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cell-communication-liana

Identify ligand-receptor interactions between cell types using LIANA+ on single-cell or spatial datasets.

3|Updated Apr 3, 2026
Search Tags:#bioinformatics#interaction#ligand-receptor#spatial-transcriptomics#scRNA-seq#liana#cell-type
One-click install
npx skills add https://github.com/chenyhvvvv/STAT-agent --skill cell-communication-liana
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cell-communication-liana
Source: https://github.com/chenyhvvvv/STAT-agent/tree/main/stat_agent/skills/cell-communication-LIANA
Command: npx skills add https://github.com/chenyhvvvv/STAT-agent --skill cell-communication-liana

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Identify significant ligand-receptor interactions between cell types to reveal communication networks from spatial and single-cell data, enabling researchers to interpret cell-type cross-talk.

Core Features & Use Cases

  • LIANA+ consensus ranking across multiple LR databases for robust interaction detection.
  • Supports cluster-level aggregation by celltype, autocrine signaling detection, and outputs full results.
  • Use Case: Analyze spatial transcriptomics to identify signaling between tumor and immune cell types.

Quick Start

Load your annotated adata with celltype labels, run the LIANA+ workflow to compute interactions, and inspect the liana_res results.

Frequently Asked Questions about cell-communication-liana

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

FAQPage Schema
How do I identify ligand-receptor interactions in single-cell RNA-seq data?▼

To map ligand-receptor interactions for cell communication in spatial transcriptomics, use LIANA+ to compute significant cross-talk between clusters. It processes prepared AnnData objects containing spatial coordinates and outputs interaction results in adata.uns['liana_res'].

How do I detect autocrine signaling across cell types in scRNA-seq?▼

Detect autocrine signaling across cell types by running the LIANA+ workflow on annotated single-cell data. It supports cluster-level aggregation by celltype and specifically enables autocrine signaling detection within the computed interaction network.

What prerequisites are needed to run LIANA+ for cell-type communication mapping?▼

To run LIANA+ for cell communication analysis, you need a prepared AnnData object with spatial coordinates, celltype annotations in adata.obs['celltype'], and species information to select the correct ligand-receptor database.

Can I use LIANA+ consensus ranking with spatial transcriptomics tumor data?▼

Yes, LIANA+ consensus ranking works with spatial transcriptomics tumor data. It identifies significant signaling interactions between tumor and immune cell types by aggregating ligand-receptor pairs at the cluster level from spatial gene expression.

What outputs does LIANA+ generate for cell communication analysis?▼

LIANA+ generates interaction results stored in adata.uns['liana_res'] and a summary dictionary. These outputs contain significant ligand-receptor interactions and coordinated cell communication patterns mapped between cell types.

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