Bayesian Sparse Multivariate Regression with Asymmetric Nonlocal Priors for Microbiome Data Analysis
Bayesian Sparse Multivariate Regression with Asymmetric Nonlocal Priors for Microbiome Data Analysis
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DOI：
10.1214/19-ba1164
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发表时间：
2020-06
影响因子：
4.4
通讯作者：
K. Shuler;M. Sison-Mangus;Juhee Lee
中科院分区：
文献类型：
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作者：
K. Shuler;M. Sison-Mangus;Juhee Lee
.我们提出了一种贝叶斯稀疏多元回归方法来建模微生物丰度和环境因素之间的关系，微生物组数据。我们模型的丰度计数的操作分类单位（OTUs）与负二项分布和相关的协变量通过回归计数。本文扩展了传统的非局部先验，构造了回归系数的非对称非局部先验，有效地识别了相关协变量及其影响方向。我们建立了一个分层模型，以促进跨OTU的信息池，从而产生具有更高准确性的简约结果。我们目前的模拟研究，比较变量选择性能的贝叶斯稀疏回归模型下的非对称和对称的局部先验和两个频率模型。仿真结果表明，该模型识别了重要的协变量，并产生了良好的精度与替代品相比，系数估计。该模型被应用于分析一段时间以来收集的海洋微生物组数据集，以研究有害藻华状况与微生物群落的关联。
. We propose a Bayesian sparse multivariate regression method to model the relationship between microbe abundance and environmental factors for micro-biome data. We model abundance counts of operational taxonomic units (OTUs) with a negative binomial distribution and relate covariates to the counts through regression. Extending conventional nonlocal priors, we construct asymmetric non-local priors for regression coefficients to efficiently identify relevant covariates and their effect directions. We build a hierarchical model to facilitate pooling of information across OTUs that produces parsimonious results with improved accuracy. We present simulation studies that compare variable selection performance under the proposed model to those under Bayesian sparse regression models with asymmetric and symmetric local priors and two frequentist models. The simulations show the proposed model identifies important covariates and yields coefficient estimates with favorable accuracy compared with the alternatives. The proposed model is applied to analyze an ocean microbiome dataset collected over time to study the association of harmful algal bloom conditions with microbial communities.