[BioC] a question on creating model.matrix using limma

[James W. MacDonald]

Hi Srinivas,

Srinivas Iyyer wrote:
> Dear Dr. Smith and group, 
> 
> apologies for simple question. 
> 
> I have data with simple plan.  There are 8 samples (4
> normal and 4 tumor). These tissue samples are from
> different patients. No replicate experiments were
> done. 
> I wish to see genes differentially expressed between
> Tumors and normals.
> 
> In such cases how can I create model.matrix
> 
> 
> Following is taken from Limma manual:
> 
> design <- model.matrix(~ -1 +
> factor(c(1,1,1,2,2,3,3,3)))
> 
> 
> my question: 
>  1. what is '~ -1' is for?

The default for model.matrix() is to fit a model that has an intercept 
(or in the case of ANOVA, a baseline level). If you specify the model 
with either ~ -1 or ~ 0, then you are saying that you don't want an 
intercept.

>  2. Will this work for my study structure. That means
> I have only two types of RNA 1. tumor  and 2. Normal. 
> design <- model.matrix(~ -1 +
> factor(c(1,1,1,1,2,2,2,2))).

This will work, but you will also need to fit a contrast between tumor 
and normal, so you will need something like this:

design <- model.matrix(~-1 + factor(rep(1:2, each=4)))
colnames(design) <- c("tumor","normal")
contrast <- makeContrasts(tumor - normal, levels = design)
fit <- lmFit(eset, design)
fit2 <- contrasts.fit(fit, contrast)
fit2 <- eBayes(fit2)
topTable(fit2)

Alternatively, you can fit an intercept and then will not need to fit a 
contrast, because it will be implicit in the model.

design <- model.matrix(~factor(rep(1:2, each=4)))
fit <- lmFit(eset, design)
fit2 <- eBayes(fit)
topTable(fit2, coef = 2)

HTH,

Jim
> 
> Thanks
> sri
> 
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-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623


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