1 Basics

1.1 Install derfinderPlot

R is an open-source statistical environment which can be easily modified to enhance its functionality via packages. derfinderPlot 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 derfinderPlot by using the following commands in your R session:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("derfinderPlot")

## Check that you have a valid Bioconductor installation
BiocManager::valid()

1.2 Required knowledge

derfinderPlot is based on many other packages and in particular in those that have implemented the infrastructure needed for dealing with RNA-seq data. A derfinderPlot user is not expected to deal with those packages directly but will need to be familiar with derfinder and for some plots with ggbio.

If you are asking yourself the question “Where do I start using Bioconductor?” you might be interested in this blog post.

1.3 Asking for help

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 derfinder or derfinderPlot tags 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.

1.4 Citing derfinderPlot

We hope that derfinderPlot will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!

## Citation info
citation("derfinderPlot")
## To cite package 'derfinderPlot' in publications use:
## 
##   Collado-Torres L, Jaffe AE, Leek JT (2017). _derfinderPlot: Plotting
##   functions for derfinder_. doi:10.18129/B9.bioc.derfinderPlot
##   <https://doi.org/10.18129/B9.bioc.derfinderPlot>,
##   https://github.com/leekgroup/derfinderPlot - R package version
##   1.44.0, <http://www.bioconductor.org/packages/derfinderPlot>.
## 
##   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>.
## 
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.

2 Introduction to derfinderPlot

derfinderPlot (Collado-Torres, Jaffe, and Leek, 2017) is an addon package for derfinder (Collado-Torres, Nellore, Frazee, Wilks, Love, Langmead, Irizarry, Leek, and Jaffe, 2017) with functions that allow you to visualize the results.

While the functions in derfinderPlot assume you generated the data with derfinder, they can be used with other GRanges objects properly formatted.

The functions in derfinderPlot are:

  • plotCluster() is a tailored ggbio (Yin, Cook, and Lawrence, 2012) plot that shows all the regions in a cluster (defined by distance). It shows the base-level coverage for each sample as well as the mean for each group. If these regions overlap any known gene, the gene and the transcript annotation is displayed.
  • plotOverview() is another tailored ggbio (Yin, Cook, and Lawrence, 2012) plot showing an overview of the whole genome. This plot can be useful to observe if the regions are clustered in a subset of a chromosome. It can also be used to check whether the regions match predominantly one part of the gene structure (for example, 3’ overlaps).
  • plotRegionCoverage() is a fast plotting function using R base graphics that shows the base-level coverage for each sample inside a specific region of the genome. If the region overlaps any known gene or intron, the information is displayed. Optionally, it can display the known transcripts. This function is most likely the easiest to use with GRanges objects from other packages.

3 Example

As an example, we will analyze a small subset of the samples from the BrainSpan Atlas of the Human Brain (BrainSpan, 2011) publicly available data.

We first load the required packages.

## Load libraries
suppressPackageStartupMessages(library("derfinder"))
library("derfinderData")
library("derfinderPlot")

3.1 Analyze data

For this example, we created a small table with the relevant phenotype data for 12 samples: 6 from fetal samples and 6 from adult samples. We chose at random a brain region, in this case the primary auditory cortex (core) and for the example we will only look at data from chromosome 21. Other variables include the age in years and the gender. The data is shown below.

library("knitr")
## Get pheno table
pheno <- subset(brainspanPheno, structure_acronym == "A1C")

## Display the main information
p <- pheno[, -which(colnames(pheno) %in% c(
    "structure_acronym",
    "structure_name", "file"
))]
rownames(p) <- NULL
kable(p, format = "html", row.names = TRUE)
gender lab Age group
1 M HSB114.A1C -0.5192308 fetal
2 M HSB103.A1C -0.5192308 fetal
3 M HSB178.A1C -0.4615385 fetal
4 M HSB154.A1C -0.4615385 fetal
5 F HSB150.A1C -0.5384615 fetal
6 F HSB149.A1C -0.5192308 fetal
7 F HSB130.A1C 21.0000000 adult
8 M HSB136.A1C 23.0000000 adult
9 F HSB126.A1C 30.0000000 adult
10 M HSB145.A1C 36.0000000 adult
11 M HSB123.A1C 37.0000000 adult
12 F HSB135.A1C 40.0000000 adult

We can load the data from derfinderData (Collado-Torres, Jaffe, and Leek, 2025) by first identifying the paths to the BigWig files with derfinder::rawFiles() and then loading the data with derfinder::fullCoverage().

