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
##   1.37.0, <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")
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

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, 2023) 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"))
## 2023-10-25 16:38:35.940752 fullCoverage: processing chromosome chr21
## 2023-10-25 16:38:35.959394 loadCoverage: finding chromosome lengths
## 2023-10-25 16:38:36.007054 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB103.bw
## 2023-10-25 16:38:36.255448 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB114.bw
## 2023-10-25 16:38:36.466673 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB123.bw
## 2023-10-25 16:38:36.628513 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB126.bw
## 2023-10-25 16:38:36.743127 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB130.bw
## 2023-10-25 16:38:36.879717 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB135.bw
## 2023-10-25 16:38:37.000587 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB136.bw
## 2023-10-25 16:38:37.115005 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB145.bw
## 2023-10-25 16:38:37.247775 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB149.bw
## 2023-10-25 16:38:37.388975 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB150.bw
## 2023-10-25 16:38:37.503811 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB154.bw
## 2023-10-25 16:38:38.73125 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB178.bw
## 2023-10-25 16:38:38.887944 loadCoverage: applying the cutoff to the merged data
## 2023-10-25 16:38:38.923386 filterData: originally there were 48129895 rows, now there are 48129895 rows. Meaning that 0 percent was filtered.
##    user  system elapsed 
##   2.930   0.100   3.052

