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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
Open Access Gold (Paid Option)
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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, Issue: , Pages: 171–178 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
Grants Deutsche Forschungsgemeinschaft (German Research Foundation)
Helmholtz Association