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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F22%3A00130147" target="_blank" >RIV/00216224:14740/22:00130147 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710656/pdf/vbac016.pdf" target="_blank" >https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710656/pdf/vbac016.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/bioadv/vbac016" target="_blank" >10.1093/bioadv/vbac016</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30204 - Oncology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Bioinformatics Advances

ISSN

2635-0041

e-ISSN

2635-0041

Svazek periodika

2

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

3

Strana od-do

1-3

Kód UT WoS článku

001153137500039

EID výsledku v databázi Scopus

2-s2.0-85132934861