If you wish, you can view this vignette online here.
regionReport (Collado-Torres, Jaffe, and Leek, 2015) creates HTML reports
styled with knitrBootstrap (Hester, 2013) for
a set of regions such as derfinder (Collado-Torres, Frazee, Love, Irizarry, et al., 2015) results.
This package includes a basic exploration for a general set of genomic regions which can be easily customized to include the appropriate conclusions and/or further exploration of the results. Such a report can be generated using renderReport(). regionReport has a separate template for running a basic exploration analysis of derfinder
results by using derfinderReport(). Both reports are written in R Markdown
format and include all the code for making the plots and explorations in the report itself. For both reports, regionReport relies on
knitr (Xie, 2014), rmarkdown
(Allaire, Cheng, Xie, McPherson, et al., 2015), and knitrBootstrap
(Hester, 2013) for generating the report.
regionReportThe plots in regionReport are powered by derfinderPlot (Collado-Torres, Jaffe, and Leek, 2015), ggbio (Yin, Cook, and Lawrence, 2012), and ggplot2 (Wickham, 2009).
The regionReport supplementary website regionReportSupp has examples of using regionReport with results from DiffBind, bumphunter, and derfinder. These represent different uses of regionReport for results from ChIP-seq, methylation, and RNA-seq data. In particular, the DiffBind example illustrates how to expand a basic report created with renderReport().
For a general use case, you first have to identify a set of genomic regions of interest and store it as a GRanges object. In a typical workflow you will have some variables measured for each of the regions, such as p-values and scores. renderReport() uses the set of regions and three main arguments:
pvalueVars: this is a character vector (named optionally) with the names of the variables that are bound between 0 and 1, such as p-values. For each of these variables, renderReport() explores the distribution by chromosome, the overall distribution, and makes a table with commonly used cutoffs.densityVars: is another character vector (named optionally) with another set of variables you wish to explore by making density graphs. This is commonly used for scores and other similar numerical variables.significantVar: is a logical vector separating the regions into by whether they are statistically significant. For example, this information is used to explore the width of all the regions and compare it the significant ones.Other parameters control the name of the report, where it'll be located, the transcripts database used to annotate the nearest genes, graphical parameters, etc.
Here is a short example of how to use renderReport(). Note that we are using regions produced by derfinder just for convenience sake. You can also run this example by using example('renderReport', 'regionReport', ask=FALSE).
## Load derfinder
library('derfinder')
regions <- genomeRegions$regions
## Assign chr length
library('GenomicRanges')
seqlengths(regions) <- c('chr21' = 48129895)
## The output will be saved in the 'derfinderReport-example' directory
dir.create('renderReport-example', showWarnings = FALSE, recursive = TRUE)
## Generate the HTML report
report <- renderReport(regions, 'Example run', pvalueVars = c(
'Q-values' = 'qvalues', 'P-values' = 'pvalues'), densityVars = c(
'Area' = 'area', 'Mean coverage' = 'meanCoverage'),
significantVar = regions$qvalues <= 0.05, nBestRegions = 20,
outdir = 'renderReport-example')
derfinder casederfinderPrior to using regionReport::derfinderReport() you must use derfinder to analyze a specific
data set. While there are many ways to do so, we recommend using
analyzeChr() with the same prefix argument. Then merging the results with
mergeResults().
Below, we run derfinder for the example data included in the package. The
steps are:
## Load derfinder
library('derfinder')
## The output will be saved in the 'report' directory
dir.create('report', showWarnings = FALSE, recursive = TRUE)
The following code runs derfinder.
## Save the current path initialPath <- getwd() setwd(file.path(initialPath, 'report')) ## Generate output from derfinder ## Collapse the coverage information collapsedFull <- collapseFullCoverage(list(genomeData$coverage), verbose=TRUE) ## Calculate library size adjustments sampleDepths <- sampleDepth(collapsedFull, probs=c(0.5), nonzero=TRUE, verbose=TRUE) ## Build the models group <- genomeInfo$pop adjustvars <- data.frame(genomeInfo$gender) models <- makeModels(sampleDepths, testvars=group, adjustvars=adjustvars) ## Analyze chromosome 21 analysis <- analyzeChr(chr='21', coverageInfo=genomeData, models=models, cutoffFstat=1, cutoffType='manual', seeds=20140330, groupInfo=group, mc.cores=1, writeOutput=TRUE, returnOutput=TRUE) ## Save the stats options for later optionsStats <- analysis$optionsStats ## Change the directory back to the original one setwd(initialPath)
For convenience, we have included the derfinder results as part of
regionReport. Note that the above functions are routinely checked as part
of derfinder.
