We will continue our series in the analysis of our azacitidine treated AML3 cell RNA-seq gene expression data set by generating a multidimensional scaling plot. This is a potentially useful way of showing variability in datasets, especially when the number of samples is large. Trawling the blogs, I found a really quick and easy way to do this in R (thanks Michael Dondrup@BioStars) that can be used to analyse the count matrix.
x<-scale(read.table("CountMatrix.xls", row.names=1, header=TRUE))
plot(cmdscale(dist(t(x))), xlab="Coordinate 1", ylab="Coordinate 2", type = "n") ; text(cmdscale(dist(t(x))), labels=colnames(x), )