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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