Integrating complementary datasets for constructing microbiome-metabolome-disease pathways

Clicks: 15
ID: 333364
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #886 of 983 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 983 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7223122748 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lei Fang, Chenglong Ye, Yue Wang
Are you one of these authors? Claim your profile
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag787
URL
https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag787/72201352/btag787.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.