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cell-communication-cellphonedb

Identify significant ligand-receptor interactions in human single-cell datasets using CellPhoneDB.

3|Updated Apr 3, 2026
Search Tags:#ligand-receptor#single-cell#cellphonedb#permutation-testing#cell-type#human-data
One-click install
npx skills add https://github.com/chenyhvvvv/STAT-agent --skill cell-communication-cellphonedb
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cell-communication-cellphonedb
Source: https://github.com/chenyhvvvv/STAT-agent/tree/main/stat_agent/skills/cell-communication-CellPhoneDB
Command: npx skills add https://github.com/chenyhvvvv/STAT-agent --skill cell-communication-cellphonedb

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzes cell-cell communication in single-cell data by identifying significant ligand-receptor interactions between cell types using CellPhoneDB’s statistical framework.

Core Features & Use Cases

  • Permutation-based identification of LR interactions across cell-type pairs in human datasets.
  • Outputs include per-interaction means, p-values, and a summarized reporting in adata.uns.
  • Use Case: researchers can map communication networks to understand tissue microenvironment dynamics.

Quick Start

Run this skill on your annotated human single-cell dataset to identify significant ligand-receptor interactions between cell types.

Frequently Asked Questions about cell-communication-cellphonedb

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

FAQPage Schema
How do I identify significant ligand-receptor interactions between cell types in single-cell data?▼

To identify significant ligand-receptor interactions, you can run CellPhoneDB-based permutation testing on annotated human single-cell datasets to calculate means and p-values for cell-type pairs.

What data format is required for CellPhoneDB permutation testing?▼

CellPhoneDB permutation testing requires human, single-slice single-cell data with existing cell-type annotations and HGNC-symbol gene expression to ensure reliable ligand-receptor detection.

How does permutation testing work for cell-cell communication analysis?▼

Permutation testing for cell-cell communication works by randomly reassigning cell-type labels to generate a null distribution, which is then used to calculate significance p-values for observed ligand-receptor means.

Can I use CellPhoneDB to analyze cell-type interactions in non-human single-cell datasets?▼

No, you cannot reliably use this approach for non-human data because the analysis specifically requires human datasets with HGNC-symbol gene expression to accurately map the ligand-receptor interactions.

What outputs does CellPhoneDB generate for cell-cell communication analysis?▼

CellPhoneDB generates outputs stored in adata.uns, including per-interaction means and p-values, which summarize the significant ligand-receptor interactions identified between cell types.

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