BBKNN: fast batch alignment of single cell transcriptomes
Abstract Motivation Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are comput...
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| Veröffentlicht in: | Bioinformatics 2020-02, Vol.36 (3), p.964-965 |
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| container_title | Bioinformatics |
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| creator | Polański, Krzysztof Young, Matthew D Miao, Zhichao Meyer, Kerstin B Teichmann, Sarah A Park, Jong-Eun |
| description | Abstract
Motivation
Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods.
Availability and implementation
BBKNN is available at https://github.com/Teichlab/bbknn, along with documentation and multiple example notebooks, and can be installed from pip.
Supplementary information
Supplementary data are available at Bioinformatics online. |
| doi_str_mv | 10.1093/bioinformatics/btz625 |
| format | Article |
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Motivation
Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods.
Availability and implementation
BBKNN is available at https://github.com/Teichlab/bbknn, along with documentation and multiple example notebooks, and can be installed from pip.
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/btz625</identifier><identifier>PMID: 31400197</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Algorithms ; Animals ; Applications Note ; Exome Sequencing ; Mice ; RNA-Seq ; Transcriptome</subject><ispartof>Bioinformatics, 2020-02, Vol.36 (3), p.964-965</ispartof><rights>The Author(s) 2019. Published by Oxford University Press. 2019</rights><rights>The Author(s) 2019. Published by Oxford University Press.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c518t-4fa1afbf3325e21a50b308623c3672a877f4d159b01601e90ab2fc38621dbee73</citedby><cites>FETCH-LOGICAL-c518t-4fa1afbf3325e21a50b308623c3672a877f4d159b01601e90ab2fc38621dbee73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883685/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883685/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,1598,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31400197$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Berger, Bonnie</contributor><creatorcontrib>Polański, Krzysztof</creatorcontrib><creatorcontrib>Young, Matthew D</creatorcontrib><creatorcontrib>Miao, Zhichao</creatorcontrib><creatorcontrib>Meyer, Kerstin B</creatorcontrib><creatorcontrib>Teichmann, Sarah A</creatorcontrib><creatorcontrib>Park, Jong-Eun</creatorcontrib><title>BBKNN: fast batch alignment of single cell transcriptomes</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>Abstract
Motivation
Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods.
Availability and implementation
BBKNN is available at https://github.com/Teichlab/bbknn, along with documentation and multiple example notebooks, and can be installed from pip.
Supplementary information
Supplementary data are available at Bioinformatics online.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Applications Note</subject><subject>Exome Sequencing</subject><subject>Mice</subject><subject>RNA-Seq</subject><subject>Transcriptome</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNqNkEtPAyEYRYnR2Fr9CZpZuhnLcx4uTGzjKzZ1o2sCFFrMzFCBmuivl6a1sTtXkHC-y_0OAOcIXiFYk6G0znbG-VZEq8JQxu8CswPQR7SAOYasPkx3UpQ5rSDpgZMQ3iFkiFJ6DHoEUQhRXfZBPRo9T6fXmREhZlJEtchEY-ddq7uYOZMF280bnSndNFn0ogvK22V0rQ6n4MiIJuiz7TkAb_d3r-PHfPLy8DS-neSKoSrm1AgkjDSEYKYxEgxKAqsCE5XKYVGVpaEzxGoJUQGRrqGQ2CiSCDSTWpdkAG42ucuVbPVMpWJeNHzpbSv8F3fC8v2Xzi743H3yuqpIUbEUcLkN8O5jpUPkrQ3rhUSn3SpwjEtUJSM1TSjboMq7ELw2u28Q5GvtfF8732hPcxd_O-6mfj0nAG4At1r-M_MHGueVnQ</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Polański, Krzysztof</creator><creator>Young, Matthew D</creator><creator>Miao, Zhichao</creator><creator>Meyer, Kerstin B</creator><creator>Teichmann, Sarah A</creator><creator>Park, Jong-Eun</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></search><sort><creationdate>20200201</creationdate><title>BBKNN: fast batch alignment of single cell transcriptomes</title><author>Polański, Krzysztof ; Young, Matthew D ; Miao, Zhichao ; Meyer, Kerstin B ; Teichmann, Sarah A ; Park, Jong-Eun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c518t-4fa1afbf3325e21a50b308623c3672a877f4d159b01601e90ab2fc38621dbee73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Applications Note</topic><topic>Exome Sequencing</topic><topic>Mice</topic><topic>RNA-Seq</topic><topic>Transcriptome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Polański, Krzysztof</creatorcontrib><creatorcontrib>Young, Matthew D</creatorcontrib><creatorcontrib>Miao, Zhichao</creatorcontrib><creatorcontrib>Meyer, Kerstin B</creatorcontrib><creatorcontrib>Teichmann, Sarah A</creatorcontrib><creatorcontrib>Park, Jong-Eun</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>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Polański, Krzysztof</au><au>Young, Matthew D</au><au>Miao, Zhichao</au><au>Meyer, Kerstin B</au><au>Teichmann, Sarah A</au><au>Park, Jong-Eun</au><au>Berger, Bonnie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>BBKNN: fast batch alignment of single cell transcriptomes</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2020-02-01</date><risdate>2020</risdate><volume>36</volume><issue>3</issue><spage>964</spage><epage>965</epage><pages>964-965</pages><issn>1367-4803</issn><eissn>1460-2059</eissn><eissn>1367-4811</eissn><abstract>Abstract
Motivation
Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods.
Availability and implementation
BBKNN is available at https://github.com/Teichlab/bbknn, along with documentation and multiple example notebooks, and can be installed from pip.
Supplementary information
Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>31400197</pmid><doi>10.1093/bioinformatics/btz625</doi><tpages>2</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 1367-4803 |
| ispartof | Bioinformatics, 2020-02, Vol.36 (3), p.964-965 |
| issn | 1367-4803 1460-2059 1367-4811 |
| language | eng |
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| source | Oxford Journals Open Access Collection; MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Alma/SFX Local Collection |
| subjects | Algorithms Animals Applications Note Exome Sequencing Mice RNA-Seq Transcriptome |
| title | BBKNN: fast batch alignment of single cell transcriptomes |
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