muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

Single-cell transcriptomics enhanced our ability to profile heterogeneous cell populations. It is not known which statistical frameworks are performant to detect subpopulation-level responses. Here, the authors developed a simulation framework to evaluate various methods across a range of scenarios.

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Detalles Bibliográficos
Autores principales:	Helena L. Crowell, Charlotte Soneson, Pierre-Luc Germain, Daniela Calini, Ludovic Collin, Catarina Raposo, Dheeraj Malhotra, Mark D. Robinson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/ca630df4491c4fa29d160b6e84f04cc4
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spelling	oai:doaj.org-article:ca630df4491c4fa29d160b6e84f04cc42021-12-02T17:31:10Zmuscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data10.1038/s41467-020-19894-42041-1723https://doaj.org/article/ca630df4491c4fa29d160b6e84f04cc42020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19894-4https://doaj.org/toc/2041-1723Single-cell transcriptomics enhanced our ability to profile heterogeneous cell populations. It is not known which statistical frameworks are performant to detect subpopulation-level responses. Here, the authors developed a simulation framework to evaluate various methods across a range of scenarios.Helena L. CrowellCharlotte SonesonPierre-Luc GermainDaniela CaliniLudovic CollinCatarina RaposoDheeraj MalhotraMark D. RobinsonNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Helena L. Crowell Charlotte Soneson Pierre-Luc Germain Daniela Calini Ludovic Collin Catarina Raposo Dheeraj Malhotra Mark D. Robinson muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data
description	Single-cell transcriptomics enhanced our ability to profile heterogeneous cell populations. It is not known which statistical frameworks are performant to detect subpopulation-level responses. Here, the authors developed a simulation framework to evaluate various methods across a range of scenarios.
format	article
author	Helena L. Crowell Charlotte Soneson Pierre-Luc Germain Daniela Calini Ludovic Collin Catarina Raposo Dheeraj Malhotra Mark D. Robinson
author_facet	Helena L. Crowell Charlotte Soneson Pierre-Luc Germain Daniela Calini Ludovic Collin Catarina Raposo Dheeraj Malhotra Mark D. Robinson
author_sort	Helena L. Crowell
title	muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data
title_short	muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data
title_full	muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data
title_fullStr	muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data
title_full_unstemmed	muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data
title_sort	muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/ca630df4491c4fa29d160b6e84f04cc4
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muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

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