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June 2010 A nested mixture model for protein identification using mass spectrometry
Qunhua Li, Michael J. MacCoss, Matthew Stephens
Ann. Appl. Stat. 4(2): 962-987 (June 2010). DOI: 10.1214/09-AOAS316

Abstract

Mass spectrometry provides a high-throughput way to identify proteins in biological samples. In a typical experiment, proteins in a sample are first broken into their constituent peptides. The resulting mixture of peptides is then subjected to mass spectrometry, which generates thousands of spectra, each characteristic of its generating peptide. Here we consider the problem of inferring, from these 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. In this way our model incorporates the evidence feedback between proteins and their constituent peptides. Using simulated data and a yeast data set, we compare and contrast our method with existing widely used approaches (PeptideProphet/ProteinProphet) and with a recently published new approach, HSM. For peptide identification, our single-stage approach yields consistently more accurate results. For protein identification the methods have similar accuracy in most settings, although we exhibit some scenarios in which the existing methods perform poorly.

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Qunhua Li. Michael J. MacCoss. Matthew Stephens. "A nested mixture model for protein identification using mass spectrometry." Ann. Appl. Stat. 4 (2) 962 - 987, June 2010. https://doi.org/10.1214/09-AOAS316

Information

Published: June 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1194.62118
MathSciNet: MR2758429
Digital Object Identifier: 10.1214/09-AOAS316

Keywords: EM algorithm , mass spectrometry , mixture model , nested structure , peptide identification , protein identification , proteomics

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.4 • No. 2 • June 2010
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