[BioC] Can subject be treated as fixed effect in linear model with microarray data

[Guilherme J. M. Rosa]

Dear Shirley:

If I understood right your design, you had both 'normal' and 'cancer' 
tissues sampled in each subject, right?
So in this case you do not have 'tissue nested in subject', like you say. 
Subjects are within gender, but crossed with tissues.

You have, as you say, a 2-way anova (factors: Gender and tissue), but you 
have repeated measurements on each subject, i.e. the two tissues assayed in 
ach subject. In this case you can see subjects as a blocking factor, but 
actually you have two sampling levels, like a split-plot design.

I would suggest a model like:

y = mu + gender + subject(gender) + tissue + gender*tissue + e

where gender, tissue and the interaction gender*tissue are considered as 
fixed effects; and subject within gender is random (which is actually the 
error term to compare the two genders).

You may want to use the MAANOVA software to run such a model 
(http://www.jax.org/staff/churchill/labsite/software/Rmaanova/index.html). 
LIMMA may not give you the flexibility to implement it.

Cheers,

Guilherme.

--

Guilherme J. M. Rosa
Assistant Professor
Department of Dairy Science
University of Wisconsin - Madison

460 Animal Science Building
1675 Observatory Dr.
Madison, WI 53706 USA

Phone: + 1 (608) 265-8617
Fax: + 1 (608) 263-9412
E-mail: grosa at wisc.edu
http://www.wisc.edu/dysci/facstaff/Rosa/index.html
----- Original Message ----- 
From: "Naomi Altman" <naomi at stat.psu.edu>
To: "shirley zhang" <shirley0818 at gmail.com>; 
<Bioconductor at stat.math.ethz.ch>
Sent: Wednesday, June 06, 2007 9:28 AM
Subject: Re: [BioC] Can subject be treated as fixed effect in linear model 
with microarray data


> Model 3 is completely illegal.  Model 2 is sometimes used when there
> are few within subject observations (as here).  However, I would not
> do that here.  I would use an eBayes method such as limma to improve 
> power.
>
> --Naomi
>
> At 11:34 AM 6/5/2007, shirley zhang wrote:
>>Dear Bioconductor,
>>
>>In a microarray data, there are 20 subjects grouped by Gender, each
>>subject has 2 tissues (normal vs. cancer).
>>
>>In fact, it is a 2-way anova (factors: Gender and tissue) with tissue
>>nested in subject. I've tried the following:
>>
>>Model 1: lme(response ~ tissue*Gender, random = ~1|subject)
>>Model 2: response ~ tissue*Gender + subject
>>Model 3: response ~ tissue*Gender
>>
>>
>>It seems like Model 1 is the correct one since my experiment design is
>>nested design. However, I got a few significant genes for Gender
>>effect from Model 1 so I want to use Model 2 or Model 3. Can anybody
>>tell me whether Model2 is
>>completely illegal?
>>
>>Thanks,
>>
>>_______________________________________________
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>
> Naomi S. Altman                                814-865-3791 (voice)
> Associate Professor
> Dept. of Statistics                              814-863-7114 (fax)
> Penn State University                         814-865-1348 (Statistics)
> University Park, PA 16802-2111
>
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