FW: [BioC] questions about Affy package from new user: onemore question

[Lizhe Xu]

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 at 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 at 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

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