[BioC] Limma Modeling

[Naomi Altman]

The problem is that you have thousands of genes which you have 
summarized as "gene expression".  This means that you can obtain a 
perfect fit with many different sets of genes.

So, usually you need to do some gene selection before you fit the 
model.  Then you could do ordinary variable selection as in ordinary 
linear regression, or possibly use a method like ridge regression.

--Naomi

At 12:00 PM 7/20/2006, Katarzyna Bryc wrote:
>Dear List,
>
>I have a question on correctly modeling a situtation to find
>significantly differentiated genes with Limma. I have Affy arrays for
>pediatric patients collected before the patients were treated with a
>drug for 4 months. After this time period, some patients had a side
>effect of significant weight gain, while others did not. I wish to find
>the genes which significantly differentiate patients who gained weight
>with the treatment from those who did not suffer this side effect. Since
>these are pediatric patients, I also wish to control for Sex and Age
>(continuous variable).
>
>I understand that with Limma I can model the following:
>
>Gene Expression = Age + Sex + Weight Gain
>
>but I actually wish to look at Weight Gain as the dependent variable,
>and Gene Expression as one of the independent variables (I still control
>for Age and Sex). Thus, I actually wish to model
>
>Weight Gain = Age + Sex + Gene Expression
>
>My questions are:
>1. Will these two models give me the same results for finding genes
>significant in predicting Weight Gain?
>2. If not, is there a way to model this using either Limma or another
>Bioconductor method?
>
>Thank you for any helpful words,
>Kasia Bryc
>
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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