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omicverse-single-cell-cellphonedb-communication

Run CellPhoneDB permutation tests on AnnData and produce visualization-ready interaction objects.

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

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 a CellPhoneDB notebook workflow into a reusable, production-ready tool that runs ligand-receptor statistical analysis on an annotated single-cell AnnData and produces a visualization-ready interaction object for downstream summary plots and pathway analyses. It removes the manual glue work of preparing counts and metadata, running permutations, formatting results for plotting, and choosing among aggregated network, pathway, bubble, chord, and signaling-role visualizations.

Core Features & Use Cases

  • Validated CellPhoneDB execution: filters cell types, checks gene-symbol conventions, manages temporary files, and runs permutation-based statistical analysis via ov.single.run_cellphonedb_v5.
  • Visualization-ready output: converts results into an AnnData with means and pvalues layers and sender/receiver observation metadata suitable for CellChat-style plotting.
  • Branch-rich downstream analysis: compute aggregated sender-receiver networks, pathway-level communication (mean/sum/max/median), extract enriched ligand-receptor pairs, render bubble and chord plots, and compute signaling-role centrality and heatmaps.
  • Use case: convert an annotated human scRNA-seq AnnData into a publication-ready communication summary highlighting key sender cell types, pathways, and contributing ligand-receptor pairs.

Quick Start

Run a CellPhoneDB analysis on an annotated AnnData to produce a visualization-ready AnnData and generate aggregated network and pathway plots using ov.single.run_cellphonedb_v5 followed by ov.pl.CellChatViz.

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?▼

To run CellPhoneDB ligand-receptor analysis, you apply permutation-based statistical tests on an annotated single-cell AnnData. This workflow manages the CellPhoneDB archive, filters cell types, checks gene-symbol conventions, and returns means and pvalues layers with sender/receiver observation metadata.

What is the best way to visualize cell-cell communication pathways from scRNA-seq data?▼

Visualizing cell-cell communication pathways is best achieved by converting statistical test results into a visualization-ready AnnData. You can then compute aggregated sender-receiver networks, render bubble or chord plots, and generate signaling-role centrality heatmaps using CellChatViz methods.

Does this CellPhoneDB workflow support pathway-level summaries and ligand-receptor extraction?▼

Yes, this CellPhoneDB workflow supports pathway-level communication summaries by calculating mean, sum, max, or median values. It also allows you to extract enriched ligand-receptor pairs and compute signaling-role centrality from the output AnnData object.

Can I use scanpy for single-cell communication analysis and get CellChat-style plots?▼

You can use scanpy to prepare your annotated single-cell AnnData for communication analysis. The workflow integrates with omicverse to run statistical tests and exposes CellChatViz methods to generate CellChat-style bubble, chord, and heatmap plots.

Why does my ligand-receptor permutation test require specific gene-symbol conventions?▼

Ligand-receptor permutation tests require specific gene-symbol conventions to accurately match interactions in the CellPhoneDB archive. The workflow validates and checks these conventions during execution to ensure statistical tests correctly identify significant sender-receiver signaling networks.

When do I need CellPhoneDB statistical testing for human single-cell datasets?▼

You need CellPhoneDB statistical testing for human single-cell datasets when you want to identify significant ligand-receptor interactions between annotated cell types. It is essential for generating publication-ready communication summaries highlighting key sender cell types and contributing pathways.

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