JSM 2011 Online Program

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Abstract Details

Activity Number: 612
Type: Topic Contributed
Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract - #302062
Title: Bayesian Models in Biomarker Discovery Using Spectral Count Data in the Label-Free Shotgun Proteomics
Author(s): Xia Wang*+ and Nell Sedransk
Companies: National Institute of Statistical Sciences and National Institute of Statistical Sciences
Address: , , ,
Keywords: Biomarker discovery ; Bayesian models ; Mixture priors ; Spectral count

Spectral counting is one of the measures used in label-free quantitative proteomics. Extensive research in biomarker discovery studies focuses on detecting differentially expressed proteins in cancer and in chronic diseases. In label-free proteomics, peptides are measured rather than whole proteins using mass spectrometry. "Shotgun" studies are designed to identify peptides present in a biological sample, typically using spectral counts from a liquid chromatography-mass spectrometry (LC-MS) system. Spectral counting is used in other applications as well, but there are several challenges inherent to LC-MS spectral counting: few replicates, sparse counts, large number of proteins, and unreliable variance estimation. A Bayesian hierarchical model using mixture priors is proposed to model and analyze these data. Compared to simple Poisson regressions and the QSpec model, the proposed approac

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