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DESeq2_gcloud.R
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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$EnsemblGene <- tmp
resdata
}
##############################################################
##############################################################
##############################################################
library(stringi)
####### RNA-iPOP-3 #######
####locate the directory containing the files####pri
dir <- "~/rsem-results/iPOP-3"
###create vector of filenames from table
samples3 <- read.table(file.path(dir, "RNA_Fiber_ipop_3.csv"), sep=",", header = TRUE)
IDs <- substring(as.character(samples3$ID), 2, 9)
IDs_last_8 <- substring(as.character(samples3$ID), 10)
library(Biostrings)
dna = DNAStringSet(stri_reverse(IDs_last_8))
IDs_last_8_rc <- complement(dna)
##import transcript-level estimates from RSEM files
files3 <- file.path(dir, paste0(IDs, IDs_last_8_rc, "_R1_concatenated_", IDs, IDs_last_8_rc, "_R2_concatenated_rsem.genes.results"))
# for (i in files3){
# print(i)
# rsem_file <- read.table(i, sep="\t", header=TRUE)
# rsem_file <-data.frame(rsem_file)
# rsem_file <- rsem_file[grepl("^E", rsem_file[,1]),]
# rsem_file <- rsem_file[!grepl("^ENSGR", rsem_file[,1]),]
# write.table(rsem_file,file=i,sep="\t",quote=FALSE)
# }
###
#tx2gene <- read.csv(file.path(dir, "tx2gene.csv"))
#head(tx2gene)
####### RNA-iPOP-4 #######
####locate the directory containing the files####pri
dir <- "~/rsem-results/iPOP-4"
###create vector of filenames from table
samples4 <- read.table(file.path(dir, "RNA_Fiber_ipop_4.csv"), sep=",", header = TRUE)
IDs <- substring(as.character(samples4$Barcode), 2, 9)
IDs_last_8 <- substring(as.character(samples4$Barcode), 10)
library(Biostrings)
dna = DNAStringSet(stri_reverse(IDs_last_8))
IDs_last_8_rc <- complement(dna)
##import transcript-level estimates from RSEM files
files4 <- file.path(dir, paste0(IDs, IDs_last_8_rc, "_R1_concatenated_", IDs, IDs_last_8_rc, "_R2_concatenated_rsem.genes.results"))
# for (i in files4){
# print(i)
# rsem_file <- read.table(i, sep="\t", header=TRUE)
# rsem_file <-data.frame(rsem_file)
# rsem_file <- rsem_file[grepl("^E", rsem_file[,1]),]
# rsem_file <- rsem_file[!grepl("^ENSGR", rsem_file[,1]),]
# write.table(rsem_file,file=i,sep="\t",quote=FALSE)
# }
###
#tx2gene <- read.csv(file.path(dir, "tx2gene.csv"))
#head(tx2gene)
####### RNA-iPOP1_2 #######
####locate the directory containing the files####
dir <- "~/rsem-results/iPOP-1-2"
setwd(dir)
#unlink("*SC_Inulin*")
#file.rename(from=list.files(dir),to=gsub("LC_Inulin","LCInulin",list.files(dir)))
#file.rename(from=list.files(dir),to=gsub("Washout_D","WashoutD",list.files(dir)))
###create vector of filenames from table
samples <- read.table(file.path(dir, "samples.txt"), header = TRUE)
##import transcript-level estimates from RSEM files
files1_2 <- file.path(dir, paste0(samples$run, "_rsem.genes.results.txt"))
