Installation

Follow these steps to set up GLAND on your local machine. We recommend using a Conda environment to maintain a clean workspace and manage dependencies effectively.

1. Clone the Repository

Start by cloning the GLAND repository from GitHub and entering the project directory:

git clone https://github.com/CSUBioGroup/GLAND.git
cd GLAND

2. Environment Setup

Create a new Conda environment with Python 3.8 and activate it:

conda create -n GLAND python=3.8
conda activate GLAND

3. Install Dependencies

GLAND requires several core bioinformatics and deep learning libraries. You can install all necessary packages directly using the provided requirements.txt file:

pip install -r requirements.txt

4. Install R Dependencies (Optional)

For high-performance spatial clustering using the mclust algorithm, you need to have R installed and the mclust library available in your environment:

# Install R base
conda install -c conda-forge r-base=4.0.3

# Install mclust package inside R
R -e "install.packages('mclust', repos='http://cran.us.r-project.org')"

Note

If you are using a GPU, please ensure that your PyTorch installation is compatible with your local CUDA version (e.g., CUDA 11.7). You may need to reinstall a specific version of torch-scatter and torch-sparse if you encounter compatibility issues.