Electronic Journal of Statistics

Analysis of proteomics data: Bayesian alignment of functions

Wen Cheng, Ian L. Dryden, David B. Hitchcock, and Huiling Le

Full-text: Open access

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.

Article information

Source
Electron. J. Statist., Volume 8, Number 2 (2014), 1734-1741.

Dates
First available in Project Euclid: 29 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1414588156

Digital Object Identifier
doi:10.1214/14-EJS900C

Mathematical Reviews number (MathSciNet)
MR3273588

Zentralblatt MATH identifier
1305.62366

Keywords
Ambient space Dirichlet Fisher-Rao Gaussian process Gibbs sampler Markov chain Monte Carlo quotient space registration, warp

Citation

Cheng, Wen; Dryden, Ian L.; Hitchcock, David B.; Le, Huiling. Analysis of proteomics data: Bayesian alignment of functions. Electron. J. Statist. 8 (2014), no. 2, 1734--1741. doi:10.1214/14-EJS900C. https://projecteuclid.org/euclid.ejs/1414588156


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References

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See also

  • Related item: Koch, I., Hoffmann, P., Marron, J. S. (2014). Proteomics profiles from mass spectrometry. Electron. J. Statist. 8(2) 1703–1713.