SynComBat is a Python package for generating biased synthetic data following the ComBat model. It provides a framework for generating synthetic datasets with built-in support for introducing biases, incorporating random effects, and applying data transformations.
You can install syncombat using pip:
pip install git+https://github.com/sssilvar/syncombat.gitTo generate synthetic data using syncombat, follow these steps:
For more detailed usage instructions, please refer to the documentation.
Contributions to syncombat are welcome! Please see the contribution guidelines for more information.
This project is licensed under the MIT License. See the LICENSE file for details.
We would like to acknowledge the contributions of the open-source community and the researchers who developed the ComBat model.
- Reference paper or relevant resources that describe the ComBat model.
you can cite the paper (pre-print):
@article{silva2023fed, title={Fed-ComBat: A Generalized Federated Framework for Batch Effect Harmonization in Collaborative Studies}, author={Silva, Santiago and Lorenzi, Marco and Altmann, Andre and Oxtoby, Neil}, journal={bioRxiv}, pages={2023--05}, year={2023}, publisher={Cold Spring Harbor Laboratory} }