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Spectral Relative Standard Deviation: A Practical Benchmark in Metabolomics
Citation:
Parsons, H. M., D. R. EKMAN, T. W. COLLETTE, AND M. R. Viant. Spectral Relative Standard Deviation: A Practical Benchmark in Metabolomics. ANALYST. Royal Society of Chemistry, Cambridge, Uk, 134(3):478-485, (2009).
Impact/Purpose:
The mission of the ERD Metabolomics Team is to study the impact of stressors on various species using NMR and other advanced analytical approaches to characterize changes in endogenous metabolites. The main focus is to define responses in ecologically-relevant organisms (e.g., small fish) upon exposure to potentially toxic xenobiotic chemicals.
Description:
Metabolomics datasets, by definition, comprise of measurements of large numbers of metabolites. Both technical (analytical) and biological factors will induce variation within these measurements that is not consistent across all metabolites. Consequently, criteria are required to assess the reproducibility of metabolomics datasets that are derived from all the detected metabolites. Here we calculate spectrum-wide relative standard deviations (RSD; also termed coefficient of variation, CV) for ten metabolomics datasets, spanning a variety of sample types from mammals, fish, invertebrates and a cell line, and display them succinctly as boxplots. We demonstrate multiple applications of spectral RSDs for characterising technical as well as inter-individual biological variation: for optimising metabolite extractions, comparing analytical techniques, investigating matrix effects, and comparing biofluids and tissue extracts from single and multiple species for optimising experimental design. Technical variation within metabolomics datasets, recorded using one- and two-dimensional NMR and mass spectrometry, range from 1.6% to 20.6% (reported as the median spectral RSD). Inter-individual biological variation is typically larger, ranging from as low as 7.2% for tissue extracts from laboratory housed rats to 58.4% for fish plasma. In addition, for some of the datasets we confirm that the spectral RSD values are largely invariant across different spectral processing methods, such as baseline correction, normalisation and binning resolution. In conclusion, we propose spectral RSDs and their median values contained herein as practical benchmarks for metabolomics studies.
URLs/Downloads:
EKMAN 08 133.PDF (PDF, NA pp, 453 KB, about PDF)Spectral Relative Standard Deviation: A Practical Benchmark in Metabolomics
