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Old 03-03-2015, 06:55 AM   #1
Junior Member
Location: Groton, CT

Join Date: Oct 2011
Posts: 1
Default Why use RNA-SEQ app for DGE rather than t-test?

Hello all, please excuse this rather na´ve 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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Old 03-03-2015, 07:50 AM   #2
Devon Ryan
Location: Freiburg, Germany

Join Date: Jul 2011
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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).
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Old 03-08-2015, 11:32 PM   #3
Gordon Smyth
Location: Melbourne, Australia

Join Date: Apr 2011
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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.
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dge, t-test

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