Phenomics is the systematic study of global cellular phenotypes that arise as a function of genotype (or metagenotype) and its environmental context (Gowen and Fong, 2009
). Differentiation of biological systems as a function of observable phenotype predates the discovery of their molecular components, which includes DNA (and its systems biology sub-discipline, genomics), epigenetic heritability (epigenomics), RNA (transcriptomics), proteins (proteomics), and metabolites (metabolomics). Nevertheless, due to the repetitive and labor intensive nature of phenotypic studies, cellular phenomics has struggled to become a vibrant functional discipline (Bochner, 2009
; Joyce and Palsson, 2006
Assay miniaturizations coupled with process automation have proven to be foundational advances that have enabled the rational design and implementation of high-throughput phenotyping (HTP) platform chemistries. Automated liquid handling systems can be used to accurately and precisely prepare low-volume microplate-based assays, while high-capacity and temperature-controlled automated microplate readers enable effectively parallel data collection (Gabrielson et al., 2002
; Bochner, 2003). These technological advancements have paved the way for the development and commercialization of standardized platform chemistries for HTP; see Gowen and Fong (2009)
for a review.
The Phenotype MicroArray (PM) platform for HTP was developed by Bochner et al. (2001)
and commercialized by Biolog, Inc., whose web site is http://www.biolog.com/
; for reviews, see Bochner et al. (2009)
and Bochner (2003a)
. The complete Phenotype MicroArray for microbial cells is comprised of twenty pre-formulated 96-well microplates (PM1 to PM20). When used together, they enable researchers to simultaneously assay up to 1,920 different cellular phenotypes as a function of time (i.e., kinetic response). Individual PM microplates contain a large and heterogeneous collection of functionally-related chemical compounds or combinations thereof (e.g., up to 96 per PM microplate). These compounds may serve as a source of carbon (PM1-2), nitrogen (PM3, PM6-PM8), and phosphorus or sulfur (PM4). Other PM microplates are used to determine sensitivity to environmental stresses, such as ions or osmolytes stress (PM9), pH (PM10), and chemical agents (PM11-20) (Bochner, 2009
Using this platform, microbial strains may be assayed individually or in parallel (e.g., a wild-type strain versus an isogenetic mutant) and subsequently compared for quantitative phenotypic differences as a function of time (i.e., kinetic activity). Many microplate-based HTP platforms monitor the accumulation of biomass directly as a function of time, which necessitates cellular growth and division. In contrast, the PM platform employs a universal colorimetric reporter system to monitor cellular metabolism that is effective even in the absence of the accumulation of biomass. This colorimetric reporter indirectly measures cellular metabolism by directly measuring the irreversible reduction of tetrazolium-based dyes (colorless) to formazan (purple, Gabrielson et al., 2002
; Bochner, 2003a
). Conceptually, maintenance of a respiration-competent state requires fewer physiological systems than does the maintenance of a replication-competent state. As such, this platform may afford a distinct advantage over cultivation based platforms for the study of microorganisms that may be viable, but not cultivatable ex vivo
, which includes most of the microorganisms present in the gastrointestinal tract of humans and animals (Savage, 1977
; Xu and Gordon, 2003
Standardized commercial chemistries for high-throughput phenotyping (HTP), such as the Biolog Phenotype Microarray (PM) platform, provide a universal platform to facilitate meta-analysis of phenomics data generated from the query of disparate biological systems.
These recent advancements have resulted in the generation of novel high-dimension phenomic data sets. The OmniLog PM system software is used to visualize phenotype curves and provides several basic analytical functions. For example, the PM Kinetic Analysis Module can be used to generate a mean kinetic phenotype curve and to (optionally) amend it (e.g., subtract background signal, crop/trim early or late time points, and several others.). The PM Parametric Analysis Module calculates summary values for each mean phenotype curve (e.g., area under the curve, min/maximum signal intensity, maximum slope, lag time, etc.), which enables two microorganisms to be compared. For example, the software allows users to select an ad hoc threshold (e.g., fixed cut-off k) that distinguishes wells that show “striking” phenotypic relative differences between microorganisms with respect to a given summary value (e.g., a k-fold change). The number of wells that differ under such an approach depends on the summary value and the stringency of the of selected threshold. Analysis of PM data using ad hoc summary values without statistical support has been reported extensively, for example (Bochner, et al., 2001
; Bochner 2003 (2); Zhou, et al., 2003
; Mukherjee, et al., 2006
). Although this approach may be used judiciously to guide biological research, ad hoc experimenter-selected thresholds have much the same flavor of the 2-fold expression changes that used to occur in microarrays, and in that context the ad hoc threshold has given way to robust statistical analysis (Slonim, 2002
As such, there is an urgent need to design robust statistical methodologies to interrogate these data and to enable sound biological inferences to be made therefrom. In this paper, we propose several simple yet effective hypothesis testing frameworks to compare phenomic data that were generated using the PM platform for high-throughput phenotyping.
The paper is organized as follows. In Section 2, we describe the experiment undertaken. Section 3 gives the methods used. Section 4 gives the results of simulation studies, while Section 5 describes the analysis of the experimental data. Section 6 has concluding remarks.