Sorry for being the millionth person to ask questions about this topic, but I haven't been able to find a clear answer to my questions.
So I'm using DESeq2 to find DE genes between two tissues. I have 20 samples of tissue 1, and 20 of tissue 2, and I use a design of ~ Tissue, and that gives me results.
But these aren't just 20 random tissue 1 and 20 random tissue 2 samples. There are relationships and structure between the samples.
My samples were taken at 4 different time points, and I want time point differences to be accounted for when calculating if the genes are differential expressed. So I do a design of Time + Tissue, and that gives me much the same genes, but with much better p-values. That's how I should set it up, if my goal is just to look at tissue differences, correcting for time point effects? (I feel like Time:Tissue is not what I want, because I'm not interested in zooming into the differences in any one particular timepoint)
My 40 samples are also paired, There is a tissue 1 and a tissue 2 from each of 20 individuals. So I could do a design of Individual + Tissue to get DE genes between tissues, and this would account for differences between individuals? (separate question: Is this a good idea with 20 individuals for 40 samples?)
So I'd really like DE genes between tissues, taking into account variation introduced by time point and individual, but Time + Individual + Tissue won't work, I presume because each individual is present at only a single time point. I can make a new column, with time and sample concatenated together, and do concat + Tissue, but now I've lost my replicates, and I'm not sure that splitting this up into 20 distinct groups is what I really want.
I can cheat a bit and make new individual numbers, that are just 1-5, so now New + Time + Tissue is full rank, because it thinks the same individuals are present in all 4 time point groups. Is this the right answer...or just a pretty good one?
What I want is for the software to understand that each sample is part of three separate groups, and for it to remove the influence from the timepoint and individual group to make the influence of the tissue group sharper. Is there an approach I am missing?