[BioC] mulitfactorial analysis, adjusting for quantitative covariates

[Ingrid Dahlman [guest]]

Hi!
I would like to use Limma to compare gene expression between two treatment groups (PO vs C). In this analysis I need to adjust for differences in a quantitative covariate (age) between samples. Is the following setup appropriate for this analysis? Do I accurately adjust for age in the final analysis?
Ingrid

 -- output of sessionInfo(): 

eset<-readExpressionSet("eset.txt","target.txt",header=TRUE)
GROUP <- factor(target$GROUP, levels=c("C","PO"))
AGE <- factor(target$AGE)
design <- model.matrix(~0+GROUP+AGE)
colnames(design) <- c("C","PO")
fit <- lmFit(eset,design)
cont.matrix <- makeContrasts(CvsPO=C-PO,levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2)
topTable(fit2, n=100, coef="CvsPO", adjust="BH")

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