Installation

TIVelo requires Python 3.8 or later. We recommend using Miniconda for managing the environment.

Step 1: Create and Activate the Conda Environment

First, create a new Conda environment with Python 3.9:

conda create -n tivelo python=3.9 -y
conda activate tivelo

Step 2: Install Dependencies

We have published the TIVelo package on PyPI. To ensure a smooth and stable installation process, we recommend installing large dependencies separately before installing TIVelo in a Conda environment.

PyTorch

Install PyTorch along with torchvision, torchaudio, and CUDA support:

conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia -y

Numba

Install Numba:

To enable CUDA GPU support for Numba, install the latest NVIDIA graphics drivers for your platform (the open-source Nouveau drivers do not support CUDA). Then install the CUDA Toolkit package.

For CUDA 12, install the following:

conda install -c conda-forge cuda-nvcc cuda-nvrtc "cuda-version>=12.0" -y

For CUDA 11, install the following:

conda install -c conda-forge cudatoolkit "cuda-version>=11.2,<12.0" -y

Note: You do not need to install the CUDA SDK from NVIDIA.

Cpu version

conda install numba

Scanpy

Install Scanpy along with additional dependencies:

conda install -c conda-forge scanpy python-igraph leidenalg -y

scVelo

Install scVelo:

pip install  scvelo==0.3.1

Optional dependencies for directed PAGA and Louvain modularity:

pip install igraph louvain

Optional dependencies for fast neighbor search via hnswlib:

pip install pybind11 hnswlib

Step 3: Install TIVelo

Finally, install TIVelo:

pip install tivelo

JupyterLab

To run the tutorials in a notebook locally, please install JupyterLab:

conda install jupyterlab -y

With these steps, TIVelo and its dependencies will be installed and ready for use.