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Old 03-11-2012, 07:19 AM   #1
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Posts: 181
Default DESeq and edgeR

i am using both programs to analyse differential expression.
i used this manual
so may script is that:

conditions <- c("1","2")
cds <- newCountDataSet(x,conditions)
cds <- estimateSizeFactors(cds)
cds <- estimateDispersions(cds,method="blind",sharingMode="maximum",fitType="local")
res <- nbinomTest(cds,condA="1",condB="2")
res[order(res$padj), ][1:4,]

sigDESeq <- res[res$padj <= 0.05, ]
sigDESeq <- na.omit(sigDESeq)
sigDESeq <- as.character(sigDESeq[,1])

group <- factor(c(1,2))
cdsR <- DGEList(counts=x, group=group)
cdsR <- estimateCommonDisp(cdsR)
cdsR <- estimateTagwiseDisp(cdsR)
et <- exactTest(cdsR, pair=c("1", "2"))

all <-, n=100000))
sigedgeR <- all[all$adj.P.Val <= 0.05, ]
sigedgeR <- na.omit(sigedgeR)
sigedgeR <- row.names(sigedgeR)

OLlist <- overLapper(setlist=list(DESeq=sigDESeq, edgeR=sigedgeR), sep="_", type="vennsets")
counts <- sapply(OLlist$Venn_List, length)

oveLapp <- OLlist$Venn_List


i want to check Differential expression between biological replicates.(here: i have only 2)
1 & 2 are biological replicates.

the problem is:
in edgeR i get 377 significant genes where in DESeq i got 0 (ZERO)

it looks weird to me, but i do'nt know what i did wrong..

Any advice!?!?

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Old 03-14-2012, 09:06 AM   #2
Wolfgang Huber
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Posts: 109

For the record, Pap cross-posted this question also to another list, where it has been addressed in the thread starting at
Wolfgang Huber
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Old 03-14-2012, 11:42 AM   #3
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Posts: 181

Hi Wolfgang Huber,
Thank you!
i didn't see that someone answer me there
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Old 05-15-2012, 07:29 PM   #4
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I had the similar issue. I used the sharingMode="fit-only", but still Deseq gave me 0 differential genes, whereas edgeR gave over 1000

I couldn't post to BioC mailist since it was in archive and I just subscribed. So I posted here.
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