases and integrative resources such as KEGG to infer
interactors and molecular functions, respectively, and produces
intuitive graphical outputs.
biocViews: SystemsBiology, Proteomics, RNASeq, Network
Author: Lang Ho Lee <lhlee@bwh.harvard.edu> and Sasha A. Singh
<sasingh@bwh.harvard.edu>
Maintainer: Lang Ho Lee <lhlee@bwh.harvard.edu> and Sasha A. Singh
<sasingh@bwh.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/XINA
git_branch: RELEASE_3_13
git_last_commit: 1ddc683
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/XINA_1.10.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/XINA_1.10.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/XINA_1.10.0.tgz
vignettes: vignettes/XINA/inst/doc/xina_user_code.html
vignetteTitles: xina_user_code
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/XINA/inst/doc/xina_user_code.R
dependencyCount: 68
Package: xmapbridge
Version: 1.50.0
Depends: R (>= 2.0), methods
Suggests: RUnit, RColorBrewer
License: LGPL-3
MD5sum: 68e6e8d222a80050ed5315ed81982003
NeedsCompilation: no
Title: Export plotting files to the xmapBridge for visualisation in
X:Map
Description: xmapBridge can plot graphs in the X:Map genome browser.
This package exports plotting files in a suitable format.
biocViews: Annotation, ReportWriting, Visualization
Author: Tim Yates <Tim.Yates@cruk.manchester.ac.uk> and Crispin J
Miller <Crispin.Miller@cruk.manchester.ac.uk>
Maintainer: Chris Wirth <Christopher.Wirth@cruk.manchester.ac.uk>
URL: http://xmap.picr.man.ac.uk, http://www.bioconductor.org
git_url: https://git.bioconductor.org/packages/xmapbridge
git_branch: RELEASE_3_13
git_last_commit: 711de11
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/xmapbridge_1.50.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/xmapbridge_1.50.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/xmapbridge_1.50.0.tgz
vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf
vignetteTitles: xmapbridge primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/xmapbridge/inst/doc/xmapbridge.R
dependencyCount: 1
Package: XNAString
Version: 1.0.2
Depends: R (>= 4.1)
Imports: utils, Biostrings, BSgenome, data.table, GenomicRanges,
IRanges, methods, Rcpp, stringi, S4Vectors, future.apply,
stringr, formattable, stats
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat,
BSgenome.Hsapiens.UCSC.hg38, pander
License: GPL-2
MD5sum: ee496e5095a7a42f389de6b173b3eae7
NeedsCompilation: yes
Title: Efficient Manipulation of Modified Oligonucleotide Sequences
Description: The XNAString package allows for description of base
sequences and associated chemical modifications in a single
object. XNAString is able to capture single stranded, as well
as double stranded molecules. Chemical modifications are
represented as independent strings associated with different
features of the molecules (base sequence, sugar sequence,
backbone sequence, modifications) and can be read or written to
a HELM notation. It also enables secondary structure prediction
using RNAfold from ViennaRNA. XNAString is designed to be
efficient representation of nucleic-acid based therapeutics,
therefore it stores information about target sequences and
provides interface for matching and alignment functions from
Biostrings package.
biocViews: SequenceMatching, Alignment, Sequencing, Genetics
Author: Anna Górska [aut], Marianna Plucinska [aut, cre], Lykke
Pedersen [aut], Lukasz Kielpinski [aut], Disa Tehler [aut],
Peter H. Hagedorn [aut]
Maintainer: Marianna Plucinska <marianna.plucinska@roche.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/XNAString
git_branch: RELEASE_3_13
git_last_commit: 9ef6642
git_last_commit_date: 2021-06-02
Date/Publication: 2021-06-03
source.ver: src/contrib/XNAString_1.0.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/XNAString_1.0.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/XNAString_1.0.2.tgz
vignettes: vignettes/XNAString/inst/doc/XNAString_vignette.html
vignetteTitles: XNAString classes and functionalities
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/XNAString/inst/doc/XNAString_vignette.R
dependencyCount: 72
Package: XVector
Version: 0.32.0
Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>=
0.27.12), IRanges (>= 2.23.9)
Imports: methods, utils, tools, zlibbioc, BiocGenerics, S4Vectors,
IRanges
LinkingTo: S4Vectors, IRanges
Suggests: Biostrings, drosophila2probe, RUnit
License: Artistic-2.0
MD5sum: f85b750229a2074ae9d529f5e5f93501
NeedsCompilation: yes
Title: Foundation of external vector representation and manipulation in
Bioconductor
Description: Provides memory efficient S4 classes for storing sequences
"externally" (e.g. behind an R external pointer, or on disk).
biocViews: Infrastructure, DataRepresentation
Author: Hervé Pagès and Patrick Aboyoun
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/XVector
BugReports: https://github.com/Bioconductor/XVector/issues
git_url: https://git.bioconductor.org/packages/XVector
git_branch: RELEASE_3_13
git_last_commit: 300392d
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/XVector_0.32.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/XVector_0.32.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/XVector_0.32.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: Biostrings, triplex
importsMe: BSgenome, ChIPsim, CNEr, compEpiTools, dada2, DECIPHER,
gcrma, GenomicFeatures, GenomicRanges, Gviz, HiLDA, IONiseR,
IsoformSwitchAnalyzeR, kebabs, MatrixRider, Modstrings,
R453Plus1Toolbox, ribosomeProfilingQC, Rsamtools, rtracklayer,
Structstrings, TFBSTools, tracktables, tRNA, tRNAscanImport,
VariantAnnotation, simMP
suggestsMe: IRanges, musicatk
linksToMe: Biostrings, CNEr, DECIPHER, kebabs, MatrixRider, Rsamtools,
rtracklayer, ShortRead, triplex, VariantAnnotation,
VariantFiltering
dependencyCount: 11
Package: yamss
Version: 1.18.0
Depends: R (>= 3.3.0), methods, BiocGenerics (>= 0.15.3),
SummarizedExperiment
Imports: IRanges, stats, S4Vectors, EBImage, Matrix, mzR, data.table,
grDevices, limma
Suggests: BiocStyle, knitr, rmarkdown, digest, mtbls2, testthat
License: Artistic-2.0
MD5sum: 3e02f7709fe8dec6a5a2bad97ea6a11b
NeedsCompilation: no
Title: Tools for high-throughput metabolomics
Description: Tools to analyze and visualize high-throughput
metabolomics data aquired using chromatography-mass
spectrometry. These tools preprocess data in a way that enables
reliable and powerful differential analysis.
biocViews: MassSpectrometry, Metabolomics, ImmunoOncology, Software
Author: Leslie Myint [cre, aut], Kasper Daniel Hansen [aut]
Maintainer: Leslie Myint <leslie.myint@gmail.com>
URL: https://github.com/hansenlab/yamss
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/yamss/issues
git_url: https://git.bioconductor.org/packages/yamss
git_branch: RELEASE_3_13
git_last_commit: 6536737
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/yamss_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/yamss_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/yamss_1.18.0.tgz
vignettes: vignettes/yamss/inst/doc/yamss.html
vignetteTitles: yamss User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/yamss/inst/doc/yamss.R
dependencyCount: 48
Package: YAPSA
Version: 1.18.0
Depends: R (>= 3.6.0), GenomicRanges, ggplot2, grid
Imports: limSolve, SomaticSignatures, VariantAnnotation, GenomeInfoDb,
reshape2, gridExtra, corrplot, dendextend, GetoptLong,
circlize, gtrellis, doParallel, PMCMR, ggbeeswarm,
ComplexHeatmap, KEGGREST, grDevices, Biostrings,
BSgenome.Hsapiens.UCSC.hg19, magrittr, pracma, dplyr, utils
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: GPL-3
Archs: i386, x64
MD5sum: 38fec893c00b903a8d3350f04bccd7f2
NeedsCompilation: no
Title: Yet Another Package for Signature Analysis
Description: This package provides functions and routines for
supervised analyses of mutational signatures (i.e., the
signatures have to be known, cf. L. Alexandrov et al., Nature
2013 and L. Alexandrov et al., Bioaxiv 2018). In particular,
the family of functions LCD (LCD = linear combination
decomposition) can use optimal signature-specific cutoffs which
takes care of different detectability of the different
signatures. Moreover, the package provides different sets of
mutational signatures, including the COSMIC and PCAWG SNV
signatures and the PCAWG Indel signatures; the latter infering
that with YAPSA, the concept of supervised analysis of
mutational signatures is extended to Indel signatures. YAPSA
also provides confidence intervals as computed by profile
likelihoods and can perform signature analysis on a stratified
mutational catalogue (SMC = stratify mutational catalogue) in
order to analyze enrichment and depletion patterns for the
signatures in different strata.
biocViews: Sequencing, DNASeq, SomaticMutation, Visualization,
Clustering, GenomicVariation, StatisticalMethod,
BiologicalQuestion
Author: Daniel Huebschmann, Lea Jopp-Saile, Carolin Andresen, Zuguang
Gu and Matthias Schlesner
Maintainer: Daniel Huebschmann <huebschmann.daniel@googlemail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/YAPSA
git_branch: RELEASE_3_13
git_last_commit: 0ce81ea
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/YAPSA_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/YAPSA_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/YAPSA_1.18.0.tgz
vignettes: vignettes/YAPSA/inst/doc/index.html,
vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.html,
vignettes/YAPSA/inst/doc/vignette_exomes.html,
vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.html,
vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.html,
vignettes/YAPSA/inst/doc/vignettes_Indel.html,
vignettes/YAPSA/inst/doc/YAPSA.html
vignetteTitles: index.html, 3. Confidence Intervals, 6. Usage of YAPSA
for WES data, 2. Signature-specific cutoffs, 4. Stratified
Analysis of Mutational Signatures, 5. Indel signature analysis,
1. Usage of YAPSA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.R,
vignettes/YAPSA/inst/doc/vignette_exomes.R,
vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.R,
vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.R,
vignettes/YAPSA/inst/doc/vignettes_Indel.R,
vignettes/YAPSA/inst/doc/YAPSA.R
dependencyCount: 186
Package: yarn
Version: 1.18.0
Depends: Biobase
Imports: biomaRt, downloader, edgeR, gplots, graphics, limma,
matrixStats, preprocessCore, readr, RColorBrewer, stats,
quantro
Suggests: knitr, rmarkdown, testthat (>= 0.8)
License: Artistic-2.0
Archs: i386, x64
MD5sum: da06a4987ed26a3fc32fa973a07fb124
NeedsCompilation: no
Title: YARN: Robust Multi-Condition RNA-Seq Preprocessing and
Normalization
Description: Expedite large RNA-Seq analyses using a combination of
previously developed tools. YARN is meant to make it easier for
the user in performing basic mis-annotation quality control,
filtering, and condition-aware normalization. YARN leverages
many Bioconductor tools and statistical techniques to account
for the large heterogeneity and sparsity found in very large
RNA-seq experiments.
