RNA-Seq Workflow Template

20 minute read

Introduction

This report describes the analysis of the RNA-Seq data set from Howard et al (2013). The corresponding FASTQ files were downloaded from GEO (Accession: SRP010938). This data set contains 18 paired-end (PE) read sets from Arabidposis thaliana. The details about all download steps are provided here.

Users want to provide here additional background information about the design of their RNA-Seq project.

Experimental design

Typically, users want to specify here all information relevant for the analysis of their NGS study. This includes detailed descriptions of FASTQ files, experimental design, reference genome, gene annotations, etc.

Workflow environment

NOTE: this section describes how to set up the proper environment (directory structure) for running systemPipeR workflows. After mastering this task the workflow run instructions can be deleted since they are not expected to be included in a final HTML/PDF report of a workflow.

1. If a remote system or cluster is used, then users need to log in to the remote system first. The following applies to an HPC cluster (e.g. HPCC cluster).

   A terminal application needs to be used to log in to a user’s cluster account. Next, one can open an interactive session on a computer node with srun. More details about argument settings for srun are available in this HPCC manual or the HPCC section of this website here. Next, load the R version required for running the workflow with module load. Sometimes it may be necessary to first unload an active software version before loading another version, e.g. module unload R.

srun --x11 --partition=gen242 --mem=20gb --cpus-per-task 8 --ntasks 1 --time 20:00:00 --pty bash -l
module unload R; module load R/4.0.3_gcc-8.3.0

2. Load a workflow template with the genWorkenvir function. This can be done from the command-line or from within R. However, only one of the two options needs to be used.

From command-line

$ Rscript -e "systemPipeRdata::genWorkenvir(workflow='rnaseq')"
$ cd rnaseq

From R

library(systemPipeRdata)
genWorkenvir(workflow = "rnaseq")
setwd("rnaseq")

3. Optional: if the user wishes to use another Rmd file than the template instance provided by the genWorkenvir function, then it can be copied or downloaded into the root directory of the workflow environment (e.g. with cp or wget).

4. Now one can open from the root directory of the workflow the corresponding R Markdown script (e.g. systemPipeChIPseq.Rmd) using an R IDE, such as nvim-r, ESS or RStudio. Subsequently, the workflow can be run as outlined below. For learning purposes it is recommended to run workflows for the first time interactively. Once all workflow steps are understood and possibly modified to custom needs, one can run the workflow from start to finish with a single command using rmarkdown::render() or runWF().

Load packages

The systemPipeR package needs to be loaded to perform the analysis steps shown in this report (H Backman and Girke 2016). The package allows users to run the entire analysis workflow interactively or with a single command while also generating the corresponding analysis report. For details see systemPipeR's main vignette.

library(systemPipeR)

To apply workflows to custom data, the user needs to modify the targets file and if necessary update the corresponding parameter (.cwl and .yml) files. A collection of pre-generated .cwl and .yml files are provided in the param/cwl subdirectory of each workflow template. They are also viewable in the GitHub repository of systemPipeRdata (see here). For more information of the structure of the targets file, please consult the documentation here. More details about the new parameter files from systemPipeR can be found here.

Import custom functions

Custem functions for the challenge projects can be imported with the source command from a local R script (here challengeProject_Fct.R). Skip this step if such a script is not available. Alternatively, these functions can be loaded from a custom R package.

source("challengeProject_Fct.R")

Experiment definition provided by targets file

The targets file defines all FASTQ files and sample comparisons of the analysis workflow. If needed the tab separated (TSV) version of this file can be downloaded from here and the corresponding Google Sheet is here.

targetspath <- "targetsPE.txt"
targets <- read.delim(targetspath, comment.char = "#")
DT::datatable(targets, options = list(scrollX = TRUE, autoWidth = TRUE))

Read preprocessing

Read quality filtering and trimming

The function preprocessReads allows to apply predefined or custom read preprocessing functions to all FASTQ files referenced in a SYSargs2 container, such as quality filtering or adapter trimming routines. The paths to the resulting output FASTQ files are stored in the output slot of the SYSargs2 object. The following example performs adapter trimming with the trimLRPatterns function from the Biostrings package. After the trimming step a new targets file is generated (here targets_trim.txt) containing the paths to the trimmed FASTQ files. The new targets file can be used for the next workflow step with an updated SYSargs2 instance, e.g. running the NGS alignments using the trimmed FASTQ files.

