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Journal of Statistical Theory and Practice

, Volume 12, Issue 2, pp 450–461 | Cite as

Dimension reduction of gene expression data

  • Jaylen Lee
  • Shannon Ciccarello
  • Mithun Acharjee
  • Kumer Das
Article

Abstract

DNA methylation of specific dinucleotides has been shown to be strongly linked with tissue age. The goal of this research is to explore different analysis techniques for microarray data in order to create a more effective predictor of age from DNA methylation level. Specifically, this study compares elastic net regression models to principal component regression, supervised principal component regression, Y-aware principal component regression, and partial least squares regression models and their ability to predict tissue age based on DNA methylation levels. It has been found that the elastic net model performs better than latent variable models when considering less than ten principal components for each method, but Y-aware principal component regression predicts more accurately (with a reasonably low testing RMSE) and captures more of the desired structure when the number of principal components increases to 20. Coding limitations inhibited forming conclusive results about the performance of supervised principal component regression as the number of components increases.

Keywords

Principal component analysis DNA methylation elastic net regression Y-aware PCR supervised PCR PLS regression 

AMS Subject Classification

62H25 62J99 62N86 

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Copyright information

© Grace Scientific Publishing, 20 Middlefield Ct, Greensboro, NC 27455 2018

Authors and Affiliations

  • Jaylen Lee
    • 1
  • Shannon Ciccarello
    • 2
  • Mithun Acharjee
    • 3
  • Kumer Das
    • 3
  1. 1.Department of Mathematics and StatisticsJames Madison UniversityHarrisonburgUSA
  2. 2.Department of Mathematics and StatisticsHollins UniversityRoanokeUSA
  3. 3.Department of MathematicsLamar UniversityBeaumontUSA

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