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Joint principal trend analysis for longitudinal high†dimensional data

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  • Yuping Zhang
  • Zhengqing Ouyang

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.

Suggested Citation

  • Yuping Zhang & Zhengqing Ouyang, 2018. "Joint principal trend analysis for longitudinal high†dimensional data," Biometrics, The International Biometric Society, vol. 74(2), pages 430-438, June.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:2:p:430-438
    DOI: 10.1111/biom.12751
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    References listed on IDEAS

    as
    1. Zhang Yuping & Tibshirani Robert J. & Davis Ronald W., 2010. "Predicting Patient Survival from Longitudinal Gene Expression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-23, November.
    2. Parkhomenko Elena & Tritchler David & Beyene Joseph, 2009. "Sparse Canonical Correlation Analysis with Application to Genomic Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-36, January.
    3. Pradeep Ravikumar & John Lafferty & Han Liu & Larry Wasserman, 2009. "Sparse additive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 1009-1030, November.
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