R is an open-source statistical environment which can be
easily modified to enhance its functionality via packages. regionReport
is a R package available via the Bioconductor
repository for packages. R can be installed on any
operating system from CRAN
after which you can install regionReport
by using the following commands in your R session:
regionReport is based on many other packages and in particular in those that have implemented the infrastructure needed for dealing with RNA-seq data. That is, packages like GenomicFeatures that allow you to import the data and DESeq2 for generating differential expression results. A regionReport user is not expected to deal with those packages directly.
If you are asking yourself the question “Where do I start using Bioconductor?” you might be interested in this blog post.
As package developers, we try to explain clearly how to use our
packages and in which order to use the functions. But R and
Bioconductor have a steep learning curve so it is critical
to learn where to ask for help. The blog post quoted above mentions some
but we would like to highlight the Bioconductor support site
as the main resource for getting help: remember to use the
regionReport tag and check the older
posts. Other alternatives are available such as creating GitHub
issues and tweeting. However, please note that if you want to receive
help you should adhere to the posting
guidelines. It is particularly critical that you provide a small
reproducible example and your session information so package developers
can track down the source of the error.
We hope that regionReport will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!
## To cite package 'regionReport' in publications use:
##
## Collado-Torres L, Jaffe AE, Leek JT (2016). "regionReport: Interactive reports for region-level and
## feature-level genomic analyses [version2; referees: 2 approved, 1 approved with reservations]."
## _F1000Research_, *4*, 105. doi:10.12688/f1000research.6379.2
## <https://doi.org/10.12688/f1000research.6379.2>, <http://f1000research.com/articles/4-105/v2>.
##
## Collado-Torres L, Nellore A, Frazee AC, Wilks C, Love MI, Langmead B, Irizarry RA, Leek JT, Jaffe AE
## (2017). "Flexible expressed region analysis for RNA-seq with derfinder." _Nucl. Acids Res._.
## doi:10.1093/nar/gkw852 <https://doi.org/10.1093/nar/gkw852>,
## <http://nar.oxfordjournals.org/content/early/2016/09/29/nar.gkw852>.
##
## Collado-Torres L, Jaffe AE, Leek JT (2017). _regionReport: Generate HTML or PDF reports for a set of
## genomic regions or DESeq2/edgeR results_. doi:10.18129/B9.bioc.regionReport
## <https://doi.org/10.18129/B9.bioc.regionReport>, https://github.com/leekgroup/regionReport - R package
## version 1.41.0, <http://www.bioconductor.org/packages/regionReport>.
##
## To see these entries in BibTeX format, use 'print(<citation>, bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.regionReport (Collado-Torres, Jaffe, and Leek, 2016) creates HTML or PDF reports for a set of genomic regions such as derfinder (Collado-Torres, Nellore, Frazee, Wilks, Love, Langmead, Irizarry, Leek, and Jaffe, 2017) results or for feature-level analyses performed with DESeq2 (Love, Huber, and Anders, 2014) or edgeR (Chen, Chen, Lun, Baldoni, and Smyth, 2024; Chen, Lun, and Smyth, 2016; Chen, Lun, and Smyth, 2014). The HTML reports are styled with rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2024) by default but can optionally be styled with knitrBootstrap (Hester, 2024).
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().
derfinderReport() is specific to single base-level approach
derfinder
results. A third template is included for exploring DESeq2 or
edgeR
differential expression results.
All reports are written in R
Markdown format and include all the code for making the plots and
explorations in the report itself. For all templates, regionReport
relies on knitr (Xie,
2014), rmarkdown
(Allaire, Xie, Dervieux et al., 2024), DT (Xie, Cheng, and
Tan, 2024) and optionally knitrBootstrap
(Hester, 2024) for generating the report. The reports can be either in
HTML or PDF format and can be easily customized.
The plots in regionReport for exploring DESeq2 are powered by ggplot2 (Wickham, 2016) and pheatmap (Kolde, 2019).
The regionReport supplementary website regionReportSupp has examples of using regionReport with DESeq2 results. In particular, please look at DESeq2.html which has the code for generating some DESeq2 results based on the DESeq2 vignette. Then it uses those results to create HTML and PDF versions of the same report. The resulting reports are available in the following locations:
Note that in both examples we changed the ggplot2 theme
to theme_bw(). Also, in the PDF version we used the option
device = 'pdf' instead of the default
device = 'png' in DESeq2Report() since PDF
figures are more appropriate for PDF reports: they look better than PNG
figures.
