How to identify significant immune differences with volcano plots

Overview

Volcano plots solve a common problem in cytometry analysis: lots of graphs with varying p-values and different effect sizes making it hard to spot most meaningful differences. By combining fold-change and statistical significance into a single view, volcano plots highlight which populations, subsets, and expression levels truly stand out.

With one glance, you can see which immune shifts are largest and which reach statistical significance. Volcano plots help you focus on meaningful signals without getting lost in the noise.

Use it to:

• Spot immune shifts that are both large and statistically significant
• Compare differences by dose, timepoint, or response
• Detect markers enriched in responders vs non-responders
• Move quickly from raw data to immune insights

Quick Start: How to Use Volcano Plots

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

Step 2 – Select Parameters

• Endpoint – Choose grouping to compare (e.g., study timepoint, age, sex, treatment line, treatment response, subject).
• Show Cells As % Of – Select the reference population (e.g., T cells, B cells, NK cells, non-granulocytes).
• Cell Type(s) – Pick the populations, subsets, or markers to include.

Step 3 – Run Volcano Plot
Click the Volcano Plot Filtering button.

Step 4 – Select Cell Populations on Volcano Plot(Optional)

Drag a box over any section of the volcano plot. Box plots on the left will update to show  the selected populations.

Note: If your volcano plot does not appear, it may be because the baseline was normalized. 

‍

Understanding the Axes

X-axis (log₂ fold-change):

• 0 = no difference between groups.
• Positive values (to the right) = population is higher in Group 1 (ex. responders).
• Negative values (to the left) = population is higher in Group 2 (ex. non-responders).
• Example: A value of +1 means the population is twice as frequent in responders. A value of –1 means it is half as frequent in responders.

Y-axis (–log₁₀ p-value):

• Converts p-values into an easy-to-read scale: the higher the value, the stronger the statistical significance.
• Example thresholds:
  
  • 1.3 ≈ p 0.05 (borderline significance) 
  • 3 ≈ p 0.001 (very strong evidence)

Interpretation: Left vs right shows the size and direction of the change. Up vs down shows how confident you can be that the change is real.

‍

Sample of Volcano Plot in Immune Changes

Comparison: Responders vs Non-Responders

1. Population Frequency

• Finding 3: CD4⁺ TEMRA T cells are more frequent in responders (fold-change ≈ 1.73, p = 0.0035).
• Interpretation: Responders show higher levels of this CD4⁺ T-cell type, which may indicate these cells help coordinate or strengthen the immune response to therapy.

2. Functional Subset Frequency

• Finding: CD38⁺HLADR⁺ CD8⁺ TEMRA T cells are more frequent in responders (fold-change ≈ 0.68, p = 0.029).
• ‍Interpretation: Responders have more of these activated CD8⁺ T cells, suggesting they may play a role in directly attacking tumor cells during treatment.

3. Marker Expression

• Finding: CCR7 expression is higher on Tregs in responders compared to non-responders (log₂ fold-change ≈ 0.97, p = 0.0007).‍
• Interpretation: Higher CCR7 may mean these cells move more effectively through the body, which could help explain why people with this pattern respond better to treatment.

If you’d like a step-by-step primer, check out our “Volcano Plots for Dummies” article. It breaks down how to interpret log2 fold-change and -log10 p-values using simple analogies.

‍