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

2007; Elsevier BV; Volume: 69; Issue: 3 Linguagem: Inglês

10.1016/j.mimet.2007.02.013

1872-8359

Kela P. Weber, Jason Grove, Matthias Gehder, William A. Anderson, Raymond L. Legge,

Economic and Environmental Valuation

BIOLOG® EcoPlates™ provide one method for determination of functional diversity indices and community-level physiological profiling of microbial populations based on carbon substrate utilization. In this study, the effect of data transformation on BIOLOG EcoPlate data derived from wetland mesocosms and biofiltration systems was examined. Homoscedasticity, normality, and the number of linear correlations between variables were quantified and evaluated for data that had been transformed using either Taylor or logarithmic transforms. Subsequent multivariate analysis was implemented using the untransformed, Taylor transformed and logarithmic transformed data sets. The effect of data transformation and its effect on principle component analysis are presented. The transforms are shown to help increase homogeneity of variance, increase normality of the data, and increase the number of significant linear correlations for the data. Separate principle component analyses and ordinations of the data showed the transforms to be well suited to this type of data and in particular illustrate the ability of the logarithmic transform to reduce the influence of high leverage or outlying observations on the overall analysis and its robustness in terms of treating data from different ecological systems. Although BIOLOG EcoPlates were used in this study to illustrate the use of transformations on multivariate data, the techniques described may be employed on similar microplate data. In addition, if homoscedasticity, normality and the number of linear correlations within a data set are not evaluated and the possibility of transforming the data, using the Taylor, logarithmic or another transform, is not considered, erroneous analysis and misleading conclusions may be attained when performing multivariate analysis on microplate data.