Yes - you could always threshold based on an effect size. An effect may be statistically significant but in the era of huge samples, the effect sizes becomes important. Tiny effects may be significant in a statistical sense but practically meaningnless. 

Note that t and F are not really effects sizes, but t can be converted to an effect size by normalizing by degrees of freedom. 

In short, yes it seems fine to threshold. 

Originally posted by bordiercecile:

Good morning!

 

Thanks a lot for your answer. 

I am running the FDR with 500 000 permutations, but you are right to be in the safe side I will increase to 1000 000.

My question about the thresholding is about the matrix NBS.test_stat. If I understood well this matrix represent the t (or f) values before the permutations. However, some of my FDR significant edges have very small value (<1) in the NBS.test_stat. So I was wondering if I should discard them afterwards when the t- or f-values are too "weak".

Thanks a lot for your help

Cecile