Statistical methods for comparative phenomics using high-throughput phenotype microarrays. Academic Article

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

• Carroll, Raymond
• Mallick, Bani

published proceedings

• Int J Biostat

author list (cited authors)

• Sturino, J., Zorych, I., Mallick, B., Pokusaeva, K., Chang, Y., Carroll, R. J., & Bliznuyk, N.

citation count

• 12

complete list of authors

• Sturino, Joseph||Zorych, Ivan||Mallick, Bani||Pokusaeva, Karina||Chang, Ying-Ying||Carroll, Raymond J||Bliznuyk, Nikolay

publication date

• August 2010

publisher

• DE GRUYTER  Publisher

published in

• International Journal of Biostatistics  Journal

keywords

• Biolog
• Data Interpretation, Statistical
• Female
• Functional Data Analysis
• High-throughput Phenotyping
• Humans
• Male
• Models, Genetic
• Models, Statistical
• Oligonucleotide Array Sequence Analysis
• Permutation Tests
• Phenomics
• Phenotype
• Phenotype Microarrays
• Principal Component Analysis
• Principal Components

PubMed Central ID

• 20865133

Digital Object Identifier (DOI)

• 10.2202/1557-4679.1227

start page

• Article

end page

• 29

volume

• 6

issue

• 1

URL

• http://dx.doi.org/10.2202/1557-4679.1227