## Copy previous results
file.copy(system.file(file.path('extdata', 'chr21'), package='derfinder',
mustWork=TRUE), 'report', recursive=TRUE)
## [1] TRUE
Next, proceed to merging the results.
## Merge the results from the different chromosomes. In this case, there's
## only one: chr21
mergeResults(chrs = 'chr21', prefix = 'report',
genomicState = genomicState$fullGenome)
Once the derfinder output has been generated and merged, use
derfinderReport() to create the HTML report.
## Load derfindeReport
library('regionReport')
## Generate the HTML report
report <- derfinderReport(prefix='report', browse=FALSE,
nBestRegions=15, makeBestClusters=TRUE, outdir='html',
fullCov=list('21'=genomeDataRaw$coverage), optionsStats=optionsStats)
Once the output is generated, you can browse the report from R using
browseURL() as shown below.
## Browse the report browseURL(report)
You can compare the resulting report with the pre-compiled report using the following code.
browseURL(system.file(file.path('basicExploration', 'basicExploration.html'),
package = 'regionReport', mustWork = TRUE))
Note that the reports require an active Internet connection to render correctly.
The report is self-explanatory and will change some of the text depending on the input options.
If the report is taking too long to compile (say more than 3 hours), you might
want to consider setting nBestCluters to a small number or even set
makeBestClusters to FALSE.
If you are interested in using the advanced arguments, use derfinder::advancedArg() as shown below:
## URLs to advanced arguemtns
derfinder::advancedArg('derfinderReport', package = 'regionReport',
browse = FALSE)
## Set browse = TRUE if you want to open them in your browser
In particular, you might be interested in specifying the output_format argument in either renderReport() or derfinderReport(). For example, setting output_format = 'pdf_document' will generate a PDF file instead. However, you will lose interactivity for toggling hiding/showing code and the tables will be static.
This package was made possible thanks to:
(R Core Team, 2015)(Collado-Torres, Frazee, Love, Irizarry, et al., 2015)(Collado-Torres, Jaffe, and Leek, 2015)(Wickham and Chang, 2015)(Arora, Morgan, Carlson, and Pages, 2015)(Lawrence, Huber, Pagès, Aboyoun, et al., 2013)(Yin, Cook, and Lawrence, 2012)(Wickham, 2009)(R Core Team, 2015)(Auguie, 2012)(Lawrence, Huber, Pagès, Aboyoun, et al., 2013)(Boettiger, 2015)(Xie, 2014)(Hester, 2013)(Neuwirth, 2014)(Allaire, Cheng, Xie, McPherson, et al., 2015)(Yin, Lawrence, and Cook, 2015)(Urbanek and Horner, 2014)(Carlson, 2015)(Jaffe, Murakami, Lee, Leek, et al., 2012)Code for creating the vignette
## Create the vignette
library('knitrBootstrap')
knitrBootstrapFlag <- packageVersion('knitrBootstrap') < '1.0.0'
if(knitrBootstrapFlag) {
## CRAN version
system.time(knit_bootstrap('regionReport.Rmd', chooser=c('boot', 'code'), show_code = TRUE))
unlink('regionReport.md')
} else {
## GitHub version
library('rmarkdown')
system.time(render('regionReport.Rmd',
'knitrBootstrap::bootstrap_document'))
}
## Note: if you prefer the knitr version use:
# library('rmarkdown')
# system.time(render('regionReport.Rmd', 'html_document'))
## Extract the R code
library('knitr')
knit('regionReport.Rmd', tangle = TRUE)
## Copy report output to be distributed with the package for comparison
## purposes
if(gsub('.*/', '', getwd()) == 'realVignettes') {
file.copy(file.path('report', 'html', 'basicExploration.html'),
file.path('..', '..', 'inst', 'basicExploration',
'basicExploration.html'), overwrite=TRUE)
} else {
file.copy(file.path('report', 'html', 'basicExploration.html'),
file.path('..', 'inst', 'basicExploration', 'basicExploration.html'),
overwrite=TRUE)
}
## Clean up
file.remove('regionReportRef.bib')
#unlink('regionReport_files', recursive=TRUE)
unlink('report', recursive = TRUE)
Date the vignette was generated.
## [1] "2015-06-09 22:37:02 PDT"
Wallclock time spent generating the vignette.
## Time difference of 52.559 secs
R session information.
