Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions

Single-cell RNA-Seq allows us to observe snapshots of how biological systems change over time at cellular resolution. Here, the authors develop a generative framework that uses time-resolved single-cell data to model how cells change in physical time, including in response to perturbations.

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Autores principales:	Grace Hui Ting Yeo, Sachit D. Saksena, David K. Gifford
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/197cf2be15834f71b3824cd58fa503a3
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spelling	oai:doaj.org-article:197cf2be15834f71b3824cd58fa503a32021-12-02T15:00:50ZGenerative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions10.1038/s41467-021-23518-w2041-1723https://doaj.org/article/197cf2be15834f71b3824cd58fa503a32021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23518-whttps://doaj.org/toc/2041-1723Single-cell RNA-Seq allows us to observe snapshots of how biological systems change over time at cellular resolution. Here, the authors develop a generative framework that uses time-resolved single-cell data to model how cells change in physical time, including in response to perturbations.Grace Hui Ting YeoSachit D. SaksenaDavid K. GiffordNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Grace Hui Ting Yeo Sachit D. Saksena David K. Gifford Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions
description	Single-cell RNA-Seq allows us to observe snapshots of how biological systems change over time at cellular resolution. Here, the authors develop a generative framework that uses time-resolved single-cell data to model how cells change in physical time, including in response to perturbations.
format	article
author	Grace Hui Ting Yeo Sachit D. Saksena David K. Gifford
author_facet	Grace Hui Ting Yeo Sachit D. Saksena David K. Gifford
author_sort	Grace Hui Ting Yeo
title	Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions
title_short	Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions
title_full	Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions
title_fullStr	Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions
title_full_unstemmed	Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions
title_sort	generative modeling of single-cell time series with prescient enables prediction of cell trajectories with interventions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/197cf2be15834f71b3824cd58fa503a3
work_keys_str_mv	AT gracehuitingyeo generativemodelingofsinglecelltimeserieswithprescientenablespredictionofcelltrajectorieswithinterventions AT sachitdsaksena generativemodelingofsinglecelltimeserieswithprescientenablespredictionofcelltrajectorieswithinterventions AT davidkgifford generativemodelingofsinglecelltimeserieswithprescientenablespredictionofcelltrajectorieswithinterventions
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Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions

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