Is Cufflinks2's normalization method reliable for differential expression analysis?

Question

[kevin.l.yang]

I am using both DESeq2 and Cufflinks2 for differential expression analysis for some mouse RNAseq data. I was reading this guide (http://chagall.med.cornell.edu/RNASEQcourse/Intro2RNAseq.pdf) for help. Page 49 of the guide says that RPKM/FPKM isn't suitable for differential expression analysis, since it doesn't take into account sequencing depth among different samples. However, I figured that going from Cufflinks to Cufflinks2, the developers would have accounted for this issue. Can I trust my results from Cufflink2? I am making a poster right now, and I don't want to put my Cufflinks2 results if they're not reliable

Answer 1

[Kevin Blighe]

You're right: for differential expression itself, I don't believe that Cufflinks is ideal due to the fact that it doesn't handle the wide variation in counts that can exist. One can end up with log base 2 fold change difference of upward of 100, which is astronomical and doesn't make much sense. You are not required to use FPKM with Cufflinks, though; If you must use Cufflinks, for whatever reason, then use geometric normalisation.

DESeq2 handles these issues very well and allows you to perform a regularised log transformation (rlog) or variance stabilised transformation (VST) on your data, which produces very nice P values and fold-changes. So, many people then try to extract raw counts from the TopHat/Cufflinks method and then do the differential expression in DESeq2, but this is not possible directly using TopHat/Cufflinks.

A much simpler way to analyse RNA-seq data is with Kallisto ( https://pachterlab.github.io/kallisto/ ), which extracts raw counts over a CDS FASTA that you supply. If you supply GENCODE's CDS FASTA ( https://www.gencodegenes.org/releases/current.html ), then you can get count abundances over upward of 200,000 transcripts and their isoforms.