[BioC] Limma vs anova (GeneSpring)

Naomi Altman naomi at stat.psu.edu
Tue Apr 18 16:43:57 CEST 2006

In general, the big differences should come from the genes with very 
small variance or very large variance, and moderate differential 
expression.  This is because the variance estimate is shrunk towards 
the mean in Limma (and SAM), whereas ANOVA uses the sample 
variance.  Since the t or F test uses the variance estimate in the 
denominator, genes with very small sample variance will be less 
significant with shrinkage, whereas genes with high variance will be 
more significant.


At 08:45 PM 4/17/2006, Wettenhall James wrote:
>I am now working in a place where GeneSpring GX is the standard
>microarray analysis tool, whereas my only microarray analysis experience
>is using R/BioC, particularly limma.  I'm sure this is not an easy
>question, but is there any recommended reading for trying to answer the
>question "Why do I get really different gene lists from the different
>statistical tests?"  (In one case where this has arisen, a thorough
>RT-PCR follow-up is being done, so that will be most interesting.)
>Here's a GeneSpring tutorial, and a quick discussion of the statistical
>test used (obviously different from limma):
>1-Way Anova
>Parameteric test, don't assume variances equal
>Multiple testing correction : Benjamini & Hochberg is the default, but
>if this gives no statistically significant genes, then users seem to
>turn this off.  One thing I don't understand is that when multiple
>testing correction is turned on, the p-value cutoff entry box is
>_replaced_ by a false discovery rate entry box - why can't I have both?
>(Probably a question for GeneSpring rather the BioC.)
>After the ANOVA test, (particularly if there are more than two
>conditions being compared), a post-hoc test (Tukey or
>Student-Newman-Keuls) can be done to determine which pairs of conditions
>the significant genes differ between.
>I have been able to get exactly the same results from normalizing /
>probe-level summary between GeneSpring and BioC.  (For Affy data,
>GeneSpring has RMA and GCRMA, but it refers to them as "pre-processing".
>"Normalization" is done later - so I suspect that some users
>over-normalize compared with what is done in BioC, not realizing that
>that RMA "pre-processing" includes a quantile normalization.)
>So if anyone can recommend any reading for comparing the "different
>worlds" of microarray analysis, I would be most interested.
>Best wishes,
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

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