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 DSL1 workflow, and Jupyter/Python 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:
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 DSL1 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.