<!-- README.md is generated from README.Rmd. Please edit that file --> # decoupleR <img src="inst/figures/logo.svg" align="right" width="120" /> <!-- badges: start --> [](https://www.tidyverse.org/lifecycle/#maturing) [](https://bioconductor.org/checkResults/release/bioc-LATEST/decoupleR) [](https://bioconductor.org/checkResults/devel/bioc-LATEST/decoupleR) [](https://github.com/saezlab/decoupleR/actions) [](https://codecov.io/gh/saezlab/decoupleR?branch=master) [](https://github.com/saezlab/decoupleR/issues) <!-- badges: end --> ## Overview There are many methods that allow us to extract biological activities from omics data. `decoupleR` is a Bioconductor package containing different statistical methods to extract biological signatures from prior knowledge within a unified framework. Additionally, it incorporates methods that take into account the sign and weight of network interactions. `decoupleR` can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase. This is the R version, for its faster and memory efficient Python implementation go [here](https://decoupler-py.readthedocs.io/en/latest/). <p align="center" width="100%"> <img src="https://github.com/saezlab/decoupleR/blob/master/inst/figures/graphical_abstract.png?raw=1" align="center" width="45%"> </p> For more information about how this package has been used with real data, please check the following links: - [decoupleR’s general usage](https://saezlab.github.io/decoupleR/articles/decoupleR.html) - [Pathway activity inference in bulk RNA-seq](https://saezlab.github.io/decoupleR/articles/pw_bk.html) - [Pathway activity inference from scRNA-seq](https://saezlab.github.io/decoupleR/articles/pw_sc.html) - [Transcription factor activity inference in bulk RNA-seq](https://saezlab.github.io/decoupleR/articles/tf_bk.html) - [Transcription factor activity inference from scRNA-seq](https://saezlab.github.io/decoupleR/articles/tf_sc.html) - [Example of Kinase and TF activity estimation](https://saezlab.github.io/kinase_tf_mini_tuto/) - [decoupleR’s manuscript repository](https://github.com/saezlab/decoupleR_manuscript) - [Python implementation](https://decoupler-py.readthedocs.io/en/latest/) # Installation `decoupleR` is an R package distributed as part of the Bioconductor project. To install the package, start R and enter: ``` r install.packages('BiocManager') BiocManager::install('saezlab/decoupleR') ``` Alternatively, if you find any error, try to install the latest version from GitHub: ```r install.packages('remotes') remotes::install_github('saezlab/decoupleR') ``` ## License Footprint methods inside `decoupleR` can be used for academic or commercial purposes, except `viper` which holds a non-commercial license. The data redistributed by `OmniPath` does not have a license, each original resource carries their own. [Here](https://omnipathdb.org/info) one can find the license information of all the resources in `OmniPath`. ## Citation 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. 2022. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics Advances. <https://doi.org/10.1093/bioadv/vbac016>