Hello all,
 
 Here is the code that I am using for finding differentially expressed genes.
 
 #Normalization
 
 library(affy)
 library(Biobase)
 library(limma)
 library(gcrma)
 
     pd<-read.phenoData("file.txt",header=TRUE,row.names=1,as.is=TRUE,sep="\t")
     Data <- ReadAffy(filenames=pData(pd)$FileName,phenoData=pd)
     print(Data)
    
     eset <- gcrma(Data)
     write.exprs(eset, file="decide-test.6-6-06.txt")
 
 #Linear Model
    
     pData(eset)
     targets<-pData(eset)
     model.matrix(~ -1 +factor(targets$Target,levels=unique(targets$Target)))
     design <- model.matrix(~ -1 +
     factor(targets$Target,levels=unique(targets$Target)))
     unique(targets$Target)
     colnames(design) <- unique(targets$Target)
     ncol(design)
     numParameters <- ncol(design)
     colnames(design)
     parameterNames <- colnames(design)
     design
     fit <- lmFit(eset,design=design)
     names(fit)
        
     contrastNames <-c(paste(parameterNames[2],parameterNames[1],sep="-"),
     paste(parameterNames[3],parameterNames[1],sep="-"),
     paste(parameterNames[4],parameterNames[1],sep="-"),
     paste(parameterNames[5],parameterNames[1],sep="-"),
     paste(parameterNames[6],parameterNames[1],sep="-"),
     paste(parameterNames[7],parameterNames[1],sep="-"))
 
     contrastsMatrix <- matrix(c(
     -1,1,0,0,0,0,0,
     -1,0,1,0,0,0,0,
     -1,0,0,1,0,0,0,
     -1,0,0,0,1,0,0,
     -1,0,0,0,0,1,0,
     -1,0,0,0,0,0,1),nrow=ncol(design))
     rownames(contrastsMatrix) <- parameterNames
     colnames(contrastsMatrix) <- contrastNames
     contrastsMatrix
    
     fit2  <- contrasts.fit(fit,contrasts=contrastsMatrix)
     names(fit2)
 
 #ebayes
 
     fit2 <- eBayes(fit2)
     names(fit2)
     numGenes <- nrow(eset@exprs)
 
 #decideTest
 
 results <- decideTests(fit2,method="nestedF",p=0.05);
 write.fit(fit2, results, "data.txt", adjust="BH");
 
 
 Is there any way for getting the adjusted p-values from the decideTests method ?
 
 Thanks,
 
 Vijay
 
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