Back to Multiple platform build/check report for BioC 3.20:   simplified   long
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This page was generated on 2024-08-07 11:45 -0400 (Wed, 07 Aug 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.4.1 (2024-06-14) -- "Race for Your Life" 4701
palomino8Windows Server 2022 Datacenterx644.4.1 (2024-06-14 ucrt) -- "Race for Your Life" 4400
lconwaymacOS 12.7.1 Montereyx86_644.4.1 (2024-06-14) -- "Race for Your Life" 4469
kjohnson3macOS 13.6.5 Venturaarm644.4.1 (2024-06-14) -- "Race for Your Life" 4418
kunpeng2Linux (openEuler 22.03 LTS-SP1)aarch644.4.1 (2024-06-14) -- "Race for Your Life" 4411
Click on any hostname to see more info about the system (e.g. compilers)      (*) as reported by 'uname -p', except on Windows and Mac OS X

Package 1950/2254HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
singleCellTK 2.15.0  (landing page)
Joshua David Campbell
Snapshot Date: 2024-08-06 14:00 -0400 (Tue, 06 Aug 2024)
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: devel
git_last_commit: 4d7a515
git_last_commit_date: 2024-04-30 11:06:02 -0400 (Tue, 30 Apr 2024)
nebbiolo2Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino8Windows Server 2022 Datacenter / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
lconwaymacOS 12.7.1 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson3macOS 13.6.5 Ventura / arm64  OK    OK    OK    OK  UNNEEDED, same version is already published
kunpeng2Linux (openEuler 22.03 LTS-SP1) / aarch64  OK    OK    OK  


CHECK results for singleCellTK on kjohnson3

To the developers/maintainers of the singleCellTK package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/singleCellTK.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results


Summary

Package: singleCellTK
Version: 2.15.0
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.15.0.tar.gz
StartedAt: 2024-08-07 00:52:03 -0400 (Wed, 07 Aug 2024)
EndedAt: 2024-08-07 00:57:39 -0400 (Wed, 07 Aug 2024)
EllapsedTime: 336.7 seconds
RetCode: 0
Status:   OK  
CheckDir: singleCellTK.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.15.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/singleCellTK.Rcheck’
* using R version 4.4.1 (2024-06-14)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Ventura 13.6.7
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.15.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... NOTE
  installed size is  6.8Mb
  sub-directories of 1Mb or more:
    extdata   1.5Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) dedupRowNames.Rd:10: Lost braces
    10 | \item{x}{A matrix like or /linkS4class{SingleCellExperiment} object, on which
       |                                       ^
checkRd: (-1) dedupRowNames.Rd:14: Lost braces
    14 | /linkS4class{SingleCellExperiment} object. When set to \code{TRUE}, will
       |             ^
checkRd: (-1) dedupRowNames.Rd:22: Lost braces
    22 | By default, a matrix or /linkS4class{SingleCellExperiment} object
       |                                     ^
checkRd: (-1) dedupRowNames.Rd:24: Lost braces
    24 | When \code{x} is a /linkS4class{SingleCellExperiment} and \code{as.rowData}
       |                                ^
checkRd: (-1) plotBubble.Rd:42: Lost braces
    42 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runClusterSummaryMetrics.Rd:27: Lost braces
    27 | \item{scale}{Option to scale the data. Default: /code{FALSE}. Selected assay will not be scaled.}
       |                                                      ^
checkRd: (-1) runEmptyDrops.Rd:66: Lost braces
    66 | provided \\linkS4class{SingleCellExperiment} object.
       |                       ^
checkRd: (-1) runSCMerge.Rd:44: Lost braces
    44 | construct pseudo-replicates. The length of code{kmeansK} needs to be the same
       |                                                ^
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
plotDoubletFinderResults 13.737  0.062  13.807
runDoubletFinder         12.739  0.040  12.786
plotScDblFinderResults   12.511  0.214  12.746
runScDblFinder            8.496  0.128   8.623
importExampleData         7.037  0.565   8.076
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 3 NOTEs
See
  ‘/Users/biocbuild/bbs-3.20-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.


Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library’
* installing *source* package ‘singleCellTK’ ...
** using staged installation
** R
** data
** exec
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (singleCellTK)

Tests output

singleCellTK.Rcheck/tests/spelling.Rout


R version 4.4.1 (2024-06-14) -- "Race for Your Life"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> if (requireNamespace('spelling', quietly = TRUE))
+   spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE)
NULL
> 
> proc.time()
   user  system elapsed 
  0.069   0.019   0.086 

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.4.1 (2024-06-14) -- "Race for Your Life"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(singleCellTK)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

The following objects are masked from 'package:matrixStats':

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
    union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:utils':

    findMatches

The following objects are masked from 'package:base':

    I, expand.grid, unname

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

The following objects are masked from 'package:matrixStats':

    anyMissing, rowMedians

Loading required package: SingleCellExperiment
Loading required package: DelayedArray
Loading required package: Matrix

Attaching package: 'Matrix'

The following object is masked from 'package:S4Vectors':

    expand

Loading required package: S4Arrays
Loading required package: abind

Attaching package: 'S4Arrays'

The following object is masked from 'package:abind':

    abind

The following object is masked from 'package:base':

    rowsum

Loading required package: SparseArray

Attaching package: 'DelayedArray'

The following objects are masked from 'package:base':

    apply, scale, sweep


Attaching package: 'singleCellTK'

The following object is masked from 'package:BiocGenerics':

    plotPCA

> 
> test_check("singleCellTK")
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 0 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 1 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 100%

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  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Uploading data to Enrichr... Done.
  Querying HDSigDB_Human_2021... Done.
Parsing results... Done.
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
No annotation package name available in the input data object.
Attempting to directly match identifiers in data to gene sets.
Estimating GSVA scores for 34 gene sets.
Estimating ECDFs with Gaussian kernels

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No annotation package name available in the input data object.
Attempting to directly match identifiers in data to gene sets.
Estimating GSVA scores for 2 gene sets.
Estimating ECDFs with Gaussian kernels

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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 390
Number of edges: 9849

Running Louvain algorithm...
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 21 | SKIP 0 | PASS 224 ]

[ FAIL 0 | WARN 21 | SKIP 0 | PASS 224 ]
> 
> proc.time()
   user  system elapsed 
 99.488   2.051 104.313 

