[BioC] RMA vs VSN

Roger Vallejo rvallejo at psu.edu
Sat Jun 19 16:06:33 CEST 2004


We have a small experiment with high FDR (around 0.40): 8 affymetrix
mouse genechips with 22k genes, 2 replications, saline and E. coli
treated mammary tissue, evaluated at 24 hr and 48 hr post injections. 

I have run both data preprocessing functions via expresso. To
subsequenctly run an lme-ANOVA.  As expected, I got lower FDR and much
smaller p-values when using VSN. The FDR was estimated using QVALUE
package. Obviously, I feel tempted to use VSN instead of RMA. However,
before proceeding I would like to hear some comments from the
Bioconductor group on this approach. The question is:

Is VSN better than RMA? 

I have read the literature and both claim to be the function to be used!


Personally, I feel more towards the use of VSN. I might be wrong, so I
would appreciate any suggestions or comments on this. 

These are the functions that I used:

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

For RMA:

> library(affy)

> Data <- ReadAffy(widget=TRUE)


> eset <- expresso(Data,bgcorrect.method="rma",
normalize.method="quantiles", pmcorrect.method="pmonly",
summary.method="medianpolish")

 

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

For VSN:

> library(affy)

> Data <- ReadAffy(widget=TRUE)                                         

> library(vsn)

> normalize.AffyBatch.methods <- c(normalize.AffyBatch.methods, "vsn")

> eset = expresso(Data, bg.correct= FALSE, normalize.method = "vsn",
pmcorrect.method = "pmonly", summary.method = "medianpolish")

 

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

Thank you very much.

Roger

 

 

Roger L. Vallejo, Ph.D.

Assist. Professor of Genomics & Bioinformatics

Genomics & Bioinformatics Laboratory

Department of Dairy & Animal Science

The Pennsylvania State University

305 Henning Building

University Park, PA 16802

Phone:       (814) 865-1846 

Email:        rvallejo at psu.edu

 


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