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=3.9
Activate your environment:
conda activate scvi-env
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.
Installing scvi-tools on a Mac with Apple Silicon is only possible using a native version of Python. A native version of Python can be installed with an Apple Silicon version of miniconda (which can be installed from a native version of homebrew). This is due to an scvi-tools dependency on jax, which cannot be run via Rosetta.
conda install scvi-tools -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]"
scvi-tools can be called from R via Reticulate.
This is only recommended for basic 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:
library(reticulate) use_condaenv("scvi-env", required=TRUE)