I think I will do the rma cross all chips regardless dis or control, since I don't have a set of control genes to normalize between two groups after RMA. With U133 set, we can expect that only few genes will change between the two groups. Thanks. Lizhe -----Original Message----- From: James MacDonald [mailto: Sent: Monday, March 15, 2004 8:11 AM To: Lizhe Xu; bioconductor@stat.math.ethz.ch Subject: Re: [BioC] questions about Affy package from new user: onemore question AH. GS==GeneSpring. If you want to join them before importing to GeneSpring, you should do this after computing expression values. You can do something like: out <- rbind(exprs(exprSetA), exprs(exprSetB)) write.table(out, "Combined expression data.txt", sep="\t", quote=F, col.names=NA) HTH, Jim James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623 >>> "Lizhe Xu" <lxu@chnola-research.org> 03/14/04 06:20PM >>> Now, I tried to load the exported data from Bioconductor to GeneSpring and found another question. Since I used U133 chip set, I wonder if I can joint the U133A and B directly and import them to GS or I should do probeset level normalization first (if so, which package in bioconductor can do it) before joint them. Thanks. Lxu _______________________________________________ Bioconductor mailing list https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor