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