Construct SYSargs2 object from cwl and yml param and targets files.

dir_path <- "param/cwl/preprocessReads/trim-pe"
trim <- loadWorkflow(targets = targetspath, wf_file = "trim-pe.cwl", 
    input_file = "trim-pe.yml", dir_path = dir_path)
trim <- renderWF(trim, inputvars = c(FileName1 = "_FASTQ_PATH1_", 
    FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_"))
trim
output(trim)[1:2]

preprocessReads(args = trim, Fct = "trimLRPatterns(Rpattern='GCCCGGGTAA', 
                subject=fq)", 
    batchsize = 1e+05, overwrite = TRUE, compress = TRUE)
writeTargetsout(x = trim, file = "targets_trim.txt", step = 1, 
    new_col = c("FileName1", "FileName2"), new_col_output_index = c(1, 
        2), overwrite = TRUE)

FASTQ quality report

The following seeFastq and seeFastqPlot functions generate and plot a series of useful quality statistics for a set of FASTQ files including per cycle quality box plots, base proportions, base-level quality trends, relative k-mer diversity, length and occurrence distribution of reads, number of reads above quality cutoffs and mean quality distribution. The results are written to a PDF file named fastqReport.pdf.

fqlist <- seeFastq(fastq = infile1(trim), batchsize = 10000, 
    klength = 8)
png("./results/fastqReport.png", height = 18 * 96, width = 4 * 
    96 * length(fqlist))
seeFastqPlot(fqlist)
dev.off()

Alignments

Read mapping with HISAT2

The following steps will demonstrate how to use the short read aligner Hisat2 (Kim, Langmead, and Salzberg 2015) in both interactive job submissions and batch submissions to queuing systems of clusters using the systemPipeR's new CWL command-line interface.

Build Hisat2 index.

dir_path <- "param/cwl/hisat2/hisat2-idx"
idx <- loadWorkflow(targets = NULL, wf_file = "hisat2-index.cwl", 
    input_file = "hisat2-index.yml", dir_path = dir_path)
idx <- renderWF(idx)
idx
cmdlist(idx)

## Run
runCommandline(idx, make_bam = FALSE)

The parameter settings of the aligner are defined in the hisat2-mapping-se.cwl and hisat2-mapping-se.yml files. The following shows how to construct the corresponding SYSargs2 object, here args.

dir_path <- "param/cwl/hisat2/hisat2-pe"
args <- loadWorkflow(targets = "targets_trim.txt", wf_file = "hisat2-mapping-pe.cwl", 
    input_file = "hisat2-mapping-pe.yml", dir_path = dir_path)
args <- renderWF(args, inputvars = c(FileName1 = "_FASTQ_PATH1_", 
    FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_"))
args

## Instance of 'SYSargs2':
##    Slot names/accessors: 
##       targets: 18 (M1A...V12B), targetsheader: 4 (lines)
##       modules: 1
##       wf: 0, clt: 1, yamlinput: 8 (components)
##       input: 18, output: 18
##       cmdlist: 18
##    WF Steps:
##       1. hisat2-mapping-pe (rendered: TRUE)

cmdlist(args)[1:2]

## $M1A
## $M1A$`hisat2-mapping-pe`
## [1] "hisat2 -S ./results/M1A.sam  -x ./data/tair10.fasta  -k 1  --min-intronlen 30  --max-intronlen 3000  -1 ./results/M1A_1.fastq_trim.gz -2 ./results/M1A_2.fastq_trim.gz --threads 4"
## 
## 
## $M1B
## $M1B$`hisat2-mapping-pe`
## [1] "hisat2 -S ./results/M1B.sam  -x ./data/tair10.fasta  -k 1  --min-intronlen 30  --max-intronlen 3000  -1 ./results/M1B_1.fastq_trim.gz -2 ./results/M1B_2.fastq_trim.gz --threads 4"

output(args)[1:2]

## $M1A
## $M1A$`hisat2-mapping-pe`
## [1] "./results/M1A.sam"
## 
## 
## $M1B
## $M1B$`hisat2-mapping-pe`
## [1] "./results/M1B.sam"

Interactive job submissions on a single machine

To simplify the short read alignment execution for the user, the command-line can be run with the runCommandline function. The execution will be on a single machine without submitting to a queuing system of a computer cluster. This way, the input FASTQ files will be processed sequentially. By default runCommandline auto detects SAM file outputs and converts them to sorted and indexed BAM files, using internally the Rsamtools package. Besides, runCommandline allows the user to create a dedicated results folder for each workflow and a sub-folder for each sample defined in the targets file. This includes all the output and log files for each step. When these options are used, the output location will be updated by default and can be assigned to the same object.

