Introduction to derfinderPlot
29 October 2019
Package
derfinderPlot 1.20.0
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')##
## Collado-Torres L, Jaffe AE, Leek JT (2017). _derfinderPlot: Plotting
## functions for derfinder_. doi: 10.18129/B9.bioc.derfinderPlot (URL:
## https://doi.org/10.18129/B9.bioc.derfinderPlot),
## https://github.com/leekgroup/derfinderPlot - R package version
## 1.20.0, <URL: 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 (URL: https://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 usingRbase 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 withGRangesobjects 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 ggplot23.1 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, 2019) 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'))## 2019-10-29 21:10:09 fullCoverage: processing chromosome chr21## 2019-10-29 21:10:09 loadCoverage: finding chromosome lengths## 2019-10-29 21:10:09 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB103.bw## 2019-10-29 21:10:10 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB114.bw## 2019-10-29 21:10:10 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB123.bw## 2019-10-29 21:10:10 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB126.bw## 2019-10-29 21:10:11 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB130.bw## 2019-10-29 21:10:11 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB135.bw## 2019-10-29 21:10:11 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB136.bw## 2019-10-29 21:10:11 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB145.bw## 2019-10-29 21:10:11 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB149.bw## 2019-10-29 21:10:12 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB150.bw## 2019-10-29 21:10:12 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB154.bw## 2019-10-29 21:10:12 loadCoverage: loading BigWig file /home/biocbuild/bbs-3.10-bioc/R/library/derfinderData/extdata/A1C/HSB178.bw## 2019-10-29 21:10:12 loadCoverage: applying the cutoff to the merged data## 2019-10-29 21:10:13 filterData: originally there were 48129895 rows, now there are 48129895 rows. Meaning that 0 percent was filtered.## user system elapsed
## 2.908 0.076 3.358Alternatively, 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)## 2019-10-29 21:10:13 sampleDepth: Calculating sample quantiles## 2019-10-29 21:10:13 sampleDepth: Calculating sample adjustments## Define models
models <- makeModels(sampleDepths, testvars = pheno$group,
adjustvars = pheno[, c('gender')])Next, we can find candidate differentially expressed regions (DERs) using as input the segments of the genome where at least one sample has coverage greater than 3. In this particular example, we chose a low theoretical F-statistic cutoff and used 20 permutations.
## Filter coverage
filteredCov <- lapply(fullCov, filterData, cutoff = 3)## 2019-10-29 21:10:13 filterData: originally there were 48129895 rows, now there are 90023 rows. Meaning that 99.81 percent was filtered.## Perform differential expression analysis
suppressPackageStartupMessages(library('bumphunter'))
system.time(results <- analyzeChr(chr = 'chr21', filteredCov$chr21,
models, groupInfo = pheno$group, writeOutput = FALSE,
cutoffFstat = 5e-02, nPermute = 20, seeds = 20140923 + seq_len(20)))## 2019-10-29 21:10:14 analyzeChr: Pre-processing the coverage data## 2019-10-29 21:10:17 analyzeChr: Calculating statistics## 2019-10-29 21:10:17 calculateStats: calculating the F-statistics## 2019-10-29 21:10:17 analyzeChr: Calculating pvalues## 2019-10-29 21:10:17 analyzeChr: Using the following theoretical cutoff for the F-statistics 5.31765507157871## 2019-10-29 21:10:17 calculatePvalues: identifying data segments## 2019-10-29 21:10:17 findRegions: segmenting information## 2019-10-29 21:10:17 findRegions: identifying candidate regions## 2019-10-29 21:10:17 findRegions: identifying region clusters## 2019-10-29 21:10:17 calculatePvalues: calculating F-statistics for permutation 1 and seed 20140924## 2019-10-29 21:10:17 findRegions: segmenting information## 2019-10-29 21:10:17 findRegions: identifying candidate regions## 2019-10-29 21:10:18 calculatePvalues: calculating F-statistics for permutation 2 and seed 20140925## 2019-10-29 21:10:18 findRegions: segmenting information## 2019-10-29 21:10:18 findRegions: identifying candidate regions## 2019-10-29 21:10:19 calculatePvalues: calculating F-statistics for permutation 3 and seed 20140926## 2019-10-29 21:10:19 findRegions: segmenting information## 2019-10-29 21:10:19 findRegions: identifying candidate regions## 2019-10-29 21:10:19 calculatePvalues: calculating F-statistics for permutation 4 and seed 20140927## 2019-10-29 21:10:19 findRegions: segmenting information## 2019-10-29 21:10:19 findRegions: identifying candidate regions## 2019-10-29 21:10:19 calculatePvalues: calculating F-statistics for permutation 5 and seed 20140928## 2019-10-29 21:10:19 findRegions: segmenting information## 2019-10-29 21:10:19 findRegions: identifying candidate regions## 2019-10-29 21:10:19 calculatePvalues: calculating F-statistics for permutation 6 and seed 20140929## 2019-10-29 21:10:19 findRegions: segmenting information## 