Mangnier Loïc, Bodein Antoine, Mariaz Margaux, Mathieu Alban, Scott-Boyer Marie-Pier, Vashist Neerja, Bramble Matthew S, Droit Arnaud
Axe Endo-Nephro, Centre de recherche du CHU de Québec-Université Laval, Québec, QC, Canada.
Department of Pathology and Laboratory Medicine, UCLA, Los Angeles, USA.
Commun Biol. 2025 Jul 25;8(1):1100. doi: 10.1038/s42003-025-08515-9.
The rapid advancement of high-throughput sequencing technologies has enabled the integration of various omic layers into computational frameworks. Among these, metagenomics and metabolomics are increasingly studied for their roles in complex diseases. However, no standard currently exists for jointly integrating microbiome and metabolome datasets within statistical models. We benchmarked nineteen integrative methods to disentangle the relationships between microorganisms and metabolites. These methods address key research goals, including global associations, data summarization, individual associations, and feature selection. Through realistic simulations, we identified the best-performing methods and validated them on real gut microbiome datasets, revealing complementary biological processes across the two omic layers. Practical guidelines are provided for specific scientific questions and data types. This work establishes a foundation for research standards in metagenomics-metabolomics integration and supports future methodological developments, while also providing guidance for designing optimal analytical strategies tailored to specific integration questions.
高通量测序技术的快速发展使得各种组学层面能够整合到计算框架中。其中，宏基因组学和代谢组学因其在复杂疾病中的作用而受到越来越多的研究。然而，目前在统计模型中联合整合微生物组和代谢组数据集尚无标准。我们对19种整合方法进行了基准测试，以理清微生物与代谢物之间的关系。这些方法解决了关键研究目标，包括全局关联、数据汇总、个体关联和特征选择。通过实际模拟，我们确定了性能最佳的方法，并在真实的肠道微生物组数据集上对其进行了验证，揭示了两个组学层面之间互补的生物学过程。针对特定的科学问题和数据类型提供了实用指南。这项工作为宏基因组学-代谢组学整合的研究标准奠定了基础，支持未来的方法学发展，同时也为设计针对特定整合问题的最佳分析策略提供了指导。
