GLEAM is a graph-based method for integrating multi-modal single-cell data and predicting missing modalities, with a focus on single-cell multi-omics (e.g., scRNA-seq, scATAC-seq, scHi-C).

Clone the repository and install in editable mode:

git clone https://github.com/keleslab/gleam.git
cd gleam
conda create -n gleam python==3.13
conda activate gleam
pip install torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu128
pip install -r requirement.txt
pip install torch_geometric==2.7.0 -f https://data.pyg.org/whl/torch-2.8.0+cu128.html
pip install pyg_lib==0.5.0 torch_scatter==2.1.2 torch_sparse==0.6.18 torch_cluster==1.6.3 torch_spline_conv==1.2.2 -f https://data.pyg.org/whl/torch-2.8.0+cu128.html
pip install -e .

Currently, this tool is under construction as I intend to unify all codes so they can all use Python instead of R. If you want to try it now, we provide basic functions for this and feel free to use the ipynb file in the "examples" folder.

The runSimulation.ipynb file is on example data.

The data (from Zhu et al. 2023) is in google drive, runRealData.ipynb is written to work on this data.

The runCP.ipynb is for conformal prediction, and it is currently under construction, as we currently just have a simple example here.