DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis

Yi Liu, Yun Yang, Wendong Chen, Feng Shen, Linhai Xie, Yingying Zhang, Yuanjun Zhai, Fuchu He, Yunping Zhu () and Cheng Chang ()
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Yi Liu: Beijing University of Technology
Yun Yang: International Academy of Phronesis Medicine (Guang Dong), No. 96 Xindao Ring South Road, Guangzhou International Bio Island
Wendong Chen: International Academy of Phronesis Medicine (Guang Dong), No. 96 Xindao Ring South Road, Guangzhou International Bio Island
Feng Shen: Naval Medical University
Linhai Xie: National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics
Yingying Zhang: National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics
Yuanjun Zhai: National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics
Fuchu He: National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics
Yunping Zhu: National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics
Cheng Chang: National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.

Date: 2023
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DOI: 10.1038/s41467-023-43909-5

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