biocViews: Software, QualityControl, GeneExpression, Sequencing,
Preprocessing, Normalization, Annotation, Visualization,
Clustering
Author: Joseph N Paulson [aut, cre], Cho-Yi Chen [aut], Camila
Lopes-Ramos [aut], Marieke Kuijjer [aut], John Platig [aut],
Abhijeet Sonawane [aut], Maud Fagny [aut], Kimberly Glass
[aut], John Quackenbush [aut]
Maintainer: Joseph N Paulson <paulson.joseph@gene.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/yarn
git_branch: RELEASE_3_13
git_last_commit: a8b8b57
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/yarn_1.18.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/yarn_1.18.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/yarn_1.18.0.tgz
vignettes: vignettes/yarn/inst/doc/yarn.pdf
vignetteTitles: YARN: Robust Multi-Tissue RNA-Seq Preprocessing and
Normalization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/yarn/inst/doc/yarn.R
dependencyCount: 157
Package: zellkonverter
Version: 1.2.1
Imports: Matrix, basilisk, reticulate, SingleCellExperiment (>=
1.11.6), SummarizedExperiment, DelayedArray, methods,
S4Vectors, utils
Suggests: covr, spelling, testthat, knitr, rmarkdown, BiocStyle,
scRNAseq, HDF5Array, rhdf5, BiocFileCache
License: MIT + file LICENSE
MD5sum: a44e95a62fceacc777e9cda9a0faa644
NeedsCompilation: no
Title: Conversion Between scRNA-seq Objects
Description: Provides methods to convert between Python AnnData objects
and SingleCellExperiment objects. These are primarily intended
for use by downstream Bioconductor packages that wrap Python
methods for single-cell data analysis. It also includes
functions to read and write H5AD files used for saving AnnData
objects to disk.
biocViews: SingleCell, DataImport, DataRepresentation
Author: Luke Zappia [aut, cre]
(<https://orcid.org/0000-0001-7744-8565>), Aaron Lun [aut]
(<https://orcid.org/0000-0002-3564-4813>)
Maintainer: Luke Zappia <luke@lazappi.id.au>
URL: https://github.com/theislab/zellkonverter
VignetteBuilder: knitr
BugReports: https://github.com/theislab/zellkonverter/issues
git_url: https://git.bioconductor.org/packages/zellkonverter
git_branch: RELEASE_3_13
git_last_commit: a3c4f31
git_last_commit_date: 2021-06-22
Date/Publication: 2021-06-22
source.ver: src/contrib/zellkonverter_1.2.1.tar.gz
win.binary.ver: bin/windows/contrib/4.1/zellkonverter_1.2.1.zip
mac.binary.ver: bin/macosx/contrib/4.1/zellkonverter_1.2.1.tgz
vignettes: vignettes/zellkonverter/inst/doc/zellkonverter.html
vignetteTitles: Converting to/from AnnData to SingleCellExperiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/zellkonverter/inst/doc/zellkonverter.R
dependsOnMe: OSCA.intro
importsMe: velociraptor
suggestsMe: HDF5Array
dependencyCount: 39
Package: zFPKM
Version: 1.14.0
Depends: R (>= 3.4.0)
Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment
Suggests: knitr, limma, edgeR, GEOquery, stringr, printr
License: GPL-3 | file LICENSE
MD5sum: a04cdac948178db82725281636ecc7a5
NeedsCompilation: no
Title: A suite of functions to facilitate zFPKM transformations
Description: Perform the zFPKM transform on RNA-seq FPKM data. This
algorithm is based on the publication by Hart et al., 2013
(Pubmed ID 24215113). Reference recommends using zFPKM > -3 to
select expressed genes. Validated with encode open/closed
chromosome data. Works well for gene level data using FPKM or
TPM. Does not appear to calibrate well for transcript level
data.
biocViews: ImmunoOncology, RNASeq, FeatureExtraction, Software,
GeneExpression
Author: Ron Ammar [aut, cre], John Thompson [aut]
Maintainer: Ron Ammar <ron.ammar@bms.com>
URL: https://github.com/ronammar/zFPKM/
VignetteBuilder: knitr
BugReports: https://github.com/ronammar/zFPKM/issues
git_url: https://git.bioconductor.org/packages/zFPKM
git_branch: RELEASE_3_13
git_last_commit: 196cddf
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/zFPKM_1.14.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/zFPKM_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/zFPKM_1.14.0.tgz
vignettes: vignettes/zFPKM/inst/doc/zFPKM.html
vignetteTitles: Introduction to zFPKM Transformation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/zFPKM/inst/doc/zFPKM.R
importsMe: DGEobj.utils
dependencyCount: 64
Package: zinbwave
Version: 1.14.2
Depends: R (>= 3.4), methods, SummarizedExperiment,
SingleCellExperiment
Imports: BiocParallel, softImpute, stats, genefilter, edgeR, Matrix
Suggests: knitr, rmarkdown, testthat, matrixStats, magrittr, scRNAseq,
ggplot2, biomaRt, BiocStyle, Rtsne, DESeq2
License: Artistic-2.0
MD5sum: 2e3265ad36f46bcdcf4daf364cade7ac
NeedsCompilation: no
Title: Zero-Inflated Negative Binomial Model for RNA-Seq Data
Description: Implements a general and flexible zero-inflated negative
binomial model that can be used to provide a low-dimensional
representations of single-cell RNA-seq data. The model accounts
for zero inflation (dropouts), over-dispersion, and the count
nature of the data. The model also accounts for the difference
in library sizes and optionally for batch effects and/or other
covariates, avoiding the need for pre-normalize the data.
biocViews: ImmunoOncology, DimensionReduction, GeneExpression, RNASeq,
Software, Transcriptomics, Sequencing, SingleCell
Author: Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny
Perraudeau [aut], Jean-Philippe Vert [aut], Clara Bagatin [aut]
Maintainer: Davide Risso <risso.davide@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/drisso/zinbwave/issues
git_url: https://git.bioconductor.org/packages/zinbwave
git_branch: RELEASE_3_13
git_last_commit: a2db5c9
git_last_commit_date: 2021-09-14
Date/Publication: 2021-09-16
source.ver: src/contrib/zinbwave_1.14.2.tar.gz
win.binary.ver: bin/windows/contrib/4.1/zinbwave_1.14.2.zip
mac.binary.ver: bin/macosx/contrib/4.1/zinbwave_1.14.2.tgz
vignettes: vignettes/zinbwave/inst/doc/intro.html
vignetteTitles: zinbwave Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/zinbwave/inst/doc/intro.R
importsMe: clusterExperiment, scBFA, singleCellTK, digitalDLSorteR
suggestsMe: MAST, splatter
dependencyCount: 72
Package: zlibbioc
Version: 1.38.0
License: Artistic-2.0 + file LICENSE
Archs: i386, x64
MD5sum: 8ef59236c632a09393687935c99a3afd
NeedsCompilation: yes
Title: An R packaged zlib-1.2.5
Description: This package uses the source code of zlib-1.2.5 to create
libraries for systems that do not have these available via
other means (most Linux and Mac users should have system-level
access to zlib, and no direct need for this package). See the
vignette for instructions on use.
biocViews: Infrastructure
Author: Martin Morgan
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/zlibbioc
BugReports: https://github.com/Bioconductor/zlibbioc/issues
git_url: https://git.bioconductor.org/packages/zlibbioc
git_branch: RELEASE_3_13
git_last_commit: b80b55e
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
source.ver: src/contrib/zlibbioc_1.38.0.tar.gz
win.binary.ver: bin/windows/contrib/4.1/zlibbioc_1.38.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/zlibbioc_1.38.0.tgz
vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.pdf
vignetteTitles: Using zlibbioc C libraries
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependsOnMe: SimRAD
importsMe: affy, affyio, affyPLM, bamsignals, ChemmineOB, MADSEQ,
makecdfenv, NanoMethViz, oligo, polyester, qckitfastq, Rhtslib,
Rsamtools, rtracklayer, ShortRead, snpStats, TransView,
VariantAnnotation, XVector, jackalope
suggestsMe: metacoder
linksToMe: bamsignals, csaw, diffHic, maftools, methylKit, mzR, Rfastp,
Rhtslib, scPipe, seqTools, ShortRead, jackalope
dependencyCount: 0
Package: BrainStars
Version: 1.35.0
Depends: RCurl, Biobase, methods
Imports: RJSONIO, Biobase
License: Artistic-2.0
Archs: i386, x64
NeedsCompilation: no
Title: query gene expression data and plots from BrainStars (B*)
Description: This package can search and get gene expression data and
plots from BrainStars (B*). BrainStars is a quantitative
expression database of the adult mouse brain. The database has
genome-wide expression profile at 51 adult mouse CNS regions.
biocViews: Microarray, OneChannel, DataImport
Author: Itoshi NIKAIDO <dritoshi@gmail.com>
Maintainer: Itoshi NIKAIDO <dritoshi@gmail.com>
git_url: https://git.bioconductor.org/packages/BrainStars
git_branch: master
git_last_commit: 7a87bab
git_last_commit_date: 2020-10-27
Date/Publication: 2021-03-09
win.binary.ver: bin/windows/contrib/4.1/BrainStars_1.35.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/BrainStars_1.35.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: ChIPSeqSpike
Version: 1.12.0
Depends: R (>= 3.5), rtracklayer (>= 1.37.6)
Imports: tools, stringr, Rsamtools, GenomicRanges, IRanges, seqplots,
ggplot2, LSD, corrplot, methods, stats, grDevices, graphics,
utils, BiocGenerics, S4Vectors
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: Artistic-2.0
NeedsCompilation: no
Title: ChIP-Seq data scaling according to spike-in control
Description: Chromatin Immuno-Precipitation followed by Sequencing
(ChIP-Seq) is used to determine the binding sites of any
protein of interest, such as transcription factors or histones
with or without a specific modification, at a genome scale. The
many steps of the protocol can introduce biases that make
ChIP-Seq more qualitative than quantitative. For instance, it
was shown that global histone modification differences are not
caught by traditional downstream data normalization techniques.
