sc-communication

Cell-cell communication analysis scores ligand-receptor interactions from single-cell RNA sequencing data. You can run this analysis using established methods like CellChat, NicheNet, LIANA, or a built-in scoring mechanism to identify interacting pathways.

Scoring ligand-receptor interactions in a tumor microenvironment is best handled by integrating multiple analysis methods. This multi-method consensus approach provides robust predictions of how immune cells communicate with tumor cells to identify therapeutic targets.

You need scanpy to run the core analysis, but R packages like CellChat or nichenetr are optional. They are only required if you want to use their specific comprehensive analysis and visualization capabilities for single-cell data.

Yes, you can use LIANA with scanpy. The analysis supports LIANA and scanpy to score cell-cell interactions from single-cell RNA sequencing data, accommodating spatial biology contexts.

Visualizing cell-cell communication networks involves generating plots of key interactions directly from the analysis results. The Skill supports visualization outputs from methods like CellChat, NicheNet, LIANA, and its built-in scoring mechanism.