Installation (pyG framework)

Software Dependencies

python: 3.8.0 torch: 1.8.1+cu111 cudnn: 9.1.0.70 numpy: 1.22.4 scanpy: 1.9.8 anndata: 0.9.2 rpy2: 3.5.12 pandas: 2.0.3 scipy: 1.10.1 scikit-learn: 1.3.2

Installation

Download the stCAMBL code from GitHub: https://github.com/AI4Bread/stCAMBL, clone the repository with the following command:

git clone git@github.com:AI4Bread/stCAMBL.git
cd stCAMBL

Then, create a new conda environment and install the required packages:

The code is tested with Python 3.8.0 and PyTorch 1.8.1+cu111 on a single NVIDIA GeForce RTX 3090 GPU. If you encounter any issues, please check the compatibility of the packages in requirements.txt with your Python version. Additionally, different versions of libraries and different GPU devices may lead to varied outcomes, so to reproduce our results, please use the same versions and hardware configuration as specified.

Dataset Directory Structure

Download the datasets and place them in the Data directory, ensuring the directory structure appears as follows:

stCAMBL
├── Data
│   ├── DLPFC
│   │   ├── 151673
│   │   ├── 151674
│   ├── Nanostring
│   ├── Human_Breast_Cancer
│   ├── Mouse_Brain_Anterior

Processed datasets can be downloaded from the following links: