Need help with SCENIC?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

196 Stars 71 Forks Other 271 Commits 105 Opened issues


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

Services available


Need anything else?

Contributors list

# 131,983
1 commit
# 123,643
1 commit


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

see the tutorials: - Introduction and setup - Running SCENIC - The output from these examples is available at:

Frequently asked questions: FAQ



2020/06/26: - The SCENICprotocol including the Nextflow workflow, and

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

(see - 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

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.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.