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#!/usr/bin/env Rscript
#
# This script makes a pca of all samples and a heatmap of sample distances.
#
# The inputs to the script are counts file, design file and the number of samples.
#
# How to run?
# Rscript summary_plots.r <counts_file> <design_file> <number_of_samples>
# Example: Rscript summary_plots.r counts.txt design.txt 6
# Design file example:
# Sample Condition
# MCVS450 MCVS
# MCVS515 MCVS
# MCVS520 MCVS
# MNS456 MNS
# MNS486 MNS
# MNS580 MNS
read <- function(counts_file, design_file,number_of_samples){
counts = read.table(counts_file, header=TRUE, sep="\t", row.names=1 )
idx = ncol(counts) - number_of_samples
# Cut out the valid columns.
if (idx > 0) counts = counts[-c(1:idx)] else counts=counts
numeric_idx = sapply(counts, mode) == 'numeric'
counts[numeric_idx] = round(counts[numeric_idx], 0)
colData = read.table(design_file, header=TRUE, sep="\t", row.names=1 )
# Create DESEq2 dataset.
dds = DESeqDataSetFromMatrix(countData=counts, colData=colData, design = ~1)
# Variance Stabilizing Transformation.
vsd = vst(dds)
names = colnames(counts)
groups = colnames(colData)
rlist <- list("vsd" = vsd, "names"=names, "groups" =groups)
return(rlist)
}
# Command line argument.
args = commandArgs(trailingOnly=TRUE)
if (length(args)!=3) {
stop("Counts file, Design file and the number of samples must be specified at the commandline", call.=FALSE)
}
# Load the library while suppressing verbose messages.
suppressPackageStartupMessages(library(DESeq2))
suppressPackageStartupMessages(library(ggplot2))
# Set the plot dimensions.
WIDTH = 12
HEIGHT = 8
# The first argument to the script -counts file
infile = args[1]
# The second argument to the script - design file
coldata_file = args[2]
# The third argument to the script - total number of samples.
sno = args[3]
sno= as.numeric(sno)
res = read(infile, coldata_file, sno)
vsd= res$vsd
names = res$names
groups = res$groups
# Open the drawing device.
pdf('pca.pdf', width = WIDTH, height = HEIGHT)
par(mfrow = c(2,1))
nudge <- position_nudge(y = 0.5)
z=plotPCA(vsd, intgroup=c(groups))
z+ geom_text(aes(label = names), position=nudge, size = 2.5) +ggtitle(aes("PCA"))
dev.off()
#
# Plot heatmap of sample distances
#
library(pheatmap)
library("RColorBrewer")
sampleDists = dist(t(assay(vsd)))
sampleDistMatrix = as.matrix(sampleDists)
colnames(sampleDistMatrix) = NULL
colors = colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
# Open the drawing device.
pdf('heatmap.pdf', width = 8, height = HEIGHT)
pheatmap(sampleDistMatrix,
clustering_distance_rows=sampleDists,
clustering_distance_cols=sampleDists,
col=colors) + geom_label(aes(label = names))
dev.off()
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