I am trying edgeR now to find deferentially expressed genes.
I have run
d <- DGEList(counts=data,group=g,lib.size=libSizes)
d <- calcNormFactors(d)
d <- estimateCommonDisp(d)
d <- estimateTagwiseDisp(d)
d <- exactTest(d)
# d.final <- topTags(de.com,n = length(data[,1]))
and got some results like
logFC logCPM PValue
A1bg 1.168034660 -3.842894 0.4137326
A1cf 0.000000000 -Inf 1.0000000
A1i3 0.000000000 -Inf 1.0000000
A2m -0.003703085 4.419204 0.9421192
A3galt2 0.437990409 2.861665 0.2611483
But I'd like to know the average/mean of the gene expressions in each condition/groups?
It seems logFC it is different from "log2(rowMeans(GrpB)/ rowMeans(GrpA))" calculating from cpm() counts.
How should I do the calculation ?
Thanks!
I have run
d <- DGEList(counts=data,group=g,lib.size=libSizes)
d <- calcNormFactors(d)
d <- estimateCommonDisp(d)
d <- estimateTagwiseDisp(d)
d <- exactTest(d)
# d.final <- topTags(de.com,n = length(data[,1]))
and got some results like
logFC logCPM PValue
A1bg 1.168034660 -3.842894 0.4137326
A1cf 0.000000000 -Inf 1.0000000
A1i3 0.000000000 -Inf 1.0000000
A2m -0.003703085 4.419204 0.9421192
A3galt2 0.437990409 2.861665 0.2611483
But I'd like to know the average/mean of the gene expressions in each condition/groups?
It seems logFC it is different from "log2(rowMeans(GrpB)/ rowMeans(GrpA))" calculating from cpm() counts.
How should I do the calculation ?
Thanks!
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