## Run single Machine
args <- runCommandline(args)

Parallelization on clusters

Alternatively, the computation can be greatly accelerated by processing many files in parallel using several compute nodes of a cluster, where a scheduling/queuing system is used for load balancing. For this the clusterRun function submits the computing requests to the scheduler using the run specifications defined by runCommandline.

To avoid over-subscription of CPU cores on the compute nodes, the value from yamlinput(args)['thread'] is passed on to the submission command, here ncpus in the resources list object. The number of independent parallel cluster processes is defined under the Njobs argument. The following example will run 18 processes in parallel using for each 4 CPU cores. If the resources available on a cluster allow running all 18 processes at the same time then the shown sample submission will utilize in total 72 CPU cores. Note, clusterRun can be used with most queueing systems as it is based on utilities from the batchtools package which supports the use of template files (*.tmpl) for defining the run parameters of different schedulers. To run the following code, one needs to have both a conf file (see .batchtools.conf.R samples here) and a template file (see *.tmpl samples here) for the queueing available on a system. The following example uses the sample conf and template files for the Slurm scheduler provided by this package.

library(batchtools)
resources <- list(walltime = 120, ntasks = 1, ncpus = 4, memory = 1024)
reg <- clusterRun(args, FUN = runCommandline, more.args = list(args = args, 
    make_bam = TRUE, dir = FALSE), conffile = ".batchtools.conf.R", 
    template = "batchtools.slurm.tmpl", Njobs = 18, runid = "01", 
    resourceList = resources)
getStatus(reg = reg)
waitForJobs(reg = reg)
args <- output_update(args, dir = FALSE, replace = TRUE, extension = c(".sam", 
    ".bam"))  ## Updates the output(args) to the right location in the subfolders
output(args)

Check whether all BAM files have been created.

outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1)
file.exists(outpaths)

Read and alignment stats

The following provides an overview of the number of reads in each sample and how many of them aligned to the reference.

read_statsDF <- alignStats(args = args)
write.table(read_statsDF, "results/alignStats.xls", row.names = FALSE, 
    quote = FALSE, sep = "\t")

The following shows the alignment statistics for a sample file provided by the systemPipeR package.

read.table("results/alignStats.xls", header = TRUE)[1:4, ]

##   FileName Nreads2x   Nalign Perc_Aligned Nalign_Primary
## 1      M1A 33609678 32136300     95.61621       32136300
## 2      M1B 53002402 43620124     82.29839       43620124
## 3      A1A 50223496 48438407     96.44571       48438407
## 4      A1B 43650000 35549889     81.44304       35549889
##   Perc_Aligned_Primary
## 1             95.61621
## 2             82.29839
## 3             96.44571
## 4             81.44304

Create symbolic links for viewing BAM files in IGV

The symLink2bam function creates symbolic links to view the BAM alignment files in a genome browser such as IGV. The corresponding URLs are written to a file with a path specified under urlfile in the results directory.

symLink2bam(sysargs = args, htmldir = c("~/.html/", "somedir/"), 
    urlbase = "http://cluster.hpcc.ucr.edu/~<username>/", urlfile = "./results/IGVurl.txt")

Read quantification

Read counting with summarizeOverlaps in parallel mode using multiple cores

Reads overlapping with annotation ranges of interest are counted for each sample using the summarizeOverlaps function (Lawrence et al. 2013). The read counting is preformed for exonic gene regions in a non-strand-specific manner while ignoring overlaps among different genes. Subsequently, the expression count values are normalized by reads per kp per million mapped reads (RPKM). The raw read count table (countDFeByg.xls) and the corresponding RPKM table (rpkmDFeByg.xls) are written to separate files in the directory of this project. Parallelization is achieved with the BiocParallel package, here using 8 CPU cores.

