[BioC] RMA normalization and MAS5.0 detection calls

[Sarwar, Rizwan]

Hi all

Regardless of whichever approach is being used, a cut-off needs to be
applied. What sort of numbers do you use as your thresholds for variance
or CV? What %age (roughly) of probesets does it exclude?

I have to admit using simple RMA signal of 150 (again another arbitrary
figure) for the higher of the 2 means in the group, and have found that
this eliminates a lot of noise

Regards

Rizwan

Dr Rizwan Sarwar
Physiological Genomics & Medicine
MRC-Clinical Sciences Centre
Imperial College London (Hammersmith Campus)
Du Cane Rd  W12 0NN
 
-----Original Message-----
From: James W. MacDonald [mailto:jmacdon at med.umich.edu] 
Sent: 07 December 2006 16:19
To: Jenny Drnevich
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] RMA normalization and MAS5.0 detection calls

Hi Jenny,

Jenny Drnevich wrote:
> Hi all,
> 
> Actually, the problem of the statistical results not always making
sense 
> with the P/A calls also happens with MAS5 values, not RMA or GCRMA 
> specifically. Many years ago I as using Affy's two sample comparison
in 
> the MAS 5.0 software, and I noticed that a probeset would be called
"A" 
> in sample 1 and "P" in sample 2, but supposedly sample 2 had higher 
> expression than sample 1!!  The calls and the expression level 
> comparisons sometimes don't correspond, but this is because they use 
> different algorithms and values in their computation, as Jim
explained. 
> I tend to like and use the calls in a conservative matter, but they
may 
> only be about 85% accurate (Choe et al. Genome Biology 2005, 6:R16).
> 
>> Another way to approach filtering probesets is based on the
variability
>> of the probesets over all samples. If the variance is low (below some
>> constant c), then you might assume that the gene is not
differentially
>> expressed in any samples (which is different than saying it is
expressed
>> or not). These genes are uninteresting by definition, and can be
removed
>> from the dataset.
> 
> 
> I still haven't convinced myself that I like this approach. And
wouldn't 
> it be better to filter on CV, which takes into account expression
level, 
> rather than variance? I know there was a recent exchange on what sort
of 
> cutoff value to use... I really need to find the time to play around 
> with filtering on some aspect of variability - unless something has
been 
> published on it?

I think you want to use CV for data that show a mean/variance 
dependence. With RMA (and I suppose GCRMA) values, most of the 
dependence has been decoupled by taking logs. For instance, a plot of 
mean expression vs variance usually shows nearly constant variance 
except at the tails, where the variance appears to go down precipitously

(which CV won't affect anyway).


Best,

Jim


> 
> Cheers,
> Jenny
> 
> 
> 
> 
>> HTH,
>>
>> Jim
>>
>>
>> >
>> >
>> > Regards!
>> >
>> >
>> > haiyan
>> >
>> >       [[alternative HTML version deleted]]
>> >
>> > _______________________________________________
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>>
>>
>> -- 
>> James W. MacDonald, M.S.
>> Biostatistician
>> Affymetrix and cDNA Microarray Core
>> University of Michigan Cancer Center
>> 1500 E. Medical Center Drive
>> 7410 CCGC
>> Ann Arbor MI 48109
>> 734-647-5623
>>
>>
>> **********************************************************
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> 
> 
> Jenny Drnevich, Ph.D.
> 
> Functional Genomics Bioinformatics Specialist
> W.M. Keck Center for Comparative and Functional Genomics
> Roy J. Carver Biotechnology Center
> University of Illinois, Urbana-Champaign
> 
> 330 ERML
> 1201 W. Gregory Dr.
> Urbana, IL 61801
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> 
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> e-mail: drnevich at uiuc.edu


-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623


**********************************************************
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