2019-10-29 21:10:19 findRegions: identifying candidate regions## 2019-10-29 21:10:19 calculatePvalues: calculating F-statistics for permutation 7 and seed 20140930## 2019-10-29 21:10:19 findRegions: segmenting information## 2019-10-29 21:10:19 findRegions: identifying candidate regions## 2019-10-29 21:10:20 calculatePvalues: calculating F-statistics for permutation 8 and seed 20140931## 2019-10-29 21:10:20 findRegions: segmenting information## 2019-10-29 21:10:20 findRegions: identifying candidate regions## 2019-10-29 21:10:20 calculatePvalues: calculating F-statistics for permutation 9 and seed 20140932## 2019-10-29 21:10:20 findRegions: segmenting information## 2019-10-29 21:10:20 findRegions: identifying candidate regions## 2019-10-29 21:10:20 calculatePvalues: calculating F-statistics for permutation 10 and seed 20140933## 2019-10-29 21:10:20 findRegions: segmenting information## 2019-10-29 21:10:20 findRegions: identifying candidate regions## 2019-10-29 21:10:20 calculatePvalues: calculating F-statistics for permutation 11 and seed 20140934## 2019-10-29 21:10:20 findRegions: segmenting information## 2019-10-29 21:10:20 findRegions: identifying candidate regions## 2019-10-29 21:10:20 calculatePvalues: calculating F-statistics for permutation 12 and seed 20140935## 2019-10-29 21:10:20 findRegions: segmenting information## 2019-10-29 21:10:21 findRegions: identifying candidate regions## 2019-10-29 21:10:21 calculatePvalues: calculating F-statistics for permutation 13 and seed 20140936## 2019-10-29 21:10:21 findRegions: segmenting information## 2019-10-29 21:10:21 findRegions: identifying candidate regions## 2019-10-29 21:10:21 calculatePvalues: calculating F-statistics for permutation 14 and seed 20140937## 2019-10-29 21:10:21 findRegions: segmenting information## 2019-10-29 21:10:21 findRegions: identifying candidate regions## 2019-10-29 21:10:21 calculatePvalues: calculating F-statistics for permutation 15 and seed 20140938## 2019-10-29 21:10:21 findRegions: segmenting information## 2019-10-29 21:10:21 findRegions: identifying candidate regions## 2019-10-29 21:10:21 calculatePvalues: calculating F-statistics for permutation 16 and seed 20140939## 2019-10-29 21:10:21 findRegions: segmenting information## 2019-10-29 21:10:21 findRegions: identifying candidate regions## 2019-10-29 21:10:21 calculatePvalues: calculating F-statistics for permutation 17 and seed 20140940## 2019-10-29 21:10:22 findRegions: segmenting information## 2019-10-29 21:10:22 findRegions: identifying candidate regions## 2019-10-29 21:10:22 calculatePvalues: calculating F-statistics for permutation 18 and seed 20140941## 2019-10-29 21:10:22 findRegions: segmenting information## 2019-10-29 21:10:22 findRegions: identifying candidate regions## 2019-10-29 21:10:22 calculatePvalues: calculating F-statistics for permutation 19 and seed 20140942## 2019-10-29 21:10:22 findRegions: segmenting information## 2019-10-29 21:10:22 findRegions: identifying candidate regions## 2019-10-29 21:10:22 calculatePvalues: calculating F-statistics for permutation 20 and seed 20140943## 2019-10-29 21:10:23 findRegions: segmenting information## 2019-10-29 21:10:23 findRegions: identifying candidate regions## 2019-10-29 21:10:23 calculatePvalues: calculating the p-values## 2019-10-29 21:10:23 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
## 53.300 0.436 53.812## Quick access to the results
regions <- results$regions$regions
## Annotation database to use
suppressPackageStartupMessages(library('TxDb.Hsapiens.UCSC.hg19.knownGene'))
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene3.2 plotOverview()
Now that we have obtained the main results using derfinder, we can proceed to visualizing the results using derfinderPlot. The easiest to use of all the functions is plotOverview() which takes a set of regions and annotation information produced by bumphunter::matchGenes().
Figure 1 shows the candidate DERs colored by whether their q-value was less than 0.10 or not.
## Q-values overview
plotOverview(regions = regions, annotation = results$annotation, type = 'qval')## 2019-10-29 21:11:08 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.
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$annotation,
type = 'annotation')## 2019-10-29 21:11:10 plotOverview: assigning chromosome lengths from hg19!!!## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
Figure 2: Location of the DERs in the genome and colored by annotation class
This plot is was designed for many chromosomes but only one is shown here for simplicity.
In this particular example, because we are only using data from one chromosome the above plot is not as informative as in a real case scenario. However, with this plot we can quickly observe that nearly all of the candidate DERs are inside an exon.
3.3 plotRegionCoverage()
The complete opposite of visualizing the candidate DERs at the genome-level is to visualize them one region at a time. plotRegionCoverage() allows us to do this quickly for a large number of regions.
Before using this function, we need to process more detailed information using two derfinder functions: annotateRegions() and getRegionCoverage() as shown below.
