I'm testing the bivariate correlations among a large dataset of plant traits. Some of these correlations have highly non-normal residuals that are normal when log-transformed, but other correlations are the other way around- they have normal residuals when untransformed that become very non-normal when logged. I didn't want to log traits for some correlations but not others, so to deal with this problem, I decided to test a spearman correlation and a rank correlation for each pair of traits, and only say that a correlation between these traits is strongly supported if both correlations are significant.
Because I have a large number of trait combinations (~250), and positive dependence among the test statistics, I want to use the Benjamini-Hochberg FDR method to adjust the p-values. But, I'm not sure how to account for the fact I'm doing two kinds of tests. Should I:
1) Conduct one B-H adjustment on the rank correlations, assuming 250 tests, and then a separate B-H adjustment on the spearman correlations, also assuming 250 tests?
2) Pool the p-values for both kinds of correlations and conduct one B-H adjustment assuming 500 tests?
or 3) Get rid of the comparison altogether and only test rank correlations?
Thanks so much for any insight!