How to use heatmaps to compare immune cell changes in cytometry data

Overview

Heatmaps give you a way to step back from individual cell populations and see how the immune system changes comparing time points, treatment lines, and responses.

Instead of focusing on one or two subsets with box plots, heatmaps layer your entire dataset into a single visual. You can quickly tell which and whether populations rise, fall, or coordinate together across treatment groups or within cohorts.

Use it to:

• Compare immune cell patterns across study groups or cohorts
• Visualize population frequencies, subset frequencies, and marker expression
• Explore grouped averages by endpoint (e.g., baseline vs on-treatment, dose groups)
• Reveal correlations between immune populations and treatment effects

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Quick Start: How to Use Heatmaps

Grouped by Endpoint Heatmaps:

Step 1 – Navigate to “Immune Changes”
In the left-hand sidebar, click Immune Changes.

Step 2 – Select Parameters

• Switch to Heatmap View – Click on Heatmap View on upper right hand corner
• Heatmap Type – Choose Grouped (by Endpoint).
• Endpoint – Pick the grouping variable (e.g., treatment response, timepoint, sex, treatment line, subject).
• Show Cells As % Of – Select the reference population (e.g., T cells, B cells, NK cells, non-granulocytes).
• Cell Type(s) – Choose the cell populations, functional subsets, or markers to include.
• Heatmap Normalization – normalize values to a specific timepoint and view results as: 
  
  • Fold change 
  • Log2 fold change 
  • Percent change‍

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Step 3 – View Heatmap
Your grouped heatmap will display instantly, showing differences across selected groups.

Correlation Heatmaps:

Step 1 – Navigate to “Immune Changes”
In the left-hand sidebar, click Immune Changes 

Step 2 – Select Parameters

• Switch to Heatmap View – Click on Heatmap View 
• Heatmap Type – Choose Correlation.
• Show Cells As % Of – Select the reference population.
• Cell Type(s) – Select the populations you want to test correlations between.

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Step 3 – View Heatmap
Your correlation heatmap will display, showing relationships between populations.

What You Can Compare

• Grouped by endpoint heatmaps: differences across cohorts (e.g., responders vs non-responders, baseline vs on-treatment).
• Correlation heatmaps: how populations rise or fall together (positive correlations) or in opposition (negative correlations).
• Output types include:
  
  • Population frequencies (% of total)
  • Functional subset frequencies (% of parent)
  • Marker expression levels

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Tips for Refining Your Analysis

• Use filters to focus on specific subgroups or timepoints. 

• For large heatmaps, draw a box around your region of interest to zoom in and analyze just that section.

Sample of Heatmap Results

1. Grouped by Endpoint Heatmap

• Finding: Non-responders show higher levels of CD38⁺ T cells, especially in CD8⁺ populations, while responders consistently show lower levels across the same subsets.‍
• Interpretation: These differences only appear after treatment begins, showing that CD38⁺ cells build up in patients who don’t respond. The contrast between the two groups highlights CD38 as a marker of resistance and a potential target for new therapies.

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2. Correlation Heatmap

• Finding: CD38⁺ levels in different T cell groups tend to rise and fall together, especially in CD8⁺ effector and CD4⁺ memory cells.
• Interpretation: This coordinated pattern suggests that CD38 is not limited to a single subset but appears across several related T cell groups, pointing to a broader role in resistance.

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