Source codes of IGCLAPS: an interpretable graph contrastive learning method with adaptive positive sampling for scRNA-seq data analysis.
cuda---12.1
python---3.12
torch---2.3.0
numpy---1.26.4
pandas--2.2.3
scikit-learn---1.5.2
torch_geometric---2.6.0
scanpy---1.10.3
munkres---1.1.4
dgl---2.1.0
h5py---3.11.0
igraph---0.11.8
Other packages can be installed by 'pip install xxx', while dgl should be installed by running
pip install dgl -f https://data.dgl.ai/wheels/torch-2.3/cu121/repo.html. Besides, after installing torch_geometric, please run pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.0+cu121.html to install dependencies.
Just run
python main.py
in a command line or run test_script.ipynb in jupyter lab.
For your own data, please make sure that the data is stored in .h5 format containing raw count matrix 'X' with cells as rows and genes as columns.
PBMC 4k: source
Darmanis: source
LaManno: source
Baron human: source
Baron mouse: source
Muraro: source
Bladder: source
Adam: source
Zanini: source
Colquitt: source
Young: source
Chen: source