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:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("derfinderPlot")

## Check that you have a valid Bioconductor installation
biocValid()

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')
## 
## Collado-Torres L, Jaffe AE and Leek JT (2017). _derfinderPlot:
## Plotting functions for derfinder_. doi:
## 10.18129/B9.bioc.derfinderPlot (URL:
## http://doi.org/10.18129/B9.bioc.derfinderPlot),
## https://github.com/leekgroup/derfinderPlot - R package version
## 1.12.3, <URL: http://www.bioconductor.org/packages/derfinderPlot>.
## 
## Collado-Torres L, Nellore A, Frazee AC, Wilks C, Love MI, Langmead
## B, Irizarry RA, Leek JT and Jaffe AE (2017). "Flexible expressed
## region analysis for RNA-seq with derfinder." _Nucl. Acids Res._.
## doi: 10.1093/nar/gkw852 (URL: http://doi.org/10.1093/nar/gkw852),
## <URL:
## 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, et al., 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, 2015) 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'))
## 2018-02-07 19:46:51 fullCoverage: processing chromosome chr21
## 2018-02-07 19:46:51 loadCoverage: finding chromosome lengths
## 2018-02-07 19:46:51 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB103.bw
## 2018-02-07 19:46:52 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB114.bw
## 2018-02-07 19:46:52 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB123.bw
## 2018-02-07 19:46:52 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB126.bw
## 2018-02-07 19:46:53 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB130.bw
## 2018-02-07 19:46:53 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB135.bw
## 2018-02-07 19:46:53 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB136.bw
## 2018-02-07 19:46:54 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB145.bw
## 2018-02-07 19:46:54 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB149.bw
## 2018-02-07 19:46:55 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB150.bw
## 2018-02-07 19:46:55 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB154.bw
## 2018-02-07 19:46:55 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.6-bioc/R/library/derfinderData/extdata/A1C/HSB178.bw
## 2018-02-07 19:46:55 loadCoverage: applying the cutoff to the merged data
## 2018-02-07 19:46:55 filterData: originally there were 48129895 rows, now there are 48129895 rows. Meaning that 0 percent was filtered.
##    user  system elapsed 
##   3.624   0.064   3.855

