Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis.
Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis.
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DOI：
10.1093/biostatistics/kxy025
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发表时间：
2019-10-01
期刊：
影响因子：
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通讯作者：
Chen, Guanhua
中科院分区：
文献类型：
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作者：
Tang, Zheng-Zheng;Chen, Guanhua
人们对使用高通量测序技术来量化微生物分类群的丰度并将丰度与人类疾病和特征联系起来的兴趣越来越高。正确的多变量分类单元计数的建模是必不可少的检测这种关联的力量。现有的模型在处理分类群计数中过多的零观测值和灵活地适应分类群之间复杂的相关结构和分散模式方面受到限制。在这篇文章中，我们开发了一个新的概率分布，零膨胀广义狄利克雷多项式（ZIGDM），克服了这些限制，在建模多变量分类群计数。基于这种分布，我们提出了一个ZIGDM回归模型，将微生物丰度与协变量（例如疾病状态）联系起来，并开发了一种快速的期望最大化算法来有效地估计模型中的参数。衍生的测试，使我们能够揭示丰富的模式，包括微分均值和分散的微生物组成的变化。通过模拟研究和肠道微生物组数据集的分析证明了所提出的方法的优点。
There is heightened interest in using high-throughput sequencing technologies to quantify abundances of microbial taxa and linking the abundance to human diseases and traits. Proper modeling of multivariate taxon counts is essential to the power of detecting this association. Existing models are limited in handling excessive zero observations in taxon counts and in flexibly accommodating complex correlation structures and dispersion patterns among taxa. In this article, we develop a new probability distribution, zero-inflated generalized Dirichlet multinomial (ZIGDM), that overcomes these limitations in modeling multivariate taxon counts. Based on this distribution, we propose a ZIGDM regression model to link microbial abundances to covariates (e.g. disease status) and develop a fast expectation-maximization algorithm to efficiently estimate parameters in the model. The derived tests enable us to reveal rich patterns of variation in microbial compositions including differential mean and dispersion. The advantages of the proposed methods are demonstrated through simulation studies and an analysis of a gut microbiome dataset.