# MulNet: a scalable framework for reconstructing intra- and intercellular signaling networks from bulk and single-cell RNA-seq data

## TL;DR

MulNet is a new framework that builds detailed gene interaction networks from RNA-seq data, revealing key regulators and communication in cancer.

## Contribution

MulNet introduces a scalable multilayer network framework to integrate diverse molecular interactions and identify biologically relevant gene modules and regulators.

## Key findings

- MulNet outperformed existing methods in identifying gene modules across cancer datasets.
- MulNet identified miR-8485 as a new therapeutic target in colon cancer and its downstream pathways.
- Analysis of single-cell data revealed communication networks between fibroblasts and cancer cells in head and neck cancer.

## Abstract

Gene expression involves complex interactions between DNA, RNA, proteins, and small molecules. However, most existing molecular networks are built on limited interaction types, resulting in a fragmented understanding of gene regulation. Here, we present MulNet, a framework that organizes diverse molecular interactions underlying gene expression data into a scalable multilayer network. Additionally, MulNet can accurately identify gene modules and key regulators within this network. When applied across diverse cancer datasets, MulNet outperformed state-of-the-art methods in identifying biologically relevant modules. MulNet analysis of RNA-seq data from colon cancer revealed numerous well-established cancer regulators and a promising new therapeutic target, miR-8485, along with several downstream pathways it governs to inhibit tumor growth. MulNet analysis of single-cell RNA-seq data from head and neck cancer revealed intricate communication networks between fibroblasts and malignant cells mediated by transcription factors and cytokines. Overall, MulNet enables high-resolution reconstruction of intra- and intercellular communication from both bulk and single-cell data. The MulNet code and application are available at https://github.com/free1234hm/MulNet.