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Copy path2016-05-18_DESeq2_1.R
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2016-05-18_DESeq2_1.R
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## Load required libraries-----
library("BiocParallel")
library("DESeq2")
library("RColorBrewer")
library("calibrate")
library("genefilter")
library("ggplot2")
library("gplots")
library("pheatmap")
save.image(paste0(dir,"/", date," ",expNum, " TGFb Genes.RData"))
## Set environment variables---------
register(MulticoreParam(4))
dir <- setwd("/Volumes/IBD/Yudanin/RNAseq/3-1A-3 RNAseq/3-1A-3 Functional Analysis/")
date <- paste0(Sys.Date())
expNum <- paste0("3-1A-3")
colorder <- c(4,7,16,19,1,10,13,5,8,17,20,2,11,14,6,9,18,3,12,15)
## Define functions and color palettes
normheatmap <- function(mtx, cluster_cols=TRUE, title=title, cex=1, h=1, w=1, ...){
pheatmap(mtx,
cex = cex,
cluster_rows=TRUE,
scale="row",
breaks = c(seq(-1.5, 1.5, length.out = 256)),
border_color = NA,
drop_levels=TRUE,
color = my_palette,
show_rownames=TRUE,
cluster_cols=cluster_cols,
annotation_col=coldata[1:3],
annotation_colors = ann_colors,
annotation_legend=FALSE,
main= paste0(expNum, " ",title),
legend=FALSE,
fontsize= 10,
treeheight_col = 20,
treeheight_row = 20,
height=h*6,
width=w*6,
filename= paste0(date," ",expNum," ",title,".pdf"))
}
resheatmap <- function(vsd, genes, samples, cluster_cols=TRUE, title=title, cex, h=1, w=1, scale="row", ...){
filtered <- assay(vsd) [genes, samples]
filtered <- filtered[rowVars(filtered)>0,]
with(vsd,
pheatmap(filtered[order( rowVars( assay(vsd)[genes,]), decreasing=TRUE),],
cex = cex,
cluster_rows=TRUE,
scale=scale,
breaks = c(seq(-2, 2, length.out = 256)),
border_color = NA,
drop_levels=TRUE,
color = my_palette,
show_rownames=TRUE,
cluster_cols=cluster_cols,
annotation_col=coldata[1:3],
annotation_colors = ann_colors,
annotation_legend=FALSE,
main= paste0(expNum, " ",title),
legend=FALSE,
fontsize= 10,
treeheight_col = 10,
treeheight_row = 20,
height=15*h,
width=6*w,
filename= paste0(date," ",expNum," ",title,".pdf"))
)
}
my_palette <- colorRampPalette(brewer.pal(11, "RdBu")) (255)
my_palette <- rev(my_palette)
ann_colors = list(
subset = c(Treg="#C7302A", ILC2="#707070", CD4="#4266F6" )[colData(dds)$subset],
diet = c(HiFat="#269040", Control="#212121")[colData(dds)$diet],
tissue = c(EWAT="#E7A626", MWAT="#9C27B0")[colData(dds)$tissue] )
## Import data-----------
countdata <- read.delim("/Volumes/IBD/Yudanin/RNAseq/3-1A-3 RNAseq/3-1A-3 DESeq2/3-1A-3 Raw Counts.txt", stringsAsFactors=FALSE)
countdata <- countdata[-(which(duplicated(countdata[,2])==TRUE)),]
row.names(countdata) <- unlist(countdata$Symbol)
## Convert to matrix
countdata <- as.matrix(countdata[,-(1:2)])
## Assign conditions and create coldata metadataframe-----------
names <- colnames(countdata)
getdiet <- function (colname){
strsplit(colname,"_")[[1]][2]
}
diet <- unlist(lapply(names, getdiet))
diet <- gsub("C","Control",diet)
diet <- gsub("F","HiFat",diet)
gettissue <- function (colname){
strsplit(colname,"_")[[1]][3]
}
tissue <- unlist(lapply(names, gettissue))
tissue <- gsub("E","EWAT",tissue)
tissue <- gsub("M","MWAT",tissue)
getsubset <- function (colname){
strsplit(colname,"_")[[1]][5]
}
subset <- unlist(lapply(names, getsubset))
rm(names)
group <- paste0(subset," ",tissue," ",diet)
