Description

This package provides a scalable Python suite for fast tree inference and advanced pseudotime downstream analysis, with a focus on fate biasing. This package is compatible with anndata object format used in scanpy or scvelo pipelines. A complete documentation of this package is available here.

Tree inference algorithms

The user have the choice between two algorithm for tree inference:

ElPiGraph

For scTree, the python implementation of the ElPiGraph algorithm is used, which include GPU accelerated principal tree inference. A self-contained description of the algorithm is available here or in the related paper

A R implementation of this algorithm is also available, coded by Luca Albergante

A native MATLAB implementation of the algorithm (coded by Andrei Zinovyev and Evgeny Mirkes) is also available

Simple PPT

A simple PPT inspired approach, translated from the crestree R package, code has been also adapted to run on GPU for accelerated tree inference.

Citations

Code for PPT inference and most of downstream pseudotime analysis was initially written in a R package by Ruslan Soldatov for the following paper:

Soldatov, R., Kaucka, M., Kastriti, M. E., Petersen, J., Chontorotzea, T., Englmaier, L., … Adameyko, I. (2019). Spatiotemporal structure of cell fate decisions in murine neural crest. Science, 364(6444).

if you are using ElPiGraph, please cite :

Albergante, L., Mirkes, E. M., Chen, H., Martin, A., Faure, L., Barillot, E., … Zinovyev, A. (2020). Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph. Entropy, 22(3), 296.

Installation

scFates 0.2 is now available on pypi, you can install it using:

pip install scFates

or the latest development version can be installed from GitHub:

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

Python dependencies

scFates gives the choice of between SimplePPT and ElPiGraph for learning a principal graph from the data. Elpigraph needs to be installed from its github repository with the following command:

pip install git+https://github.com/j-bac/elpigraph-python.git

R dependencies

scFates rely on the R package mgcv to perform testing and fitting of the features on the peudotime tree. Package is installed in an R session with the following command:

install.packages('mgcv')

GPU dependencies (optional)

If you have a nvidia GPU, scFates can leverage CUDA computations for speedups in some functions, for that you will need Rapids 0.17 installed.

Docker container

scFates can be run on a Docker container based on Rapids 0.17 container, which provide a gpu enabled environment with Jupyter Lab. Use the following command:

docker run --rm -it --gpus all -p 8888:8888 -p 8787:8787 -p 8786:8786 \
    louisfaure/scfates:version-0.2