## Determine the files to use and fix the names
files <- rawFiles(system.file("extdata", "A1C", package = "derfinderData"),
    samplepatt = "bw", fileterm = NULL
)
names(files) <- gsub(".bw", "", names(files))

## Load the data from disk
system.time(fullCov <- fullCoverage(files = files, chrs = "chr21"))
## 2025-10-29 23:27:13.289329 fullCoverage: processing chromosome chr21
## 2025-10-29 23:27:13.311467 loadCoverage: finding chromosome lengths
## 2025-10-29 23:27:13.347618 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB103.bw
## 2025-10-29 23:27:13.587473 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB114.bw
## 2025-10-29 23:27:13.805728 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB123.bw
## 2025-10-29 23:27:14.003213 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB126.bw
## 2025-10-29 23:27:14.135551 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB130.bw
## 2025-10-29 23:27:14.308494 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB135.bw
## 2025-10-29 23:27:14.457081 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB136.bw
## 2025-10-29 23:27:14.587541 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB145.bw
## 2025-10-29 23:27:14.742242 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB149.bw
## 2025-10-29 23:27:14.921135 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB150.bw
## 2025-10-29 23:27:15.054479 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB154.bw
## 2025-10-29 23:27:15.247315 loadCoverage: loading BigWig file /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/derfinderData/extdata/A1C/HSB178.bw
## 2025-10-29 23:27:15.448322 loadCoverage: applying the cutoff to the merged data
## 2025-10-29 23:27:15.496017 filterData: originally there were 48129895 rows, now there are 48129895 rows. Meaning that 0 percent was filtered.
##    user  system elapsed 
##   2.132   0.125   2.297

Alternatively, since the BigWig files are publicly available from BrainSpan (see here), we can extract the relevant coverage data using derfinder::fullCoverage(). Note that as of rtracklayer 1.25.16 BigWig files are not supported on Windows: you can find the fullCov object inside derfinderData to follow the examples.

## Determine the files to use and fix the names
files <- pheno$file
names(files) <- gsub(".A1C", "", pheno$lab)

## Load the data from the web
system.time(fullCov <- fullCoverage(files = files, chrs = "chr21"))

Once we have the base-level coverage data for all 12 samples, we can construct the models. In this case, we want to find differences between fetal and adult samples while adjusting for gender and a proxy of the library size.

## Get some idea of the library sizes
sampleDepths <- sampleDepth(collapseFullCoverage(fullCov), 1)
## 2025-10-29 23:27:16.023607 sampleDepth: Calculating sample quantiles
## 2025-10-29 23:27:16.163739 sampleDepth: Calculating sample adjustments
## Define models
models <- makeModels(sampleDepths,
    testvars = pheno$group,
    adjustvars = pheno[, c("gender")]
)

Next, we can find candidate differentially expressed regions (DERs) using as input the segments of the genome where at least one sample has coverage greater than 3. In this particular example, we chose a low theoretical F-statistic cutoff and used 20 permutations.