# for (i in files1_2){
# print(i)
# rsem_file <- read.table(i, sep="\t", header=TRUE)
# rsem_file <-data.frame(rsem_file)
# rsem_file <- rsem_file[grepl("^E", rsem_file[,1]),]
# rsem_file <- rsem_file[!grepl("^ENSGR", rsem_file[,1]),]
# write.table(rsem_file,file=i,sep="\t",quote=FALSE)
# }
if (FALSE) {
####### RNA-iPOP5-6-7 #######
####locate the directory containing the files####
dir <- "~/rnaseq-storage/rsem-results/iPOP-5-6-7"
samples5_7 <- read.table(file.path(dir, "ReplaceRNASeq_library_prep.csv"), sep=",", header = TRUE)
##import transcript-level estimates from RSEM files
files5_7 <- file.path(dir, paste0(samples5_7$adapters, ".genes.results"))
# for (i in files5_7){
# print(i)
# rsem_file <- read.table(i, sep="\t", header=TRUE)
# rsem_file <-data.frame(rsem_file)
# to_remove <- c("TPM_coefficient_of_quartile_variation", "FPKM_coefficient_of_quartile_variation")
# rsem_file <- rsem_file[,which(!colnames(rsem_file) %in% to_remove)]
# colnames(rsem_file)[2] <- "transcript_id(s)"
# write.table(rsem_file,file=i,sep="\t",quote=FALSE)
# }
}
samplesa <- samples[,-1]
samples4a <- cbind(samples4[,7],samples4[,8:9],samples4[,6],samples4[,10])
colnames(samples4a) <- colnames(samplesa)
samples3a <- cbind(samples3[,7],samples3[,8:9],samples3[,6],samples3[,10])
colnames(samples3a) <- colnames(samplesa)
samples5_7a <- samples5_7[,6:10]
###
#
samplesa <- rbind(samplesa, samples3a, samples4a, samples5_7a)
rownames(samplesa) <- paste0("sample", 1:nrow(samplesa))
#What going on here is I've made all the samples placed them in order and then generated the list of files from
#those samples and listed them 1:end of samples. That way file 1 will match sample1
#,files5_7
files <- c(files1_2,files3, files4)
names(files) <- paste0("sample", 1:nrow(samplesa))
txi <- tximport(files, type = "rsem")
names(txi)
library(BiocParallel)
bpparam <- SnowParam(15, log = TRUE, stop.on.error = FALSE)
register(MulticoreParam(15))
###remove zero length transcripts
txi$length[txi$length == 0] <- 1
###create dataframe with proper rownames
sampleTable <- data.frame(condition = factor(samplesa$fiber))
sampleTable[,2] <- data.frame(week = factor(samplesa$week))
rownames(sampleTable) <- colnames(txi$counts)
########################################
#fiber_week
########################################
#dds <- DESeqDataSetFromTximport(txi, sampleTable, ~ week:condition)
sampleTable[,3] <- data.frame(fiber_week = factor(interaction(sampleTable$condition,sampleTable$week)))
#sampleTable$fiber_week <- interaction(samples$fiber, samples$week)
sampleTable[,4] <- data.frame(batch = factor(samplesa$batch))
#~batch+
dds <- DESeqDataSetFromTximport(txi, sampleTable, ~fiber_week)
###prefilter low count genes
dds <- dds[ rowMeans(counts(dds)) > 0.2, ]
###set factor levels
#factor_levels <- unique(sampleTable$condition)
#dds$condition <- factor(dds$condition, levels = factor_levels)
#This loop is slightly clunky, but it works well enough for now. Next time perhaps I will
#create a loop for every factor. That should clean things up nicely.
