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 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 (ZI

SUBMITTER: Jiang S 

PROVIDER: S-EPMC7283552 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature