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.

保留时间 (RT) 比对是基于液相色谱-质谱 (LC-MS) 的蛋白质组学和代谢组学实验中的关键步骤，特别是对于大型队列研究。最流行的对齐工具是基于扭曲函数方法和直接匹配方法。然而，现有工具很难同时处理单调和非单调 RT 平移。在这里，我们开发了一种基于深度学习的 RT 对齐工具 DeepRTAlign，用于大型队列 LC-MS 数据分析。通过在多个真实世界和模拟蛋白质组学和代谢组学数据集上与当前最先进的方法进行基准测试，DeepRTAlign 已被证明具有改进的性能。结果还表明，DeepRTAlign 可以在不影响定量准确性的情况下提高识别灵敏度。此外，使用 DeepRTAlign 对齐的 MS 特征，我们训练并验证了一个强大的分类器来预测肝细胞癌的早期复发。DeepRTAlign 为大型队列 LC-MS 研究中的 RT 比对提供了先进的解决方案，这是目前蛋白质组学和代谢组学研究的主要瓶颈。