[BioC] Limma vs GEE
Gordon K Smyth
smyth at wehi.EDU.AU
Thu Oct 3 01:39:09 CEST 2013
Hi Micahel,
As Aaron Mackey has said in separate email, limma has the obvious
advantage of borrowing information between genes.
I have trouble thinking of any possible motivation for using a GEE in your
context, and the fact that you can't find any applications to microarrays
is a sign of this.
GEEs are not actually used to fit generalized linear models (glms). If
one wanted to fit a glm, one would simply do so using the usual likelihood
method. GEEs are actually used to estimate glms with correlation
structures. The reason why a "generalized" (approximate) estimating
equation is needed is that such models don't correspond to any well
defined probability distribution. The GEE equations don't maximize any
optimality criteria such as a likelihood or sum of squares.
In your case you don't even have glms. You have normal data from
Affymetrix arrays for which likelihood methods are readily available. So
there is no need to use glms or GEEs.
With your data, the potential motivation for fitting a correlation
structure would be to take account of correlation between repeated time
course measurements on the same samples (if that is what you actually
have). limma allows you to fit a constant correlation between the
repeated measures. That should be sufficient unless you have large number
of longitudinal observations on the same samples. If you did need to go
outside the limma framework to fit a more complex correlation structure
(and forgo the benefits of information borrowing), you would probably want
to use one of the many normal-based mixed model tools rather than GEEs.
Best wishes
Gordon
On Wed, 2 Oct 2013, Michael Breen wrote:
> 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 at 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 at gmail.com**>
>>> To: "bioconductor at r-project.org" <bioconductor at r-project.org>,
>>> "Bioconductor Mailing List" <bioconductor at stat.math.ethz.**ch<bioconductor at 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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