Squidpy: a scalable framework for spatial omics analysis

Palla G, Spitzer H, Klein M, Fischer D, Schaar AC, Kuemmerle LB, Rybakov S, Ibarra IL, Holmberg O, Virshup I, Lotfollahi M, Richter S, Theis FJ (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Nature Methods Nature Publishing Group

Book Volume: 19

Pages Range: 171-178

Journal Issue: 2

DOI: 10.1038/s41592-021-01358-2

Abstract

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.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) The University of Melbourne AU Australia (AU)

How to cite

APA:

Palla, G., Spitzer, H., Klein, M., Fischer, D., Schaar, A.C., Kuemmerle, L.B.,... Theis, F.J. (2022). Squidpy: a scalable framework for spatial omics analysis. Nature Methods, 19(2), 171-178. https://doi.org/10.1038/s41592-021-01358-2

MLA:

Palla, Giovanni, et al. "Squidpy: a scalable framework for spatial omics analysis." Nature Methods 19.2 (2022): 171-178.

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