Computational challenges and opportunities in spatially resolved transcriptomic data analysis

Spatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated computational challenges as well as highlight the opportunities for standardized benchmarking metrics and data-sharing infrastructure in spurring innov...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Lyla Atta, Jean Fan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/30b39018bedf4a9f9b290b200f3c9857
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:30b39018bedf4a9f9b290b200f3c9857
record_format	dspace
spelling	oai:doaj.org-article:30b39018bedf4a9f9b290b200f3c98572021-12-02T17:41:08ZComputational challenges and opportunities in spatially resolved transcriptomic data analysis10.1038/s41467-021-25557-92041-1723https://doaj.org/article/30b39018bedf4a9f9b290b200f3c98572021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25557-9https://doaj.org/toc/2041-1723Spatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated computational challenges as well as highlight the opportunities for standardized benchmarking metrics and data-sharing infrastructure in spurring innovation moving forward.Lyla AttaJean FanNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-5 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Lyla Atta Jean Fan Computational challenges and opportunities in spatially resolved transcriptomic data analysis
description	Spatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated computational challenges as well as highlight the opportunities for standardized benchmarking metrics and data-sharing infrastructure in spurring innovation moving forward.
format	article
author	Lyla Atta Jean Fan
author_facet	Lyla Atta Jean Fan
author_sort	Lyla Atta
title	Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title_short	Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title_full	Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title_fullStr	Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title_full_unstemmed	Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title_sort	computational challenges and opportunities in spatially resolved transcriptomic data analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/30b39018bedf4a9f9b290b200f3c9857
work_keys_str_mv	AT lylaatta computationalchallengesandopportunitiesinspatiallyresolvedtranscriptomicdataanalysis AT jeanfan computationalchallengesandopportunitiesinspatiallyresolvedtranscriptomicdataanalysis
_version_	1718379696109912064

Computational challenges and opportunities in spatially resolved transcriptomic data analysis

Ejemplares similares