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)
## 2023-10-25 16:38:39.010371 sampleDepth: Calculating sample quantiles
## 2023-10-25 16:38:39.152982 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)
## 2023-10-25 16:38:39.4745 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)
))
## 2023-10-25 16:38:40.61564 analyzeChr: Pre-processing the coverage data
## 2023-10-25 16:38:42.409442 analyzeChr: Calculating statistics
## 2023-10-25 16:38:42.412474 calculateStats: calculating the F-statistics
## 2023-10-25 16:38:42.607766 analyzeChr: Calculating pvalues
## 2023-10-25 16:38:42.608136 analyzeChr: Using the following theoretical cutoff for the F-statistics 5.31765507157871
## 2023-10-25 16:38:42.609356 calculatePvalues: identifying data segments
## 2023-10-25 16:38:42.617314 findRegions: segmenting information
## 2023-10-25 16:38:42.645255 findRegions: identifying candidate regions
## 2023-10-25 16:38:42.702203 findRegions: identifying region clusters
## 2023-10-25 16:38:42.866828 calculatePvalues: calculating F-statistics for permutation 1 and seed 20140924
## 2023-10-25 16:38:43.021402 findRegions: segmenting information
## 2023-10-25 16:38:43.057745 findRegions: identifying candidate regions
## 2023-10-25 16:38:43.122962 calculatePvalues: calculating F-statistics for permutation 2 and seed 20140925
## 2023-10-25 16:38:43.2753 findRegions: segmenting information
## 2023-10-25 16:38:43.299026 findRegions: identifying candidate regions
## 2023-10-25 16:38:43.353766 calculatePvalues: calculating F-statistics for permutation 3 and seed 20140926
## 2023-10-25 16:38:43.509216 findRegions: segmenting information
## 2023-10-25 16:38:43.533254 findRegions: identifying candidate regions
## 2023-10-25 16:38:43.580324 calculatePvalues: calculating F-statistics for permutation 4 and seed 20140927
## 2023-10-25 16:38:43.742309 findRegions: segmenting information
## 2023-10-25 16:38:43.76602 findRegions: identifying candidate regions
## 2023-10-25 16:38:43.819848 calculatePvalues: calculating F-statistics for permutation 5 and seed 20140928
## 2023-10-25 16:38:43.992078 findRegions: segmenting information
## 2023-10-25 16:38:44.032826 findRegions: identifying candidate regions
## 2023-10-25 16:38:44.078399 calculatePvalues: calculating F-statistics for permutation 6 and seed 20140929
## 2023-10-25 16:38:44.23452 findRegions: segmenting information
## 2023-10-25 16:38:44.258667 findRegions: identifying candidate regions
## 2023-10-25 16:38:44.304484 calculatePvalues: calculating F-statistics for permutation 7 and seed 20140930
## 2023-10-25 16:38:44.486611 findRegions: segmenting information
## 2023-10-25 16:38:44.510411 findRegions: identifying candidate regions
## 2023-10-25 16:38:44.555716 calculatePvalues: calculating F-statistics for permutation 8 and seed 20140931
## 2023-10-25 16:38:44.714598 findRegions: segmenting information
## 2023-10-25 16:38:44.74874 findRegions: identifying candidate regions
## 2023-10-25 16:38:44.794604 calculatePvalues: calculating F-statistics for permutation 9 and seed 20140932
## 2023-10-25 16:38:44.949329 findRegions: segmenting information
## 2023-10-25 16:38:44.973148 findRegions: identifying candidate regions
## 2023-10-25 16:38:45.01942 calculatePvalues: calculating F-statistics for permutation 10 and seed 20140933
## 2023-10-25 16:38:45.180309 findRegions: segmenting information
## 2023-10-25 16:38:45.204364 findRegions: identifying candidate regions
## 2023-10-25 16:38:45.251414 calculatePvalues: calculating F-statistics for permutation 11 and seed 20140934
## 2023-10-25 16:38:45.424242 findRegions: segmenting information
## 2023-10-25 16:38:45.448086 findRegions: identifying candidate regions
## 2023-10-25 16:38:45.494753 calculatePvalues: calculating F-statistics for permutation 12 and seed 20140935
## 2023-10-25 16:38:45.662753 findRegions: segmenting information
## 2023-10-25 16:38:45.686234 findRegions: identifying candidate regions
## 2023-10-25 16:38:45.73187 calculatePvalues: calculating F-statistics for permutation 13 and seed 20140936
## 2023-10-25 16:38:45.912068 findRegions: segmenting information
## 2023-10-25 16:38:45.935916 findRegions: identifying candidate regions
## 2023-10-25 16:38:45.981508 calculatePvalues: calculating F-statistics for permutation 14 and seed 20140937
## 2023-10-25 16:38:46.133564 findRegions: segmenting information
## 2023-10-25 16:38:46.166258 findRegions: identifying candidate regions
## 2023-10-25 16:38:46.211924 calculatePvalues: calculating F-statistics for permutation 15 and seed 20140938
## 2023-10-25 16:38:46.365468 findRegions: segmenting information
## 2023-10-25 16:38:46.389531 findRegions: identifying candidate regions
## 2023-10-25 16:38:46.446679 calculatePvalues: calculating F-statistics for permutation 16 and seed 20140939
## 2023-10-25 16:38:46.597473 findRegions: segmenting information
## 2023-10-25 16:38:46.621372 findRegions: identifying candidate regions
## 2023-10-25 16:38:46.667001 calculatePvalues: calculating F-statistics for permutation 17 and seed 20140940
## 2023-10-25 16:38:46.827261 findRegions: segmenting information
## 2023-10-25 16:38:46.851243 findRegions: identifying candidate regions
## 2023-10-25 16:38:46.896916 calculatePvalues: calculating F-statistics for permutation 18 and seed 20140941
## 2023-10-25 16:38:47.057441 findRegions: segmenting information
## 2023-10-25 16:38:47.093731 findRegions: identifying candidate regions
## 2023-10-25 16:38:47.140122 calculatePvalues: calculating F-statistics for permutation 19 and seed 20140942
## 2023-10-25 16:38:47.299069 findRegions: segmenting information
## 2023-10-25 16:38:47.322805 findRegions: identifying candidate regions
## 2023-10-25 16:38:47.368382 calculatePvalues: calculating F-statistics for permutation 20 and seed 20140943
## 2023-10-25 16:38:47.547834 findRegions: segmenting information
## 2023-10-25 16:38:47.571759 findRegions: identifying candidate regions
## 2023-10-25 16:38:47.64336 calculatePvalues: calculating the p-values
## 2023-10-25 16:38:47.722038 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: stats4
## 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.
## Getting exons.
## Annotating genes.
## ...
##    user  system elapsed 
##  61.710   1.252  62.964
## 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")
## 2023-10-25 16:39:43.770353 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"
)
## 2023-10-25 16:39:45.862149 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)
## 2023-10-25 16:39:46.787497 annotateRegions: counting
## 2023-10-25 16:39:46.88305 annotateRegions: annotating
regionCov <- getRegionCoverage(fullCov, regions)
## 2023-10-25 16:39:47.016447 getRegionCoverage: processing chr21
## 2023-10-25 16:39:47.079102 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: Transformation introduced infinite values in continuous y-axis
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, 2023)
  • 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 and Seidel, 2022)
  • biovizBase (Yin, Lawrence, and Cook, 2023)
  • 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, 2023)
  • sessioninfo (Wickham, Chang, Flight, Müller, and Hester, 2021)
  • knitr (Xie, 2014)
  • BiocStyle (Oleś, 2023)
  • RefManageR (McLean, 2017)
  • rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2023)
  • testthat (Wickham, 2011)
  • TxDb.Hsapiens.UCSC.hg19.knownGene (Carlson and Maintainer, 2015)

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] "2023-10-25 16:40:15 EDT"

Wallclock time spent generating the vignette.

