differential expression analysis from microarray data

Question

[au.rinki.bio]

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.

[igor]

Do you mean you only get positive values?

[au.rinki.bio]

i got fold change with only positive value. I want to filterer differentially expressed genes with log fc>±2 .

[igor]

Have you tried running topTable() without lfc parameter? This way you can see the distribution of your fold changes. Maybe none of the negative fold changes are more than 2.

[WouterDeCoster]

positive value of what?

[au.rinki.bio]

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.