CEFCON

CEFCON is a computational tool for deciphering driver regulators of cell fate decisions from single-cell RNA-seq data. CEFCON takes a prior gene interaction network and expression profiles from scRNA-seq data as inputs, and consists of three main components, including cell-lineage-specific gene regulatory network (GRN) construction, driver regulator identification and regulon-like gene module (RGM) identification.

Installation

Requirements

• python>=3.8
• numpy, scipy, pandas, scikit-learn, tqdm
• pytorch>=1.8.0
• torch-geometric>=2.1.0
• scanpy>=1.9.0
• networkx>=2.8.0
• cvxpy>=1.2.0
• gurobipy>=9.5.0
• pyscenic>=0.12.0
• Recommended: An NVIDIA GPU with CUDA support for GPU acceleration

Optional (for evaluation and other analyses)

• rpy2>=3.4.1
• R>=3.6
  • PRROC (R package)
• matplotlib-venn
• palantir

Install using pip

pip install git+https://github.com/WPZgithub/CEFCON.git

Using GUROBI

We recommend using GRUOBI to solve the integer linear programming (ILP) problem for identifying driver genes. GUROBI is a commercial solver that requires licenses to run. Thankfully, it provides free licenses in academia, as well as trial licenses outside academia. If there is no problem about the licenses, you need to install the gurobipy package.

If you have difficulty using GUROBI, a non-commercial solver, SCIP, will be used. But it does not ensure a successful solution.

Using GPU

We recommend using GPU. If so, you will need to install the GPU version of PyTorch.

Input data

The pre-processed data in the paper can be downloaded from here.

• Prior gene interaction network: an edgelist formatted network file.
     We provide prior gene interaction networks for human and mouse respectively, located in /prior_data.
• scRNA-seq data: a '.csv' file in which rows represent cells and columns represent genes, or a '.h5ad' formatted file with AnnData objects.
• Differential expression level: a 'csv' file contains the log fold change of each gene.
  Examples of input data are located in /example_data

Usage example

Command line usage

python CEFCON.py [-h] --input_expData PATH --input_priorNet PATH [--input_genesDE PATH] [--TFs PATH] [--additional_edges_pct ADDITIONAL_EDGES_PCT] [--cuda CUDA] [--seed SEED] [--hidden_dim HIDDEN_DIM]
                 [--output_dim OUTPUT_DIM] [--heads HEADS] [--attention {COS,AD,SD}] [--miu MIU] [--epochs EPOCHS] [--repeats REPEATS] [--edge_threshold_param EDGE_THRESHOLD_PARAM] [--remove_self_loops]
                 [--topK_drivers TOPK_DRIVERS] --out_dir OUT_DIR

Please use python CEFCON.py -h to view parameters information.
You can run the run_CEFCON.sh bash file for a usage example.

• Output (in the output folder ${OUT_DIR}/):
  • A cell-lineage-specific GRN with default name "cl_GRN.csv"
  • Gene embeddings with default name "gene_embs.csv"
  • A list of driver regulators with default name "driver_regulators.csv"
  • A list of RGMs with default name "RGMs.csv"

Citation

Please cite the following paper, if you find the repository or the paper useful.

Peizhuo Wang, Xiao Wen, Peng Lang, Han Li, Hantao Shu, Lin Gao, Dan Zhao and Jianyang Zeng, A network-based framework for deciphering driver regulators of cell fate decisions from single-cell RNA-seq data, Preprint, 2023

@article{wang2023cefcon,
  title={A network-based framework for deciphering driver regulators of cell fate decisions from single-cell RNA-seq data},
  author={Wang, peizhuo and Wen, Xiao and Lang, Peng and Li, Han and Shu, Hantao and Gao, Lin and Zhao, Dan and Zeng, Jianyang},
  journal={Preprint},
  year={2023}
}

Bugs & Suggestions

Please contact [email protected] or raise an issue in the github repo with any questions.