{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T16:34:48Z","timestamp":1784306088187,"version":"3.55.0"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000015","name":"Department of Energy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"name":"owa State University Plant Sciences Institute Scholars Program"},{"name":"Nonclinical Biostatistics Scholarship from the Biopharmaceutical Section of the American Statistical Association"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>High-throughput sequencing technologies have greatly facilitated microbiome research and have generated a large volume of microbiome data with the potential to answer key questions regarding microbiome assembly, structure and function. Cluster analysis aims to group features that behave similarly across treatments, and such grouping helps to highlight the functional relationships among features and may provide biological insights into microbiome networks. However, clustering microbiome data are challenging due to the sparsity and high dimensionality.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We propose a model-based clustering method based on Poisson hurdle models for sparse microbiome count data. We describe an expectation\u2013maximization algorithm and a modified version using simulated annealing to conduct the cluster analysis. Moreover, we provide algorithms for initialization and choosing the number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. We also apply the proposed method to a sorghum rhizosphere microbiome dataset that results in interesting biological findings.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>R package is freely available for download at https:\/\/cran.r-project.org\/package=PHclust.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac782","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T20:07:17Z","timestamp":1670270837000},"source":"Crossref","is-referenced-by-count":9,"title":["Poisson hurdle model-based method for clustering microbiome features"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8154-0898","authenticated-orcid":false,"given":"Zhili","family":"Qiao","sequence":"first","affiliation":[{"name":"Department of Statistics, Iowa State University , Ames, IA 50011, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elle","family":"Barnes","sequence":"additional","affiliation":[{"name":"Department of Energy, Joint Genome Institute , Berkeley, CA 94720, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susannah","family":"Tringe","sequence":"additional","affiliation":[{"name":"Department of Energy, Joint Genome Institute , Berkeley, CA 94720, USA"},{"name":"Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory , Berkeley, CA 94720, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1807-4369","authenticated-orcid":false,"given":"Daniel P","family":"Schachtman","sequence":"additional","affiliation":[{"name":"Department of Agronomy and Horticulture, University of Nebraska , Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Statistics, Iowa State University , Ames, IA 50011, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"key":"2023010805355143700_btac782-B1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/j.2517-6161.1982.tb01195.x","article-title":"The statistical analysis of compositional data","volume":"44","author":"Aitchison","year":"1982","journal-title":"J. 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