Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

Visualisation tools that use dimensionality reduction, such as t-SNE, provide poor visualisation on large data sets of millions of observations. Here the authors present opt-SNE, that automatically finds data set-tailored parameters for t-SNE to optimise visualisation and improve analysis.

Bibliographic Details
Main Authors:	Anna C. Belkina, Christopher O. Ciccolella, Rina Anno, Richard Halpert, Josef Spidlen, Jennifer E. Snyder-Cappione
Format:	Article
Language:	English
Published:	Nature Publishing Group 2019-11-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-019-13055-y

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spelling	doaj-e5126a248205487bb5a7c54c11c0bcc32021-05-11T12:12:47ZengNature Publishing GroupNature Communications2041-17232019-11-0110111210.1038/s41467-019-13055-yAutomated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasetsAnna C. Belkina0Christopher O. Ciccolella1Rina Anno2Richard Halpert3Josef Spidlen4Jennifer E. Snyder-Cappione5Department of Pathology and Laboratory Medicine, Boston University School of MedicineOmiq, IncDepartment of Mathematics, Kansas State UniversityBD Life Sciences–FlowJoBD Life Sciences–FlowJoFlow Cytometry Core Facility, Boston University School of MedicineVisualisation tools that use dimensionality reduction, such as t-SNE, provide poor visualisation on large data sets of millions of observations. Here the authors present opt-SNE, that automatically finds data set-tailored parameters for t-SNE to optimise visualisation and improve analysis.https://doi.org/10.1038/s41467-019-13055-y
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Anna C. Belkina Christopher O. Ciccolella Rina Anno Richard Halpert Josef Spidlen Jennifer E. Snyder-Cappione
spellingShingle	Anna C. Belkina Christopher O. Ciccolella Rina Anno Richard Halpert Josef Spidlen Jennifer E. Snyder-Cappione Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets Nature Communications
author_facet	Anna C. Belkina Christopher O. Ciccolella Rina Anno Richard Halpert Josef Spidlen Jennifer E. Snyder-Cappione
author_sort	Anna C. Belkina
title	Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
title_short	Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
title_full	Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
title_fullStr	Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
title_full_unstemmed	Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
title_sort	automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2019-11-01
description	Visualisation tools that use dimensionality reduction, such as t-SNE, provide poor visualisation on large data sets of millions of observations. Here the authors present opt-SNE, that automatically finds data set-tailored parameters for t-SNE to optimise visualisation and improve analysis.
url	https://doi.org/10.1038/s41467-019-13055-y
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Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

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