GRACE

Installation

python setup.py install
* After installation, change form numpy == 1.19.2 to numpy in  ../gae.egg-info/requires.txt (remove '==1.19.2')
* and then try installation again => python setup.py install

Requirements (automatically installed by installation)

• TensorFlow (1.0 or later)
• python 3.8.15
• networkx
• scikit-learn
• scipy

Extra Requirements

• pip install umap-learn
• pip install scipy==1.8.0

conda

conda create -n GRACE python=3.8.15 pip
conda activate GRACE
conda install -c anaconda tensorflow-gpu==2.6.0

Run the demo

python GRACE.py

Data

Set the route. Have a look at the my_data(data_file) function in input_data_my.py

file = pd.read_csv('.../{}.txt'.format(data_file), sep='\t')

file_label = pd.read_csv('.../{}_label.txt'.format(data_file), names=['order', 'target'], sep='\t')

Set the route. Have a look at the svm_m(names, PRED_elbow_kmean) function in support_vector_machine.py. File Norm_data_{} is provided.

svm_file = pd.read_csv('.../Norm_data_Umap/Norm_data_{}.txt'.format(names), sep='\t')

You can adjust a data(single-cell RNA seqeuncing datasets) as follows:

python GRACE.py --dataset usoskin

Models

You can choose between the following models:

• gcn_ae: Graph Auto-Encoder (with GCN encoder)
• gcn_vae: Variational Graph Auto-Encoder (with GCN encoder)

Cite

Please cite our paper if you use this code in your own work:

@article{ha2023grace,
  title={GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning},
  author={Ha, Jun Seo and Jeong, Hyundoo},
  journal={Plos one},
  volume={18},
  number={4},
  pages={e0284527},
  year={2023},
  publisher={Public Library of Science San Francisco, CA USA}
}