DESeq2 Factor Level Design

I have 3 muscles I am trying to perform differential expression with.

For each muscle type, I have 3 replicates.

How can I get pairwise comparisons between muscle 1 vs muscle 2, muscle 1 vs muscle 3, muscle 2 vs muscle 3. Within the DESeq2 tool I have factor set to muscle, then level 1 as muscle 1, level 2 and muscle 2, etc. Then within each level/,muscle type, I select the three replicate data count files.

I read that DESeq2 takes the first input as the reference (so that would be muscle 1) and then compares everything to that. But that’s not necessarily what I want. I have been using Cuffdiff to perform analyses but wanted to use a more updated tool but I can’t seem to set it up as it needs to be. Can anyone give me some guidance?


Trying to work through similar problems…

I’m aware of the math to an extent, but ultimately, when you’re building a model of expression change in a gene and comparing it to a normal distribution, the model incorporates each sample’s expression level, so idk how to get pairwise comparisons of LFCs/significance from a matrix with multiple factors/variables and the levels therein.

I have a bunch of RNAseq data from mutant mice at different ages; and the data clusters better after including age as an additional factor (genotype is my primary factor), and the test results are different if I remember right, but ultimately, how do you do a pairwise test (contrast) or examine interactions on Galaxy? The DESeq2 paper is very intuitive until you dig deeper into the methods. The documentation in Bioconductor for the DESeq2 R package is good, but is there the appropriate functionality here on Galaxy for this?


It almost seems that it’s not available as part of the Galaxy wrapper. Ultimately, I ended up ditching DESeq2 and ended up using edgeR as I found that their set up made more sense to me. I also found it easier to manipulate.


Yes, EdgeR and Limma offer more functions and the tool forms are structured a bit differently.

Search with the keywork “edger” here to review prior Q&A and tutorial links, including tutorials that are still in progress for final publication to the Galaxy Training Network (GTN):