Using single cell atlas data to reconstruct regulatory networks
Abstract Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)–gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is...
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| Veröffentlicht in: | Nucleic acids research 2023-04, Vol.51 (7), p.e38-e38 |
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| container_title | Nucleic acids research |
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| creator | Song, Qi Ruffalo, Matthew Bar-Joseph, Ziv |
| description | Abstract
Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)–gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task. |
| doi_str_mv | 10.1093/nar/gkad053 |
| format | Article |
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Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)–gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.</description><identifier>ISSN: 0305-1048</identifier><identifier>EISSN: 1362-4962</identifier><identifier>DOI: 10.1093/nar/gkad053</identifier><identifier>PMID: 36762475</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Algorithms ; Atlases as Topic ; Computational Biology ; Gene Regulatory Networks ; Methods Online ; Single-Cell Analysis ; Systems Biology</subject><ispartof>Nucleic acids research, 2023-04, Vol.51 (7), p.e38-e38</ispartof><rights>The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. 2023</rights><rights>The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c413t-a054e93c478e9cca364b33c375e61829d2047e18b51fc273a0e6075dce71851e3</citedby><cites>FETCH-LOGICAL-c413t-a054e93c478e9cca364b33c375e61829d2047e18b51fc273a0e6075dce71851e3</cites><orcidid>0000-0003-2222-6169 ; 0000-0003-3430-6051 ; 0000-0001-5420-4031</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/PMC10123116/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10123116/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,1598,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36762475$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Song, Qi</creatorcontrib><creatorcontrib>Ruffalo, Matthew</creatorcontrib><creatorcontrib>Bar-Joseph, Ziv</creatorcontrib><title>Using single cell atlas data to reconstruct regulatory networks</title><title>Nucleic acids research</title><addtitle>Nucleic Acids Res</addtitle><description>Abstract
Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)–gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.</description><subject>Algorithms</subject><subject>Atlases as Topic</subject><subject>Computational Biology</subject><subject>Gene Regulatory Networks</subject><subject>Methods Online</subject><subject>Single-Cell Analysis</subject><subject>Systems Biology</subject><issn>0305-1048</issn><issn>1362-4962</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNp9kE1LAzEQhoMotlZP3mVPIsjaTD53T0WKX1DwYs8hzaZ17XZTk6zSf29Kq-jFy8ww8_DOzIvQOeAbwCUdttoPF0tdYU4PUB-oIDkrBTlEfUwxzwGzoodOQnjDGBhwdox6VEhBmOR9NJqGul1k29DYzNimyXRsdMgqHXUWXeatcW2IvjMx1Yuu0dH5Tdba-On8Mpyio7lugj3b5wGa3t-9jB_zyfPD0_h2khsGNOYac2ZLapgsbGmMpoLNKDVUciugIGVFMJMWihmHuSGSamwFlrwyVkLBwdIBGu10191sZVO_jV43au3rlfYb5XSt_k7a-lUt3IcCDIQCiKRwtVfw7r2zIapVHbYP69a6LigiJU-3AEBCr3eo8S4Eb-c_ewCrrecqea72nif64vdpP-y3yQm43AGuW_-r9AX4RYu5</recordid><startdate>20230424</startdate><enddate>20230424</enddate><creator>Song, Qi</creator><creator>Ruffalo, Matthew</creator><creator>Bar-Joseph, Ziv</creator><general>Oxford University Press</general><scope>TOX</scope><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/0000-0003-2222-6169</orcidid><orcidid>https://orcid.org/0000-0003-3430-6051</orcidid><orcidid>https://orcid.org/0000-0001-5420-4031</orcidid></search><sort><creationdate>20230424</creationdate><title>Using single cell atlas data to reconstruct regulatory networks</title><author>Song, Qi ; Ruffalo, Matthew ; Bar-Joseph, Ziv</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c413t-a054e93c478e9cca364b33c375e61829d2047e18b51fc273a0e6075dce71851e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Atlases as Topic</topic><topic>Computational Biology</topic><topic>Gene Regulatory Networks</topic><topic>Methods Online</topic><topic>Single-Cell Analysis</topic><topic>Systems Biology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Song, Qi</creatorcontrib><creatorcontrib>Ruffalo, Matthew</creatorcontrib><creatorcontrib>Bar-Joseph, Ziv</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><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>Nucleic acids research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Song, Qi</au><au>Ruffalo, Matthew</au><au>Bar-Joseph, Ziv</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using single cell atlas data to reconstruct regulatory networks</atitle><jtitle>Nucleic acids research</jtitle><addtitle>Nucleic Acids Res</addtitle><date>2023-04-24</date><risdate>2023</risdate><volume>51</volume><issue>7</issue><spage>e38</spage><epage>e38</epage><pages>e38-e38</pages><issn>0305-1048</issn><eissn>1362-4962</eissn><abstract>Abstract
Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)–gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>36762475</pmid><doi>10.1093/nar/gkad053</doi><orcidid>https://orcid.org/0000-0003-2222-6169</orcidid><orcidid>https://orcid.org/0000-0003-3430-6051</orcidid><orcidid>https://orcid.org/0000-0001-5420-4031</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Oxford Journals Open Access Collection; MEDLINE; DOAJ Directory of Open Access Journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Algorithms Atlases as Topic Computational Biology Gene Regulatory Networks Methods Online Single-Cell Analysis Systems Biology |
| title | Using single cell atlas data to reconstruct regulatory networks |
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