Hello, I have two questions concerning the same data set. I have a number of time series data (say, S1 - S10). Each time series consists of a few time points (T1-T5), without replicates (you play what you are dealt...). First question: there is a hypothesis that one of the time series stands out in respect to a number of genes. The goal would be to find these genes. How can I test whether the single (no repeats) time series for one gene is different for S1 than S2-S9? I was thinking along this lines: for one time point, assuming normality of S2-S9, I can calculate the probability that S1 is from the same distribution as S2-S9. I can then combine the p-values from the different time points to form a joint probability, and then correct that one for multiple testing. I think that this would be even too conservative; is there a better way? Or, maybe, simply defining the rule that at least one time point should show a significant adjusted p value? I can clearly see that certain genes behave differently, and I have a validation (sort of) in that all genes from one operon behave in a similar way, e.g. over-expressed at certain time points for S1 but not S2-S9. Here comes the next question: is there a standard way of testing the changes in expression of operons rather than genes? Assuming that I have a mapping gene->operon in form of a data frame, how can I easily test whether the expression of an operon differs between the ten time series? Best regards, January --