title: Statistical methods for comparative phenomics using high-throughput phenotype microarrays.
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.
authors.affiliation: Texas A&M University, TX, USA.
authors.forename: Joseph
authors.initials: J
authors.lastname: Sturino
authors.forename: Ivan
authors.initials: I
authors.lastname: Zorych
authors.forename: Bani
authors.initials: B
authors.lastname: Mallick
authors.forename: Karina
authors.initials: K
authors.lastname: Pokusaeva
authors.forename: Ying-Ying
authors.initials: YY
authors.lastname: Chang
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Carroll
authors.forename: Nikolay
authors.initials: N
authors.lastname: Bliznuyk
mesh_terms: D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data*; D010641:Phenotype; D025341:Principal Component Analysis / Q000379:methods*