Installation
GPU Acceleration
UniTVelo is designed based on TensorFlow’s automatic differentiation architecture. Please make sure TensorFlow and relative CUDA dependencies are correctly installed.
Please use the following scripts to confirm TensorFlow is using the GPU:
import tensorflow as tf
print ("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
If GPU is not available, UniTVelo will automatically switch to CPU for model fitting or it can be spcified in config.py (see Getting Started).
Main Module
UniTVelo requires Python 3.7 or later. We recommend to use Anaconda environment for version control and to avoid potential conflicts:
conda create -n unitvelo python=3.7
conda activate unitvelo
UniTVelo package can be conveniently installed via PyPI (for stable version)
pip install unitvelo
or directly from GitHub repository (for development version):
pip install git+https://github.com/StatBiomed/UniTVelo
Dependencies
Most required dependencies are automatically installed, e.g.
scvelo for a few pre- and post-processing analysis
statsmodels for regression analysis
jupyter for running RNA velocity within notebooks
If you run into any issues or errors are raised during the installation process, feel free to contact us at GitHub.