Dear list,

 

When we have paired samples, one way to analyze the data would be to
consider a block effect, as it is shown in point 8.3 of the limma user
guide. 

 

File       block    treatment

1          1          c

2          1          t

3          2          c

4          2          t

5          3          c

6          3          t 

 

block=factor(c(“1”,”1”,”2”,”2”,”3”,”3”))

contrasts(block)=contr.sum(3)

 

treat=factor(c(“c”,”t”,”c”,”t”,”c”,”t”))

contrasts(treat)=contr.sum(2)

 

design=model.matrix(~ -1 + treat + block)

fit=lmFit(eset,design) 

            fit2=contrasts.fit(fit,CM)

            fit2=eBayes(fit2)

  

MY DOUBT IS IF WE CAN ANALYZE THIS PAIRED DATA USING DUPLICATECORRELATION (
) FUNCTION IN ORDER TO TAKE INTO ACCOUNT THE BLOCK EFFECT. I guess that this
is right, with the difference that we are considering the block effect as a
random effect. Am I right? 

 

famrep=c(1,1,2,2,3,3)

corfit=duplicateCorrelation(eset,ndups=1,block=famrep)

 

treat=factor(c(“c”,”t”,”c”,”t”,”c”,”t”))

contrasts(treat)=contr.sum(2)

 

design=model.matrix(~ -1 + treat + block)

 

fit=lmFit(eset,design,block=famrep,cor=corfit$consensus)

            fit2=contrasts.fit(fit,CM)

            fit2=eBayes(fit2)

  

 

Can anyone tell me whether or not I am right? 

Thank you very much 

 

Pedro.- 

 

 

 

 


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