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We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R package and Shiny application are available through Github at https:\/\/github.com\/Zhangxf-ccnu\/EnImpute.<\/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\/btz435","type":"journal-article","created":{"date-parts":[[2019,5,21]],"date-time":"2019-05-21T11:18:00Z","timestamp":1558437480000},"page":"4827-4829","source":"Crossref","is-referenced-by-count":33,"title":["EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5052-9725","authenticated-orcid":false,"given":"Xiao-Fei","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Statistics, School of Mathematics and Statistics, Central China Normal University , Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Ou-Yang","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University , Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine, Wuhan Number 1 Hospital , Wuhan 430022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing-Ming","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University , Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computing and Informatics, Drexel University , Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, City University of Hong Kong , Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,5,24]]},"reference":[{"key":"2023013108365654600_btz435-B1","author":"Chang","year":"2017"},{"key":"2023013108365654600_btz435-B2","author":"Chen","year":"2018"},{"key":"2023013108365654600_btz435-B3","doi-asserted-by":"crossref","first-page":"390.","DOI":"10.1038\/s41467-018-07931-2","article-title":"Single-cell RNA-seq denoising using a deep count autoencoder","volume":"10","author":"Eraslan","year":"2019","journal-title":"Nat. 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