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, scRNA-seq data and bulk TF ChIP-seq data. We consider the co-occurrence of TF expression and chromatin accessibility at TF binding sites in each cell. ChIP-seq data allows motif-agonistic activity inference of transcriptional coregulators or TF harboring neomorphic mutations.

Schematics of epiregulon workflow:

Main functions of epiregulon:

This documentation presents epiregulon v2. Review NEWS file to learn about the changes introduced with the version 2.

Check out our publication:

Włodarczyk T, Lun A, Wu D, Shi M, Ye X, Menon S, Toneyan S, Seidel K, Wang L, Tan J, Chen S-Y, Keyes T, Chlebowski A, Waddell A, Zhou W, Wang Y, Yuan Q, Guo Y, Chen L-F, Daniel B, Hafner A, He M, Chibly A, Liang Y, Duren Z, Metcalfe C, Hafner M, Siebel C, Corces M. R, Yauch R, Xie S, Yao X (2025) Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states. Nature Communications 16: 7118. doi:10.1038/s41467-025-62252-5