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A Zero-inflated Beta-binomial Model for Microbiome Data Analysis.

ABSTRACT: The microbiome is increasingly recognized as an important aspect of the health of host species, involved in many biological pathways and processes and potentially useful as health biomarkers. Taking advantage of high-throughput sequencing technologies, modern bacterial microbiome studies are metagenomic, interrogating thousands of taxa simultaneously. Several data analysis frameworks have been proposed for microbiome sequence read count data and determining the most significant features. However, there is still room for improvement. We introduce a zero-inflated beta-binomial (ZIBB) to model the distribution of microbiome count data and to determine association with a continuous or categorical phenotype of interest. The approach can exploit mean-variance relationships to improve power and adjust for covariates. The proposed method is a mixture model with two components: (i) a zero model accounting for excess zeros and (ii) a count model to capture the remaining component by beta-binomial regression, allowing for overdispersion effects. Simulation studies show that our proposed method effectively controls type I error and has higher power than competing methods to detect taxa associated with phenotype. An R package ZIBBSeqDiscovery is available on R CRAN.

SUBMITTER: Hu T

PROVIDER: S-EPMC6124506 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A Zero-inflated Beta-binomial Model for Microbiome Data Analysis.

Hu Tao T Gallins Paul P Zhou Yi-Hui YH

Stat (International Statistical Institute) 20180619 1

The microbiome is increasingly recognized as an important aspect of the health of host species, involved in many biological pathways and processes and potentially useful as health biomarkers. Taking advantage of high-throughput sequencing technologies, modern bacterial microbiome studies are metagenomic, interrogating thousands of taxa simultaneously. Several data analysis frameworks have been proposed for microbiome sequence read count data and determining the most significant features. However ...[more]

PMID: 30197785

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Project description:BackgroundTwo characteristics of commonly used outcomes in medical research are zero inflation and non-negative integers; examples include the number of hospital admissions or emergency department visits, where the majority of patients will have zero counts. Zero-inflated regression models were devised to analyze this type of data. However, the performance of zero-inflated regression models or the properties of data best suited for these analyses have not been thoroughly investigated.MethodsWe conducted a simulation study to evaluate the performance of two generalized linear models, negative binomial and zero-inflated negative binomial, for analyzing zero-inflated count data. Simulation scenarios assumed a randomized controlled trial design and varied the true underlying distribution, sample size, and rate of zero inflation. We compared the models in terms of bias, mean squared error, and coverage. Additionally, we used logistic regression to determine which data properties are most important for predicting the best-fitting model.ResultsWe first found that, regardless of the rate of zero inflation, there was little difference between the conventional negative binomial and its zero-inflated counterpart in terms of bias of the marginal treatment group coefficient. Second, even when the outcome was simulated from a zero-inflated distribution, a negative binomial model was favored above its ZI counterpart in terms of the Akaike Information Criterion. Third, the mean and skewness of the non-zero part of the data were stronger predictors of model preference than the percentage of zero counts. These results were not affected by the sample size, which ranged from 60 to 800.ConclusionsWe recommend that the rate of zero inflation and overdispersion in the outcome should not be the sole and main justification for choosing zero-inflated regression models. Investigators should also consider other data characteristics when choosing a model for count data. In addition, if the performance of the NB and ZINB regression models is reasonably comparable even with ZI outcomes, we advocate the use of the NB regression model due to its clear and straightforward interpretation of the results.

Dataset Information

A Zero-inflated Beta-binomial Model for Microbiome Data Analysis.

Publications

A Zero-inflated Beta-binomial Model for Microbiome Data Analysis.

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