Uncovering cell identity through differential stability with Cepo

Kim, HJ; Wang, K; Chen, C; Lin, Y; Tam, PPL; Lin, DM; Yang, JYH; Yang, P

Uncovering cell identity through differential stability with Cepo

Kim, HJ Wang, K Chen, C Lin, Y Tam, PPL Lin, DM Yang, JYH Yang, P

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Publisher:: Nature Research
Publication Type:: Journal Article
Citation:: Nature Computational Science, 2021, 1, (12), pp. 784-790
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Kim, HJ
dc.contributor.author	Wang, K
dc.contributor.author	Chen, C
dc.contributor.author	Lin, Y
dc.contributor.author	Tam, PPL
dc.contributor.author	Lin, DM
dc.contributor.author	Yang, JYH
dc.contributor.author	Yang, P
dc.date.accessioned	2022-05-27T01:53:02Z
dc.date.available	2022-05-27T01:53:02Z
dc.date.issued	2021-12-01
dc.identifier.citation	Nature Computational Science, 2021, 1, (12), pp. 784-790
dc.identifier.issn	2662-8457
dc.identifier.issn	2662-8457
dc.identifier.uri	http://hdl.handle.net/10453/157739
dc.description.abstract	The use of single-cell RNA-sequencing (scRNA-seq) allows observation of different cells at multi-tiered complexity in the same microenvironment. To get insights into cell identity using scRNA-seq data, we present Cepo, which generates cell-type-specific gene statistics of differentially stable genes from scRNA-seq data to define cell identity. When applied to multiple datasets, Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells and lineage inference of single cells.
dc.language	en
dc.publisher	Nature Research
dc.relation.ispartof	Nature Computational Science
dc.relation.isbasedon	10.1038/s43588-021-00172-2
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Uncovering cell identity through differential stability with Cepo
dc.type	Journal Article
utslib.citation.volume	1
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-05-27T01:52:56Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	1
utslib.citation.issue	12

Abstract:

The use of single-cell RNA-sequencing (scRNA-seq) allows observation of different cells at multi-tiered complexity in the same microenvironment. To get insights into cell identity using scRNA-seq data, we present Cepo, which generates cell-type-specific gene statistics of differentially stable genes from scRNA-seq data to define cell identity. When applied to multiple datasets, Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells and lineage inference of single cells.

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http://hdl.handle.net/10453/157739