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 prefference for running it in 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).

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

• Introduction and setup
• running SCENIC

The output from the examples is available at: http://scenic.aertslab.org/examples/

Frequently asked questions: FAQ

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.