Statistical strategies for avoiding false discoveries in metabolomics and related experiments

By David Broadhurst, Douglas B. Kell. Metabolomics, 2007

833 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/c54728e2602a9f026995d077a004d70b0ef3a8c1

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Citations in the corpus

Metabolomics reveals unhealthy alterations in rumen metabolism with increased proportion of cereal grain in the diet of dairy cows (2010, methodology): “…(mg/L) 0.64 0.81 6.31 7.90 0.80 \0.01 0.001 Rumen pH 6.81 6.70 6.85 6.52 0.08 0.32 0.210 a BON Bonferroni correction, FDR false discovery rate (Broadhurst and Kell 2006); Significances were considered at p = 0.05 b Rumen fluid for the analysis was collected shortly before the morning…”
Multivariate Analysis in Metabolomics. (2012, methodology): “Truly honest model validation requires partitioning the data into a training set used to build a model and a validation set used to assess predictive ability of the model, where the validation set is in no way used to generate the trained model [103, 104].”
A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. (2015, methodology): “Similar statistics were observed nearly a decade ago by Broadhurst and Kell, where these authors highlighted the fact that, in most cases, the statistical approaches that have been employed are not described in sufficient detail, nor are the models appropriately validated [14].”
Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community (2019, background): “Data integration has been characterized as the key bottleneck to all multi‐omics studies [33].”
Analytical Methods in Untargeted Metabolomics: State of the Art in 2015 (2015, positive): “Although a very conservative approach, especially when the hypotheses tested are not independent, many researchers advocate its use in metabolomic studies (Broadhurst and Kell, 2006).”
Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism (2009, methodology): “The Bonferroni correction was applied to counter the multiple hypothesis testing that results from the multivariate nature of metabonomic data (24).”
Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry (2011, background): “To enable a greater understanding of the metabolic status of humans, medium-to-large-scale epidemiological studies are required in order to take into account the substantial diversity observed in physiology, metabolic status and lifestyle in the general human population, as discussed by Broadhurst a”