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https://github.com/saezlab/dorothea
R package to access DoRothEA's regulons
https://github.com/saezlab/dorothea
bioconductor-package functional-analysis gene-expression regulons tf-activities transcription-factor
Last synced: 11 days ago
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R package to access DoRothEA's regulons
- Host: GitHub
- URL: https://github.com/saezlab/dorothea
- Owner: saezlab
- License: gpl-3.0
- Created: 2017-03-27T16:10:28.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2024-02-24T12:21:16.000Z (11 months ago)
- Last Synced: 2024-05-17T00:10:31.319Z (9 months ago)
- Topics: bioconductor-package, functional-analysis, gene-expression, regulons, tf-activities, transcription-factor
- Language: R
- Homepage: https://saezlab.github.io/dorothea/
- Size: 393 MB
- Stars: 120
- Watchers: 15
- Forks: 25
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DoRothEA: collection of human and mouse regulons 
[](https://github.com/saezlab/dorothea/actions)
[](https://bioconductor.org/checkResults/release/data-experiment-LATEST/dorothea)
[](https://codecov.io/gh/saezlab/dorothea)
## Overview
DoRothEA is a gene regulatory network (GRN) containing signed transcription factor
(TF) - target gene interactions. DoRothEA regulons, the collection of a TF and
its transcriptional targets, were curated and collected from different types of
evidence for both human and mouse. A confidence level was assigned to each
TF-target interaction based on the number of supporting evidence.
These regulons, coupled with any statistical method, can be
used to infer TF activities from bulk or single-cell transcriptomics.
This is an R package for storing the regulons. To infer TF
activities, please check out
[decoupleR](https://doi.org/10.1093/bioadv/vbac016), available in
[R](https://saezlab.github.io/decoupleR/) or
[python](https://github.com/saezlab/decoupler-py).
## Installation
DoRothEA is available in
[Bioconductor](http://bioconductor.org/packages/release/data/experiment/html/dorothea.html).
In addition, one can install the development version from the Github repository:
```r
## To install the package from Bioconductor
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("dorothea")
## To install the development version from the Github repo:
devtools::install_github("saezlab/dorothea")
```
## Updates
Since the original release, we have implemented some extensions in DoRothEA:
1. **Extension to mouse**:
Originally DoRothEA was developed for the application to human data.
In a benchmark study we showed that DoRothEA is also applicable to mouse data,
as described in
[Holland et al., 2019](https://doi.org/10.1016/j.bbagrm.2019.194431).
Accordingly, we included new parameters to run mouse version of DoRothEA by
transforming the human genes to their mouse orthologs.
2. **Extension to single-cell RNA-seq data**:
We showed that DoRothEA can be applied to scRNA-seq data, as described in
[Holland et al., 2020](https://doi.org/10.1186/s13059-020-1949-z)
3. **Extension to other databases**
We have released a new literature based GRN with increased coverage and better
performance at identifying perturbed TFs, called [CollecTRI](https://github.com/saezlab/CollecTRI).
We encourage users to use CollecTRI instead of DoRothEA. Vignettes on how to
obtain activities are available at the [decoupleR package](https://saezlab.github.io/decoupleR/).
## License
DoRothEA is intended only for academic use as in contains resources
whose licenses don't permit commercial use. However, we developed a non-academic
version of DoRothEA by removing those resources (mainly TRED from the curated
databases). You can find the non-academic package with the regulons [here](https://github.com/saezlab/dorothea/tree/non-academic).
## Citation
If you use the DoRothEA resource, please cite:
> Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J.
Benchmark and integration of resources for the estimation of human
transcription factor activities. _Genome Research._ 2019. DOI: [10.1101/gr.240663.118](https://doi.org/10.1101/gr.240663.118).
If you infer TF activities, please cite:
> Badia-i-Mompel P., Vélez Santiago J., Braunger J., Geiss C., Dimitrov
D., Müller-Dott S., Taus P., Dugourd A., Holland C.H., Ramirez Flores R.O.
and Saez-Rodriguez J. decoupleR: Ensemble of computational methods to
infer biological activities from omics data. 2022. _Bioinformatics Advances_.
DOI: [10.1093/bioadv/vbac016](https://doi.org/10.1093/bioadv/vbac016)
If you use the CollecTRI resource, please cite:
> Müller-Dott S., Tsirvouli E., Vázquez M., Ramirez Flores R.O.,
Badia-i-Mompel P., Fallegger R., Lægreid A. and Saez-Rodriguez J.
Expanding the coverage of regulons from high-confidence prior knowledge for
accurate estimation of transcription factor activities. _bioRxiv_. 2023. DOI:
[10.1101/2023.03.30.534849](https://doi.org/10.1101/2023.03.30.534849)