Will it be ok to use FPKM normalized RNA seq data for differential
gene expression analysis ????
It will not be okay to use FPKM values for differential expression analyses, even if you have seen it in some major publication. Statisticians / bio-statisticians are not accustom to peer review those publications, but they ought to.
Please read these:
1, What the FPKM? A review of RNA-Seq expression units
In bold, at the top (regarding RPKM, FPKM, TPM, and CPM):
The first thing one should remember is that without between sample
normalization (a topic for a later post), NONE of these units are
comparable across experiments. This is a result of RNA-Seq being a
relative measurement, not an absolute one.
2, A
comprehensive evaluation of normalization methods for Illumina
high-throughput RNA sequencing data analysis
In their key points:
The Total Count and RPKM normalization methods, both of which are
still widely in use, are ineffective and should be definitively
abandoned in the context of differential analysis.
[Note - FPKM is essentially the same as RPKM]
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As sophiespo mentions, try to obtain the raw counts and then feed these into one of the more serious differential expression analysis tools.
Kevin
