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

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.
Original languageEnglish (US)
JournalThe International Journal of Biostatistics
Issue number1
StatePublished - Jan 24 2010
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: Zorych and Bliznyuk were supported by the Texas A&M Postdoctoral Training Program of the National Cancer Institute (CA90301). The research of Carroll was supported by a grant from the National Cancer Institute (CA57030). Carroll and Mallick were also supported by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST). The research of Sturino was supported by the United States Department of Agriculture, Cooperative State Research, Education and Extension Service, Hatch project TEX 09436. Acquisition of the Biolog Omnilog Phenotype Microaarray was supported by the State of Texas Permanent University Fund with matching funds from Texas AgriLife Research and Texas A&M University. In addition, the authors would like to thank the editor and anonymous reviewers for their valuable comments.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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