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Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R package is available at https:\/\/github.com\/Zhangxf-ccnu\/scTSSR.<\/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\/btaa108","type":"journal-article","created":{"date-parts":[[2020,2,12]],"date-time":"2020-02-12T20:16:18Z","timestamp":1581538578000},"page":"3131-3138","source":"Crossref","is-referenced-by-count":29,"title":["scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation"],"prefix":"10.1093","volume":"36","author":[{"given":"Ke","family":"Jin","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics , Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le","family":"Ou-Yang","sequence":"additional","affiliation":[{"name":"College of Information Engineering , Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4531-3970","authenticated-orcid":false,"given":"Xing-Ming","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence , Fudan University, Shanghai 200433, China"},{"name":"Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) , Ministry of Education, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering , City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5052-9725","authenticated-orcid":false,"given":"Xiao-Fei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics , Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan 430079, China"},{"name":"Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) , Ministry of Education, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,2,19]]},"reference":[{"key":"2023013112031504300_btaa108-B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1837-6","article-title":"Deepimpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data","volume":"20","author":"Arisdakessian","year":"2019","journal-title":"Genome Biol"},{"key":"2023013112031504300_btaa108-B2","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","article-title":"A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure","volume":"3","author":"Baron","year":"2016","journal-title":"Cell Systems"},{"key":"2023013112031504300_btaa108-B3","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat. 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