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The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.

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1557-4679
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Statistical Methods for Comparative Phenomics Using High-Throughput Phenotype Microarrays

Joseph Sturino / Ivan Zorych / Bani Mallick / Karina Pokusaeva / Ying-Ying Chang / Raymond J Carroll / Nikolay Bliznuyk
Published Online: 2010-08-24 | DOI: https://doi.org/10.2202/1557-4679.1227

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.

Keywords: functional data analysis; principal components; permutation tests; phenotype microarrays; high-throughput phenotyping; phenomics; Biolog

About the article

Published Online: 2010-08-24


Citation Information: The International Journal of Biostatistics, Volume 6, Issue 1, ISSN (Online) 1557-4679, DOI: https://doi.org/10.2202/1557-4679.1227.

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