Skip to content

Latest commit

 

History

History
29 lines (18 loc) · 2.93 KB

README.md

File metadata and controls

29 lines (18 loc) · 2.93 KB
Raw

🐝 BEELINE: Benchmarking gEnE reguLatory network Inference from siNgle-cEll transcriptomic data 🐝

Overview of BEELINE

This is the main repository for BEELINE. The documentation is available at: https://murali-group.github.io/Beeline/.

Quick setup:

  • To install docker on Ubuntu 18.04, follow the steps mentioned here
  • Setup docker to run docker without sudo using sudo usermod -aG docker $USER, if you haven't already. See more details here
  • The Docker images of 12 algorithms tested in BEELINE are available at https://hub.docker.com/u/grnbeeline. Alternatively, to build the docker containers from scratch (instead of using pre-built versions) for each of the algorithms run . initialize.sh (this step will take a while)
  • We recommend using Anaconda for Python. Run the . 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 here

We provided an example dataset under inputs/example/GSD/ and a corresponding configuration file necessary for running GRN inference using 12 methods described in BEELINE.

  • To compute proposed reconstructions on the example dataset, run 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.
  • To compute areas under the ROC and PR curves for the proposed reconstructions, run 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