TY - JOUR
T1 - Harmonies
T2 - A hybrid approach for microbiome networks inference via exploiting sparsity
AU - Jiang, Shuang
AU - Xiao, Guanghua
AU - Koh, Andrew Y.
AU - Chen, Yingfei
AU - Yao, Bo
AU - Li, Qiwei
AU - Zhan, Xiaowei
N1 - Publisher Copyright:
© 2020 Jiang, Xiao, Koh, Chen, Yao, Li and Zhan.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Bayesian statistics
KW - Dirichlet process prior
KW - Gaussian graphical model
KW - Hierarchical model
KW - Microbiome network
UR - http://www.scopus.com/inward/record.url?scp=85087036199&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087036199&partnerID=8YFLogxK
U2 - 10.3389/fgene.2020.00445
DO - 10.3389/fgene.2020.00445
M3 - Article
C2 - 32582274
AN - SCOPUS:85087036199
SN - 1664-8021
VL - 11
JO - Frontiers in Genetics
JF - Frontiers in Genetics
M1 - 445
ER -