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Related Experiment Video

Updated: Oct 23, 2025

Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans

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Published on: September 13, 2022

A compositional mediation model for a binary outcome: Application to microbiome studies.

Michael B Sohn¹, Jiarui Lu², Hongzhe Li²

¹Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA.

Bioinformatics (Oxford, England)

|August 20, 2021

Summary

This summary is machine-generated.

A new compositional mediation model helps understand how the microbiome links diet to health. This method identifies causal effects, aiding microbiome research for therapeutic and preventative applications.

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Last Updated: Oct 23, 2025

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Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

Area of Science:

Microbiome research
Causal inference
Statistical modeling

Background:

The human microbiome plays a critical role in health, influenced by diet and xenobiotics.
Understanding microbiome-mediated effects is key for developing microbiome-based therapies and preventative strategies.

Purpose of the Study:

To introduce a novel sparse compositional mediation model for binary outcomes.
To estimate and test mediation effects of the microbiome.
To enable causal inference in microbiome research.

Main Methods:

Utilized compositional algebra on the simplex space.
Applied a linear zero-sum constraint on probit regression coefficients.
Developed a sensitivity analysis for unmeasured confounding.

Main Results:

Demonstrated identifiability of causal direct and indirect effects under standard causal assumptions.
Validated the method through extensive simulation studies.
Applied the model to real microbiome data to investigate the link between fat intake and overweight/obesity.

Conclusions:

The proposed sparse compositional mediation model is a powerful tool for microbiome research.
The method allows for robust estimation and testing of microbiome-mediated effects.
This work facilitates the translation of microbiome insights into clinical applications.