GRN-guided in silico simulation of single-cell RNA-seq data using Causal Generative Adversarial Networks

Implementation of GRouNdGAN as described in:

Yazdan Zinati, Abdulrahman Takiddeen, Amin Emad, GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks, bioRxiv, 2023-07, https://doi.org/10.1101/2023.07.25.550225

Simulated Datasets and Ground Truth GRNs

Simulated dataset and their underlying ground truth GRNs are available for download on our website.

Tutorials and Documentation

For a detailed tutorial and comprehensive API references, please visit our project's documentation here.

BibTex Citation

@article{zinati2023groundgan,
  title={GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks},
  author={Zinati, Yazdan and Takiddeen, Abdulrahman and Emad, Amin},
  journal={bioRxiv},
  pages={2023--07},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}

License

Copyright (C) 2023 Emad's COMBINE Lab: Yazdan Zinati, Abdulrahman Takiddeen, and Amin Emad.

GRouNdGAN is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

GRouNdGAN is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with GRouNdGAN. If not, see https://www.gnu.org/licenses/.