Squidpy: a scalable framework for spatial omics analysis

TLDR

Spatial omics data are advancing tissue organization and cellular communication studies, but flexible tools are needed to store, integrate, and visualize their large diversity. The authors present Squidpy, a Python framework that integrates omics and image analysis tools to enable scalable description of spatial molecular data such as transcriptomes or multivariate proteins. Squidpy offers efficient infrastructure, analysis methods, and extensibility, enabling storage, manipulation, interactive visualization, and integration with existing libraries for scalable spatial omics analysis.

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.