First of all, I would like to ask you if you know good literature that explains the theoretical underpinnings of multiple testing. Although I think I understand pretty well the basic principles, I would like to really understand it in order to apply it correctly in all the different situations I encounter.
I analyze activity of neurons in a simulated neural network containing 1000 neurons. I am interested in knowing how many neurons are influenced by different variables (
C) and their interactions (
A*B*C) with ANOVA. So I do multiple testing on each neuron, and so far, with correction independently for each neuron. But I suppose I should adjust also between neurons (with FDR) because the chances that I find neurons significant is higher with more neurons in the simulations.
What is the procedure here? Should I correct the ANOVA posthoc tests p-values first and then do a second correction on all the neurons? If so, in the second correction, should I correct each test independently (p-value correction for influence of factor
A for all neurons, then same for factor
B, and so on...) or correct all previously corrected tests with all neurons?
Or should I correct the p-values of all tests and all neurons at once?