[BioC] multi-level design - a simplified question - corrected table

[Rao,Xiayu]

Hello all,

I do need some help on analyzing such unorganized data. Please help me out. Thank you so much!
I basically followed the analysis of multi-level experiments in limma user guide. But I do not feel right about the code below. Please give me some suggestions.

# I want to compare Normal vs. Tumor negative,  and Normal vs Tumor positive. There are partial pairing (subject) and batch effect (chip).
Treat <- factor(paste(targets$chip,targets$type,sep="."))
design <- model.matrix(~0+Treat)
colnames(design) <- levels(Treat)

corfit <- duplicateCorrelation(y,design,block=targets$subject)
corfit$consensus
fit <- lmFit(y,design,block=targets$subject,correlation=corfit$consensus)
cm <- makeContrasts(TposvsN=(a1.Tpos+a2.Tpos+a3.Tpos)/3-(a1.N+a2.N)/2, TnegvsN=(a1.Tneg+a3.Tneg)/2-(a1.N+a2.N)/2, levels=design)        ????
fit2 <- contrasts.fit(fit, cm)
fit2 <- eBayes(fit2)
topTable(fit2, coef=1, sort.by="p")

sample	type	subject	chip 
s1	Tneg	1	a1
s2	N	1	a1
s3	Tpos	2	a1
s4	N	2	a1
s5	Tneg	3	a1
s6	N	3	a1
s7	Tpos	4	a1
s8	N	4	a1
s9	Tpos	5	a2
s10	N	5	a2
s11	N	6	a2
s12	Tpos	7	a2
s13	N	7	a2
s14	Tpos	8	a2
s15	N	8	a2
s16	Tneg	9	a3
s17	Tneg	10	a3
s18	Tneg	11	a3
s19	Tpos	6	a3
s20	Tpos	12	a3
s21	Tneg	13	a3
s22	Tpos	14	a3


Thanks,
Xiayu