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SBA_deseq2_iterations_scg.R
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library(tximport)
require(genefilter)
require(calibrate)
library(readr)
library(EnsDb.Hsapiens.v79)
library(DESeq2)
library(RColorBrewer)
library(gplots)
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=0.05, 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"))
}
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))
}
}
entrezid <- function( resdata ) {
a = resdata$Gene #the column to iterate over will be different if I'm using res vs resdata
tmp=gsub("\\..*","",a)
tmp <- as.character(tmp)
txdb <- EnsDb.Hsapiens.v79
df <- AnnotationDbi::select(txdb, keys = tmp, keytype = "GENEID", columns = "ENTREZID")
ENTREZID <- c()
counter1 <- 0
for (i in tmp) {
counter1 <- counter1 + 1
j <- match(i,df$GENEID)
ENTREZID <- c(ENTREZID, toString(df[j,][2]))}
resdata$ENTREZID <- ENTREZID
resdata
}
##############################################################
##############################################################
##############################################################
####locate the directory containing the files####
dir <- "/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/rsem_genes_results_all_trimmed"
#dir <- "/Users/SLancaster/Desktop/rsem_genes_results_all_trimmed"
###create vector of filenames from table
samples <- read.table(file.path(dir, "samples.txt"), header = TRUE)
##import transcript-level estimates from RSEM files
files <- file.path(dir, samples$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$length == 0] <- 1
###create dataframe with proper rownames
sampleTable <- data.frame(condition = factor(samples$fiber))
sampleTable[,2] <- 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, ~ condition + week)
###prefilter low count genes
dds <- dds[ rowSums(counts(dds)) > 1, ]
###set factor levels
#factor_levels <- unique(sampleTable$condition)
#dds$condition <- factor(dds$condition, levels = factor_levels)
#This loop is slightly clunky, but it works well enough for now. Next time perhaps I will
#create a loop for every factor. That should clean things up nicely.
#The first one will be for "condition" i.e. fiber type
for (i in colnames(sampleTable)[1]) {
print(i)
factor_levels <- unique(sampleTable[[i]])
print("factor levels:")
print(factor_levels)
ddsDESeq <- dds
ddsDESeq[[i]] <- factor(ddsDESeq[[i]], levels = factor_levels)
for (j in unique(sampleTable[[i]])) {
print(j)
ddsDESeq[[i]] <- relevel(ddsDESeq[[i]], ref = j)
###Differential analysis
ddsDESeq <- DESeq(ddsDESeq)
print("Results Names:")
print(resultsNames(ddsDESeq))
for (h in resultsNames(ddsDESeq)[2:4]){
print(h)
res <- results(ddsDESeq, contrast = list(h), altHypothesis = "greaterAbs")
table(res$padj<0.05)
res <- res[order(res$padj), ]
head(res)
res <- subset(res, baseMean > 0.1) #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"
## Write results
resdata <- entrezid(resdata)
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, blind=FALSE)
vsd <- varianceStabilizingTransformation(ddsDESeq, blind=FALSE) #blind=false because dds has already been run estimating dispersion values
head(assay(rld))
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"rld_hist.png",sep=""))
hist(assay(rld))
dev.off()
print("rld finsihed")
# 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("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",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()
png(paste("/Users/SLancaster/Desktop/RNAseq_results/",h,"qc-vsd-pca.png",sep=""), 1000, 1000, pointsize=20)
rld_pca(vsd, colors=mycols, intgroup="condition", xlim=c(-75, 35))
dev.off()
# Principal components analysis
## Could do with built-in DESeq2 function:
## DESeq2::plotPCA(rld, intgroup="condition")
## I like this better:
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="condition", xlim=c(-75, 35))
dev.off()
## 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=50, col="grey")
dev.off()
## 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)
## Use this instead though:
print("resLFC")
# resLFC <- lfcShrink(ddsDESeq, coef=2, res=res)
# png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"built-in-maplot.png",sep=""), 1500, 1000, pointsize=20)
# plotMA(resLFC, ylim=c(-1,1), cex=1)
# dev.off()
# png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"diffexpr-maplot.png",sep=""), 1500, 1000, pointsize=20)
# maplot(resLFC, ylim = c(-.2,.2), main="MA Plot")
# dev.off()
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",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("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"diffexpr-volcanoplot.png",sep=""), 1200, 1000, pointsize=20)
volcanoplot(resdata, lfcthresh=1, sigthresh=0.05, textcx=.8, xlim=c(-1, 1))
dev.off()
}}}
########################################
#The second one is now for column "week"
########################################
for (i in colnames(sampleTable)[2]) {
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)
###Differential analysis
ddsDESeq <- DESeq(ddsDESeq)
resultsNames(ddsDESeq)
for (h in resultsNames(ddsDESeq)[5:length(resultsNames(ddsDESeq))]){
