1. An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles
- Author
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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
- Subjects
Science - 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.
- Published
- 2024
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