Abstract
The metagenomics sequencing data provide valuable resources for investigating the associations between the microbiome and host environmental/clinical factors and the dynamic changes of microbial abundance over time. The distinct properties of microbiome measurements include varied total sequence reads across samples, over-dispersion and zero-inflation. Additionally, microbiome studies usually collect samples longitudinally, which introduces time-dependent and correlation structures among the samples and thus further complicates the analysis and interpretation of microbiome count data. In this article, we propose negative binomial mixed models (NBMMs) for longitudinal microbiome studies. The proposed NBMMs can efficiently handle over-dispersion and varying total reads, and can account for the dynamic trend and correlation among longitudinal samples. We develop an efficient and stable algorithm to fit the NBMMs. We evaluate and demonstrate the NBMMs method via extensive simulation studies and application to a longitudinal microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of flexible framework for modeling correlation structures and detecting dynamic effects. We have developed an R package NBZIMM to implement the proposed method, which is freely available from the public GitHub repository http://github.com//nyiuab//NBZIMM and provides a useful tool for analyzing longitudinal microbiome data.
Highlights
IntroductionThe complex microbiome is inherently dynamic and interacts with the host and the environmental factors over time (Gerber, 2014a)
The human microbiome plays an important role in human health and disease
We show that the negative binomial mixed models (NBMMs) outperform the previously used linear mixed models (LMMs) in terms of detecting dynamic effects in longitudinal microbiome count data
Summary
The complex microbiome is inherently dynamic and interacts with the host and the environmental factors over time (Gerber, 2014a). These complex dynamics start from the birth with increasingly richness in the communities of microbiota over time (Palmer et al, 2007; Koenig et al, 2011; Wu et al, 2011; De Muinck et al, 2013; Gerber, 2014a). NBMMs for Longitudinal Microbiome status (Turnbaugh et al, 2009), and host environment (Dominguez-Bello et al, 2010). To decipher the relationship between the dynamic changes in microbiome and human diseases, high-throughput sequencing technologies, such as the 16S ribosome RNA (rRNA) gene sequencing or shotgun metagenomics sequencing, have been widely applied in longitudinal microbiome studies (Matsen et al, 2010; Ghodsi et al, 2011; Gilbert et al, 2011; La Rosa et al, 2014)
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