I'm looking to run a bunch of t-tests, and I'm trying to figure out the appropriate time to apply an FDR correction.
I have four conditions and am doing pairwise comparisons amongst these conditions, so I have six pairwise comparisons/t-tests to run on each variable. I have 30 variables.
Does it make more sense to:
Do all of the pairwise t-tests, take all of the p-values, and perform one FDR correction? I.e.: I have 6 p-values (6 pairwise comparisons) for the 30 variables and run one FDR correction on the 180 p-values?
Or to perform an FDR correction for each pairwise t-test for each variable? I.e.: Run an FDR correction for the 6 p-values for each variable, for a total of 30 FDR corrections.
To clarify, my variables are not dependent (my title was edited)--perhaps 'variables' is not the correct term. I am looking at changes in gene expression for 30 different unrelated genes. The conditions are the same for each gene though. So for gene 1, I am running pairwise t-tests for condition A vs B, A vs C, A vs D, B vs C, B vs D, and C vs D, and then repeating this for each gene.
I've just noticed this question, in which the asker has a similar set-up to me: Multiple testing and FDR on multiple-pairs
I hadn't thought about running FDR for each pairwise comparison (6 FDR corrections for my case), though I'm not sure this would be the best option. Advice?