[BioC] time series analysis with limma package

Gordon K Smyth smyth at wehi.EDU.AU
Wed Jul 20 00:52:23 CEST 2011

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

> Date: Mon, 18 Jul 2011 10:23:50 -0700
> From: Xiaokuan Wei <weixiaokuan at yahoo.com>
> To: bioconductor <bioconductor at 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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