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Two-Way Analysis of High-Dimensional Collinear Data

  • Ilkka Huopaniemi
  • Tommi Suvitaival
  • Janne Nikkilä
  • Matej Orešič
  • Samuel Kaski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)

Abstract

We present a Bayesian model for two-way ANOVA-type analysis of high-dimensional, small sample-size datasets with highly correlated groups of variables. Modern cellular measurement methods are a main application area; typically the task is differential analysis between diseased and healthy samples, complicated by additional covariates requiring a multi-way analysis. The main complication is the combination of high dimensionality and low sample size, which renders classical multivariate techniques useless. We introduce a hierarchical model which does dimensionality reduction by assuming that the input variables come in similarly-behaving groups, and performs an ANOVA-type decomposition for the set of reduced-dimensional latent variables. We apply the methods to study lipidomic profiles of a recent large-cohort human diabetes study.

Keywords

ANOVA factor analysis hierarchical model metabolomics multi-way analysis small sample-size 

References

  1. 1.
    Huopaniemi, I., Suvitaival, T., Nikkilä, J., Orešič, M., Kaski, S.: Two-Way Analysis of High-Dimensional Collinear Data. Data Mining and Knowledge Discovery (2009) doi: 10.1007/s10618-009-0137-2Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ilkka Huopaniemi
    • 1
  • Tommi Suvitaival
    • 1
  • Janne Nikkilä
    • 1
    • 2
  • Matej Orešič
    • 3
  • Samuel Kaski
    • 1
  1. 1.Department of Information and Computer ScienceHelsinki University of TechnologyFinland
  2. 2.Department of Basic Veterinary Sciences, (Division of Microbiology and Epidemiology), Faculty of Veterinary MedicineUniversity of HelsinkiFinland
  3. 3.VTT Technical Research Centre of FinlandEspooFinland

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