I am analysing some microarray data. For a set of participants, I have their RNA expression before an intervention ("Baseline") and after the intervention ("Followup"). I want to know which genes are differentially expressed between the two timepoints. As suggested in the Limma user guide when working with human in vivo data, I have used the arrayWeights() function to weight the arrays according to their quality. From what I understand, this function performs a regression on all samples and then weights the samples according to their distance from this regression. However, as there are two treatment groups ("Baseline" and "Followup"), I would like to know if it is suitable to use a single regression in this way, as I am expecting there to be differences between the two groups. Does it make sense instead to perform two array weightings - one for the "Baseline" samples and one for the "Followup" samples? If so, how would I go about altering my code to incorporate this?
My current code:
y <- neqc(x) design <- model.matrix(~Participant+Time) arrayw <- arrayWeights(y) fitw <- lmFit(y,design,weights=arrayw) fitw <- eBayes(fitw) topTable(fitw,coef="TimeFollowup")
Thanks in advance for any help!