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 performs 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).

Spatial multi-omic map of human myocardial infarction

“…To infer a transcription factor regulon activity score, we estimated the mean expression of the target genes in each cell-type-specific regulon. Cell-type pseudo-bulk profiles were filtered to contain only genes with at least 10 counts in 5% of the samples, before the estimation of normalized weighted means using decoupleR’s 70 (v1.1.0) wmean function with 1,000 permutations. Regulon activities were standardized and correlated with transcription factor binding activities using Spearman correlations.…”

Deep Phosphoproteomic Elucidation of Metformin-Signaling in Heterogenous Colorectal Cancer Cells

“…To infer the kinase activity response in a cell line-specific manner, we performed an enrichment on the phosphoproteomic data using decoupleR (Badia-i-Mompel et al, 2022), leveraging the kinase-P-site interactions from the OmniPath database (Türei et al ., 2016; Türei et al ., 2021). The group of kinases that showed mostly downregulated activities across the cell lines ( Figure 5A , green cluster) include many kinases regulating cellular growth and proliferation, such as mTOR, CDKs, mitotic regulators AURKA, AURKB, and PLK1, MAPKs, but also several tyrosine kinases such as EGFR, FYN, and ABL1 and a dual specificity kinase DYRK1A, which is not evident from the above P-site level analyses.…”

“…Interactions reported exclusively in the ProtMapper database were removed after noticing inconsistent interactions leading to a total of 29,445 signed kinase-P-site interactions of 580 different kinases. Additionally, t-values from the differential analysis of each P-site after metformin stimulation using the limma R-package were passed to the run_viper function from the decoupleR R-package ( Alvarez et al, 2016 ; Badia-i-Mompel et al ., 2022 ) . Only kinases with at least five measured targets were included in the kinase activity estimation.…”

Phosphoproteomic analysis of metformin signaling in colorectal cancer cells elucidates mechanism of action and potential therapeutic opportunities

“…Additionally, t ‐values from the differential analysis of each P‐site after metformin stimulation using the limma R‐package were passed to the run_viper function from the decoupleR R package. 74 , 75 Only kinases with at least five measured targets were included in the kinase activity estimation.…”