gsMap

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Introduction

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.

Key Features

• Spatially-aware High-Resolution Trait Mapping
• Spatial Region Identification
• Putative Causal Genes Identification

🛠️ Installation

Install using pip:

conda create -n gsMap python>=3.10
conda activate gsMap
pip install gsMap

Install using conda:

conda create -n gsMap python>=3.10
conda activate gsMap
conda install bioconda::gsmap

Install from source:

git clone https://github.com/JianYang-Lab/gsMap
cd gsMap
pip install -e .

Verify the installation by running the following command:

gsmap --help

🤖 AI-assisted Analysis

gsMap 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/

📘 Usage

Please check out the documentation and tutorials at gsMap Documentation.

🌐 Online Visualization

To visualize the traits-cell association spatial maps, please refer to gsMap Visualization.

📖 Citation

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.

✨ Research Highlight

gsMap was highlighted in Nature Methods.
gsMap was highlighted in Nature Review Genetics.

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