Good evening everyone,

I was wondering if anyone has ever tried to perform a differential expression analysis using pathway/network information as prior knowledge. To be more specific, I am wondering if it is possible to feed DESeq2 the prior knowledge of pathways so that the algorithm is more prone to consider statistically significant genes part of an up-/down-regulated pathway even without a big LFC (and vice-versa).

ANY help here will be highly appreciated!

To answer your exact question, you could use IHW to estimate weights for partitions of the genes. It would give a better chance to those genes in a partition that has more power under an alternative hypothesis. As long as the partition is independent of the p-values under the null, which it should be.

One important note: users should not run gene set testing on a dataset, find the significant sets and then rerun with IHW using a particular partition based on the first run results. This would obviously violate the independence of the p-values and the partition under the null, but just to be very clear about that. The partition has to be decided prior to looking at the results.

However, I think a more straightforward approach is simply gene set testing. I like to use goseq following DESeq2, but other options are roast and camera from the limma package (these cannot be used directly with DESeq2).