Significance determination of individual metabolic abundance changes owing to environmental impacts: Factorial design t-distribution spectral representations

Abstract

Currently quantitative metabolic analysis is work-intensive and time-consuming normally demanding the use of both chromatography and mass spectrometry. Screening spectral data with principal component analysis is fast and helps identify metabolites. However, it has not been used for quantitative analysis as it cannot determine the statistical significance of individual metabolite abundance changes owing to simulated environmental impacts. Factorial design t univariate spectral representations provide a relatively fast and simple method to determine the statistical significance of individual NMR channels forming peaks and help fill the gap between qualitative and quantitative metabolic analysis of plants suffering environmental impacts. These composite spectral representations, introduced and described for the first time, are univariate statistical t values calculated from factorial design spectra plotted as a function of the analytical channels. They are simple to understand by chemists and biologists with limited statistical knowledge as they only use one basic statistical t distribution equation. We demonstrate their usefulness with factorial design analyses of ¹H NMR spectra of yerba mate samples obtained from different solvent extracts of a mixture design. The spectral representation peak locations are almost the same as those of principal component loadings of ASCA effect matrices although their peak heights are much different and correspond to the statistical significance levels of individual metabolic abundance changes. Spectral representations have ordinates of calculated t values and results for different data sets can be analyzed simultaneously on a common graph whereas this is not possible for loadings that correspond to different PCA coordinate spaces.