Hello,

What is the most appropriate way to calculate Cohen's D while using DESeq2 package for differential expression analysis?

Can one calculate it using log2FC and lfcSE from summary stats as follows: log2FC/(lfcSE*sqrt(sample_size)) ?

We found that the abs(Cohen'sD) values are always less than 0.6 using the above approach.

Any feedback would be great!

Thank you!

Here's an old answer of mine from another site

https://www.biostars.org/p/140976/#141046

Copying my answer here for posterity:

DESeq2's posterior log fold changes are "reliable" effect sizes, that is, directly comparable across experiments, because the fold changes from genes with less information (low counts, high variability) are moderated toward zero using Bayes theorem. We lay out the argument in our paper. We also provide Wald statistics in the results table, but this is not exactly what you are asking for (dividing by SE of the estimate, not SD of the data). You could use the expected variance formula for log counts to add your standardized effect size: V = 1/mean + dispersion. So divide log fold change by sqrt(1/mu + dispersion), where mu is the mean of normalized counts for the gene.