Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry

TL;DR

This paper introduces an efficient, automatic partitioning algorithm for Gaussian mixture modeling of proteomic mass spectra, enabling improved peak detection and analysis of complex spectral data.

Contribution

The paper presents a novel automatic partitioning algorithm that enhances Gaussian mixture modeling of mass spectra, allowing systematic analysis and comparison with existing methods.

Findings

Improved peak detection efficiency using Gaussian mixture models.

Effective application to real proteomic datasets of varying resolution.

Demonstrated advantages over existing peak detection algorithms.

Abstract

Mixture - modeling of mass spectra is an approach with many potential applications including peak detection and quantification, smoothing, de-noising, feature extraction and spectral signal compression. However, existing algorithms do not allow for automatic analyses of whole spectra. Therefore, despite highlighting potential advantages of mixture modeling of mass spectra of peptide/protein mixtures and some preliminary results presented in several papers, the mixture modeling approach was so far not developed to the stage enabling systematic comparisons with existing software packages for proteomic mass spectra analyses. In this paper we present an efficient algorithm for Gaussian mixture modeling of proteomic mass spectra of different types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automatic partitioning of protein mass spectral signal into fragments. The obtained fragments are separately decomposed into Gaussian mixture models. The parameters of the mixture models of fragments are then aggregated to form the mixture model of the whole spectrum. We compare the elaborated algorithm to existing algorithms for peak detection and we demonstrate improvements of peak detection efficiency obtained by using Gaussian mixture modeling. We also show applications of the elaborated algorithm to real proteomic datasets of low and high resolution.