MulNet

MulNet organizes diverse molecular interactions underlying gene expression data into a scalable multilayer network to reconstruct intra- and intercellular signaling networks from bulk and single-cell RNA-seq data.

Key Features:

• Multilayer network construction: MulNet integrates diverse molecular interaction types underlying gene expression data into a unified multilayer network framework.
• Gene module identification: MulNet accurately identifies biologically relevant gene modules within the reconstructed network.
• Key regulator identification: MulNet identifies key regulators within the network, including novel therapeutic targets such as miR-8485.
• Cross-platform compatibility: MulNet processes both bulk RNA-seq and single-cell RNA-seq data.
• Intercellular communication analysis: MulNet reconstructs communication networks between different cell types, such as fibroblasts and malignant cells mediated by transcription factors and cytokines.

Scientific Applications:

• Cancer biology: MulNet identified well-established cancer regulators and the novel therapeutic target miR-8485 with its downstream tumor growth-inhibiting pathways in colon cancer.
• Tumor microenvironment analysis: MulNet revealed intricate communication networks between fibroblasts and malignant cells mediated by transcription factors and cytokines in head and neck cancer single-cell data.
• Comparative method evaluation: MulNet outperformed state-of-the-art methods in identifying biologically relevant modules across diverse cancer datasets.

Methodology:

MulNet organizes diverse molecular interactions into a scalable multilayer network and applies this framework to reconstruct intra- and intercellular signaling networks from RNA-seq data.