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@email.arizona.edu [[alternative HTML version deleted]]

Hi, I'd just like to ask for a point of clarification here: On Thu, Apr 1, 2010 at 4:46 PM, Kaitlin Louise Bergfield <kshupe at="" email.arizona.edu=""> wrote: > 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. [snip] > 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) The "vast numbers of identical values in the dataset" you mention, do you mean that the numbers across the rows in `datamatrix` are identical? Or do you mean that *all of the numbers* are quite similar? Also, I've been working with *-seq data for a while so I forget, but after the data is rma normalized, are the expression values returned in log-space or no? (Are the numbers in `datamatrix` in the 2-14 range, or in the 1000's range?) -steve -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact