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Model-based microbiome data ordination: A variational approximation approach
Journal of Computational and Graphical Statistics ( IF 2.4 ) Pub Date : 2021-02-02
Yanyan Zeng, Hongyu Zhao, Tao Wang

Abstract

The coevolution between human and bacteria colonizing the human body has profound implications for heath and development, with a growing body of evidence linking the altered microbiome composition with a wide array of disease states. Yet dimension reduction and visualization analysis of microbiome data are still in their infancy and many challenges exist. In this paper we introduce a general framework, Zero-Inflated Probabilistic PCA (ZIPPCA), for dimension reduction and data ordination of multivariate abundance data, and propose an efficient variational approximation method for estimation, inference, and prediction. Extensive simulations show that the proposed method outperforms algorithm-based methods and compares favorably with existing model-based methods. We further apply our method to a gut microbiome dataset for visualization analysis of community composition across age and geography. The method is implemented in R and available at https://github.com/YanyZeng/ZIPPCA. Supplemental materials are available online.



中文翻译:

基于模型的微生物组数据排序:一种变分近似方法

摘要

人类与定居在细菌之间的共同进化对健康和发育具有深远的影响,越来越多的证据表明改变的微生物组组成与多种疾病状态相关。然而,微生物组数据的降维和可视化分析仍处于起步阶段,并且存在许多挑战。在本文中,我们介绍了一个通用框架,即零膨胀概率PCA(ZIPPCA),用于多维丰度数据的降维和数据排序,并提出了一种用于估计,推断和预测的有效变分逼近方法。大量的仿真表明,所提出的方法优于基于算法的方法,并且与现有的基于模型的方法相比具有优势。我们进一步将我们的方法应用于肠道微生物组数据集,以便跨年龄和地理区域对社区组成进行可视化分析。该方法在R中实现,可从https://github.com/YanyZeng/ZIPPCA获得。补充材料可在线获得。

更新日期:2021-02-02
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