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

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 <i>t</i>_R–<i>m</i>/<i>z</i> plane of LC-MS data is divided into equal-sized segments of twelve seconds by one <i>m</i>/<i>z</i> unit each, and the total ion currents in corresponding segments as specified by the <i>t</i>_R–<i>m</i>/<i>z</i> pair from multiple LC-MS runs are evaluated to generate mean ion currents (<i>μ</i>) and standard deviations (<i>σ</i>). The <i>μ</i>’s and <i>σ</i>’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 <i>Z</i>-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 <i>Aesculus</i> to the leaf-miner <i>Cameraria ohridella</i> were detected

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

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 <i>t</i>_R–<i>m</i>/<i>z</i> plane of LC-MS data is divided into equal-sized segments of twelve seconds by one <i>m</i>/<i>z</i> unit each, and the total ion currents in corresponding segments as specified by the <i>t</i>_R–<i>m</i>/<i>z</i> pair from multiple LC-MS runs are evaluated to generate mean ion currents (<i>μ</i>) and standard deviations (<i>σ</i>). The <i>μ</i>’s and <i>σ</i>’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 <i>Z</i>-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 <i>Aesculus</i> to the leaf-miner <i>Cameraria ohridella</i> were detected