A case study reported no differences in histone H3 lysine-27
trimethyl (H3K27me3) upon Ezh2 inhibitor treatment. To tackle
this problem, external spike-in control were used to keep track
of technical biases between conditions. Exogenous DNA from a
different non-closely related species was inserted during the
protocol to infer scaling factors that enabled an accurate
normalization, thus revealing the inhibitor effect.
ChIPSeqSpike offers tools for ChIP-Seq spike-in normalization.
Ready to use scaled bigwig files and scaling factors values are
obtained as output. ChIPSeqSpike also provides tools for
ChIP-Seq spike-in assessment and analysis through a versatile
collection of graphical functions.
biocViews: ImmunoOncology, ChIPSeq, Sequencing, Normalization,
Transcription, Coverage, DifferentialMethylation, Epigenetics,
DataImport, HistoneModification
Author: Nicolas Descostes
Maintainer: Nicolas Descostes <nicolas.descostes@gmail.com>
VignetteBuilder: knitr
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/ChIPSeqSpike
git_branch: RELEASE_3_13
git_last_commit: 8b73542
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
win.binary.ver: bin/windows/contrib/4.1/ChIPSeqSpike_1.12.0.zip
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: destiny
Version: 3.6.0
Depends: R (>= 3.4.0)
Imports: methods, graphics, grDevices, utils, stats, Matrix, Rcpp (>=
0.10.3), RcppEigen, RSpectra (>= 0.14-0), irlba, pcaMethods,
Biobase, BiocGenerics, SummarizedExperiment,
SingleCellExperiment, ggplot2, ggplot.multistats, tidyr,
tidyselect, ggthemes, VIM, knn.covertree, proxy, RcppHNSW,
smoother, scales, scatterplot3d
LinkingTo: Rcpp, RcppEigen, grDevices
Suggests: nbconvertR (>= 1.3.2), igraph, testthat, FNN, tidyr
Enhances: rgl, SingleCellExperiment
License: GPL
NeedsCompilation: yes
Title: Creates diffusion maps
Description: Create and plot diffusion maps.
biocViews: CellBiology, CellBasedAssays, Clustering, Software,
Visualization
Author: Philipp Angerer [cre, aut]
(<https://orcid.org/0000-0002-0369-2888>), Laleh Haghverdi
[ctb], Maren Büttner [ctb]
(<https://orcid.org/0000-0002-6189-3792>), Fabian Theis [ctb]
(<https://orcid.org/0000-0002-2419-1943>), Carsten Marr [ctb]
(<https://orcid.org/0000-0003-2154-4552>), Florian Büttner
[ctb] (<https://orcid.org/0000-0001-5587-6761>)
Maintainer: Philipp Angerer <philipp.angerer@helmholtz-muenchen.de>
URL: https://theislab.github.io/destiny/,
https://github.com/theislab/destiny/,
https://www.helmholtz-muenchen.de/icb/destiny,
https://bioconductor.org/packages/destiny,
https://doi.org/10.1093/bioinformatics/btv715
SystemRequirements: C++11, jupyter nbconvert (see nbconvertR’s INSTALL
file)
VignetteBuilder: nbconvertR
BugReports: https://github.com/theislab/destiny/issues
git_url: https://git.bioconductor.org/packages/destiny
git_branch: RELEASE_3_13
git_last_commit: c3cef14
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-27
win.binary.ver: bin/windows/contrib/4.1/destiny_3.6.0.zip
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: ENVISIONQuery
Version: 1.40.0
Depends: rJava, XML, utils
License: GPL-2
NeedsCompilation: no
Title: Retrieval from the ENVISION bioinformatics data portal into R
Description: Tools to retrieve data from ENVISION, the Database for
Annotation, Visualization and Integrated Discovery portal
biocViews: Annotation
Author: Alex Lisovich, Roger Day
Maintainer: Alex Lisovich <all67@pitt.edu>, Roger Day <day01@pitt.edu>
git_url: https://git.bioconductor.org/packages/ENVISIONQuery
git_branch: RELEASE_3_13
git_last_commit: 1cb737f
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-27
win.binary.ver: bin/windows/contrib/4.1/ENVISIONQuery_1.40.0.zip
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: gramm4R
Version: 1.5.0
Depends: R (>= 3.6.0)
Imports: basicTrendline,investr,minerva,psych,grDevices, graphics,
stats,DelayedArray,SummarizedExperiment,DMwR,phyloseq
Suggests: knitr, rmarkdown
License: GPL-2
NeedsCompilation: no
Title: Generalized correlation analysis and model construction strategy
for metabolome and microbiome
Description: Generalized Correlation Analysis for Metabolome and
Microbiome (GRaMM), for inter-correlation pairs discovery among
metabolome and microbiome.
biocViews: GraphAndNetwork,Microbiome
Author: Mengci Li, Dandan Liang, Tianlu Chen and Wei Jia
Maintainer: Tianlu Chen <chentianlu@sjtu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gramm4R
git_branch: master
git_last_commit: 960fa29
git_last_commit_date: 2020-10-27
Date/Publication: 2021-03-08
win.binary.ver: bin/windows/contrib/4.1/gramm4R_1.5.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/gramm4R_1.5.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: Onassis
Version: 1.14.0
Depends: R (>= 4.0), rJava, OnassisJavaLibs
Imports: GEOmetadb, RSQLite, data.table, methods, tools, utils,
AnnotationDbi, RCurl, stats, DT, data.table, knitr, Rtsne,
dendextend, clusteval, ggplot2, ggfortify
Suggests: BiocStyle, rmarkdown, htmltools, org.Hs.eg.db, gplots,
GenomicRanges, kableExtra
License: GPL-2
NeedsCompilation: no
Title: OnASSIs Ontology Annotation and Semantic SImilarity software
Description: A package that allows the annotation of text with ontology
terms (mainly from OBO ontologies) and the computation of
semantic similarity measures based on the structure of the
ontology between different annotated samples.
biocViews: Annotation, DataImport, Clustering, Network, Software,
GeneTarget
Author: Eugenia Galeota
Maintainer: Eugenia Galeota <eugenia.galeota@gmail.com>
SystemRequirements: Java (>= 1.8)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Onassis
git_branch: RELEASE_3_13
git_last_commit: e143c4b
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
win.binary.ver: bin/windows/contrib/4.1/Onassis_1.14.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/Onassis_1.14.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: SRGnet
Version: 1.17.0
Depends: R (>= 3.3.1), EBcoexpress, MASS, igraph, pvclust (>= 2.0-0),
gbm (>= 2.1.1), limma, DMwR (>= 0.4.1), matrixStats, Hmisc
Suggests: knitr, rmarkdown
License: GPL-2
NeedsCompilation: no
Title: SRGnet: An R package for studying synergistic response to gene
mutations from transcriptomics data
Description: We developed SRGnet to analyze synergistic regulatory
mechanisms in transcriptome profiles that act to enhance the
overall cell response to combination of mutations, drugs or
environmental exposure. This package can be used to identify
regulatory modules downstream of synergistic response genes,
prioritize synergistic regulatory genes that may be potential
intervention targets, and contextualize gene perturbation
experiments.
biocViews: Software, StatisticalMethod, Regression
Author: Isar Nassiri [aut, cre], Matthew McCall [aut, cre]
Maintainer: Isar Nassiri <isar_nassiri@urmc.rochester.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SRGnet
git_branch: master
git_last_commit: fde0f26
git_last_commit_date: 2020-10-27
Date/Publication: 2021-03-08
win.binary.ver: bin/windows/contrib/4.1/SRGnet_1.17.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/SRGnet_1.17.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: synapter
Version: 2.16.0
Depends: R (>= 3.1.0), methods, MSnbase (>= 2.1.2)
Imports: RColorBrewer, lattice, qvalue, multtest, utils, tools,
Biobase, knitr, Biostrings, cleaver (>= 1.3.3), readr (>= 0.2),
rmarkdown (>= 1.0)
Suggests: synapterdata (>= 1.13.2), xtable, testthat (>= 0.8), BRAIN,
BiocStyle
License: GPL-2
Archs: i386, x64
NeedsCompilation: no
Title: Label-free data analysis pipeline for optimal identification and
quantitation
Description: The synapter package provides functionality to reanalyse
label-free proteomics data acquired on a Synapt G2 mass
spectrometer. One or several runs, possibly processed with
additional ion mobility separation to increase identification
accuracy can be combined to other quantitation files to
maximise identification and quantitation accuracy.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, QualityControl
Author: Laurent Gatto, Nick J. Bond, Pavel V. Shliaha and Sebastian
Gibb.
