Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution
scNiche is a computational framework to identify and characterize cell niches from single-cell spatial omics data
For scNiche, the Python version need is over 3.9. If you have already installed a lower version of Python, consider installing Anaconda, and then you can create a new environment.
conda create -n scniche python=3.9
conda activate scniche
We developed scNiche in a CUDA 11.3 environment. Here is an example of installing PyTorch and DGL with CUDA11.3:
# install PyTorch
pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
# install DGL
pip install dgl==1.1.0+cu113 -f https://data.dgl.ai/wheels/cu113/repo.html
The version of PyTorch and DGL should be suitable to the CUDA version of your machine. You can find the appropriate version on the PyTorch and DGL website.
cd scNiche-main
pip install -r requirements.txt
python setup.py build
python setup.py install
scNiche requires the single-cell spatial omics data (stored as .h5ad format) as input, where cell population label of each cell needs to be provided.
Here are examples of scNiche on simulated and biological datasets:
- Demonstration of scNiche on the simulated data
- Demonstration of scNiche on the mouse spleen CODEX data
- Demonstration of scNiche on the human upper tract urothelial carcinoma (UTUC) IMC data
scNiche also offers a downstream analytical framework for characterizing cell niches more comprehensively.
Here are examples of scNiche on two biological datasets:
- Demonstration of scNiche on the human triple-negative breast cancer (TNBC) MIBI-TOF data
- Demonstration of scNiche on the mouse liver Seq-Scope data
scNiche was developed by Jingyang Qian. Should you have any questions, please contact Jingyang Qian at qianjingyang@zju.edu.cn.