Abstract Summary Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across methods perform better than other methods at predicting perturbed regulators. Availability and Implementation decoupleR is open source available in Bioconductor ( https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html ). The code to reproduce the results is in Github ( https://github.com/saezlab/decoupleR_manuscript ) and the data in Zenodo ( https://zenodo.org/record/5645208 ). Contact Julio Saez-Rodriguez at pub.saez@uni-heidelberg.de .

decoupleR: Ensemble of computational methods to infer biological activities from omics data