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Related Experiment Video

Updated: May 13, 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 Generalized Linear Models for Analyzing Compositional and Sub-Compositional Microbiome Data via EM

Li Zhang¹, Zhenying Ding², Jinhong Cui²

¹Biostatistics and Bioinformatics Facility, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA.

Statistics in Medicine

|April 14, 2025

Summary

This summary is machine-generated.

We developed a Bayesian generalized linear model for analyzing microbiome data, improving accuracy and reducing prediction error in high-dimensional settings. This method aids in identifying microbial links to diseases like inflammatory bowel disease (IBD).

Keywords:

Bayesian GLMs EM algorithm compositional data microbiome spike‐and‐slab priors sum‐to‐zero constraint

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Area of Science:

Microbiome Research
Statistical Modeling
Bioinformatics

Background:

Analyzing compositional microbiome data is crucial for understanding microbial roles in health and disease.
Existing methods like penalized regression and MCMC have limitations in handling sum-to-zero constraints for high-dimensional data.

Purpose of the Study:

To propose a novel Bayesian generalized linear model (GLM) for compositional and sub-compositional microbiome data analysis.
To address limitations of existing methods by offering better uncertainty assessment and computational efficiency.

Main Methods:

Developed Bayesian GLMs with a spike-and-slab double-exponential prior for microbiome coefficients.
Implemented a sum-to-zero constraint using soft-centering with prior distributions.
Created a fast and stable algorithm combining Expectation-Maximization (EM) with Iteratively Reweighted Least Squares (IWLS).

Main Results:

Extensive simulations demonstrated superior performance compared to existing methods, showing higher coefficient estimation accuracy and lower prediction error.
The proposed method effectively handles high-dimensional microbiome data.

Conclusions:

The novel Bayesian GLM provides an accurate and efficient approach for analyzing compositional microbiome data.
The method was successfully applied to identify microorganisms associated with inflammatory bowel disease (IBD).
An R package, BhGLM, is available for public use.