title: A hybrid approach to protein differential expression in mass spectrometry-based proteomics.
abstract: BACKGROUND
  Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical MS-based proteomics datasets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis.
  
  
  RESULTS
  We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of 'presence/absence,' we enable the selection of proteins not typically amenable to quantitative analysis; e.g. 'one-state' proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence/absence analysis of a given dataset in a principled way, resulting in a single list of selected proteins with a single-associated false discovery rate.
  
  
  BACKGROUND
  All R code available here: http://www.stat.tamu.edu/~adabney/share/xuan_code.zip.
authors.affiliation: Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.
authors.forename: Xuan
authors.initials: X
authors.lastname: Wang
authors.forename: Gordon A
authors.initials: GA
authors.lastname: Anderson
authors.forename: Richard D
authors.initials: RD
authors.lastname: Smith
authors.forename: Alan R
authors.initials: AR
authors.lastname: Dabney
mesh_terms: D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D013058:Mass Spectrometry / Q000379:methods*; D011506:Proteins / Q000032:analysis*; D040901:Proteomics / Q000379:methods*
chemical_list: D011506:Proteins