title: Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.
abstract: BACKGROUND
  Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets.
  
  
  RESULTS
  Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions.
  
  
  BACKGROUND
  The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials.
  
  
  BACKGROUND
  ctekwe@stat.tamu.edu.
authors.affiliation: Department of Statistics, 3143 TAMU, College Station, TX 77843-3143, USA. ctekwe@stat.tamu.edu
authors.forename: Carmen D
authors.initials: CD
authors.lastname: Tekwe
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Carroll
authors.forename: Alan R
authors.initials: AR
authors.lastname: Dabney
mesh_terms: D002853:Chromatography, Liquid / Q000379:methods; D003198:Computer Simulation; D003920:Diabetes Mellitus / Q000378:metabolism; D006801:Humans; D016013:Likelihood Functions; D013058:Mass Spectrometry / Q000379:methods; D015233:Models, Statistical; D011506:Proteins / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D012984:Software; D018709:Statistics, Nonparametric; D053719:Tandem Mass Spectrometry / Q000379:methods
chemical_list: D011506:Proteins