Hi James,

thanks for your reply! My intention to use the above mentioned (although
improper as you mentioned) method was to be able to form clear tests. I
assumed that when I am testing for genes responding to the covariate e.g.
significant "cov" I am testing against the Baseline  in the reference group
(the group that has been assigned as reference by limma or lm). But I want
to test against the baseline in all groups. Basically I wanted to adapt the
procedure that has been proposed for factoral designs in the limma
userguide (Section 8.7).
I now realize that testing for significant coefficients for cov and
interaction of group only seems to be a good choice for me. However I was a
little puzzled by the fact that the effect size for cov would be only the
effect size / slope within GroupA, which I somehow confused with a
signifcant nonzero slope of the covariate exclusively in GroupA.

Best,

Moritz



2013/8/21 James W. MacDonald <jmacdon@uw.edu>

> Hi Moritz,
>
>
> On 8/20/2013 2:30 PM, Moritz Hess wrote:
>
>> Hi,
>> I am investigating the global gene expression response to a continuous
>> environmental covariate in two groups of individuals using limma.
>> I am interested in genes whose  expression is:
>> A) correlated with the covariate
>> B) differentially correlated with the covariate in the two groups of
>> individuals (Interaction of Group and Covariate)
>>
>> In order to be able to set up the proper contrasts I split the Covariate
>> in
>> two Vectors where one Vector contains only the samples with the lowest
>> level of the covariate
>> e.g.  CovBase = c(0,0,0,3,0,3,0,0)
>> and where the other vector contains all the samples with higher levels of
>> the covariate
>> e.g. Cov = c(34,2,5,0,5,0,2,34)
>> and set up a design matrix without intercept:
>>
>> ~ Group + CovBase + Cov
>>
>>
>> The contrasts I am testing are specified as follows:
>> Effect of Covariate: Cov-CovBase
>> Interaction of Group and Covariate: (GroupA-GroupB) - (Cov-CovBase)
>>
>> Does this procedure makes sense ?
>>
>
> It doesn't make sense to me. In the first place the formula you use will
> create an intercept. Secondly, if you want the covariate to be continuous,
> then why are you splitting it like that? And why do you think values of 3
> are lower than values of 2?
>
> If I were trying to do what you say you are doing, then I would just do
>
> cov <- c(34,2,5,3,5,3,2,34)
> group <- factor(whateveryourgroupsare)
>
> design <- model.matrix(~cov*group)
>
> Best,
>
> Jim
>
>
>
>> Thank you very much in advance
>>
>> Moritz
>>
>>
>>
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>
> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
>
>


-- 

*Moritz Heß
PhD Candidate
*
*Research associate
Forest Research Institute
of Baden Württemberg (FVA)
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