Hi,

    I am using edgeR for 65 pairs Tumor-Normal differential expression genes analysis. I want to get genes that differentially express between Tumor samples and Normal samples. The methods I used are refered to section 4.6 "Pro les of Unrelated Nigerian Individuals" of edgeR User's Guide. But the results are not ideal and that result in interrupted analysis. Could you please help me to check on  the R codes? The codes are as follows:

library(Rsubread)

library(limma)

library(edgeR)

library(splines)

Counts <- read.table("readcounts.txt",head=T,row.names=1)

gr <- c("N","T")

group <- rep(gr, times=65)

x <- DGEList(counts=Counts,group=group)

x$samples$lib.size <- colSums(x$counts)

x <- calcNormFactors(x)

Patient <- factor(rep(15:79,each=2))                       ####The sample names are "CH15N" "CH15T" "CH16N" "CH16T" ...... "CH79N" "CH79T".

Tissue <- factor(group)

data.frame(Sample=colnames(x),Patient,Tissue)

design <- model.matrix(~Patient+Tissue)

rownames(design) <- colnames(x)

x <- estimateGLMCommonDisp(x, design, verbose=TRUE)

x <- estimateGLMTrendedDisp(x, design)

x <- estimateGLMTagwiseDisp(x, design)

fit <- glmFit(x, design)

lrt <- glmLRT(fit)

write.table(topTags(lrt, n=dim(lrt$table)[1]),file="result.txt",row.names=TRUE,sep="\t")

    If there is any mistake or question, please let me know. I look forward to hearing from you.

    Thanks!

We run the code and get more than 500 differential expression genes. And then we use these genes to do pathway enrichment analysis. But the pathways do not include our interested genes. So we doubt if the differential expression genes are right.