Get access
Advertisement

Clustering based on periodicity in high-throughput time course data

Authors

  • Anna J. Blackstock,

    1. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
    Search for more papers by this author
  • Amita K. Manatunga,

    Corresponding author
    1. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
    • Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
    Search for more papers by this author
  • Youngja Park,

    1. Department of Medicine, Emory University, Atlanta, GA 30322, USA
    Search for more papers by this author
  • Dean P. Jones,

    1. Department of Medicine, Emory University, Atlanta, GA 30322, USA
    Search for more papers by this author
  • Tianwei Yu

    Corresponding author
    1. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
    • Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
    Search for more papers by this author

Abstract

Nuclear magnetic resonance (NMR) spectroscopy, traditionally used in analytical chemistry, has recently been introduced to studies of metabolite composition of biological fluids and tissues. Metabolite levels change over time, and providing a tool for better extraction of NMR peaks exhibiting periodic behavior is of interest. We propose a method in which NMR peaks are clustered based on periodic behavior. Periodic regression is used to obtain estimates of the parameter corresponding to period for individual NMR peaks. A mixture model is then used to develop clusters of peaks, taking into account the variability of the regression parameter estimates. Methods are applied to NMR data collected from human blood plasma over a 24-h period. Simulation studies show that the extra variance component due to the estimation of the parameter estimate should be accounted for in the clustering procedure. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 579–589, 2011

Get access to the full text of this article

Ancillary