Squidpy: a scalable framework for spatial omics analysis.
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
02 2022
02 2022
Historique:
received:
19
02
2021
accepted:
21
11
2021
pubmed:
2
2
2022
medline:
26
2
2022
entrez:
1
2
2022
Statut:
ppublish
Résumé
Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.
Identifiants
pubmed: 35102346
doi: 10.1038/s41592-021-01358-2
pii: 10.1038/s41592-021-01358-2
pmc: PMC8828470
doi:
Banques de données
figshare
['10.6084/m9.figshare.c.5273297.v1']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
171-178Informations de copyright
© 2022. The Author(s).
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