Plate IV.152 · Publications
SpatialData: an open and universal data framework for spatial omics
Article in Nature Methods, 20 March 2024.
Abstract
Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.
Versions2
- PublishedNature Methods10.1038/s41592-024-02212-x
- PreprintbioRxiv10.1101/2023.05.05.539647
Cite
@article{marconato2024spatialdata,
title = {{SpatialData: an open and universal data framework for spatial omics}},
author = {Marconato, Luca and Palla, Giovanni and Yamauchi, Kevin A. and Virshup, Isaac and Heidari, Elyas and Treis, Tim and Vierdag, Wouter-Michiel and Toth, Marcella and Stockhaus, Sonja and Shrestha, Rahul B. and Rombaut, Benjamin and Pollaris, Lotte and Lehner, Laurens and Vöhringer, Harald and Kats, Ilia and Saeys, Yvan and Saka, Sinem K. and Huber, Wolfgang and Gerstung, Moritz and Moore, Josh and Theis, Fabian J. and Stegle, Oliver},
journal = {Nature Methods},
publisher = {Springer Science and Business Media LLC},
year = {2024},
month = mar,
doi = {10.1038/s41592-024-02212-x},
url = {https://doi.org/10.1038/s41592-024-02212-x},
pmid = {38509327},
pmcid = {PMC11725494}
}