"Alex F. Bokov" <firstname.lastname@example.org> writes:
> Wow, that was fast! Thanks for your answer.
> rossini-at-blindglobe.net (A.J. Rossini) |bioconductor.org| wrote:
>> It would be good to be clear -- ANOVA at the gene level, or are you
>> thinking of mixing in experiment-level parameters? For the
>> Biobase would suffice, though other packages (limma, nlme) might be
>> useful as well. This assumes that you've already got normalized
>> (or else you'd need the appropriate packages)
> It's a two-way experiment. Young vs. old, mutant vs. wildtype, with
> 8 to 10 replicate arrays in each of the four possible
> combinations. Would that mean it's experiment-level or gene-level
> (sorry, I don't yet know the proper terminology)?
I'm assuming that you want to fit a 2-way ANOVA to each gene or tag.
That would be easy, the overview being to stick the data into an
exprSet, and then use esApply to compute p-values from which to
evaluate via multtest. The question would be if you wanted to include
a parameter for modeling/testing that was the same for all genes.
> The sort of thing that in SAS would involve PROC GLM... except the
> data is probably not normally distributed even after log2 (thus
> necessitating the empirical null distribution) and of course the
> multiple comparison problem (thus necessitating multtest or qvalue).
> I'm surprised that a two-way ANOVA using an empirical null dist and
> then adjusted p-values isn't the default analysis everyone does on
> their microarrays... could it be that everyone in the field is doing
> t-test style comparisons only?
Depends on all of the following: the technology and chips used, the
experiment (and different scientific areas seem to have different uses
for expression arrays), and how well normalization works. Not all
experiments are in the format you described (very simple 2-way
layout), and not all observed datasets are appropriate for ANOVA.
A.J. Rossini / email@example.com / firstname.lastname@example.org
Biomedical/Health Informatics and Biostatistics, University of
Biostatistics, HVTN/SCHARP, Fred Hutchinson Cancer Research Center.
FHCRC: 206-667-7025 (fax=4812)|Voicemail is pretty sketchy/use Email
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