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 STATEMENT OF CONFIDENTIALITY The contents of this e-mail message and any attachments are confidential and are intended solely for addressee. The information may also be legally privileged. This transmission is sent in trust, for the sole purpose of delivery to the intended recipient. If you have received this transmission in error, any use, reproduction or dissemination of this transmission is strictly prohibited. If you are not the intended recipient, please immediately notify the sender by reply e-mail or at (859) 323-8033 and delete this message and its attachments, if any. [[alternative HTML version deleted]]

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 > >STATEMENT OF CONFIDENTIALITY > >The contents of this e-mail message and any attachments are confidential >and are intended solely for addressee. The information may also be legally >privileged. This transmission is sent in trust, for the sole purpose of >delivery to the intended recipient. If you have received this transmission >in error, any use, reproduction or dissemination of this transmission is >strictly prohibited. If you are not the intended recipient, please >immediately notify the sender by reply e-mail or at (859) 323-8033 and >delete this message and its attachments, if any. > [[alternative HTML version deleted]] > >_______________________________________________ >Bioconductor mailing list >Bioconductor@stat.math.ethz.ch >https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor 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

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@stat.psu.edu> >Subject: Re: [BioC] Testing for significant genes with RMA >To: Eric <emblal@uky.edu>, bioconductor@stat.math.ethz.ch >Message-ID: <6.0.0.22.2.20031219102013.01e11050@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 STATEMENT OF CONFIDENTIALITY The contents of this e-mail message and any attachments are confidential and are intended solely for addressee. The information may also be legally privileged. This transmission is sent in trust, for the sole purpose of delivery to the intended recipient. If you have received this transmission in error, any use, reproduction or dissemination of this transmission is strictly prohibited. If you are not the intended recipient, please immediately notify the sender by reply e-mail or at (859) 323-8033 and delete this message and its attachments, if any. [[alternative HTML version deleted]]