| Version |   |
Status |   |
| Activity |   |
Quality |   |
| CI/CD |   |
Community |   |
| Downloads |  |
License |   |
| Platform |  |
Contribute |   |
gsMap (genetically informed spatial mapping of cells for complex traits)
integrates spatial transcriptomics (ST) data with genome-wide association study (GWAS)
summary statistics to map cells to human complex traits, including diseases,
in a spatially resolved manner.
- Spatially-aware High-Resolution Trait Mapping
- Spatial Region Identification
- Putative Causal Genes Identification
Install using pip:
conda create -n gsMap python>=3.10
conda activate gsMap
pip install gsMapInstall using conda:
conda create -n gsMap python>=3.10
conda activate gsMap
conda install bioconda::gsmapInstall from source:
git clone https://github.com/JianYang-Lab/gsMap
cd gsMap
pip install -e .Verify the installation by running the following command:
gsmap --helpgsMap supports AI-assisted workflows that allow users to run spatial GWAS analysis using modern AI coding agents and Vibe coding environments. To add the gsMap MCP server and SKILLs in Claude Code:
cd gsMap/gsmap_vibe
uv pip install -e .
claude mcp add gsmap -- python -m gsmap_mcp
cp -r gsmap_skill ~/.claude/skills/Please check out the documentation and tutorials at gsMap Documentation.
To visualize the traits-cell association spatial maps, please refer to gsMap Visualization.
Song, L., Chen, W., Hou, J., Guo, M. & Yang, J. Spatially resolved mapping of cells associated with human complex traits. Nature (2025).
Please cite the paper and give us a STAR if you find gsMap useful for your research.
gsMap was highlighted in Nature Methods.
gsMap was highlighted in Nature Review Genetics.