RMA vs. MAS 5.0 fold change compression (was Re: [BioC] RMA vs VSN)

Naomi Altman naomi at stat.psu.edu
Tue Jun 22 16:09:07 CEST 2004

If there are no replicates, t-tests and SAM cannot be used.


At 11:35 PM 6/21/2004 +0100, Adaikalavan Ramasamy wrote:
>I believe this is due to the fact that you have used Fold Change to
>filter your gene list. Try filtering your genes by t-test or SAM and see
>how the two lists compare.
>In the last paragraph of the Results section of Irizarry et al, 2003
>(Pubmed ID : 12582260), the authors mention that RMA compressed the Fold
>Change estimates by 10-20%. This could be due to the quantile
>But in reality I often find 50-60% compression and am wondering why is
>this myself. If anyone could shed light into this area, it would be much
>On Mon, 2004-06-21 at 11:39, peter robinson wrote:
> > I'd like to throw another observation with request for comments into this
> > round. Our group has been using affymetrix murine chips with pooled 
> samples
> > but no technical replicates as a way of identifying candidate genes for
> > further characterization by RT-PCR or in situ hybridization and other
> > techniques. We have used a simple fold-change threshold between samples 
> taken
> > from different developmental stages or between wt and ko models to 
> identify
> > candidates.
> >
> > Then, we are able to confirm about 80% of genes predicted following mas5
> > analysis (original or bioconductor is very similar).
> > However, rma analysis has produced lists of genes that are much shorter 
> and in
> > general do not correspond well to the confirmed lists of genes produced by
> > mas5 (or to our biological prejudices as to what genes should be observed).
> >
> > Have others notices similar discrepancies between mas5 and rma?  Are there
> > perhaps other issues I have overlooked?
> >
> > Thanks
> >
> > Peter
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch

Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

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