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Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics

Author

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  • Lea A I Vaas
  • Johannes Sikorski
  • Victoria Michael
  • Markus Göker
  • Hans-Peter Klenk

Abstract

Background: The 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. Methodology: The 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. Conclusions: We 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.

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  • Lea A I Vaas & Johannes Sikorski & Victoria Michael & Markus Göker & Hans-Peter Klenk, 2012. "Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-18, April.
    • Handle: RePEc:plo:pone00:0034846
    DOI: 10.1371/journal.pone.0034846
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    References listed on IDEAS

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    1. Minna Vehkala & Mikhail Shubin & Thomas R Connor & Nicholas R Thomson & Jukka Corander, 2015. "Novel R Pipeline for Analyzing Biolog Phenotypic Microarray Data," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    2. Vincent Somerville & Nadine Thierer & Remo S. Schmidt & Alexandra Roetschi & Lauriane Braillard & Monika Haueter & Hélène Berthoud & Noam Shani & Ueli Ah & Florent Mazel & Philipp Engel, 2024. "Genomic and phenotypic imprints of microbial domestication on cheese starter cultures," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Meen Chul Kim & Yongjun Zhu & Chaomei Chen, 2016. "How are they different? A quantitative domain comparison of information visualization and data visualization (2000–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 123-165, April.

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