update

README.md

@@ -1,33 +1,33 @@
 # CEFCON
-This is the PyTorch implementation of the paper:
 
-Peizhuo Wang, Xiao Wen, Peng Lang, Hantao Shu, Lin Gao, Dan Zhao and Jianyang Zeng, 
-[Deciphering Driver Regulators of Cell Fate Decisions by Constructing and Controlling Cell-Lineage-Specific Gene Regulatory Networks](https://) 
+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.
 
-![Overview.png](https://github.com/WPZgithub/CEFCON/blob/main/Overview.png)
-
-CEFCON is a computational tool to characterize gene regulatory dynamics from a perspective of network control theory  and identifies putative regulators that
-drive cell fate decisions. 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 GRN construction, driver regulator
-identification and regulon-like gene module identification.
+![figs/Overview.png](https://github.com/WPZ/CEFCON/main/figs/Overview.png)
 
 ## Installation
 ### Requirements
-- python>=3.7
+- python>=3.8
 - numpy, scipy, pandas, scikit-learn, tqdm
 - [pytorch>=1.8.0](https://pytorch.org/get-started/locally/) 
 - [torch-geometric>=2.1.0](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)
-- anndata>=0.7.0
+- [scanpy>=1.9.0](https://scanpy.readthedocs.io/en/stable/installation.html)
 - networkx>=2.8.0
 - cvxpy>=1.2.0
 - gurobipy>=9.5.0
-- pyscenic>=0.12.0
+- [pyscenic>=0.12.0](https://pyscenic.readthedocs.io/en/latest/installation.html)
 - Recommended: An NVIDIA GPU with CUDA support for GPU acceleration
-### Optional
+### Optional (for evaluation and other analyses)
+- rpy2>=3.4.1
+- R>=3.6
+  - PRROC (R package)
 - matplotlib-venn
-- rpy2
+- palantir
 ### Install using pip
 ```
-pip install git+https://github.com/WPZgithub/CEFCON
+pip install git+https://github.com/WPZgithub/CEFCON.git
 ```
 
 ### Using GUROBI
@@ -37,36 +37,19 @@ GUROBI is a commercial solver that requires licenses to run. Thankfully, it prov
 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](https://www.scipopt.org/), will be used by default.
+If you have difficulty using GUROBI, a non-commercial solver, [SCIP](https://www.scipopt.org/), 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.
 
-## TODO
-- [ ] Add support for visualization
-- [ ] Add notebook file for analyses
-
 ## Input data
-The pre-processed data in the paper can be downloaded from [here](https://www.dropbox.com/s/48oe7shjq0ih151/data.tar.gz?dl=0). 
-- `scRNA-seq data`: a 'csv' file in which rows represent cells and columns represent genes
-- `Prior gene interaction network`: a edgelist formatted network file
-```
-   From      To      Directed
-   Gene1     Gene1   True
-   Gene1     Gene2   True
-   ...
-   GeneX     GeneY   False
-```
-We provide prior gene interaction networks for human and mouse respectively, located in `/prior_data`.
-- `Differential expression level`: 
-```
-   GeneName    absLogFC
-   Gene1       3.25
-   Gene2       1.06
-   ...
-   GeneX       0.71
-```
+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
@@ -75,24 +58,24 @@ python CEFCON.py [-h] --input_expData PATH --input_priorNet PATH [--input_genesD
                  [--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
 ```
-You can run the `run_CEFCON.sh` bash file for an usage example.
+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, Hantao Shu, Lin Gao, Dan Zhao and Jianyang Zeng, [Deciphering Driver Regulators of Cell Fate Decisions by Constructing and Controlling Cell-Lineage-Specific Gene Regulatory Networks](https://arxiv.org/abs/2102.07810), Preprint, 2023 
+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](https://github.com/WPZgithub/CEFCON), Preprint, 2023 
 
 ```
-@article{wang2022cefcon,
-  title={Deciphering Driver Regulators of Cell Fate Decisions by Constructing and Controlling Cell-Lineage-Specific Gene Regulatory Networks},
-  author={Wang, peizhuo and Wen, Xiao and Lang, Peng and Shu, Hantao and Gao, Lin and Zhao, Dan and Zeng, Jianyang},
+@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}
 }