Easter greetings, and a question ;-) I have 8 genotypes with 3 biological replicas per genotype. I also have a quantified variable (growth) for each. Currently I use a model like x <- c(1.5,1.5,1.5,2.3,2.3,2.3,3.6,3.6,3.6,4.2,4.2,4.2,4.8,4.8,4.8,5.2,5.2, 5. 2,5.5,5.5,5.5,6,6,6) design <- model.matrix(~x) fit <- lmFit(esetgcrma, design) ebfit <- eBayes(fit) Firstly, obviously it is fitting each of the 3 replica arrays to the same value. However, we have measured the growth several times and although they are under the same conditions these are not directly paired to the 3 replica. So the growth is really 1.5 +/- 0.3, 2.3 +/- 0.2...etc. Is there any way to take this into account (ie: if different growth have bigger SD than others)? Should the design be fit against 8 groups and then compared to 8 values - can/how would you do this? Secondly, the eBayes is moderating the p-values for the returned coefficients - intercept and slope. The intercept one is obviously not of interest. The slope one is the probability of it being different from zero so I assume that tells you if a gene correlates with growth. Is the eBayes removing genes that have low p-values but very shallow slopes as I would expect or is there more to consider? Finally, I'd like to ask about permutation testing and using the results to correct the p-values using an fdr procedure. As there are only 8 groups permutating the arrays randomly will also be testing if the groupings make sense. I think the better way is to just permutate the 8 growth values but preserve the group structure as this will indicate how many genes correlate by chance. My question is once I've done my (say) 1000 permutations what should I do with the computed p-values and how do I use them to correct the 'real' p.values. I'm looking for practical advice/code. Thanks in advance, Matt