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
##   1.38.0, <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, 2024) 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"))
## 2024-04-30 23:28:56.786262 fullCoverage: processing chromosome chr21
## 2024-04-30 23:28:56.805601 loadCoverage: finding chromosome lengths
## 2024-04-30 23:28:56.839838 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB103.bw
## 2024-04-30 23:28:57.069542 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB114.bw
## 2024-04-30 23:28:57.272225 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB123.bw
## 2024-04-30 23:28:57.417187 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB126.bw
## 2024-04-30 23:28:57.521126 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB130.bw
## 2024-04-30 23:28:57.647348 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB135.bw
## 2024-04-30 23:28:57.758154 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB136.bw
## 2024-04-30 23:28:57.865021 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB145.bw
## 2024-04-30 23:28:57.98599 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB149.bw
## 2024-04-30 23:28:58.121904 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB150.bw
## 2024-04-30 23:28:58.226656 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB154.bw
## 2024-04-30 23:28:58.370596 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.19-bioc/R/site-library/derfinderData/extdata/A1C/HSB178.bw
## 2024-04-30 23:28:58.506769 loadCoverage: applying the cutoff to the merged data
## 2024-04-30 23:28:58.539235 filterData: originally there were 48129895 rows, now there are 48129895 rows. Meaning that 0 percent was filtered.
##    user  system elapsed 
##   1.745   0.057   1.822

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)
## 2024-04-30 23:28:58.648558 sampleDepth: Calculating sample quantiles
## 2024-04-30 23:28:58.790282 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)
## 2024-04-30 23:28:59.126109 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)
))
## 2024-04-30 23:29:00.223104 analyzeChr: Pre-processing the coverage data
## 2024-04-30 23:29:01.958786 analyzeChr: Calculating statistics
## 2024-04-30 23:29:01.962221 calculateStats: calculating the F-statistics
## 2024-04-30 23:29:03.061647 analyzeChr: Calculating pvalues
## 2024-04-30 23:29:03.062094 analyzeChr: Using the following theoretical cutoff for the F-statistics 5.31765507157871
## 2024-04-30 23:29:03.063313 calculatePvalues: identifying data segments
## 2024-04-30 23:29:03.07119 findRegions: segmenting information
## 2024-04-30 23:29:03.102149 findRegions: identifying candidate regions
## 2024-04-30 23:29:03.156948 findRegions: identifying region clusters
## 2024-04-30 23:29:03.309376 calculatePvalues: calculating F-statistics for permutation 1 and seed 20140924
## 2024-04-30 23:29:03.469488 findRegions: segmenting information
## 2024-04-30 23:29:03.49534 findRegions: identifying candidate regions
## 2024-04-30 23:29:03.557575 calculatePvalues: calculating F-statistics for permutation 2 and seed 20140925
## 2024-04-30 23:29:03.724544 findRegions: segmenting information
## 2024-04-30 23:29:03.748766 findRegions: identifying candidate regions
## 2024-04-30 23:29:03.79348 calculatePvalues: calculating F-statistics for permutation 3 and seed 20140926
## 2024-04-30 23:29:03.937539 findRegions: segmenting information
## 2024-04-30 23:29:03.962041 findRegions: identifying candidate regions
## 2024-04-30 23:29:04.007526 calculatePvalues: calculating F-statistics for permutation 4 and seed 20140927
## 2024-04-30 23:29:04.160019 findRegions: segmenting information
## 2024-04-30 23:29:04.183781 findRegions: identifying candidate regions
## 2024-04-30 23:29:04.228312 calculatePvalues: calculating F-statistics for permutation 5 and seed 20140928
## 2024-04-30 23:29:04.373183 findRegions: segmenting information
## 2024-04-30 23:29:04.39712 findRegions: identifying candidate regions
## 2024-04-30 23:29:04.44181 calculatePvalues: calculating F-statistics for permutation 6 and seed 20140929
## 2024-04-30 23:29:04.595706 findRegions: