Direct
and Unbiased Information Recovery from Liquid
Chromatography–Mass Spectrometry Raw Data for Phenotype-Differentiating
Metabolites Based on Screening Window Coefficient of Ion Currents
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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 <i>t</i><sub>R</sub>–<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><sub>R</sub>–<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