Gordon, 

Thank you for your prompt response and clear explainations.
This is exactly I am looking for.

-Xiaokuan



________________________________
From: Gordon K Smyth <smyth@wehi.EDU.AU>

Cc: Bioconductor mailing list <bioconductor@r-project.org>
Sent: Tue, July 19, 2011 6:52:23 PM
Subject: time series analysis with limma package

Dear Xiaokuan,

You are correct that the time course example in the limma User's Guide assumes 
that all the samples are independent.  When the time course is of a repeated 
measures nature, you can estimate the correlation between the repeated measures 
using the duplicateCorrelation() function in limma, with the block argument 
indicating each time course replicate.  The correlation is then input to the 
lmFit() function and carried through all the analysis.  This was done for 
example in the following paper:

Peart, MJ., Smyth, GK., van Laar, RK., Richon, VM., Holloway, AJ, Johnstone, RW 
(2005). Identification and functional significance of genes regulated by 
structurally diverse histone deacetylase inhibitors. Proceedings of the National 
Academy of Sciences of the United States of America 102, 3697-3702.

Best wishes
Gordon

> Date: Mon, 18 Jul 2011 10:23:50 -0700

> To: bioconductor <bioconductor@stat.math.ethz.ch>
> Subject: [BioC] time series analysis with limma package
> 
> Dear List,
> 
> I have been thinking with using limma package to perform some time series
> analysis. There is a simple example in limma's manual. However, it seems that
> the analysis in the manual does not consider the repeated measurement effect 
>for
> time series data.
> I am wondering if limma has developed any method to deal with such time series
> data. Or I have to manually add random effects term in the model. But I really
> don't know how to do this. Could some one or Gordon clarify on this topic?
> My apology first, if this topic has been intensively discussed.
> Thank you.
> 
> Xiaokuan

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