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Palla, G. ; Spitzer, H. ; Klein, M. ; Fischer, D.S. ; Schaar, A. ; Kuemmerle, L. ; Rybakov, S. ; Ibarra Del Rio, I.A. ; Holmberg, O. ; Virshup, I.* ; Lotfollahi, M. ; Richter, S. ; Theis, F.J.

Squidpy: A scalable framework for spatial omics analysis.

Nat. Methods 19, 171–178 (2022)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Transcriptomics; Resolution
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Quellenangaben Band: 19, Heft: , Seiten: 171–178 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
Förderungen Deutsche Forschungsgemeinschaft (German Research Foundation)
Helmholtz Association