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Squidpy: A scalable framework for spatial single cell analysis.

bioRxiv, DOI: 10.1101/2021.02.19.431994 (2021)
Preprint DOI
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Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. 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 both infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data.

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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Zeitschrift bioRxiv
Verlag Cold Spring Harbor Laboratory Press
Verlagsort Cold Spring Harbor
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Helmholtz AI - HMGU (HAI - HMGU)
Förderungen Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI
Chan Zuckerberg Initiative DAF
European Union’s Horizon 2020 research and innovation programme