Hi Aaron and Gordon,
Thanks for your entirely straightforward replies to our broad question.
In fact this was not yet critic from a reviewer, rather constructive
criticism from a colleague. Although, now we have a better idea about these
types of tests. I find your summarization of GEE rather helpful in that
they do not maximize any optimality criteria such as they don't correspond
to any well defined probability distribution and are poor when maximizing
likelihood and sum of squares.
Thanks again for your time and answers!
Michael
On Thu, Oct 3, 2013 at 12:39 AM, Gordon K Smyth wrote:
> 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 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
>>>> To: "bioconductor@r-project.org" ,
>>>> "Bioconductor Mailing List" >>> *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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