## setting value ## version R version 3.2.1 beta (2015-06-08 r68489) ## system x86_64, linux-gnu ## ui X11 ## language en_US: ## collate C ## tz <NA>
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This vignette was generated using knitrBootstrap (Hester, 2013)
with knitr (Xie, 2014) and rmarkdown (Allaire, Cheng, Xie, McPherson, et al., 2015) running behind the scenes.
Citations made with knitcitations (Boettiger, 2015).
[1] J. Allaire, J. Cheng, Y. Xie, J. McPherson, et al. rmarkdown: Dynamic Documents for R. R package version 0.6.1. 2015. URL: http://CRAN.R-project.org/package=rmarkdown.
[2] S. Arora, M. Morgan, M. Carlson and H. Pages. GenomeInfoDb: Utilities for manipulating chromosome and other 'seqname' identifiers. R package version 1.4.0. 2015.
[3] B. Auguie. gridExtra: functions in Grid graphics. R package version 0.9.1. 2012. URL: http://CRAN.R-project.org/package=gridExtra.
[4] C. Boettiger. knitcitations: Citations for Knitr Markdown Files. R package version 1.0.6. 2015. URL: http://CRAN.R-project.org/package=knitcitations.
[5] M. Carlson. TxDb.Hsapiens.UCSC.hg19.knownGene: Annotation package for TxDb object(s). R package version 3.1.2. 2015.
[6] L. Collado-Torres, A. C. Frazee, M. I. Love, R. A. Irizarry, et al. “derfinder: Software for annotation-agnostic RNA-seq differential expression analysis”. In: bioRxiv (2015). DOI: 10.1101/015370. URL: http://www.biorxiv.org/content/early/2015/02/19/015370.abstract.
[7] L. Collado-Torres, A. E. Jaffe and J. T. Leek. derfinderPlot: Plotting functions for derfinder. https://github.com/lcolladotor/derfinderPlot - R package version 1.2.0. 2015. URL: http://www.bioconductor.org/packages/release/bioc/html/derfinderPlot.html.
[8] L. Collado-Torres, A. E. Jaffe and J. T. Leek. regionReport: Generate HTML reports for exploring a set of regions. https://github.com/leekgroup/regionReport - R package version 1.2.1. 2015. URL: http://www.bioconductor.org/packages/release/bioc/html/regionReport.html.
[9] J. Hester. knitrBootstrap: Knitr Bootstrap framework. R package version 0.9.0. 2013. URL: http://CRAN.R-project.org/package=knitrBootstrap.
[10] A. E. Jaffe, P. Murakami, H. Lee, J. T. Leek, et al. “Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies”. In: International journal of epidemiology 41.1 (2012), pp. 200–209. DOI: 10.1093/ije/dyr238.
[11] M. Lawrence, W. Huber, H. Pagès, P. Aboyoun, et al. “Software for Computing and Annotating Genomic Ranges”. In: PLoS Computational Biology 9 (8 2013). DOI: 10.1371/journal.pcbi.1003118. URL: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118}.
[12] E. Neuwirth. RColorBrewer: ColorBrewer Palettes. R package version 1.1-2. 2014. URL: http://CRAN.R-project.org/package=RColorBrewer.
[13] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2015. URL: http://www.R-project.org/.
[14] S. Urbanek and J. Horner. Cairo: R graphics device using cairo graphics library for creating high-quality bitmap (PNG, JPEG, TIFF), vector (PDF, SVG, PostScript) and display (X11 and Win32) output. R package version 1.5-6. 2014. URL: http://CRAN.R-project.org/package=Cairo.
[15] H. Wickham. ggplot2: elegant graphics for data analysis. Springer New York, 2009. ISBN: 978-0-387-98140-6. URL: http://had.co.nz/ggplot2/book.
[16] H. Wickham and W. Chang. devtools: Tools to Make Developing R Packages Easier. R package version 1.8.0. 2015. URL: http://CRAN.R-project.org/package=devtools.
[17] Y. Xie. “knitr: A Comprehensive Tool for Reproducible Research in R”. In: Implementing Reproducible Computational Research. Ed. by V. Stodden, F. Leisch and R. D. Peng. ISBN 978-1466561595. Chapman and Hall/CRC, 2014. URL: http://www.crcpress.com/product/isbn/9781466561595.
[18] T. Yin, D. Cook and M. Lawrence. “ggbio: an R package for extending the grammar of graphics for genomic data”. In: Genome Biology 13.8 (2012), p. R77.
[19] T. Yin, M. Lawrence and D. Cook. biovizBase: Basic graphic utilities for visualization of genomic data. R package version 1.16.0. 2015.