Maintainer: Laurent Gatto <lg390@cam.ac.uk> and Sebastian Gibb
<mail@sebastiangibb.de>
URL: https://lgatto.github.io/synapter/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/synapter
git_branch: RELEASE_3_13
git_last_commit: 93006a2
git_last_commit_date: 2021-05-19
Date/Publication: 2021-05-19
win.binary.ver: bin/windows/contrib/4.1/synapter_2.16.0.zip
mac.binary.ver: bin/macosx/contrib/4.1/synapter_2.16.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: affyQCReport
Version: 1.70.0
Depends: Biobase (>= 1.13.16), affy, lattice
Imports: affy, affyPLM, Biobase, genefilter, graphics, grDevices,
lattice, RColorBrewer, simpleaffy, stats, utils, xtable
Suggests: tkWidgets (>= 1.5.23), affydata (>= 1.4.1)
License: LGPL (>= 2)
Title: QC Report Generation for affyBatch objects
Description: This package creates a QC report for an AffyBatch object.
The report is intended to allow the user to quickly assess the
quality of a set of arrays in an AffyBatch object.
biocViews: Microarray,OneChannel,QualityControl
Author: Craig Parman <craig.parman@bifx.org>, Conrad Halling
<conrad.halling@bifx.org>, Robert Gentleman
Maintainer: Craig Parman <craig.parman@bifx.org>
PackageStatus: Deprecated
Package: pcot2
Version: 1.60.0
Depends: R (>= 2.0.0), grDevices, Biobase, amap
Suggests: multtest, hu6800.db, KEGG.db, mvtnorm
License: GPL (>= 2)
Title: Principal Coordinates and Hotelling's T-Square method
Description: PCOT2 is a permutation-based method for investigating
changes in the activity of multi-gene networks. It utilizes
inter-gene correlation information to detect significant
alterations in gene network activities. Currently it can be
applied to two-sample comparisons.
biocViews: Microarray, DifferentialExpression, KEGG, GeneExpression,
Network
Author: Sarah Song, Mik Black
Maintainer: Sarah Song <qson003@stat.auckland.ac.nz>
PackageStatus: Deprecated
Package: SAGx
Version: 1.66.0
Depends: R (>= 2.5.0), stats, multtest, methods
Imports: Biobase, stats4
Suggests: KEGG.db, hu6800.db, MASS
License: GPL-3
Title: Statistical Analysis of the GeneChip
Description: A package for retrieval, preparation and analysis of data
from the Affymetrix GeneChip. In particular the issue of
identifying differentially expressed genes is addressed.
biocViews: Microarray, OneChannel, Preprocessing, DataImport,
DifferentialExpression, Clustering, MultipleComparison,
GeneExpression, GeneSetEnrichment, Pathways, Regression, KEGG
Author: Per Broberg
Maintainer: Per Broberg, <pi.broberg@gmail.com>
URL: http://home.swipnet.se/pibroberg/expression_hemsida1.html
PackageStatus: Deprecated
Package: AffyExpress
Version: 1.58.0
Depends: R (>= 2.10), affy (>= 1.23.4), limma
Suggests: simpleaffy, R2HTML, affyPLM, hgu95av2cdf, hgu95av2, test3cdf,
genefilter, estrogen, annaffy, gcrma
License: LGPL
Title: Affymetrix Quality Assessment and Analysis Tool
Description: The purpose of this package is to provide a comprehensive
and easy-to-use tool for quality assessment and to identify
differentially expressed genes in the Affymetrix gene
expression data.
biocViews: Microarray, OneChannel, QualityControl, Preprocessing,
DifferentialExpression, Annotation, ReportWriting,
Visualization
Author: Xiwei Wu <xwu@coh.org>, Xuejun Arthur Li <xueli@coh.org>
Maintainer: Xuejun Arthur Li <xueli@coh.org>
PackageStatus: Deprecated
Package: OutlierD
Version: 1.56.0
Depends: R (>= 2.3.0), Biobase, quantreg
License: GPL (>= 2)
Title: Outlier detection using quantile regression on the M-A
scatterplots of high-throughput data
Description: This package detects outliers using quantile regression on
the M-A scatterplots of high-throughput data.
biocViews: Microarray
Author: HyungJun Cho <hj4cho@korea.ac.kr>
Maintainer: Sukwoo Kim <s4kim@korea.ac.kr>
URL: http://www.korea.ac.kr/~stat2242/
PackageStatus: Deprecated
Package: yaqcaffy
Version: 1.52.0
Depends: simpleaffy (>= 2.19.3), methods
Imports: stats4
Suggests: MAQCsubsetAFX, affydata, xtable, tcltk2, tcltk
License: Artistic-2.0
Title: Affymetrix expression data quality control and reproducibility
analysis
Description: Quality control of Affymetrix GeneChip expression data and
reproducibility analysis of human whole genome chips with the
MAQC reference datasets.
biocViews: Microarray,OneChannel,QualityControl,ReportWriting
Author: Laurent Gatto
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
PackageStatus: Deprecated
Package: BiocCaseStudies
Version: 1.54.0
Depends: tools, methods, utils, Biobase
Suggests: affy (>= 1.17.3), affyPLM (>= 1.15.1), affyQCReport (>=
1.17.0), ALL (>= 1.4.3), annaffy (>= 1.11.1), annotate (>=
1.17.3), AnnotationDbi (>= 1.1.6), apComplex (>= 2.5.0),
Biobase (>= 1.17.5), bioDist (>= 1.11.3), biocGraph (>= 1.1.1),
biomaRt (>= 1.13.5), CCl4 (>= 1.0.6), CLL (>= 1.2.4), Category
(>= 2.5.0), class (>= 7.2-38), cluster (>= 1.11.9), convert (>=
1.15.0), gcrma (>= 2.11.1), genefilter (>= 1.17.6), geneplotter
(>= 1.17.2), GO.db (>= 2.0.2), GOstats (>= 2.5.0), graph (>=
1.17.4), GSEABase (>= 1.1.13), hgu133a.db (>= 2.0.2),
hgu95av2.db, hgu95av2cdf (>= 2.0.0), hgu95av2probe (>= 2.0.0),
hopach (>= 1.13.0), KEGG.db (>= 2.0.2), kohonen (>= 2.0.2),
lattice (>= 0.17.2), latticeExtra (>= 0.3-1), limma (>=
2.13.1), MASS (>= 7.2-38), MLInterfaces (>= 1.13.17), multtest
(>= 1.19.0), org.Hs.eg.db (>= 2.0.2), ppiStats (>= 1.5.4),
randomForest (>= 4.5-20), RBGL (>= 1.15.6), RColorBrewer (>=
1.0-2), Rgraphviz (>= 1.17.11), vsn (>= 3.4.0), weaver (>=
1.5.0), xtable (>= 1.5-2), yeastExpData (>= 0.9.11)
License: Artistic-2.0
Title: BiocCaseStudies: Support for the Case Studies Monograph
Description: Software and data to support the case studies.
biocViews: Infrastructure
Author: R. Gentleman, W. Huber, F. Hahne, M. Morgan, S. Falcon
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
PackageStatus: Deprecated
Package: PCpheno
Version: 1.54.0
Depends: R (>= 2.10), Category, ScISI (>= 1.3.0), SLGI, ppiStats,
ppiData, annotate (>= 1.17.4)
Imports: AnnotationDbi, Biobase, Category, GO.db, graph, graphics,
GSEABase, KEGG.db, methods, ScISI, stats, stats4
Suggests: KEGG.db, GO.db, org.Sc.sgd.db
License: Artistic-2.0
Title: Phenotypes and cellular organizational units
Description: Tools to integrate, annotate, and link phenotypes to
cellular organizational units such as protein complexes and
pathways.
biocViews: GraphAndNetwork, Proteomics, Network
Author: Nolwenn Le Meur and Robert Gentleman
Maintainer: Nolwenn Le Meur <nlemeur@gmail.com>
PackageStatus: Deprecated
Package: ArrayTools
Version: 1.52.0
Depends: R (>= 2.7.0), affy (>= 1.23.4), Biobase (>= 2.5.5), methods
Imports: affy, Biobase, graphics, grDevices, limma, methods, stats,
utils, xtable
Suggests: simpleaffy, R2HTML, affydata, affyPLM, genefilter, annaffy,
gcrma, hugene10sttranscriptcluster.db
License: LGPL (>= 2.0)
Title: geneChip Analysis Package
Description: This package is designed to provide solutions for quality
assessment and to detect differentially expressed genes for the
Affymetrix GeneChips, including both 3' -arrays and gene 1.0-ST
arrays. The package generates comprehensive analysis reports in
HTML format. Hyperlinks on the report page will lead to a
series of QC plots, processed data, and differentially
expressed gene lists. Differentially expressed genes are
reported in tabular format with annotations hyperlinked to
online biological databases.
biocViews: Microarray, OneChannel, QualityControl, Preprocessing,
StatisticalMethod, DifferentialExpression, Annotation,
ReportWriting, Visualization
Author: Xiwei Wu, Arthur Li
Maintainer: Arthur Li <xueli@coh.org>
PackageStatus: Deprecated
Package: RNAither
Version: 2.40.0
Depends: R (>= 2.10), topGO, RankProd, prada
Imports: geneplotter, limma, biomaRt, car, splots, methods
License: Artistic-2.0
Title: Statistical analysis of high-throughput RNAi screens
Description: RNAither analyzes cell-based RNAi screens, and includes
quality assessment, customizable normalization and statistical
tests, leading to lists of significant genes and biological
processes.
biocViews: CellBasedAssays, QualityControl, Preprocessing,
Visualization, Annotation, GO, ImmunoOncology
Author: Nora Rieber and Lars Kaderali, University of Heidelberg,
Viroquant Research Group Modeling, Im Neuenheimer Feld 267,
69120 Heidelberg, Germany
Maintainer: Lars Kaderali <lars.kaderali@uni-greifswald.de>
PackageStatus: Deprecated
Package: SSPA
Version: 2.32.1
Depends: R (>= 2.12), methods
Imports: graphics, stats, qvalue, lattice, limma
Suggests: BiocStyle, knitr, rmarkdown, genefilter, edgeR, DESeq
License: GPL (>= 2)
Title: General Sample Size and Power Analysis for Microarray and
Next-Generation Sequencing Data
Description: General Sample size and power analysis for microarray and
next-generation sequencing data.
