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Negative binomial mixed models for analyzing microbiome count data.

Xinyan Zhang¹, Himel Mallick^2,3, Zaixiang Tang⁴

¹Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA.

BMC Bioinformatics

|January 5, 2017

Summary

This summary is machine-generated.

We introduce negative binomial mixed models (NBMMs) to analyze correlated microbiome data, effectively handling over-dispersion and varying sequence reads. This method improves association detection between microbiomes and host factors.

Keywords:

Correlated measures Count data Metagenomics Microbiome Negative binomial model Penalized Quasi-likelihood Random effects

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Area of Science:

Microbiome research
Statistical modeling
Bioinformatics

Background:

Next-generation sequencing (NGS) generates large metagenomic datasets.
Microbiome data present challenges like over-dispersion, zero-inflation, and hierarchical structures causing correlations.
Analyzing these complex datasets requires advanced statistical approaches.

Purpose of the Study:

To propose negative binomial mixed models (NBMMs) for analyzing microbiome count data with hierarchical structures.
To address the challenge of correlated microbiome samples in association studies.
To provide a robust statistical framework for microbiome data analysis.

Main Methods:

Developed negative binomial mixed models (NBMMs) incorporating random effects.
Utilized an Iterative Weighted Least Squares (IWLS) algorithm for model fitting.
Focused on handling over-dispersion and varying total reads in microbiome data.

Main Results:

The proposed NBMMs effectively account for sample correlations.
The method efficiently handles over-dispersion and varying sequence reads.
Demonstrated superior performance in simulation studies and real data application.

Conclusions:

NBMMs offer improved power and Type I error control for microbiome association studies.
The method outperforms existing approaches for correlated microbiome data.
A user-friendly R package (BhGLM) is available for implementing the NBMMs.