Mark,

Thank you for your prompt response. 
In this experiment, I consider all these 3 strains are almost the same (wild type). And there are only 3 mice, each received different treatment at diffierent time. (Sorry, forgetting to mention this important info, even 0 t1 and 0 t2 are received at different time).
the repeated treatment to each mouse need to be included in the model. That's why I average the technical replicates and then used each mouse as block.

I did try to use technical replicates as block. But I am not sure how to capture the repeated measurements. The data frame I have here is:
   V1    V2 V3 V4
1   1    B6  0 t1
2   2    B6  0 t1
3   3    B6  0 t1
4   1    B6 14 t1
5   2    B6 14 t1
6   3    B6 14 t1
7   1    B6  0 t2
8   2    B6  0 t2
9   3    B6  0 t2
10  1    B6 14 t2
11  2    B6 14 t2
12  3    B6 14 t2
13  1 Balbc  0 t1
14  2 Balbc  0 t1
15  3 Balbc  0 t1
16  1 Balbc 14 t1
17  2 Balbc 14 t1
18  3 Balbc 14 t1
19  1 Balbc  0 t2
20  2 Balbc  0 t2
21  3 Balbc  0 t2
22  1  J129  0 t1
23  2  J129  0 t1
24  3  J129  0 t1
25  1  J129  0 t2
26  2  J129  0 t2
27  3  J129  0 t2
28  1  J129 14 t2
29  2  J129 14 t2
30  3  J129 14 t2

create factors (I only care time and treatment):

 f<-paste(df$V3,"_",df$V4,sep="")

thus, we have factor: 0_t1,0_t2,14_t1,14_t2.

create block using technical replecates:
block<-rep(1:10,each=3)

then make the design:
design<-model.matrix(~ -1+factor(f, levels=unique(f))
colnames(design)<-unique(f)

biocor<-duplicateCorrelation(eset,desgin,block=block)
fit<-lmFit(eset,design=design,block=block,correlation=biocor$consensus)


contrast.matrix<-makeContrasts(0_t2-0_t1, 14_t2-14_t1, 14_t2-14_t1, 0_t2-0_t1)

As you can see from the above analysis, I just treat each set experiment independently after considering technical replicates, and did not include repeated measurement effects for each mouse into the model.
The goal here is to compare the t2 and t1 at day0 and day14. These 3 mice are considered pathologically same even they have some different genetic background.

Do you have any suggestion to include repeated measurement of each mouse into the model? 

Thank you very much.

-Xiaokuan




 





 




________________________________
From: Mark Cowley <m.cowley0@gmail.com>

Cc: bioconductor <bioconductor@stat.math.ethz.ch>
Sent: Wed, June 9, 2010 8:23:09 PM
Subject: Re: [BioC] Repeat Measurement with Limma Fw: limma matrix desgin question?

hi Xiaokuan,
i'd be treating the technical replicates as the block, and strain, day and treatment as the biological effects of interest
block <- rep(1:10, each=3)
that way limma has triple the number of arrays to use in its variance estimation

you have a few options for your design matrix, such as strain + day + treatment + any interaction effects you're interested in
or you can simply treat these samples as 10 different groups and then setup contrasts to identify the effects of interest

cheers,
mark
-----------------------------------------------------
Mark Cowley, PhD

Peter Wills Bioinformatics Centre
Garvan Institute of Medical Research, Sydney, Australia
-----------------------------------------------------


On 10/06/2010, at 6:39 AM, Xiaokuan Wei wrote:

> 
> 
> 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 ----

> 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 
> 
> 
> 
> 
>     [[alternative HTML version deleted]]
> 
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