15 months ago
Republic of Ireland
Regarding differential expression analysis and FPKM, please read these:
You should abandon RPKM / FPKM. They are not ideal where cross-sample differential expression analysis is your aim; indeed, they render samples incomparable via differential expression analysis:
Please read this: A
comprehensive evaluation of normalization methods for Illumina
high-throughput RNA sequencing data analysis
The Total Count and RPKM [FPKM] normalization methods, both of which are
still widely in use, are ineffective and should be definitively
abandoned in the context of differential analysis.
Also, by Harold Pimental: What the FPKM? A review of RNA-Seq expression units
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.
You should aim to obtain the raw counts for your dataset of interest and then reprocess these using a normalisation strategy that is more amenable to differential expression (like those implemented in DESeq2, EdgeR, and Limma in R Programming Language).
You will not find much advice for conducting statistical comparisons on FPKM data in Excel on this forum, but I could be proved incorrect on that.