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Jiang S, Xiao G, Koh AY, et al. HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity. Front Genet. 2020;11:445doi: 10.3389/fgene.2020.00445.
Jiang, S., Xiao, G., Koh, A. Y., Chen, Y., Yao, B., Li, Q., & Zhan, X. (2020). HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity. Frontiers in genetics, 11445. https://doi.org/10.3389/fgene.2020.00445
Jiang, Shuang, et al. "HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity." Frontiers in genetics vol. 11 (2020): 445. doi: https://doi.org/10.3389/fgene.2020.00445
Jiang S, Xiao G, Koh AY, Chen Y, Yao B, Li Q, Zhan X. HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity. Front Genet. 2020 Jun 03;11:445. doi: 10.3389/fgene.2020.00445. eCollection 2020. PMID: 32582274; PMCID: PMC7283552.
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Front Genet. 2020 Jun 03;11:445. doi: 10.3389/fgene.2020.00445. eCollection 2020.

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

Frontiers in genetics

Shuang Jiang, Guanghua Xiao, Andrew Y Koh, Yingfei Chen, Bo Yao, Qiwei Li, Xiaowei Zhan

Affiliations

  1. Department of Statistical Science, Southern Methodist University, Dallas, TX, United States.
  2. Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States.
  3. Departments of Pediatrics, Departments of Microbiology, University of Texas Southwestern Medical Center, Dallas, TX, United States.
  4. Lyda Hill Department of Bioinformatics, Bioinformatics High Performance Computing, University of Texas Southwestern Medical Center, Dallas, TX, United States.
  5. Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX, United States.

PMID: 32582274 PMCID: PMC7283552 DOI: 10.3389/fgene.2020.00445

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 (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.

Copyright © 2020 Jiang, Xiao, Koh, Chen, Yao, Li and Zhan.

Keywords: Bayesian statistics; Dirichlet process prior; Gaussian graphical model; hierarchical model; microbiome network

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