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Joint principal trend analysis for longitudinal high‐dimensional data
Author(s) -
Zhang Yuping,
Ouyang Zhengqing
Publication year - 2018
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12751
Subject(s) - joint (building) , computer science , longitudinal data , principal component analysis , principal (computer security) , statistics , econometrics , data mining , mathematics , artificial intelligence , engineering , computer security , architectural engineering
Summary 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.