Direct and Unbiased Information Recovery from Liquid Chromatography–Mass Spectrometry Raw Data for Phenotype-Differentiating Metabolites Based on Screening Window Coefficient of Ion Currents

Abstract

A reworking of a data mining strategy, in which statistical treatment of raw data from liquid chromatography-mass spectrometry (LC-MS) precedes recognition of chromatographic peaks, is presented. In this algorithm the tR-m/z plane of LC-MS data is divided into equal-sized segments of twelve seconds by one m/z unit each, and the total ion currents in corresponding segments as specified by the tR-m/z pair from multiple LC-MS runs are evaluated to generate mean ion currents (μ) and standard deviations (σ). The μ's and σ's of the segments, derived from contrasting classes of LC-MS data set (e.g., resistant-susceptible, case-control, etc.), are used to calculate the Z-factor (screening window coefficient) which is in turn used to rank the segments. Chromatographic peaks are recognized only where the ion currents are shown to differentiate the classes. The result-reporting format enables detection of positive as well as negative correlations between ion intensities and biological traits under study and thus points to the presence of potentially phenotype-discriminating metabolites. Examples of data analyses are presented, in which ions that may distinguish resistant and susceptible species of Aesculus to the leaf-miner Cameraria ohridella were detected.