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

nameusersystemelapsed
MitoGenes0.0000.0010.002
SEG0.0010.0020.003
calcEffectSizes0.0560.0020.058
combineSCE0.4480.0050.453
computeZScore0.0950.0030.098
convertSCEToSeurat1.4780.0551.532
convertSeuratToSCE0.1380.0020.140
dedupRowNames0.3630.0010.364
detectCellOutlier2.0640.0412.112
diffAbundanceFET0.0220.0010.023
discreteColorPalette0.0020.0000.003
distinctColors0.0010.0010.001
downSampleCells0.2230.0260.249
downSampleDepth0.1570.0110.167
expData-ANY-character-method0.0780.0020.080
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method0.0880.0020.091
expData-set0.0850.0030.088
expData0.0810.0080.089
expDataNames-ANY-method0.0820.0030.085
expDataNames0.0750.0010.076
expDeleteDataTag0.0140.0010.016
expSetDataTag0.0100.0010.011
expTaggedData0.0110.0010.012
exportSCE0.0100.0020.012
exportSCEtoAnnData0.0420.0010.043
exportSCEtoFlatFile0.0410.0020.043
featureIndex0.0140.0010.015
generateSimulatedData0.0190.0010.021
getBiomarker0.0210.0020.023
getDEGTopTable0.2450.0150.260
getDiffAbundanceResults0.0190.0000.020
getEnrichRResult0.1220.0181.526
getFindMarkerTopTable0.8460.0100.855
getMSigDBTable0.0020.0010.002
getPathwayResultNames0.0110.0010.011
getSampleSummaryStatsTable0.0830.0010.084
getSoupX0.0000.0000.001
getTSCANResults0.4800.0100.489
getTopHVG0.3660.0070.373
importAnnData0.0010.0000.000
importBUStools0.0700.0010.072
importCellRanger0.3050.0090.316
importCellRangerV2Sample0.0720.0010.074
importCellRangerV3Sample0.1170.0040.122
importDropEst0.0900.0010.091
importExampleData7.0370.5658.076
importGeneSetsFromCollection0.2410.0240.266
importGeneSetsFromGMT0.0240.0020.026
importGeneSetsFromList0.0400.0020.043
importGeneSetsFromMSigDB0.9280.0400.976
importMitoGeneSet0.0190.0030.022
importOptimus0.0010.0000.001
importSEQC0.0720.0010.073
importSTARsolo0.0860.0020.090
iterateSimulations0.1090.0030.112
listSampleSummaryStatsTables0.1220.0020.124
mergeSCEColData0.2080.0070.215
mouseBrainSubsetSCE0.0180.0020.021
msigdb_table0.0010.0010.002
plotBarcodeRankDropsResults0.2760.0060.281
plotBarcodeRankScatter0.2690.0030.272
plotBatchCorrCompare4.5910.0384.634
plotBatchVariance0.0970.0080.106
plotBcdsResults3.0590.0793.138
plotBubble0.2880.0170.306
plotClusterAbundance0.2270.0040.231
plotCxdsResults2.4780.0422.531
plotDEGHeatmap0.9360.0340.971
plotDEGRegression1.0760.0161.092
plotDEGViolin1.2860.0501.341
plotDEGVolcano0.3380.0040.348
plotDecontXResults2.9990.0313.030
plotDimRed0.0840.0020.086
plotDoubletFinderResults13.737 0.06213.807
plotEmptyDropsResults2.1200.0042.125
plotEmptyDropsScatter2.1520.0032.156
plotFindMarkerHeatmap1.3670.0081.374
plotMASTThresholdGenes0.4850.0100.504
plotPCA0.1500.0020.153
plotPathway0.2550.0040.261
plotRunPerCellQCResults0.6100.0050.618
plotSCEBarAssayData0.0660.0020.069
plotSCEBarColData0.0620.0030.065
plotSCEBatchFeatureMean0.0590.0010.060
plotSCEDensity0.0660.0020.069
plotSCEDensityAssayData0.0530.0030.055
plotSCEDensityColData0.0640.0020.065
plotSCEDimReduceColData0.1930.0040.196
plotSCEDimReduceFeatures0.1480.0040.151
plotSCEHeatmap0.1690.0020.171
plotSCEScatter0.0990.0030.102
plotSCEViolin0.0710.0020.074
plotSCEViolinAssayData0.0920.0030.096
plotSCEViolinColData0.0710.0020.073
plotScDblFinderResults12.511 0.21412.746
plotScanpyDotPlot0.0130.0010.014
plotScanpyEmbedding0.0120.0010.012
