April 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 modelling 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.
Keywords: Bayes; kernel regression; Lévy random field; nonparametric regression; proteomics; reversible jump Markov chain Monte Carlo; wavelets.
The manuscript is available in pdf format (2.4 Mb).
@InCollection{Clyd:Hous:Wolp:2006,
Author = "Merlise A. Clyde and Leanna House and Robert L. Wolpert",
Title = "Nonparametric Models for Proteomic Peak Identification and
Quantification",
BookTitle = "Bayesian Inference for Gene Expression and Proteomics",
Editor = "Kim-Anh Do and Peter M{\"{u}}ller and Marina Vannucci",
Publisher = cup,
Address = uk:cam,
Chapter = 15,
Pages = "293--308",
Year = 2006,
}
This material is based upon work supported by the National Science Foundation under Grant Number DMS-0342172, DMS-0422400 and DMS-0406115. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.