Tutorial: Group Comparison Workflow

Core Feature

This tutorial covers features available in all MATISSE Explorer deployments.

This tutorial demonstrates how to compare different cell populations or tissue regions using MATISSE Explorer's visualization and subsetting tools.

Scenario

You want to:

1. Define two groups for comparison (e.g., tumor vs. stroma)
2. Compare gene expression between groups
3. Visualize differences using scatter plots
4. Export group assignments for statistical analysis

Prerequisites

• Completed the Basic Exploration Workflow
• Understanding of Lasso Selection
• Familiarity with Scatter Plot

Step 1: Define Comparison Groups

Identify Groups to Compare

Common comparisons:

• Tumor region vs. Normal tissue
• Different anatomical regions
• Treatment vs. Control areas
• Different cell type populations

Example: Tumor vs. Stroma

We'll compare cells in the tumor region to cells in the surrounding stroma.

Step 2: Select Group 1 (Tumor)

Navigate to Tumor Region

1. Open Explorer in Spatial view
2. Identify the tumor region visually
3. You might color by a marker to help identify (e.g., tumor marker)

Draw Lasso Around Tumor

1. Switch to Selection mode
2. Draw a lasso around the tumor region
3. Note the count: "X items selected"

Save as Subset Condition

1. Click Items dropdown
2. Select Add selection to subset
3. The condition is named automatically (e.g., "Lasso Selection (Spatial)")

Rename for Clarity

In the Subset:

1. Find the lasso condition
2. Note or rename to "Tumor Region" for reference

Step 3: Select Group 2 (Stroma)

Clear Current Selection

1. Clear the current subset to see all cells
2. Return to Selection mode

Draw Lasso Around Stroma

1. Draw a lasso around the stromal region
2. Note the count of selected cells

Save as Second Condition

1. Add this selection to subset
2. Now you have two lasso conditions available

Step 4: Compare Using Color Mapping

View Tumor Cells Only

1. Apply subset: Tumor Region only
2. Color by a gene of interest
3. Observe expression pattern and range

View Stroma Cells Only

1. Change subset: Stroma Region only
2. Keep same gene coloring
3. Compare expression pattern to tumor

Note Observations

• Is expression higher in tumor or stroma?
• Are there spatial gradients?
• Do specific sub-regions show different patterns?

Step 5: Compare Using Scatter Plot

Configure Scatter Plot

1. Open the Plot extension
2. Set X axis: Gene A expression
3. Set Y axis: Gene B expression
4. Apply: No subset (show all cells)

Color by Region

Create a visual comparison:

1. If you have a region feature, color by it
2. Or use cell type coloring
3. Observe where different populations fall

Alternative: Subset Toggle

1. Apply Tumor Region subset
2. View scatter plot
3. Switch to Stroma Region subset
4. Compare the distributions

Step 6: Create Signatures for Comparison

Tumor Signature Genes

1. Create Signature: "Tumor Markers"
2. Add genes associated with tumor cells
3. Save the signature

Stroma Signature Genes

1. Create Signature: "Stroma Markers"
2. Add genes associated with stromal cells
3. Save the signature

Compare Signatures

1. Open Scatter Plot
2. X axis: Tumor Markers (Max or Sum)
3. Y axis: Stroma Markers (Max or Sum)
4. View the separation of populations

Step 7: Quantify Differences

Export Group 1 Data

1. Apply Tumor Region subset
2. Export gene expression or features
3. Save as "tumor_cells.csv"

Export Group 2 Data

1. Apply Stroma Region subset
2. Export same data
3. Save as "stroma_cells.csv"

Statistical Analysis

Use exported data for:

• Differential expression analysis
• Statistical tests (t-test, Wilcoxon)
• Visualization in R/Python

Step 8: Document Comparison

Save Subsets

1. Save "Tumor Region" as a saved subset
2. Save "Stroma Region" as a saved subset
3. Easily return to these views

Screenshot Key Findings

1. Capture comparison visualizations
2. Save scatter plots showing separation
3. Document expression patterns

Record Methods

Note your analysis steps:

• How regions were defined
• Which genes/markers used
• Subset configurations
• Statistical methods applied

Advanced Comparison Techniques

Three-Way Comparison

Compare multiple regions:

Subset (OR):
├── Group 1: Tumor Core
├── Group 2: Tumor Margin
└── Group 3: Normal Tissue

Cell Type Within Region

Nested comparison:

1. Subset: Tumor Region
2. Then subdivide by cell type
3. Compare T cells in tumor vs. stroma

Using UMAP for Comparison

1. Switch to UMAP projection
2. Color by region assignment
3. See if regions cluster separately
4. Draw lasso in UMAP space for transcriptional groups

Summary

In this tutorial, you learned to:

Step	Technique
1-3	Define comparison groups with lasso
4	Compare using color mapping
5	Compare using scatter plots
6	Create signatures
7	Export for quantitative analysis
8	Document your comparison

Next Steps

• Export data for statistical testing in R/Python
• Create publication-quality figures
• Apply similar workflow to other comparisons
• Explore Subset for complex group definitions