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 article, we introduce a general framework, zero-inflated probabilistic principal component analysis (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.

摘要

人和细菌之间的共同进化对健康和发育具有深远的影响，越来越多的证据将改变的微生物组组成与各种疾病状态联系起来。然而，微生物组数据的降维和可视化分析仍处于起步阶段，存在许多挑战。在本文中，我们引入了一个通用框架，零膨胀概率主成分分析（ZIPPCA），用于多元丰度数据的降维和数据排序，并提出了一种用于估计、推理和预测的有效变分逼近方法。大量的模拟表明，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，以对不同年龄和地域的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。