Spectral relative standard deviation: a practical benchmark in metabolomics

摘要

Metabolomics datasets, by definition, comprise of measurements of large numbers of metabolites. Bothntechnical (analytical) and biological factors will induce variation within these measurements that is notnconsistent across all metabolites. Consequently, criteria are required to assess the reproducibility ofnmetabolomics datasets that are derived from all the detected metabolites. Here we calculate spectrum-nwide relative standard deviations (RSDs; also termed coefficient of variation, CV) for tenmetabolomicsndatasets, spanning a variety of sample types from mammals, fish, invertebrates and a cell line, andndisplay them succinctly as boxplots. We demonstrate multiple applications of spectral RSDs forncharacterising technical as well as inter-individual biological variation: for optimising metabolitenextractions, comparing analytical techniques, investigating matrix effects, and comparing biofluids andntissue extracts from single and multiple species for optimising experimental design. Technical variationnwithin metabolomics datasets, recorded using one- and two-dimensional NMR and mass spectrometry,nranges from 1.6 to 20.6% (reported as the median spectral RSD). Inter-individual biological variation isntypically larger, ranging from as low as 7.2% for tissue extracts from laboratory-housed rats to 58.4%nfor fish plasma. In addition, for some of the datasets we confirmthat the spectral RSDvalues are largelyninvariant across different spectral processing methods, such as baseline correction, normalisation andnbinning resolution. In conclusion, we propose spectral RSDs and their median values contained hereinnas practical benchmarks for metabolomics studies.