Hi
I have a microarray data comprised of 3 treatment conditions (N,M,I) and 
2 time points (4dpi and 5dpi).
I used limma - ebayes fit and toptable with coefficient for a specific 
contrast to get the differentially expressed genes and adjusted P value. 
I also did a ANOVA test, and I want to know which one is better to 
identify differentially expressed genes.

CODE: for eBAYES and Toptable

design <- model.matrix(~ 0+factor(c(1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6)))
colnames(design) <- c("N_4dpi", "M_4dpi", "I_4dpi","N_5dpi", "M_5dpi", 
"I_5dpi")
fit <- lmFit(eset, design)
contrast.matrix <- 
makeContrasts("I_4dpi-M_4dpi","I_5dpi-M_5dpi","I_4dpi-N_4dpi","I_5dpi-N_5dpi","M_4dpi-N_4dpi","M_5dpi-N_5dpi", 
levels=design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)
2) #If we do one comparison at a time I/M 4dpi
tab_all<-topTable(fit2, coef=1,adjust="BH",number=100,p.value=1)
head(tab_all)
write.table(tab_all,"I-M-4dpi.txt",sep="\t",quote=FALSE)

This gives zero significant genes.
############################################################

CODE for ANOVA


targets <- readTargets("target_data.txt")
data <- ReadAffy(filenames=targets$filename)
experiment <- targets$experiment
time <- targets$time
treatment <- targets$treatment


##normalization

eset <- rma(data)
exprs.eset <- exprs(eset)
exprs.eset.df <- data.frame(exprs.eset)
aof <- function(x) {
   m<-data.frame(time, treatment, x);
   anova(aov(x ~ time + treatment + time * treatment, m))
}

anovaresults <- apply(exprs.eset, 1, aof)

This gives  over a thousand genes with treatment_Pr.F <0.05 , Does this 
need to be corrected for multiple testing, why is the huge difference?
Appreciate your help.


-- 
With Regards,
Konika Chawla
NTNU, Norway
Phone +4772821344


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