[BioC] Best way of presenting "absolute" expression values (edgeR)

[Gordon K Smyth]

Yes, your code is fine for getting normalized RPKM from featureCounts and 
edgeR.

You code is similar to the public case study:

  http://bioinf.wehi.edu.au/RNAseqCaseStudy

In the latest version of edgeR, you can even simplify the code to

   expr_norm <- rpkm(expr)

Gordon

> Date: Fri, 09 May 2014 04:18:06 +0200
> From: Sindre Lee <sindre.lee at studmed.uio.no>
> To: <bioconductor at r-project.org>
> Subject: [BioC] Best way of presenting "absolute" expression values
> 	(edgeR)
>
> Hi!
>
> We want to classify a new type of glands by ranking genes by expression
> level using RNAseq. We don't have any good controls, so we just want to
> see a ranked list of genes.
>
> I have used Cufflinks RPKM values, but if I want to use edgeR, is this
> a valid way of doing it using featureCounts:
>
> fc <- featureCounts(files=targets$Targets,nthreads=8,
> isGTFAnnotationFile=TRUE, GTF.attrType="gene_id",
> GTF.featureType="exon", useMetaFeatures=TRUE, annot.ext="genes.gtf")
>
> x <- DGEList(counts=fc$counts, genes=fc$annotation)
>
> expr <- calcNormFactors(x)
>
> expr_norm <- rpkm(expr, log=FALSE,gene.length=x$genes$Length) # Getting
> gene length from FeatureCounts, using rkpm() in the edgeR package, not
> Rsubread..
>
>
>
> Then just write out this table..
>
>
> Thanks!

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