Hi,
I've got a paired (or blocks really) experiment with the setup like the below.
Five blocks and four conditions (A, B, C, D). I want to contrast all vs all (A vs B, A vs C, A vs D and etc.). The Xs in the table below indicate a sample present for each block (I think it's clearer in table format rather than colData format). Clearly there are a lot of "missing pairs", but there are at least two paired samples when comparing any of the conditions.
Block | A | B | C | D |
---|---|---|---|---|
1 | x | x | ||
2 | x | x | ||
3 | x | x | x | |
4 | x | x | ||
5 | x | x | x | x |
So, my question is what would be the best way to do an analysis with DESeq2?
As far as I can see, it's either keep all the data and use the design ~Block + condition then extract each contrast from the results object, or subset the data and keep the design balanced. It's noted that block 3 and 5 (-B5) cover half the contrasts.
Reading previous forum posts the consensus seems to be subset the data or use limma-voom, but the posts I've read don't deal with multiple conditions.
Greg