非靶向代谢组学数据自动对齐的最优传输方法
Optimal transport for automatic alignment of untargeted metabolomic data
AI总结：
本文提出GromovMatcher算法，利用最优传输自动对齐非靶向代谢组学LC-MS数据集，实现高精度鲁棒的特征对齐，并应用于癌症患者研究以发现与酒精摄入相关的代谢标志物。
摘要：
通过液相色谱-质谱联用（LC-MS）进行的非靶向代谢组学分析可测量生物样本中的大量代谢物，从而推动药物开发、疾病诊断和风险预测。然而，LC-MS的低通量对生物标志物发现、注释和实验比较构成了重大挑战，因此需要合并多个数据集。现有的数据合并方法因对数据变异和超参数依赖的敏感性而面临实际限制。在此，我们介绍了GromovMatcher，一种灵活且用户友好的算法，利用最优传输自动合并LC-MS数据集。通过利用特征强度相关结构，GromovMatcher相比现有方法提供了更优越的对齐精度和鲁棒性。该算法可扩展到数千个特征，且只需最少的超参数调整。在非靶向代谢组学领域，用于验证对齐算法的手工整理数据集有限，因此我们开发了一种数据集分割程序，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。将我们的方法应用于肝癌和胰腺癌的实验性患者研究，我们发现了与患者酒精摄入相关的共享代谢特征，展示了GromovMatcher如何促进与多种癌症类型相关的生活方式风险因素生物标志物的搜索。
英文摘要：
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.
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