print(h)
res <- results(ddsDESeq, lfcThreshold = 1, contrast = list(h), altHypothesis = "greaterAbs")
table(res$padj<0.05)
res <- res[order(res$padj), ]
head(res)
res <- subset(res, baseMean > 0.1) #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"
head(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")[1:length(unique(factor_levels))])
# Sample distance heatmap
sampleDists <- as.matrix(dist(t(assay(rld))))
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"qc-heatmap-samples.png",sep=""), w=1000, h=1000, pointsize=20)
heatmap.2(as.matrix(sampleDists), key=F, trace="none",
col=colorpanel(100, "black", "white"),
ColSideColors=mycols[ddsDESeq[[i]]], RowSideColors=mycols[ddsDESeq[[i]]],
margin=c(10, 10), main="Sample Distance Matrix")
dev.off()
# 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="condition", xlim=c(-75, 35))
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$pvalue, breaks=50, col="grey")
dev.off()
## Examine independent filtering
#attr(res, "filterThreshold")
#plot(attr(res,"filterNumRej"), type="b", xlab="quantiles of baseMean", ylab="number of rejections")
## MA plot
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"diffexpr-maplot-noLFC.png",sep=""), 1500, 1000, pointsize=20)
maplot(resdata, ylim = c(-1,1), main="MA Plot")
dev.off()
## Volcano plot with "significant" genes labeled
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"diffexpr-volcanoplot.png",sep=""), 1200, 1000, pointsize=20)
volcanoplot(resdata, lfcthresh=1, sigthresh=0.05, textcx=.8, xlim=c(-1, 1))
dev.off()
}}}
#dds <- DESeqDataSetFromTximport(txi, sampleTable, ~ week:condition)
sampleTable[,3] <- data.frame(fiber_week = factor(interaction(sampleTable$condition,sampleTable$week)))
#sampleTable$fiber_week <- interaction(samples$fiber, samples$week)
dds <- DESeqDataSetFromTximport(txi, sampleTable, ~fiber_week)
###prefilter low count genes
dds <- dds[ rowSums(counts(dds)) > 1, ]
###set factor levels
#factor_levels <- unique(sampleTable$condition)
#dds$condition <- factor(dds$condition, levels = factor_levels)
#This loop is slightly clunky, but it works well enough for now. Next time perhaps I will
#create a loop for every factor. That should clean things up nicely.
#The first one will be for "condition" i.e. fiber type
for (i in colnames(sampleTable)[3]) {
print(i)
factor_levels <- unique(sampleTable[[i]])
ddsDESeq <- dds
print(factor_levels)
ddsDESeq[[i]] <- factor(ddsDESeq[[i]], levels = factor_levels)
for (j in unique(sampleTable[[i]])) {
print(j)
ddsDESeq[[i]] <- relevel(ddsDESeq[[i]], ref = j)
###Differential analysis
ddsDESeq <- DESeq(ddsDESeq)
resultsNames(ddsDESeq)
for (h in resultsNames(ddsDESeq)[11:length(resultsNames(ddsDESeq))]){
print(h)
res <- results(ddsDESeq, contrast = list(h), lfcThreshold = 1, altHypothesis = "greaterAbs")
table(res$padj<0.05)
res <- res[order(res$padj), ]
head(res)
res <- subset(res, baseMean > 0.1) #thowing out the extrememly low read count samples.
resdata <- merge(as.data.frame(res), as.data.frame(counts(ddsDESeq, normalized=TRUE)), by="row.names", sort=FALSE)
names(resdata)[1] <- "Gene"
head(resdata)
## Write results
write.csv(resdata, file=paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"-diffexpr-results.csv",sep=""))
###Visualization and data collection
# Plot dispersions
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"qc-dispersions.png",sep=""), 1000, 1000, pointsize=20)
plotDispEsts(ddsDESeq, main="Dispersion plot")
dev.off()
# Regularized log transformation for clustering/heatmaps, etc
print("rld")
rld <- rlogTransformation(ddsDESeq)
head(assay(rld))
hist(assay(rld))
# Colors for plots below
## Ugly:
## (mycols <- 1:length(unique(condition)))
## Use RColorBrewer, better looking
(mycols <- brewer.pal(8, "Dark2")[1:length(unique(factor_levels))])
# Sample distance heatmap
sampleDists <- as.matrix(dist(t(assay(rld))))
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"qc-heatmap-samples.png",sep=""), w=1000, h=1000, pointsize=20)
heatmap.2(as.matrix(sampleDists), key=F, trace="none",
col=colorpanel(100, "black", "white"),
ColSideColors=mycols[ddsDESeq[[i]]], RowSideColors=mycols[ddsDESeq[[i]]],
margin=c(10, 10), main="Sample Distance Matrix")
dev.off()
# Principal components analysis
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"qc-pca.png",sep=""), 1000, 1000, pointsize=20)
rld_pca(rld, colors=mycols, intgroup="condition", xlim=c(-75, 35))
dev.off()
# Get differential expression results
## Examine plot of p-values
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"res-pvalue.png",sep=""), 1000, 1000, pointsize=20)
hist(res$pvalue, breaks=50, col="grey")
dev.off()
## Examine independent filtering
#attr(res, "filterThreshold")
#plot(attr(res,"filterNumRej"), type="b", xlab="quantiles of baseMean", ylab="number of rejections")
## MA plot
png(paste("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",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("/srv/gsfs0/projects/snyder/slancast/fiber/rnaseq/RNAseq_results/",h,"diffexpr-volcanoplot.png",sep=""), 1200, 1000, pointsize=20)
volcanoplot(resdata, lfcthresh=1, sigthresh=0.05, textcx=.8, xlim=c(-1, 1))
dev.off()
}}}