AI Article Synopsis
- Advances in spatial omics technologies have enhanced our understanding of cellular organization, leading to complex data that necessitates new tools for management and visualization.
- The Spatial Omics Database (SODB) was created to provide a standardized data storage format and interactive visualization options, with Pysodb being a Python tool specifically for exploring and loading spatial datasets from SODB.
- The protocol is user-friendly for researchers with limited computational backgrounds, and it includes a website for benchmarking analysis, detailing cases where Pysodb interacts with various methods to ensure reproducibility and data integration.
Article Abstract
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.
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| http://dx.doi.org/10.1038/s41596-023-00925-5 | DOI Listing |
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