Template for analysis with DESeq2

deseq2-analysis-template.R

## RNA-seq analysis with DESeq2
## Stephen Turner, @genetics_blog

# RNA-seq data from GSE52202
# http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse52202. All patients with
# ALS, 4 with C9 expansion ("exp"), 4 controls without expansion ("ctl")

# Import & pre-process ----------------------------------------------------

# Import data from featureCounts
## Previously ran at command line something like this:
## featureCounts -a genes.gtf -o counts.txt -T 12 -t exon -g gene_id GSM*.sam
countdata <- read.table("counts.txt", header=TRUE, row.names=1)

# Remove first five columns (chr, start, end, strand, length)
countdata <- countdata[ ,6:ncol(countdata)]

# Remove .bam or .sam from filenames
colnames(countdata) <- gsub("\\.[sb]am$", "", colnames(countdata))

# Convert to matrix
countdata <- as.matrix(countdata)
head(countdata)

# Assign condition (first four are controls, second four contain the expansion)
(condition <- factor(c(rep("ctl", 4), rep("exp", 4))))

# Analysis with DESeq2 ----------------------------------------------------

library(DESeq2)

# Create a coldata frame and instantiate the DESeqDataSet. See ?DESeqDataSetFromMatrix
(coldata <- data.frame(row.names=colnames(countdata), condition))
dds <- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design=~condition)
dds

# Run the DESeq pipeline
dds <- DESeq(dds)

# Plot dispersions
png("qc-dispersions.png", 1000, 1000, pointsize=20)
plotDispEsts(dds, main="Dispersion plot")
dev.off()

# Regularized log transformation for clustering/heatmaps, etc
rld <- rlogTransformation(dds)
head(assay(rld))
hist(assay(rld))

# Colors for plots below
## Ugly:
## (mycols <- 1:length(unique(condition)))
## Use RColorBrewer, better
library(RColorBrewer)
(mycols <- brewer.pal(8, "Dark2")[1:length(unique(condition))])

# Sample distance heatmap
sampleDists <- as.matrix(dist(t(assay(rld))))
library(gplots)
png("qc-heatmap-samples.png", w=1000, h=1000, pointsize=20)
heatmap.2(as.matrix(sampleDists), key=F, trace="none",
          col=colorpanel(100, "black", "white"),
          ColSideColors=mycols[condition], RowSideColors=mycols[condition],
          margin=c(10, 10), main="Sample Distance Matrix")
dev.off()

# Principal components analysis
## Could do with built-in DESeq2 function:
## DESeq2::plotPCA(rld, intgroup="condition")
## I like mine better:
rld_pca <- function (rld, intgroup = "condition", ntop = 500, colors=NULL, legendpos="bottomleft", main="PCA Biplot", textcx=1, ...) {
  require(genefilter)
  require(calibrate)
  require(RColorBrewer)
  rv = rowVars(assay(rld))
  select = order(rv, decreasing = TRUE)[seq_len(min(ntop, length(rv)))]
  pca = prcomp(t(assay(rld)[select, ]))
  fac = factor(apply(as.data.frame(colData(rld)[, intgroup, drop = FALSE]), 1, paste, collapse = " : "))
  if (is.null(colors)) {
    if (nlevels(fac) >= 3) {
      colors = brewer.pal(nlevels(fac), "Paired")
    }   else {
      colors = c("black", "red")
    }
  }
  pc1var <- round(summary(pca)$importance[2,1]*100, digits=1)
  pc2var <- round(summary(pca)$importance[2,2]*100, digits=1)
  pc1lab <- paste0("PC1 (",as.character(pc1var),"%)")
  pc2lab <- paste0("PC1 (",as.character(pc2var),"%)")
  plot(PC2~PC1, data=as.data.frame(pca$x), bg=colors[fac], pch=21, xlab=pc1lab, ylab=pc2lab, main=main, ...)
  with(as.data.frame(pca$x), textxy(PC1, PC2, labs=rownames(as.data.frame(pca$x)), cex=textcx))
  legend(legendpos, legend=levels(fac), col=colors, pch=20)
  #     rldyplot(PC2 ~ PC1, groups = fac, data = as.data.frame(pca$rld),
  #            pch = 16, cerld = 2, aspect = "iso", col = colours, main = draw.key(key = list(rect = list(col = colours),
  #                                                                                         terldt = list(levels(fac)), rep = FALSE)))
}
png("qc-pca.png", 1000, 1000, pointsize=20)
rld_pca(rld, colors=mycols, intgroup="condition", xlim=c(-75, 35))
dev.off()


# Get differential expression results
res <- results(dds)
table(res$padj<0.05)
## Order by adjusted p-value
res <- res[order(res$padj), ]
## Merge with normalized count data
resdata <- merge(as.data.frame(res), as.data.frame(counts(dds, normalized=TRUE)), by="row.names", sort=FALSE)
names(resdata)[1] <- "Gene"
head(resdata)
## Write results
write.csv(resdata, file="diffexpr-results.csv")

## Examine plot of p-values
hist(res$pvalue, breaks=50, col="grey")

## Examine independent filtering
attr(res, "filterThreshold")
plot(attr(res,"filterNumRej"), type="b", xlab="quantiles of baseMean", ylab="number of rejections")

