cellchat-cell-communication

You can infer cell-cell communication networks from single-cell RNA-seq data by identifying ligand-receptor interactions between cell groups using the CellChat R package, which aggregates interactions into signaling pathways.

Yes, you can visualize single-cell signaling pathways using chord diagrams, heatmaps, and bubble plots to explore communication patterns and quantify cell group roles as senders, receivers, or influencers.

Yes, CellChat works with Seurat objects for cell communication analysis, allowing you to directly infer and visualize intercellular communication pathways from your prepared single-cell RNA-seq data.

Cell-cell communication analysis in the tumor microenvironment identifies how tumor cells communicate with immune cells via ligand-receptor pairs, revealing signaling pathways to help identify potential therapeutic targets.

Yes, CellChat supports both human and mouse single-cell RNA-seq data, allowing you to map intercellular communication networks and analyze signaling pathways across these two model organisms.

The best way to identify ligand-receptor interactions mediating communication between cell groups is using CellChat, which quantifies cell group roles and aggregates interactions into signaling pathways for comprehensive network analysis.