Comprehensive Analysis of LC/MS Data Using Pseudocolor Plots

Abstract

We have developed new applications of the pseudocolor plot for the analysis of LC/MS data. These applications include spectral averaging, analysis of variance, differential comparison of spectra, and qualitative filtering by compound class. These applications have been motivated by the need to better understand LC/ MS data generated from analysis of human biofluids. The examples presented use data generated to profile steroid hormones in urine extracts from a Cushing’s disease patient relative to a healthy control, but are general to any discovery-based scanning mass spectrometry technique. In addition to new visualization techniques, we introduce a new metric of variance: the relative maximum difference from the mean. We also introduce the concept of substructure-dependent analysis of steroid hormones using precursor ion scans. These new analytical techniques provide an alternative approach to traditional untargeted metabolomics workflow. We present an approach to discovery using MS that essentially eliminates alignment or preprocessing of spectra. Moreover, we demonstrate the concept that untargeted metabolomics can be achieved using low mass resolution instrumentation.

Advances in mass spectrometry-based clinical biomarker discovery

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The promise of metabolic phenotyping in gastroenterology and hepatology

“…In parallel with spectroscopic developments, many computer-based preprocessing and multivariate model ling techniques have been created to facilitate the analysis and interpretation of complex high-density spectral data. Computational modelling tools are able to analyse, map and interpret spectroscopic data and can be roughly divided into different categories: preprocessing methods (such as spectral alignment and de noising [34][35][36] ); multivariate analysis methods (extensions to linear projection methods, Bayesian probabilistic models and so on 37-39 ); biomarker extraction algorithms (based on statistical spectro scopic tools 40 ) and pathway integration and mapping tools (such as Metabonetworks 41 and MetaMapp 42 ). Data-filtering strategies and other preprocessing methods can be used to optimize models and to increase the interpretability of models focusing on important biochemical changes relating to single or combinatorial pathologies by removing extraneous variation.…”

A short review of applications of liquid chromatography mass spectrometry based metabolomics techniques to the analysis of human urine

“…The application of this strategy reduced the number missing values in data sets 85 A procedure using pseudocolor plots was used for the analysis of LC/MS data providing an alternative approach to traditional untargeted metabolomics workflow and eliminating alignment and preprocessing of spectra. 86…”