A0547
Title: Sparse functional canonical correlation analysis for multiview data integration
Authors: Limeng Liu - University of Minnesota (United States) [presenting]
Guannan Wang - College of William & Mary (United States)
Sandra Safo - University of Minnesota (United States)
Abstract: The current landscape of functional data analysis (FDA) predominantly caters to single variables on one view of the dataset, overlooking the prevalence of multiview multivariate functional data in biomedical research. While canonical correlation analysis (CCA) stands out as a popular choice for integrative analysis, its applicability is limited to cross-sectional data, failing to address longitudinal or functional data scenarios. In response to these limitations, an innovative, integrative, sparse functional canonical correlation analysis approach is proposed for multiview data. This novel framework aims to tackle the challenges posed by multiview functional datasets, seamlessly integrating both cross-sectional and longitudinal/functional data while accounting for sparsity. The method aims to identify linear combinations of variable functions for each view such that the correlation between the sets of linear combinations is maximized. The method will also identify interpretable variables that maximize such association over time. Simulation studies are conducted to evaluate the effectiveness of the approach. The method is used to investigate multi-omics biomarkers during the onset of inflammatory bowel disease.
