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Abstract Details

Activity Number: 121
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #306493
Title: Statistical Approach for Large-Scale Proteome Assay
Author(s): Lisa Chung*+ and Christopher Colangelo and Hongyu Zhao
Companies: and Yale University and Yale School of Public Health
Address: 123 York St. #7C, New Haven, CT, 06511, United States
Keywords: large-scale proteomics assay ; protein expression

Large-scale targeted proteome assays enable researchers to simultaneously quantify a hundreds of targeted proteins/peptides in a single analysis. Similarly with microarrays, peptide-level expression can be obtained by measuring the mass of different fragment ions through a process called selective reaction monitoring (SRM). SRM is an emerging technology that enables researchers to complement discovery proteomic analysis by targeted specific peptides for reliable quantification in complex mixtures. In an SRM experiment, a predefined precursor ion and one of its fragments (transition) are selected and monitored over time for precise quantification.

Statistical analysis of SRM data requires a modeling for different levels of variation, for example, variability among transitions, replicated samples, and experimental groups. Here, we propose a statistical approach to estimate peptide-level expression by taking account the variance across transitions used to investigate the same peptide. The modeling further involves the test for differential expression by considering the experimental group structure. We apply our model to two initial targeted proteomics assay data sets.

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