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Dissecting the Influence of Batch Effects on Immunomics Pattern Discovery in High-Dimensional Mass Cytometry
Martin Wasser1,2, Joo Guan Yeo1,2,3, Valerie Chew1,2
1Translational Immunology Institute (TII), SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore.
Summary
Group Similarity Analysis (GSA) is a new method to evaluate cytometry data clustering. It helps identify immune biomarkers by reducing batch effects, improving the accuracy of disease diagnosis and treatment strategies.
Area of Science:
- Immunology
- Computational Biology
- Data Science
Background:
- High-dimensional cytometry (e.g., CyTOF) enables single-cell protein expression analysis.
- AI-driven analytics can identify immune biomarkers for disease diagnosis and treatment.
- Batch effects from instrument variability can obscure biological patterns in cytometry data.
Purpose of the Study:
- To develop the ImmuneMapBuilder app for integrating, annotating, and clustering CyTOF data.
- To introduce Group Similarity Analysis (GSA) for evaluating biological stratification and batch effects in clustering.
- To assess the effectiveness of batch normalization methods on cytometry data.
Main Methods:
- Development of the ImmuneMapBuilder app.
- Creation of a two-step visualization method, Group Similarity Analysis (GSA).
- GSA involves 2D embedding projection and silhouette analysis; a mix-and-match strategy was used to dissect marker contributions.
Main Results:
- GSA effectively evaluates biological stratification and batch effects in cytometry data clustering.
- Batch-effect correction enhanced the separation of age and tissue groups across datasets.
- GSA scores correlate with decreased Earth Mover's Distance (EMD), indicating improved signal alignment.
Conclusions:
- GSA is a versatile tool for assessing cytometry data clustering results.
- Batch normalization improves biological signal separation and reduces technical noise.
- The study confirmed CD62L expression's significance in T cells for age-based stratification.
