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DESeq2_WashoutD10.r
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#[email protected]:/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq
#This program will be to run DESeq2 on the WashoutD10 samples, which in explicably didn't work on the cluster
#I troubleshooted it (see recap, 10/5/17) but to no avail. It seemed to work on my desktop,
#so I'm going to proceed with that.
library(gplots)
library(RColorBrewer)
library(DESeq2)
library(readr)
library(tximport)
library(fdrtool)
options(bitmapType='cairo') #work around for X11 missing on cluster
##############################################################
##############################################################
##############################################################
#functions here
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)))
}
volcanoplot <- function (res, lfcthresh=2, sigthresh=0.1, 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"))
}
#Must be run with resdata, not res! This is because of the lab=Gene
maplot <- function (res, thresh=0.1, 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))
}
}
entrezid <- function( resdata ) {
require(EnsDb.Hsapiens.v79)
a = resdata$Gene #the column to iterate over will be different if I'm using res vs resdata
tmp=gsub("\\..*","",a)
tmp <- as.character(tmp)
txdb <- EnsDb.Hsapiens.v79
df <- AnnotationDbi::select(txdb, keys = tmp, keytype = "GENEID", columns = "ENTREZID")
df2 <- AnnotationDbi::select(txdb, keys = tmp, keytype = "GENEID", columns = "SYMBOL")
ENTREZID <- c()
SYMBOL <- c()
counter1 <- 0
for (i in tmp) {
counter1 <- counter1 + 1
j <- match(i,df$GENEID)
ENTREZID <- c(ENTREZID, toString(df[j,][2]))
SYMBOL <- c(SYMBOL, toString(df2[j,][2]))}
resdata$ENTREZID <- ENTREZID
resdata$SYMBOL <- SYMBOL
resdata
}
##############################################################
##############################################################
##############################################################
####locate the directory containing the files####pri
#dir <- "/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/Projects/Fiber/Data/rsem_genes_results_all_trimmed"
dir <- "/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/rsem_genes_results_all_trimmed"
###create vector of filenames from table
samples <- read.table(file.path(dir, "samples.txt"), header = TRUE)
##import transcript-level estimates from RSEM files
files <- file.path(dir, samples$run, paste0(samples$run, ".genes.results"))
names(files) <- paste0("sample", 1:nrow(samples))
###
#tx2gene <- read.csv(file.path(dir, "tx2gene.csv"))
#head(tx2gene)
###
txi <- tximport(files, type = "rsem")
names(txi)
###remove zero length transcripts
txi$length[txi$length == 0] <- 1
###create dataframe with proper rownames
sampleTable <- data.frame(week = factor(samples$week))
rownames(sampleTable) <- colnames(txi$counts)
###construct a DESeqDataSet from the txi object and sample info in samples.txt file
dds <- DESeqDataSetFromTximport(txi, sampleTable, ~week)
###prefilter low count genes
dds <- dds[ rowMeans(counts(dds)) > 0.2, ]
ddsDESeq <- dds
for (j in unique(sampleTable$week)) {
print(j)
#change to/from j
ddsDESeq$week <- relevel(ddsDESeq$week, ref = "WashoutD10")
###Differential analysis
ddsDESeq <- DESeq(ddsDESeq)
resultsNames(ddsDESeq)
print(resultsNames(ddsDESeq)[2:length(resultsNames(ddsDESeq))])
for (h in resultsNames(ddsDESeq)[2:length(resultsNames(ddsDESeq))]){
print(h)
res <- results(ddsDESeq, contrast = list(h), altHypothesis = "greaterAbs")
res.fixed <- fdrtool(res$stat, statistic = "normal") #adjusting for batch effects (https://support.bioconductor.org/p/99685/; http://www-huber.embl.de/users/klaus/Teaching/DESeq2Predoc2014.html)
res.fixed <- data.frame(res.fixed)
padjusted <- p.adjust(res.fixed$pval, method = "BH", n = nrow(res.fixed))
# res$pvalue <- res.fixed$pval
# res$padj <- padjusted
table(res$padj<0.1)
res <- res[order(res$padj), ]
head(res)
resLFC <- lfcShrink(ddsDESeq, coef=2, res=res)
## Examine independent filtering
#attr(res, "filterThreshold")
#plot(attr(res,"filterNumRej"), type="b", xlab="quantiles of baseMean", ylab="number of rejections")
print(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"resLFC-maplot.png",sep=""))
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"resLFC-maplot.png",sep=""), 1500, 1000, pointsize=20)
plotMA(resLFC, ylim=c(-1,1), cex=1)
dev.off()
# res <- subset(res, baseMean > 0.2) #thowing out the extrememly low read count samples.
resdata <- merge(as.data.frame(res), as.data.frame(counts(ddsDESeq, normalized=TRUE)), by="row.names", sort=FALSE)
names(resdata)[1] <- "Gene"
resdata <- entrezid(resdata)
## Write results
write.csv(resdata, file=paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"-diffexpr-results.csv",sep=""))
###Visualization and data collection
# Plot dispersions
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"qc-dispersions.png",sep=""), 1000, 1000, pointsize=20)
plotDispEsts(ddsDESeq, main="Dispersion plot")
dev.off()
# Regularized log transformation for clustering/heatmaps, etc
print("rld")
rld <- rlogTransformation(ddsDESeq)
head(assay(rld))
hist(assay(rld))
# Colors for plots below
## Ugly:
## (mycols <- 1:length(unique(condition)))
## Use RColorBrewer, better looking
(mycols <- brewer.pal(8, "Dark2"))
# Heatmap was removed, unecessary and needed to troubleshoot
# Principal components analysis
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"qc-pca.png",sep=""), 1000, 1000, pointsize=20)
rld_pca(rld, colors=mycols, intgroup="week")
dev.off()
# Get differential expression results
## Examine plot of p-values
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"res-pvalue.png",sep=""), 1000, 1000, pointsize=20)
hist(res$pvalue, breaks=250, col="grey")
dev.off()
## MA plot
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"diffexpr-maplot-resdata.png",sep=""), 1500, 1000, pointsize=20)
maplot(resdata, ylim = c(-1,1), main="MA Plot")
dev.off()
## Volcano plot with "significant" genes labeled
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"diffexpr-volcanoplot.png",sep=""), 1200, 1000, pointsize=20)
volcanoplot(resdata, sigthresh=0.1, textcx=.8, xlim=c(-5, 5))
dev.off()
}}