| :warning: WARNING | |:----------------------------| | SCENIC is deprecated, use pySCENIC instead. |

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), 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).

Tutorials

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.

• 1. Run SCENIC from VSN
• 2. Explore SCENIC output (with SCope and R)

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.

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

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

Frequently asked questions: FAQ

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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.