This is the main repository for BEELINE. The documentation is available at: https://murali-group.github.io/Beeline/.
Quick setup:
sudo usermod -aG docker $USER
, if you haven't already. See more details here. initialize.sh
(this step will take a while). setupAnacondaVENV.sh
command to automatically create an Anaconda virtual environment named BEELINE from requirements.txt and install necessary libraries required to run BEELINE. Alternatively, you can create virtual environment for python using vnev from requirements.txt as detailed hereWe provided an example dataset under inputs/example/GSD/ and a corresponding configuration file necessary for running GRN inference using 12 methods described in BEELINE.
python BLRunner.py --config config-files/config.yaml
. Running this script for the first time can be slow as it involves downloading the contianers from Docker hub.python BLEvaluator.py --config config-files/config.yaml --auc
. To display the complete list of evalutation options, run python BLEvaluator.py --help
.If you use BEELINE in your research, please cite:
Pratapa, A., Jalihal, A.P., Law, J.N., Bharadwaj, A., Murali, T. M. (2020) "Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data." Nature Methods, 17, 147–154.
Link to the pubication: https://www.nature.com/articles/s41592-019-0690-6
The preprint version of this article is available at: https://doi.org/10.1101/642926
The repository for BoolODE is located at: https://github.com/Murali-group/BoolODE
The input datasets used in BEELINE are available at: https://doi.org/10.5281/zenodo.3378975
Twitter thread link: https://twitter.com/t_m_murali/status/1215302095601119234?s=20