Nonparametric models for proteomic peak identification and quantification. Bayesian Inference for Gene Expression and Proteomics

Abstract

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