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@article{2244045,
author = {BadiaandiandMompel, P. and Santiago, JV. and Braunger, J. and Geiss, C. and Dimitrov, D. and MüllerandDott, S. and Tauš, Petr and Dugourd, A. and Holland, Ch. and Flores, RR and SaezandRodriguez, J.},
article_location = {Spojené království},
article_number = {1},
doi = {http://dx.doi.org/10.1093/bioadv/vbac016},
keywords = {computational methods; omics data},
language = {eng},
issn = {2635-0041},
journal = {Bioinformatics Advances},
title = {decoupleR: Ensemble of computational methods to infer biological activities from omics data},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710656/pdf/vbac016.pdf},
volume = {2},
year = {2022}
}
TY - JOUR
ID - 2244045
AU - Badia-i-Mompel, P. - Santiago, JV. - Braunger, J. - Geiss, C. - Dimitrov, D. - Müller-Dott, S. - Tauš, Petr - Dugourd, A. - Holland, Ch. - Flores, RR - Saez-Rodriguez, J.
PY - 2022
TI - decoupleR: Ensemble of computational methods to infer biological activities from omics data
JF - Bioinformatics Advances
VL - 2
IS - 1
SP - 1-3
EP - 1-3
PB - Oxford University Press
SN - 26350041
KW - computational methods
KW - omics data
UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710656/pdf/vbac016.pdf
N2 - 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.
ER -
BADIA-I-MOMPEL, P., JV. SANTIAGO, J. BRAUNGER, C. GEISS, D. DIMITROV, S. MÜLLER-DOTT, Petr TAUŠ, A. DUGOURD, Ch. HOLLAND, RR FLORES and J. SAEZ-RODRIGUEZ. decoupleR: Ensemble of computational methods to infer biological activities from omics data. \textit{Bioinformatics Advances}. Spojené království: Oxford University Press, 2022, vol.~2, No~1, p.~1-3. ISSN~2635-0041. Available from: https://dx.doi.org/10.1093/bioadv/vbac016.
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