{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T14:05:50Z","timestamp":1777644350144,"version":"3.51.4"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T00:00:00Z","timestamp":1588550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000199","name":"United States Department of Agriculture","doi-asserted-by":"publisher","award":["ARZT-1360830-H22-138"],"award-info":[{"award-number":["ARZT-1360830-H22-138"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"United States Department of Agriculture","doi-asserted-by":"publisher","award":["ARZT-1361620-H22-149"],"award-info":[{"award-number":["ARZT-1361620-H22-149"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Single-cell RNA-sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of \u2018drop-out\u2019 events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this article, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification and differential expression detection in scRNA-seq data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>R code is available at https:\/\/github.com\/anlingUA\/scDoc.<\/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\/btaa283","type":"journal-article","created":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T07:09:22Z","timestamp":1587712162000},"page":"4233-4239","source":"Crossref","is-referenced-by-count":25,"title":["scDoc: correcting drop-out events in single-cell RNA-seq data"],"prefix":"10.1093","volume":"36","author":[{"given":"Di","family":"Ran","sequence":"first","affiliation":[{"name":"Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanshan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Graduate Interdisciplinary Program in Statistics and Data Science"},{"name":"School of Plant Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas","family":"Lytal","sequence":"additional","affiliation":[{"name":"Graduate Interdisciplinary Program in Statistics and Data Science"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingling","family":"An","sequence":"additional","affiliation":[{"name":"Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health"},{"name":"Graduate Interdisciplinary Program in Statistics and Data Science"},{"name":"Department of Biosystems Engineering, University of Arizona , Tucson, AZ 85721, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,5,4]]},"reference":[{"key":"2023062312040784300_btaa283-B1","doi-asserted-by":"crossref","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol"},{"key":"2023062312040784300_btaa283-B2","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.12688\/f1000research.16613.1","article-title":"False signals induced by single-cell imputation","volume":"7","author":"Andrews","year":"2018","journal-title":"F1000Research"},{"key":"2023062312040784300_btaa283-B3","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.mam.2017.07.002","article-title":"Identifying cell populations with scRNASeq","volume":"59","author":"Andrews","year":"2018","journal-title":"Mol. 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