## Get required information for the plots
annoRegs <- annotateRegions(regions, genomicState$fullGenome)## 2019-10-29 21:11:11 annotateRegions: counting## 2019-10-29 21:11:11 annotateRegions: annotatingregionCov <- getRegionCoverage(fullCov, regions)## 2019-10-29 21:11:11 getRegionCoverage: processing chr21## 2019-10-29 21:11:11 getRegionCoverage: done processing chr21Once 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 3, 4, 5, 6, 7, 8, 9, 10, 11, 12).
## 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)
Figure 3: Base-pair resolution plot of differentially expressed region 1
Figure 4: Base-pair resolution plot of differentially expressed region 2
Figure 5: Base-pair resolution plot of differentially expressed region 3
Figure 6: Base-pair resolution plot of differentially expressed region 4
Figure 7: Base-pair resolution plot of differentially expressed region 5
Figure 8: Base-pair resolution plot of differentially expressed region 6
Figure 9: Base-pair resolution plot of differentially expressed region 7
Figure 10: Base-pair resolution plot of differentially expressed region 8
Figure 11: Base-pair resolution plot of differentially expressed region 9
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$group,
titleUse = 'pval')## Parsing transcripts...## Parsing exons...## Parsing cds...## Parsing utrs...## ------exons...## ------cdss...## ------introns...## ------utr...## aggregating...## Done## Constructing graphics...## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Figure 13: Cluster plot for cluster 1 using ggbio
The second cluster (Figure 14) shows a larger number of potential DERs (again without q-values less than 0.10) in a segment of the genome where the coverage data is highly variable. This is a common occurrence with RNA-seq data.
## Second cluster
plotCluster(idx = 2, regions = regions, annotation = results$annotation,
coverageInfo = fullCov$chr21, txdb = txdb, groupInfo = pheno$group,
titleUse = 'pval')## Warning in getIdeoGR(data): geom(ideogram) need valid seqlengths information for accurate mapping,
## now use reduced information as ideogram...## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead
## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead## Parsing transcripts...## Parsing exons...## Parsing cds...## Parsing utrs...## ------exons...## ------cdss...## ------introns...## ------utr...## aggregating...## Done## Constructing graphics...## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead
## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead
## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead
## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead
## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead
## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead
## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead
## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead
## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead## Coordinate system already present. Adding new coordinate system, which will replace the existing one.## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.## Warning: Transformation introduced infinite values in continuous y-axis
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)
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, 2019)
- GenomeInfoDb (Arora, Morgan, Carlson, and Pagès, 2017)
- GenomicRanges (Lawrence, Huber, Pagès, Aboyoun, et al., 2013)
- ggbio (Yin, Cook, and Lawrence, 2012)
- ggplot2 (Wickham, 2016)
- IRanges (Lawrence, Huber, Pagès, Aboyoun, et al., 2013)
- plyr (Wickham, 2011)
- RColorBrewer (Neuwirth, 2014)
- reshape2 (Wickham, 2007)
- scales (Wickham, 2018)
- biovizBase (Yin, Lawrence, and Cook, 2019)
- bumphunter (Jaffe, Murakami, Lee, Leek, et al., 2012) and (Jaffe, Murakami, Lee, Leek, et al., 2012)
- derfinder (Collado-Torres, Nellore, Frazee, Wilks, et al., 2017)
- derfinderData (Collado-Torres, Jaffe, and Leek, 2019)
- sessioninfo (Csárdi, core, Wickham, Chang, et al., 2018)
- knitcitations (Boettiger, 2019)
- knitr (Xie, 2014)
- BiocStyle (Oleś, Morgan, and Huber, 2019)
- rmarkdown
- 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)
file.remove('derfinderPlotRef.bib')## [1] TRUEDate the vignette was generated.
## [1] "2019-10-29 21:11:47 EDT"Wallclock time spent generating the vignette.
## Time difference of 1.902 minsR session information.
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.1 (2019-07-05)
## os Ubuntu 18.04.3 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate C
## ctype en_US.UTF-8
## tz America/New_York
## date 2019-10-29
##
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## package * version date lib source
## acepack 1.4.1 2016-10-29 [2] CRAN (R 3.6.1)
## AnnotationDbi * 1.48.0 2019-10-29 [2] Bioconductor
## AnnotationFilter 1.10.0 2019-10-29 [2] Bioconductor
## askpass 1.1 2019-01-13 [2] CRAN (R 3.6.1)
## assertthat 0.2.1 2019-03-21 [2] CRAN (R 3.6.1)
## backports 1.1.5 2019-10-02 [2] CRAN (R 3.6.1)
## base64enc 0.1-3 2015-07-28 [2] CRAN (R 3.6.1)
## bibtex 0.4.2 2017-06-30 [2] CRAN (R 3.6.1)
## Biobase * 2.46.0 2019-10-29 [2] Bioconductor
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##
## [1] /tmp/RtmpA6t12W/Rinst17121ae4ee84
## [2] /home/biocbuild/bbs-3.10-bioc/R/library5 Bibliography
This vignette was generated using BiocStyle (Oleś, Morgan, and Huber, 2019) with knitr (Xie, 2014) and rmarkdown running behind the scenes.
Citations made with knitcitations (Boettiger, 2019).
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