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Activity Number: 19
Type: Topic Contributed
Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Consulting
Abstract - #308713
Title: Multivariate Survival Approaches to Detect Differential Expressions in LC-MS/MS Proteomics Data
Author(s): Carmen Tekwe*+ and Raymond J. Carroll and Alan R. Dabney
Companies: Texas A&M Health Science Center and Texas A&M University and Texas A&M University
Keywords: Proteomics ; Multivariate Survival Analysis ; Missing Data ; Left censoring ; LC-MS/MS
Abstract:

Shotgun proteomics is a technique for identifying and quantifying proteins in complex mixtures, using a combination of liquid chromatography and mass spectrometry (LC-MS) on proteolytic digests. The quantitative information in shotgun proteomic data is represented by spectral peak intensities, which are frequently left-censored due to detection limits of the technology. Furthermore, since shotgun proteomics involves MS analysis of peptides, there is usually a need to "roll-up" the peptide-level quantitative MS data to enable protein-level inference. In our previous work, we proposed the application of the survival analysis models to the quantitative analysis of shotgun proteomics data, after using an averaging routine to roll-up the peptide peak intensities to the protein level. In this paper, we extend our previous work to the application of multivariate survival analysis methods for the quantitative analysis of shotgun proteomics data. Using both real and simulated data, we demonstrate that the proposed multivariate methods which are easily accessible in R, improve the power to detect differentially expressed proteins.


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