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