Hello Jeremy and Marco, it seems like you both have very similar cases with qPCR data analysis in HTqPCR, so I'm writing to you jointly. If I understand it correctly, you both have qPCR data from 384-well plates, but your plates are designed to contain multiple samples per card, i.e. either 192 features x 2 samples or 96 features x 4 samples. I've made a test version of a function in HTqPCR, changeCtDim(shown below my message), for splitting such data into e.g. a 96x4 matrix instead of 384x1 for a qPCRset. Note that this is for *after* importing the data with readCtdata(). This function requires a qPCRset, and a factor with the order of the samples on the arrays, so that they correspond to the order of featureNames. An example for 2 or 4 samples per array would be: > sample2.order <- factor(rep(c("A", "B"), each=192)) > sample4.order <- factor(rep(c("A", "B", "C", "D"), each=96)) > data(qPCRraw) > q2 <- changeCtDim(qPCRraw, sample.order=sample2.order) > q4 <- changeCtDim(qPCRraw, sample.order=sample4.order) Depending on the circumstances of your data, you might also want to do this after normalising within plates and performing other quality measures, rather than right after reading in the data. However, it should hopefully make some of the data characterisation as well as the differential testing simpler, since data from each sample are present in separate columns. If this does not work for your data, or if it was something completely different you had in mind, then please let me know and I'll see what I can do. If the changeCtDim() functions does what you require, I'll add it to the next version of HTqPCR. HTH \Heidi # Paste this function into R to run the examples above. changeCtDim <- function(q, sample.order) { # Split Ct values X <- split(as.data.frame(exprs(q)), sample.order) Xnew <- do.call("cbind", X) # Split categories cats <- split(as.data.frame(featureCategory(q)), sample.order) catsnew <- do.call("cbind", cats) # Split flags flags <- split(as.data.frame(flag(q)), sample.order) flagsnew <- do.call("cbind", flags) # Make new qPCRset out <- new("qPCRset", exprs=as.matrix(Xnew), flag=flagsnew, featureCategory=catsnew) # Add some extra info (not in matrix or data.frame form) sampleNames(out) <- paste(rep(levels(sample.order), each=n.samples(q)), sampleNames(q), sep=":") featureNames(out) <- featureNames(q)[as.numeric(sample.order)==1] featureType(out) <- featureType(q)[as.numeric(sample.order)==1] featureClass(out) <- featureClass(q)[as.numeric(sample.order)==1] out at history <- rbind(q at history, capture.output(match.call(changeCtDim))) # Return the new object out } ------------------//------------------ Heidi Dvinge European Bioinformatics Institute Wellcome Trust Genome Campus Cambridge, CB10 1SD heidi at ebi.ac.uk