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Statistical Methods for Comparative Phenomics Using High-Throughput Phenotype Microarrays

Author

Listed:
  • Sturino Joseph

    (Texas A&M University)

  • Zorych Ivan

    (Texas A&M University)

  • Mallick Bani

    (Texas A&M University)

  • Pokusaeva Karina

    (Texas A&M University)

  • Chang Ying-Ying

    (Texas A&M University)

  • Carroll Raymond J

    (Texas A&M University)

  • Bliznuyk Nikolay

    (Texas A&M University)

Abstract

We propose statistical methods for comparing phenomics data generated by the Biolog Phenotype Microarray (PM) platform for high-throughput phenotyping. Instead of the routinely used visual inspection of data with no sound inferential basis, we develop two approaches. The first approach is based on quantifying the distance between mean or median curves from two treatments and then applying a permutation test; we also consider a permutation test applied to areas under mean curves. The second approach employs functional principal component analysis. Properties of the proposed methods are investigated on both simulated data and data sets from the PM platform.

Suggested Citation

  • Sturino Joseph & Zorych Ivan & Mallick Bani & Pokusaeva Karina & Chang Ying-Ying & Carroll Raymond J & Bliznuyk Nikolay, 2010. "Statistical Methods for Comparative Phenomics Using High-Throughput Phenotype Microarrays," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-21, August.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:1:n:29
    DOI: 10.2202/1557-4679.1227
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    References listed on IDEAS

    as
    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
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    Cited by:

    1. Minna Vehkala & Mikhail Shubin & Thomas R Connor & Nicholas R Thomson & Jukka Corander, 2015. "Novel R Pipeline for Analyzing Biolog Phenotypic Microarray Data," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    2. Lea A I Vaas & Johannes Sikorski & Victoria Michael & Markus Göker & Hans-Peter Klenk, 2012. "Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-18, April.
    3. Livio Corain & Viatcheslav Melas & Andrey Pepelyshev & Luigi Salmaso, 2014. "New insights on permutation approach for hypothesis testing on functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 339-356, September.

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