library("GenomicFeatures")
library(BiocParallel)
txdb <- makeTxDbFromGFF(file = "data/tair10.gff", format = "gff", 
    dataSource = "TAIR", organism = "Arabidopsis thaliana")
saveDb(txdb, file = "./data/tair10.sqlite")
txdb <- loadDb("./data/tair10.sqlite")
outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1)
# (align <- readGAlignments(outpaths[1])) # Demonstrates how
# to read bam file into R
eByg <- exonsBy(txdb, by = c("gene"))
bfl <- BamFileList(outpaths, yieldSize = 50000, index = character())
multicoreParam <- MulticoreParam(workers = 4)
register(multicoreParam)
registered()
counteByg <- bplapply(bfl, function(x) summarizeOverlaps(eByg, 
    x, mode = "Union", ignore.strand = TRUE, inter.feature = FALSE, 
    singleEnd = FALSE))
countDFeByg <- sapply(seq(along = counteByg), function(x) assays(counteByg[[x]])$counts)
rownames(countDFeByg) <- names(rowRanges(counteByg[[1]]))
colnames(countDFeByg) <- names(bfl)
rpkmDFeByg <- apply(countDFeByg, 2, function(x) returnRPKM(counts = x, 
    ranges = eByg))
write.table(countDFeByg, "results/countDFeByg.xls", col.names = NA, 
    quote = FALSE, sep = "\t")
write.table(rpkmDFeByg, "results/rpkmDFeByg.xls", col.names = NA, 
    quote = FALSE, sep = "\t")

Shows count table generated in previous step (countDFeByg.xls). To avoid slowdowns of the load time of this page, ony 200 rows of the source table are imported into the below datatable view .

countDF <- read.delim("results/countDFeByg.xls", row.names = 1, 
    check.names = FALSE)[1:200, ]
library(DT)
datatable(countDF, options = list(scrollX = TRUE, autoWidth = TRUE))

A data slice of RPKM table (rpkmDFeByg.xls) is shown here.

read.delim("results/rpkmDFeByg.xls", row.names = 1, check.names = FALSE)[1:4, 
    1:4]

##                 M1A       M1B       A1A       A1B
## AT1G01010 15.552350 15.855557 15.515099 11.482534
## AT1G01020  5.663586  8.550121  5.550872  8.069877
## AT1G01030  6.294920  4.918521  6.180994  3.092568
## AT1G01040 13.909390 12.846007 11.638283 11.304143

Note, for most statistical differential expression or abundance analysis methods, such as edgeR or DESeq2, the raw count values should be used as input. The usage of RPKM values should be restricted to specialty applications required by some users, e.g. manually comparing the expression levels among different genes or features.

Sample-wise correlation analysis

The following computes the sample-wise Spearman correlation coefficients from the rlog transformed expression values generated with the DESeq2 package. After transformation to a distance matrix, hierarchical clustering is performed with the hclust function and the result is plotted as a dendrogram (also see file sample_tree.pdf).

library(DESeq2, quietly = TRUE)
library(ape, warn.conflicts = FALSE)
countDF <- as.matrix(read.table("./results/countDFeByg.xls"))
colData <- data.frame(row.names = targets.as.df(targets(args))$SampleName, 
    condition = targets.as.df(targets(args))$Factor)
dds <- DESeqDataSetFromMatrix(countData = countDF, colData = colData, 
    design = ~condition)
d <- cor(assay(rlog(dds)), method = "spearman")
hc <- hclust(dist(1 - d))
png("results/sample_tree.png")
plot.phylo(as.phylo(hc), type = "p", edge.col = "blue", edge.width = 2, 
    show.node.label = TRUE, no.margin = TRUE)
dev.off()

Analysis of DEGs

The analysis of differentially expressed genes (DEGs) is performed with the glm method of the edgeR package (Robinson, McCarthy, and Smyth 2010). The sample comparisons used by this analysis are defined in the header lines of the targets.txt file starting with <CMP>.

Run edgeR

library(edgeR)
countDF <- read.delim("results/countDFeByg.xls", row.names = 1, 
    check.names = FALSE)
targets <- read.delim("targetsPE.txt", comment = "#")
cmp <- readComp(file = "targetsPE.txt", format = "matrix", delim = "-")
edgeDF <- run_edgeR(countDF = countDF, targets = targets, cmp = cmp[[1]], 
    independent = FALSE, mdsplot = "")

Add gene descriptions

library("biomaRt")
m <- useMart("plants_mart", dataset = "athaliana_eg_gene", host = "plants.ensembl.org")
desc <- getBM(attributes = c("tair_locus", "description"), mart = m)
desc <- desc[!duplicated(desc[, 1]), ]
descv <- as.character(desc[, 2])
names(descv) <- as.character(desc[, 1])
edgeDF <- data.frame(edgeDF, Desc = descv[rownames(edgeDF)], 
    check.names = FALSE)
write.table(edgeDF, "./results/edgeRglm_allcomp.xls", quote = FALSE, 
    sep = "\t", col.names = NA)

