
Dear Bioc,

   I am trying to find DE genes in a time course experiment, and I am a bit confused at how to setup the matrix model. I wasn't able to follow the example in manual page 48-49 because I don't know what the variable "targets" refer to. But I have setup a model based on Section 7.2. 

 

  My layout is: I have 10 time points, time 0,1,3,5,7,9...

  Each time point has two replicates.

  My aim is to find DE genes by comparing each time point to the reference point (i.e. at time 0).

 

Below are my codes related to setting up the model. It would help me a lot if someone could confirm if I'm doing this right, or whether some changes that need to be done.

Thanks a lot in advance!

 

design<-model.matrix(~0+factor(c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10)));  ##1=at time 0, 2=at time 1...and so on
colnames(design)<-c("WT0hr","WT1hr","WT2hr","WT6hr","WT12hr","WT18hr","WT24hr","WT30hr","WT48hr","WT72hr");
fit<-lmFit(allDatagcRMA, design); ##allDatagcRMA contains the expressions normalized by gcrma
contrast.matrix <- makeContrasts(WT1hr-WT0hr, WT2hr-WT0hr, WT6hr-WT0hr, WT12hr-WT0hr, WT18hr-WT0hr, WT24hr-WT0hr, WT30hr-WT0hr, WT48hr-WT0hr, WT72hr-WT0hr, levels=design);
fit2<-contrasts.fit(fit,contrast.matrix);
fit2<-eBayes(fit2);
topTableF(fit2, adjust="BH"); ##To get the top 10 genes that respond to at least one of the comparisons above

 

 

Thanks again!

Sincerely,

Casper
 		 	   		  
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