  Analysis and clustering of multivariate time-series data attract growing
interest in immunological and clinical studies. In such applications,
researchers are interested in clustering subjects based on potentially
high-dimensional longitudinal features, and in investigating how clinical
covariates may affect the clustering results. These studies are often
challenging due to high dimensionality, as well as the sparse and irregular
nature of sample collection along the time dimension. We propose a smoothed
probabilistic PARAFAC model with covariates (SPACO) to tackle these two
problems while utilizing auxiliary covariates of interest. We provide intensive
simulations to test different aspects of SPACO and demonstrate its use on
immunological data sets from two recent cohorts of SARs-CoV-2 patients.
