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

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