Advancing spatial biology analysis across programming languages

The rapid growth and increasing diversity of spatial profiling technologies, combined with the demand for accelerated innovation cycles, have created a fragmented landscape characterized by incompatible file formats and isolated analysis methods. This fragmentation significantly hinders scientific reproducibility. To address these challenges, scverse, in collaboration with napari and researchers from the Open Microscopy Environment (OME), developed the SpatialData framework. This framework provides a language-agnostic, standardized storage format derived from the OME-Zarr format and OME-NGFF specification, specifically designed to maximize interoperability for spatial omics. Additionally, the SpatialData framework includes a suite of Python libraries tailored for high-performance processing and flexible visualization of spatial omics data. SpatialData aims to ease interoperability of analysis and visualization tools via its standardized storage format. In this talk, I will share recent developments of the SpatialData framework, emphasizing efforts aimed at extending interoperability beyond Python. I will present preliminary results from ongoing collaborations with the Bioconductor, and bioimaging (napari, OME) communities, highlighting key challenges, implemented solutions, and our vision for fully interoperable, cross-language analyses of spatial omics data. These efforts aim to leverage the unique strengths of each programming language and their respective analysis communities. Furthermore, I will stress the critical importance of adopting open, reusable standards to mitigate redundancy and fragmentation. Specifically, I will discuss ongoing efforts within the bioimaging community to develop a canonical parser for OME-NGFF and OME-Zarr formats in Python, which will significantly enhance interoperability and facilitate the development of sustainable, maintainable software solutions. Similar efforts within Bioconductor have the potential to significantly broaden accessibility, foster community collaboration, and unlock powerful, integrative spatial omics analyses across diverse programming environments.