biocViews: ImmunoOncology, GeneExpression, RNASeq, Microarray,
StatisticalMethod
Author: Maarten van Iterson
Maintainer: Maarten van Iterson <mviterson@gmail.com>
URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: GeneAnswers
Version: 2.34.0
Depends: R (>= 3.0.0), igraph, KEGGREST, RCurl, annotate, Biobase (>=
1.12.0), methods, XML, RSQLite, MASS, Heatplus, RColorBrewer
Imports: RBGL, annotate, downloader
Suggests: GO.db, reactome.db, biomaRt, AnnotationDbi, org.Hs.eg.db,
org.Rn.eg.db, org.Mm.eg.db, org.Dm.eg.db, graph
License: LGPL (>= 2)
NeedsCompilation: no
Title: Integrated Interpretation of Genes
Description: GeneAnswers provides an integrated tool for biological or
medical interpretation of the given one or more groups of genes
by means of statistical test.
biocViews: Infrastructure, DataRepresentation, Visualization,
GraphsAndNetworks
Author: Lei Huang, Gang Feng, Pan Du, Tian Xia, Xishu Wang, Jing, Wen,
Warren Kibbe and Simon Lin
Maintainer: Lei Huang <lhuang1998@gmail.com> and Gang Feng
<gilbert.feng@qq.com>
git_url: https://git.bioconductor.org/packages/GeneAnswers
git_branch: RELEASE_3_12
git_last_commit: a310951
git_last_commit_date: 2021-02-21
Date/Publication: 2021-02-21
Package: eisa
Version: 1.44.0
Depends: isa2, Biobase (>= 2.17.8), AnnotationDbi, methods
Imports: BiocGenerics, Category, genefilter, DBI
Suggests: igraph (>= 0.6), Matrix, GOstats, GO.db, KEGG.db, biclust,
MASS, xtable, ALL, hgu95av2.db, targetscan.Hs.eg.db,
org.Hs.eg.db
License: GPL (>= 2)
Title: Expression data analysis via the Iterative Signature Algorithm
Description: The Iterative Signature Algorithm (ISA) is a biclustering
method; it finds correlated blocks (transcription modules) in
gene expression (or other tabular) data. The ISA is capable of
finding overlapping modules and it is resilient to noise. This
package provides a convenient interface to the ISA, using
standard BioConductor data structures; and also contains
various visualization tools that can be used with other
biclustering algorithms.
biocViews: Classification, Visualization, Microarray, GeneExpression
Author: Gabor Csardi <csardi.gabor@gmail.com>
Maintainer: Gabor Csardi <csardi.gabor@gmail.com>
PackageStatus: Deprecated
Package: ExpressionView
Version: 1.44.0
Depends: caTools, bitops, methods, isa2, eisa, GO.db, KEGG.db,
AnnotationDbi
Imports: methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi
Suggests: ALL, hgu95av2.db, biclust, affy
License: GPL (>= 2)
Title: Visualize biclusters identified in gene expression data
Description: ExpressionView visualizes possibly overlapping biclusters
in a gene expression matrix. It can use the result of the ISA
method (eisa package) or the algorithms in the biclust package
or others. The viewer itself was developed using Adobe Flex and
runs in a flash-enabled web browser.
biocViews: Classification, Visualization, Microarray, GeneExpression,
GO, KEGG
Author: Andreas Luscher <andreas.luescher@a3.epfl.ch>
Maintainer: Gabor Csardi <csardi.gabor@gmail.com>
PackageStatus: Deprecated
Package: rnaSeqMap
Version: 2.50.0
Depends: R (>= 2.11.0), methods, Biobase, Rsamtools, GenomicAlignments
Imports: GenomicRanges , IRanges, edgeR, DESeq, DBI
License: GPL-2
Title: rnaSeq secondary analyses
Description: The rnaSeqMap library provides classes and functions to
analyze the RNA-sequencing data using the coverage profiles in
multiple samples at a time
biocViews: ImmunoOncology, Annotation, ReportWriting, Transcription,
GeneExpression, DifferentialExpression, Sequencing, RNASeq,
SAGE, Visualization
Author: Anna Lesniewska <alesniewska@cs.put.poznan.pl>; Michal
Okoniewski <michal@fgcz.ethz.ch>
Maintainer: Michal Okoniewski <michal@fgcz.ethz.ch>
PackageStatus: Deprecated
Package: AnnotationFuncs
Version: 1.42.0
Depends: R (>= 2.7.0), AnnotationDbi
Imports: DBI
Suggests: org.Bt.eg.db, GO.db, org.Hs.eg.db, hom.Hs.inp.db
License: GPL-2
Title: Annotation translation functions
Description: Functions for handling translating between different
identifieres using the Biocore Data Team data-packages (e.g.
org.Bt.eg.db).
biocViews: AnnotationData, Software
Author: Stefan McKinnon Edwards <stefan.hoj-edwards@agrsci.dk>
Maintainer: Stefan McKinnon Edwards <stefan.hoj-edwards@agrsci.dk>
URL: http://www.iysik.com/index.php?page=annotation-functions
PackageStatus: Deprecated
Package: genoset
Version: 1.48.0
Depends: R (>= 2.10), BiocGenerics (>= 0.11.3), GenomicRanges (>=
1.17.19), SummarizedExperiment (>= 1.1.6)
Imports: S4Vectors (>= 0.27.3), GenomeInfoDb (>= 1.1.3), IRanges (>=
2.5.12), methods, graphics
Suggests: testthat, knitr, BiocStyle, rmarkdown, DNAcopy, stats,
BSgenome, Biostrings
Enhances: parallel
License: Artistic-2.0
Title: A RangedSummarizedExperiment with methods for copy number
analysis
Description: GenoSet provides an extension of the
RangedSummarizedExperiment class with additional API features.
This class provides convenient and fast methods for working
with segmented genomic data. Additionally, GenoSet provides the
class RleDataFrame which stores runs of data along the genome
for multiple samples and provides very fast summaries of
arbitrary row sets (regions of the genome).
biocViews: Infrastructure, DataRepresentation, Microarray, SNP,
CopyNumberVariation
Author: Peter M. Haverty
Maintainer: Peter M. Haverty <phaverty@gene.com>
URL: https://github.com/phaverty/genoset
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: RchyOptimyx
Version: 2.32.0
Depends: R (>= 2.10)
Imports: Rgraphviz, sfsmisc, graphics, methods, graph, grDevices,
flowType (>= 2.0.0)
Suggests: flowCore
License: Artistic-2.0
Title: Optimyzed Cellular Hierarchies for Flow Cytometry
Description: Constructs a hierarchy of cells using flow cytometry for
maximization of an external variable (e.g., a clinical outcome
or a cytokine response).
biocViews: FlowCytometry
Author: Adrin Jalali, Nima Aghaeepour
Maintainer: Adrin Jalali <adrin.jalali@gmail.com>, Nima Aghaeepour
<naghaeep@gmail.com>
PackageStatus: Deprecated
Package: CancerMutationAnalysis
Version: 1.34.0
Depends: R (>= 2.10.0), qvalue
Imports: AnnotationDbi, limma, methods, stats
Suggests: KEGG.db
License: GPL (>= 2) + file LICENSE
Title: Cancer mutation analysis
Description: This package implements gene and gene-set level analysis
methods for somatic mutation studies of cancer. The gene-level
methods distinguish between driver genes (which play an active
role in tumorigenesis) and passenger genes (which are mutated
in tumor samples, but have no role in tumorigenesis) and
incorporate a two-stage study design. The gene-set methods
implement a patient-oriented approach, which calculates
gene-set scores for each sample, then combines them across
samples; a gene-oriented approach which uses the Wilcoxon test
is also provided for comparison.
biocViews: Genetics, Software
Author: Giovanni Parmigiani, Simina M. Boca
Maintainer: Simina M. Boca <smb310@georgetown.edu>
PackageStatus: Deprecated
Package: KEGGprofile
Version: 1.34.0
Imports:
AnnotationDbi,png,TeachingDemos,XML,KEGG.db,KEGGREST,biomaRt,RCurl,ggplot2,reshape2
License: GPL (>= 2)
Title: An annotation and visualization package for multi-types and
multi-groups expression data in KEGG pathway
Description: KEGGprofile is an annotation and visualization tool which
integrated the expression profiles and the function annotation
in KEGG pathway maps. The multi-types and multi-groups
expression data can be visualized in one pathway map.
KEGGprofile facilitated more detailed analysis about the
specific function changes inner pathway or temporal
correlations in different genes and samples.
biocViews: Pathways, KEGG
Author: Shilin Zhao, Yan Guo, Yu Shyr
Maintainer: Shilin Zhao <shilin.zhao@vanderbilt.edu>
Package: DBChIP
Version: 1.36.0
Depends: R (>= 2.15.0), edgeR, DESeq
Suggests: ShortRead, BiocGenerics
License: GPL (>= 2)
Title: Differential Binding of Transcription Factor with ChIP-seq
Description: DBChIP detects differentially bound sharp binding sites
across multiple conditions, with or without matching control
samples.
biocViews: ChIPSeq, Sequencing, Transcription, Genetics
Author: Kun Liang
Maintainer: Kun Liang <kliang@stat.wisc.edu>
PackageStatus: Deprecated
Package: EasyqpcR
Version: 1.34.0
Imports: plyr, matrixStats, plotrix, gWidgetsRGtk2
Suggests: SLqPCR, qpcrNorm, qpcR, knitr
License: GPL (>=2)
Title: EasyqpcR for low-throughput real-time quantitative PCR data
analysis
Description: This package is based on the qBase algorithms published by
Hellemans et al. in 2007. The EasyqpcR package allows you to
import easily qPCR data files as described in the vignette.
