Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM
Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM
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DOI：
10.1080/10618600.2021.1935971
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发表时间：
2021-07-16
影响因子：
2.4
通讯作者：
Vannucci, Marina
中科院分区：
文献类型：
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作者：
Osborne, Nathan;Peterson, Christine B.;Vannucci, Marina
网络估计和变量选择在统计文献中得到了广泛的研究，但直到最近才同时解决了这两个挑战。在这篇文章中，我们试图开发一种新的方法，同时估计网络的相互作用和相关协变量的计数数据，特别是成分数据，其中有一个固定的总和约束。我们使用一个分层贝叶斯模型与潜在层，并采用穗和板先验的边缘和协变量的选择。对于后验推理，我们开发了一种新的变分推理方案的期望最大化的步骤，使有效的估计。通过仿真研究，我们证明了该模型优于现有的方法在其准确性的网络恢复。我们展示了我们的模型的实际效用，通过微生物组数据的应用。人体微生物组已被证明有助于人体的太多功能，并且与许多疾病有关。在我们的应用中，我们寻求更好地理解微生物与相关协变量之间的相互作用，以及微生物之间的相互作用。我们称我们的算法为网络和协变量的同时推理，并提供了一个Python实现，可以在线获得。
Network estimation and variable selection have been extensively studied in the statistical literature, but only recently have those two challenges been addressed simultaneously. In this article, we seek to develop a novel method to simultaneously estimate network interactions and associations to relevant covariates for count data, and specifically for compositional data, which have a fixed sum constraint. We use a hierarchical Bayesian model with latent layers and employ spike-and-slab priors for both edge and covariate selection. For posterior inference, we develop a novel variational inference scheme with an expectation-maximization step, to enable efficient estimation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of network recovery. We show the practical utility of our model via an application to microbiome data. The human microbiome has been shown to contribute too many of the functions of the human body, and also to be linked with a number of diseases. In our application, we seek to better understand the interaction between microbes and relevant covariates, as well as the interaction of microbes with each other. We call our algorithm simultaneous inference for networks and covariates and provide a Python implementation, which is available online.