Bayesian Inference for Gene Expression and Proteomics
Nonparametric Models for Proteomic Peak Identification and Quantification
Publication
, Chapter
Clyde, M; House, L; Wolpert, RL
2006
We present model-based inference for proteomic peak identification and quantification from mass spectroscopy data, focusing on nonparametric Bayesian models. Using experimental data generated from MALDI-TOF mass spectroscopy (matrix-assisted laser desorption ionization time-of-flight) we model observed intensities in spectra with a hierarchical nonparametric model for expected intensity as a function of time-of-flight. We express the unknown intensity function as a sum of kernel functions, a natural choice of basis functions for modeling spectral peaks. We discuss how to place prior distributions on the unknown functions using Lévy random fields and describe posterior inference via a reversible jump Markov chain Monte Carlo algorithm.
Duke Scholars
DOI
ISBN
9780511584589
Publication Date
2006
Start / End Page
293 / 308
Publisher
Cambridge University Press
Citation
APA
Chicago
ICMJE
MLA
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Clyde, M., House, L., & Wolpert, R. L. (2006). Nonparametric Models for Proteomic Peak Identification and Quantification. In K. A. Do, P. Muller, & M. Vannucci (Eds.), Bayesian Inference for Gene Expression and Proteomics (pp. 293–308). Cambridge University Press. https://doi.org/10.1017/CBO9780511584589.016
Clyde, M., L. House, and R. L. Wolpert. “Nonparametric Models for Proteomic Peak Identification and Quantification.” In Bayesian Inference for Gene Expression and Proteomics, edited by K. A. Do, P. Muller, and M. Vannucci, 293–308. Cambridge University Press, 2006. https://doi.org/10.1017/CBO9780511584589.016.
Clyde M, House L, Wolpert RL. Nonparametric Models for Proteomic Peak Identification and Quantification. In: Do KA, Muller P, Vannucci M, editors. Bayesian Inference for Gene Expression and Proteomics. Cambridge University Press; 2006. p. 293–308.
Clyde, M., et al. “Nonparametric Models for Proteomic Peak Identification and Quantification.” Bayesian Inference for Gene Expression and Proteomics, edited by K. A. Do et al., Cambridge University Press, 2006, pp. 293–308. Manual, doi:10.1017/CBO9780511584589.016.
Clyde M, House L, Wolpert RL. Nonparametric Models for Proteomic Peak Identification and Quantification. In: Do KA, Muller P, Vannucci M, editors. Bayesian Inference for Gene Expression and Proteomics. Cambridge University Press; 2006. p. 293–308.
DOI
ISBN
9780511584589
Publication Date
2006
Start / End Page
293 / 308
Publisher
Cambridge University Press