Coping with a label imbalance in DESeq2

Question

[bkellman]

I'm trying to do differential expression on label imbalanced data; my case:control ratio is 2:1. I know that regressions are at the core of DESeq2 machinery and I know regressions have internal machinery for coping with such imbalances. Specifically, you can down-weight observations from the over-represented group to force an equal contribution to the learning. Is observation weighting available to the user in DESeq2?

glm( gene_i ~ effect , data[,c('a','a','a','a','b','b')] ) --> group 'a' over-represented in learning

glm( gene_i ~ effect , data[,c('a','a','a','a','b','b')] , weight=c(.5,.5,.5,.5,1,1) ) --> group 'a' and 'b' equal representation in learning

Answer 1

[Michael Love]

You should note the cross post so people can see if it has already been answered:

https://support.bioconductor.org/p/87507/

[Devon Ryan]

Thanks for the follow-up link, Ryan Thompson's replies there are excellent!