Hi, I'm trying to do an analysis of some tag3 Affy microarray data with the limma package, and I've run into a problem I'm not sure how to solve. The data comes from a series of arrays recorded at different time points. Each probe on the array represents a DNA tag from a yeast gene deletion library. There are several different time points and technical replicates for some of them. All I need to do is a linear regression of the expression levels against time and find the slope of that line. So far so easy, but the problem I've run into is that each yeast gene deletion mutant is represented on the array by multiple probes (sometimes 2, sometimes more). What I'd like to do is fit all the data for each deletion mutant simultaneously rather than on a probe-by- probe basis. Hopefully this will improve the quality of the linear regression. So, is there any trick in limma (or in the preprocessing step prior to limma) for combining probe level data (i.e. rows of the expression matrix)? I could just average the data across probe sets for each deleton, but that seems like it wouldn't be as powerful as fitting all the data (I think?). Alternatively, am I better off just taking the expression values and doing the linear regression using the standard R lm function? In that case, could anyone point me towards a method for accounting for the technical replicates (which limma knows how to handle and does the 'right thing'). I've tried reading gls.series, but it's a bit scary for a biologist. Alex Gutteridge Systems Biology Centre University of Cambridge