## Filter coverage
filteredCov <- lapply(fullCov, filterData, cutoff = 3)
## 2025-10-29 23:27:16.552088 filterData: originally there were 48129895 rows, now there are 90023 rows. Meaning that 99.81 percent was filtered.
## Perform differential expression analysis
suppressPackageStartupMessages(library("bumphunter"))
system.time(results <- analyzeChr(
    chr = "chr21", filteredCov$chr21,
    models, groupInfo = pheno$group, writeOutput = FALSE,
    cutoffFstat = 5e-02, nPermute = 20, seeds = 20140923 + seq_len(20)
))
## 2025-10-29 23:27:17.879779 analyzeChr: Pre-processing the coverage data
## 2025-10-29 23:27:19.580193 analyzeChr: Calculating statistics
## 2025-10-29 23:27:19.58555 calculateStats: calculating the F-statistics
## 2025-10-29 23:27:20.842582 analyzeChr: Calculating pvalues
## 2025-10-29 23:27:20.843509 analyzeChr: Using the following theoretical cutoff for the F-statistics 5.31765507157871
## 2025-10-29 23:27:20.845527 calculatePvalues: identifying data segments
## 2025-10-29 23:27:20.855622 findRegions: segmenting information
## 2025-10-29 23:27:20.876285 findRegions: identifying candidate regions
## 2025-10-29 23:27:20.947789 findRegions: identifying region clusters
## 2025-10-29 23:27:21.124541 calculatePvalues: calculating F-statistics for permutation 1 and seed 20140924
## 2025-10-29 23:27:21.273858 findRegions: segmenting information
## 2025-10-29 23:27:21.288362 findRegions: identifying candidate regions
## 2025-10-29 23:27:21.363784 calculatePvalues: calculating F-statistics for permutation 2 and seed 20140925
## 2025-10-29 23:27:21.537557 findRegions: segmenting information
## 2025-10-29 23:27:21.550977 findRegions: identifying candidate regions
## 2025-10-29 23:27:21.602854 calculatePvalues: calculating F-statistics for permutation 3 and seed 20140926
## 2025-10-29 23:27:21.715391 findRegions: segmenting information
## 2025-10-29 23:27:21.732926 findRegions: identifying candidate regions
## 2025-10-29 23:27:21.791125 calculatePvalues: calculating F-statistics for permutation 4 and seed 20140927
## 2025-10-29 23:27:21.949868 findRegions: segmenting information
## 2025-10-29 23:27:21.967055 findRegions: identifying candidate regions
## 2025-10-29 23:27:22.012374 calculatePvalues: calculating F-statistics for permutation 5 and seed 20140928
## 2025-10-29 23:27:22.173373 findRegions: segmenting information
## 2025-10-29 23:27:22.19059 findRegions: identifying candidate regions
## 2025-10-29 23:27:22.241857 calculatePvalues: calculating F-statistics for permutation 6 and seed 20140929
## 2025-10-29 23:27:22.403128 findRegions: segmenting information
## 2025-10-29 23:27:22.42066 findRegions: identifying candidate regions
## 2025-10-29 23:27:22.483951 calculatePvalues: calculating F-statistics for permutation 7 and seed 20140930
## 2025-10-29 23:27:22.625462 findRegions: segmenting information
## 2025-10-29 23:27:22.646233 findRegions: identifying candidate regions
## 2025-10-29 23:27:22.706149 calculatePvalues: calculating F-statistics for permutation 8 and seed 20140931
## 2025-10-29 23:27:22.858527 findRegions: segmenting information
## 2025-10-29 23:27:22.875024 findRegions: identifying candidate regions
## 2025-10-29 23:27:22.932827 calculatePvalues: calculating F-statistics for permutation 9 and seed 20140932
## 2025-10-29 23:27:23.05889 findRegions: segmenting information
## 2025-10-29 23:27:23.077052 findRegions: identifying candidate regions
## 2025-10-29 23:27:23.140059 calculatePvalues: calculating F-statistics for permutation 10 and seed 20140933
## 2025-10-29 23:27:23.298166 findRegions: segmenting information
## 2025-10-29 23:27:23.313619 findRegions: identifying candidate regions
## 2025-10-29 23:27:23.373119 calculatePvalues: calculating F-statistics for permutation 11 and seed 20140934
## 2025-10-29 23:27:23.523105 findRegions: segmenting information
## 2025-10-29 23:27:23.543177 findRegions: identifying candidate regions
## 2025-10-29 23:27:23.600941 calculatePvalues: calculating F-statistics for permutation 12 and seed 20140935
## 2025-10-29 23:27:23.762425 findRegions: segmenting information
## 2025-10-29 23:27:23.780438 findRegions: identifying candidate regions
## 2025-10-29 23:27:23.83458 calculatePvalues: calculating F-statistics for permutation 13 and seed 20140936
## 2025-10-29 23:27:23.979398 findRegions: segmenting information
## 2025-10-29 23:27:23.995063 findRegions: identifying candidate regions
## 2025-10-29 23:27:24.040939 calculatePvalues: calculating F-statistics for permutation 14 and seed 20140937
## 2025-10-29 23:27:24.186671 findRegions: segmenting information
## 2025-10-29 23:27:24.20831 findRegions: identifying candidate regions
## 2025-10-29 23:27:24.266113 calculatePvalues: calculating F-statistics for permutation 15 and seed 20140938
## 2025-10-29 23:27:24.399649 findRegions: segmenting information
## 2025-10-29 23:27:24.417245 findRegions: identifying candidate regions
## 2025-10-29 23:27:24.474074 calculatePvalues: calculating F-statistics for permutation 16 and seed 20140939
## 2025-10-29 23:27:24.624662 findRegions: segmenting information
## 2025-10-29 23:27:24.648038 findRegions: identifying candidate regions
## 2025-10-29 23:27:24.702993 calculatePvalues: calculating F-statistics for permutation 17 and seed 20140940
## 2025-10-29 23:27:24.861006 findRegions: segmenting information
## 2025-10-29 23:27:24.879344 findRegions: identifying candidate regions
## 2025-10-29 23:27:24.924938 calculatePvalues: calculating F-statistics for permutation 18 and seed 20140941
## 2025-10-29 23:27:25.066109 findRegions: segmenting information
## 2025-10-29 23:27:25.086249 findRegions: identifying candidate regions
## 2025-10-29 23:27:25.143303 calculatePvalues: calculating F-statistics for permutation 19 and seed 20140942
## 2025-10-29 23:27:25.280404 findRegions: segmenting information
## 2025-10-29 23:27:25.294417 findRegions: identifying candidate regions
## 2025-10-29 23:27:25.350249 calculatePvalues: calculating F-statistics for permutation 20 and seed 20140943
## 2025-10-29 23:27:25.515663 findRegions: segmenting information
## 2025-10-29 23:27:25.533078 findRegions: identifying candidate regions
## 2025-10-29 23:27:25.619628 calculatePvalues: calculating the p-values
## 2025-10-29 23:27:25.707703 analyzeChr: Annotating regions
## No annotationPackage supplied. Trying org.Hs.eg.db.
## Loading required package: org.Hs.eg.db
## Loading required package: AnnotationDbi
## 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")'.
## 
## Getting TSS and TSE.
## Getting CSS and CSE.
## Warning in .set_group_names(grl, use.names, txdb, by): some group names are NAs
## or duplicated
## Getting exons.
## Warning in .set_group_names(grl, use.names, txdb, by): some group names are NAs
## or duplicated
## Annotating genes.
## ...
##    user  system elapsed 
##  83.677   1.805  85.901
## Quick access to the results
regions <- results$regions$regions

