Squidpy: a scalable framework for spatial omics analysis
Giovanni Palla(Helmholtz Zentrum München), Hannah Spitzer(Helmholtz Zentrum München), Michal Klein(Helmholtz Zentrum München), David S. Fischer(Helmholtz Zentrum München), Anna C. Schaar(Helmholtz Zentrum München), Louis B. Kuemmerle(Helmholtz Zentrum München), Sergei Rybakov(Helmholtz Zentrum München), Ignacio L. Ibarra(Helmholtz Zentrum München), Olle Holmberg(Helmholtz Zentrum München), Isaac Virshup(The University of Melbourne), Mohammad Lotfollahi(Helmholtz Zentrum München), Sabrina Richter(Helmholtz Zentrum München), Fabian J. Theis(Helmholtz Zentrum München)
Cited by 1,062Open Access
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.
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