decoupleR_manuscript

Code to reproduce the results from decoupleR's manuscript

Abstract

Computational methods allow the extraction of mechanistic signatures from omics data based on prior knowledge resources, reducing the dimensionality of the data for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. Using decoupleR, we evaluated the performance of contemporary methods on transcriptomic and phospho-proteomic perturbation experiments.

Benchmark pipeline

In this manuscript we have built a separate R package to benchmark decoupleR, called decoupleRBench. You can install it by running:

devtools::install_github('saezlab/decoupleRBench')

You can check the source code in: https://github.com/saezlab/decoupleRBench

Data

To retrieve all the necessary data, please run:

Rscript R/process/get_data.R

Analysis

To reproduce all the analyses performed in the manuscript, please run:

# Run perturbation benchmark
Rscript R/process/run_rna_bench.R
Rscript R/process/run_php_bench.R

# Run noise analysis (This might take a while)
Rscript R/process/run_rna_noise.R #(~6 hours)
Rscript R/process/run_php_noise.R #(~2 hours)

# Run scalablity analysis (This might take a while)
Rscript R/process/get_simmulated_data.R
bash run_scale_bench.R

Figures

To generate all the figures shown in the manuscript, please run:

Rscript R/plot/supp_fig_1.R
Rscript R/plot/supp_fig_2.R
Rscript R/plot/supp_fig_3.R
Rscript R/plot/supp_fig_4.R
Rscript R/plot/supp_fig_5.R
Rscript R/plot/supp_fig_6.R
Rscript R/plot/supp_fig_7.R