Hi Kaitlin, Incorporating the eighth timepoint into your experiment will be difficult, given that the chips used are likely quite different from the version 1.0 chips that you used for the other time points. Since you only used one timepoint with these chips you have aliased any biological differences with the chip type, so it will be impossible to determine if any differences are due to biology, technical differences or a combination of the two. The fact that you have a large number of identical probeset values is an artifact of the median polish procedure that arises when you use three (or five) chips. This is not really the problem here, however. Although it is not ideal to estimate the expression values with such a small number of chips, the much larger problem is the chip-timepoint aliasing that I mentioned above. Best, Jim James W. MacDonald, M.S. Biostatistician Douglas Lab 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826 >>> Kaitlin Louise Bergfield 04/01/10 4:47 PM >>> Hello, We are using Affymetrix Drosophila microarrays to investigate central nervous system gene expression profiles at eight timepoints spanning metamorphosis. Each of the eight timepoints consists of three biological replicate samples. Unfortunately, our final eighth timepoint had to be hybridized to version 2.0 arrays, while all our other samples were hybridized to version 1.0 arrays. We have found no way to normalize these 24 samples all together. I have attempted to use RMA normalization on the 3 replicates from the final timepoint, but find when I do this that I end up with vast numbers of identical values in the dataset. These are highly correlated (98-99%) with the raw values of the arrays, but I feel that with only 3 replicates in this normalization I might be forcing artificial conformity to a range of discrete normalized values. My concern is that running RMA normalization on only 3 replicates is not a valid use of the method. Can anyone offer advice on the number of replicates necessary to run RMA normalization, or if other methods are more useful for this sort of analysis? I have been using the following code: cels <-dir("F:/Restifo Lab/Microarray files/A1 files", pattern=".*.CEL", full.names=TRUE) batch <- ReadAffy(filenames=cels) eset <- rma(batch) datamatrix <-exprs(eset) Thank you very much, Kaitlin Bergfield -- Kaitlin Bergfield Neuroscience Graduate Interdisciplinary Program Brain Imaging, Behavior & Aging Lab University of Arizona kshupe at email.arizona.edu [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues