{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T11:48:58Z","timestamp":1784029738283,"version":"3.55.0"},"reference-count":23,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R00HG007368"],"award-info":[{"award-number":["R00HG007368"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010232","name":"National Institute of Development Administration","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010232","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Single\u2010cell RNA\u2010sequencing (scRNA\u2010seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA\u2010seq still has several limitations, including high rates of dropouts, which result in a large number of genes having zero read count in the scRNA\u2010seq data, and complicate downstream analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To overcome this problem, we treat zeros as missing values and develop nonparametric deep learning methods for imputation. Specifically, our LATE (Learning with AuToEncoder) method trains an autoencoder with random initial values of the parameters, whereas our TRANSLATE (TRANSfer learning with LATE) method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On both simulated and real data, LATE and TRANSLATE outperform existing scRNA\u2010seq imputation methods, achieving lower mean squared error in most cases, recovering nonlinear gene\u2010gene relationships, and better separating cell types. They are also highly scalable and can efficiently process over 1\u00a0million cells in just a few hours on a GPU.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s40484-019-0192-7","type":"journal-article","created":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T20:02:48Z","timestamp":1579809768000},"page":"78-94","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Imputation of single\u2010cell gene expression with an autoencoder neural network"],"prefix":"10.1002","volume":"8","author":[{"given":"Md. Bahadur","family":"Badsha","sequence":"first","affiliation":[{"name":"<!--1--> Department of Statistical Science Institute for Bioinformatics and Evolutionary Studies Institute for Modeling Collaboration &amp; Innovation University of Idaho Moscow ID 83844 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[{"name":"<!--1--> Department of Statistical Science Institute for Bioinformatics and Evolutionary Studies Institute for Modeling Collaboration &amp; Innovation University of Idaho Moscow ID 83844 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"<!--2--> Department of Biology Stanford University Stanford CA 94305 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang I.","family":"Li","sequence":"additional","affiliation":[{"name":"<!--3--> Section of Genetic Medicine University of Chicago Chicago IL 60637 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Xian","sequence":"additional","affiliation":[{"name":"<!--4--> Department of Computer Science University of Idaho Idaho Falls ID 83401 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas E.","family":"Banovich","sequence":"additional","affiliation":[{"name":"<!--5--> The Translational Genomics Research Institute Phoenix AZ 85004 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Audrey","family":"Qiuyan Fu","sequence":"additional","affiliation":[{"name":"<!--1--> Department of Statistical Science Institute for Bioinformatics and Evolutionary Studies Institute for Modeling Collaboration &amp; Innovation University of Idaho Moscow ID 83844 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,3]]},"reference":[{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.molcel.2015.04.005"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.molcel.2017.01.023"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010018\u201003405\u20107"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592\u2010018\u20100033\u2010z"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.05.061"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010018\u201007931\u20102"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592\u2010018\u20100229\u20102"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13059\u2010016\u20100927\u2010y"},{"key":"e_1_2_12_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3833"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"e_1_2_12_12_2","unstructured":"Bengio Y.(2012)Deep learning of representations for unsupervised and transfer learning. 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Bahadur Badsha, Rui Li, Boxiang Liu, Yang I. Li, Min Xian, Nicholas E. Banovich and Audrey Qiuyan Fu declare that they have no conflicts of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethics Guidelines"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethics Guidelines"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}