[BioC] Testing for significant genes with RMA

[Eric]

Hi, thanks for the reply. I don't think I said that clearly enough.
I believe the situation is closer to:

(y-x)/m<>y-x

The p-values from one-way ANOVA on the different data sets have extremely 
poor correlation (R2 ~ .1). It is my understanding that a straight 
subtraction/addition would not alter variance and so the p-values would be 
exactly the same, while a division/multiplication would alter variance- 
which appears to be the case when I compare the results of justRMA output 
to those of the "log of the fold change of justRMA using a control group".

At 09:00 AM 12/22/2003 +0100, you wrote:
>Date: Fri, 19 Dec 2003 10:20:59 -0500
>From: Naomi Altman <naomi at stat.psu.edu>
>Subject: Re: [BioC] Testing for significant genes with RMA
>To: Eric <emblal at uky.edu>, bioconductor at stat.math.ethz.ch
>Message-ID: <6.0.0.22.2.20031219102013.01e11050 at stat.psu.edu>
>Content-Type: text/plain; charset="us-ascii"; format=flowed
>
>Eric,
>If I understand your question, it doesn't matter.  (y-m)-(x-m)=y-x.
>
>--Naomi

At 10:00 AM 12/19/2003, Eric wrote:
 >Hi,
 >
 >Which would be more appropriate to use as the initial data for a 'per gene
 >parametric test', the output of justRMA or the output of justRMA normalized
 >to the mean of a control group (log of fold change)?



Eric Blalock, PhD
Dept Pharmacology, UKMC
859 323-8033

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