Plot DEG results

Filter and plot DEG results for up and down regulated genes. The definition of up and down is given in the corresponding help file. To open it, type ?filterDEGs in the R console.

edgeDF <- read.delim("results/edgeRglm_allcomp.xls", row.names = 1, 
    check.names = FALSE)
png("results/DEGcounts.png")
DEG_list <- filterDEGs(degDF = edgeDF, filter = c(Fold = 2, FDR = 20))
dev.off()
write.table(DEG_list$Summary, "./results/DEGcounts.xls", quote = FALSE, 
    sep = "\t", row.names = FALSE)

Venn diagrams of DEG sets

The overLapper function can compute Venn intersects for large numbers of sample sets (up to 20 or more) and plots 2-5 way Venn diagrams. A useful feature is the possibility to combine the counts from several Venn comparisons with the same number of sample sets in a single Venn diagram (here for 4 up and down DEG sets).

vennsetup <- overLapper(DEG_list$Up[6:9], type = "vennsets")
vennsetdown <- overLapper(DEG_list$Down[6:9], type = "vennsets")
png("results/vennplot.png")
vennPlot(list(vennsetup, vennsetdown), mymain = "", mysub = "", 
    colmode = 2, ccol = c("blue", "red"))
dev.off()

GO term enrichment analysis

Obtain gene-to-GO mappings

The following shows how to obtain gene-to-GO mappings from biomaRt (here for A. thaliana) and how to organize them for the downstream GO term enrichment analysis. Alternatively, the gene-to-GO mappings can be obtained for many organisms from Bioconductor’s *.db genome annotation packages or GO annotation files provided by various genome databases. For each annotation this relatively slow preprocessing step needs to be performed only once. Subsequently, the preprocessed data can be loaded with the load function as shown in the next subsection.

library("biomaRt")
listMarts()  # To choose BioMart database
listMarts(host = "plants.ensembl.org")
m <- useMart("plants_mart", host = "plants.ensembl.org")
listDatasets(m)
m <- useMart("plants_mart", dataset = "athaliana_eg_gene", host = "plants.ensembl.org")
listAttributes(m)  # Choose data types you want to download
go <- getBM(attributes = c("go_id", "tair_locus", "namespace_1003"), 
    mart = m)
go <- go[go[, 3] != "", ]
go[, 3] <- as.character(go[, 3])
go[go[, 3] == "molecular_function", 3] <- "F"
go[go[, 3] == "biological_process", 3] <- "P"
go[go[, 3] == "cellular_component", 3] <- "C"
go[1:4, ]
dir.create("./data/GO")
write.table(go, "data/GO/GOannotationsBiomart_mod.txt", quote = FALSE, 
    row.names = FALSE, col.names = FALSE, sep = "\t")
catdb <- makeCATdb(myfile = "data/GO/GOannotationsBiomart_mod.txt", 
    lib = NULL, org = "", colno = c(1, 2, 3), idconv = NULL)
save(catdb, file = "data/GO/catdb.RData")

Batch GO term enrichment analysis

Apply the enrichment analysis to the DEG sets obtained the above differential expression analysis. Note, in the following example the FDR filter is set here to an unreasonably high value, simply because of the small size of the toy data set used in this vignette. Batch enrichment analysis of many gene sets is performed with the function. When method=all, it returns all GO terms passing the p-value cutoff specified under the cutoff arguments. When method=slim, it returns only the GO terms specified under the myslimv argument. The given example shows how a GO slim vector for a specific organism can be obtained from BioMart.

