{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T06:05:03Z","timestamp":1790834703736,"version":"4.1.0"},"reference-count":167,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T00:00:00Z","timestamp":1640044800000},"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\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1UL1RR025767"],"award-info":[{"award-number":["1UL1RR025767"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM113245"],"award-info":[{"award-number":["R01GM113245"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NCI Cancer Center Shared Resources","award":["P30CA54174"],"award-info":[{"award-number":["P30CA54174"]}]},{"name":"NCI Cancer Center Shared Resources","award":["K99CA248944"],"award-info":[{"award-number":["K99CA248944"]}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2051113"],"award-info":[{"award-number":["2051113"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004917","name":"Cancer Prevention and Research Institute of Texas","doi-asserted-by":"publisher","award":["RP160732"],"award-info":[{"award-number":["RP160732"]}],"id":[{"id":"10.13039\/100004917","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004917","name":"Cancer Prevention and Research Institute of Texas","doi-asserted-by":"publisher","award":["RP190346"],"award-info":[{"award-number":["RP190346"]}],"id":[{"id":"10.13039\/100004917","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018154","name":"Fund for Innovation in Cancer Informatics","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018154","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioned as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here, we survey a total of 25 DL algorithms and their applicability for a specific step in the single cell RNA-seq processing pipeline. Specifically, we establish a unified mathematical representation of variational autoencoder, autoencoder, generative adversarial network and supervised DL models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such a presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative uses of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.<\/jats:p>","DOI":"10.1093\/bib\/bbab531","type":"journal-article","created":{"date-parts":[[2021,11,20]],"date-time":"2021-11-20T15:09:30Z","timestamp":1637420970000},"source":"Crossref","is-referenced-by-count":67,"title":["Deep learning tackles single-cell analysis\u2014a survey of deep learning for scRNA-seq analysis"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1255-3983","authenticated-orcid":false,"given":"Mario","family":"Flores","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhentao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tinghe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Musaddaqui","family":"Hasib","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1647-8634","authenticated-orcid":false,"given":"Yu-Chiao","family":"Chiu","sequence":"additional","affiliation":[{"name":"Greehey Children\u2019s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenqing","family":"Ye","sequence":"additional","affiliation":[{"name":"Greehey Children\u2019s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA"},{"name":"Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karla","family":"Paniagua","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumin","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqiu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shou-Jiang","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, PA 15232, USA"},{"name":"UPMC Hillman Cancer Center, University of Pittsburgh, PA 15232, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Fang","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yidong","family":"Chen","sequence":"additional","affiliation":[{"name":"Greehey Children\u2019s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA"},{"name":"Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Medicine, School of Medicine, University of Pittsburgh, PA 15232, USA"},{"name":"UPMC Hillman Cancer Center, University of Pittsburgh, PA 15232, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,12,21]]},"reference":[{"issue":"1","key":"2022012000325954400_ref1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13059-020-1926-6","article-title":"Eleven grand challenges in single-cell data science","volume":"21","author":"Lahnemann","year":"2020","journal-title":"Genome Biol"},{"issue":"3","key":"2022012000325954400_ref2","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1038\/nmeth.4154","article-title":"Sequencing thousands of single-cell genomes with combinatorial indexing","volume":"14","author":"Vitak","year":"2017","journal-title":"Nat Methods"},{"issue":"15","key":"2022012000325954400_ref3","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.2174\/1568026617666161116145358","article-title":"Single-cell sequencing for drug discovery and drug development","volume":"17","author":"Wu","year":"2017","journal-title":"Curr Top Med 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