Both very popular differential expression packages assume a raw gene matrix count. This makes sense because the statistical model models the library depth. But what if I normalize my counts with say, the ERCC RNA spiked-in standard? Would that affect the statistical power? False-positive? Why exactly is it not recommended to give a normalized count matrix?

  • $\begingroup$ This question should have edgeR and deseq2 tag, but I don't have enough reputation (require 300). Would somebody else add the tags for me? $\endgroup$ – HelloWorld Sep 16 '15 at 13:09
  • $\begingroup$ No need for specific tags. There are hardly any DEseq/edgeR specific questions. $\endgroup$ – WYSIWYG Sep 16 '15 at 13:40

The advice is to not generally use ERCC spike-ins at all because of variations introduced by pipetting at the volumes they recommend.

The thread also explains how to use DESeq and EdgeR with spike-in normalisation, with the process being easier significantly with DESeq, where you can use the calcSizeFactors on a count matrix of spike-in reads alone. With edgeR you will have to pass values using the lib.sizes parameter in the apposite functions.

If you want to use fractional counts through limma-voom should work, I think; I've had good results using voom on RSEM counts, which are fractional.


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