Hello Bioconductor Gurus!

(I apologize if this goes through more than once)

We are currently using limma (through the voom() function) to analyze
RNA-seq data, represented as RSEM counts. We currently have 246 samples
(including replicates) and our design matrix has 65 columns.

My question is in regard to how much we should be filtering our data before
running it through the analysis pipeline. Our current approach is to look
for a CPM of greater than 2 in at least half of the samples. The code is:

keep <- rowSums(cpm(dge) > 2) >= round(ncol(dge)/2)

This brings down our transcript count from 73,761 to less than 20,000.
While we do see groupings and batch effects we expect to see in the MDS
plots, we are afraid we might be filtering too severely.

So finally my question: What is a good metric for determining how well we
have filtered the data?

Thank you,

Mark Lawson, PhD

	[[alternative HTML version deleted]]

