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Tutorial 03: featureCounts and DESeq2

Ownership

Wang Lab at HKUST

Data Illustration

The raw sequencing data of these patients will be only used for tutorials. All data will be deleted after this semester’s BIEN3320/LIFS4320 course.

Introduction

• featureCounts is a read summarization program suitable for counting reads generated from either RNA or DNA sequencing experiments. Liao, Y., Smyth, G. K., & Shi, W. (2014). featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923-930.

• DESeq2 is a method for differential analysis of count data. Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology, 15(12), 1-21. The DESeq2 package is available at (http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html).

Prerequisites of featureCounts

• A count matrix (DESeq2/Input/count.csv)

• A sample information table (DESeq2/Input/info.csv)

step 1

Open your RStudio and create a R script.

step 2

Find the directory of featureCounts (eg. yourpath/subread-2.0.1-MacOS-x86_64/bin/featureCounts)

step 3

Run featureCounts

yourpath/subread-2.0.1-MacOS-x86_64/bin/featureCounts -p -T 12 -B -t exon -g gene_name \
-a yourpath/featureCounts/Input/gtf/chr21.hg37.gtf \
-o outputpath/Control1.txt yourpath/featureCounts/Input/bam/Control1.bam

Note: you need to change the path to your path.

Prerequisites of DESeq2

• A count matrix (DESeq2/Input/count.csv)

• A sample information table (DESeq2/Input/info.csv)

step 1

Install RStudio, open your RStudio and create a R script (File->New File->R script).

step 2

Install the “DESeq2” package.

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("DESeq2")
library("DESeq2")

step 3

Prepare your data: A count matrix and A sample information table

setwd("~/Dropbox/course/BIEN3320/Tutorial/T03/")

countMatrix <- as.matrix(read.csv("count.csv",sep=",",row.names="gene"))
info <- read.csv("info.csv", row.names=1)
info$condition <- factor(info$condition)

#The columns of the count matrix and the rows of the information table are in the same order.
all(rownames(info) == colnames(countMatrix))

step 4

Run DESeq2: two-group comparison

dds <- DESeqDataSetFromMatrix(countData = countMatrix,
                              colData = info,
                              design = ~ condition)
dds <- dds[rowSums(counts(dds)) > 0,]
dds$condition <- factor(dds$condition, levels = c("control","tumor"))
dds <- DESeq(dds)
results(dds)
comparison=resultsNames(dds)[2]
res <- results(dds, name=comparison)   
print(comparison)
res<-as.data.frame(res[complete.cases(res),])
write.csv(res,'./deseq2.csv')

step 5

Volcano Plot to show the differentially expressed genes

par(mfrow=c(1,1))
# Make a basic volcano plot
with(res, plot(log2FoldChange, -log10(pvalue), pch=20, main="Volcano plot"))
# Add colored points: blue if padj<0.05 & log2FoldChange<0, red if pvalue<.05 & log2FoldChange>0)
with(subset(res, pvalue<.05 & log2FoldChange<0), points(log2FoldChange, -log10(pvalue), pch=20, col="blue", cex = 2))
with(subset(res, pvalue<.05 & log2FoldChange>0), points(log2FoldChange, -log10(pvalue), pch=20, col="red", cex = 2))

15 Feb 2021