MassGAT: a graph-based collective learning approach for untargeted detection and annotation of LC-MS data

Abstract

MotivationThe untargeted processing of Liquid Chromatography coupled to High-Resolution Mass Spectrometry (LC-HRMS) data is a major challenge for the comprehensive and robust characterisation of metabolites. In particular, peak detection and annotation are two challenging tasks due to the size and complexity of the data, that are currently addressed independently in existing pipelines, without taking into account the redundancy of information between the ion species of the same compound. Results: We developed an innovative and efficient approach that combines detection and annotation, by 1) representing signals putatively originating from the same molecule as a graph, and 2) inferring the validity of the peaks and their connections within each component using a Graph Attention Network (GAT). We demonstrate on real data sets that the resulting MassGAT model outperforms current approaches in terms of both detection and annotation. Availability and implementation: The MassGAT open-source Python module is publicly available at https://github.com/odisce/MassGAT.