Design Matrix

Dear all,

I need some help on matrix design and B statistics by using limma package.
I want to compare gene expression in 2 groups of cDNA samples.
The experiment compares 4 treated mice(#1,#2,#3,#4) and 4 control mice
(#5,#6,#7,#8).

The target file is
FileName     Cy3           Cy5
mice1.spot   Control_#5   Treat_#1
mice2.spot   Treat_#1     Control_#5
mice3.spot   Control_#6   Treat_#2
mice4.spot   Treat_#3     Control_#7
mice5.spot   Control_#8   Treat_#4

The first slide (mice1.spot) and the second slide(mice2.spot) are
dye-swap. There is no common reference. There are 3 replicated spots of
each gene on each array (384 genes in total).

MA is an object of class marrayNorm, below is what I did.

design <- c(1,-1,1,-1,1)
cor <- duplicateCorrelation(MA,design,ndups=3)
cor$consensus.correlation

[1] 0.506

fit <- lmFit(MA,design,ndups=3,correlation=cor$consensus.correlation)
fit <- eBayes(fit)
topTable(fit,n=20,adjust="fdr")

The result is,

ID	M	A	t	P.Value	B
348	-1.3	10.8	-3.98	0.577	-4.47
371	-1.91	11.5	-3.36	0.577	-4.47
172	-2.56	13.4	-3.36	0.577	-4.47
273	-0.98	10.3	-3.22	0.577	-4.48
...

It seems this is no evidence of differential expression. But if I use the
first three slides to do analysis, design <- c(1,-1,1),the result is good,
B>5, P.Value is very small. I am wondering if my design matrix is right?

Many thanks in advance and best regards.
Michelle