## Annotation database to use
suppressPackageStartupMessages(library("TxDb.Hsapiens.UCSC.hg19.knownGene"))
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene

3.2 plotOverview()

Now that we have obtained the main results using derfinder, we can proceed to visualizing the results using derfinderPlot. The easiest to use of all the functions is plotOverview() which takes a set of regions and annotation information produced by bumphunter::matchGenes().

Figure 1 shows the candidate DERs colored by whether their q-value was less than 0.10 or not.

## Q-values overview
plotOverview(regions = regions, annotation = results$annotation, type = "qval")
## 2025-10-29 23:28:44.102183 plotOverview: assigning chromosome lengths from hg19!
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
Location of the DERs in the genome. This plot is was designed for many chromosomes but only one is shown here for simplicity.

Figure 1: Location of the DERs in the genome
This plot is was designed for many chromosomes but only one is shown here for simplicity.

Figure 2 shows the candidate DERs colored by the type of gene feature they are nearest too.

## Annotation overview
plotOverview(
    regions = regions, annotation = results$annotation,
    type = "annotation"
)
## 2025-10-29 23:28:46.232345 plotOverview: assigning chromosome lengths from hg19!
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
Location of the DERs in the genome and colored by annotation class. This plot is was designed for many chromosomes but only one is shown here for simplicity.

Figure 2: Location of the DERs in the genome and colored by annotation class
This plot is was designed for many chromosomes but only one is shown here for simplicity.

In this particular example, because we are only using data from one chromosome the above plot is not as informative as in a real case scenario. However, with this plot we can quickly observe that nearly all of the candidate DERs are inside an exon.

3.3 plotRegionCoverage()

The complete opposite of visualizing the candidate DERs at the genome-level is to visualize them one region at a time. plotRegionCoverage() allows us to do this quickly for a large number of regions.

