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]]

