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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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Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
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