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CARGO: A Cytometry Analysis framework via Regularized Graph Optimal-transport
Abida Sanjana Shemonti1, Grzegorz B Gmyrek2, Katrien L A Quintelier3,4,5
1Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America.
Plos Computational Biology
|June 23, 2026
Summary
This study introduces a novel graph-based visualization for high-parameter flow cytometry data, improving biological interpretation and quantitative analysis beyond traditional methods.
Area of Science:
- Computational Biology
- Data Visualization
- Immunology
Background:
- Current single-cell analysis visualization methods (e.g., t-SNE, UMAP) struggle with high-parameter flow cytometry data.
- These methods oversimplify biological complexity and lack quantitative analysis frameworks.
Purpose of the Study:
- To develop a graph-based visualization framework for intuitive understanding and quantitative analysis of flow cytometry data.
- To address limitations of conventional visualization techniques in high-parameter single-cell analysis.
Main Methods:
- A graph-based framework using optimal transport theory (Sinkhorn distance) to define cell populations and quantify inter-population similarity.
- Biologically consistent 2D graph layouts generated using phenotype-aware Hamming distance.
- Customized graph-edit distance to characterize structural differences between sample graphs.
Main Results:
- Demonstrated on clinical immunotherapy and acute myeloid leukemia flow cytometry datasets.
- The framework provides robust, quantitative visual summaries of cell populations.
- Enabled statistical analysis of graph edit distances for disease and treatment insights.
Conclusions:
- The graph-based approach bridges the gap between flow cytometry data visualization and biological interpretation.
- Offers a principled framework for downstream quantitative analysis and statistical insights.
- Enhances understanding of disease progression and treatment response through improved data visualization.
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