plot

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.

For full documentation, please refer to the epiregulon book.

plot There are three related packages. The core epiregulon package supports SingleCellExperiment objects. If the users would like to start from ArchR projects, they may choose to use epiregulon.archr package, which allows for seamless integration with the ArchR package. Moreover, we provide a suite of tools in epiregulon.extra package for enrichment analysis, visualization, and network analysis which can be run on the epireglon or epiregulon.archr output.

Installation

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

# install basic epiregulon package
devtools::install_github(repo='xiaosaiyao/epiregulon')

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

# install extended version of epiregulon
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")

System Requirements

Hardware Requirements

The epiregulon package has been tested on a standard MacBook with 16GB of RAM and 8 cores

Software Requirements

The epiregulon package is supported for macOS, Linux and Windows. The package has been tested on the following systems:

macOS: Monterey (12.7.1)
Linux: Ubuntu 22.04.2 LTS
Windows: Windows 2022

Users should have R version 4.3.0 or higher

Functions

Functions in the suite of Epiregulon packages plot

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.

xiaosaiyao / epiregulon

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