Spectral relative standard deviation: A practical benchmark in metabolomics
Analyst, ISSN: 1364-5528, Vol: 134, Issue: 3, Page: 478-485
2009
- 159Citations
- 154Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations159
- Citation Indexes159
- 159
- CrossRef150
- Academic Citation Index (ACI) - airiti1
- Captures154
- Readers154
- 154
Article 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 (RSDs; 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, ranges 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. © The Royal Society of Chemistry.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=60649117985&origin=inward; http://dx.doi.org/10.1039/b808986h; http://www.ncbi.nlm.nih.gov/pubmed/19238283; https://xlink.rsc.org/?DOI=B808986H; http://xlink.rsc.org/?DOI=B808986H; http://pubs.rsc.org/en/content/articlepdf/2009/AN/B808986H; https://dx.doi.org/10.1039/b808986h; https://pubs.rsc.org/en/content/articlelanding/2009/an/b808986h
Royal Society of Chemistry (RSC)
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