#The first one will be for "condition" i.e. fiber type
i <- colnames(sampleTable)[3]
print(i)
factor_levels <- unique(sampleTable[[i]])
ddsDESeq <- dds
ddsDESeq[[i]] <- factor(ddsDESeq[[i]], levels = factor_levels)
for (j in unique(sampleTable[[i]])) {
print(j)
ddsDESeq[[i]] <- relevel(ddsDESeq[[i]], ref = j)
print("releveled")
#betaPrior = TRUE,
ddsDESeq <- DESeq(ddsDESeq, parallel = TRUE, BPPARAM=MulticoreParam(15)) #The DESeq function must come here because of the releveling
###Differential analysis
print("names:")
resultsNames(ddsDESeq)
for (h in unique(sampleTable[[i]])){
if (h == j) next
print(h)
res <- results(ddsDESeq, contrast = c("fiber_week",j, h), altHypothesis = "greaterAbs", parallel = TRUE, BPPARAM=MulticoreParam(15), cooksCutoff=FALSE, independentFiltering=FALSE)
#res <- na.omit(res)
print("LFCShrink")
#resLFC <- lfcShrink(ddsDESeq, coef=2, res=res,parallel = TRUE, BPPARAM=MulticoreParam(30))
print("LFCShrinkDone")
# names(resLFC)[1] <- "Gene"
## Examine independent filtering
#attr(res, "filterThreshold")
#plot(attr(res,"filterNumRej"), type="b", xlab="quantiles of baseMean", ylab="number of rejections")
#png(paste("~/rnaseq-storage/rsem-results/deseq_results/",j,h,"res-maplot.png",sep=""), 1500, 1000, pointsize=20)
#plotMA(res, ylim=c(-1,1), cex=1)
#dev.off()
# res <- subset(res, baseMean > 0.2) #thowing out the extrememly low read count samples.
print("res created")
res.fixed <- fdrtool(res$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
res <- res[order(res$padj), ]
head(res)
resdata <- merge(as.data.frame(res), as.data.frame(counts(ddsDESeq, normalized=TRUE)), by="row.names", sort=FALSE)
names(resdata)[1] <- "Gene"
resdata <- entrezid(resdata)
## Write results
write.csv(resdata, file=paste("~/rnaseq-storage/rsem-results/deseq_results/",j,h,"-diffexpr-resultsb.csv",sep=""))
###Visualization and data collection
# Plot dispersions
png(paste("~/rnaseq-storage/rsem-results/deseq_results/",j,h,"qc-dispersions.png",sep=""), 1000, 1000, pointsize=20)
plotDispEsts(ddsDESeq, main="Dispersion plot")
dev.off()
# Regularized log transformation for clustering/heatmaps, etc
print("rld")
#rld <- bplapply(ddsDESeq, rlogTransformation(ddsDESeq), BPPARAM=MulticoreParam(15))
#head(assay(rld))
#hist(assay(rld))
# Colors for plots below
## Ugly:
## (mycols <- 1:length(unique(condition)))
## Use RColorBrewer, better looking
#(mycols <- brewer.pal(8, "Dark2")[1:length(unique(factor_levels))])
# Sample distance heatmap
#sampleDists <- as.matrix(dist(t(assay(rld))))
#png(paste("~/rnaseq-storage/rsem-results/deseq_results/",j,h,"qc-heatmap-samples.png",sep=""), w=1000, h=1000, pointsize=20)
#heatmap.2(as.matrix(sampleDists), key=F, trace="none",
#col=colorpanel(100, "green", "red"),
#ColSideColors=mycols[ddsDESeq[[i]]], RowSideColors=mycols[ddsDESeq[[i]]],
#margin=c(10, 10), main="Sample Distance Matrix")
#dev.off()
# Principal components analysis
#png(paste("~/rnaseq-storage/rsem-results/deseq_results/",j,h,"qc-pca.png",sep=""), 1000, 1000, pointsize=20)
#rld_pca(rld, colors=mycols, intgroup="condition")
#dev.off()
# Get differential expression results
## Examine plot of p-values
png(paste("~/rnaseq-storage/rsem-results/deseq_results/",j,h,"res-pvalue.png",sep=""), 1000, 1000, pointsize=20)
hist(res$pvalue, breaks=250, col="grey")
dev.off()
## MA plot
png(paste("~/rnaseq-storage/rsem-results/deseq_results/",j,h,"diffexpr-maplot-noLFC.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("~/rnaseq-storage/rsem-results/deseq_results/",j,h,"diffexpr-volcanoplot.png",sep=""), 1200, 1000, pointsize=20)
volcanoplot(resdata, sigthresh=0.1, textcx=.8, xlim=c(-5, 5))
dev.off()
}}