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

Author

Listed:
  • Yongjie Deng

    (Sun Yat-sen University)

  • Yao Yao

    (Sun Yat-sen University Cancer Center
    Sun Yat-sen University)

  • Yanni Wang

    (Sun Yat-sen University)

  • Tiantian Yu

    (Sun Yat-sen University
    Sun Yat-sen University Cancer Center
    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
    Sun Yat-sen University)

  • Peng Huang

    (Sun Yat-sen University Cancer Center
    Sun Yat-sen University)

  • Weizhong Li

    (Sun Yat-sen University
    Sun Yat-Sen University
    Sun Yat-sen University)

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.

Suggested Citation

  • 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 & Weizhong Li, 2024. "An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51433-3
    DOI: 10.1038/s41467-024-51433-3
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    1. Yangzi Chen & Bohong Wang & Yizi Zhao & Xinxin Shao & Mingshuo Wang & Fuhai Ma & Laishou Yang & Meng Nie & Peng Jin & Ke Yao & Haibin Song & Shenghan Lou & Hang Wang & Tianshu Yang & Yantao Tian & Pen, 2024. "Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Taiyun Kim & Owen Tang & Stephen T. Vernon & Katharine A. Kott & Yen Chin Koay & John Park & David E. James & Stuart M. Grieve & Terence P. Speed & Pengyi Yang & Gemma A. Figtree & John F. O’Sullivan , 2021. "A hierarchical approach to removal of unwanted variation for large-scale metabolomics data," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    3. Xiaotao Shen & Ruohong Wang & Xin Xiong & Yandong Yin & Yuping Cai & Zaijun Ma & Nan Liu & Zheng-Jiang Zhu, 2019. "Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    4. Yao Yao & Xueping Wang & Jian Guan & Chuanbo Xie & Hui Zhang & Jing Yang & Yao Luo & Lili Chen & Mingyue Zhao & Bitao Huo & Tiantian Yu & Wenhua Lu & Qiao Liu & Hongli Du & Yuying Liu & Peng Huang & T, 2023. "Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
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