>
> Hello List,
>
> I'm just starting with R and wanted to analyze my data for differential
> expression using edgeR. Here is the code which is working for me but I want
> to check if I'm missing something as I get more number of differentially
> expressed genes compared to DESeq
>
> MY Sample data
>
> GeneID        CR1 CR2 MR1 MR2
> 3119s00200.1 78 78 148 124
> 3119s00202.1    33 68 168 198
> 3119s00232.1     52 73 135 99
>
> CR1 and CR2 are replicates of controls and MR1 and MR2 are treated sample
> replicates
>
> library(edgeR)
> library(limma)
> raw.data <- read.delim("RNAseq-MycReadCountData-EdgeR.txt")
> names(raw.data)
>
> d <- raw.data[, 2:5]
> rownames(d) <- raw.data[, 1]
> group <- c(rep("CR", 2), rep("MR", 2))
> d <- DGEList(counts = d, group = group)
> d <- estimateCommonDisp(d)
> et <- exactTest(d)
> topTags(et)
> etTag = rownames(topTags(et)$table)
> sum(et$table$p.value <0.05)
> sum(p.adjust(et$table$p.value,method="BH") < 0.1)
> good = sum(et$table$p.value <0.05)
> goodList = topTags(et, n=good)
> sink("edgeR-MR-pVal005.csv")
> goodList
> sink()
>
> I compared the result with DESeq and I get about 2000 genes more in edgeR
> at pVal < 0.05, however, the the matched genes showed same log2foldchanges.
> Is it usual that edgeR gives more number of diff.expr genes?
>
> Thank you for your time,
> Avinash
>

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