{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T12:53:49Z","timestamp":1768827229950,"version":"3.49.0"},"reference-count":6,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2022,9,9]],"date-time":"2022-09-09T00:00:00Z","timestamp":1662681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"National Institute of Health","award":["R21 HG011662"],"award-info":[{"award-number":["R21 HG011662"]}]},{"name":"National Institute of Health","award":["R01GM144351"],"award-info":[{"award-number":["R01GM144351"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS2113359"],"award-info":[{"award-number":["DMS2113359"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS2113360"],"award-info":[{"award-number":["DMS2113360"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Due to the sparsity and high dimensionality, microbiome data are routinely summarized into pairwise distances capturing the compositional differences. Many biological insights can be gained by analyzing the distance matrix in relation to some covariates. A microbiome sampling method that characterizes the inter-sample relationship more reproducibly is expected to yield higher statistical power. Traditionally, the intraclass correlation coefficient (ICC) has been used to quantify the degree of reproducibility for a univariate measurement using technical replicates. In this work, we extend the traditional ICC to distance measures and propose a distance-based ICC (dICC). We derive the asymptotic distribution of the sample-based dICC to facilitate statistical inference. We illustrate dICC using a real dataset from a metagenomic reproducibility study.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>dICC is implemented in the R CRAN package GUniFrac.<\/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\/btac618","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T21:00:53Z","timestamp":1662670853000},"page":"4969-4971","source":"Crossref","is-referenced-by-count":4,"title":["dICC: distance-based intraclass correlation coefficient for metagenomic reproducibility studies"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1273-5624","authenticated-orcid":false,"given":"Jun","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Quantitative Health Sciences, Division of Computational Biology, Mayo Clinic , Rochester, MN 55905, USA"}]},{"given":"Xianyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Statistics, Texas A&M University , College Station, TX 77840, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,9,9]]},"reference":[{"key":"2023090900204697600_btac618-B1","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1002\/uog.5256","article-title":"Reliability, repeatability and reproducibility: analysis of measurement errors in continuous variables","volume":"31","author":"Bartlett","year":"2008","journal-title":"Ultrasound Obstet. Gynecol"},{"key":"2023090900204697600_btac618-B2","doi-asserted-by":"crossref","first-page":"e15216","DOI":"10.1371\/journal.pone.0015216","article-title":"Disordered microbial communities in the upper respiratory tract of cigarette smokers","volume":"5","author":"Charlson","year":"2010","journal-title":"PLoS One"},{"key":"2023090900204697600_btac618-B3","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1093\/bioinformatics\/bts342","article-title":"Associating microbiome composition with environmental covariates using generalized UniFrac distances","volume":"28","author":"Chen","year":"2012","journal-title":"Bioinformatics"},{"key":"2023090900204697600_btac618-B4","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1038\/s41579-018-0029-9","article-title":"Best practices for analysing microbiomes","volume":"16","author":"Knight","year":"2018","journal-title":"Nat. Rev. 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