Hi,

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):
http://hsc.unm.edu/som/micro/genomics/tutorial.html#OLE_LINK6

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,
James



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