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I work with one-channel arrays, where
on each slide the sample is repeated three times (in three blocks
called subarrays).
In order to perform the differential
expression analysis (usually a two-groups comparison), up to now we
always averaged the three repetitions (after normalization).
But I always wanted to use more
effectively the information that we get from the repetitions.



For example now we have two groups to
compare, 15 + 15 samples. Looking at the userguide and at the mailing
list archive I came up with this design.



# group 2 vs. 3



require(limma)
res.P23 <- cbind(res.grp2, res.grp3)
CR23 <- c(rep("g2",
ncol(res.grp2)), rep("g3", ncol(res.grp3)))
design23<-
model.matrix(~0+factor(CR23))
colnames(design23) <- unique(CR23)
biolrep23 <-  sort(rep(1:30,3))  # 
1,1,1,2,2,2,3,3,3 … 30,30,30
corfit23<-
duplicateCorrelation(res.P23, design23, ndups=1, block= biolrep23)
fit23 <- lmFit(res.P23, design23,
ndups=1, block=biolrep23, cor=corfit23$consensus)
cont.matrix23 <-
makeContrasts(g2vsg3 = g2-g3, levels=design23)
fit2.23 <- contrasts.fit(fit23,
cont.matrix23)
fit2.23 <- eBayes(fit2.23)



I know that it is a banal question, but I would like to know if the
formulation of the biolrep sequence (and the rest) is correct. This because I get
really different results compared with the analysis done by using
averaged arrays. I tried with a few different datasets, with very
different  corfit$consensus value. I'm not shocked by these results,
at the contrary, but I just want to be sure, especially after I've read somewhere that it shouldn't change that much.
I thank you in advance,





 		 	   		  
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