{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T19:05:32Z","timestamp":1771009532645,"version":"3.50.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2018,8,23]],"date-time":"2018-08-23T00:00:00Z","timestamp":1534982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Early Career Scheme","award":["24301416"],"award-info":[{"award-number":["24301416"]}]},{"name":"General Research Fund","award":["14306417"],"award-info":[{"award-number":["14306417"]}]},{"name":"Research Grants Council"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not consider the interactions between variables\u2014microbial taxa in microbial studies\u2014and the overdispersion of the microbiome data. Therefore, they are not applicable to microbiome data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We develop a new method, Bayesian Dirichlet-multinomial regression meta-analysis (BDMMA), to simultaneously model the batch effects and detect the microbial taxa associated with phenotypes. BDMMA automatically models the dependence among microbial taxa and is robust to the high dimensionality of the microbiome and their association sparsity. Simulation studies and real data analysis show that BDMMA can successfully adjust batch effects and substantially reduce false discoveries in microbial meta-analyses.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>An R package\u201d BDMMA\u201d for Windows and Linux is available at https:\/\/github.com\/DAIZHENWEI\/BDMMA\/BDMMA, and a version for MacOS is provided at https:\/\/github.com\/DAIZHENWEI\/BDMMA\/BDMMA_MacOS.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty729","type":"journal-article","created":{"date-parts":[[2018,8,22]],"date-time":"2018-08-22T19:19:20Z","timestamp":1534965560000},"page":"807-814","source":"Crossref","is-referenced-by-count":38,"title":["Batch effects correction for microbiome data with Dirichlet-multinomial regression"],"prefix":"10.1093","volume":"35","author":[{"given":"Zhenwei","family":"Dai","sequence":"first","affiliation":[{"name":"Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Hong Kong"},{"name":"Gastrointestinal Cancer Biology & Therapeutics Laboratory, CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong"}]},{"given":"Sunny H","family":"Wong","sequence":"additional","affiliation":[{"name":"Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Hong Kong"},{"name":"Gastrointestinal Cancer Biology & Therapeutics Laboratory, CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong"}]},{"given":"Jun","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Hong Kong"},{"name":"Gastrointestinal Cancer Biology & Therapeutics Laboratory, CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong"}]},{"given":"Yingying","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong, Hong Kong"}]}],"member":"286","published-online":{"date-parts":[[2018,8,23]]},"reference":[{"key":"2023013107250190500_bty729-B1","first-page":"32","article-title":"A new method for non-parametric multivariate analysis of variance","volume":"26","author":"Anderson","year":"2001","journal-title":"Austral Ecology"},{"key":"2023013107250190500_bty729-B2","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.1111\/j.1440-1746.2008.05490.x","article-title":"Real-time polymerase chain reaction quantification of specific butyrate-producing bacteria, desulfovibrio and enterococcus faecalis in the feces of patients with colorectal cancer","volume":"23","author":"Balamurugan","year":"2008","journal-title":"J. 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