Bayesian nonparametric models for peak identification in MALDI-TOF mass spectroscopy

TL;DR

This paper introduces a Bayesian nonparametric model using LARK for analyzing MALDI-TOF mass spectrometry data, enabling accurate protein peak identification and quantification with interpretable parameters.

Contribution

It develops a novel Bayesian approach with informative priors and MCMC inference for peak detection in spectral data, improving over existing methods.

Findings

High true-positive rates in simulations

Effective peak resolution in complex spectra

Successful application to real patient data

Abstract

We present a novel nonparametric Bayesian approach based on L\'{e}vy Adaptive Regression Kernels (LARK) to model spectral data arising from MALDI-TOF (Matrix Assisted Laser Desorption Ionization Time-of-Flight) mass spectrometry. This model-based approach provides identification and quantification of proteins through model parameters that are directly interpretable as the number of proteins, mass and abundance of proteins and peak resolution, while having the ability to adapt to unknown smoothness as in wavelet based methods. Informative prior distributions on resolution are key to distinguishing true peaks from background noise and resolving broad peaks into individual peaks for multiple protein species. Posterior distributions are obtained using a reversible jump Markov chain Monte Carlo algorithm and provide inference about the number of peaks (proteins), their masses and abundance. We show through simulation studies that the procedure has desirable true-positive and false-discovery rates. Finally, we illustrate the method on five example spectra: a blank spectrum, a spectrum with only the matrix of a low-molecular-weight substance used to embed target proteins, a spectrum with known proteins, and a single spectrum and average of ten spectra from an individual lung cancer patient.