Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types

Single cell profiling yields high dimensional data of very large numbers of cells, posing challenges of visualization and analysis. Here the authors introduce a method for analysis of mass cytometry data that can handle very large datasets and allows their intuitive and hierarchical exploration.

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Autores principales:	Vincent van Unen, Thomas Höllt, Nicola Pezzotti, Na Li, Marcel J. T. Reinders, Elmar Eisemann, Frits Koning, Anna Vilanova, Boudewijn P. F. Lelieveldt
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/e50b0012ac854b22b12f11d3326c67c4
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spelling	oai:doaj.org-article:e50b0012ac854b22b12f11d3326c67c42021-12-02T15:38:51ZVisual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types10.1038/s41467-017-01689-92041-1723https://doaj.org/article/e50b0012ac854b22b12f11d3326c67c42017-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-017-01689-9https://doaj.org/toc/2041-1723Single cell profiling yields high dimensional data of very large numbers of cells, posing challenges of visualization and analysis. Here the authors introduce a method for analysis of mass cytometry data that can handle very large datasets and allows their intuitive and hierarchical exploration.Vincent van UnenThomas HölltNicola PezzottiNa LiMarcel J. T. ReindersElmar EisemannFrits KoningAnna VilanovaBoudewijn P. F. LelieveldtNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Vincent van Unen Thomas Höllt Nicola Pezzotti Na Li Marcel J. T. Reinders Elmar Eisemann Frits Koning Anna Vilanova Boudewijn P. F. Lelieveldt Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
description	Single cell profiling yields high dimensional data of very large numbers of cells, posing challenges of visualization and analysis. Here the authors introduce a method for analysis of mass cytometry data that can handle very large datasets and allows their intuitive and hierarchical exploration.
format	article
author	Vincent van Unen Thomas Höllt Nicola Pezzotti Na Li Marcel J. T. Reinders Elmar Eisemann Frits Koning Anna Vilanova Boudewijn P. F. Lelieveldt
author_facet	Vincent van Unen Thomas Höllt Nicola Pezzotti Na Li Marcel J. T. Reinders Elmar Eisemann Frits Koning Anna Vilanova Boudewijn P. F. Lelieveldt
author_sort	Vincent van Unen
title	Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
title_short	Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
title_full	Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
title_fullStr	Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
title_full_unstemmed	Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
title_sort	visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/e50b0012ac854b22b12f11d3326c67c4
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Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types

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