A systematic benchmark of integrative strategies for microbiome-metabolome data

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.