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.
- 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
2021/03/26:
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New tutorials to run SCENIC from VSN and explore its output (with SCope and R)
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Tutorial to create new databases
2020/06/26:
- The SCENICprotocol including the Nextflow workflow, and
pySCENICnotebooks are now officially released. For details see the Github repository, and the associated publication in Nature Protocols.
2019/01/24:
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.