CellCommuNet

CellCommuNet maps and analyzes cell-cell communication networks from single-cell RNA sequencing (scRNA-seq) data to characterize ligand–receptor interactions and compare communication patterns across human and mouse healthy and diseased tissues.

Key Features:

• Extensive dataset collection: Contains 376 individual scRNA-seq datasets and 118 comparative datasets pairing disease and normal samples from the same studies covering human and mouse tissues.
• Ligand–receptor interaction analysis: Infers and quantifies ligand–receptor interactions from scRNA-seq data to map molecular communication between cell types.
• Pathway and pair exploration: Enables querying signaling pathways, specific ligand–receptor pairs, and cell types of interest.
• Comparative differential analysis: Computes and illustrates differences in communication strength between disease and control samples to identify disease-associated changes.
• Network visualization: Produces visual representations of cell–cell communication networks across states to support interpretation of interaction patterns.

Scientific Applications:

• Disease research: Characterizes alterations in cell–cell communication between healthy and diseased tissues, supporting analysis of disease mechanisms including cancers.
• Biological function studies: Investigates how cellular interactions maintain tissue function and microenvironmental homeostasis.

Methodology:

Uses scRNA-seq data to map cell-cell communication networks by analyzing ligand–receptor interactions, supports querying of signaling pathways/ligand–receptor pairs/cell types, and performs comparative analysis of communication strength between disease and control samples.