{"doi":"10.3389/fgene.2020.00445","title":"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 (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.","journal":"Frontiers in Genetics","year":2020,"id":94797,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":37,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9341,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":27515,"name":"Guanghua Xiao","orcid":"0000-0001-9387-9883","position":1,"is_corresponding":false},{"id":237268,"name":"Andrew Y. Koh","orcid":"0000-0003-2172-5126","position":2,"is_corresponding":false},{"id":471754,"name":"Yingfei Chen","orcid":"0000-0002-1392-3483","position":3,"is_corresponding":false},{"id":471755,"name":"Bo Yao","orcid":"0009-0002-2225-9956","position":4,"is_corresponding":false},{"id":352535,"name":"Qiwei Li","orcid":"0000-0002-1020-3050","position":5,"is_corresponding":false},{"id":14650,"name":"Xiaowei Zhan","orcid":"0000-0002-6249-7193","position":6,"is_corresponding":false},{"id":301025,"name":"Shuang Jiang","orcid":"0000-0002-4675-7143","position":0,"is_corresponding":true}],"reference_count":61,"raw_metadata":null,"created_at":"2026-07-18T22:33:00.770265Z","pmid":"32582274","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}