[BioC] test for differential expresssion

[Rao,Xiayu]

Thank you very much! It is great to know. 

I am curious that how limma can recognize if our samples are paired or not, and then the corresponding ttest for the contrast comparison would be paired or unpaired? Was it coming from the targets file? This is kind of important.

Thanks,
Xiayu

-----Original Message-----
From: Gordon K Smyth [mailto:smyth at wehi.EDU.AU] 
Sent: Thursday, June 27, 2013 6:53 PM
To: Rao,Xiayu
Cc: Bioconductor mailing list
Subject: test for differential expresssion

Dear Xiayu,

> Date: Wed, 26 Jun 2013 17:47:22 +0000
> From: "Rao,Xiayu" <XRao at mdanderson.org>
> To: "'bioconductor at r-project.org'" <bioconductor at r-project.org>
> Subject: [BioC] test for differential expresssion
>
> Hello,
>
> Thank you in advance for your help!
>
> I have 16 tumor samples and each one has a corresponding normal 
> sample, which are 16 pairs. For one particular pair, there is a 
> matched metastasis sample. When I analyzed the data using limma 
> package and followed their protocol, is it right that I fit a linear 
> model, and compare two contrasts of Tumor vs. Normal, and Metastasis vs. Normal?

This is fine.

Of course the genewise variability has to estimated purely from the tumor samples; the metastisized sample doesn't contribute to the variability estimate because there is only one.  Also the statistical test of metastasis vs normal will not be very powerful, because it is based on only one sample.  Nevertheless, it can all be done.

> Or should I ignore that 1 metastasis sample and just use paired ttest 
> to test between Tumor and Normal?

It makes no difference.  The limma paired t-test of tumor vs normal will be exactly the same whether or not the metastasis sample is included. 
The metastasis sample doesn't contribute to the genewise variability estimate, so it makes no difference to the test of tumor vs normal whether or not it is incuded in the data.

You may as well include all the data.  That is the philosophy behind the limma package.

Best wishes
Gordon

> Best,
> Xiayu

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