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SKALKO@clinic.ub.es
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40
@skalkoclinicubes-1118
Last seen 10.2 years ago
Dear all,
I have a question on a subject that I think was not discussed here
before.
I am using limma package for the detection of significant differential
expression in an affy experiment:
3 "Healthy" (group1) indiv. before treatment and the same indiv. after
treatment (group4)
3 "ill-low" (group2) " " "
" " (group 5)
2 "ill-high" (group3) " " "
" " (group 6)
the interest is comparing effects of the treatment (i.e.
group4-group1,
group5-group2, etc). I used these commands:
>library(affy)
>library(limma)
>library("hgu133a")
>x<-ReadAffy()
>eset<-rma(x)
>design <- model.matrix(~
-1+factor(c(1,1,1,2,2,2,3,3,4,4,4,5,5,5,6,6)))
>colnames(design) <-
c("group1","group2","group3","group4","group5","group6")
>fit<-lmFit(eset,design)
>contrast.matrix <-
makeContrasts(group2-group1,group4-group1,group5-group2,
group6-group3,levels=design)
>fit2 <- contrasts.fit(fit, contrast.matrix)
>fit2 <- eBayes(fit2)
The question is: How has to be taken into account that the
individuals
are the same before and after the treatment?
I red about block in lmFit but I am not sure how to do that. Here it
would be some correlation, but no so high as
in the case of real technical replicates.
Thanking you in advance,
Susana Kalko
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