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LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data

Author

Listed:
  • Sun Jiehuan

    (Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA)

  • Herazo-Maya Jose D.
  • Kaminski Naftali

    (Internal Medicine: Pulmonary, Critical Care and Sleep Medicine, Yale School of Medcine, New Haven, CT, USA)

  • Wang Jane-Ling

    (Department of Statistics, University of California, Davis, CA, USA)

  • Zhao Hongyu

    (Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06510, USA)

Abstract

Longitudinal genomics data and survival outcome are common in biomedical studies, where the genomics data are often of high dimension. It is of great interest to select informative longitudinal biomarkers (e.g. genes) related to the survival outcome. In this paper, we develop a computationally efficient tool, LCox, for selecting informative biomarkers related to the survival outcome using the longitudinal genomics data. LCox is powerful to detect different forms of dependence between the longitudinal biomarkers and the survival outcome. We show that LCox has improved performance compared to existing methods through extensive simulation studies. In addition, by applying LCox to a dataset of patients with idiopathic pulmonary fibrosis, we are able to identify biologically meaningful genes while all other methods fail to make any discovery. An R package to perform LCox is freely available at https://CRAN.R-project.org/package=LCox.

Suggested Citation

  • Sun Jiehuan & Herazo-Maya Jose D. & Kaminski Naftali & Wang Jane-Ling & Zhao Hongyu, 2019. "LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(2), pages 1-9, April.
    • Handle: RePEc:bpj:sagmbi:v:18:y:2019:i:2:p:9:n:1
    DOI: 10.1515/sagmb-2017-0060
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    References listed on IDEAS

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    1. Fushing Hsieh & Yi-Kuan Tseng & Jane-Ling Wang, 2006. "Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited," Biometrics, The International Biometric Society, vol. 62(4), pages 1037-1043, December.
    2. Tianxi Cai & Giulia Tonini & Xihong Lin, 2011. "Kernel Machine Approach to Testing the Significance of Multiple Genetic Markers for Risk Prediction," Biometrics, The International Biometric Society, vol. 67(3), pages 975-986, September.
    3. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    4. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
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