Installation

scFates is continuously tested with python 3.11, it is recommended to use a Miniconda environment.

PyPI

scFates is available on pypi, you can install it using:

pip install scFates

or the latest development version can be installed from GitHub using:

pip install git+https://github.com/LouisFaure/scFates

With all dependencies

• scFates.pp.find_overdispersed(), scFates.tl.test_association(), scFates.tl.fit(), scFates.tl.test_fork(), scFates.tl.activation(): Require R package mgcv interfaced via python package rpy2:

  conda create -n scFates -c conda-forge -c r python=3.11 r-mgcv rpy2 -y
  conda activate scFates
  pip install scFates
  

to avoid any possible crashes due to rpy2 not finding the R install on conda, run the following import command:

import os, sys
os.environ['R_HOME'] = sys.exec_prefix+"/lib/R/"
import scFates

• scFates.tl.cellrank_to_tree(): Requires cellrank to be installed in order to function:

  pip install cellrank
  

On Apple Silicon

Installing mgcv using conda/mamba on Apple Silicon lead to the package not being able to find some dynamic libraries (BLAS). In that case it is recommended to install it separately:

mamba create -n scFates -c conda-forge -c bioconda -c defaults python numpy=1.24.4 "libblas=*=*accelerate" rpy2 -y
mamba activate scFates
Rscript -e 'install.packages("mgcv",repos = "http://cran.us.r-project.org")'

GPU dependencies (optional)

If you have a nvidia GPU, scFates can leverage CUDA computations for speedups for the following functions:

scFates.pp.filter_cells(), scFates.pp.batch_correct(), scFates.pp.diffusion(), scFates.tl.tree(), scFates.tl.cluster()

The latest version of rapids framework is required (at least 0.17) it is recommanded to create a new conda environment:

conda create -n scFates-gpu -c rapidsai -c nvidia -c conda-forge -c defaults cuml=21.12 cugraph=21.12 python=3.8 cudatoolkit=11.0 -y
conda activate scFates-gpu
pip install git+https://github.com/j-bac/elpigraph-python.git
pip install scFates