{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:14:12Z","timestamp":1772766852139,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"20","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: The high-throughput sequencing technologies have provided a powerful tool to study the microbial organisms living in various environments. Characterizing microbial interactions can give us insights into how they live and work together as a community. Metagonomic data are usually summarized in a compositional fashion due to varying sampling\/sequencing depths from one sample to another. We study the co-occurrence patterns of microbial organisms using their relative abundance information. Analyzing compositional data using conventional correlation methods has been shown prone to bias that leads to artifactual correlations.<\/jats:p><jats:p>Results: We propose a novel method, regularized estimation of the basis covariance based on compositional data (REBACCA), to identify significant co-occurrence patterns by finding sparse solutions to a system with a deficient rank. To be specific, we construct the system using log ratios of count or proportion data and solve the system using the l1-norm shrinkage method. Our comprehensive simulation studies show that REBACCA (i) achieves higher accuracy in general than the existing methods when a sparse condition is satisfied; (ii) controls the false positives at a pre-specified level, while other methods fail in various cases and (iii) runs considerably faster than the existing comparable method. REBACCA is also applied to several real metagenomic datasets.<\/jats:p><jats:p>Availability and implementation: The R codes for the proposed method are available at http:\/\/faculty.wcas.northwestern.edu\/\u223chji403\/REBACCA.htm<\/jats:p><jats:p>Contact: \u00a0hongmei@northwestern.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv364","type":"journal-article","created":{"date-parts":[[2015,6,17]],"date-time":"2015-06-17T00:49:51Z","timestamp":1434502191000},"page":"3322-3329","source":"Crossref","is-referenced-by-count":77,"title":["Investigating microbial co-occurrence patterns based on metagenomic compositional data"],"prefix":"10.1093","volume":"31","author":[{"given":"Yuguang","family":"Ban","sequence":"first","affiliation":[{"name":"1 Department of Statistics, Northwestern University, Evanston, IL 60208, USA,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingling","family":"An","sequence":"additional","affiliation":[{"name":"2 Interdisciplinary Program in Statistics, University of Arizona, Tucson, AZ 85721, USA and"},{"name":"3 Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ 85721, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongmei","family":"Jiang","sequence":"additional","affiliation":[{"name":"1 Department of Statistics, Northwestern University, Evanston, IL 60208, USA,"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,6,16]]},"reference":[{"key":"2023020202310418200_btv364-B1","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/BF01031393","article-title":"A new approach to null correlations of proportions","volume":"13","author":"Aitchison","year":"1981","journal-title":"J. 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