Before using this function, we need to process more detailed information using two derfinder functions: annotateRegions() and getRegionCoverage() as shown below.

## Get required information for the plots
annoRegs <- annotateRegions(regions, genomicState$fullGenome)
## 2025-10-29 23:28:49.238831 annotateRegions: counting
## 2025-10-29 23:28:49.336383 annotateRegions: annotating
regionCov <- getRegionCoverage(fullCov, regions)
## 2025-10-29 23:28:49.483657 getRegionCoverage: processing chr21
## 2025-10-29 23:28:49.5335 getRegionCoverage: done processing chr21

Once we have the relevant information we can proceed to plotting the first 10 regions. In this case, we will supply plotRegionCoverage() with the information it needs to plot transcripts overlapping these 10 regions (Figures ??, ??, ??, ??, ??, ??, ??, ??, ??, ??).

## Plot top 10 regions
plotRegionCoverage(
    regions = regions, regionCoverage = regionCov,
    groupInfo = pheno$group, nearestAnnotation = results$annotation,
    annotatedRegions = annoRegs, whichRegions = 1:10, txdb = txdb, scalefac = 1,
    ask = FALSE, verbose = FALSE
)
Base-pair resolution plot of differentially expressed region 1.

Figure 3: Base-pair resolution plot of differentially expressed region 1

Base-pair resolution plot of differentially expressed region 2.

Figure 4: Base-pair resolution plot of differentially expressed region 2

Base-pair resolution plot of differentially expressed region 3.

Figure 5: Base-pair resolution plot of differentially expressed region 3

Base-pair resolution plot of differentially expressed region 4.

Figure 6: Base-pair resolution plot of differentially expressed region 4

Base-pair resolution plot of differentially expressed region 5.

Figure 7: Base-pair resolution plot of differentially expressed region 5

Base-pair resolution plot of differentially expressed region 6.

Figure 8: Base-pair resolution plot of differentially expressed region 6

Base-pair resolution plot of differentially expressed region 7.

Figure 9: Base-pair resolution plot of differentially expressed region 7

Base-pair resolution plot of differentially expressed region 8.

Figure 10: Base-pair resolution plot of differentially expressed region 8

Base-pair resolution plot of differentially expressed region 9.

Figure 11: Base-pair resolution plot of differentially expressed region 9

Base-pair resolution plot of differentially expressed region 10.

Figure 12: Base-pair resolution plot of differentially expressed region 10

The base-level coverage is shown in a log2 scale with any overlapping exons shown in dark blue and known introns in light blue.

3.4 plotCluster()

In this example, we noticed with the plotRegionCoverage() plots that most of the candidate DERs are contained in known exons. Sometimes, the signal might be low or we might have used very stringent cutoffs in the derfinder analysis. One way we can observe this is by plotting clusters of regions where a cluster is defined as regions within 300 bp (default option) of each other.

To visualize the clusters, we can use plotCluster() which takes similar input to plotOverview() with the notable addition of the coverage information as well as the idx argument. This argument specifies which region to focus on: it will be plotted with a red bar and will determine the cluster to display.

In Figure 13 we observe one large candidate DER with other nearby ones that do not have a q-value less than 0.10. In a real analysis, we would probably discard this region as the coverage is fairly low.

## First cluster
plotCluster(
    idx = 1, regions = regions, annotation = results$annotation,
    coverageInfo = fullCov$chr21, txdb = txdb, groupInfo = pheno$group,
    titleUse = "pval"
)
## Parsing transcripts...
## Parsing exons...
## Parsing cds...
## Parsing utrs...
## ------exons...
## ------cdss...
## ------introns...
## ------utr...
## aggregating...
## Done
## Constructing graphics...
Cluster plot for cluster 1 using ggbio.

Figure 13: Cluster plot for cluster 1 using ggbio

The second cluster (Figure 14) shows a larger number of potential DERs (again without q-values less than 0.10) in a segment of the genome where the coverage data is highly variable. This is a common occurrence with RNA-seq data.

