Differential Analysis

Overview

This skill answers: What genes/features differ between groups in this spatial data?

Three main comparison types:

1. Marker genes: What defines each cluster/cell type?
2. Condition comparison: How does treatment/disease change expression?
3. Regional comparison: What differs between spatial domains?

Decision Tree: Which Comparison?

Q: What are you comparing?
│
├─ Clusters or cell types
│   └─ Marker Finding
│       ├─ Wilcoxon rank-sum - Default, robust
│       ├─ t-test - Parametric, fast
│       └─ Logistic regression - Handles confounders
│
├─ Experimental conditions (treated vs control)
│   └─ Condition Comparison
│       ├─ Pseudobulk + DESeq2 - Gold standard for replicates
│       ├─ MAST - Single-cell aware
│       └─ Mixed models - Complex designs
│
└─ Spatial domains or regions
    └─ Regional Comparison
        ├─ Same as marker finding
        └─ Consider spatial autocorrelation

Marker Gene Finding

Purpose

Identify genes that distinguish one group from all others (1 vs rest) or from a specific group (pairwise).

Method Selection

Method	When to Use	Strengths
Wilcoxon	Default choice	Non-parametric, robust
t-test	Large datasets	Fast, well-understood
Logreg	Confounders present	Adjusts for covariates

Workflow

Step 1: Define Groups

Ensure grouping column exists in adata.obs:

• Clusters: leiden, louvain
• Cell types: cell_type, annotation
• Domains: spatial_domain

Step 2: Run Marker Finding

Use find_markers tool with:

• groupby: Column name defining groups
• method: "wilcoxon", "t-test", or "logreg"
• n_genes: Number of top markers to return per group

Step 3: Interpret Results

Key output columns:

• names: Gene name
• scores: Test statistic
• pvals_adj: Adjusted p-value (FDR corrected)
• logfoldchanges: Log2 fold change
• pct_nz_group: Percent expressing in group
• pct_nz_reference: Percent expressing in other groups

Quality markers criteria:

Strong markers typically have:
- logFC > 1 (2-fold change) or < -1
- padj < 0.05
- pct_nz_group > 0.25 (expressed in >25% of group)
- pct_nz_group >> pct_nz_reference (specificity)

Condition Comparison

Purpose

Compare expression between experimental conditions (e.g., disease vs healthy, treated vs control).

Considerations

Replicates matter:

• With replicates: Use pseudobulk + DESeq2/edgeR
• Without replicates: Treat as exploratory only

Confounders:

• Batch effects: Include batch as covariate
• Cell type composition: Control or stratify

Workflow

Step 1: Set Up Comparison

Identify:

• Condition column in adata.obs
• Reference group (baseline/control)
• Confounding variables to adjust

Step 2: Run Comparison

Use compare_conditions tool with:

• condition_key: Column with conditions
• reference: Reference/control group name
• method: "pseudobulk", "mast", or "wilcoxon"
• covariates: Optional confounders to adjust

Step 3: Interpret Results

Volcano plot interpretation:

• X-axis: Log2 fold change (effect size)
• Y-axis: -log10(p-value) (significance)
• Upper right: Upregulated in condition
• Upper left: Downregulated in condition

Thresholds:

Significant DEGs typically:
- |logFC| > 0.5 (at least 1.4-fold change)
- padj < 0.05
- For stricter: |logFC| > 1, padj < 0.01

Regional/Spatial Domain Comparison

Purpose

Compare expression between different spatial regions or domains.

Special Considerations

Spatial autocorrelation:

• Nearby spots are not independent
• Standard tests may be anti-conservative
• Consider spatial-aware methods or pseudobulk

Workflow

Step 1: Define Regions

Regions can come from:

• Spatial domain identification
• Manual annotation
• Anatomical boundaries

Step 2: Run Comparison

Use find_markers with regions as groups:

• Pairwise: Compare specific domains
• 1 vs rest: Find domain-specific genes

Step 3: Validate Spatially

Visualize top DEGs on spatial map:

• Confirm pattern matches expected region
• Check for artifacts at boundaries

Visualization

Essential Plots

1. Volcano plot: Overview of all comparisons
2. Heatmap: Top markers across groups
3. Violin plots: Distribution of key genes
4. Spatial maps: Expression in tissue context

Use visualize_data with:

• plot_type="volcano" for volcano plots
• plot_type="heatmap" for expression heatmaps
• plot_type="violin" for distributions

Multiple Testing Correction

Why It Matters

Testing thousands of genes inflates false positives:

• 20,000 genes × 0.05 = 1,000 false positives expected

Correction Methods

Method	Stringency	When to Use
Benjamini-Hochberg (FDR)	Moderate	Default, controls false discovery rate
Bonferroni	Strict	When false positives are costly
None	None	Exploratory only

Always use adjusted p-values for final results!

Common Analysis Scenarios

Scenario 1: Find Cell Type Markers

Goal: What genes define each annotated cell type?
1. find_markers with groupby="cell_type"
2. Filter: padj < 0.05, logFC > 1, pct_nz > 0.25
3. Validate known markers present
4. Visualize top 3-5 per type

Scenario 2: Disease vs Healthy

Goal: What changes in disease state?
1. compare_conditions with condition_key="disease_state"
2. Set reference="healthy"
3. Add covariates if batch effects present
4. Volcano plot to visualize
5. Pathway analysis on significant DEGs

Scenario 3: Tumor Margin Analysis

Goal: What genes differ at tumor-stroma boundary?
1. Define regions: "tumor_core", "margin", "stroma"
2. find_markers with groupby="region"
3. Focus on margin-specific genes
4. Cross-reference with communication analysis

Troubleshooting

Issue	Cause	Solution
No significant DEGs	Low power or no effect	Check sample size, relax threshold
Too many DEGs	Loose threshold or batch	Tighten threshold, check batches
Known markers missing	Expression too low	Lower min expression filter
Inconsistent results	Technical noise	Use pseudobulk, add replicates

Output Summary

Successful differential analysis provides:

• DEG list: Ranked genes with statistics
• Effect sizes: Log fold changes
• Significance: Adjusted p-values
• Visualizations: Volcano, heatmap, spatial maps
• Downstream input: Gene lists for enrichment analysis