I agree with Lila - you should not perform differential expression analysis on FPKM expression levels. If you don't believe me, then take it from the developer of limma, where some suggestions are also made: https://support.bioconductor.org/p/56275/#56299
The first thing one should remember is that without between sample
normalization (a topic for a later post), NONE of these units arecomparable across experiments. This is a result of RNA-Seq being arelative measurement, not an absolute one.
Remarkably, I still see publications coming out where people are comparing groups of samples based on FPKM counts, even though this makes no sense. As an example, FPKM of 10 in one sample may be the equivalent of 50 in another, due to the way that FPKM counts are produced, i.e., with no cross-library / sample normalisation.
Hi, I do not recommend to use FPKM for DE analysis. Is better to use the normalized counts (vst/vsd) generated by DESeq2. (For more info have a look in here).
However, there is a function in DESeq2 called fpkm() that allow you to do that