Genetically informed spatial mapping of cells for complex traits

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  • License: MIT License
  • Author: liyang
  • Requires: Python >=3.10
  • Provides-Extra: doc , tests
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Project description

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

Model Architecture

🛠️ 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

📘 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.

Project details

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
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Avatar for Wenhao-Chen from gravatar.com Wenhao-Chen

Unverified details

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Project links
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  • License: MIT License
  • Author: liyang
  • Requires: Python >=3.10
  • Provides-Extra: doc , tests
Classifiers

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