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Updated: Feb 21, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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Published on: September 25, 2021

A GLM-based latent variable ordination method for microbiome samples.

Michael B Sohn¹, Hongzhe Li¹

¹Department of Biostatistics and Epidemiology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania 19104, U.S.A.

|October 10, 2017

Summary

This summary is machine-generated.

A new method, the Generalized Linear Model-based Ordination Method for Microbiome Samples (GOMMS), accurately analyzes microbiome data, especially when dispersion effects are significant, unlike traditional methods.

Keywords:

16S sequencing Factor models Microbiome Zero-inflated models

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Area of Science:

Microbiome research
Statistical modeling
Bioinformatics

Background:

Distance-based ordination methods like PCoA are common for microbiome data analysis.
These methods may misinterpret compositional differences due to dispersion effects.
Microbiome data often exhibits high sparsity and overdispersion.

Purpose of the Study:

To introduce a novel ordination method, GOMMS, robust to sparsity and overdispersion in microbiome data.
To address the limitations of distance-based methods in the presence of varying dispersion effects.
To provide a statistically sound approach for microbiome community analysis.

Main Methods:

Developed a Generalized Linear Model-based Ordination Method for Microbiome Samples (GOMMS).
Utilized a zero-inflated quasi-Poisson (ZIQP) latent factor model.
Employed an EM algorithm based on quasi-likelihood for parameter estimation.

Main Results:

GOMMS performs comparably to distance-based methods when dispersion is negligible.
GOMMS consistently outperforms distance-based methods when dispersion effects are strong.
Distance-based methods can yield undesirable results with significant dispersion.

Conclusions:

GOMMS offers a more reliable approach for microbiome data analysis, particularly with high dispersion.
The method's latent factors can be used for associating microbiome communities with covariates.
Further confirmatory experiments can explore these associations.