An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles
Yongjie Deng, Yao Yao, Yanni Wang, T. Semiglazova Yu., Wen-Hao Cai, Dingli Zhou, Feng Yin, Wanli Liu, Yuying Liu, Chuanbo Xie, Jian Guan, Yumin Hu, Peng Huang, Weizhong Li
Abstract
Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present DeepMSProfiler, an explainable deep-learning-based method, enabling end-to-end analysis on raw metabolic signals with output of high accuracy and reliability. Using cross-hospital 859 human serum samples from lung adenocarcinoma, benign lung nodules, and healthy individuals, DeepMSProfiler successfully differentiates the metabolomic profiles of different groups (AUC 0.99) and detects early-stage lung adenocarcinoma (accuracy 0.961). Model flow and ablation experiments demonstrate that DeepMSProfiler overcomes inter-hospital variability and effects of unknown metabolites signals. Our ensemble strategy removes background-category phenomena in multi-classification deep-learning models, and the novel interpretability enables direct access to disease-related metabolite-protein networks. Further applying to lipid metabolomic data unveils correlations of important metabolites and proteins. Overall, DeepMSProfiler offers a straightforward and reliable method for disease diagnosis and mechanism discovery, enhancing its broad applicability. Untargeted metabolomic analysis provides comprehensive metabolic profiling but faces challenges in medical application. Here, the authors present an explainable deep learning method for end-to-end analysis on raw metabolic signals to differentiate metabolomic profiles of cancers with high accuracy.