How to use Top Statistical Results to identify the most significant immune shifts

Overview

Large cytometry datasets often contain thousands of possible comparisons, and important signals are easy to miss without extensive manual searching. Filtering combinations can take hours, and even then, key differences may remain hidden.

Top Statistical Results removes this burden by automatically testing all combinations of populations, markers, analysis types, and endpoints, then surfacing the top 100 statistically significant results for you. Instead of digging through data to find what changed, you immediately see the most meaningful immune shifts across your study.

You can use Top Statistical Results to:

• Quickly identify the strongest immune differences across your dataset (i.e. biggest change from baseline, largest change between responders and non-responders, or dose)
• Compare ‘from baseline’ changes using Baseline Only and full cross-timepoint comparisons using All Pairwise‍
• Accelerate interpretation by sending results to Pocket Immunologist

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Quick Start: How to use Top Statistical Results

Step 1 – Navigate to “Summary”

In the left-hand sidebar, click Summary.

Step 2 – Select the ‘Analyze Results’ button 

Step 3 – Choose Your Comparison Mode

• Baseline Only
  Compares any time point directly to the first time point in the study.
• All Pairwise
  Compares every possible combination of timepoints across the dataset.

Step 4 – Review the Ranked Output

Each result includes:

• Population
• Marker
• Analysis Type
• Magnitude
• P-Value
• Endpoint

Sort any column to prioritize the metrics most relevant to your analysis.

Step 5 – Examine Magnitude and Direction

• Positive fold changes indicate increases relative to the comparison group.
• Negative fold changes indicate decreases.

Step 6 – Send Results to Pocket Immunologist

Click Pocket Immunologist to generate AI-driven interpretation.

What You Can Compare

Top Statistical Results supports multiple readouts, including:

• Population frequencies (percent of total or percent of parent)
• Marker expression levels (median or mean intensity)
• Functional subset frequencies (activated, proliferating, cytokine-positive, exhausted)
• Absolute cell counts (cells per microliter or per sample)

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Tips for Refining Your Analysis

• Use Baseline Only for early pharmacodynamic effects anchored to the first timepoint.
• Use All Pairwise to explore mid-study and late-study differences across all timepoints.
• Filter subjects to focus on response groups or dose cohorts.
• Sort by magnitude to highlight the largest fold changes.
• Sort by p-value to prioritize the most statistically significant results.