Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.

<h4>Background</h4>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, wher...

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Autores principales:	Lea A I Vaas, Johannes Sikorski, Victoria Michael, Markus Göker, Hans-Peter Klenk
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2f4706e21ded4edba4b58d433599b02e
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spelling	oai:doaj.org-article:2f4706e21ded4edba4b58d433599b02e2021-11-18T07:21:28ZVisualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.1932-620310.1371/journal.pone.0034846https://doaj.org/article/2f4706e21ded4edba4b58d433599b02e2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22536335/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Methodology</h4>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.<h4>Conclusions</h4>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.Lea A I VaasJohannes SikorskiVictoria MichaelMarkus GökerHans-Peter KlenkPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 4, p e34846 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lea A I Vaas Johannes Sikorski Victoria Michael Markus Göker Hans-Peter Klenk Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.
description	<h4>Background</h4>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.<h4>Methodology</h4>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.<h4>Conclusions</h4>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.
format	article
author	Lea A I Vaas Johannes Sikorski Victoria Michael Markus Göker Hans-Peter Klenk
author_facet	Lea A I Vaas Johannes Sikorski Victoria Michael Markus Göker Hans-Peter Klenk
author_sort	Lea A I Vaas
title	Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.
title_short	Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.
title_full	Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.
title_fullStr	Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.
title_full_unstemmed	Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.
title_sort	visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/2f4706e21ded4edba4b58d433599b02e
work_keys_str_mv	AT leaaivaas visualizationandcurveparameterestimationstrategiesforefficientexplorationofphenotypemicroarraykinetics AT johannessikorski visualizationandcurveparameterestimationstrategiesforefficientexplorationofphenotypemicroarraykinetics AT victoriamichael visualizationandcurveparameterestimationstrategiesforefficientexplorationofphenotypemicroarraykinetics AT markusgoker visualizationandcurveparameterestimationstrategiesforefficientexplorationofphenotypemicroarraykinetics AT hanspeterklenk visualizationandcurveparameterestimationstrategiesforefficientexplorationofphenotypemicroarraykinetics
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Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.

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