SpatialData: an open and universal data framework for spatial omics
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....
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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
20 March 2024
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| In: |
Nature methods
Year: 2024, Pages: 1-5 |
| ISSN: | 1548-7105 |
| DOI: | 10.1038/s41592-024-02212-x |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41592-024-02212-x Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41592-024-02212-x 
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| Verfasserangaben: | Luca Marconato, Giovanni Palla, Kevin A. Yamauchi, Isaac Virshup, Elyas Heidari, Tim Treis, Wouter-Michiel Vierdag, Marcella Toth, Sonja Stockhaus, Rahul B. Shrestha, Benjamin Rombaut, Lotte Pollaris, Laurens Lehner, Harald Vöhringer, Ilia Kats, Yvan Saeys, Sinem K. Saka, Wolfgang Huber, Moritz Gerstung, Josh Moore, Fabian J. Theis & Oliver Stegle |
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| 520 | |a 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. | ||
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| 650 | 4 | |a Molecular imaging | |
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