cytoGPNet: Enhancing clinical outcome prediction accuracy using longitudinal cytometry data in small cohort studies.

Abstract

Cytometry data, including flow and mass cytometry, are widely used in immunological studies such as cancer immunotherapy and vaccine trials. These data provide rich insights into immune cell dynamics and their relationship to clinical outcomes. However, traditional analyses based on summary statistics may overlook critical single-cell information. To address this, we introduce cytoGPNet, a novel method for predicting individual-level outcomes from cytometry data. cytoGPNet addresses four key challenges: (1) accommodating varying numbers of cells per sample, (2) analyzing longitudinal cytometry data to capture temporal patterns, (3) maintaining robustness despite limited sample sizes, and (4) ensuring interpretability for biomarker discovery. We apply cytoGPNet across multiple immunological studies with diverse designs and show that it consistently outperforms existing methods in predictive accuracy. Importantly, cytoGPNet also offers interpretable insights at multiple levels, enhancing our understanding of immune responses. These results highlight cytoGPNet's potential to advance cytometry-based analysis in immunological research.