A Multiresolution Hierarchical Approach to Peak Picking for High Resolution Mass Spectrometry Data Analysis

Motivation Accurate peak detection is a critical first step in high-resolution mass spectrometry (HRMS) data analysis. Most existing tools rely on centroiding and grid-based assumptions, which simplify the raw profile data at the cost of information loss and reduced detection accuracy. Results We present MRH, a novel peak detection method that operates directly on raw HRMS data using a hierarchical, multi-resolution decomposition of the (RT, m/z ) space. Peaks are identified within resolution-dependent regions of interest using image-processing techniques, and an ambiguity score is introduced to quantify detection confidence. Benchmarking against widely used methods (apLCMS, centWave, and gridmass) shows that MRH consistently achieves higher F1 scores across concentrations, particularly excelling at low concentrations where competing tools fail. MRH also provides accurate localization of peaks and low ambiguity in detections, highlighting both robustness and precision. Availability A proof-of-concept implementation of MRH and the evaluation dataset are freely available at https://github.com/VojtechBarton/MRH .