
Hi,
 

I was wondering if anybody could helping me in figuring out how to optimize explanatory variable selection using limma. 
Following is what I have been trying to do:
 
library("limma")

x.norm <- read.table("rma_data.txt", header=T, row.names=1, sep="\t")
target <- read.table("target_wb01.txt", header=T, row.names=1, sep="\t")
plasma <- as.factor(target$Plasma)
stimulation <- as.factor(target$Stimulation)
donor <- as.factor(target$Donor)
design<- model.matrix(~ 0 + stimulation + plasma + donor)
fit1<- lmFit(x.norm, design)
fit2 <- contrasts.fit(fit1, cont.WT)
fit3 <- eBayes(fit2)
 
Is there any way to generate an anova table which gives me an idea as to which of the covariates; 'stimulation', 'plasma' or 'donor', in this case are informative and which aren't for all the probesets taken together.
 
Any help would be greatly appreciated.
 
Thanks. 		 	   		  
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