Hi Gordon,

We are just about finished with a write-up of a manuscript where we
describe longitudinal differences within subjects between two different
groups from baseline to an outcome.

We used a factorial design in limma and are happy with its results and
robustness.

Recently, a colleague mentioned had GEE as a means to test for DE between
groups. I have yet to find any microarray differential testing done with
it. GEE is a generalized estimated equation used to estimate parameters of
a glm, it measures population-averaged effects. Truthfully, I dont what is
is about and was hoping to gain a bit more of insight which google could
not offer. Often this mail listing brings me resolution in a much more
explicit and unambigous manner.

Yours,

Michael





On Wed, Oct 2, 2013 at 2:13 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Dear Michael,
>
> It would help if you explained what you mean by "GEE" and why you think it
> might be relevant for your problem.
>
> Best wishes
> Gordon
>
>  Date: Tue, 1 Oct 2013 15:28:36 +0100
>> From: Michael Breen <breenbioinformatics@gmail.com**>
>> To: "bioconductor@r-project.org" <bioconductor@r-project.org>,
>>         "Bioconductor   Mailing List" <bioconductor@stat.math.ethz.**ch<bioconductor@stat.math.ethz.ch>
>> >
>> Subject: [BioC] Limma vs GEE
>>
>> Hi all,
>>
>> Consider 20 samples at baseline later exposed to treatment. 10 develop a
>> disease and 10 do not develop a disease. Here we want to make a
>> longitudinal assessment of gene expression in the diseased vs disease-free.
>> All done on Affy microarrays.
>>
>> Are there any obvious reasons why one would consider limma over GEE for
>> testing for conditional or disease related outcomes?
>>
>> Cheers,
>>
>> Michael
>>
>
> ______________________________**______________________________**__________
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