[BioC] RMA verse GCRMA

[Adaikalavan Ramasamy]

I know very little biology but my biologist collaborators are usually
more interested in low signal genes, so you might want to think
carefully before deleting the genes with low correlation. 

Furthermore, if you compared expressions from RMA (or GCRMA) with MAS
5.0, I believe you might find similar results. i.e. Good correlation
among high signal genes but poor correlation for low signal genes.

You results might be simply saying that the RMA and GCRMA expression
measures are very similar for high signal genes but they differ for low
signal genes.

Regards, Adai



On Fri, 2005-03-04 at 13:32 -0800, Fangxin Hong wrote:
> Thank you. Actually I just found this out from one of my tests, genes with
> low correlation are all in the low intensity end. I am thinking actually
> this give me clue to delecte those non-expressed genes from further study.
> 
> This is a hrad evidence that we should filter genes first.
> 
> Thanks.
> Fangxin
> 
> 
> 
> > Hi Fangxin,
> >
> > do you expect that 100% of the genes that are assayed by your chips are
> > expressed all the time in the system you are investigating? (you never
> > told us which chips and which plant or animal)
> >
> > And if not - say if only 50% of genes are expressed, then the data for
> > the remaining 50% should just be pure noise and there is no reason why
> > intensities from RMA and GCRMA should be correlated.
> >
> > I think you have just learned something about your measurement
> > instrument (and this has little to do with normalization methods).
> >
> >    Best wishes
> >    Wolfgang
> >
> > Fangxin Hong wrote:
> >> Hi list;
> >> I met a strange problem regarding the normalization methods,
> >>
> >> For an experiment with 24 arrays (time order), I normalized the data by
> >> both RMA and GCRMA. Then I tested the correlation between the normalized
> >> data for each gene. Surprisingly, I found that about 25% genes with
> >> correlation less than 0.7 between value normalized by RMA and GCRMA, and
> >> only less than 50% genes have correlation >0.9. I studies the profile of
> >> some genes, they look quite different under two methods.
> >>
> >>
> >> Anybody met this problem before?  Which method we should trust? Any
> >> comments/idea is appreciated. Or is it possible that I did something
> >> wrong, I couldn't find it myself.
> >
> >
> > -------------------------------------
> > Wolfgang Huber
> > European Bioinformatics Institute
> > European Molecular Biology Laboratory
> > Cambridge CB10 1SD
> > England
> > Phone: +44 1223 494642
> > Fax:   +44 1223 494486
> > Http:  www.ebi.ac.uk/huber
> > -------------------------------------
> >
> >
> 
>