COMUNET

COMUNET represents and analyzes intercellular communication from single-cell transcriptomic data using multiplex network models to characterize and visualize cell–cell signaling patterns.

Key Features:

• Multiplex Network Representation: Employs multiplex networks to represent and cluster potential communication pathways between different cell types.
• Pattern Search Capability: Implements algorithms to search for specific communication patterns within multiplex network layers.
• Comparative Analysis: Performs comparative analyses of cell–cell communication between two distinct biological conditions to identify changes in signaling dynamics.

Scientific Applications:

• Single-Cell Transcriptomic Data Analysis: Applied to single-cell transcriptomic datasets to infer and visualize communication patterns, with examples including mouse embryos and an acute myeloid leukemia patient at diagnosis and post-treatment.
• Biomedical Research: Enables investigation of disease mechanisms and treatment-associated changes in cell communication networks, such as cancer microenvironment interactions.

Methodology:

Implemented as an R package, COMUNET uses multiplex network representations, clustering of communication pathways, pattern-search algorithms, and comparative analysis between two biological conditions.