Abstract
BackgroundThe Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed ‘-omics’ techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves.MethodologyThe free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats.ConclusionsWe provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data.
Highlights
The so called ‘-omics’ techniques yielded tremendous insights in the biology of cellular organisms
The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native Phenotype MicroArray (PM) software for calculating both point estimates and confidence intervals
These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions
Summary
The so called ‘-omics’ techniques yielded tremendous insights in the biology of cellular organisms. Other ‘-omics’ techniques are MicroRNomics, probiogenomics, lipidomics and fluxomics [4,5,6,7] Their unifying theme is the study of the cellular totality of the organisms of interest to obtain a systematic insight into basic biology [8,9] and to reconstruct complex metabolic networks and flow-charts of fluxes [10,11,12,13]. The Phenotype MicroArray (OmniLogH PM) system is able to simultaneously capture a large number of phenotypes by recording an organism’s respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed ‘-omics’ techniques merely study components that contribute to it. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves
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