Cell Communication

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially pandas and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for ligand-receptor communication inference in single-cell or spatial data with sender-receiver summaries and cautious interpretation.

When To Use This Skill

• use when the task is cell-cell communication or ligand-receptor analysis
• use when the dataset already has reasonable cell type annotations or spatial neighborhoods
• use when the user needs network, heatmap, or pathway-style communication outputs

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• annotated single-cell or spatial object
• ligand-receptor resource
• group or condition metadata

Expected Outputs

• interaction tables
• sender-receiver summaries
• communication visualizations

Preferred Tools

• pandas
• networkx
• seaborn
• matplotlib

Starter Pattern

Preferred starting point: pandas
Inputs: annotated single-cell or spatial object, ligand-receptor resource, group or condition metadata
Outputs: interaction tables, sender-receiver summaries, communication visualizations

Workflow

1. Confirm annotation quality

Communication analysis depends on robust cell labels or spatial domains.

2. Define comparison units

Choose whether to infer communication across clusters, cell types, neighborhoods, or conditions.

3. Run interaction scoring

Compute ligand-receptor evidence and apply filtering for expression support and redundancy.

4. Aggregate to interpretable views

Summarize signals by sender, receiver, pathway, or condition.

5. Report caveats

State clearly that inferred communication is hypothesis-generating unless validated experimentally.

Output Artifacts

• Recommended output layout:
  • results/ for final tables and serialized objects
  • figures/ for plots and static visual exports
  • qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• interaction tables
• sender-receiver summaries
• communication visualizations

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Review embeddings together with QC metrics and batch structure before labeling biology.
• Preserve the processed object with metadata and embeddings for downstream reuse.

Anti-Patterns

• running communication analysis on unstable or weak annotations
• equating expression correlation with validated signaling
• reporting dense uninterpretable networks without summarization

Related Skills

• scRNA Preprocessing And Clustering
• Cell Annotation
• Trajectory And Lineage
• Multiome And scATAC

Optional Supplements

• string-database