An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles

Yongjie Deng, Yao Yao, Yanni Wang, Tiantian Yu, Wenhao Cai, Dingli Zhou, Feng Yin, Wanli Liu, Yuying Liu, Chuanbo Xie, Jian Guan, Yumin Hu (), Peng Huang () and Weizhong Li ()
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Yongjie Deng: Sun Yat-sen University
Yao Yao: Sun Yat-sen University Cancer Center
Yanni Wang: Sun Yat-sen University
Tiantian Yu: Sun Yat-sen University
Wenhao Cai: Sun Yat-sen University
Dingli Zhou: Sun Yat-sen University
Feng Yin: Sun Yat-sen University Cancer Center
Wanli Liu: Sun Yat-sen University Cancer Center
Yuying Liu: Sun Yat-sen University Cancer Center
Chuanbo Xie: Sun Yat-sen University Cancer Center
Jian Guan: The First Affiliated Hospital of Sun Yat-sen University
Yumin Hu: Sun Yat-sen University Cancer Center
Peng Huang: Sun Yat-sen University Cancer Center
Weizhong Li: Sun Yat-sen University

Nature Communications, 2024, vol. 15, issue 1, 1-17

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-51433-3

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