[BioC] RMA verse GCRMA
fhong at salk.edu
Fri Mar 4 22:32:49 CET 2005
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.
> 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
> 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
> Phone: +44 1223 494642
> Fax: +44 1223 494486
> Http: www.ebi.ac.uk/huber
Fangxin Hong, Ph.D.
Plant Biology Laboratory
The Salk Institute
10010 N. Torrey Pines Rd.
La Jolla, CA 92037
E-mail: fhong at salk.edu
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