NeuCA web server: a neural network-based cell annotation tool with web-app and GUI
Abstract Summary Correctly annotating individual cell’s type is an important initial step in single-cell RNA sequencing (scRNA-seq) data analysis. Here, we present NeuCA web server, a neural network-based scRNA-seq cell annotation tool with web-app portal and graphical user interface, for automatica...
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| Veröffentlicht in: | Bioinformatics 2022-04, Vol.38 (8), p.2361-2363 |
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| container_title | Bioinformatics |
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| creator | Duan, Daoyu He, Sijia Huang, Emina Li, Ziyi Feng, Hao |
| description | Abstract
Summary
Correctly annotating individual cell’s type is an important initial step in single-cell RNA sequencing (scRNA-seq) data analysis. Here, we present NeuCA web server, a neural network-based scRNA-seq cell annotation tool with web-app portal and graphical user interface, for automatically assigning cell labels. NeuCA algorithm is accurate and exhaustive, maximizing the usage of measured cells for downstream analysis. NeuCA web server provides over 20 ready-to-use pre-trained classifiers for commonly used tissue types. As the first web-app tool with neural-network infrastructure implemented, NeuCA web will facilitate the research community in analyzing and annotating scRNA-seq data.
Availability and implementation
NeuCA web server is implemented with R Shiny application online at https://statbioinfo.shinyapps.io/NeuCA/.
Supplementary information
Supplementary data are available at Bioinformatics online. |
| doi_str_mv | 10.1093/bioinformatics/btac108 |
| format | Article |
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Summary
Correctly annotating individual cell’s type is an important initial step in single-cell RNA sequencing (scRNA-seq) data analysis. Here, we present NeuCA web server, a neural network-based scRNA-seq cell annotation tool with web-app portal and graphical user interface, for automatically assigning cell labels. NeuCA algorithm is accurate and exhaustive, maximizing the usage of measured cells for downstream analysis. NeuCA web server provides over 20 ready-to-use pre-trained classifiers for commonly used tissue types. As the first web-app tool with neural-network infrastructure implemented, NeuCA web will facilitate the research community in analyzing and annotating scRNA-seq data.
Availability and implementation
NeuCA web server is implemented with R Shiny application online at https://statbioinfo.shinyapps.io/NeuCA/.
Supplementary information
Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btac108</identifier><identifier>PMID: 35176143</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Algorithms ; Applications Note ; Computers ; Mobile Applications ; Neural Networks, Computer ; Software</subject><ispartof>Bioinformatics, 2022-04, Vol.38 (8), p.2361-2363</ispartof><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2022</rights><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c355t-331d21132ef9017107f2a015337fd2426f59592c7428a82fe83e8e097e9771753</cites><orcidid>0000-0002-3147-2006 ; 0000-0001-8359-0533 ; 0000-0003-2243-9949</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9004646/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9004646/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,1598,27901,27902,53766,53768</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/bioinformatics/btac108$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35176143$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Duan, Daoyu</creatorcontrib><creatorcontrib>He, Sijia</creatorcontrib><creatorcontrib>Huang, Emina</creatorcontrib><creatorcontrib>Li, Ziyi</creatorcontrib><creatorcontrib>Feng, Hao</creatorcontrib><title>NeuCA web server: a neural network-based cell annotation tool with web-app and GUI</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>Abstract
Summary
Correctly annotating individual cell’s type is an important initial step in single-cell RNA sequencing (scRNA-seq) data analysis. Here, we present NeuCA web server, a neural network-based scRNA-seq cell annotation tool with web-app portal and graphical user interface, for automatically assigning cell labels. NeuCA algorithm is accurate and exhaustive, maximizing the usage of measured cells for downstream analysis. NeuCA web server provides over 20 ready-to-use pre-trained classifiers for commonly used tissue types. As the first web-app tool with neural-network infrastructure implemented, NeuCA web will facilitate the research community in analyzing and annotating scRNA-seq data.
Availability and implementation
NeuCA web server is implemented with R Shiny application online at https://statbioinfo.shinyapps.io/NeuCA/.
