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PROGENy: Pathway RespOnsive GENes for activity inference

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Overview

Aberrant cell signaling is known to cause cancer and many other diseases, as well as a focus of treatment. A common approach is to infer its activity on the level of pathways using gene expression. However, mapping gene expression to pathway components disregards the effect of post-translational modifications, and downstream signatures represent very specific experimental conditions. Here we present PROGENy, a method that overcomes both limitations by leveraging a large compendium of publicly available perturbation experiments to yield a common core of Pathway RespOnsive GENes. Unlike existing methods, PROGENy can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways for patient survival. Collectively, these results show that PROGENy more accurately infers pathway activity from gene expression than other methods.

This is an R package for using the method described in Nature Communications.

@article{Schubert2018,
  doi = {10.1038/s41467-017-02391-6},
  url = {https://doi.org/10.1038/s41467-017-02391-6},
  year  = {2018},
  month = {jan},
  publisher = {Springer Nature},
  volume = {9},
  number = {1},
  author = {Michael Schubert and Bertram Klinger and Martina Kl\"{u}nemann and Anja Sieber and Florian Uhlitz and Sascha Sauer and Mathew J. Garnett and Nils Bl\"{u}thgen and Julio Saez-Rodriguez},
  title = {Perturbation-response genes reveal signaling footprints in cancer gene expression},
  journal = {Nature Communications}
}

Installation

Progeny is available in Bioconductor. In addition, one can install the development version from the Github repository:

## To install the package from Bioconductor
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("progeny")

## To install the development version from the Github repo:
devtools::install_github("saezlab/progeny")

Updates

Since the original release, we have implemented some extensions in PROGENy:

Update #1 - Extension to mouse

Originally PROGENy was developed for the application to human data. In a benchmark study we showed that PROGENy is also applicable to mouse data, as described in Holland et al., 2019. Accordingly, we included new parameters to run mouse version of PROGENy by transforming the human genes to their mouse orthologs.

Update #2 - Expanding Pathway Collection

We expanded human and mouse PROGENy with the pathways Androgen, Estrogen and WNT.

Update #3 - Extension to single-cell RNA-seq data

Recent technological advances in single-cell RNA-seq enable the profiling of gene expression at the individual cell level. We showed that PROGENy can be applied to scRNA-seq data, as described in Holland et al., 2020

Citing PROGENy

Besides the original paper, there are two additional publication describing expansions of PROGENy usage.

  • If you use PROGENy for your research please cite the original publication:

Schubert M, Klinger B, Klünemann M, Sieber A, Uhlitz F, Sauer S, Garnett MJ, Blüthgen N, Saez-Rodriguez J. “Perturbation-response genes reveal signaling footprints in cancer gene expression.” Nature Communications: 10.1038/s41467-017-02391-6

  • If you use for mouse or you use the expanded version containing 14 pathways, please cite additionally:

Holland CH, Szalai B, Saez-Rodriguez J. "Transfer of regulatory knowledge from human to mouse for functional genomics analysis." Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms. 2019. DOI: 10.1016/j.bbagrm.2019.194431.

  • If you apply PROGENy on single-cell RNA-seq data please cite additionally:

Holland CH, Tanevski J, Perales-Patón J, Gleixner J, Kumar MP, Mereu E, Joughin BA, Stegle O, Lauffenburger DA, Heyn H, Szalai B, Saez-Rodriguez, J. "Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data." Genome Biology. 2020. DOI: 10.1186/s13059-020-1949-z.

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R package for Pathway RespOnsive GENe activity inference

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