[BioC] stats on probes instead of summary values?

[Jenny Drnevich]

Hello all,

I have been following the recent exchanges Re: ttest or fold change with a 
lot of interest, particularly the limitations of small sample sizes (i.e., 
3 chips per treatment). The question I would like to raise is that for Affy 
chips, why not use the probe-level values instead of summary values for 
statistical tests as Chu, Weir & Wolfinger (2002, Math. Biosci. 176:35-51) 
suggest? It seems like you throw away a lot of power to detect differences 
when you summarize 14 or 20 PM probes into one summary value. I In fact, 
the median polish used by RMA to summarize is a linear additive model 
somewhat similar to the mixed model used by Chu et al.; RMA only considers 
the probes from one chip, while Chu et al's uses the probes from all chips, 
along with cell line, treatment, and interaction effects (I still use the 
gcrma background correction and normalization on PM values). I'm not 
suggesting that this is a good substitute for conducting more replicates 
(I, too, am from a behavioral ecology background and tend to think an 
adequate sample size is at least 15-20), but I think it is a way to get 
more accurate information on differential expression from only a few 
replicates. I would like to get your thoughts on whether this is or isn't a 
valid method for analysis and why.

Thanks,
Jenny





Jenny Drnevich, Ph.D.
Department of Animal Biology
University of Illinois
515 Morrill Hall
505 S Goodwin Ave
Urbana, IL 61801
USA

ph: 217-244-6826
fax: 217-244-4565
e-mail: drnevich at uiuc.edu