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

Naomi Altman naomi at stat.psu.edu
Thu Dec 18 16:44:07 MET 2003

Currently I am telling the biologists to consider microarrays as screening 
experiments.  Mostly, they use the results for second stage analyses, which 
may be:

e.g. statistical analyses such as clustering etc
       bioinformatics analyses such as GO, BLAST or sequence analyses
       lab analyses such as Northern blots, in situs, etc

Given the huge number of genes on most arrays, I do want a reasonably 
reliable method of screening.  On the other hand, I sometimes just rank the 
genes by test score, rather than attempt to determine some suitable 
alpha-level, FDR or FNR.

Incidentally, distinguishing between technical replicates and biological 
replicates can make a huge different to ANOVA test scores, so I think we 
should insist that our analyses should account for this.


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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