README.md

In saezlab/progeny: Pathway RespOnsive GENes for activity inference from gene expression

PROGENy: Pathway RespOnsive GENes for activity inference

Overview

PROGENy is resource that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway responsive genes for human and mouse. These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics.

This is an R package for storing the pathway signatures. To infer pathway activities, please check out decoupleR, available in R or python.

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:

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.
2. Expanding Pathway Collection: We expanded human and mouse PROGENy with the pathways Androgen, Estrogen and WNT.
3. Extension to single-cell RNA-seq data: We showed that PROGENy can be applied to scRNA-seq data, as described in Holland et al., 2020

Citation

Schubert M, Klinger B, Klünemann M, Sieber A, Uhlitz F, Sauer S, Garnett MJ, Blüthgen N, Saez-Rodriguez J. 2018. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nature Communications: 10.1038/s41467-017-02391-6

saezlab/progeny documentation built on Feb. 9, 2023, 2:21 p.m.