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Novel R Pipeline for Analyzing Biolog Phenotypic Microarray Data

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  • Minna Vehkala
  • Mikhail Shubin
  • Thomas R Connor
  • Nicholas R Thomson
  • Jukka Corander

Abstract

Data produced by Biolog Phenotype MicroArrays are longitudinal measurements of cells’ respiration on distinct substrates. We introduce a three-step pipeline to analyze phenotypic microarray data with novel procedures for grouping, normalization and effect identification. Grouping and normalization are standard problems in the analysis of phenotype microarrays defined as categorizing bacterial responses into active and non-active, and removing systematic errors from the experimental data, respectively. We expand existing solutions by introducing an important assumption that active and non-active bacteria manifest completely different metabolism and thus should be treated separately. Effect identification, in turn, provides new insights into detecting differing respiration patterns between experimental conditions, e.g. between different combinations of strains and temperatures, as not only the main effects but also their interactions can be evaluated. In the effect identification, the multilevel data are effectively processed by a hierarchical model in the Bayesian framework. The pipeline is tested on a data set of 12 phenotypic plates with bacterium Yersinia enterocolitica. Our pipeline is implemented in R language on the top of opm R package and is freely available for research purposes.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0118392
    DOI: 10.1371/journal.pone.0118392
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    References listed on IDEAS

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    1. Sturino Joseph & Zorych Ivan & Mallick Bani & Pokusaeva Karina & Chang Ying-Ying & Carroll Raymond J & Bliznuyk Nikolay, 2010. "Statistical Methods for Comparative Phenomics Using High-Throughput Phenotype Microarrays," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-21, August.
    2. 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.
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    1. Mikhail Shubin & Katharina Schaufler & Karsten Tedin & Minna Vehkala & Jukka Corander, 2016. "Identifying Multiple Potential Metabolic Cycles in Time-Series from Biolog Experiments," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-14, September.

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