Hi, Rather than use the linear step-up FDR procedure I'd like to use a permutation method to correct p-values from limma. I use linear regression of 8 groups with 3 replicates against a numerical measure for the eight groups. ie: x <- c(1,1,1,3,3,3,6,6,6,4,4,4,8,8,8,7,7,7,5.5,5.5,5.5,6,6,6) design <- model.matrix(~x) I want to run 1000 permutations to get p-values I can then use to estimate the null distribution to do an FDR correction of the p-values. I guess I can quantile normalise the p-values to combine them into a single distribution to estimate the null - but is this ok?? If I want to control at a specific FDR then I could look at say p=0.001 and find 1000 significant in the test set and 10 in the null and conclude I'm controlling at an FDR of 0.01. However, I'm looking for a continuous correction like for the step-up FDR where the FDR corrected p-values are the output and you can select whatever level you choose. Has anyone got some practical advice/code/link to relevant function that would allow this. Thanks in advance, Matt