[BioC] LIMMA paired T-test

[Gordon K Smyth]

Your design matrix is not sufficient to answer questions 2 and 3.  Your 
questions presume an interaction between treatment and disease, i.e., 
distinct effects for treatment for disease and healthy, whereas your model 
formula assumes no interaction.

You need:

   design <- model.matrix(~patient + dis + dis:tx)

Then last two coefficients answer questions 2 and 3.

Gordon

---------------------------------------------
Professor Gordon K Smyth,
Bioinformatics Division,
Walter and Eliza Hall Institute of Medical Research,
1G Royal Parade, Parkville, Vic 3052, Australia.
http://www.wehi.edu.au
http://www.statsci.org/smyth

On Tue, 3 Jul 2012, somnath bandyopadhyay wrote:

>
> Hi Gordon and LIMMA users,
>
> I am sure this question has been answered before and I tried looking into the archives for some answer but did n't have any success there.
>
> My experimental design has diseased and healthy volunteers blood treated with a drug. I have gene expression data for both before and after treatment. So, I have disease, treatment and patient_ID (before vs. after treatment) as covariates. What I am interested in are as follows:
>
> 1. What genes change in untreated disease vs. untreated healthy volunteers?
> 2. What genes change in treated disease vs. untreated disease blood samples?
> 3. What genes change in treated healthy volunteers vs. untreated healthy volunteers blood samples?
>
> Design of the experiment:
> design <- model.matrix(~ dis + tx + patient)
>
> Based on the above design I am able to answer question 1. I was 
> wondering how I would answer question 2 and 3 in a paired T -test (to 
> account for before vs. after treatment). Do I need to do some contrasts 
> because I have been trying to work off the lmfit.
>
> Any help would be greatly apreciated.
>
> Thanks,
> Som.
>
>
>
>
>

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