Question: differential expression analysis from microarray data
0
gravatar for au.rinki.bio
7 days ago by
au.rinki.bio0 wrote:

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.

limma microarray • 73 views
ADD COMMENTlink written 7 days ago by au.rinki.bio0

Do you mean you only get positive values?

ADD REPLYlink written 7 days ago by igor5.7k

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

ADD REPLYlink written 7 days ago by au.rinki.bio0

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.

ADD REPLYlink written 6 days ago by igor5.7k

positive value of what?

ADD REPLYlink written 7 days ago by WouterDeCoster27k

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.

ADD REPLYlink written 7 days ago by au.rinki.bio0
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