Fisher ratio method applied to third-order separation data to identify significant chemical components of metabolite extracts

This report is about applying a Fisher ratio method to entire four dimensional (4D) data sets from third-order instrumentation data. The Fisher ratio method uses a novel indexing scheme to discover the unknown chemical differences among known classes of complex samples. This is the first report of a Fisher ratio analysis procedure applied to entire 4D data sets of third-order separation data, which, in this case, is comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry analyses of metabolite extracts using all of the collected mass channels. Current analysis methods for third-order separation data use only user-defined subsets of the 4D data set. First, in a validation study, the Fisher ratio method was demonstrated to objectively evaluate and determine the chemical differences between three controlled urine samples that differed by known spiked chemical components. It was determined that, out of more than 600 recognizable chemical components in a single sample, the six spiked components, along with only two other matrix components, differed most significantly in concentration among the control samples. In a second study, the Fisher ratio method was used in a novel application to discover the unknown chemical differences between urine metabolite samples from pregnant women and nonpregnant women. A brief list of the top 11 components that were most significantly different in concentration between the pregnant and nonpregnant samples was generated. Because the Fisher ratio calculation statistically differentiates regions of the chromatogram with large class-to-class variations from regions containing large within-class variations, the Fisher ratio method should generally be robust against biological diversity in a sample population. Indeed, application of principal component analysis in this second study failed due to biological diversity of the samples.