## Second cluster
plotCluster(
    idx = 2, regions = regions, annotation = results$annotation,
    coverageInfo = fullCov$chr21, txdb = txdb, groupInfo = pheno$group,
    titleUse = "pval"
)
## Parsing transcripts...
## Parsing exons...
## Parsing cds...
## Parsing utrs...
## ------exons...
## ------cdss...
## ------introns...
## ------utr...
## aggregating...
## Done
## Constructing graphics...
## Warning in !vapply(ggl, fixed, logical(1L)) & !vapply(PlotList, is, "Ideogram",
## : longer object length is not a multiple of shorter object length
## Warning in scale_y_continuous(trans = log2_trans()): log-2 transformation
## introduced infinite values.
Cluster plot for cluster 2 using ggbio.

Figure 14: Cluster plot for cluster 2 using ggbio

These plots show an ideogram which helps quickly identify which region of the genome we are focusing on. Then, the base-level coverage information for each sample is displayed in log2. Next, the coverage group means are shown in the log2 scale. The plot is completed with the potential and candidate DERs as well as any known transcripts.

3.5 vennRegions

derfinder has functions for annotating regions given their genomic state. A typical visualization is to then view how many regions overlap known exons, introns, intergenic regions, none of them or several of these groups in a venn diagram. The function vennRegions() makes this plot using the output from derfinder::annotateRegions() as shown in Figure 15.

## Make venn diagram
venn <- vennRegions(annoRegs)
Venn diagram of regions by annotation class.

Figure 15: Venn diagram of regions by annotation class

## It returns the actual venn counts information
venn
##   exon intergenic intron Counts
## 1    0          0      0      0
## 2    0          0      1      2
## 3    0          1      0      4
## 4    0          1      1      0
## 5    1          0      0    259
## 6    1          0      1     35
## 7    1          1      0      0
## 8    1          1      1      0
## attr(,"class")
## [1] "VennCounts"

4 Reproducibility

This package was made possible thanks to:

  • R (R Core Team, 2025)
  • GenomeInfoDb (Arora, Morgan, Carlson, and Pagès, 2017)
  • GenomicRanges (Lawrence, Huber, Pagès, Aboyoun, Carlson, Gentleman, Morgan, and Carey, 2013)
  • ggbio (Yin, Cook, and Lawrence, 2012)
  • ggplot2 (Wickham, 2016)
  • IRanges (Lawrence, Huber, Pagès et al., 2013)
  • plyr (Wickham, 2011)
  • RColorBrewer (Neuwirth, 2022)
  • reshape2 (Wickham, 2007)
  • scales (Wickham, Pedersen, and Seidel, 2025)
  • biovizBase (Yin, Lawrence, and Cook, 2025)
  • bumphunter (Jaffe, Murakami, Lee, Leek, Fallin, Feinberg, and Irizarry, 2012) and (Jaffe, Murakami, Lee, Leek, Fallin, Feinberg, and Irizarry, 2012)
  • derfinder (Collado-Torres, Nellore, Frazee et al., 2017)
  • derfinderData (Collado-Torres, Jaffe, and Leek, 2025)
  • sessioninfo (Wickham, Chang, Flight, Müller, and Hester, 2025)
  • knitr (Xie, 2014)
  • BiocStyle (Oleś, 2025)
  • RefManageR (McLean, 2017)
  • rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2025)
  • testthat (Wickham, 2011)
  • TxDb.Hsapiens.UCSC.hg19.knownGene (Team and Maintainer, 2025)

Code for creating the vignette

## Create the vignette
library("rmarkdown")
system.time(render("derfinderPlot.Rmd", "BiocStyle::html_document"))

## Extract the R code
library("knitr")
knit("derfinderPlot.Rmd", tangle = TRUE)
## Clean up
unlink("chr21", recursive = TRUE)

Date the vignette was generated.

## [1] "2025-10-29 23:29:47 EDT"

Wallclock time spent generating the vignette.

## Time difference of 2.893 mins

R session information.

## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.5.1 Patched (2025-09-10 r88807)
##  os       macOS Monterey 12.7.6
##  system   x86_64, darwin20
##  ui       X11
##  language (EN)
##  collate  C
##  ctype    en_US.UTF-8
##  tz       America/New_York
##  date     2025-10-29
##  pandoc   2.7.3 @ /usr/local/bin/ (via rmarkdown)
##  quarto   1.4.553 @ /usr/local/bin/quarto
## 
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5 Bibliography

This vignette was generated using BiocStyle (Oleś, 2025) with knitr (Xie, 2014) and rmarkdown (Allaire, Xie, Dervieux et al., 2025) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

[1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents for R. R package version 2.30. 2025. URL: https://github.com/rstudio/rmarkdown.

[2] S. Arora, M. Morgan, M. Carlson, et al. GenomeInfoDb: Utilities for manipulating chromosome and other ‘seqname’ identifiers. 2017. DOI: 10.18129/B9.bioc.GenomeInfoDb.

[3] BrainSpan. “Atlas of the Developing Human Brain [Internet]. Funded by ARRA Awards 1RC2MH089921-01, 1RC2MH090047-01, and 1RC2MH089929-01.” 2011. URL: http://www.brainspan.org/.

[4] L. Collado-Torres, A. E. Jaffe, and J. T. Leek. derfinderPlot: Plotting functions for derfinder. https://github.com/leekgroup/derfinderPlot - R package version 1.44.0. 2017. DOI: 10.18129/B9.bioc.derfinderPlot. URL: http://www.bioconductor.org/packages/derfinderPlot.

[5] L. Collado-Torres, A. Jaffe, and J. Leek. derfinderData: Processed BigWigs from BrainSpan for examples. R package version 2.27.0. 2025. DOI: 10.18129/B9.bioc.derfinderData. URL: https://bioconductor.org/packages/derfinderData.

[6] L. Collado-Torres, A. Nellore, A. C. Frazee, et al. “Flexible expressed region analysis for RNA-seq with derfinder”. In: Nucl. Acids Res. (2017). DOI: 10.1093/nar/gkw852. URL: http://nar.oxfordjournals.org/content/early/2016/09/29/nar.gkw852.

[7] A. E. Jaffe, P. Murakami, H. Lee, 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.

[8] A. E. Jaffe, P. Murakami, H. Lee, et al. “Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies”. In: International Journal of Epidemiology (2012).

[9] M. Lawrence, W. Huber, H. Pagès, 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}.

[10] M. W. McLean. “RefManageR: Import and Manage BibTeX and BibLaTeX References in R”. In: The Journal of Open Source Software (2017). DOI: 10.21105/joss.00338.

[11] E. Neuwirth. RColorBrewer: ColorBrewer Palettes. R package version 1.1-3. 2022. DOI: 10.32614/CRAN.package.RColorBrewer. URL: https://CRAN.R-project.org/package=RColorBrewer.

[12] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.38.0. 2025. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle.

[13] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2025. URL: https://www.R-project.org/.

[14] B. C. Team and B. P. Maintainer. TxDb.Hsapiens.UCSC.hg19.knownGene: Annotation package for TxDb object(s). R package version 3.22.1. 2025.

[15] H. Wickham. “Reshaping Data with the reshape Package”. In: Journal of Statistical Software 21.12 (2007), pp. 1–20. URL: http://www.jstatsoft.org/v21/i12/.

[16] H. Wickham. “The Split-Apply-Combine Strategy for Data Analysis”. In: Journal of Statistical Software 40.1 (2011), pp. 1–29. URL: https://www.jstatsoft.org/v40/i01/.

[17] H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. ISBN: 978-3-319-24277-4. URL: https://ggplot2.tidyverse.org.

[18] H. Wickham. “testthat: Get Started with Testing”. In: The R Journal 3 (2011), pp. 5–10. URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf.

[19] H. Wickham, W. Chang, R. Flight, et al. sessioninfo: R Session Information. R package version 1.2.3. 2025. DOI: 10.32614/CRAN.package.sessioninfo. URL: https://CRAN.R-project.org/package=sessioninfo.

[20] H. Wickham, T. Pedersen, and D. Seidel. scales: Scale Functions for Visualization. R package version 1.4.0. 2025. DOI: 10.32614/CRAN.package.scales. URL: https://CRAN.R-project.org/package=scales.

[21] 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.

[22] 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.

[23] T. Yin, M. Lawrence, and D. Cook. biovizBase: Basic graphic utilities for visualization of genomic data. R package version 1.58.0. 2025. DOI: 10.18129/B9.bioc.biovizBase. URL: https://bioconductor.org/packages/biovizBase.