Thereafter, you can calculate amplification efficiencies,
relative quantities and their standard errors, normalization
factors based on the best reference genes choosen (using the
SLqPCR package), and then the normalized relative quantities,
the NRQs scaled to your control and their standard errors. This
package has been created for low-throughput qPCR data analysis.
biocViews: qPCR, GeneExpression
Author: Le Pape Sylvain
Maintainer: Le Pape Sylvain <sylvain.le.pape@univ-poitiers.fr>
PackageStatus: Deprecated
Package: bigmemoryExtras
Version: 1.40.0
Depends: R (>= 2.12), bigmemory (>= 4.5.31)
Imports: methods
Suggests: testthat, BiocGenerics, BiocStyle, knitr
License: Artistic-2.0
OS_type: unix
Title: An extension of the bigmemory package with added safety,
convenience, and a factor class
Description: This package defines a "BigMatrix" ReferenceClass which
adds safety and convenience features to the
filebacked.big.matrix class from the bigmemory package.
BigMatrix protects against segfaults by monitoring and
gracefully restoring the connection to on-disk data and it also
protects against accidental data modification with a
filesystem-based permissions system. We provide utilities for
using BigMatrix-derived classes as assayData matrices within
the Biobase package's eSet family of classes. BigMatrix
provides some optimizations related to attaching to, and
indexing into, file-backed matrices with dimnames.
Additionally, the package provides a "BigMatrixFactor" class, a
file-backed matrix with factor properties.
biocViews: Infrastructure, DataRepresentation
Author: Peter M. Haverty
Maintainer: Peter M. Haverty <phaverty@gene.com>
URL: https://github.com/phaverty/bigmemoryExtras
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: dexus
Version: 1.32.0
Depends: R (>= 2.15), methods, BiocGenerics
Imports: stats
Suggests: parallel, statmod, DESeq, RColorBrewer
License: LGPL (>= 2.0)
Title: DEXUS - Identifying Differential Expression in RNA-Seq Studies
with Unknown Conditions or without Replicates
Description: DEXUS identifies differentially expressed genes in RNA-Seq
data under all possible study designs such as studies without
replicates, without sample groups, and with unknown conditions.
DEXUS works also for known conditions, for example for RNA-Seq
data with two or multiple conditions. RNA-Seq read count data
can be provided both by the S4 class Count Data Set and by read
count matrices. Differentially expressed transcripts can be
visualized by heatmaps, in which unknown conditions,
replicates, and samples groups are also indicated. This
software is fast since the core algorithm is written in C. For
very large data sets, a parallel version of DEXUS is provided
in this package. DEXUS is a statistical model that is selected
in a Bayesian framework by an EM algorithm. DEXUS does not need
replicates to detect differentially expressed transcripts,
since the replicates (or conditions) are estimated by the EM
method for each transcript. The method provides an
informative/non-informative value to extract differentially
expressed transcripts at a desired significance level or power.
biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression,
DifferentialExpression, CellBiology, Classification,
QualityControl
Author: Guenter Klambauer
Maintainer: Guenter Klambauer <klambauer@bioinf.jku.at>
PackageStatus: Deprecated
Package: RDAVIDWebService
Version: 1.30.0
Depends: R (>= 2.14.1), methods, graph, GOstats, ggplot2
Imports: Category, GO.db, RBGL, rJava
Suggests: Rgraphviz
License: GPL (>=2)
Title: An R Package for retrieving data from DAVID into R objects using
Web Services API.
Description: Tools for retrieving data from the Database for
Annotation, Visualization and Integrated Discovery (DAVID)
using Web Services into R objects. This package offers the main
functionalities of DAVID website including: i) user friendly
connectivity to upload gene/background list/s, change
gene/background position, select current specie/s, select
annotations, etc. ii) Reports of the submitted Gene List,
Annotation Category Summary, Gene/Term Clusters, Functional
Annotation Chart, Functional Annotation Table
biocViews: Visualization, DifferentialExpression, GraphAndNetwork
Author: Cristobal Fresno and Elmer A. Fernandez
Maintainer: Cristobal Fresno <cfresno@bdmg.com.ar>
URL: http://www.bdmg.com.ar, http://david.abcc.ncifcrf.gov/
PackageStatus: Deprecated
Package: CexoR
Version: 1.30.1
Depends: R (>= 2.10.0), S4Vectors, IRanges
Imports: Rsamtools, GenomeInfoDb, GenomicRanges, rtracklayer, idr,
RColorBrewer, genomation
Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown
License: Artistic-2.0 | GPL-2 + file LICENSE
Title: An R package to uncover high-resolution protein-DNA interactions
in ChIP-exo replicates
Description: Strand specific peak-pair calling in ChIP-exo replicates.
The cumulative Skellam distribution function (package
'skellam') is used to detect significant normalised count
differences of opposed sign at each DNA strand (peak-pairs).
Irreproducible discovery rate (IDR) for overlapping peak-pairs
across biological replicates is estimated using the package
'idr'.
biocViews: Transcription, Genetics, Sequencing
Author: Pedro Madrigal <bioinformatics.engineer@gmail.com>
Maintainer: Pedro Madrigal <pmadrigal@ebi.ac.uk>
PackageStatus: Deprecated
Package: ELBOW
Version: 1.28.0
Depends: R (>= 2.15.0)
Imports: graphics, stats, utils
Suggests: DESeq, GEOquery, limma, simpleaffy, affyPLM, RColorBrewer,
hgu133plus2cdf, hgu133plus2probe
License: file LICENSE
License_is_FOSS: yes
License_restricts_use: no
Title: ELBOW - Evaluating foLd change By the lOgit Way
Description: Elbow an improved fold change test that uses cluster
analysis and pattern recognition to set cut off limits that are
derived directly from intrareplicate variance without assuming
a normal distribution for as few as 2 biological replicates.
Elbow also provides the same consistency as fold testing in
cross platform analysis. Elbow has lower false positive and
false negative rates than standard fold testing when both are
evaluated using T testing and Statistical Analysis of
Microarray using 12 replicates (six replicates each for initial
and final conditions). Elbow provides a null value based on
initial condition replicates and gives error bounds for results
to allow better evaluation of significance.
biocViews: ImmunoOncology, Technology, Microarray, RNASeq, Sequencing,
Sequencing, Software, MultiChannel, OneChannel, TwoChannel,
GeneExpression
Author: Xiangli Zhang, Natalie Bjorklund, Graham Alvare, Tom Ryzdak,
Richard Sparling, Brian Fristensky
Maintainer: Graham Alvare <alvare@cc.umanitoba.ca>, Xiangli Zhang
<justinzhang.xl@gmail.com>
PackageStatus: Deprecated
Package: EDDA
Version: 1.30.0
Depends: Rcpp (>= 0.10.4),parallel,methods,ROCR,DESeq,baySeq,snow,edgeR
Imports: graphics, stats, utils, parallel, methods, ROCR, DESeq,
baySeq, snow, edgeR
LinkingTo: Rcpp
License: GPL (>= 2)
Title: Experimental Design in Differential Abundance analysis
Description: EDDA can aid in the design of a range of common
experiments such as RNA-seq, Nanostring assays, RIP-seq and
Metagenomic sequencing, and enables researchers to
comprehensively investigate the impact of experimental
decisions on the ability to detect differential abundance. This
work was published on 3 December 2014 at Genome Biology under
the title "The importance of study design for detecting
differentially abundant features in high-throughput
experiments" (http://genomebiology.com/2014/15/12/527).
biocViews: ImmunoOncology, Sequencing, ExperimentalDesign,
Normalization, RNASeq, ChIPSeq
Author: Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan
Maintainer: Chia Kuan Hui Burton <chiakhb@gis.a-star.edu.sg>, Niranjan
Nagarajan <nagarajann@gis.a-star.edu.sg>
URL: http://edda.gis.a-star.edu.sg/,
http://genomebiology.com/2014/15/12/527
PackageStatus: Deprecated
Package: Polyfit
Version: 1.26.0
Depends: DESeq
Suggests: BiocStyle
License: GPL (>= 3)
Title: Add-on to DESeq to improve p-values and q-values
Description: Polyfit is an add-on to the packages DESeq which ensures
the p-value distribution is uniform over the interval [0, 1]
for data satisfying the null hypothesis of no differential
expression, and uses an adpated Storey-Tibshiran method to
calculate q-values.
biocViews: ImmunoOncology, DifferentialExpression, Sequencing, RNASeq,
GeneExpression
Author: Conrad Burden
Maintainer: Conrad Burden <conrad.burden@anu.edu.au>
PackageStatus: Deprecated
Package: seqplots
Version: 1.30.0
Depends: R (>= 3.2.0)
Imports: methods, IRanges, BSgenome, digest, rtracklayer,
GenomicRanges, Biostrings, shiny (>= 0.13.0), DBI, RSQLite,
plotrix, fields, grid, kohonen, parallel, GenomeInfoDb, class,
S4Vectors, ggplot2, reshape2, gridExtra, jsonlite, DT (>=
0.1.0), RColorBrewer, Rsamtools, GenomicAlignments, BiocManager
Suggests: testthat, BiocStyle, knitr, rmarkdown, covr
License: GPL-3
Title: An interactive tool for visualizing NGS signals and sequence
motif densities along genomic features using average plots and
heatmaps
Description: SeqPlots is a tool for plotting next generation sequencing
(NGS) based experiments' signal tracks, e.g. reads coverage
from ChIP-seq, RNA-seq and DNA accessibility assays like
DNase-seq and MNase-seq, over user specified genomic features,
e.g. promoters, gene bodies, etc. It can also calculate
sequence motif density profiles from reference genome. The data
are visualized as average signal profile plot, with error
estimates (standard error and 95% confidence interval) shown as
fields, or as series of heatmaps that can be sorted and
clustered using hierarchical clustering, k-means algorithm and
self organising maps. Plots can be prepared using R programming
language or web browser based graphical user interface (GUI)
implemented using Shiny framework. The dual-purpose
implementation allows running the software locally on desktop
or deploying it on server. SeqPlots is useful for both for
exploratory data analyses and preparing replicable, publication
quality plots. Other features of the software include
collaboration and data sharing capabilities, as well as ability
to store pre-calculated result matrixes, that combine many
sequencing experiments and in-silico generated tracks with
multiple different features. These binaries can be further used
to generate new combination plots on fly, run automated batch
operations or share with colleagues, who can adjust their
plotting parameters without loading actual tracks and
recalculating numeric values. SeqPlots relays on Bioconductor
packages, mainly on rtracklayer for data input and BSgenome
packages for reference genome sequence and annotations.
