Okay, I guess this is more of a statistics question than a program question, but I just want to clarify something dealing with the correct usage of FDR with multiple tests.
General Experiment Set:
3 Biological Replicates of samples collected from 4 time points (t1, t2, t3, t4).
The question is how is t1 different from each of t2, t3, and t4 individually.
So using DESeq, I basically did 3 pairwise tests: t1 vs t2, t1 vs t3, t1 vs t4. And of course, DESeq gives FDR values for each of those test pairs.
However, since I am technically doing 3 tests on the same data set, is the appopriate statistical approach to carry out an FDR correction on all of the tests as a whole?
In other works, take the raw p-values from all 3 tests, and combine them in a single list and then FDR correct (like using p.adjust, method="BH"), and afterwords break the whole set back into the original 3 tests again.
Or is just using the FDR values generated from the 3 pairwise tests separately statistically valid?
General Experiment Set:
3 Biological Replicates of samples collected from 4 time points (t1, t2, t3, t4).
The question is how is t1 different from each of t2, t3, and t4 individually.
So using DESeq, I basically did 3 pairwise tests: t1 vs t2, t1 vs t3, t1 vs t4. And of course, DESeq gives FDR values for each of those test pairs.
However, since I am technically doing 3 tests on the same data set, is the appopriate statistical approach to carry out an FDR correction on all of the tests as a whole?
In other works, take the raw p-values from all 3 tests, and combine them in a single list and then FDR correct (like using p.adjust, method="BH"), and afterwords break the whole set back into the original 3 tests again.
Or is just using the FDR values generated from the 3 pairwise tests separately statistically valid?
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