Joint principal trend analysis for longitudinal high-dimensional data.

Authors

Yuping Zhang
Zhengqing Ouyang, The Jackson LaboratoryFollow

Document Type

Article

Publication Date

6-2018

JAX Source

Biometrics 2018 Jun; 74(2):430-438

Volume

74

Issue

2

First Page

430

Last Page

438

ISSN

1541-0420

PMID

28759699

DOI

https://doi.org/10.1111/biom.12751

Grant

Research Starter Grant in Informatics from PhRMA Foundation

Abstract

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.

Recommended Citation

Zhang Y, Ouyang Z. Joint principal trend analysis for longitudinal high-dimensional data. Biometrics 2018 Jun; 74(2):430-438