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Squidpy: A scalable framework for spatial omics analysis.
Nat. Methods 19, 171–178 (2022)
Publ. Version/Full Text
Research data
DOI
PMC
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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Transcriptomics; Resolution
ISSN (print) / ISBN
1548-7091
e-ISSN
1548-7105
Journal
Nature Methods
Quellenangaben
Volume: 19,
Pages: 171–178
Publisher
Nature Publishing Group
Publishing Place
New York, NY
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
Grants
Deutsche Forschungsgemeinschaft (German Research Foundation)
Helmholtz Association
Helmholtz Association