Integrating complementary datasets for constructing microbiome–metabolome–disease pathways
Abstract
Motivation: Microbiome–metabolome–disease pathways play a central role in host health, but studying them is limited by the data available. Large clinical microbiome cohorts often lack metabolomic measurements, while multi-omics studies with paired profiles are typically small or incompletely phenotyped. Combining such complementary datasets is challenging: the metabolite is not measured in the clinical cohort, microbiome data are high-dimensional and compositional, and log-ratio analyses depend on an arbitrarily chosen reference taxon.
Results: We propose a framework that integrates a large target cohort that has microbiome and outcome data with a smaller external study that has paired microbiome–metabolome profiles. A high-dimensional structural equation model jointly links the microbiome, the metabolome, and the disease outcome. It allows for unmeasured confounding and groups microbes into functional categories. We address compositionality with the additive log-ratio (ALR) transformation, estimate parameters by penalized two-stage least squares, and test metabolite–disease associations with a regularized permutation test. A novel multiple-reference procedure treats each microbe in turn as the ALR reference. It combines the resulting P-values with the Cauchy combination test and classifies microbes by stability selection, which makes inference robust to the choice of reference. In simulations, the method controls the type I error rate, and its power increases with both target and external sample sizes. An application to inflammatory bowel disease, integrating the FRAN and iHMP cohorts, reveals biologically interpretable microbiome–metabolome–disease links.
Availability and implementation: The method is implemented in the R package MMDP, freely available at https://github.com/lei-fang-stat/MMDP.
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BibTeX
@article{fang2026integrating,
title = {{Integrating complementary datasets for constructing microbiome–metabolome–disease pathways}},
author = {Lei Fang and Chenglong Ye and Yue Wang},
journal = {Bioinformatics},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag787},
url = {https://doi.org/10.1093/bioinformatics/btag787},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrating complementary datasets for constructing microbiome–metabolome–disease pathways
AU - Lei Fang
AU - Chenglong Ye
AU - Yue Wang
PY - 2026
DA - 2026/10/20
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag787
UR - https://doi.org/10.1093/bioinformatics/btag787
AB - Motivation: Microbiome–metabolome–disease pathways play a central role in host health, but studying them is limited by the data available. Large clinical microbiome cohorts often lack metabolomic measurements, while multi-omics studies with paired profiles are typically small or incompletely phenotyped. Combining such complementary datasets is challenging: the metabolite is not measured in the clinical cohort, microbiome data are high-dimensional and compositional, and log-ratio analyses depend on an arbitrarily chosen reference taxon. Results: We propose a framework that integrates a large target cohort that has microbiome and outcome data with a smaller external study that has paired microbiome–metabolome profiles. A high-dimensional structural equation model jointly links the microbiome, the metabolome, and the disease outcome. It allows for unmeasured confounding and groups microbes into functional categories. We address compositionality with the additive log-ratio (ALR) transformation, estimate parameters by penalized two-stage least squares, and test metabolite–disease associations with a regularized permutation test. A novel multiple-reference procedure treats each microbe in turn as the ALR reference. It combines the resulting P-values with the Cauchy combination test and classifies microbes by stability selection, which makes inference robust to the choice of reference. In simulations, the method controls the type I error rate, and its power increases with both target and external sample sizes. An application to inflammatory bowel disease, integrating the FRAN and iHMP cohorts, reveals biologically interpretable microbiome–metabolome–disease links. Availability and implementation: The method is implemented in the R package MMDP, freely available at https://github.com/lei-fang-stat/MMDP.
SN - 1367-4803
SN - 1367-4811
ER -