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.
For more information about how this package has been used with real data, please check the following links:
- decoupleR’s general usage
- Pathway activity inference in bulk RNA-seq
- Pathway activity inference from scRNA-seq
- Transcription factor activity inference in bulk RNA-seq
- Transcription factor activity inference from scRNA-seq
- Example of Kinase and TF activity estimation
- decoupleR’s manuscript repository
- Python implementation
decoupleR is an R package distributed as part of the Bioconductor
project. To install the package, start R and enter:
install.packages('BiocManager')
BiocManager::install('saezlab/decoupleR')Alternatively, if you find any error, try to install the latest version from GitHub:
install.packages('remotes')
remotes::install_github('saezlab/decoupleR')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
one can find the license information of all the resources in OmniPath.
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