High-Parameter Spatial Multi-Omics through Histology-Anchored Integration
SpatialEx is a powerful tool for high-parameter spatial multi-omics analysis through histology-anchored integration. This repository contains the source code and implementation for our published work.
SpatialEx enables high-parameter spatial multi-omics analysis by integrating histology information with multi-omics data. The published version and preprint of our work are available at:
- Published Version: Nature Methods
- Preprint: BioRxiv
📘 Step-by-step tutorials are available at our documentation site.
Install SpatialEx directly from PyPI:
pip install SpatialExAlternatively, install from the requirements file:
pip install -r requirements.txtIf you prefer to install packages individually to avoid potential conflicts, here are the key dependencies:
pip install anndata==0.8.0
pip install scanpy==1.9.3
pip install numpy==1.23.5
pip install pandas==2.0.3
pip install cellpose==3.0.10
pip install scikit-image==0.21.0
pip install scikit-learn==1.3.2
pip install scikit-misc==0.2.0
pip install torch==2.3.1
pip install huggingface-hub==0.24.6
pip install timm==1.0.8
pip install torchvision==0.18.1
⚠️ Note: We recommend installing the above Python packages one by one to avoid potential dependency conflicts.
We have packaged our implementation into an easy-to-use Python library for the research community.
- 📖 Detailed Installation Guide: Tutorial Documentation
- 📓 Comprehensive Examples: See
Demonstration.ipynbfor detailed guides to all applications in the paper
The processed data generated in this study are available on Google Drive:
- Source: 10x Genomics Dataset Explorer
- Source: 10x Genomics Public Datasets
- Source: Mendeley Data
The preprocessed IF data for Xenium Human Breast Cancer Rep1 and mouse brain SMA data were obtained from another study:
- 📄 Citation: NicheTrans
- 🔗 Data Repository: Zenodo
- 📘 Tutorials: NicheTrans Tutorials
⚠️ Important: Please cite the NicheTrans study when using their preprocessed data.
If you find our work useful, please cite our paper:
@article{liu2025high,
title={High-Parameter Spatial Multi-Omics through Histology-Anchored Integration},
author={Liu, Yonghao and Wang, Chuyao and Wang, Zhikang and Chen, Liang and Li, Zhi and Song, Jiangning and Zou, Qi and Gao, Rui and Qian, Binzhi and Feng, Xiaoyue and Guan, Renchu and Yuan, Zhiyuan},
journal={Nature Methods},
year={2025}
}If you have any questions or need support, please feel free to contact us:
- 📩 Yonghao Liu: yonghao20@mails.jlu.edu.cn
- 📩 Chuyao Wang: wcy22@mails.jlu.edu.cn