Joint Principal Trend Analysis for Longitudinal High-Dimensional Data

• Published in: Biometrics Vol. 74; no. 2; pp. 430 - 438
• Main Authors: Zhang, Yuping, Ouyang, Zhengqing
• Format: Journal Article
• Language: English
• Published: United States Wiley-Blackwell 01.06.2018; Blackwell Publishing Ltd
• Subjects: Biological activity; BIOMETRIC METHODOLOGY: DISCUSSION PAPER; biometry; Biometry - methods; Cell cycle; Cell Cycle - genetics; Computer Simulation; data collection; Data Interpretation, Statistical; Data mining; Datasets as Topic; Dimension reduction; Feature extraction; Gene expression; Gene Expression Profiling; High‐dimensional data analysis; Humans; Influenza; Influenza, Human - genetics; information sources; Joint principal trend analysis; Latent models; Longitudinal data analysis; mammals; Models, Statistical; Transcription; transcription (genetics); Trend analysis; Trends; Dimension reduction; High-dimensional data analysis; Joint principal trend analysis; Latent models; Longitudinal data analysis
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.12751

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.