differential expression analysis from microarray data
hello, I am working microarray data analysis using limma package. my data consist 4 normal sample and 8 cancerous sample using the following command line l
ibrary(affyio)
library(affy)
ovarian<-make.cdf.env("HGU133A_Hs_ENSG.cdf")
data<-ReadAffy(cdfname='ovarian')
data
data<-ReadAffy()
eset <- rma(data)
exprSet <- exprs(eset)
ph<-pData(eset)
ph
head(pData(eset))
p_disease<- c("control","control","control","control","case","case","case","case","case","case","case","case")
p_disease
design <- model.matrix(~factor(p_disease))
d<-colnames(design) <- c("case","control")
d
contrast.matrix <- makeContrasts(case-control, levels=design)
design
fit <- lmFit(eset, design)
r<-eBayes(fit)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit3 <- eBayes(fit2)
options(digits=2)
genes
getwd()
gene<- topTable(fit3, coef=1, adjust="BH",lfc=2, p.value=0.05)
write.table(gene,"d1.xls",sep="\t",col.names = NA)
the problem is that i get differentially expressed genes with positive value how is it possible.
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au.rinki.bio •
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Do you mean you only get positive values?
i got fold change with only positive value. I want to filterer differentially expressed genes with log fc>±2 .
Have you tried running
topTable()withoutlfcparameter? This way you can see the distribution of your fold changes. Maybe none of the negative fold changes are more than 2.positive value of what?
sorry for miscommunication, actually I want to filter differentially expressed gene with log fc> ±2. but I got genes with only positive value. I hope it is clear to you.