Joint principal trend analysis for longitudinal high-dimensional data.

Yuping Zhang
Zhengqing Ouyang, The Jackson Laboratory


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. Biometrics 2017 Jul 31 [Epub ahead of print]