HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity

• Main Authors: Shuang Jiang, Guanghua Xiao, Andrew Y. Koh, Yingfei Chen, Bo Yao, Qiwei Li, Xiaowei Zhan
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-06-01
• Series: Frontiers in Genetics
• Subjects: Bayesian statistics; microbiome network; Gaussian graphical model; Dirichlet process prior; hierarchical model
• Online Access: https://www.frontiersin.org/article/10.3389/fgene.2020.00445/full

The human microbiome is a collection of microorganisms. They form complex communities and collectively affect host health. Recently, the advances in next-generation sequencing technology enable the high-throughput profiling of the human microbiome. This calls for a statistical model to construct microbial networks from the microbiome sequencing count data. As microbiome count data are high-dimensional and suffer from uneven sampling depth, over-dispersion, and zero-inflation, these characteristics can bias the network estimation and require specialized analytical tools. Here we propose a general framework, HARMONIES, Hybrid Approach foR MicrobiOme Network Inferences via Exploiting Sparsity, to infer a sparse microbiome network. HARMONIES first utilizes a zero-inflated negative binomial (ZINB) distribution to model the skewness and excess zeros in the microbiome data, as well as incorporates a stochastic process prior for sample-wise normalization. This approach infers a sparse and stable network by imposing non-trivial regularizations based on the Gaussian graphical model. In comprehensive simulation studies, HARMONIES outperformed four other commonly used methods. When using published microbiome data from a colorectal cancer study, it discovered a novel community with disease-enriched bacteria. In summary, HARMONIES is a novel and useful statistical framework for microbiome network inference, and it is available at https://github.com/shuangj00/HARMONIES.