Hello Wei,

okay, not actually an answer to your question, but since you have a  
time series you might also want to consider the package "timecourse".  
I've found it quite useful for medium to long time series.

Best wishes
\Heidi

===================
Loans that change lives
https://www.kiva.org/

On 5 May 2010, at 14:09, wei tie wrote:

> 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
>
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