## MA plot
## Could do with built-in DESeq2 function:
## DESeq2::plotMA(dds, ylim=c(-1,1), cex=1)
## I like mine better:
maplot <- function (res, thresh=0.05, labelsig=TRUE, textcx=1, ...) {
  with(res, plot(baseMean, log2FoldChange, pch=20, cex=.5, log="x", ...))
  with(subset(res, padj<thresh), points(baseMean, log2FoldChange, col="red", pch=20, cex=1.5))
  if (labelsig) {
    require(calibrate)
    with(subset(res, padj<thresh), textxy(baseMean, log2FoldChange, labs=Gene, cex=textcx, col=2))
  }
}
png("diffexpr-maplot.png", 1500, 1000, pointsize=20)
maplot(resdata, main="MA Plot")
dev.off()

## Volcano plot with "significant" genes labeled
volcanoplot <- function (res, lfcthresh=2, sigthresh=0.05, main="Volcano Plot", legendpos="bottomright", labelsig=TRUE, textcx=1, ...) {
  with(res, plot(log2FoldChange, -log10(pvalue), pch=20, main=main, ...))
  with(subset(res, padj<sigthresh ), points(log2FoldChange, -log10(pvalue), pch=20, col="red", ...))
  with(subset(res, abs(log2FoldChange)>lfcthresh), points(log2FoldChange, -log10(pvalue), pch=20, col="orange", ...))
  with(subset(res, padj<sigthresh & abs(log2FoldChange)>lfcthresh), points(log2FoldChange, -log10(pvalue), pch=20, col="green", ...))
  if (labelsig) {
    require(calibrate)
    with(subset(res, padj<sigthresh & abs(log2FoldChange)>lfcthresh), textxy(log2FoldChange, -log10(pvalue), labs=Gene, cex=textcx, ...))
  }
  legend(legendpos, xjust=1, yjust=1, legend=c(paste("FDR<",sigthresh,sep=""), paste("|LogFC|>",lfcthresh,sep=""), "both"), pch=20, col=c("red","orange","green"))
}
png("diffexpr-volcanoplot.png", 1200, 1000, pointsize=20)
volcanoplot(resdata, lfcthresh=1, sigthresh=0.05, textcx=.8, xlim=c(-2.3, 2))
dev.off()

Thank you, this was extremely helpful.

Thank you, this was extremely helpful.

Hi thanks for sharing this code. I'm starting to use DESeq2 in command line in R. Basically I can understand how to fuse featureCounts output into one matrix (I will use counts file generated in Galaxy), but this misses the coldata info and I was trying to search how to create it and put it into the deseqdataset object.
Basically, you create it between line 25 and 34 if I understand correctly. I want to assign 2 different batches as conditions and in each I have 3 conditions (not similar between 2 batches) first batch has duplicates and 2nd has triplicates.
so should I use:
(condition <- factor(c(rep("cond1", 2), rep("cond2", 2), rep("cond3", 2), rep("cond4", 3), rep("cond5", 3), rep("cond6", 3))))
(batch <- factor(c(rep("batch1", 6), rep("batch2", 9))))
(coldata <- data.frame(row.names=colnames(countdata), condition, batch))
dds <- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design=~condition, batch)

is this correct? also I'm not sure why we need brackets from the beginning of these lines?
thank you for any help,
best

please how do i solve this sort of error

Error in read.table(file = file, header = header, sep = sep, quote = quote, :
duplicate 'row.names' are not allowed

you can't have duplicates rownames, so one option is to detect them and remove or you could also rename the duplicates adding some extra information like _1 and _2. El mié, 3 feb 2021 a las 9:25, Aduragbemi1992 (<notifications@github.com>) escribió:

…

***@***.**** commented on this gist. ------------------------------ please how do i solve this sort of error Error in read.table(file = file, header = header, sep = sep, quote = quote, : duplicate 'row.names' are not allowed — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <https://gist.github.com/f60c1934405c127f09a6#gistcomment-3618044>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AJU7ALPWR3JMZM27QM7CUNTS5EB57ANCNFSM4HP6ES3Q> .

Could anyone tell me how to download the template RNA-seq data from GSE52202? I tried the GEOquery package in R and unzipped the GSE52202_iPSCMN_RPKM_RS10162012.txt.gz. After that, I only received the GSE52202_iPSCMN_RPKM_RS10162012.txt file, which seemed not like the counts.txt that described in the R script. Thanks!

Super helpful. I am ran dds <- DESeqDataSetFromMatrix(countData= cc_all, colData=coldata, design= ~condition) just now and it returned : an error message saying NA values are not allowed in the count matrix. Should I use DESeqTransform or simply removed the NA values. Changing them to "0" is not recommended?

yeah, this code helped a lot with my current project.

Super helpful. I am ran dds <- DESeqDataSetFromMatrix(countData= cc_all, colData=coldata, design= ~condition) just now and it returned : an error message saying NA values are not allowed in the count matrix. Should I use DESeqTransform or simply removed the NA values. Changing them to "0" is not recommended?

• For my project I am using ~mode_of_action. Also, I had to go back and fix my counts file with Multmerge and get rid of all my 0's before running DESeq since there is division (which will return NA's when there are 0's)

Hi,
Thanks for sharing this. I was wondering how I can regress out batch effect using DESeq analysis. Didn't find any argument which is specified for this.
Thanks