[BioC] Questions about using Limma

[wei tie]

Hello ,

I am currently working on 30 Affymetrix arrays for a time course
experiment using Limma package. I have two groups (A and B) with
samples taken at 0hr, 1hr, 6hr, 12hrs and 24hrs.

The following is how my targets file looks like:
FileName      Targets
Files 1-3       A.0hr
Files 4-6       A.1hr
Files 7-9       A.6hr
Files 10-12     A.12hr
Files 13-15     A.24hr
Files 16-18     B.0hr
Files 19-21     B.1hr
Files 22-24     B.6hr
Files 25-27     B.12hr
Files 28-30     B.24hr

If I apply the lmFit() function to all the 30 arrays as follows:

sample<-c("A0","A1","A6","A12","A24","B0","B1","B6","B12","B24")
all<-factor(rep(sample,each=3),levels=sample)
design<-model.matrix(~0+all)
colnames(design)<-sample
fit1 <- lmFit(eset,design)
contrast<- makeContrasts(B1-B0,A1-A0, (B1-B0)-(A1-A0),levels=design)
fit2<- contrasts.fit(fit1, contrast)
fit2<- eBayes(fit2)
results1<-decideTests(fit2,method="global",adjust.method="BH",p.value=0.05,lfc=1)
summary(results1)
     B1 - B0      A1 - A0          (B1 - B0) - (A1 - A0)
-1      1941        1133                        1155
0      26434       27285                       28635
1       1879        1836                         464

the result is that 1619(1155+464) genes changed at the first 1hr
differently between group A and group B.

If I apply the lmFit() function only to the 12 samples taken at 0hr
and 1hr , and use the following target file:
FileName      Targets
Files 1-3       A.0hr
Files 4-6       A.1hr
Files 7-9       B.0hr
Files 10-12     B.1hr

then use the following script:
sample1<-c("A0","A1","B0","B1")
part<-factor(rep(sample,each=3),levels=sample)
design<-model.matrix(~0+part)
colnames(design)<-sample1
fit3 <- lmFit(eset1,design)
contrast<- makeContrasts(B1-B0,A1-A0, (B1-B0)-(A1-A0),levels=design)
fit4<- contrasts.fit(fit3, contrast)
fit4<- eBayes(fit4)
results2<-decideTests(fit4,method="global",adjust.method="BH",p.value=0.05,lfc=1)
summary(results2)
     B1 - B0      A1 - A0           (B1 - B0) - (A1 - A0)
-1      1501         825                         563
0      27478       28194                       29483
1       1275        1235                         208

the genes changing differently between group A and group B in terms of
interaction decreased to 771(563+208).

If I use the method="separate" in decideTests()
Results3<-decideTests(fit4,method="separate",adjust.method="BH",p.value=0.05,lfc=1)
summary(results3)
      B1 - B0     A1 - A0           (B1 - B0) - (A1 - A0)
-1      1763         876                          67
0      26915       28058                       30169
1       1576        1320                          18

the genes which changed differently between group A and group B are
much fewer (67+18=85).

Why do I get different lists of differentially expressed genes when I
use the three approaches? Which result should I choose?

I would really appreciate if someone can give some help.

Thank you,
Regards,
Wei Tie