Hi, I've got a microarray dataset (Illumina) coming from a blood assay with a case-control factor of interest. I also have several other covariates (gender, weight, etc...). I know that the experimental design is highly unbalanced with respect to Gender: female male control 12 7 case 7 17 Therefore, if there is a Gender effect, then it really needs to be included into any subsequent analysis (differential expression with limma, classifications). I do not want to find differences between cases-controls that are actually due to Gender. Some questions around that: - what would be the "best practice" way of find if the Gender (or any other covariates) actually has an effect that needs to be dealt with (as I would rather not bother about it). What I did: run limma on ~ Status + Gender, looking at the q-values for Gender (?) - one part of the genes claims for a Gender effect, whereas the other part doesn't. In that case is it a good thing to include the Gender for all? Can we use two different models? What about the multiple testing correction in that case? - supposing we decide to take into account the gender in the analysis, do you know classification methods that enables to include some correction for a covariate (I cannot correct my original data for gender without including the case-control status, because I think would then remove a lot of the effect of interest (cf. unbalanced design). Therefore, I need to cross-validated any gender-correction if I do not want to bias the classification result. This increase the complexity of the classification methods, as well as reducing the actual choice of the method, since not all method give access to the internal machinery (cf. Random Forest: can I hook the splitting method to use a gender- corrected split?) - any other suggestion to deal properly with this kind of very annoying unbalanced design? Thanks for your help and comments.