Smooth and probabilistic PARAFAC model with auxiliary covariates
Smooth and probabilistic PARAFAC model with auxiliary covariates
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DOI：
10.1080/10618600.2023.2257783
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发表时间：
2021-04
影响因子：
2.4
通讯作者：
Leying Guan
中科院分区：
文献类型：
--
作者：
Leying Guan
在免疫学和临床研究中，矩阵值时间序列数据聚类越来越受欢迎。研究人员感兴趣的是基于潜在的高维纵向特征找到受试者的低维嵌入，并研究静态临床协变量与嵌入之间的关系。这些研究往往是具有挑战性的，由于高维，以及稀疏和不规则的性质，样本收集沿着时间维。我们提出了一个平滑的概率PARAFAC模型与协变量（SPACO）来解决这两个问题，同时利用辅助协变量的兴趣。我们提供了密集的模拟来测试SPACO的不同方面，并展示了它在SARS-CoV-2感染患者的免疫学数据集上的应用。
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.