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)
## 2018-02-07 19:46:55 sampleDepth: Calculating sample quantiles
## 2018-02-07 19:46:55 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)
## 2018-02-07 19:46:56 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)))
## 2018-02-07 19:46:57 analyzeChr: Pre-processing the coverage data
## 2018-02-07 19:47:00 analyzeChr: Calculating statistics
## 2018-02-07 19:47:00 calculateStats: calculating the F-statistics
## 2018-02-07 19:47:01 analyzeChr: Calculating pvalues
## 2018-02-07 19:47:01 analyzeChr: Using the following theoretical cutoff for the F-statistics 5.31765507157871
## 2018-02-07 19:47:01 calculatePvalues: identifying data segments
## 2018-02-07 19:47:01 findRegions: segmenting information
## 2018-02-07 19:47:01 findRegions: identifying candidate regions
## 2018-02-07 19:47:01 findRegions: identifying region clusters
## 2018-02-07 19:47:01 calculatePvalues: calculating F-statistics for permutation 1 and seed 20140924
## 2018-02-07 19:47:01 findRegions: segmenting information
## 2018-02-07 19:47:01 findRegions: identifying candidate regions
## 2018-02-07 19:47:01 calculatePvalues: calculating F-statistics for permutation 2 and seed 20140925
## 2018-02-07 19:47:02 findRegions: segmenting information
## 2018-02-07 19:47:02 findRegions: identifying candidate regions
## 2018-02-07 19:47:02 calculatePvalues: calculating F-statistics for permutation 3 and seed 20140926
## 2018-02-07 19:47:02 findRegions: segmenting information
## 2018-02-07 19:47:02 findRegions: identifying candidate regions
## 2018-02-07 19:47:02 calculatePvalues: calculating F-statistics for permutation 4 and seed 20140927
## 2018-02-07 19:47:02 findRegions: segmenting information
## 2018-02-07 19:47:02 findRegions: identifying candidate regions
## 2018-02-07 19:47:02 calculatePvalues: calculating F-statistics for permutation 5 and seed 20140928
## 2018-02-07 19:47:02 findRegions: segmenting information
## 2018-02-07 19:47:02 findRegions: identifying candidate regions
## 2018-02-07 19:47:02 calculatePvalues: calculating F-statistics for permutation 6 and seed 20140929
## 2018-02-07 19:47:03 findRegions: segmenting information
## 2018-02-07 19:47:03 findRegions: identifying candidate regions
## 2018-02-07 19:47:03 calculatePvalues: calculating F-statistics for permutation 7 and seed 20140930
## 2018-02-07 19:47:03 findRegions: segmenting information
## 2018-02-07 19:47:03 findRegions: identifying candidate regions
## 2018-02-07 19:47:03 calculatePvalues: calculating F-statistics for permutation 8 and seed 20140931
## 2018-02-07 19:47:03 findRegions: segmenting information
## 2018-02-07 19:47:03 findRegions: identifying candidate regions
## 2018-02-07 19:47:03 calculatePvalues: calculating F-statistics for permutation 9 and seed 20140932
## 2018-02-07 19:47:03 findRegions: segmenting information
## 2018-02-07 19:47:03 findRegions: identifying candidate regions
## 2018-02-07 19:47:03 calculatePvalues: calculating F-statistics for permutation 10 and seed 20140933
## 2018-02-07 19:47:03 findRegions: segmenting information
## 2018-02-07 19:47:03 findRegions: identifying candidate regions
## 2018-02-07 19:47:03 calculatePvalues: calculating F-statistics for permutation 11 and seed 20140934
## 2018-02-07 19:47:04 findRegions: segmenting information
## 2018-02-07 19:47:04 findRegions: identifying candidate regions
## 2018-02-07 19:47:04 calculatePvalues: calculating F-statistics for permutation 12 and seed 20140935
## 2018-02-07 19:47:04 findRegions: segmenting information
## 2018-02-07 19:47:04 findRegions: identifying candidate regions
## 2018-02-07 19:47:04 calculatePvalues: calculating F-statistics for permutation 13 and seed 20140936
## 2018-02-07 19:47:04 findRegions: segmenting information
## 2018-02-07 19:47:04 findRegions: identifying candidate regions
## 2018-02-07 19:47:04 calculatePvalues: calculating F-statistics for permutation 14 and seed 20140937
## 2018-02-07 19:47:04 findRegions: segmenting information
## 2018-02-07 19:47:04 findRegions: identifying candidate regions
## 2018-02-07 19:47:04 calculatePvalues: calculating F-statistics for permutation 15 and seed 20140938
## 2018-02-07 19:47:04 findRegions: segmenting information
## 2018-02-07 19:47:04 findRegions: identifying candidate regions
## 2018-02-07 19:47:04 calculatePvalues: calculating F-statistics for permutation 16 and seed 20140939
## 2018-02-07 19:47:05 findRegions: segmenting information
## 2018-02-07 19:47:05 findRegions: identifying candidate regions
## 2018-02-07 19:47:05 calculatePvalues: calculating F-statistics for permutation 17 and seed 20140940
## 2018-02-07 19:47:05 findRegions: segmenting information
## 2018-02-07 19:47:05 findRegions: identifying candidate regions
## 2018-02-07 19:47:05 calculatePvalues: calculating F-statistics for permutation 18 and seed 20140941
## 2018-02-07 19:47:05 findRegions: segmenting information
## 2018-02-07 19:47:05 findRegions: identifying candidate regions
## 2018-02-07 19:47:05 calculatePvalues: calculating F-statistics for permutation 19 and seed 20140942
## 2018-02-07 19:47:05 findRegions: segmenting information
## 2018-02-07 19:47:05 findRegions: identifying candidate regions
## 2018-02-07 19:47:05 calculatePvalues: calculating F-statistics for permutation 20 and seed 20140943
## 2018-02-07 19:47:05 findRegions: segmenting information
## 2018-02-07 19:47:06 findRegions: identifying candidate regions
## 2018-02-07 19:47:06 calculatePvalues: calculating the p-values
## 2018-02-07 19:47:06 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 
##  63.768   0.240  64.067
## 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')
## 2018-02-07 19:48:01 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')
## 2018-02-07 19:48:04 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)
## 2018-02-07 19:48:06 annotateRegions: counting
## 2018-02-07 19:48:06 annotateRegions: annotating
regionCov <- getRegionCoverage(fullCov, regions)
## 2018-02-07 19:48:06 getRegionCoverage: processing chr21
## 2018-02-07 19:48:07 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)