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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Zero-Inflated gaussian mixed models for analyzing longitudinal microbiome data.

Xinyan Zhang¹, Boyi Guo², Nengjun Yi²

¹Department of Statistics and Data Analytics, Kennesaw State University, Kennesaw, GA, United States of America.

|November 9, 2020

Summary

This summary is machine-generated.

Analyzing longitudinal microbiome data is challenging due to sparsity and correlations. We introduce zero-inflated Gaussian mixed models (ZIGMMs) to effectively analyze this complex data, improving accuracy in identifying microbial associations with health and disease.

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Area of Science:

Microbiome Research
Statistical Bioinformatics
Computational Biology

Background:

Human microbiome data is highly variable and dynamic.
Longitudinal studies are crucial for understanding microbiome stability and disease-associated dysbiosis.
Analyzing sparse, correlated, and zero-inflated longitudinal microbiome data presents significant statistical challenges.

Purpose of the Study:

To propose a novel statistical method, zero-inflated Gaussian mixed models (ZIGMMs), for analyzing longitudinal microbiome data.
To develop an efficient Expectation-Maximization (EM) algorithm for fitting ZIGMMs.
To evaluate the performance of ZIGMMs against existing methods in detecting associations in microbiome studies.

Main Methods:

Development and implementation of zero-inflated Gaussian mixed models (ZIGMMs).
Utilized an Expectation-Maximization (EM) algorithm for model fitting, leveraging standard linear mixed model procedures.
Comparative analysis through extensive simulations and application to two public longitudinal microbiome datasets.

Main Results:

ZIGMMs demonstrated superior performance over linear mixed models (LMMs), negative binomial mixed models (NBMMs), and zero-inflated Beta regression mixed models (ZIBR) in simulations.
The developed EM algorithm for ZIGMMs is computationally efficient.
The method effectively identified dynamic effects of associated taxa in real-world longitudinal microbiome datasets.

Conclusions:

ZIGMMs provide a robust and flexible framework for analyzing complex longitudinal microbiome data, including both proportion and count data.
The proposed method accurately handles zero-inflation and within-subject correlations inherent in microbiome datasets.
ZIGMMs offer improved power for detecting microbial associations relevant to health and disease states.