Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and visualizing spatial omics data. The Spatial Omics Database (SODB) was established to offer a unified format for data storage and interactive visualization modules. Here we detail the use of Pysodb, a Python-based tool designed to enable the efficient exploration and loading of spatial datasets from SODB within a Python environment. We present seven case studies using Pysodb, detailing the interaction with various computational methods, ensuring reproducibility of experimental data and facilitating the integration of new data and alternative applications in SODB. The approach offers a reference for method developers by outlining label and metadata availability in representative spatial data that can be loaded by Pysodb. The tool is supplemented by a website (https://protocols-pysodb.readthedocs.io/) with detailed information for benchmarking analysis, and allows method developers to focus on computational models by facilitating data processing. This protocol is designed for researchers with limited experience in computational biology. Depending on the dataset complexity, the protocol typically requires ~12 h to complete.
Key points: Pysodb allows researchers to load and explore spatial omics data in a Python environment. Data loaded using Pysodb follow the AnnData format, thus providing a unified format for storing over 3,000 datasets and facilitating benchmarking and reuse of data.Alternative packages such as Scanpy, Squidpy and Giotto focus on data analysis; Pysodb complements them by providing a support platform for data storage and handling.
The procedure guides inexperienced users interested in handling spatial omics data in a Python environment to streamline data analysis and to facilitate benchmarking analysis via the spatial omics database.