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Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

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Fast zero-inflated negative binomial mixed modeling approach for analyzing longitudinal metagenomics data.

Xinyan Zhang¹, Nengjun Yi²

¹Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30458, USA.

Bioinformatics (Oxford, England)

|January 7, 2020

Summary

A new Fast Zero-Inflated Negative Binomial Mixed Modeling (FZINBMM) approach effectively analyzes complex longitudinal metagenomic data. This method offers superior computational efficiency and statistical accuracy for microbiome research.

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Area of Science:

Microbiome research
Computational biology
Statistical modeling

Background:

Longitudinal metagenomics data, including 16S rRNA and whole-metagenome shotgun sequencing, are crucial for understanding disease associations.
Existing analytical tools struggle with high-dimensionality, sample dependence, and zero-inflation inherent in longitudinal metagenomic data.

Purpose of the Study:

To develop a novel computational approach for analyzing high-dimensional longitudinal metagenomic count data.
To address the challenges of data complexity, including high dimensionality, sample dependence, and zero-inflation.

Main Methods:

Proposed a Fast Zero-Inflated Negative Binomial Mixed Modeling (FZINBMM) approach.
Utilized zero-inflated negative binomial mixed models (ZINBMMs) and a fast expectation-maximization iteratively reweighted least squares (EM-IWLS) algorithm.
Leveraged procedures for fitting linear mixed models to incorporate fixed/random effects and correlation structures.

Main Results:

FZINBMM demonstrated superior computational efficiency compared to existing R packages (GLMMadaptive, glmmTMB).
Statistical performance was comparable to methods using numerical integration.
FZINBMM outperformed previous methods like linear mixed models, negative binomial mixed models, and zero-inflated Gaussian mixed models in simulations and real data analyses.

Conclusions:

FZINBMM provides an effective and efficient solution for analyzing complex longitudinal metagenomic data.
The developed method enhances the ability to study dynamic microbiome-disease associations.
The FZINBMM approach is implemented in the R package NBZIMM for broader accessibility.