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2014 Analysis of proteomics data: Bayesian alignment of functions
Wen Cheng, Ian L. Dryden, David B. Hitchcock, Huiling Le
Electron. J. Statist. 8(2): 1734-1741 (2014). DOI: 10.1214/14-EJS900C

Abstract

A Bayesian approach to function alignment is introduced. A model is proposed in the ambient space, with a Dirichlet prior for the derivative of the warping function and a Gaussian process for the square root velocity function. Posterior inference is carried out via Markov chain Monte Carlo simulation. The methodology is applied to a dataset of mass spectrometry scans. Good alignment is obtained for most of the known proteins, with more uncertainty at either end of each scan.

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Wen Cheng. Ian L. Dryden. David B. Hitchcock. Huiling Le. "Analysis of proteomics data: Bayesian alignment of functions." Electron. J. Statist. 8 (2) 1734 - 1741, 2014. https://doi.org/10.1214/14-EJS900C

Information

Published: 2014
First available in Project Euclid: 29 October 2014

zbMATH: 1305.62366
MathSciNet: MR3273588
Digital Object Identifier: 10.1214/14-EJS900C

Rights: Copyright © 2014 The Institute of Mathematical Statistics and the Bernoulli Society

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Vol.8 • No. 2 • 2014
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