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KEMREP: A New Qualitative Method for the Assessment of an Analyst’s Ability to Generate a Metabolomics Data Matrix by Gas Chromatography– Mass Spectrometry

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The analytical procedures required to generate a quantified metabolomics data matrix include many and widely different potential sources of error, complicating the generation of reliable data. The methods generally used to assess precision of such data all have distinct merits but some clear limitations as well. In this paper we describe KEMREP (kernel method for the assessment of repeatability and reproducibility), a new method with the advantage and focus aimed specifically at analysis of the reliability of metabolomics data. Repeatability and reproducibility were assessed on gas chromatography- mass spectrometry (GC-MS) generated metabolomics data matrices produced by and between analysts and across laboratories, using cerebrospinal fluid (CSF) and urine as biological samples for analysis. KEMREP provides a visual overlay of the smoothed and scaled versions of the data from repeated samples for a direct and easy qualitative assessment of repeatability or reproducibility of a distinct chromatographic region (univariate) or for the experiment as a whole (multivariate). The KEMREP method can also be extended by the imposition of confidence bounds which provide lower and upper limits that indicate quantitatively whether the experiment was repeatable or reproducible at a predefined input coefficient of variation (CV). KEMREP is thus a novel approach which supplements existing methods of assessment of reliability of metabolomics data; provides a benchmark for assessing the quality of practical work performed by analysts; monitors the sequence of data pre-treatment steps; and tests the robustness of an experimentally designed protocol for metabolomics.
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Keywords: Gas chromatography-mass spectrometry; kernel density; metabolomics; qualitative; repeatability; reproducibility

Document Type: Research Article

Publication date: January 1, 2014

More about this publication?
  • Current Metabolomics publishes timely reviews, original research articles, thematic issues, and technical notes covering all aspects of the recent advancements and applications of metabolomics technology to systems biology, disease diagnosis, personalized medicine, and drug discovery. Original research articles and reviews in the following areas of metabolomics are of special interest to the readers of this journal:

    Application to humans, plants, microbes, cell cultures, tissues, organs, and biofluids
    Automation and high-throughput technology
    Bioinformatics
    Chemical and structural characterization of new metabolites
    Chemometrics, statistical analysis, and other approaches to data analysis
    Databases, including collections of reference spectra
    Disease biomarkers
    Drug discovery, efficacy, toxicity, and personalized medicine
    Integration of analytical techniques and/or various OMICS datasets
    Metabolic profiling and directed metabolite analysis
    New analytical experiments and techniques (NMR, MS, FTIR, etc)
    Novel sensors and instrumentation for detecting metabolites
    Sample preparation, extraction techniques and chromatography
    Software

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