{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:21Z","timestamp":1772138061979,"version":"3.50.1"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"11","funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM141074"],"award-info":[{"award-number":["R01GM141074"]}],"id":[{"id":"10.13039\/100000002","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>Summary<\/jats:title>\n                    <jats:p>There are compelling reasons to test compositional hypotheses about microbiome data. We present here linear decomposition model-centered log ratio (LDM-clr), an extension of our LDM approach to allow fitting linear models to centered-log-ratio-transformed taxa count data. As LDM-clr is implemented within the existing LDM program, this extension enjoys all the features supported by LDM, including a compositional analysis of differential abundance at both the taxon and community levels, while allowing for a wide range of covariates and study designs for either association or mediation analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>LDM-clr has been added to the R package LDM, which is available on GitHub at https:\/\/github.com\/yijuanhu\/LDM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad668","type":"journal-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T10:20:43Z","timestamp":1698747643000},"source":"Crossref","is-referenced-by-count":7,"title":["Compositional analysis of microbiome data using the linear decomposition model (LDM)"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2171-9041","authenticated-orcid":false,"given":"Yi-Juan","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Emory University , Atlanta, GA 30322, United States"}]},{"given":"Glen A","family":"Satten","sequence":"additional","affiliation":[{"name":"Department of Gynecology and Obstetrics, Emory University School of Medicine , Atlanta, GA 30322, United States"}]}],"member":"286","published-online":{"date-parts":[[2023,11,1]]},"reference":[{"key":"2023111117015595100_btad668-B1","doi-asserted-by":"crossref","DOI":"10.1007\/978-94-009-4109-0","volume-title":"The Statistical Analysis of Compositional Data","author":"Aitchison","year":"1986"},{"key":"2023111117015595100_btad668-B2","doi-asserted-by":"crossref","first-page":"2648","DOI":"10.1214\/22-AOAS1607","article-title":"Testing for differential abundance in compositional counts data, with 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