CoMMiT: Co-informed inference of microbiome-metabolome interactions via transfer learning

2025/06/30 by Ling-Ling Li, Chenglong Ye, Li, Leiyue +13
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #Genomics (q-bio.GN) #Gut microbiota and health #Metabolomics and Mass Spectrometry Studies #Methodology (stat.ME) #Microbial Metabolic Engineering and Bioproduction

paper · pdf · doi:10.48550/arxiv.2506.24013

openalex publication_date 2025/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Recent multi-omic microbiome studies enable integrative analysis of microbes and metabolites, uncovering their associations with various host conditions. Such analyses require multivariate models capable of accounting for the complex correlation structures between microbes and metabolites. However, existing multivariate models often suffer from low statistical power for detecting microbiome-metabolome interactions due to small sample sizes and weak biological signals. To address these challenges, we introduce CoMMiT, Co-informed inference of Microbiome-Metabolome Interactions via novel Transfer learning models. Unlike conventional transfer-learning methods that borrow information from external datasets, CoMMiT leverages similarities across metabolites within a single cohort, reducing the risk of negative transfer often caused by differences in sequencing platforms and bioinformatic pipelines across studies. CoMMiT operates under the flexible assumption that auxiliary metabolites are collectively informative for the target metabolite, without requiring individual auxiliary metabolites to be informative. CoMMiT uses a novel data-driven approach to selecting the optimal set of auxiliary metabolites. Using this optimal set, CoMMiT employs a de-biasing framework to enable efficient calculation of p-values, facilitating the identification of statistically significant microbiome-metabolome interactions. Applying CoMMiT to a feeding study reveals biologically meaningful microbiome-metabolome interactions under a low glycemic load diet, demonstrating the diet-host link through gut metabolism.

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