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Updated: Jul 10, 2025

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

Bayesian compositional generalized linear models for analyzing microbiome data.

Li Zhang¹, Xinyan Zhang², Nengjun Yi¹

¹Department of Biostatistics, University of Alabama at Birmingham, Alabama, USA.

Statistics in Medicine

|November 21, 2023

Summary

This summary is machine-generated.

This study introduces Bayesian compositional generalized linear models (BCGLM) to analyze complex microbiome data, improving disease prediction and personalized medicine by accurately estimating microbial impacts on health conditions like IBD.

Keywords:

Bayesian models MCMC compositional data microbiome sum-to-zero restriction

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Published on: September 13, 2022

Area of Science:

Microbiome research
Statistical modeling
Bioinformatics

Background:

The human microbiome significantly impacts health and disease, driving research into personalized medicine.
Conventional models struggle with microbiome data's compositional nature, high dimensionality, and feature similarity.
Accurate analysis is crucial for linking microbial patterns to health outcomes.

Purpose of the Study:

To develop advanced statistical models for analyzing compositional microbiome data.
To address challenges of high dimensionality and feature similarity in microbiome datasets.
To improve the prediction of diseases and inform personalized medicine strategies.

Main Methods:

Proposed Bayesian compositional generalized linear models (BCGLM).
Incorporated a structured regularized horseshoe prior for compositional coefficients.
Utilized Markov Chain Monte Carlo (MCMC) algorithms via the R package rstan.
Implemented a soft sum-to-zero restriction on coefficients through prior distribution.

Main Results:

BCGLM demonstrated superior performance over existing methods in simulation studies.
Achieved higher accuracy in coefficient estimation and reduced prediction error.
Successfully identified microorganisms associated with inflammatory bowel disease (IBD).

Conclusions:

BCGLM offers a robust framework for analyzing complex microbiome data.
The method enhances understanding of microbiome-host interactions and disease links.
Provides a reproducible approach for microbiome data analysis and discovery.