biocViews: ImmunoOncology, ChIPSeq, RNASeq, Sequencing, Software,
Visualization
Author: Przemyslaw Stempor <ps562@cam.ac.uk>
Maintainer: Przemyslaw Stempor <ps562@cam.ac.uk>
URL: http://github.com/przemol/seqplots
VignetteBuilder: knitr
BugReports: http://github.com/przemol/seqplots/issues
PackageStatus: Deprecated
Package: simulatorZ
Version: 1.26.0
Depends: R (>= 3.5), Biobase, SummarizedExperiment, survival, CoxBoost,
BiocGenerics
Imports: graphics, stats, gbm, Hmisc, GenomicRanges, methods
Suggests: RUnit, BiocStyle, curatedOvarianData, parathyroidSE
License: Artistic-2.0
NeedsCompilation: yes
Title: Simulator for Collections of Independent Genomic Data Sets
Description: simulatorZ is a package intended primarily to simulate
collections of independent genomic data sets, as well as
performing training and validation with predicting algorithms.
It supports ExpressionSet and RangedSummarizedExperiment
objects.
biocViews: Survival
Author: Yuqing Zhang, Christoph Bernau, Levi Waldron
Maintainer: Yuqing Zhang <zhangyuqing.pkusms@gmail.com>
URL: https://github.com/zhangyuqing/simulatorZ
BugReports: https://github.com/zhangyuqing/simulatorZ
PackageStatus: Deprecated
Package: ToPASeq
Version: 1.26.0
Depends: R(>= 3.5.0), graphite
Imports: Rcpp, graph, methods, Biobase, RBGL, SummarizedExperiment,
gRbase, limma, corpcor
LinkingTo: Rcpp
Suggests: BiocStyle, airway, knitr, rmarkdown, DESeq2, DESeq, edgeR,
plotrix, breastCancerVDX, EnrichmentBrowser
License: AGPL-3
Title: Topology-based pathway analysis of RNA-seq data
Description: Implementation of methods for topology-based pathway
analysis of RNA-seq data. This includes Topological Analysis of
Pathway Phenotype Association (TAPPA; Gao and Wang, 2007),
PathWay Enrichment Analysis (PWEA; Hung et al., 2010), and the
Pathway Regulation Score (PRS; Ibrahim et al., 2012).
biocViews: ImmunoOncology, GeneExpression, RNASeq,
DifferentialExpression, GraphAndNetwork, Pathways,
NetworkEnrichment, Visualization
Author: Ivana Ihnatova, Eva Budinska, Ludwig Geistlinger
Maintainer: Ivana Ihnatova <ihnatova@iba.muni.cz>
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: mdgsa
Version: 1.24.0
Depends: R (>= 2.14)
Imports: AnnotationDbi, DBI, GO.db, KEGG.db, cluster, Matrix
Suggests: BiocStyle, knitr, rmarkdown, limma, ALL, hgu95av2.db, RUnit,
BiocGenerics
License: GPL
Title: Multi Dimensional Gene Set Analysis.
Description: Functions to preform a Gene Set Analysis in several
genomic dimensions. Including methods for miRNAs.
biocViews: GeneSetEnrichment, Annotation, Pathways, GO
Author: David Montaner <dmontaner@cipf.es>
Maintainer: David Montaner <dmontaner@cipf.es>
URL: https://github.com/dmontaner/mdgsa, http://www.dmontaner.com
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: RNAprobR
Version: 1.24.0
Depends: R (>= 3.1.1), GenomicFeatures(>= 1.16.3), plyr(>= 1.8.1),
BiocGenerics(>= 0.10.0)
Imports: Biostrings(>= 2.32.1), GenomicRanges(>= 1.16.4), IRanges(>=
2.10.5), Rsamtools(>= 1.16.1), rtracklayer(>= 1.24.2),
GenomicAlignments(>= 1.5.12), S4Vectors(>= 0.14.7), graphics,
stats, utils
Suggests: BiocStyle
License: GPL (>=2)
Title: An R package for analysis of massive parallel sequencing based
RNA structure probing data
Description: This package facilitates analysis of Next Generation
Sequencing data for which positional information with a single
nucleotide resolution is a key. It allows for applying
different types of relevant normalizations, data visualization
and export in a table or UCSC compatible bedgraph file.
biocViews: Coverage, Normalization, Sequencing, GenomeAnnotation
Author: Lukasz Jan Kielpinski <kielpinski@bio.ku.dk>, Nikos
Sidiropoulos <nikos.sidiro@gmail.com>, Jeppe Vinther
<jvinther@bio.ku.dk>
Maintainer: Nikos Sidiropoulos <nikos.sidiro@gmail.com>
PackageStatus: Deprecated
Package: ENCODExplorer
Version: 2.18.0
Depends: R (>= 3.6)
Imports: methods, tools, jsonlite, RCurl, tidyr, data.table, dplyr,
stringr, stringi, utils, AnnotationHub, GenomicRanges,
rtracklayer, S4Vectors, GenomeInfoDb, ENCODExplorerData
Suggests: RUnit,BiocGenerics,knitr, curl, httr, shiny, shinythemes, DT
License: Artistic-2.0
Title: A compilation of ENCODE metadata
Description: This package allows user to quickly access ENCODE project
files metadata and give access to helper functions to query the
ENCODE rest api, download ENCODE datasets and save the database
in SQLite format.
biocViews: Infrastructure, DataImport
Author: Charles Joly Beauparlant
<charles.joly-beauparlant@crchul.ulaval.ca>, Audrey Lemacon
<lemacon.audrey@ulaval.ca>, Eric Fournier
<Fournier.Eric.2@crchudequebec.ulaval.ca>, Louis Gendron
<louisg.212@gmail.com>, Astrid-Louise Deschenes
<astrid-louise.deschenes@crchudequebec.ulaval.ca>, Arnaud Droit
<arnaud.droit@crchudequebec.ulaval.ca>
Maintainer: Charles Joly Beauparlant
<charles.joly-beauparlant@crchul.ulaval.ca>
VignetteBuilder: knitr
BugReports: https://github.com/CharlesJB/ENCODExplorer/issues
Package: XBSeq
Version: 1.24.0
Depends: DESeq2, R (>= 3.3)
Imports: pracma, matrixStats, locfit, ggplot2, methods, Biobase, dplyr,
magrittr, roar
Suggests: knitr, DESeq, rmarkdown, BiocStyle, testthat
License: GPL (>=3)
Title: Test for differential expression for RNA-seq data
Description: We developed a novel algorithm, XBSeq, where a statistical
model was established based on the assumption that observed
signals are the convolution of true expression signals and
sequencing noises. The mapped reads in non-exonic regions are
considered as sequencing noises, which follows a Poisson
distribution. Given measureable observed and noise signals from
RNA-seq data, true expression signals, assuming governed by the
negative binomial distribution, can be delineated and thus the
accurate detection of differential expressed genes.
biocViews: ImmunoOncology, RNASeq, DifferentialExpression, Sequencing,
Software, ExperimentalDesign
Author: Yuanhang Liu
Maintainer: Yuanhang Liu <liuy12@uthscsa.edu>
URL: https://github.com/Liuy12/XBSeq
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: Imetagene
Version: 1.22.0
Depends: R (>= 3.2.0), metagene, shiny
Imports: d3heatmap, shinyBS, shinyFiles, shinythemes, ggplot2
Suggests: knitr, BiocStyle, rmarkdown
License: Artistic-2.0 | file LICENSE
NeedsCompilation: no
Title: A graphical interface for the metagene package
Description: This package provide a graphical user interface to the
metagene package. This will allow people with minimal R
experience to easily complete metagene analysis.
biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment,
Sequencing
Author: Audrey Lemacon <audrey.lemacon.1@ulaval.ca>, Charles Joly
Beauparlant <charles.joly-beauparlant@crchul.ulaval.ca>, Arnaud
Droit <arnaud.droit@crchuq.ulaval.ca>
Maintainer: Audrey Lemacon <audrey.lemacon.1@ulaval.ca>
VignetteBuilder: knitr
BugReports: https://github.com/andronekomimi/Imetagene/issues
PackageStatus: Deprecated
Package: metagenomeFeatures
Version: 2.12.0
Depends: R (>= 3.5), Biobase (>= 2.17.8)
Imports: Biostrings (>= 2.36.4), S4Vectors (>= 0.23.18), dplyr (>=
0.7.0), dbplyr(>= 1.0.0), stringr (>= 1.0.0), lazyeval (>=
0.1.10), RSQLite (>= 1.0.0), magrittr (>= 1.5), methods (>=
3.3.1), lattice (>= 0.20.33), ape (>= 3.5), DECIPHER (>= 2.4.0)
Suggests: knitr (>= 1.11), testthat (>= 0.10.0), rmarkdown (>= 1.3),
devtools (>= 1.13.5), ggtree(>= 1.8.2), BiocStyle (>= 2.8.2),
phyloseq (>= 1.24.2), forcats (>= 0.3.0), ggplot2 (>= 3.0.0)
License: Artistic-2.0
NeedsCompilation: no
Title: Exploration of marker-gene sequence taxonomic annotations
Description: metagenomeFeatures was developed for use in exploring the
taxonomic annotations for a marker-gene metagenomic sequence
dataset. The package can be used to explore the taxonomic
composition of a marker-gene database or annotated sequences
from a marker-gene metagenome experiment.
biocViews: ImmunoOncology, Microbiome, Metagenomics, Annotation,
Infrastructure, Sequencing, Software
Author: Nathan D. Olson, Joseph Nathaniel Paulson, Hector Corrada Bravo
Maintainer: Nathan D. Olson <nolson@umiacs.umd.edu>
URL: https://github.com/HCBravoLab/metagenomeFeatures
VignetteBuilder: knitr
BugReports: https://github.com/HCBravoLab/metagenomeFeatures/issues
PackageStatus: Deprecated
Package: samExploreR
Version: 1.16.0
Depends: ggplot2,Rsubread,RNAseqData.HNRNPC.bam.chr14,edgeR,R (>=
3.4.0)
Imports: grDevices, stats, graphics
Suggests: BiocStyle,RUnit,BiocGenerics,Matrix
License: GPL-3
Title: samExploreR package: high-performance read summarisation to
count vectors with avaliability of sequencing depth reduction
simulation
Description: This R package is designed for subsampling procedure to
simulate sequencing experiments with reduced sequencing depth.
