Question: Best way of presenting "absolute" expression values (edgeR)
gravatar for Sindre Lee
5.0 years ago by
Sindre Lee100
Sindre Lee100 wrote:
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!
rnaseq edger • 1.3k views
ADD COMMENTlink modified 5.0 years ago • written 5.0 years ago by Sindre Lee100
Hi! I know a lot of people do the following: 1. Take a list of differentially expressed genes 2. Fetch the FASTA files for protein coding genes 3. Predict if secretory by using SignalP My question is, does it exist a list/database of known/predicted secretory protein coding genes from hg19? That would be much more efficient than many people predicting the same proteins a lot of times..
ADD REPLYlink written 5.0 years ago by Sindre Lee100
Answer: Best way of presenting "absolute" expression values (edgeR)
gravatar for Gordon Smyth
5.0 years ago by
Gordon Smyth37k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth37k wrote:

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

Your code is similar to the public case study:

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

expr_norm <- rpkm(expr)


ADD COMMENTlink modified 23 months ago • written 5.0 years ago by Gordon Smyth37k
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