library("biomaRt")
load("data/GO/catdb.RData")
DEG_list <- filterDEGs(degDF = edgeDF, filter = c(Fold = 2, FDR = 50), 
    plot = FALSE)
up_down <- DEG_list$UporDown
names(up_down) <- paste(names(up_down), "_up_down", sep = "")
up <- DEG_list$Up
names(up) <- paste(names(up), "_up", sep = "")
down <- DEG_list$Down
names(down) <- paste(names(down), "_down", sep = "")
DEGlist <- c(up_down, up, down)
DEGlist <- DEGlist[sapply(DEGlist, length) > 0]
BatchResult <- GOCluster_Report(catdb = catdb, setlist = DEGlist, 
    method = "all", id_type = "gene", CLSZ = 2, cutoff = 0.9, 
    gocats = c("MF", "BP", "CC"), recordSpecGO = NULL)
write.table(BatchResult, "results/GOBatchAll.xls", row.names = FALSE, 
    quote = FALSE, sep = "\t")
library("biomaRt")
m <- useMart("plants_mart", dataset = "athaliana_eg_gene", host = "plants.ensembl.org")
goslimvec <- as.character(getBM(attributes = c("goslim_goa_accession"), 
    mart = m)[, 1])
BatchResultslim <- GOCluster_Report(catdb = catdb, setlist = DEGlist, 
    method = "slim", id_type = "gene", myslimv = goslimvec, CLSZ = 10, 
    cutoff = 0.01, gocats = c("MF", "BP", "CC"), recordSpecGO = NULL)
write.table(BatchResultslim, "results/GOBatchSlim.xls", row.names = FALSE, 
    quote = FALSE, sep = "\t")

Shows GO term enrichment results from previous step. The last gene identifier column (10) of this table has been excluded in this viewing instance to minimze the complexity of the result. To avoid slowdowns of the load time of this page, ony 10 rows of the source table are shown below.

BatchResult <- read.delim("results/GOBatchAll.xls")[1:10, ]
knitr::kable(BatchResult[, -10])

CLID	CLSZ	GOID	NodeSize	SampleMatch	Phyper	Padj	Term	Ont
M1-A1_up_down	26	GO:0050291	4	1	0.0039621	0.0396207	sphingosine N-acyltransferase activity	MF
M1-A1_up_down	26	GO:0004345	6	1	0.0059375	0.0593750	glucose-6-phosphate dehydrogenase activity	MF
M1-A1_up_down	26	GO:0050664	11	1	0.0108597	0.1085975	oxidoreductase activity, acting on NAD(P)H, oxygen as acceptor	MF
M1-A1_up_down	26	GO:0052593	11	1	0.0108597	0.1085975	tryptamine:oxygen oxidoreductase (deaminating) activity	MF
M1-A1_up_down	26	GO:0052594	11	1	0.0108597	0.1085975	aminoacetone:oxygen oxidoreductase(deaminating) activity	MF
M1-A1_up_down	26	GO:0052595	11	1	0.0108597	0.1085975	aliphatic-amine oxidase activity	MF
M1-A1_up_down	26	GO:0052596	11	1	0.0108597	0.1085975	phenethylamine:oxygen oxidoreductase (deaminating) activity	MF
M1-A1_up_down	26	GO:0052793	12	1	0.0118414	0.1184141	pectin acetylesterase activity	MF
M1-A1_up_down	26	GO:0008131	15	1	0.0147808	0.1478083	primary amine oxidase activity	MF
M1-A1_up_down	26	GO:0016018	16	1	0.0157588	0.1575878	cyclosporin A binding	MF

Plot batch GO term results

The data.frame generated by GOCluster can be plotted with the goBarplot function. Because of the variable size of the sample sets, it may not always be desirable to show the results from different DEG sets in the same bar plot. Plotting single sample sets is achieved by subsetting the input data frame as shown in the first line of the following example.

gos <- BatchResultslim
png("results/GOslimbarplotMF.png")
goBarplot(gos, gocat = "MF")
dev.off()
png("results/GOslimbarplotBP.png")
goBarplot(gos, gocat = "BP")
dev.off()
png("results/GOslimbarplotCC.png")
goBarplot(gos, gocat = "CC")
dev.off()

Clustering and heat maps

The following example performs hierarchical clustering on the rlog transformed expression matrix subsetted by the DEGs identified in the above differential expression analysis. It uses a Pearson correlation-based distance measure and complete linkage for cluster joining.

library(pheatmap)
geneids <- unique(as.character(unlist(DEG_list[[1]])))
y <- assay(rlog(dds))[geneids, ]
png("results/heatmap1.png")
pheatmap(y, scale = "row", clustering_distance_rows = "correlation", 
    clustering_distance_cols = "correlation")
dev.off()

Version Information

sessionInfo()

