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

Abstract

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 zeroinflation, these characteristics can bias the network estimation and require specialized analytical tools. Here we propose a general framework, HARMONIES, a 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

Network analysis methods for studying microbial communities: A mini review

“…Discovered a unique subnetwork of Fusobacterium, Peptostreptococcus, and Parvimonas in healthy patients compared to Colorectal cancer patients [122] …”

“…Hybrid Approach foR MicrobiOme Network Inferences via Exploiting Sparsity (HARMONIES) [49] employs the zero-inflated negative binomial distribution (ZINB) and a Dirichlet prior to deal with overdispersion and the large number of zero counts. HARMONIES then uses a graphical lasso approach to infer interactions with favourable results compared to SPIEC-EASI (using both Glasso and the Meinhausen-Bühlmann method), and CClasso on synthetic data, in particular when additional zeros were added.…”