Hi All, I've some question regarding the affyPLM package. Maybe you can give some hints ... . fitPLM is *not* a normalisation method, is it? I mean, it performs a normalisation such as RMA background correction and e.g. quantile cross-chip normalisation, but it doesn't summarize the probes that belong to one probe set into a single expression value. Instead, a robust (?) linear model is fit through *all* the probes of each probeset on each chip. Is this kind of interpretation correct? How do I then interpret the coefficients of the PlmSet object? Say I've a 40 chips for measuring gene expression after 4h and 24h treatment with a drug with doses 0mM, 0.1mM, 0.25mM, 0.5mM and 1.0mM (this is a typical design for me). I'd create factors time <- factor(c('04h','24h')) dose <- factor(c('0mM', '0.1mM', '0.25mM', '0.5mM', '1.0mM')) and then do the fit with an intercept plm <- fitPLM(affybatch, model = PM ~ probes + dose + time) I'm not sure what to do with the coefficients of the result, what do they tell me? A while ago I've analysed my data with linear model and anova in a way like foreach gene in genes: mylm <- lm(intensity ~ dose + time, data=all_chips_dataframe_for_gene) myanova <- anova(lm) Then I've extracted the p-values for the dose and time factor for each gene to see what's differentially regulated ... . In the example above I'm using the linear model for an anova - which makes sense to me, and again the coefficients of "lm" wouldn' tell me much. Could I use the the PlmSet for an anova, too? thanks for your comments +kind regards, Arne