TFvelo

This is the code of TFvelo: gene regulation inspired RNA velocity estimation. Paper is avaliable at: Li, Jiachen, et al. "TFvelo: gene regulation inspired RNA velocity estimation." bioRxiv (2023): 2023-07.

Due to the wide usage of scVelo (Bergen, Volker, et al. "Generalizing RNA velocity to transient cell states through dynamical modeling." Nature biotechnology 38.12 (2020): 1408-1414, link) and its clean, well-organized codes, we develop TFvelo based on the framework of scvelo.

In TFvelo, the gene regulatory relationship is taken into consideration for modeling the time derivative of RNA abundance, which allows a more accurate phase portrait fitting for each gene.

TFvelo_run_demo.py provides the demo for runing TFvelo, and TFvelo_analysis_demo.py is for results visualization. The package TFvelo can be directly downloaded for usage.

Please decompress the ENCODE TF-target database file firstly after downloading this package. On linux, you can run:

unzip ENCODE.zip

Environment:

conda create -n TFvelo_env python=3.8.12
conda activate TFvelo_env
pip install pandas==1.2.3 
pip install anndata==0.8.0 
pip install scanpy==1.8.2
pip install numpy==1.21.6
pip install scipy==1.10.1 
pip install numba==0.57.0 
pip install matplotlib==3.3.4
pip install scvelo==0.2.4
pip install typing_extensions

Reproduce:

Running the program with default parameters can reproduce the results in manuscript.

To reproduce TFvelo on pancreas:

python TFvelo_run_demo.py --dataset_name pancreas

This will automatically download, preprocess and run TF model on pancrease dataset. The result will be stored in 'TFvelo_pancreas/rc.h5ad'.

After that, the visualization of results can be obtained by

python TFvelo_analysis_demo.py --dataset_name pancreas

This will show the pseudotime and streamplot on UMAP, and also the phase portrait fitting of best fitted genes. The result will be stored in 'TFvelo_pancreas_demo/TFvelo.h5ad', and figures will be saved in folder 'figures'.

Usage:

To apply TFvelo to any other scRNA-seq datasets:

you can define a personalized name for the dataset, and simply add the following codes into the preprocess() function in TFvelo_run_demo.py:

if args.dataset_name == your_dataset_name:
  adata = ad.read_h5ad(your_h5ad_file_path)

Then run the code with:

python TFvelo_run_demo.py --dataset_name your_dataset_name
python TFvelo_analysis_demo.py --dataset_name your_dataset_name

As a result, all generated h5ad files will be puted in the folder named: "TFvelo_"+your_data_name+"_demo". And figures will be saved in the folder "figures".

Hyperparameters:

--n_jobs: The number of CPUs to use
--init_weight_method: The method to initialize the weights. Correlation is adopted by default.
--WX_method: The method to optimize weight. lsq_linear is adopted by default.
--n_neighbors: The number of neighbors.
--WX_thres: The max absolute value for weights.
--TF_databases: The way to select candidate TFs. Use ENCODE and ChEA by default.
--max_n_TF: Max number of TFs used for modeling each gene.
--max_iter: Max number of iterations in the generalized EM algorithm.
--n_time_points: The number of time points in the time assinment (E step of the generalized EM algorithm). 
--save_name: The name of folder which all generated files will be put in.

Baselines and metrics:

The code for baselines and metrics are provided in the folder "baselines". You may need to create an environment and install the required packages for each baseline method.

xiaoyeye / TFvelo