My data is RNAseq data and I want to know which all genes (which I am looking for) are differentially expressed with age. I have tried the Kruskal-Wallis test and fold change methods. Do you have any better suggestions?

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    $\begingroup$ Someone here may be able to answer your question, but if not I would try SE Bioinformatics, which might have been a better bet in the first place. $\endgroup$
    – David
    May 20, 2021 at 16:31

1 Answer 1


A standard pipeline for DGE would be Salmon for (pseudo)mapping + DESeq2 for statistical analysis.

Salmon is one of a set of modern, fast and accurate mapping software. Requires a transcriptome to be defined.

DESeq2 is a mature R (Bioconductor) package which can handle reasonably complex designs, including time series, but not mixed models. The vignettes are pretty thorough. It can handle data from pseudomapping software as well as data generated using more traditional methods, e.g. Star + FeatureCounts.

Using a pre-made RNAseq statistical package is a much better idea than trying to do, e.g. a Poisson/Neg.bin GLM manually for each gene (even though ultimately this is what DESeq2 does).

(agree this question is a better fit for SE Bioinf)


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