scvi-tools can be installed via conda or pip. If you don’t know which to choose, we recommend conda for beginner users.
Install Conda. We typically use the Miniconda Python distribution. Use Python version >=3.7.
Create a new conda environment:
conda create -n scvi-env python=3.7
Activate your environment:
source activate scvi-env
Install Python, we prefer the pyenv version management system, along with pyenv-virtualenv.
Install PyTorch. If you have an Nvidia GPU, be sure to install a version of PyTorch that supports it – scvi-tools runs much faster with a discrete GPU.
Install scvi-tools in one of the following ways:
conda install scvi-tools -c bioconda -c conda-forge
pip install scvi-tools
Through pip with packages to run notebooks. This installs scanpy, etc.:
pip install scvi-tools[tutorials]
Nightly version - clone this repo and run:
pip install .
For development - clone this repo and run:
pip install -e .[dev,docs]
If you wish to use multiple GPUs for hyperparameter tuning, install MongoDb.
scvi-tools can be called from R via Reticulate.
This is only recommended for basic scvi-tools functionality (getting the latent space, normalized expression, differential expression). For more involved analyses with scvi-tools, we highly recommend using it from Python.
The easiest way to install scvi-tools for R is via conda.
Install Conda Prerequisites (see above).
Then in your R code: