Application of Survival Analysis Methodology to the Quantitative Analysis of LC-MS Proteomics Data

• Activity Number: 641
• Type: Topic Contributed
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Carmen Tekwe*+ and Alan Dabney and Raymond Carroll
• Companies: Texas A&M University and and Texas A&M University
• Keywords: LC-MS ; Left censoring ; Missing Data ; Proteomics ; Survival Analysis

Abstract

Protein abundance in quantitative proteomics is often based on observed spectral features derived from 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, accelerated failure time model with lognormal, loglogistic 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 data sets.