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Data transformations in the analysis of community-level substrate utilization data from microplates.

Kela P Weber1, Jason A Grove, Matthias Gehder

  • 1Department of Chemical Engineering, University of Waterloo, 200 University Avenue W., Waterloo, Ontario, Canada.

Journal of Microbiological Methods
|April 3, 2007
PubMed
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Data transformation enhances analysis of microbial community data from BIOLOG EcoPlates. Logarithmic transforms improve homogeneity, normality, and correlations, ensuring robust ecological system analysis.

Area of Science:

  • Microbial Ecology
  • Environmental Science
  • Data Analysis

Background:

  • BIOLOG EcoPlates are used for microbial community-level physiological profiling based on carbon substrate utilization.
  • Data transformation is crucial for accurate multivariate analysis of such data.
  • Previous studies have not fully explored the impact of specific transformations on BIOLOG EcoPlate data.

Purpose of the Study:

  • To investigate the effect of data transformation on BIOLOG EcoPlate data from wetland and biofiltration systems.
  • To evaluate how Taylor and logarithmic transforms impact data properties like homoscedasticity, normality, and linear correlations.
  • To assess the influence of these transformations on multivariate analyses, particularly principal component analysis (PCA).

Main Methods:

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  • Collected BIOLOG EcoPlate data from wetland mesocosms and biofiltration systems.
  • Applied Taylor and logarithmic data transformations.
  • Quantified homoscedasticity, normality, and linear correlations for transformed and untransformed data.
  • Performed multivariate analysis, including PCA, on all data sets.

    • Main Results:

    • Data transformations, especially logarithmic, increased homogeneity of variance and normality.
    • Transforms significantly increased the number of significant linear correlations within the data.
    • PCA and ordination revealed that transformations are well-suited for this data, with logarithmic transforms mitigating outlier influence.

    Conclusions:

    • Data transformation is essential for reliable multivariate analysis of BIOLOG EcoPlate data.
    • Logarithmic transformation offers robustness and reduces the impact of outliers, making it suitable for diverse ecological systems.
    • Failure to consider data transformation can lead to erroneous conclusions in microplate data analysis.