Microarray analysis using limma multiple groups

Question

[Paul]

I am using a dataset GSE1297, which contains gene expression for multiple groups ( Control, Incipient, Moderate,Severe).

My contrast matrix for analyzing the dataset looks like the following

contrast.matrix <- makeContrasts(
  CI = Control - Incipient,
  CM = Control - Moderate,
  HM = Control - Severe,
  levels = design
)

And with the following written code for the dataset GSE1297, I don't get any significant genes with fold change value >=2.

setwd("C:\\GSE1297\\GSE1297\\")

library(limma) 
library(affy) 
library(affyio) 
library(gcrma) 
library(oligo) 
library(pd.hg.u133.plus.2)
library(hgu133plus2.db)
library(annotate)


celFiles <- list.celfiles('C:\\GSE1297\\GSE1297\\', full.names=TRUE)
rawData <- read.celfiles(celFiles)
rmaRes <- rma(rawData)   # normalization using RMA
eset <- exprs(rmaRes)


labels <- factor(
  c(rep('Control', 9), rep('Incipient', 7), rep('Moderate', 8),rep('Severe', 7)),
  levels = c('Control', 'Incipient', 'Moderate','Severe')
)

design <- model.matrix(~ 0 + labels)
colnames(design) <- levels(labels)


contrast.matrix <- makeContrasts(
  CI = Control - Incipient,
  CM = Control - Moderate,
  HM = Control - Severe,
  levels = design
)


fit <- lmFit(eset, design)
fit.cont <- contrasts.fit(fit, contrast.matrix)
fit.eb   <- eBayes(fit.cont)

genes<- topTable(fit.eb, coef=2, n=nrow(eset), adjust="fdr")

write.table(genes,"C:\\GSE1297\\limma_rma.xls",sep="\t",col.names = NA)

Please let me know where am I going wrong. I read many tutorials but unable to find out what is wrong with the code.

I had similar kind of problem in my previous post for which I did not find any answer

C: lesser fold change value using RMA background correction