Introduction to derfinderPlot

In derfinderPlot: Plotting functions for derfinder

Basics

Install r Biocpkg('derfinderPlot')

R is an open-source statistical environment which can be easily modified to enhance its functionality via packages. r Biocpkg('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 r Biocpkg('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()

Required knowledge

r Biocpkg('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 r Biocpkg('derfinderPlot') user is not expected to deal with those packages directly but will need to be familiar with r Biocpkg('derfinder') and for some plots with r Biocpkg('ggbio').

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

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.

Citing r Biocpkg('derfinderPlot')

We hope that r Biocpkg('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")

Introduction to r Biocpkg('derfinderPlot')

## Track time spent on making the vignette
startTime <- Sys.time()

## Bib setup
library("RefManageR")

## Write bibliography information
bib <- c(
    derfinderPlot = citation("derfinderPlot")[1],
    BiocStyle = citation("BiocStyle"),
    knitr = citation("knitr")[3],
    RefManageR = citation("RefManageR")[1],
    rmarkdown = citation("rmarkdown")[1],
    derfinder = citation("derfinder")[1],
    ggbio = citation("ggbio"),
    brainspan = RefManageR::BibEntry(
        bibtype = "Unpublished",
        key = "brainspan",
        title = "Atlas of the Developing Human Brain [Internet]. Funded by ARRA Awards 1RC2MH089921-01, 1RC2MH090047-01, and 1RC2MH089929-01.",
        author = "BrainSpan", year = 2011, url = "http://www.brainspan.org/"
    ),
    R = citation(),
    IRanges = citation("IRanges"),
    sessioninfo = citation("sessioninfo"),
    testthat = citation("testthat"),
    GenomeInfoDb = RefManageR::BibEntry(
        bibtype = "manual",
        key = "GenomeInfoDb",
        author = "Sonali Arora and Martin Morgan and Marc Carlson and H. Pagès",
        title = "GenomeInfoDb: Utilities for manipulating chromosome and other 'seqname' identifiers",
        year = 2017, doi = "10.18129/B9.bioc.GenomeInfoDb"
    ),
    GenomicRanges = citation("GenomicRanges"),
    ggplot2 = citation("ggplot2"),
    plyr = citation("plyr"),
    RColorBrewer = citation("RColorBrewer"),
    reshape2 = citation("reshape2"),
    scales = citation("scales"),
    biovizBase = citation("biovizBase"),
    bumphunter = citation("bumphunter")[1],
    TxDb.Hsapiens.UCSC.hg19.knownGene = citation("TxDb.Hsapiens.UCSC.hg19.knownGene"),
    bumphunterPaper = RefManageR::BibEntry(
        bibtype = "article",
        key = "bumphunterPaper",
        title = "Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies",
        author = "Jaffe, Andrew E and Murakami, Peter and Lee, Hwajin and Leek, Jeffrey T and Fallin, M Daniele and Feinberg, Andrew P and Irizarry, Rafael A",
        year = 2012, journal = "International Journal of Epidemiology"
    ),
    derfinderData = citation("derfinderData")
)

r Biocpkg('derfinderPlot') r Citep(bib[['derfinderPlot']]) is an addon package for r Biocpkg('derfinder') r Citep(bib[['derfinder']]) with functions that allow you to visualize the results.

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

The functions in r Biocpkg('derfinderPlot') are:

• plotCluster() is a tailored r Biocpkg('ggbio') r Citep(bib[['ggbio']]) 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 r Biocpkg('ggbio') r Citep(bib[['ggbio']]) 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.

Example

As an example, we will analyze a small subset of the samples from the BrainSpan Atlas of the Human Brain r Citep(bib[['brainspan']]) publicly available data.

We first load the required packages.

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

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)

We can load the data from r Biocexptpkg('derfinderData') r Citep(bib[['derfinderData']]) 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"))

## Load data in Windows case
foo <- function() {
    load(system.file("extdata", "fullCov", "fullCovA1C.RData",
        package = "derfinderData"
    ))
    return(fullCovA1C)
}
fullCov <- foo()

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 r Biocpkg('rtracklayer') 1.25.16 BigWig files are not supported on Windows: you can find the fullCov object inside r Biocexptpkg('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)

## 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)

## 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)
))

## 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

plotOverview()

Now that we have obtained the main results using r Biocpkg('derfinder'), we can proceed to visualizing the results using r Biocpkg('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 \@ref(fig:plotOverview) 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")

Figure \@ref(fig:plotOverview2) 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"
)

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.

