Department of Statistics, Colorado State University, Fort Collins, Colorado, USA.
Biometrics. 2023 Dec;79(4):3239-3251. doi: 10.1111/biom.13853. Epub 2023 Apr 3.
The Dirichlet-multinomial (DM) distribution plays a fundamental role in modern statistical methodology development and application. Recently, the DM distribution and its variants have been used extensively to model multivariate count data generated by high-throughput sequencing technology in omics research due to its ability to accommodate the compositional structure of the data as well as overdispersion. A major limitation of the DM distribution is that it is unable to handle excess zeros typically found in practice which may bias inference. To fill this gap, we propose a novel Bayesian zero-inflated DM model for multivariate compositional count data with excess zeros. We then extend our approach to regression settings and embed sparsity-inducing priors to perform variable selection for high-dimensional covariate spaces. Throughout, modeling decisions are made to boost scalability without sacrificing interpretability or imposing limiting assumptions. Extensive simulations and an application to a human gut microbiome dataset are presented to compare the performance of the proposed method to existing approaches. We provide an accompanying R package with a user-friendly vignette to apply our method to other datasets.
Dirichlet-multinomial（DM）分布在现代统计方法学的发展和应用中起着至关重要的作用。由于其能够适应数据的组成结构和过离散性，最近，DM 分布及其变体已被广泛用于对组学研究中高通量测序技术生成的多元计数数据进行建模。DM 分布的一个主要局限性是它无法处理实践中通常遇到的超额零值，这可能会导致推断出现偏差。为了弥补这一空白，我们提出了一种用于具有超额零值的多元组合计数数据的新颖的贝叶斯零膨胀 DM 模型。然后，我们将我们的方法扩展到回归设置，并嵌入稀疏诱导先验，以便在高维协变量空间中进行变量选择。整个过程中，我们做出建模决策以提高可扩展性，而不会牺牲可解释性或施加限制假设。我们进行了广泛的模拟，并将其应用于人类肠道微生物组数据集，以比较所提出的方法与现有方法的性能。我们提供了一个附带用户友好教程的 R 包，以将我们的方法应用于其他数据集。
