

Dear List,

I think I know how to do this with limma.
I first calculate the correlation treating each strain as a block
block<-rep(1:3,c(4,3,2))
biocor<-duplicateCorrelation(eset,design,block=block)
then
fit<-lmFit(eset,design=design,block=block,correlation=biocor$consensus)
then create contrast matrix and extract coef of each comparison.

Is this right?

However, I have a further question. In fact, each chip has 3 technical replicates. In order to simply the anlaysis, I just average the replicates and then use limma to do the job.
How could I include such technical replicate information and repeat measurement information together with using Limma. Could Gordon or someone give me some hints or example?
Thank you very much.


replicate Strain Day Treatment 
1 B6 0 t1 
2 B6 0 t1 
3 B6 0 t1 
1 B6 14 t1 
2 B6 14 t1 
3 B6 14 t1 
1 B6 0 t2 
2 B6 0 t2 
3 B6 0 t2 
1 B6 14 t2 
2 B6 14 t2 
3 B6 14 t2 
1 Balbc 0 t1 
2 Balbc 0 t1 
3 Balbc 0 t1 
1 Balbc 14 t1 
2 Balbc 14 t1 
3 Balbc 14 t1 
1 Balbc 0 t2 
2 Balbc 0 t2 
3 Balbc 0 t2 
1 J129 0 t1 
2 J129 0 t1 
3 J129 0 t1 
1 J129 0 t2 
2 J129 0 t2 
3 J129 0 t2 
1 J129 14 t2 
2 J129 14 t2 
3 J129 14 t2 






-Xiaokuan












----- Forwarded Message ----
From: Xiaokuan Wei <weixiaokuan@yahoo.com>
To: bioconductor <bioconductor@stat.math.ethz.ch>
Sent: Wed, June 9, 2010 11:53:57 AM
Subject: limma matrix desgin question?


Dear List,
 
I have an experiment trying to evaluate two treatment for mice. I have 3 normal strains, two treatment and two time points.
The goal is to compare t2 vs t1 Day14 vs Day0.
All these mice considered normal. So I can create factor such as day0_t1, day0_t2, day14_t1, and day14_t2.
and make contrasts, such as day0_t2-day0_t1, day14_t2-day14_t1, day14_t2-day14_t1...
 
But how can I include strain information into the comparison? Since each strain's data will be correlated? 
 
Thank you.
 
Xiaokuan
 
 
 
 
 
 
Strain Day Treatment 
B6 0 t1 
B6 14 t1 
B6 0 t2 
B6 14 t2 
Balbc 0 t1 
Balbc 14 t1 
Balbc 0 t2 
J129 0 t1 
J129 0 t2 
J129 14 t2 



      
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