# Modeling combinatorial regulation from single-cell multi-omics provides regulatory units underpinning cell type landscape using cRegulon

## TL;DR

This paper introduces cRegulon, a new method that identifies regulatory modules from single-cell data to better understand how gene regulation shapes different cell types.

## Contribution

cRegulon models combinatorial regulation to infer reusable regulatory modules from single-cell multi-omics data.

## Key findings

- cRegulon outperforms existing methods in identifying transcription factor combinatorial modules.
- The method improves cell type annotation using regulatory units derived from GRNs.

## Abstract

Advances in single-cell technology enable large-scale generation of omics data, promising for clarifying gene regulatory networks governing different cell type/states. Nonetheless, prevailing methods fail to account for universal and reusable regulatory modules in GRNs, which are fundamental underpinnings of cell type landscape. We introduce cRegulon to infer regulatory modules by modeling combinatorial regulation of transcription factors based on diverse GRNs from single-cell multi-omics data. Through benchmarking and applications using simulated datasets and real datasets, cRegulon outperforms existing approaches in identifying TF combinatorial modules as regulatory units and annotating cell types. cRegulon offers new insights and methodology into combinatorial regulation.

The online version contains supplementary material available at 10.1186/s13059-025-03680-w.