Plate IV.253 · Publications
Squidpy: a scalable framework for spatial omics analysis
Article in Nature Methods, 31 January 2022.
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.
Cite
@article{palla2022squidpy,
title = {{Squidpy: a scalable framework for spatial omics analysis}},
author = {Palla, Giovanni and Spitzer, Hannah and Klein, Michal and Fischer, David and Schaar, Anna Christina and Kuemmerle, Louis Benedikt and Rybakov, Sergei and Ibarra, Ignacio L. and Holmberg, Olle and Virshup, Isaac and Lotfollahi, Mohammad and Richter, Sabrina and Theis, Fabian J.},
journal = {Nature Methods},
publisher = {Springer Science and Business Media LLC},
year = {2022},
month = jan,
doi = {10.1038/s41592-021-01358-2},
url = {https://doi.org/10.1038/s41592-021-01358-2},
pmid = {35102346},
pmcid = {PMC8828470}
}