Tutorial: Analysis Workflow

Core Feature

This tutorial covers features available in all MATISSE Explorer deployments.

This tutorial demonstrates how to use advanced filtering and analysis features to investigate specific biological questions in your spatial transcriptomics data.

Scenario

You want to:

1. Find cells that meet multiple criteria
2. Use variable sets to analyze pathway activity
3. Combine spatial and transcriptional filters
4. Save complex filter configurations for reuse

Prerequisites

• Completed the Basic Exploration Workflow
• Familiarity with Filter

Step 1: Define Your Question

Example Question

"Find T cells in the tumor region that have high immune checkpoint expression."

This requires:

• Cell type filter (T cells)
• Spatial filter (tumor region)
• Expression filter (checkpoint genes)

Step 2: Create a Variable Set

First, create a variable set for immune checkpoint genes.

Open Variable Sets

1. In Explorer sidebar, click Variable Sets tab
2. Click Create Variable Set

Define the Gene Set

1. Name: "Immune Checkpoints"
2. Description: "Key immune checkpoint molecules"
3. Variables:

   PDCD1
   CTLA4
   LAG3
   HAVCR2
   TIGIT

4. Click Create

Verify Matches

1. View the created variable set
2. Check how many genes matched your data
3. Note any unmatched genes

Step 3: Define Tumor Region

Use lasso selection to define the tumor region.

Switch to Spatial View

1. Ensure you're in Spatial projection
2. Color by a relevant marker to identify tumor region
3. Navigate to see the full tissue

Draw Tumor Region

1. Click Selection mode
2. Draw a lasso around the tumor region
3. Note the selected cell count

Save to Filter

1. Click Items dropdown
2. Select Add selection to filter
3. The lasso condition is saved

Step 4: Build the Complex Filter

Now combine all criteria in the Filter.

Open Filter

1. Open the Filter Panel
2. You should see your lasso condition already added

Add Cell Type Condition

1. Click Add Condition in the same group
2. Select Column type
3. Choose cell type column
4. Select "T cell" category
5. Ensure operator is AND

Add Variable Set Condition

1. Click Add Condition
2. Select Variable Set type
3. Choose "Immune Checkpoints"
4. Set combination method to Max
5. Set comparison: > 1.0

Review the Filter

Your filter should now read:

(Tumor Region Lasso) AND (cell_type = T cell) AND (Immune Checkpoints Max > 1.0)

Step 5: Apply and Analyze

Apply the Filter

1. Click Apply
2. View the filtered population
3. Note the final cell count

Explore the Results

1. Color by individual checkpoint genes
2. Compare expression levels
3. Examine spatial distribution within the tumor

View in UMAP

1. Switch to UMAP projection
2. See where these cells fall in transcriptional space
3. Check if they cluster together

Step 6: Refine the Analysis

Adjust Thresholds

If results are too few or too many:

1. Edit the variable set condition
2. Adjust the threshold (e.g., > 0.5 or > 2.0)
3. Re-apply and check results

Add Additional Conditions

To further refine:

1. Add more expression criteria
2. Adjust spatial region
3. Change combination method

Step 7: Save Your Filter

Save as Preset

1. Click Save Preset
2. Name: "Tumor T cells - High Checkpoint"
3. Click Save

Verify Preset

1. Clear the filter
2. Load the preset
3. Confirm filter is restored

Step 8: Export Results

Export Filtered Data

1. With filter active, export annotation data
2. Download cell IDs and annotations
3. Use in downstream analysis

Document Analysis

Record your analysis:

• Filter configuration
• Variable set contents
• Result counts
• Screenshots

Advanced Techniques

Using OR Logic

Find cells matching any of several criteria:

Group 1 (OR):
├── High PDCD1 (> 2.0)
├── High CTLA4 (> 2.0)
└── High LAG3 (> 2.0)

Combining Regions

Compare cells across multiple regions:

Filter (OR):
├── Group 1: Tumor Region AND T cell
└── Group 2: Stroma Region AND T cell

Nested Logic

Complex queries with groups:

Filter (AND):
├── Group 1 (OR):
│   ├── T cell
│   └── NK cell
└── Group 2 (AND):
    ├── Tumor Region
    └── High Checkpoint

Summary

In this tutorial, you learned to:

Step	Skill
1-2	Create variable sets for gene groups
3	Define spatial regions with lasso
4	Build multi-condition filters
5-6	Apply, analyze, and refine results
7-8	Save presets and export data

Next Steps

• Group Comparison Workflow - Compare populations
• Variable Sets - More on gene groups
• Filter Presets - Managing presets