{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:25Z","timestamp":1740185125746,"version":"3.37.3"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2020,2,28]],"date-time":"2020-02-28T00:00:00Z","timestamp":1582848000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>We developed 2DImpute, an imputation method for correcting false zeros (known as dropouts) in single-cell RNA-sequencing (scRNA-seq) data. It features preventing excessive correction by predicting the false zeros and imputing their values by making use of the interrelationships between both genes and cells in the expression matrix. We showed that 2DImpute outperforms several leading imputation methods by applying it on datasets from various scRNA-seq protocols.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R package of 2DImpute is freely available at GitHub (https:\/\/github.com\/zky0708\/2DImpute).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Contact<\/jats:title>\n                  <jats:p>d.anastassiou@columbia.edu<\/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\/btaa148","type":"journal-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T20:16:28Z","timestamp":1582661788000},"page":"3588-3589","source":"Crossref","is-referenced-by-count":12,"title":["2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0111-0848","authenticated-orcid":false,"given":"Kaiyi","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Systems Biology , Columbia University, New York, NY 10032, USA"},{"name":"Department of Electrical Engineering , New York, NY 10027, USA"}]},{"given":"Dimitris","family":"Anastassiou","sequence":"additional","affiliation":[{"name":"Department of Systems Biology , Columbia University, New York, NY 10032, USA"},{"name":"Department of Electrical Engineering , New York, NY 10027, USA"},{"name":"Data Science Institute , Columbia University, New York, NY 10027, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,2,28]]},"reference":[{"key":"2023062312020917100_btaa148-B1","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1016\/j.cell.2018.05.060","article-title":"Single-cell map of diverse immune phenotypes in the breast tumor microenvironment","volume":"174","author":"Azizi","year":"2018","journal-title":"Cell"},{"key":"2023062312020917100_btaa148-B2","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1186\/s13059-018-1575-1","article-title":"VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies","volume":"19","author":"Chen","year":"2018","journal-title":"Genome Biol"},{"key":"2023062312020917100_btaa148-B3","doi-asserted-by":"crossref","first-page":"e1002920","DOI":"10.1371\/journal.pcbi.1002920","article-title":"Biomolecular events in cancer revealed by attractor metagenes","volume":"9","author":"Cheng","year":"2013","journal-title":"PLoS Comput. 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