In immunological and clinical studies, matrix-valued time-series data clustering is increasingly popular. Researchers are interested in finding low-dimensional embedding of subjects based on potentially high-dimensional longitudinal features and investigating relationships between static clinical covariates and the embedding. 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 an immunological data set from patients with SARs-CoV-2 infection.

翻译：在免疫学和临床研究中,矩阵估价时间序列数据组群越来越受欢迎,研究人员有兴趣根据潜在的高维纵向特征找到低维嵌入的课题,并调查静态临床共变体与嵌入体之间的关系,这些研究往往具有挑战性,因为高度的维度,以及取样收集在时间方面的稀少和不规律性质,我们建议采用一个与共变体(SPACO)的顺利的概率模型,以便解决这两个问题,同时利用有兴趣的辅助共变体。我们提供密集模拟,以测试SPACO的不同方面,并展示它用于SARs-COV-2感染病人的一组免疫学数据。