Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers

TL;DR

This paper introduces a novel peak detection algorithm for MALDI MSI data using sparse frame multipliers, improving accuracy and robustness without prior preprocessing, and incorporating spatial information.

Contribution

The paper presents a new peak detection method based on sparse frame multipliers that enhances accuracy and robustness in MALDI MSI data analysis.

Findings

Outperforms state-of-the-art algorithms in simulated data

Robust to baseline and noise effects

Effective on real MALDI-TOF datasets with spatial information

Abstract

MALDI mass spectrometry imaging (MALDI MSI) is a spatially resolved analytical tool for biological tissue analysis by measuring mass-to-charge ratios of ionized molecules. With increasing spatial and mass resolution of MALDI MSI data, appropriate data analysis and interpretation is getting more and more challenging. A reliable separation of important peaks from noise (aka peak detection) is a prerequisite for many subsequent processing steps and should be as accurate as possible. We propose a novel peak detection algorithm based on sparse frame multipliers, which can be applied to raw MALDI MSI data without prior preprocessing. The accuracy is evaluated on a simulated data set in comparison with a state-of-the-art algorithm. These results also show the proposed method's robustness to baseline and noise effects. In addition, the method is evaluated on two real MALDI-TOF data sets, whereby spatial information can be included in the peak picking process.