Enhancing Outcome Prediction Accuracy with cytoGPNet Using Longitudinal Cytometry Data in Small Cohort HIV Studies

Music City Center 

Cytometry data, including flow cytometry and mass cytometry, are now standard in numerous immunological studies, such as HIV vaccine trials. These data enable the monitoring of an individual's peripheral immune status over time, providing detailed insights into immune cells and their role in clinical outcomes. However, traditional analyses relying on summary statistics, such as cell subset proportions and mean fluorescence intensity, may overlook critical single-cell information. To address this limitation, we introduce cytoGPNet, a novel approach that harnesses extensive cytometry data to predict individual-level outcomes. cytoGPNet is designed to address four key challenges: (1) accommodating varying numbers of cells per sample; (2) analyzing the longitudinal cytometry data to understand temporal relationships; (3) maintaining robustness under the constraints of limited individual samples in HIV vaccine trials; and (4) ensuring interpretability to facilitate biomarker identification. We apply cytoGPNet to data from four diverse studies, each with unique characteristics. Despite these differences, cytoGPNet consistently outperforms other popular methods in terms of prediction accuracy. Moreover, cytoGPNet provides interpretable results at multiple levels, offering valuable insights.

Outcome Prediction Accuracy