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

13 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


Dive into the research topics of 'Statistical Methods for Comparative Phenomics Using High-Throughput Phenotype Microarrays'. Together they form a unique fingerprint.

Cite this