EdgeR for proteomics data
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@phinney-brett-6324
Last seen 8.1 years ago
Hi everyone, I have been experimenting with using EdgeR with proteomics data (spectral counts for now). I was a little confused how the TMM normalization works on proteomics data. I basically just read in my spectral counting data as a data matrix And then normFactors <- calcNormFactors(counts) but I'm not sure exactly how it is calculating the normalization factors? Any help would be greatly appreciated Cheers Brett --- Brett S. Phinney, Ph D. UC Davis Genome Center www.proteomics.ucdavis.edu<http: www.proteomics.ucdavis.edu=""> cell = 530-771-7055 [[alternative HTML version deleted]]
Proteomics Normalization edgeR Proteomics Normalization edgeR • 3.4k views
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@ryan-c-thompson-5618
Last seen 2.1 years ago
Scripps Research, La Jolla, CA
Hi, As mentoined in the help text for calcNormFactors, the TMM normalization method is described in the paper "A scaling normalization method for differential expression analysis of RNA-seq data" by Robinson & Oshlack. The best way to familiarize yourself with this method would be to read the paper: http://genomebiology.com/2010/11/3/r25 For what it's worth, one of my colleagues used edgeR on some proteomic data and decided that the default normalization strategy was not suitable for his data. I don't remember exactly what he ended up using instead. -Ryan Thompson On Mon Jan 13 17:32:36 2014, Phinney, Brett wrote: > Hi everyone, I have been experimenting with using EdgeR with proteomics data (spectral counts for now). I was a little confused how the TMM normalization works on proteomics data. I basically just read in my spectral counting data as a data matrix > > And then > > normFactors <- calcNormFactors(counts) > > but I'm not sure exactly how it is calculating the normalization factors? > > Any help would be greatly appreciated > > Cheers > > Brett > > > --- > Brett S. Phinney, Ph D. > UC Davis Genome Center > www.proteomics.ucdavis.edu<http: www.proteomics.ucdavis.edu=""> > cell = 530-771-7055 > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
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Hello, I have used RNA-seq statistics to analyze proteomics data myself. I tried using edgeR but think that the spectral count numbers in proteomics data are too small that you get artefacts, especially of lowly expressed proteins. I then turned to limma/voom to estimated mean-variance relationship for the data and then did analysis with limma/eBayes. I was pleased with the results and the speciicity of the method, limma/voom is generally a bit more conservative than edgeR (but not much less sensitive). I experimented a bit with the loess span for the voom (increasing it to 0.75 I think), since there was a dip in the distribution otherwise (probably due to low amount of data) that could lead to some spurious hits. I think that RNA-seq analysis packages are great for label-free proteomics, but one neds to be a bit careful with them. Otherwise the procedure is the same (starting with a matrix of counts). Best, Pekka 2014/1/14 Ryan <rct at="" thompsonclan.org="">: > Hi, > > As mentoined in the help text for calcNormFactors, the TMM normalization > method is described in the paper "A scaling normalization method for > differential expression analysis of RNA-seq data" by Robinson & Oshlack. The > best way to familiarize yourself with this method would be to read the > paper: http://genomebiology.com/2010/11/3/r25 > > For what it's worth, one of my colleagues used edgeR on some proteomic data > and decided that the default normalization strategy was not suitable for his > data. I don't remember exactly what he ended up using instead. > > -Ryan Thompson > > > On Mon Jan 13 17:32:36 2014, Phinney, Brett wrote: >> >> Hi everyone, I have been experimenting with using EdgeR with proteomics >> data (spectral counts for now). I was a little confused how the TMM >> normalization works on proteomics data. I basically just read in my >> spectral counting data as a data matrix >> >> And then >> >> normFactors <- calcNormFactors(counts) >> >> but I'm not sure exactly how it is calculating the normalization factors? >> >> Any help would be greatly appreciated >> >> Cheers >> >> Brett >> >> >> --- >> Brett S. Phinney, Ph D. >> UC Davis Genome Center >> www.proteomics.ucdavis.edu<http: www.proteomics.ucdavis.edu=""> >> cell = 530-771-7055 >> >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor > > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor
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