Supplementary information
Supplementary data are available at Bioinformatics online.</description><subject>Algorithms</subject><subject>Applications Note</subject><subject>Computers</subject><subject>Mobile Applications</subject><subject>Neural Networks, Computer</subject><subject>Software</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNUctu2zAQJIoUjeP2FwIee1HD5UOUeihgGHkYMFqgaM4EJa0SpbKokJSN_H1p2AniW0-7wM7Mzu4QcgnsG7BSXFWd64bW-Y2NXR2uqmhrYMUHMgOZs4wzVZ6lXuQ6kwUT5-QihCfGFEgpP5FzoUDnIMWM_P6J03JBd1jRgH6L_ju1dMDJ2z6VuHP-b1bZgA2tse-pHQYX00o30OhcT3ddfNyTMzuOadjQ2_vVZ_KxtX3AL8c6J_c313-Wd9n61-1quVhntVAqZkJAwwEEx7ZkoIHpllsGSgjdNlzyvFWlKnmtJS9swVssBBbISo2l1qCVmJMfB91xqjbY1DjE5NqMvttY_2Kc7czpZOgezYPbmpIxmcs8CXw9Cnj3PGGIZtOF_Zl2QDcFw3ORXg3JRoLmB2jtXQge27c1wMw-EHMaiDkGkoiX702-0V4TSAA4ANw0_q_oP5pKnbk</recordid><startdate>20220412</startdate><enddate>20220412</enddate><creator>Duan, Daoyu</creator><creator>He, Sijia</creator><creator>Huang, Emina</creator><creator>Li, Ziyi</creator><creator>Feng, Hao</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3147-2006</orcidid><orcidid>https://orcid.org/0000-0001-8359-0533</orcidid><orcidid>https://orcid.org/0000-0003-2243-9949</orcidid></search><sort><creationdate>20220412</creationdate><title>NeuCA web server: a neural network-based cell annotation tool with web-app and GUI</title><author>Duan, Daoyu ; He, Sijia ; Huang, Emina ; Li, Ziyi ; Feng, Hao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c355t-331d21132ef9017107f2a015337fd2426f59592c7428a82fe83e8e097e9771753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Applications Note</topic><topic>Computers</topic><topic>Mobile Applications</topic><topic>Neural Networks, Computer</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Duan, Daoyu</creatorcontrib><creatorcontrib>He, Sijia</creatorcontrib><creatorcontrib>Huang, Emina</creatorcontrib><creatorcontrib>Li, Ziyi</creatorcontrib><creatorcontrib>Feng, Hao</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Duan, Daoyu</au><au>He, Sijia</au><au>Huang, Emina</au><au>Li, Ziyi</au><au>Feng, Hao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>NeuCA web server: a neural network-based cell annotation tool with web-app and GUI</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2022-04-12</date><risdate>2022</risdate><volume>38</volume><issue>8</issue><spage>2361</spage><epage>2363</epage><pages>2361-2363</pages><issn>1367-4803</issn><eissn>1460-2059</eissn><eissn>1367-4811</eissn><abstract>Abstract
Summary
Correctly annotating individual cell’s type is an important initial step in single-cell RNA sequencing (scRNA-seq) data analysis. Here, we present NeuCA web server, a neural network-based scRNA-seq cell annotation tool with web-app portal and graphical user interface, for automatically assigning cell labels. NeuCA algorithm is accurate and exhaustive, maximizing the usage of measured cells for downstream analysis. NeuCA web server provides over 20 ready-to-use pre-trained classifiers for commonly used tissue types. As the first web-app tool with neural-network infrastructure implemented, NeuCA web will facilitate the research community in analyzing and annotating scRNA-seq data.
Availability and implementation
NeuCA web server is implemented with R Shiny application online at https://statbioinfo.shinyapps.io/NeuCA/.
Supplementary information
Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>35176143</pmid><doi>10.1093/bioinformatics/btac108</doi><tpages>3</tpages><orcidid>https://orcid.org/0000-0002-3147-2006</orcidid><orcidid>https://orcid.org/0000-0001-8359-0533</orcidid><orcidid>https://orcid.org/0000-0003-2243-9949</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Bioinformatics, 2022-04, Vol.38 (8), p.2361-2363 |
| issn | 1367-4803 1460-2059 1367-4811 |
| language | eng |
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| source | Oxford Journals Open Access Collection |
| subjects | Algorithms Applications Note Computers Mobile Applications Neural Networks, Computer Software |
| title | NeuCA web server: a neural network-based cell annotation tool with web-app and GUI |
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