This package can be used to anlayze data generated from all
major sequencing platforms such as Illumina GA, HiSeq, MiSeq,
Roche GS-FLX, ABI SOLiD and LifeTech Ion PGM Proton sequencers.
It supports multiple operating systems incluidng Linux, Mac OS
X, FreeBSD and Solaris. Was developed with usage of Rsubread.
biocViews: ImmunoOncology, Sequencing, SequenceMatching, RNASeq,
ChIPSeq, DNASeq, WholeGenome, GeneTarget, Alignment,
GeneExpression, GeneticVariability, GeneRegulation,
Preprocessing, GenomeAnnotation, Software
Author: Alexey Stupnikov, Shailesh Tripathi and Frank Emmert-Streib
Maintainer: shailesh tripathi <shailesh.tripathy@gmail.com>
PackageStatus: Deprecated
Package: POST
Version: 1.16.0
Depends: R (>= 3.4.0)
Imports: stats, CompQuadForm, Matrix, survival, Biobase, GSEABase
License: GPL (>= 2)
Title: Projection onto Orthogonal Space Testing for High Dimensional
Data
Description: Perform orthogonal projection of high dimensional data of
a set, and statistical modeling of phenotye with projected
vectors as predictor.
biocViews: Microarray, GeneExpression
Author: Xueyuan Cao <xueyuan.cao@stjude.org> and Stanley.pounds
<stanley.pounds@stjude.org>
Maintainer: Xueyuan Cao <xueyuan.cao@stjude.org>
PackageStatus: Deprecated
Package: cytofast
Version: 1.8.0
Depends: R (>= 3.6.0)
Imports: flowCore, ggplot2, ggridges, RColorBrewer, reshape2, stats,
grDevices, Rdpack, methods, grid, FlowSOM
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
Title: cytofast - A quick visualization and analysis tool for CyTOF
data
Description: Multi-parametric flow and mass cytometry allows
exceptional high-resolution exploration of the cellular
composition of the immune system. Together with tools like
FlowSOM and Cytosplore it is possible to identify novel cell
types. By introducing cytofast we hope to offer a workflow for
visualization and quantification of cell clusters for an
efficient discovery of cell populations associated with
diseases or other clinical outcomes.
biocViews: FlowCytometry, Visualization, Clustering
Author: K.A. Stam <k.a.stam@hotmail.com>, G. Beyrend
<G.Beyrend@lumc.nl>
Maintainer: K.A. Stam <k.a.stam@hotmail.com>
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: CoRegFlux
Version: 1.8.0
Depends: R (>= 3.6)
Imports: CoRegNet, sybil
Suggests: glpkAPI, testthat, knitr, rmarkdown, digest, R.cache,
ggplot2, plyr, igraph, methods, latex2exp,
rBayesianOptimization
License: GPL-3
Title: CoRegFlux
Description: CoRegFlux aims at providing tools to integrate reverse
engineered gene regulatory networks and gene-expression into
metabolic models to improve prediction of phenotypes, both for
metabolic engineering, through transcription factor or gene
(TF) knock-out or overexpression in various conditions as well
as to improve our understanding of the interactions and cell
inner-working.
biocViews:
GeneRegulation,Network,SystemsBiology,GeneExpression,Transcription,GenePrediction
Author: Pauline Trébulle, Daniel Trejo-Banos, Mohamed Elati
Maintainer: Pauline Trébulle and Mohamed Elati <coregflux@gmail.com>
SystemRequirements: GLPK (>= 4.42)
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: HCABrowser
Version: 1.8.0
Depends: R (>= 3.6.0), dplyr, AnVIL
Imports: utils, methods, tibble, BiocFileCache, googleAuthR, httr,
jsonlite, readr, rlang
Suggests: BiocStyle, knitr, rmarkdown, stringr, testthat
License: Artistic-2.0
Title: Browse the Human Cell Atlas data portal
Description: Search, browse, reference, and download resources from the
Human Cell Atlas data portal. Development of this package is
supported through funds from the Chan / Zuckerberg initiative.
biocViews: DataImport, Sequencing, SingleCell
Author: Daniel Van Twisk <Daniel.VanTwisk@RoswellPark.org>, Martin
Morgan <Martin.Morgan@RoswellPark.org>, Bioconductor Package
Maintainer <maintainer@bioconductor.org>
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://github.com/Bioconductor/HCABrowser
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/HCABrowser/issues
PackageStatus: Deprecated
Package: CrossICC
Version: 1.6.0
Depends: R (>= 3.5), MASS
Imports: data.table, methods, MergeMaid, ConsensusClusterPlus, limma,
cluster, dplyr, Biobase, grDevices, stats, graphics, utils
Suggests: rmarkdown, testthat, knitr, shiny, shinydashboard,
shinyWidgets, shinycssloaders, DT, ggthemes, ggplot2, pheatmap,
RColorBrewer, tibble, ggalluvial
License: GPL-3 | file LICENSE
Title: An Interactive Consensus Clustering Framework for Multi-platform
Data Analysis
Description: CrossICC utilizes an iterative strategy to derive the
optimal gene set and cluster number from consensus similarity
matrix generated by consensus clustering and it is able to deal
with multiple cross platform datasets so that requires no
between-dataset normalizations. This package also provides
abundant functions for visualization and identifying subtypes
of cancer. Specially, many cancer-related analysis methods are
embedded to facilitate the clinical translation of the
identified cancer subtypes.
biocViews: Software, GeneExpression, DifferentialExpression, GUI,
GeneSetEnrichment, Classification, Clustering,
FeatureExtraction, Survival, Microarray, RNASeq, BatchEffect,
Normalization, Preprocessing, Visualization
Author: Yu Sun <suny226@mail2.sysu.edu.cn>, Qi Zhao
<zhaoqi@sysucc.org.cn>
Maintainer: Yu Sun <suny226@mail2.sysu.edu.cn>
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: HCAExplorer
Version: 1.6.0
Depends: R (>= 3.6.0), dplyr
Imports: BiocFileCache, HCAMatrixBrowser, S4Vectors, LoomExperiment,
vctrs, curl, httr, jsonlite, methods, plyr, readr, rlang,
tibble, tidygraph, utils, xml2
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0)
License: Artistic-2.0
Title: Browse the Human Cell Atlas data portal
Description: Search, browse, reference, and download resources from the
Human Cell Atlas data portal. Development of this package is
supported through funds from the Chan / Zuckerberg initiative.
biocViews: DataImport, Sequencing
Author: Daniel Van Twisk <Daniel.VanTwisk@RoswellPark.org>, Martin
Morgan <Martin.Morgan@RoswellPark.org>, Bioconductor Package
Maintainer <maintainer@bioconductor.org>
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://github.com/Bioconductor/HCABrowser
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/HCABrowser/issues
PackageStatus: Deprecated
Package: HCAMatrixBrowser
Version: 1.2.0
Depends: R (>= 4.0.0), AnVIL
Imports: BiocFileCache, digest, dplyr, httr, jsonlite, Matrix, methods,
progress, rlang, SingleCellExperiment, stats, utils
Suggests: BiocStyle, knitr, HCABrowser, LoomExperiment (>= 1.5.3),
readr
License: Artistic-2.0
Title: Extract and manage matrix data from the Human Cell Atlas project
Description: The HCAMatrixBrowser queries the HCA matrix endpoint to
download expression data and returns a standard Bioconductor
object. It uses the LoomExperiment package to serve matrix data
that is downloaded as HDF5 loom format.
biocViews: Infrastructure, DataRepresentation, Software
Author: Marcel Ramos <marcel.ramos@roswellpark.org>, Martin Morgan
<mtmorgan.bioc@gmail.com>
Maintainer: Marcel Ramos <marcel.ramos@roswellpark.org>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/HCAMatrixBrowser
PackageStatus: Deprecated
Package: MouseFM
Version: 1.2.0
Depends: R (>= 4.0.0)
Imports: httr, curl, GenomicRanges, dplyr, ggplot2, reshape2, scales,
gtools, tidyr, data.table, jsonlite, rlist, GenomeInfoDb,
methods, biomaRt, stats, IRanges
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: GPL-3
Title: In-silico methods for genetic finemapping in inbred mice
Description: This package provides methods for genetic finemapping in
inbred mice by taking advantage of their very high homozygosity
rate (>95%).
biocViews: Genetics, SNP, GeneTarget, VariantAnnotation,
GenomicVariation, MultipleComparison, SystemsBiology,
MathematicalBiology, PatternLogic, GenePrediction,
BiomedicalInformatics, FunctionalGenomics
Author: Matthias Munz <matthias.munz@gmx.de>, Inken Wohlers
<inken.wohlers@uni-luebeck.de.de>, Hauke Busch
<hauke.busch@uni-luebeck.de.de>
Maintainer: Matthias Munz <matthias.munz@gmx.de>
VignetteBuilder: knitr
BugReports: https://github.com/matmu/MouseFM/issues