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. 2022 Mar 8;2(1):vbac016.
doi: 10.1093/bioadv/vbac016. eCollection 2022.

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

Pau Badia-I-Mompel  1   2 , Jesús Vélez Santiago  1   2 , Jana Braunger  1   2 , Celina Geiss  1   2 , Daniel Dimitrov  1   2 , Sophia Müller-Dott  1   2 , Petr Taus  3 , Aurelien Dugourd  1   2 , Christian H Holland  1   2 , Ricardo O Ramirez Flores  1   2 , Julio Saez-Rodriguez  1   2
Affiliations

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

Pau Badia-I-Mompel et al. Bioinform Adv. .

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 and Python 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, which are not present in other frameworks. Moreover, it leverages OmniPath, a meta-resource comprising over 100 databases of prior knowledge. 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 top methods perform better than other methods at predicting perturbed regulators.

Availability and implementation: decoupleR's open-source code is available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html) for R and in GitHub (https://github.com/saezlab/decoupler-py) for Python. 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).

Supplementary information: Supplementary data are available at Bioinformatics Advances online.

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Figures

Fig. 1.
Fig. 1.
Inference of biological activities with decoupleR’s workflow. (A) decoupleR’s workflow, it contains a collection of computational methods that coupled with prior knowledge resources estimates biological activities from omics data molecular readouts such as normalized counts or log fold changes. (B) Spearman correlation across methods and (C) predictive performance across methods in the RNA-seq data-set
See this image and copyright information in PMC

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