[BioC] help with paired-test in limma

[Ariel Chernomoretz]

Dear list,

This is my first time with limma (and also with linear models!).
After reading the vignettes, papers and previously posted messages I came out 
with something, but I am not sure it is the correct procedure....in particular 
I have doubts related to how to handle with limma the paired nature of my 
design. I would greatly appreciate some remarks, comments, help, etc. 

I want to study the differences between two treatments: allocationA and 
allocationB, using single color affymetrix arrays, in a paired design.
Two samples were taken from 13 patients, one before, and the other after 
treatment. There were 6 and 7 patients treated with allocationA, and 
allocationB, respectively. Each sample was technically replicated, so at the 
end we have 4 chip per patient.
The experimental setup looks like this:

Sample	BeforeAfter	Patient	allocation
1		0			1		A
2		0			1		A
3		1			1		A
4		1			1		A
5		0			2		A
6		0			2		A
7		1			2		A
8		1			2		A
9		0			3		B
10		0			3		B
11		1			3		B
12		1			3		B
.		.			.		.
.		.			.		.
52		1			13		B


After reading some posts I decided to take the average of technical 
replicates. Then I used a block in order to get the lmFit, and I calculated 
the contrasts of interest:
> design<-model.matrix(~allocation*AntesDespues,data=pData(eset))
> bblock<-rep(1:13,each=2)
> fit2<-lmFit(eset,design,block=bblock)
>
> cont.matrix<-cbind(A.AfterVSBefore=c(0,0,1,0),
>                                    B.AfterVSBefore=c(0,0,1,1),
>                                    Interac                =c(0,0,0,1))
> fit2<-contrasts.fit(fit2,cont.matrix)
> fit2eb<-eBayes(fit2)

I think that in this way I am implicitly assuming an intrablock correlation 
level of 0.75. Is this somehow appropriate in order to consider this 
a paired t-test like analysis? 

With duplicateCorrelation, I get a value of 0.21 (!)
Is this value the one I should consider for lmFit ?
Using this rather low value isn't like disregarding
the before-after pairing for each patient?  Shouldn't I tweak the correlation 
level to an artificially higher value (for instance 0.9) instead?

Any comments would be highly appreciated
Thanks
Ariel./






-- 
Ariel Chernomoretz, Ph.D.
Centre de recherche du CHUL
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