[BioC] get over it/WAKE uP and SMELL the COFFEE

Naomi Altman naomi at stat.psu.edu
Thu Dec 18 16:49:28 MET 2003

One thing that makes me very cautious about over-interpreting tests, 
however, is the following:

We have tried several options for normalizing arrays, and found that the 
resulting expression values (for the methods we used) were correlated 
98-99%.  But if we then test for differential expression, we find the 
overlap in the list of "top genes" is only 50-60%.


At 09:09 AM 12/18/2003, Stephen Henderson wrote:
>I agree with some of WHAT you say CHAD, the PROBLEM is THAT MOST
>multiVARIATE methods are BUILt on top OF the marginal tests. FOR instance
>machine learning methods are based on gene subsets for each of k CROSS
>validations. USE of the appropriate TEST (fold/T/F/cyber-T/etc..)for subset
>selection is IMHO the most IMPORTANT!! choice .
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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

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