Hello all, please excuse this rather naïve question but why should I use an RNA-SEQ specific application (such as edgeR, DESeq, etc.) on counts rather than a simple t-test on FPKM or RPKM values? I've seen many papers comparing the performance of different RNA-SEQ specific applications but I don't recall seeing any compared to a simple t-test. I presume there would be a breakdown of reliability at extreme values but has anyone seen an actual analysis? Thanks.
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Have a read through the original limma paper. This was for microarrays, but the point is the same. Also, make sure you create you FPKMs/RPKMs from normalized values rather than using the classical FPKM/RPKM formulas. That latter are well known to be unreliable (not to mention that you lose precision when you convert to FPKM/RPKM). -
For a comparison that includes the t-test, see the voom paper at
As expected, the t-test does very poorly compared to the best of the RNA-seq specific methods.Comment
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This convergence of genetics, immunology, and computation...09-01-2026, 05:41 AM
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