Interpretation of T cell states from single-cell transcriptomics data using reference atlases

One challenge of single cell RNA sequencing analysis is how to consistently identify cell subtypes and states across different datasets. Here the authors propose the use of a reference single-cell atlas as a stable system of coordinates to characterize T cell states across studies, diseases and spec...

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Autores principales:	Massimo Andreatta, Jesus Corria-Osorio, Sören Müller, Rafael Cubas, George Coukos, Santiago J. Carmona
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/9f2f292884da42c48b6390b29c262e36
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spelling	oai:doaj.org-article:9f2f292884da42c48b6390b29c262e362021-12-02T15:52:23ZInterpretation of T cell states from single-cell transcriptomics data using reference atlases10.1038/s41467-021-23324-42041-1723https://doaj.org/article/9f2f292884da42c48b6390b29c262e362021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23324-4https://doaj.org/toc/2041-1723One challenge of single cell RNA sequencing analysis is how to consistently identify cell subtypes and states across different datasets. Here the authors propose the use of a reference single-cell atlas as a stable system of coordinates to characterize T cell states across studies, diseases and species.Massimo AndreattaJesus Corria-OsorioSören MüllerRafael CubasGeorge CoukosSantiago J. CarmonaNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Massimo Andreatta Jesus Corria-Osorio Sören Müller Rafael Cubas George Coukos Santiago J. Carmona Interpretation of T cell states from single-cell transcriptomics data using reference atlases
description	One challenge of single cell RNA sequencing analysis is how to consistently identify cell subtypes and states across different datasets. Here the authors propose the use of a reference single-cell atlas as a stable system of coordinates to characterize T cell states across studies, diseases and species.
format	article
author	Massimo Andreatta Jesus Corria-Osorio Sören Müller Rafael Cubas George Coukos Santiago J. Carmona
author_facet	Massimo Andreatta Jesus Corria-Osorio Sören Müller Rafael Cubas George Coukos Santiago J. Carmona
author_sort	Massimo Andreatta
title	Interpretation of T cell states from single-cell transcriptomics data using reference atlases
title_short	Interpretation of T cell states from single-cell transcriptomics data using reference atlases
title_full	Interpretation of T cell states from single-cell transcriptomics data using reference atlases
title_fullStr	Interpretation of T cell states from single-cell transcriptomics data using reference atlases
title_full_unstemmed	Interpretation of T cell states from single-cell transcriptomics data using reference atlases
title_sort	interpretation of t cell states from single-cell transcriptomics data using reference atlases
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9f2f292884da42c48b6390b29c262e36
work_keys_str_mv	AT massimoandreatta interpretationoftcellstatesfromsinglecelltranscriptomicsdatausingreferenceatlases AT jesuscorriaosorio interpretationoftcellstatesfromsinglecelltranscriptomicsdatausingreferenceatlases AT sorenmuller interpretationoftcellstatesfromsinglecelltranscriptomicsdatausingreferenceatlases AT rafaelcubas interpretationoftcellstatesfromsinglecelltranscriptomicsdatausingreferenceatlases AT georgecoukos interpretationoftcellstatesfromsinglecelltranscriptomicsdatausingreferenceatlases AT santiagojcarmona interpretationoftcellstatesfromsinglecelltranscriptomicsdatausingreferenceatlases
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Interpretation of T cell states from single-cell transcriptomics data using reference atlases

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