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omicverse-single-cell-liana-communication

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

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill removes the manual burden of running ligand-receptor inference on annotated single-cell data and turning the result into interpretable communication plots.

Core Features & Use Cases

  • LIANA+ inference: Runs consensus or single-method ligand-receptor scoring on labeled AnnData objects for cell-cell communication analysis.
  • Pathway-aware reshaping: Converts results into a communication AnnData and classifies interactions with CellChat or family-level pathway labels.
  • Flexible visualisation: Produces dot, tile, aggregation heatmap, focused heatmap, pathway bubble, role heatmap, and role network views for sender-receiver and pathway-specific questions.
  • Use case: A researcher can analyze a PBMC cohort, compare baseline versus stimulated conditions, and quickly inspect which clusters send or receive specific signaling pathways.

Quick Start

Ask the Skill to run LIANA on your annotated single-cell AnnData, reshape the results into a communication AnnData, and generate the most appropriate ccc_heatmap view for your sender, receiver, or pathway question.

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

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

FAQPage Schema
How do I infer cell-cell communication patterns from single-cell AnnData?▼

You can infer cell-cell communication from single-cell AnnData by running LIANA+ consensus ranking, reshaping results into a communication AnnData, and visualizing sender-receiver interactions across dot, tile, or heatmap formats.

Can I use CellChat pathway classification with ligand-receptor inference results?▼

Yes, this Skill converts ligand-receptor inference results into a communication AnnData and classifies interactions using CellChat pathway labels for pathway-specific analysis.

Do I need log-normalized expression data for single-cell communication analysis?▼

Yes, single-cell communication analysis requires a labeled AnnData object with a populated group-by column and log-normalized expression data to run ligand-receptor inference.

What is the best way to visualize multi-condition cell-cell communication differences?▼

The best way to visualize multi-condition cell-cell communication differences is by generating focused heatmaps, pathway bubble plots, or role networks to compare sender-receiver signaling across baseline and stimulated conditions.

How does LIANA consensus ranking work for single-cell data?▼

LIANA consensus ranking aggregates multiple ligand-receptor scoring methods on labeled single-cell AnnData to produce robust cell-cell communication scores and p-values for downstream visualization.

Why does my cell-cell communication heatmap require a populated group-by column?▼

Your cell-cell communication heatmap requires a populated group-by column because ligand-receptor inference relies on labeled clusters to identify sender and receiver cell populations for accurate scoring.

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