[BioC] Repeat Measurement with Limma Fw: limma matrix desgin question?

[Mark Cowley]

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 ----
> From: Xiaokuan Wei <weixiaokuan at yahoo.com>
> To: bioconductor <bioconductor at 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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