title: 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.
authors.affiliation: Jodrell Laboratory, Royal Botanic Gardens, Kew , Richmond, Surrey TW9 3DS, United Kingdom.
authors.forename: Tetsuo
authors.initials: T
authors.lastname: Kokubun
authors.forename: Lilla
authors.initials: L
authors.lastname: D'Costa
mesh_terms: D002853:Chromatography, Liquid / Q000379:methods; D016208:Databases, Factual; D007477:Ions; D013058:Mass Spectrometry / Q000379:methods; D010641:Phenotype; D010936:Plant Extracts / Q000032:analysis*; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D007477:Ions; D010936:Plant Extracts