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Streamlining spatial omics data analysis with Pysodb

Nature Protocols volume 19, pages 831–895 (2024)Cite this article

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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.

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.

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Fig. 1: Overview of the protocol.
Fig. 2: Pysodb can interact with spatially variable gene detection.
Fig. 3: Pysodb can interact with spatial clustering analysis.
Fig. 4: Pysodb can interact with pseudo-spatiotemporal analysis.
Fig. 5: Pysodb can interact with spatial data integration.
Fig. 6: Pysodb can interact with spatial data alignment.
Fig. 7: Pysodb can interact with spatial spot deconvolution.
Fig. 8: Detailed information about representative datasets available for Pysodb.

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Data availability

The spatial datasets discussed in this protocol are available from SODB (https://gene.ai.tencent.com/SpatialOmics/). We provide guidelines on how to load and visualize these data at https://protocols-pysodb.readthedocs.io/en/latest/SOView/SOView.html#. The mouse cortex single-cell data are provided at https://figshare.com/articles/dataset/Visium/22332667. The human PDAC single-cell data are provided at https://figshare.com/articles/dataset/PDAC/22332574.

Code availability

Pysodb is a freely available software package written in the Python programming language. Source code can be found at https://github.com/TencentAILabHealthcare/pysodb. Installation instructions can be found at https://pysodb.readthedocs.io/en/latest/. The code used in this paper can be found at https://protocols-sodb.readthedocs.io/en/latest/. A Python version of SOView code can be found at https://github.com/yuanzhiyuan/SOView. An SOView tutorial can be found at https://soview-doc.readthedocs.io/en/latest/index.html.

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Acknowledgements

This work was supported by by Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission, Shanghai Science and Technology Development Funds (23YF1403000), Tencent AI Lab Rhino-Bird Focused Research Program (RBFR2023008), Shanghai Municipal Science and Technology Major Project (no. 2018SHZDZX01), ZJ Lab, Shanghai Center for Brain Science and Brain-Inspired Technology and 111 Project (no. B18015). The Innovation Fund of Institute of Computing and Technology, CAS (E161080, E161030); Beijing Natural Science Foundation Haidian Origination and Innovation Joint Fund (L222007).

Author information

Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Senlin Lin, Fangyuan Zhao & Yi Zhao

  2. University of Chinese Academy of Sciences, Beijing, China

    Senlin Lin, Fangyuan Zhao & Yi Zhao

  3. Tencent AI Lab, Shenzhen, China

    Zihan Wu & Jianhua Yao

  4. Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China

    Zhiyuan Yuan

  5. Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China

    Zhiyuan Yuan

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Contributions

Y.Z. and Z.Y. conceived and designed the study. Z.Y. designed the pipeline and collected the methods and datasets. S.L. and Z.Y. completed the pipeline. Z.Y. and S.L. analyzed the results and generated the figures. J.Y. and Z.W. maintained the database. Z.Y., S.L., Z.F. and Y.Z. wrote the manuscript.

Corresponding authors

Correspondence to Yi Zhao or Zhiyuan Yuan.

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Key reference using this protocol

Yuan, Z. et al. Nat. Methods 20, 387–399 (2023): https://doi.org/10.1038/s41592-023-01773-7

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Supplementary Table 2 and Supplementary Protocols.

Reporting Summary

Supplementary Table 1

Summary of parameters.

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Lin, S., Zhao, F., Wu, Z. et al. Streamlining spatial omics data analysis with Pysodb. Nat Protoc 19, 831–895 (2024). https://doi.org/10.1038/s41596-023-00925-5

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