{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T14:01:22Z","timestamp":1783605682012,"version":"3.55.0"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,25]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>In microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical issues. We introduce a surprisingly simple, interpretable and efficient method for the estimation of compositional data regression through the lens of a novel high-dimensional log-error-in-variable regression model. The proposed method provides corrections on sequencing data with possible overdispersion and simultaneously avoids any subjective imputation of zero read counts. We provide theoretical justifications with matching upper and lower bounds for the estimation error. The merit of the procedure is illustrated through real data analysis and simulation studies.<\/jats:p>","DOI":"10.1093\/biomet\/asab020","type":"journal-article","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T19:11:20Z","timestamp":1617045080000},"page":"405-420","source":"Crossref","is-referenced-by-count":12,"title":["High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis"],"prefix":"10.1093","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2455-2622","authenticated-orcid":false,"given":"Pixu","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Biostatistics & Bioinformatics, Duke University, 2424 Erwin Road, Durham, North Carolina 27710, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Biostatistics & Bioinformatics, Duke University, 2424 Erwin Road, Durham, North Carolina 27710, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anru R","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, Wisconsin 53706, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"2022052609543739900_B1","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1093\/biomet\/71.2.323","article-title":"Log contrast models for experiments with mixtures","volume":"71","author":"Aitchison,","year":"1984","journal-title":"Biometrika"},{"key":"2022052609543739900_B2","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1186\/1476-511X-10-116","article-title":"Antiobesity and lipid-lowering effects of Bifidobacterium spp. in high fat diet-induced obese rats","volume":"10","author":"An,","year":"2011","journal-title":"Lipids Health Dis."},{"key":"2022052609543739900_B3","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1111\/rssb.12196","article-title":"Linear and conic programming estimators in high dimensional errors-in-variables models","volume":"79","author":"Belloni,","year":"2017","journal-title":"J. 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