Cell-Cell Interaction Analysis

Overview

This skill answers: How do cells communicate with each other in this tissue?

Cell communication fundamentally requires:

1. A sender cell expressing a ligand
2. A receiver cell expressing a receptor
3. Spatial proximity enabling the interaction

Prerequisites

Before running cell communication analysis:

• Cell type annotations available (from deconvolution or direct annotation)
• Species identified (human or mouse)
• Biological question defined (specific pathways? global patterns?)

Method Selection

Decision Framework

Q: What level of analysis do you need?
│
├─ Ligand-receptor pairs only
│   └─ LIANA+ (Python) - Multi-method consensus, recommended default
│
├─ Signaling pathway analysis
│   └─ CellChat (R) - Pathway-level insights, communication networks
│
├─ Fast computation needed
│   └─ FastCCC (C++) - Human only, very fast
│
└─ Classic/benchmark comparison
    └─ CellPhoneDB - Original method, good for comparisons

Method Comparison

Aspect	LIANA+	CellChat	CellPhoneDB	FastCCC
Language	Python	R	Python	C++
Speed	Fast	Moderate	Moderate	Very Fast
Output	LR pairs	Pathways + Networks	LR pairs	LR pairs
Species	Human/Mouse	Human/Mouse	Human	Human
Strength	Multi-method consensus	Pathway interpretation	Literature standard	Scale

Workflow

Step 1: Verify Cell Type Annotations

Required in adata.obs:
- Cell type column (e.g., 'cell_type', 'annotation')
- Clean labels (no "unknown", "unassigned")
- Biologically meaningful categories

Step 2: Configure Species

Species-specific databases:
- Human: liana_resource="consensus" (default)
- Mouse: liana_resource="mouseconsensus"

Step 3: Run Analysis

Use analyze_cell_communication tool with:

• cell_type_key: Column name for cell type annotations
• species: "human" or "mouse"
• method: Selected from decision framework

Step 4: Interpret Results

Key outputs:

• Ligand-receptor pairs: Specific molecular interactions
• Interaction scores: Strength/significance of communication
• Cell type pairs: Which cells are talking to which
• Pathways (CellChat): Grouped signaling pathways

Step 5: Visualize

Use visualize_data with plot_type="communication":

• subtype="dotplot": Overview of all interactions
• subtype="circle_plot": Network of cell type communication
• subtype="tileplot": Heatmap of interaction strength

Spatial Considerations

Adding Spatial Context

Standard cell communication ignores distance. To incorporate spatial proximity:

1. Pre-filter by spatial neighbors: Only consider cells that are spatially adjacent
2. Use spatial statistics: Correlate communication scores with co-localization

Interpreting Spatial Patterns

Questions to consider:

• Are communicating cell types spatially co-localized?
• Do interaction hotspots correspond to specific tissue regions?
• How does communication differ across spatial domains?

Common Analysis Scenarios

Scenario 1: Tumor Microenvironment

Focus on:
- Tumor-immune interactions
- CAF-tumor communication
- Immune checkpoint pathways (PD-1/PD-L1)

Scenario 2: Development/Differentiation

Focus on:
- Signaling gradients (Wnt, BMP, Notch)
- Niche-stem cell interactions
- Temporal progression of communication

Scenario 3: Tissue Architecture

Focus on:
- ECM-cell interactions
- Structural organization signals
- Regional communication patterns

Output Interpretation Guide

Significant Interactions

Look for:

• High specificity scores (interaction specific to certain cell pairs)
• Biological relevance (known pathways in tissue type)
• Spatial coherence (co-localization of interacting cells)

Common Patterns

Pattern	Interpretation
Hub cell type	Central coordinator of tissue communication
Reciprocal pairs	Bidirectional signaling between cell types
Pathway enrichment	Dominant signaling mechanism
Spatial clustering	Localized communication zones

Troubleshooting

Issue	Solution
No significant interactions	Lower threshold, check cell type balance
Too many interactions	Increase stringency, focus on specific pathways
CellChat R errors	Verify rpy2 installation, check R packages
Species mismatch	Ensure correct database for species