Joint principal trend analysis for longitudinal high-dimensional data.
Biometrics 2018 Jun; 74(2):430-438
Research Starter Grant in Informatics from PhRMA Foundation
We consider a research scenario motivated by integrating multiple sources of information for better knowledge discovery in diverse dynamic biological processes. Given two longitudinal high-dimensional datasets for a group of subjects, we want to extract shared latent trends and identify relevant features. To solve this problem, we present a new statistical method named as joint principal trend analysis (JPTA). We demonstrate the utility of JPTA through simulations and applications to gene expression data of the mammalian cell cycle and longitudinal transcriptional profiling data in response to influenza viral infections.
Zhang, Yuping and Ouyang, Zhengqing, "Joint principal trend analysis for longitudinal high-dimensional data." (2018). Faculty Research 2018. 152.