segmenting information
## 2024-04-30 23:29:04.619505 findRegions: identifying candidate regions
## 2024-04-30 23:29:04.663464 calculatePvalues: calculating F-statistics for permutation 7 and seed 20140930
## 2024-04-30 23:29:04.825274 findRegions: segmenting information
## 2024-04-30 23:29:04.849316 findRegions: identifying candidate regions
## 2024-04-30 23:29:04.893988 calculatePvalues: calculating F-statistics for permutation 8 and seed 20140931
## 2024-04-30 23:29:05.056787 findRegions: segmenting information
## 2024-04-30 23:29:05.080664 findRegions: identifying candidate regions
## 2024-04-30 23:29:05.126073 calculatePvalues: calculating F-statistics for permutation 9 and seed 20140932
## 2024-04-30 23:29:05.272693 findRegions: segmenting information
## 2024-04-30 23:29:05.29658 findRegions: identifying candidate regions
## 2024-04-30 23:29:05.340988 calculatePvalues: calculating F-statistics for permutation 10 and seed 20140933
## 2024-04-30 23:29:05.506359 findRegions: segmenting information
## 2024-04-30 23:29:05.530146 findRegions: identifying candidate regions
## 2024-04-30 23:29:05.574757 calculatePvalues: calculating F-statistics for permutation 11 and seed 20140934
## 2024-04-30 23:29:05.719385 findRegions: segmenting information
## 2024-04-30 23:29:05.74308 findRegions: identifying candidate regions
## 2024-04-30 23:29:05.787247 calculatePvalues: calculating F-statistics for permutation 12 and seed 20140935
## 2024-04-30 23:29:05.938506 findRegions: segmenting information
## 2024-04-30 23:29:05.974214 findRegions: identifying candidate regions
## 2024-04-30 23:29:06.018815 calculatePvalues: calculating F-statistics for permutation 13 and seed 20140936
## 2024-04-30 23:29:06.163003 findRegions: segmenting information
## 2024-04-30 23:29:06.186687 findRegions: identifying candidate regions
## 2024-04-30 23:29:06.230112 calculatePvalues: calculating F-statistics for permutation 14 and seed 20140937
## 2024-04-30 23:29:06.373776 findRegions: segmenting information
## 2024-04-30 23:29:06.397446 findRegions: identifying candidate regions
## 2024-04-30 23:29:06.441573 calculatePvalues: calculating F-statistics for permutation 15 and seed 20140938
## 2024-04-30 23:29:06.594259 findRegions: segmenting information
## 2024-04-30 23:29:06.618108 findRegions: identifying candidate regions
## 2024-04-30 23:29:06.662585 calculatePvalues: calculating F-statistics for permutation 16 and seed 20140939
## 2024-04-30 23:29:06.806798 findRegions: segmenting information
## 2024-04-30 23:29:06.830672 findRegions: identifying candidate regions
## 2024-04-30 23:29:06.875106 calculatePvalues: calculating F-statistics for permutation 17 and seed 20140940
## 2024-04-30 23:29:07.028631 findRegions: segmenting information
## 2024-04-30 23:29:07.052385 findRegions: identifying candidate regions
## 2024-04-30 23:29:07.096866 calculatePvalues: calculating F-statistics for permutation 18 and seed 20140941
## 2024-04-30 23:29:07.244694 findRegions: segmenting information
## 2024-04-30 23:29:07.275663 findRegions: identifying candidate regions
## 2024-04-30 23:29:07.319548 calculatePvalues: calculating F-statistics for permutation 19 and seed 20140942
## 2024-04-30 23:29:07.463417 findRegions: segmenting information
## 2024-04-30 23:29:07.487394 findRegions: identifying candidate regions
## 2024-04-30 23:29:07.531512 calculatePvalues: calculating F-statistics for permutation 20 and seed 20140943
## 2024-04-30 23:29:07.684787 findRegions: segmenting information
## 2024-04-30 23:29:07.713157 findRegions: identifying candidate regions
## 2024-04-30 23:29:07.786402 calculatePvalues: calculating the p-values
## 2024-04-30 23:29:07.858875 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.
## Getting exons.
## Annotating genes.
## ...
##    user  system elapsed 
##  59.348   1.067  60.417
## 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")
## 2024-04-30 23:30:00.815421 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"
)
## 2024-04-30 23:30:02.782418 plotOverview: assigning chromosome lengths from hg19!
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.