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yep, mixed effects models, see our technical report at: http://www.bepress.com/bioconductor/ on synthesizing microarray experiments. The best advice I can give is to not jointly normalize (treat them seperately and use a random effect term for the experiment). Your problem is a bit easier since you don't need to worry about matching probes there is a package which needs to appear somewhere, but I think has not yet done so Robert Lana Schaffer wrote: > Bioconductor users, > We have two groups of samples on HU133plus2 chips done 1 year apart. > After I normalized with RMA, the two groups are clustering into separate > clusters at a low level of correlation. > Can someone suggest a way to analyze these samples together? > > Lana Schaffer > Biostatistics/Informatics > The Scripps Research Institute > DNA Array Core Facility > La Jolla, CA 92037 > (858) 784-2263 > (858) 784-2994 > schaffer at scripps.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 > -- Robert Gentleman, PhD Program in Computational Biology Division of Public Health Sciences Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N, M2-B876 PO Box 19024 Seattle, Washington 98109-1024 206-667-7700 rgentlem at fhcrc.org