## Time difference of 1.976 mins

R session information.

## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
##  setting  value
##  version  R Under development (unstable) (2023-10-22 r85388)
##  os       Ubuntu 22.04.3 LTS
##  system   x86_64, linux-gnu
##  ui       X11
##  language (EN)
##  collate  C
##  ctype    en_US.UTF-8
##  tz       America/New_York
##  date     2023-10-25
##  pandoc   2.7.3 @ /usr/bin/ (via rmarkdown)
## 
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
##  package                           * version   date (UTC) lib source
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##  BiocParallel                        1.37.0    2023-10-25 [2] Bioconductor
##  BiocStyle                         * 2.31.0    2023-10-25 [2] Bioconductor
##  biomaRt                             2.59.0    2023-10-25 [2] Bioconductor
##  Biostrings                          2.71.1    2023-10-25 [2] Bioconductor
##  biovizBase                          1.51.0    2023-10-25 [2] Bioconductor
##  bit                                 4.0.5     2022-11-15 [2] CRAN (R 4.4.0)
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##  checkmate                           2.3.0     2023-10-25 [2] CRAN (R 4.4.0)
##  cli                                 3.6.1     2023-03-23 [2] CRAN (R 4.4.0)
##  cluster                             2.1.4     2022-08-22 [3] CRAN (R 4.4.0)
##  codetools                           0.2-19    2023-02-01 [3] CRAN (R 4.4.0)
##  colorspace                          2.1-0     2023-01-23 [2] CRAN (R 4.4.0)
##  crayon                              1.5.2     2022-09-29 [2] CRAN (R 4.4.0)
##  curl                                5.1.0     2023-10-02 [2] CRAN (R 4.4.0)
##  data.table                          1.14.8    2023-02-17 [2] CRAN (R 4.4.0)
##  DBI                                 1.1.3     2022-06-18 [2] CRAN (R 4.4.0)
##  dbplyr                              2.3.4     2023-09-26 [2] CRAN (R 4.4.0)
##  DelayedArray                        0.29.0    2023-10-25 [2] Bioconductor
##  derfinder                         * 1.37.0    2023-10-25 [2] Bioconductor
##  derfinderData                     * 2.19.0    2023-10-24 [2] Bioconductor
##  derfinderHelper                     1.37.0    2023-10-25 [2] Bioconductor
##  derfinderPlot                     * 1.37.0    2023-10-25 [1] Bioconductor
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##  foreign                             0.8-85    2023-09-09 [3] CRAN (R 4.4.0)
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##  generics                            0.1.3     2022-07-05 [2] CRAN (R 4.4.0)
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##  GenomeInfoDbData                    1.2.11    2023-10-23 [2] Bioconductor
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##  htmltools                           0.5.6.1   2023-10-06 [2] CRAN (R 4.4.0)
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## 
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##  [2] /home/biocbuild/bbs-3.19-bioc/R/site-library
##  [3] /home/biocbuild/bbs-3.19-bioc/R/library
## 
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

5 Bibliography

This vignette was generated using BiocStyle (Oleś, 2023) with knitr (Xie, 2014) and rmarkdown (Allaire, Xie, Dervieux et al., 2023) 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 2.25. 2023. 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] M. Carlson and B. P. Maintainer. TxDb.Hsapiens.UCSC.hg19.knownGene: Annotation package for TxDb object(s). R package version 3.2.2. 2015.

[5] L. Collado-Torres, A. E. Jaffe, and J. T. Leek. derfinderPlot: Plotting functions for derfinder. https://github.com/leekgroup/derfinderPlot - R package version 1.37.0. 2017. DOI: 10.18129/B9.bioc.derfinderPlot. URL: http://www.bioconductor.org/packages/derfinderPlot.

[6] L. Collado-Torres, A. Jaffe, and J. Leek. derfinderData: Processed BigWigs from BrainSpan for examples. R package version 2.19.0. 2023. DOI: 10.18129/B9.bioc.derfinderData. URL: https://bioconductor.org/packages/derfinderData.

[7] 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.

[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 41.1 (2012), pp. 200–209. DOI: 10.1093/ije/dyr238.

[9] 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).

[10] 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}.

[11] 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.

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

[13] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.31.0. 2023. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle.

[14] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2023. URL: https://www.R-project.org/.

[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 1.2.2. 2021. URL: https://CRAN.R-project.org/package=sessioninfo.

[20] H. Wickham and D. Seidel. scales: Scale Functions for Visualization. R package version 1.2.1. 2022. 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 1.51.0. 2023. DOI: 10.18129/B9.bioc.biovizBase. URL: https://bioconductor.org/packages/biovizBase.