plotScanpyHVG0.0140.0020.017
plotScanpyHeatmap0.0120.0000.013
plotScanpyMarkerGenes0.0110.0000.012
plotScanpyMarkerGenesDotPlot0.0110.0010.011
plotScanpyMarkerGenesHeatmap0.0110.0010.012
plotScanpyMarkerGenesMatrixPlot0.0110.0010.012
plotScanpyMarkerGenesViolin0.0110.0010.012
plotScanpyMatrixPlot0.0110.0010.012
plotScanpyPCA0.0110.0000.012
plotScanpyPCAGeneRanking0.0110.0000.013
plotScanpyPCAVariance0.0110.0010.011
plotScanpyViolin0.0110.0010.011
plotScdsHybridResults3.5280.0623.648
plotScrubletResults0.0110.0010.013
plotSeuratElbow0.0110.0010.011
plotSeuratHVG0.0110.0020.012
plotSeuratJackStraw0.0110.0000.012
plotSeuratReduction0.0110.0010.012
plotSoupXResults000
plotTSCANClusterDEG1.4990.0101.512
plotTSCANClusterPseudo0.6460.0110.664
plotTSCANDimReduceFeatures0.6530.0060.664
plotTSCANPseudotimeGenes0.6430.0070.652
plotTSCANPseudotimeHeatmap0.6970.0070.704
plotTSCANResults0.6340.0070.641
plotTSNE0.1500.0040.159
plotTopHVG0.1730.0060.179
plotUMAP2.7160.0162.731
readSingleCellMatrix0.0020.0000.002
reportCellQC0.0490.0020.051
reportDropletQC0.0110.0010.013
reportQCTool0.0490.0010.051
retrieveSCEIndex0.0130.0000.014
runBBKNN000
runBarcodeRankDrops0.1230.0020.126
runBcds1.1830.0401.223
runCellQC0.0500.0020.052
runClusterSummaryMetrics0.1770.0040.181
runComBatSeq0.1510.0050.156
runCxds0.1530.0050.158
runCxdsBcdsHybrid0.5960.0250.622
runDEAnalysis0.2140.0050.218
runDecontX2.5880.0062.595
runDimReduce0.1200.0020.122
runDoubletFinder12.739 0.04012.786
runDropletQC0.0120.0020.014
runEmptyDrops2.0430.0032.046
runEnrichR0.1140.0161.300
runFastMNN0.5190.0140.533
runFeatureSelection0.0730.0020.074
runFindMarker0.8900.0120.902
runGSVA0.9920.0161.009
runHarmony0.0090.0000.011
runKMeans0.1200.0030.123
runLimmaBC0.0200.0010.020
runMNNCorrect0.1670.0020.169
runModelGeneVar0.1240.0020.125
runNormalization0.8700.0110.880
runPerCellQC0.1400.0030.143
runSCANORAMA000
runSCMerge0.0010.0000.002
runScDblFinder8.4960.1288.623
runScanpyFindClusters0.0120.0010.014
runScanpyFindHVG0.0110.0010.012
runScanpyFindMarkers0.0100.0010.011
runScanpyNormalizeData0.0570.0010.059
runScanpyPCA0.0120.0020.014
runScanpyScaleData0.0110.0020.013
runScanpyTSNE0.0110.0010.012
runScanpyUMAP0.0100.0020.012
runScranSNN0.2080.0050.213
runScrublet0.0120.0030.015
runSeuratFindClusters0.0110.0010.012
runSeuratFindHVG0.2220.0170.239
runSeuratHeatmap0.0110.0010.013
runSeuratICA0.0110.0010.012
runSeuratJackStraw0.0110.0010.012
runSeuratNormalizeData0.0110.0000.012
runSeuratPCA0.0110.0000.012
runSeuratSCTransform2.1660.0532.263
runSeuratScaleData0.0110.0010.013
runSeuratUMAP0.0100.0010.011
runSingleR0.0100.0010.012
runSoupX000
runTSCAN0.4010.0210.422
runTSCANClusterDEAnalysis0.4290.0160.444
runTSCANDEG0.4060.0060.410
runTSNE0.3240.0130.337
runUMAP2.7070.0302.758
runVAM0.1610.0020.163
runZINBWaVE0.0020.0000.002
sampleSummaryStats0.0860.0020.088
scaterCPM0.0590.0020.061
scaterPCA0.1960.0120.209
scaterlogNormCounts0.0920.0060.099
sce0.0120.0020.013
sctkListGeneSetCollections0.0290.0010.051
sctkPythonInstallConda000
sctkPythonInstallVirtualEnv000
selectSCTKConda000
selectSCTKVirtualEnvironment000
setRowNames0.0310.0070.037
setSCTKDisplayRow0.1400.0090.153
singleCellTK000
subDiffEx0.1820.0100.191
subsetSCECols0.0540.0020.056
subsetSCERows0.1190.0030.121
summarizeSCE0.0280.0020.029
trimCounts0.0850.0050.090