Hi BioC List, I'm working with an affymetrix data set where the batches are completely confounded with the factor of interest for one contrast, treatment time. From my understanding I can use something like fRMA to partially mitigate the effect, but otherwise not much I can do. However, we do have the original tissue samples and the option to re- extract/reprocess some samples in a new batch. Due to the study size, rerunning all samples with a proper randomized design is out of the question. Are there any studies describing how we might rerun a small subset of samples to recover the contrast of interest? Or does anyone have any advice? For example, can I run 10 samples from each time point in a new batch - could that be sufficient? Clearly it could depend on the size of the batch effect, so I understand that there is probably no definitive answer... Here's the sample breakdown with the relevant treatments/covariates - you can see that timepoints t1 and t2 are completely confounded with batch, while timepoints t2 and t3 are not. I also included another covariate that we want to account for, Ethnicity, and the number of samples in each group: Batch Timepoint Ethnicity Num_Samples b1 t1 A 21 b1 t1 B 54 b2 t1 A 20 b2 t1 B 56 b3 t2 A 10 b3 t2 B 35 b3 t3 A 9 b3 t3 B 38 b4 t2 B 49 b4 t3 A 1 b4 t3 B 43 b5 t2 A 28 b5 t2 B 4 b5 t3 A 25 b5 t3 B 21 Here's the setup that I would like (note that I'm also including patientID in the design matrix since there are many patients that have a sample at multiple time points): design <- model.matrix(~ -1 + Timepoint + Ethnicity + Batch + PatientID) fit <- lmFit(eset, design) contrast.matrix <- makeContrasts(Timepointt2 - Timepointt1, levels=design) fit2 <- contrasts.fit(fit, contrast.matrix) fit <- eBayes(fit) If we do reprocess samples, do I need to ensure even representation of the two ethnicities in the new batch? Thanks in advance for the guidance! Ty [[alternative HTML version deleted]]