I have a list of say 50,000 e-values (multiple testing corrected P values) for a statistical test I am doing that essentially matches patterns together. Essentially the e-value is the expectation that these two patterns would match purely by chance.
Most people who do this will do another round of multiple testing correction on their list of e-values which are already corrected for the number of tests that generate that e-value. For example each E-value may be the result of 100,000 tests matching different patterns together. The theory is when you have 50,000 of these e-values you will still have a good number of type I error's associated with your list.
Can someone give me a some theory on why it's a good idea to apply multiple testing correction to values that were already corrected for multiple testing?
Is there a good paper I can read that will help me understand? Conceptually I can understand why you want to do this, because that list of 50,000 e-vales will have some false positives, but if there is a paper someone can point me to that would be great.