If you want to create a similar HTML report as the one linked in this
section, simply run
example('DESeq2Report', 'regionReport', ask=FALSE). The
only difference will be the ggplot2 theme
for the plots.
regionReport
has the edgeReport() function that takes as input a
DGEList object and the results from the differential
expression analysis using edgeR.
edgeReport() internally uses DEFormats
to convert the results to DESeq2’s
format and then uses DESeqReport() to create the final
report. The report looks nearly the same whether you performed the
differential expression analysis with DESeq2 or
edgeR in
order to make more homogenous the exploratory data analysis step.
The regionReport supplementary website regionReportSupp has examples of using regionReport with edgeR results. In particular, please look at edgeR.html which has the code for generating some random data with DEFormats and performing the differential expression analysis with edgeR. Then it uses those results to create HTML and PDF versions of the same report. The resulting reports are available in the following locations:
Note that in both examples we changed the ggplot2 theme
to theme_linedraw(). Also, in the PDF version we used the
option device = 'pdf' instead of the default
device = 'png' in edgeReport() since PDF
figures are more appropriate for PDF reports: they look better than PNG
figures.
If you want to create a similar HTML report as the one linked in this
section, simply run
example('edgeReport', 'regionReport', ask=FALSE). The only
difference will be the ggplot2 theme
for the plots and the amount of data simulated with DEFormats.
The plots in regionReport for region reports are powered by derfinderPlot (Collado-Torres, Jaffe, and Leek, 2017), ggbio (Yin, Cook, and Lawrence, 2012), and ggplot2 (Wickham, 2016).
The regionReport
supplementary website regionReportSupp
has examples of using regionReport
with results from DiffBind
and derfinder.
Included as a vignette, this package also has an example using a small
data set derived from bumphunter.
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.
## 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"
)See the report created by this example here.
For derfinder
results created via the expressed regions-level approach you can use
renderReport() to explore the results. If you use DESeq2 to
perform the differential expression analysis of the expressed regions
you can then use DESeq2Report().
Prior 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(). This is the recommended pipeline for
the single base-level approach.
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 "derfinderReport-example" directory
dir.create("derfinderReport-example", showWarnings = FALSE, recursive = TRUE)The following code runs derfinder.
## Save the current path
initialPath <- getwd()
setwd(file.path(initialPath, "derfinderReport-example"))
## 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
), "derfinderReport-example", recursive = TRUE)## [1] TRUENext, proceed to merging the results.
## Merge the results from the different chromosomes. In this case, there's
## only one: chr21
mergeResults(
chrs = "chr21", prefix = "derfinderReport-example",
genomicState = genomicState$fullGenome
)## 2024-10-31 04:50:53.320706 mergeResults: Saving options used## 2024-10-31 04:50:53.321979 Loading chromosome chr21## Neither 'cutoffFstatUsed' nor 'optionsStats' were supplied, so the FWER calculation step will be skipped.## 2024-10-31 04:50:53.395837 mergeResults: Saving fullNullSummary## 2024-10-31 04:50:53.396714 mergeResults: Re-calculating the p-values## 2024-10-31 04:50:53.50146 mergeResults: Saving fullRegions## 2024-10-31 04:50:53.502743 mergeResults: assigning genomic states## 2024-10-31 04:50:53.606714 annotateRegions: counting## 2024-10-31 04:50:53.660347 annotateRegions: annotating## 2024-10-31 04:50:53.678666 mergeResults: Saving fullAnnotatedRegions## 2024-10-31 04:50:53.679539 mergeResults: Saving fullFstats## 2024-10-31 04:50:53.680254 mergeResults: Saving fullTime## Load optionsStats
load(file.path("derfinderReport-example", "chr21", "optionsStats.Rdata"), verbose = TRUE)## Loading objects:
## optionsStatsOnce the derfinder output has been generated and merged, use derfinderReport() to create the HTML report.
## Generate the HTML report
report <- derfinderReport(
prefix = "derfinderReport-example", browse = FALSE,
nBestRegions = 15, makeBestClusters = TRUE,
fullCov = list("21" = genomeDataRaw$coverage), optionsStats = optionsStats
)Once the output is generated, you can browse the report from
R using browseURL() as shown below.
You can view a pre-compiled version of this report here.
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.
This package was made possible thanks to:
Code for creating the vignette
## Create the vignette
library("rmarkdown")
system.time(render("regionReport.Rmd", "BiocStyle::html_document"))
## Extract the R code
library("knitr")
knit("regionReport.Rmd", tangle = TRUE)Date the vignette was generated.
## [1] "2024-10-31 04:50:53 UTC"Wallclock time spent generating the vignette.
## Time difference of 1.048 secsR session information.
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.1 (2024-06-14)
## os Ubuntu 24.04.1 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate C
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2024-10-31
## pandoc 3.2.1 @ /usr/local/bin/ (via rmarkdown)
##
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## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────This vignette was generated using BiocStyle (Oleś, 2024) with knitr (Xie, 2014) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
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