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https://github.com/aertslab/SCENIC

SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.
https://github.com/aertslab/SCENIC

Last synced: 23 days ago
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SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.

Host: GitHub
URL: https://github.com/aertslab/SCENIC
Owner: aertslab
License: gpl-3.0
Created: 2016-07-13T12:04:50.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2024-04-05T09:56:19.000Z (3 months ago)
Last Synced: 2024-04-05T10:44:40.075Z (3 months ago)
Language: HTML
Homepage: http://scenic.aertslab.org
Size: 267 MB
Stars: 379
Watchers: 22
Forks: 96
Open Issues: 101
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome_single_cell - SCENIC - [R] - SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data. [SCENIC: single-cell regulatory network inference and clustering](https://www.nature.com/articles/nmeth.4463) (Software packages / RNA-seq)
awesome-single-cell - SCENIC - [R] - SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data. [SCENIC: single-cell regulatory network inference and clustering](https://www.nature.com/articles/nmeth.4463) (Software packages / Gene regulatory network identification)
awesome-single-cell - SCENIC - [R] - SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data. [SCENIC: single-cell regulatory network inference and clustering](https://www.nature.com/articles/nmeth.4463) (Software packages / RNA-seq)
awesome-grn-inference - SCENIC - [R, Python] - SCENIC is a package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data. [SCENIC: single-cell regulatory network inference and clustering](https://www.nature.com/articles/nmeth.4463) (Software packages / RNA-seq)

README

        | :warning: WARNING           |

|:----------------------------|

| [SCENIC](https://github.com/aertslab/SCENIC) is deprecated, use [pySCENIC](https://github.com/aertslab/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)](https://www.nature.com/articles/nmeth.4463).

There are currently **implementations** of SCENIC in R (this repository), in Python ([pySCENIC](https://github.com/aertslab/pySCENIC)), as well as wrappers to automate analyses with Nextflow ([VSN-pipelines](https://vsn-pipelines.readthedocs.io/en/latest/)).

The **output** from any of the implementations can be explored either in R, Python or [SCope](https://scope.aertslab.org) (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](http://htmlpreview.github.io/?https://github.com/aertslab/SCENIC/blob/master/Tutorials_JupyterNotebooks/SCENIC_tutorial_1-RunningVSN.html) 

- [2. Explore SCENIC output (with SCope and R)](http://htmlpreview.github.io/?https://github.com/aertslab/SCENIC/blob/master/Tutorials_JupyterNotebooks/SCENIC_tutorial_2-ExploringOutput.html)

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](http://htmlpreview.github.io/?https://github.com/aertslab/SCENIC/blob/master/inst/doc/SCENIC_Setup.html)

  - [Running SCENIC](http://htmlpreview.github.io/?https://github.com/aertslab/SCENIC/blob/master/inst/doc/SCENIC_Running.html)

  - The output from these examples is available at: [https://scenic.aertslab.org/scenic_paper/examples/](https://scenic.aertslab.org/scenic_paper/examples/)

**Python/Jupyter notebooks** with examples running SCENIC in different settings are available in the [SCENIC protocol repository](https://github.com/aertslab/SCENICprotocol/).

Frequently asked questions: [FAQ](https://github.com/aertslab/SCENIC/blob/master/vignettes/FAQ.md)

---

### News

2021/03/26:

- New tutorials to [run SCENIC from VSN](http://htmlpreview.github.io/?https://github.com/aertslab/SCENIC/blob/master/Tutorials_JupyterNotebooks/SCENIC_tutorial_1-RunningVSN.html) 

and [explore its output (with SCope and R)](http://htmlpreview.github.io/?https://github.com/aertslab/SCENIC/blob/master/Tutorials_JupyterNotebooks/SCENIC_tutorial_2-ExploringOutput.html)

- Tutorial to [create new databases](https://github.com/aertslab/create_cisTarget_databases)

2020/06/26:

- The **SCENICprotocol** including the Nextflow workflow, and `pySCENIC` notebooks are now officially released. For details see the [Github repository](https://github.com/aertslab/SCENICprotocol/), and the associated publication in [Nature Protocols](https://doi.org/10.1038/s41596-020-0336-2).

2019/01/24:

  - [Tutorial](https://rawcdn.githack.com/aertslab/SCENIC/0a4c96ed8d930edd8868f07428090f9dae264705/inst/doc/importing_pySCENIC.html)

    for importing [pySCENIC](http://pyscenic.readthedocs.io) results in SCENIC by using [loom](http://scope.aertslab.org/) 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](https://bioconductor.org/packages/RcisTarget) is now available in Bioconductor.

  - The new databases can be downloaded from [https://resources.aertslab.org/cistarget/](https://resources.aertslab.org/cistarget/). 

2018/03/30: New releases

  - [pySCENIC](https://pyscenic.readthedocs.io): lightning-fast python implementation of the SCENIC pipeline.

  - [Arboreto](https://arboreto.readthedocs.io) 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](https://resources.aertslab.org/cistarget/) for RcisTarget.