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omicverse-single-cell-cellphonedb-communication

Analyze single-cell ligand-receptor communication from annotated AnnData objects.

13|2|Updated Apr 5, 2026
Search Tags:#pathway analysis#ligand-receptor#single-cell#omicverse#anndata#cellphonedb#cellchat
One-click install
npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-cellphonedb-communication-omicverse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: omicverse-single-cell-cellphonedb-communication
Source: https://github.com/omicverse/omicverse-skills/tree/main/src/omicverse_skills/skills/single-cell-cellphonedb-communication
Command: npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-cellphonedb-communication-omicverse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires omicverse, scanpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill turns an annotated single-cell AnnData object into a reusable CellPhoneDB communication workflow, so you can move from raw interaction results to interpretable cell-cell signaling summaries without rebuilding the analysis each time.

Core Features & Use Cases

  • CellPhoneDB execution: Runs statistical ligand-receptor analysis on annotated single-cell data with validation of cell-type labels, gene-symbol conventions, and matrix compatibility.
  • Communication summarization: Builds aggregated sender-receiver networks, pathway-level communication scores, ligand-receptor contribution tables, and signaling-role summaries from the same processed result object.
  • Visualization branches: Supports CellChat-style circle, hierarchy, bubble, chord, centrality, and heatmap views for downstream interpretation of interaction patterns.
  • Use Case: A researcher can take a clustered AnnData object, run CellPhoneDB once, then explore which cell types signal most strongly, which pathways dominate, and how specific ligand-receptor pairs contribute to the result.

Quick Start

Use the omicverse single-cell CellPhoneDB skill on your annotated AnnData to run communication analysis and then choose the downstream visualization branch you need.

Frequently Asked Questions about omicverse-single-cell-cellphonedb-communication

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run CellPhoneDB ligand-receptor analysis on an annotated single-cell AnnData object?▼

To run CellPhoneDB ligand-receptor analysis, you need an annotated single-cell AnnData object with a valid cell-type column and human gene symbols. The workflow applies statistical testing to your expression matrix to identify significant cell-cell interactions.

Can I generate CellChat-style visualizations from CellPhoneDB results?▼

Yes, you can generate CellChat-style visualizations from CellPhoneDB results, including circle, hierarchy, bubble, chord, centrality, and heatmap plots. These views help interpret interaction patterns and signaling roles directly from the processed communication data.

Do I need scanpy and omicverse to perform single-cell communication analysis?▼

Yes, you need both scanpy and omicverse installed to perform this single-cell communication analysis. These dependencies provide the foundational AnnData structures and the specific run_cellphonedb_v5 methods required to execute the workflow.

What is needed to summarize pathway-level communication scores from single-cell data?▼

Summarizing pathway-level communication scores requires a processed CellPhoneDB result object derived from your annotated AnnData. The workflow builds aggregated sender-receiver networks and ligand-receptor contribution tables to identify dominant signaling pathways.

Why does my single-cell communication analysis require human gene symbols?▼

Single-cell communication analysis requires human gene symbols because the underlying CellPhoneDB ligand-receptor database is mapped to human gene conventions. Validating your AnnData expression matrix against these symbols ensures accurate statistical interaction testing.

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