s-aibar/SCENIC

SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.

SCENIC

SCENIC (Single-Cell rEgulatory Network Inference and Clustering) is a computational method to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.

The description of the method and some usage examples are available in Nature Methods (2017).

There are currently implementations of SCENIC in R (this repository), and in Python. If you don't have a strong preference for using R, we would recommend to check out the SCENIC protocol repository, which contains the Nextflow workflow, and Python/Jupyter notebooks to easily run SCENIC (highly recommended for running it in batch or bigger datasets). The output from any of the implementations can then be explored either in R, Python or SCope (a web interface).

For more details and installation instructions on running SCENIC in R see the tutorials:

• Introduction and setup
• Running SCENIC
• The output from these examples is available at: https://scenic.aertslab.org/scenic_paper/examples/

Frequently asked questions: FAQ

News

2021/03/26:

• New tutorials to run SCENIC from VSN and explore its output (with SCope and R)

• Tutorial to create new databases

2020/06/26:

• The SCENICprotocol including the Nextflow workflow, and pySCENIC notebooks are now officially released. For details see the Github repository, and the associated publication in Nature Protocols.

2019/01/24:

• Tutorial for importing pySCENIC results in SCENIC by using loom files.

2018/06/20:

• Added function export2scope() (see http://scope.aertslab.org/).
• Version bump to 1.0.

2018/06/01:

• Updated SCENIC pipeline to support the new version of RcisTarget and AUCell.

2018/05/01:

• RcisTarget is now available in Bioconductor.
• The new databases can be downloaded from https://resources.aertslab.org/cistarget/.

2018/03/30: New releases

• pySCENIC: lightning-fast python implementation of the SCENIC pipeline.
• Arboreto package including GRNBoost2 and scalable GENIE3:
  • Easy to install Python library that supports distributed computing.
  • It allows fast co-expression module inference (Step1) on large datasets, compatible with both, the R and python implementations of SCENIC.
• Drosophila databases for RcisTarget.