Introduction

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and cell states. The main function of the epiregulon package is to construct gene regulatory networks and infer transcription factor (TF) activity in single cells by integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data.

epiregulon.archr is extended version of epiregulon. It is designed to allow for downstream analysis of the single cell data prepared with the ArchR package. In particular, gene expression, chromatin availability and transcription factor motif matches data retrieved from the ArchR project are utilized to construct model of the gene regulatory network. Building upon epiregulon, epiregulon.archr inherits all its features. Likewise, it is complemented by epiregulon.extra, which provides tools for data visualization and network analysis.

For full documentation, please refer to the epiregulon book.

Installation

# install devtools
if(!require(devtools)) install.packages("devtools")

# install epiregulon.archr
devtools::install_github(repo='xiaosaiyao/epiregulon.archr')

# install epiregulon.extra (optional)
devtools::install_github(repo='xiaosaiyao/epiregulon.extra)

Example data included in the tutorial are available from scMultiome

if (!require("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("scMultiome")

Software Requirements

Users should have R version 4.4 or higher.

Reference

Tomasz Włodarczyk, Aaron Lun, Diana Wu, Shreya Menon, Shushan Toneyan, Kerstin Seidel, Liang Wang, Jenille Tan, Shang-Yang Chen, Timothy Keyes, Aleksander Chlebowski, Yu Guo, Ciara Metcalfe, Marc Hafner, Christian W. Siebel, M. Ryan Corces, Robert Yauch, Shiqi Xie, Xiaosai Yao. 2023. "Inference of single-cell transcription factor activity to dissect mechanisms of lineage plasticity and drug response" bioRxiv 2023.11.27.568955; doi: https://doi.org/10.1101/2023.11.27.568955

Contact: Xiaosai Yao, Genentech Inc.