Hi to all, I'm performing a time-series analysis on 2 cell lines with 4 time points, with 2 replicates at each time. I imported the data with "Affy" library, and I normalized with RMA. QC parameters (average background, scale factors, % present calls, ratios 5'-3') are all in the suggested range (according to the book of Gentleman et al., chapter 3), but when I look at probes expressions, they have one behaviour in 2 time points (1st-3rd), and a different behaviour (wider expression) in the remaining 2 time points (2nd-4th), so probes seem to go up and down during time. Also, I detected genes d.e. at each time point with linear models, and clustering analysis with these d.e. genes groups samples of time points 1-3 in one major group, and those of 2-4 in a second-one, regardless of the cell line type. In fact, the arrays have been hybridized in different moments, one for time-points 1-3, months later for time-points 2-4. So, technically everything seems ok, but all the genes have this behaviour of "up and down" across time and this seems to affect following analysis. - Which could be a normalization that can reduce this (probably technical) bias? - Could be a good idea normalizing e.g. on the control probes and is this approach reasonable for this case? How this can be done? - Or you suggest that because QC is ok to leave things as they are with RMA normalization? Thanks for your time, Andrea