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Updated: Jan 31, 2026
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
Published on: September 5, 2025
761
Mapping biology in space: from spatial transcriptomics platforms to analytical tools and databases
Zi-Zhen Guo1, Renyan Wu2, Weixiang Li3
1Center for Immune-Related Diseases at Shanghai Institute of Immunology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Department of Plastic Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai 200233, China.
Science Bulletin
|January 29, 2026
Summary
Spatial transcriptomics (ST) tools are rapidly advancing, creating challenges for researchers. This review systematically summarizes 594 ST analysis tools and 77 ST technologies, aiding in tool selection for spatial biology research.
Area of Science:
- Genomics and Bioinformatics
- Molecular Biology
- Biomedical Research
Background:
- Spatial transcriptomics (ST) offers gene expression insights within tissue context.
- Rapid advancements in ST technologies and analysis present challenges in tool selection and data interpretation.
- A comprehensive understanding of available ST tools and methods is crucial for effective research.
Purpose of the Study:
- To systematically review and categorize 594 spatial transcriptomics analysis tools across 77 ST technologies.
- To outline the complete analytical workflow for ST data, covering preprocessing to multi-omics integration.
- To introduce SpatialToolDB, a curated platform for ST tool and database integration to facilitate method comparison and selection.
Main Methods:
- Systematic literature review and data extraction of ST analysis tools and technologies.
- Categorization of tools based on analytical tasks: preprocessing, denoising, pattern identification, cellular composition, trajectory analysis, cell-cell communication, and multi-omics integration.
- Development and curation of SpatialToolDB (https://www.spatialtooldb.yelab.site/) integrating ST technologies, methods, and databases.
Main Results:
- A comprehensive summary of 594 ST analysis tools and 77 ST technologies is presented.
- Key analytical tasks and platform-dependent considerations for ST data analysis are discussed.
- SpatialToolDB is launched as a centralized resource for ST tool and method discovery.
Conclusions:
- The review and SpatialToolDB provide a data-driven foundation for selecting appropriate ST platforms and analytical strategies.
- Addressing challenges in ST tool selection and methodological comparison is vital for advancing spatial biology.
- Future directions include AI-driven computation, standardization, and improved experimental validation for spatial omics.
Keywords:
Analytical workflowST technologySpatial multi-omics integrationSpatial transcriptomicsSpatialToolDBMore Related Videos
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