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June 2011 Bayesian nonparametric models for peak identification in MALDI-TOF mass spectroscopy
Leanna L. House, Merlise A. Clyde, Robert L. Wolpert
Ann. Appl. Stat. 5(2B): 1488-1511 (June 2011). DOI: 10.1214/10-AOAS450


We present a novel nonparametric Bayesian approach based on Lévy Adaptive Regression Kernels (LARK) to model spectral data arising from MALDI-TOF (Matrix Assisted Laser Desorption Ionization Time-of-Flight) mass spectrometry. This model-based approach provides identification and quantification of proteins through model parameters that are directly interpretable as the number of proteins, mass and abundance of proteins and peak resolution, while having the ability to adapt to unknown smoothness as in wavelet based methods. Informative prior distributions on resolution are key to distinguishing true peaks from background noise and resolving broad peaks into individual peaks for multiple protein species. Posterior distributions are obtained using a reversible jump Markov chain Monte Carlo algorithm and provide inference about the number of peaks (proteins), their masses and abundance. We show through simulation studies that the procedure has desirable true-positive and false-discovery rates. Finally, we illustrate the method on five example spectra: a blank spectrum, a spectrum with only the matrix of a low-molecular-weight substance used to embed target proteins, a spectrum with known proteins, and a single spectrum and average of ten spectra from an individual lung cancer patient.


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Leanna L. House. Merlise A. Clyde. Robert L. Wolpert. "Bayesian nonparametric models for peak identification in MALDI-TOF mass spectroscopy." Ann. Appl. Stat. 5 (2B) 1488 - 1511, June 2011.


Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1223.62012
MathSciNet: MR2849783
Digital Object Identifier: 10.1214/10-AOAS450

Keywords: Gamma random field , kernel regression , Lévy random fields , reversible jump Markov chain Monte Carlo , Wavelets

Rights: Copyright © 2011 Institute of Mathematical Statistics

Vol.5 • No. 2B • June 2011
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