## R version 4.0.5 (2021-03-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 10 (buster)
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.8.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.8.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices
## [6] utils     datasets  methods   base     
## 
## other attached packages:
##  [1] batchtools_0.9.14           ape_5.4-1                  
##  [3] ggplot2_3.3.2               systemPipeR_1.24.5         
##  [5] ShortRead_1.48.0            GenomicAlignments_1.26.0   
##  [7] SummarizedExperiment_1.20.0 Biobase_2.50.0             
##  [9] MatrixGenerics_1.2.0        matrixStats_0.57.0         
## [11] BiocParallel_1.24.1         Rsamtools_2.6.0            
## [13] Biostrings_2.58.0           XVector_0.30.0             
## [15] GenomicRanges_1.42.0        GenomeInfoDb_1.26.1        
## [17] IRanges_2.24.0              S4Vectors_0.28.0           
## [19] BiocGenerics_0.36.0         BiocStyle_2.18.0           
## 
## loaded via a namespace (and not attached):
##   [1] colorspace_2.0-0         rjson_0.2.20            
##   [3] hwriter_1.3.2            ellipsis_0.3.1          
##   [5] bit64_4.0.5              AnnotationDbi_1.52.0    
##   [7] xml2_1.3.2               codetools_0.2-18        
##   [9] splines_4.0.5            knitr_1.30              
##  [11] jsonlite_1.7.1           annotate_1.68.0         
##  [13] GO.db_3.12.1             dbplyr_2.0.0            
##  [15] png_0.1-7                pheatmap_1.0.12         
##  [17] graph_1.68.0             BiocManager_1.30.10     
##  [19] compiler_4.0.5           httr_1.4.2              
##  [21] backports_1.2.0          GOstats_2.56.0          
##  [23] assertthat_0.2.1         Matrix_1.3-2            
##  [25] limma_3.46.0             formatR_1.7             
##  [27] htmltools_0.5.1.1        prettyunits_1.1.1       
##  [29] tools_4.0.5              gtable_0.3.0            
##  [31] glue_1.4.2               GenomeInfoDbData_1.2.4  
##  [33] Category_2.56.0          dplyr_1.0.2             
##  [35] rsvg_2.1                 rappdirs_0.3.1          
##  [37] V8_3.4.0                 Rcpp_1.0.5              
##  [39] jquerylib_0.1.3          vctrs_0.3.5             
##  [41] nlme_3.1-149             blogdown_1.2            
##  [43] rtracklayer_1.50.0       xfun_0.22               
##  [45] stringr_1.4.0            lifecycle_0.2.0         
##  [47] XML_3.99-0.5             edgeR_3.32.0            
##  [49] zlibbioc_1.36.0          scales_1.1.1            
##  [51] BSgenome_1.58.0          VariantAnnotation_1.36.0
##  [53] hms_0.5.3                RBGL_1.66.0             
##  [55] RColorBrewer_1.1-2       yaml_2.2.1              
##  [57] curl_4.3                 memoise_1.1.0           
##  [59] sass_0.3.1               biomaRt_2.46.0          
##  [61] latticeExtra_0.6-29      stringi_1.5.3           
##  [63] RSQLite_2.2.1            genefilter_1.72.0       
##  [65] checkmate_2.0.0          GenomicFeatures_1.42.1  
##  [67] DOT_0.1                  rlang_0.4.8             
##  [69] pkgconfig_2.0.3          bitops_1.0-6            
##  [71] evaluate_0.14            lattice_0.20-41         
##  [73] purrr_0.3.4              bit_4.0.4               
##  [75] tidyselect_1.1.0         GSEABase_1.52.0         
##  [77] AnnotationForge_1.32.0   magrittr_2.0.1          
##  [79] bookdown_0.21            R6_2.5.0                
##  [81] generics_0.1.0           base64url_1.4           
##  [83] DelayedArray_0.16.0      DBI_1.1.0               
##  [85] withr_2.3.0              pillar_1.4.7            
##  [87] survival_3.2-10          RCurl_1.98-1.2          
##  [89] tibble_3.0.4             crayon_1.3.4            
##  [91] BiocFileCache_1.14.0     rmarkdown_2.7           
##  [93] jpeg_0.1-8.1             progress_1.2.2          
##  [95] locfit_1.5-9.4           grid_4.0.5              
##  [97] data.table_1.13.2        blob_1.2.1              
##  [99] Rgraphviz_2.34.0         digest_0.6.27           
## [101] xtable_1.8-4             brew_1.0-6              
## [103] openssl_1.4.3            munsell_0.5.0           
## [105] bslib_0.2.4              askpass_1.1

Funding

This project was supported by funds from the National Institutes of Health (NIH).

References