Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

翻译：通过液相色谱-质谱联用技术进行的非靶向代谢组学分析可测量生物样本中大量代谢物，从而推动药物开发、疾病诊断和风险预测。然而，液相色谱-质谱联用技术的低通量特性对生物标志物发现、注释及实验比较构成了重大挑战，亟需整合多组数据集。现有数据合并方法因易受数据变异影响且过度依赖超参数，在实际应用中存在明显局限。本文提出GromovMatcher算法——一种基于最优传输原理、灵活易用的液相色谱-质谱联用数据集自动整合方法。该算法通过利用特征强度相关性结构，相较于现有方法实现了更优的对齐精度与鲁棒性。该方法可扩展至处理数千个特征，且仅需极少的超参数调整。由于非靶向代谢组学领域缺乏用于验证对齐算法的人工标注数据集，我们开发了数据集分割流程以生成验证数据集对，用于测试GromovMatcher及其他方法产生的对齐结果。将本方法应用于肝癌与胰腺癌的临床实验研究后，我们发现了与患者酒精摄入相关的共享代谢特征，这证明了GromovMatcher如何促进寻找与多种癌症相关的生活方式风险因素相关联的生物标志物。