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SCENIC is deprecated, use pySCENIC instead.


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), in Python (pySCENIC), as well as wrappers to automate analyses with Nextflow (VSN-pipelines).

The output from any of the implementations can be explored either in R, Python or SCope (a web interface).


If you have access to Nextflow and a container system (e.g. Docker or Singularity), we recommend to run SCENIC through the VSN-pipeline.

This option is specially useful for running SCENIC on large datasets, or in batch on multiple samples.

If you prefer to use R for the whole analysis, these are the main tutorials:

The tutorials in R include a more detailed explanation of the workflow and source code.

Python/Jupyter notebooks with examples running SCENIC in different settings are available in the SCENIC protocol repository.

Frequently asked questions: FAQ




  • 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.




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


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.