Dataset Information

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

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.

SUBMITTER: Badia-I-Mompel P

PROVIDER: S-EPMC9710656 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

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

Badia-I-Mompel Pau P Vélez Santiago Jesús J Braunger Jana J Geiss Celina C Dimitrov Daniel D Müller-Dott Sophia S Taus Petr P Dugourd Aurelien A Holland Christian H CH Ramirez Flores Ricardo O RO Saez-Rodriguez Julio J

Bioinformatics advances 20220308 1

<h4>Summary</h4>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 an ...[more]

PMID: 36699385

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