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Article: PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells

TitlePARC: ultrafast and accurate clustering of phenotypic data of millions of single cells
Authors
Issue Date2020
PublisherOxford University Press (OUP): Policy B - Oxford Open Option B. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2020, v. 36, p. 2778-2786 How to Cite?
AbstractMOTIVATION: New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity. RESULTS: We introduce a highly scalable graph-based clustering algorithm PARC - Phenotyping by Accelerated Refined Community-partitioning - for large-scale, high-dimensional single-cell data (> 1 million cells). Using large single cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without sub-sampling of cells, including Phenograph, FlowSOM, and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single cell data set of 1.1M cells within 13 minutes, compared to > 2 hours for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis. AVAILABILITY: https://github.com/ShobiStassen/PARC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.
Persistent Identifierhttp://hdl.handle.net/10722/281997
ISSN
2020 Impact Factor: 6.937
2015 SCImago Journal Rankings: 4.643
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorStassen, SV-
dc.contributor.authorSIU, DMD-
dc.contributor.authorLEE, KCM-
dc.contributor.authorHo, JWK-
dc.contributor.authorSo, HKH-
dc.contributor.authorTsia, KK-
dc.date.accessioned2020-04-19T03:33:55Z-
dc.date.available2020-04-19T03:33:55Z-
dc.date.issued2020-
dc.identifier.citationBioinformatics, 2020, v. 36, p. 2778-2786-
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10722/281997-
dc.description.abstractMOTIVATION: New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity. RESULTS: We introduce a highly scalable graph-based clustering algorithm PARC - Phenotyping by Accelerated Refined Community-partitioning - for large-scale, high-dimensional single-cell data (> 1 million cells). Using large single cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without sub-sampling of cells, including Phenograph, FlowSOM, and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single cell data set of 1.1M cells within 13 minutes, compared to > 2 hours for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis. AVAILABILITY: https://github.com/ShobiStassen/PARC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.-
dc.languageeng-
dc.publisherOxford University Press (OUP): Policy B - Oxford Open Option B. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/-
dc.relation.ispartofBioinformatics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titlePARC: ultrafast and accurate clustering of phenotypic data of millions of single cells-
dc.typeArticle-
dc.identifier.emailHo, JWK: jwkho@hku.hk-
dc.identifier.emailSo, HKH: hso@eee.hku.hk-
dc.identifier.emailTsia, KK: tsia@hku.hk-
dc.identifier.authorityHo, JWK=rp02436-
dc.identifier.authoritySo, HKH=rp00169-
dc.identifier.authorityTsia, KK=rp01389-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1093/bioinformatics/btaa042-
dc.identifier.pmid31971583-
dc.identifier.scopuseid_2-s2.0-85084379617-
dc.identifier.hkuros309737-
dc.identifier.hkuros314128-
dc.identifier.volume36-
dc.identifier.spage2778-
dc.identifier.epage2786-
dc.identifier.isiWOS:000537450900018-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl1367-4803-

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