(coldata <- data.frame(row.names=colnames(countdata), diet, subset, tissue, group))
coldata <- coldata[order(coldata$subset,coldata$tissue, coldata$diet),]
## Instantiate DESeq Dataset
dds <- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design= ~subset + diet + tissue)
dds <- DESeq(dds, parallel=TRUE)
## Import gene list of interest and export corresponding counts-----------
ILC2genes <- read.table("/Volumes/IBD/Yudanin/RNAseq/3-1A-3 RNAseq/ILC2_genes.txt",
quote="\"", comment.char="", stringsAsFactors=FALSE)
ILC2genes <- ILC2genes[[1]]
ILC2genes <- ILC2genes[which(ILC2genes %in% rownames(counts(dds)))]
ILC2genes <- ILC2genes[order( rowMeans( assay(dds)[ILC2genes,]), decreasing=TRUE)]
write.csv(counts(dds, normalized=TRUE)[ILC2genes,colorder],
file=paste0(date," ",expNum," ILC2 Genes Normalized Counts.csv" ))
write.csv(counts(dds, normalized=FALSE)[ILC2genes,colorder],
file=paste0(date," ",expNum," ILC2 Genes Raw Counts.csv" ))
TGFbgenes <- read.table("/Volumes/IBD/Yudanin/RNAseq/3-1A-3 RNAseq/3-1A-3 Functional Analysis/Genes.txt",
quote="\"", comment.char="", stringsAsFactors=FALSE)
TGFbgenes <- TGFbgenes[[1]]
TGFbgenes <- unique(TGFbgenes)
TGFbgenes <- TGFbgenes[which(TGFbgenes %in% rownames(counts(dds)))]
TGFbgenes <- TGFbgenes[which(rowMin(counts(dds)[TGFbgenes,])>100)]
TGFbgenes <- TGFbgenes[order( rowVars( assay(dds)[TGFbgenes,]), decreasing=TRUE)]
TGFbgenes <- TGFbgenes[which(rowVars(counts(dds)[TGFbgenes,])>10)]
### Selected Genes Heatmaps ----------------------------------------------------
ILC2normcounts <- counts(dds, normalized=TRUE)[ILC2genes[1:30],]
TGFbnormcounts <- counts(dds, normalized=TRUE)[TGFbgenes,]
normheatmap(ILC2normcounts[,grep("ILC2",colnames(dds))],
cex= 1,
h=1.5,
w=0.5,
title= "ILC2 Genes Heatmap"
)
dev.off()
normheatmap(TGFbnormcounts[,grep("ILC2",colnames(dds))],
cex= 1,
h=1.5,
w=0.5,
title= "ILC2 TGFb Genes Heatmap"
)
dev.off()
## Selected Gene Bar Plots ---------------------
library(ggplot2)
library(gridExtra)
library(reshape2)
TGFbcounts <- t(counts(dds, normalized=TRUE)[TGFbgenes,])
TGFbcounts <- cbind2(coldata, TGFbcounts)
meltedSC <- melt(TGFbcounts, variable.name = "gene", value.name = "count" )
meltedSC$group <- paste0(meltedSC$subset, " ",meltedSC$tissue, " ",meltedSC$diet)
meltedSC$subsetdiet <- paste0(meltedSC$subset,"_",meltedSC$diet)
subsetdietcolors <- c(Treg_Control="#E39794",
ILC2_Control="#d4d4d4",
CD4_Control="#b3c1fb",
Treg_HiFat="#C7302A",
ILC2_HiFat="#707070",
CD4_HiFat="#4266F6")[meltedSC$subsetdiet]
subsetcolors <- c(Treg="#4f1310",
ILC2="#212121",
CD4="#131e49")[meltedSC$subset]
cairo_pdf(paste0(date," ",expNum," ILC2 TGFb Genes.pdf"), w=10, h=13)
ggplot(data=meltedSC, aes(x=group, y=log2(count+1), fill=subsetdiet, color=subset)) +
facet_wrap( ~ gene, scales="fixed", ncol = 6, dir="h") +
stat_summary (fun.y = "mean", geom = "bar", position = position_dodge(0.9), width=0.75) +
scale_fill_manual(values = subsetdietcolors) +
scale_color_manual(values = subsetcolors ) +
labs(title=paste0(expNum," ILC2 TGFb Genes")) +
theme(legend.position='none',
strip.background = element_blank(),
panel.background = element_rect(color="#707070", linetype="solid", fill=NA, size=0.5),
panel.grid.major = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
plot.title = element_text(size = rel(2)))
dev.off()