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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.
Insights
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.
Abstract:
High-dimensional cytometry, such as mass cytometry (CyTOF), measures protein expression in single cells. When paired with AI-enhanced data analytics, it facilitates the discovery of immune biomarkers that can assist in diagnosing and treating immune-related diseases. However, fluctuating instrument readouts, known as batch effects, can obscure biological patterns. To enhance our previously reported EPIC immune atlas platform, we developed the ImmuneMapBuilder app to integrate, annotate, and cluster CyTOF data. To evaluate biological stratification and batch effects in clustering results, we created a two-step visualization method, called Group Similarity Analysis (GSA). First, multidimensional immune profiles are projected into two-dimensional embeddings to reveal similarities between samples. Second, silhouette analysis of embedding coordinates quantifies cohesion within technical and biological groups. We illustrate the scope and effectiveness of GSA using six batch normalization methods across three datasets, demonstrating that batch-effect correction enhances the separation of age groups and tissue types. The goals of batch normalization are increased biological and decreased technical GSA scores, both of which correlate with decreased Earth Mover's Distance (EMD), an alignment indicator of signal distributions. To dissect the contributions of individual marker misalignments to batch effects, we introduce a mix-and-match strategy that combines normalized and raw channels. GSA also helps to compare meta-clustering outputs. In a case study, we confirmed the significance of CD62L expression in T cells for age-based stratification. Overall, GSA is a versatile method to evaluate clustering results from cytometry data.
