decoupleR: ensemble of computational methods to infer biological activities from omics data
Status PubMed-not-MEDLINE Jazyk angličtina Země Velká Británie, Anglie Médium electronic-ecollection
Typ dokumentu časopisecké články
PubMed
36699385
PubMed Central
PMC9710656
DOI
10.1093/bioadv/vbac016
PII: vbac016
Knihovny.cz E-zdroje
- Publikační typ
- časopisecké články MeSH
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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Aibar S. et al. (2017) Scenic: single-cell regulatory network inference and clustering. Nat. Methods, 14, 1083–1086. PubMed PMC
Alhamdoosh M. et al. (2017) Combining multiple tools outperforms individual methods in gene set enrichment analyses. Bioinformatics, 33, 414–424. PubMed PMC
Alvarez M.J. et al. (2016) Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet., 48, 838–847. PubMed PMC
Dugourd A., Saez-Rodriguez J. (2019) Footprint-based functional analysis of multiomic data. Curr. Opin. Syst. Biol., 15, 82–90. PubMed PMC
Garcia-Alonso L. et al. (2019) Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res., 29, 1363–1375. PubMed PMC
Geistlinger L. et al. (2016) Bioconductor’s enrichment browser: seamless navigation through combined results of set- & network-based enrichment analysis. BMC Bioinformatics, 17, 45. PubMed PMC
Hänzelmann S. et al. (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 14, 7. PubMed PMC
Hernandez-Armenta C. et al. (2017) Benchmarking substrate-based kinase activity inference using phosphoproteomic data. Bioinformatics, 33, 1845–1851. PubMed PMC
Holland C.H. et al. (2020) Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. Genome Biol., 21, 36. PubMed PMC
Korotkevich G. et al. (2021) Fast gene set enrichment analysis. bioRxiv. DOI: https://doi.org/10.1101/060012.
Teschendorff A.E., Wang N. (2020) Improved detection of tumor suppressor events in single-cell RNA-seq data. NPJ Genomic Med., 5, 43. PubMed PMC
Türei D. et al. (2021) Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol. Syst. Biol., 17, e9923. PubMed PMC
Väremo L. et al. (2013) Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res., 41, 4378–4391. PubMed PMC