This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 281
Type: Topic Contributed
Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #308312
Title: A Nested Mixture Model for Protein Identification Using Mass Spectrometry
Author(s): Qunhua Li*+ and Michael MacCoss and Matthew Stephens
Companies: University of California, Berkeley and University of Washington and The University of Chicago
Address: , , ,
Keywords: mixture model ; nested structure ; EM algorithm ; protein identification / peptide identification ; mass spectrometry ; proteomics
Abstract:

Mass spectrometry is a high-throughput way to identify proteins in biological samples. We consider the problem of inferring, from tandem mass spectra, which proteins and peptides are present in the sample. We develop a statistical approach to the problem based on a nested mixture model. In contrast to commonly-used two-stage approaches, this model provides a one-stage solution that simultaneously identifies which proteins are present, and which peptides are correctly identified. Our model incorporates the evidence feedback between proteins and their constituent peptides. We compare our method with a widely-used approach and a recently-published approach. For peptide identification, our approach yields consistently more accurate results. For protein identification the methods have similar accuracy in most settings, although we exhibit some cases in which existing methods perform poorly.


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