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 r Biocpkg('derfinder') functions: annotateRegions() and getRegionCoverage() as shown below.

## Get required information for the plots
annoRegs <- annotateRegions(regions, genomicState$fullGenome)
regionCov <- getRegionCoverage(fullCov, regions)

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 \@ref(fig:plotRegCov1), \@ref(fig:plotRegCov2), \@ref(fig:plotRegCov3), \@ref(fig:plotRegCov4), \@ref(fig:plotRegCov5), \@ref(fig:plotRegCov6), \@ref(fig:plotRegCov7), \@ref(fig:plotRegCov8), \@ref(fig:plotRegCov9), \@ref(fig:plotRegCov10)).

## 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
)

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

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 r Biocpkg('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 \@ref(fig:plotCluster) 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"
)

The second cluster (Figure \@ref(fig:plotCluster2)) 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"
)

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.

vennRegions

r Biocpkg('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 \@ref(fig:vennRegions).

## Make venn diagram
venn <- vennRegions(annoRegs)

## It returns the actual venn counts information
venn

Reproducibility

This package was made possible thanks to:

• R r Citep(bib[['R']])
• r Biocpkg('GenomeInfoDb') r Citep(bib[['GenomeInfoDb']])
• r Biocpkg('GenomicRanges') r Citep(bib[['GenomicRanges']])
• r Biocpkg('ggbio') r Citep(bib[['ggbio']])
• r CRANpkg('ggplot2') r Citep(bib[['ggplot2']])
• r Biocpkg('IRanges') r Citep(bib[['IRanges']])
• r CRANpkg('plyr') r Citep(bib[['plyr']])
• r CRANpkg('RColorBrewer') r Citep(bib[['RColorBrewer']])
• r CRANpkg('reshape2') r Citep(bib[['reshape2']])
• r CRANpkg('scales') r Citep(bib[['scales']])
• r Biocpkg('biovizBase') r Citep(bib[['biovizBase']])
• r Biocpkg('bumphunter') r Citep(bib[['bumphunter']]) and r Citep(bib[['bumphunterPaper']])
• r Biocpkg('derfinder') r Citep(bib[['derfinder']])
• r Biocexptpkg('derfinderData') r Citep(bib[['derfinderData']])
• r CRANpkg('sessioninfo') r Citep(bib[['sessioninfo']])
• r CRANpkg('knitr') r Citep(bib[['knitr']])
• r Biocpkg('BiocStyle') r Citep(bib[['BiocStyle']])
• r CRANpkg("RefManageR") r Citep(bib[["RefManageR"]])
• r CRANpkg('rmarkdown') r Citep(bib[['rmarkdown']])
• r CRANpkg('testthat') r Citep(bib[['testthat']])
• r Biocannopkg('TxDb.Hsapiens.UCSC.hg19.knownGene') r Citep(bib[['TxDb.Hsapiens.UCSC.hg19.knownGene']])

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.

## Date the vignette was generated
Sys.time()

Wallclock time spent generating the vignette.

## Processing time in seconds
totalTime <- diff(c(startTime, Sys.time()))
round(totalTime, digits = 3)

R session information.

## Session info
library("sessioninfo")
options(width = 120)
session_info()

Bibliography

This vignette was generated using r Biocpkg('BiocStyle') r Citep(bib[['BiocStyle']]) with r CRANpkg('knitr') r Citep(bib[['knitr']]) and r CRANpkg('rmarkdown') r Citep(bib[['rmarkdown']]) running behind the scenes.

Citations made with r CRANpkg('RefManageR') r Citep(bib[['RefManageR']]).

## Print bibliography
PrintBibliography(bib, .opts = list(hyperlink = "to.doc", style = "html"))

derfinderPlot documentation built on Dec. 20, 2020, 2:01 a.m.