Hi,

I've been using the DESeq2 package lately with tximport to import the counts generated by Salmon. I wanted to do some pathway analysis and I used tximport with the tx2gene argument set to a mapping between transcript ids and pathway name.

> head(pw_txdb)
           TXNAME                 pathway
1 ENST00000585714 mitochondrial_transport
2 ENST00000495634 mitochondrial_transport
3 ENST00000492580 mitochondrial_transport
4 ENST00000340001 mitochondrial_transport
5 ENST00000460872 mitochondrial_transport
6 ENST00000370732 mitochondrial_transport
> txi <- tximport(files, type="salmon", tx2gene=pw_txdb)

Then run the DESeq2 standard DE analysis.

My assumption here is that the counts can be summed up regardless of the definition of a "region" (gene or gene set). However, I'm wondering if the assumptions behind the DESeq model behind still hold (e.g. negative binomial distribution)? In addition, I'm afraid that overlapping regions (genes in common between pathways) would violate the assumption of independence in the multiple tests to correct for... Perhaps permutation of labels would be a better way to go about it?

Thank you,

Gon

You don't want to sum up counts to the pathway level. Imagine if you have a pathway with two genes, and one increases in expression and the other goes down. This will be tossed. You should do either gene and/or transcript-level analysis (tximport gives code for gene-level analysis; for transcript-level, there are many options, and we're still evaluating these in our lab, but you can use a differential transcript usage detection method like DRIMSeq on Salmon quantifications). You can then use a gene-set tool like goseq (you can use this downstream of DESeq2), or you can use the gene-set methods in limma: roast and camera (not downstream of DESeq2 though).