{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:56Z","timestamp":1772138036531,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T00:00:00Z","timestamp":1588118400000},"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\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007431","name":"NRF","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007431","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korea Government"},{"DOI":"10.13039\/501100014188","name":"MSIT","doi-asserted-by":"publisher","award":["NRF-2019R1G1A1004803"],"award-info":[{"award-number":["NRF-2019R1G1A1004803"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code for the proposed method is freely available at https:\/\/github.com\/hyundoo\/PRIME.<\/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\/btaa278","type":"journal-article","created":{"date-parts":[[2020,4,22]],"date-time":"2020-04-22T15:11:22Z","timestamp":1587568282000},"page":"4021-4029","source":"Crossref","is-referenced-by-count":20,"title":["PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing"],"prefix":"10.1093","volume":"36","author":[{"given":"Hyundoo","family":"Jeong","sequence":"first","affiliation":[{"name":"Department of Mechatronics Engineering , Incheon National University, Incheon, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7608-0831","authenticated-orcid":false,"given":"Zhandong","family":"Liu","sequence":"additional","affiliation":[{"name":"Jan and Dan Duncan Neurological Research Institute , Texas Children\u2019s Hospital"},{"name":"Department of Pediatrics , Baylor College of Medicine, Houston, TX 77030, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,4,29]]},"reference":[{"key":"2023062312040644800_btaa278-B1","first-page":"475","author":"Andersen","year":"2006"},{"key":"2023062312040644800_btaa278-B2","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J. 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