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.