Marconato Luca, Palla Giovanni, Yamauchi Kevin A, Virshup Isaac, Heidari Elyas, Treis Tim, Vierdag Wouter-Michiel, Toth Marcella, Stockhaus Sonja, Shrestha Rahul B, Rombaut Benjamin, Pollaris Lotte, Lehner Laurens, Vöhringer Harald, Kats Ilia, Saeys Yvan, Saka Sinem K, Huber Wolfgang, Gerstung Moritz, Moore Josh, Theis Fabian J, Stegle Oliver
European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany.
Nat Methods. 2025 Jan;22(1):58-62. doi: 10.1038/s41592-024-02212-x. Epub 2024 Mar 20.
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.
空间分辨组学技术正在改变我们对生物组织的理解。然而,由于数据量庞大、数据类型的异质性以及缺乏灵活的、具有空间感知能力的数据结构,单模态和多模态空间组学数据集的处理仍然是一项挑战。在此,我们引入了SpatialData框架,该框架建立了一个统一且可扩展的多平台文件格式,对大于内存的数据进行惰性表示,进行转换并与通用坐标系对齐。SpatialData便于进行空间注释以及跨模态聚合与分析,其效用在多个示例中得到了说明,包括对多模态Xenium和Visium乳腺癌研究的综合分析。