Spatial Pattern Analysis

Overview

This skill answers: Which genes/features show significant spatial organization in this tissue?

Two complementary analyses:

1. Spatially Variable Genes (SVG): Find genes with non-random spatial expression
2. Spatial Statistics: Quantify and test spatial patterns

Decision Tree: Which Analysis?

Q: What do you want to know?
│
├─ Which genes have spatial patterns?
│   └─ SVG Detection
│       ├─ SpatialDE - Gaussian Process, handles noise well
│       ├─ SPARK-X - Fast, scalable to large datasets
│       └─ Moran's I per gene - Classic, interpretable
│
├─ Are spatial patterns statistically significant?
│   └─ Spatial Statistics
│       ├─ Global Moran's I - Overall spatial autocorrelation
│       ├─ Getis-Ord Gi* - Local hot/cold spot detection
│       └─ Ripley's K/L - Point pattern analysis
│
└─ Where are the spatial hotspots?
    └─ Local Spatial Statistics
        ├─ LISA - Local Moran's I
        └─ Getis-Ord Gi* - Hot spot analysis

Spatially Variable Gene Detection

Method Selection

Method	Speed	Best For	Output
SpatialDE	Moderate	Noise robustness	p-values, effect sizes
SPARK-X	Fast	Large datasets (>50k spots)	p-values, adjusted
Moran's I	Fast	Simple interpretation	I statistic, p-value
Sepal	Moderate	Multi-scale patterns	Scale-specific genes

Workflow

Step 1: Run SVG Detection

Use find_spatial_genes tool with:

• method: "spatialDE", "sparkx", or "moran"
• n_top_genes: Number of top genes to return (default 100)

Step 2: Interpret Results

Key output columns:

• pval / padj: Statistical significance
• moranI / effect_size: Strength of spatial pattern
• gene: Gene identifier

Filtering criteria:

Recommended thresholds:
- Adjusted p-value < 0.05 (stringent: < 0.01)
- Effect size > 0.1 (moderate spatial pattern)
- Expression level: Filter very low-expressed genes

Step 3: Visualize Top SVGs

Use visualize_data with plot_type="spatial":

• Show expression of top SVGs spatially
• Overlay with tissue domains for context

Spatial Statistics Analysis

Global Statistics

Moran's I: Measures overall spatial autocorrelation

• Range: -1 (dispersed) to +1 (clustered)
• 0 = random distribution
• Use for: Overall pattern assessment

Geary's C: Alternative to Moran's I

• Range: 0 (clustered) to 2 (dispersed)
• 1 = random
• More sensitive to local variation

Local Statistics

LISA (Local Moran's I): Identifies local clusters

• High-High: Hot spots (high values surrounded by high)
• Low-Low: Cold spots (low values surrounded by low)
• High-Low / Low-High: Spatial outliers

Getis-Ord Gi*: Hot spot analysis

• Positive z-score: Hot spot (high value cluster)
• Negative z-score: Cold spot (low value cluster)
• Use for: Precise hotspot identification

Workflow

Step 1: Compute Spatial Statistics

Use analyze_spatial_statistics tool with:

• statistic: "moran", "geary", "getis_ord", or "ripley"
• feature: Gene or obs column to analyze

Step 2: Interpret Results

Statistic	Interpretation
Moran's I > 0.3	Strong positive spatial autocorrelation
Moran's I < -0.3	Strong negative autocorrelation (checkerboard)
Getis-Ord z > 1.96	Significant hot spot (p < 0.05)
Getis-Ord z < -1.96	Significant cold spot (p < 0.05)

Common Analysis Scenarios

Scenario 1: Find Tissue Boundary Genes

Goal: Genes marking transitions between regions
Approach:
1. Run SVG detection (SpatialDE recommended)
2. Overlay top SVGs with domain boundaries
3. Look for genes with gradient patterns

Scenario 2: Identify Hot Spots of Activity

Goal: Where is gene X most active?
Approach:
1. Run Getis-Ord Gi* on gene expression
2. Map significant hot spots spatially
3. Cross-reference with cell type composition

Scenario 3: Compare Spatial Organization

Goal: Do two conditions differ in spatial organization?
Approach:
1. Compute Moran's I for each condition separately
2. Compare I statistics
3. Test for significant difference

Biological Interpretation

What SVGs Tell You

• Boundary markers: Genes defining tissue compartments
• Gradient genes: Continuous spatial variation
• Spot-specific: Localized expression patterns
• Tissue architecture: Structural organization genes

Combining with Other Analyses

Combine With	Insight
Spatial domains	Which genes drive domain identity
Cell composition	Cell type-specific spatial patterns
Cell communication	Spatially organized signaling

Troubleshooting

Issue	Cause	Solution
No significant SVGs	Low spatial variation	Lower threshold, check data quality
Too many SVGs	Loose threshold	Use stricter adjusted p-value
Unexpected patterns	Batch effects	Check for technical artifacts
Slow computation	Large dataset	Use SPARK-X, subsample for exploration

Output Summary

Successful spatial pattern analysis provides:

• SVG list: Ranked spatially variable genes
• Statistics: Significance and effect sizes
• Spatial maps: Visualization of top patterns
• Hotspots: Localized areas of interest