{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T16:07:37Z","timestamp":1769098057208,"version":"3.49.0"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"vor","delay-in-days":16,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12101425"],"award-info":[{"award-number":["12101425"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Microbes are essential components in the ecosystem and participate in most biological procedures in environments. The high-throughput sequencing technologies help researchers directly quantify the abundance of microbes in a natural environment. Microbiome studies explore the construction, stability, and function of microbial communities with the aid of sequencing technology. However, sequencing technologies only provide relative abundances of microbes, and this kind of data is called compositional data in statistics. The constraint of the constant-sum requires flexible statistical methods for analyzing microbiome data. Current statistical analysis of compositional data mainly focuses on one compositional vector such as bacterial communities. The fungi are also an important component in microbial communities and are always measured by sequencing internal transcribed spacer instead of 16S rRNA genes for bacteria. The different sequencing methods between fungi and bacteria bring two compositional vectors in microbiome studies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a novel statistical method, called gmcoda, based on an additive logistic normal distribution for estimating the partial correlation matrix for cross-domain interactions. A majorization\u2013minimization algorithm is proposed to solve the optimization problem involved in gmcoda. Through simulation studies, gmcoda is demonstrated to work well in estimating partial correlations between two compositional vectors. Gmcoda is also applied to infer cross-domain interactions in a real microbiome dataset and finds potential interactions between bacteria and fungi.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Gmcoda is open source and freely available from https:\/\/github.com\/huayingfang\/gmcoda under GNU LGPL v3.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad700","type":"journal-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T14:44:13Z","timestamp":1700145853000},"source":"Crossref","is-referenced-by-count":2,"title":["gmcoda: Graphical model for multiple compositional vectors in microbiome studies"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7693-8260","authenticated-orcid":false,"given":"Huaying","family":"Fang","sequence":"first","affiliation":[{"name":"Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University , Beijing 100048, China"},{"name":"Academy of Multidisciplinary Studies, Capital Normal University , Beijing 100048, China"}]}],"member":"286","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"2023112802455518800_btad700-B1","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1126\/science.286.5439.509","article-title":"Emergence of scaling in random networks","volume":"286","author":"Barab\u00e1si","year":"1999","journal-title":"Science"},{"key":"2023112802455518800_btad700-B2","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1080\/01621459.2018.1442340","article-title":"Large covariance estimation for compositional data via composition-adjusted thresholding","volume":"114","author":"Cao","year":"2019","journal-title":"J Am Stat Assoc"},{"key":"2023112802455518800_btad700-B3","doi-asserted-by":"crossref","first-page":"3172","DOI":"10.1093\/bioinformatics\/btv349","article-title":"CCLasso: correlation inference for compositional data through lasso","volume":"31","author":"Fang","year":"2015","journal-title":"Bioinformatics"},{"key":"2023112802455518800_btad700-B4","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1089\/cmb.2017.0054","article-title":"gCoda: conditional dependence network inference for compositional data","volume":"24","author":"Fang","year":"2017","journal-title":"J Comput Biol"},{"key":"2023112802455518800_btad700-B5","doi-asserted-by":"crossref","first-page":"e1002687","DOI":"10.1371\/journal.pcbi.1002687","article-title":"Inferring correlation networks from genomic survey data","volume":"8","author":"Friedman","year":"2012","journal-title":"PLoS Comput Biol"},{"key":"2023112802455518800_btad700-B6","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1093\/biostatistics\/kxm045","article-title":"Sparse inverse covariance estimation with the graphical lasso","volume":"9","author":"Friedman","year":"2008","journal-title":"Biostatistics"},{"key":"2023112802455518800_btad700-B7","doi-asserted-by":"crossref","first-page":"e01148\u201320","DOI":"10.1128\/mSystems.01148-20","article-title":"Integrative transkingdom analysis of the gut microbiome in antibiotic perturbation and critical illness","volume":"6","author":"Haak","year":"2021","journal-title":"mSystems"},{"key":"2023112802455518800_btad700-B8","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3390\/pathogens8020070","article-title":"Fungal-bacterial interactions in health and disease","volume":"8","author":"Kr\u00fcger","year":"2019","journal-title":"Pathogens"},{"key":"2023112802455518800_btad700-B9","doi-asserted-by":"crossref","first-page":"e1004226","DOI":"10.1371\/journal.pcbi.1004226","article-title":"Sparse and compositionally robust inference of microbial ecological networks","volume":"11","author":"Kurtz","year":"2015","journal-title":"PLoS Comput Biol"},{"key":"2023112802455518800_btad700-B10","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1038\/s42003-021-02036-x","article-title":"Gut mycobiota alterations in patients with Covid-19 and H1N1 infections and their associations with clinical features","volume":"4","author":"Lv","year":"2021","journal-title":"Commun Biol"},{"key":"2023112802455518800_btad700-B11","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1136\/postgradmedj-2015-133285","article-title":"Gut microbiota, obesity and diabetes","volume":"92","author":"Patterson","year":"2016","journal-title":"Postgrad Med J"},{"key":"2023112802455518800_btad700-B12","first-page":"911","article-title":"Association of streptococcus mutans, candida albicans and oral health practices with activity status of caries lesions among 5-year-old children with early childhood caries","volume":"18","author":"Sridhar","year":"2020","journal-title":"Oral Health Prev Dent"},{"key":"2023112802455518800_btad700-B13","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s40168-017-0393-0","article-title":"Fungi stabilize connectivity in the lung and skin microbial ecosystems","volume":"6","author":"Tipton","year":"2018","journal-title":"Microbiome"},{"key":"2023112802455518800_btad700-B14","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1093\/biomet\/asm018","article-title":"Model selection and estimation in the Gaussian graphical model","volume":"94","author":"Yuan","year":"2007","journal-title":"Biometrika"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btad700\/53483101\/btad700.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/11\/btad700\/53863025\/btad700.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/11\/btad700\/53863025\/btad700.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T02:46:13Z","timestamp":1701139573000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btad700\/7425448"}},"subtitle":[],"editor":[{"given":"Alfonso","family":"Valencia","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,11,1]]},"references-count":14,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btad700","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,11,1]]},"published":{"date-parts":[[2023,11,1]]},"article-number":"btad700"}}