129 results found
Peluso A, Ebbels T, Glen R, 2018, Empirical estimation of permutation-based metabolome-wide significance thresholds, Publisher: bioRxiv
A key issue in the omics literature is the search of statistically significant relationships between molecular markers and phenotype. The aim is to detect disease-related discriminatory features while controlling for false positive associations at adequate power. Metabolome-wide association studies have revealed significant relationships of metabolic phenotypes with disease risk by analysing hundreds to tens of thousands of molecular variables leading to multivariate data which are highly noisy and collinear. In this context, Bonferroni or Sidak correction are rather useful as these are valid for independent tests, while permutation procedures allow for the estimation of p-values from the null distribution without assuming independence among features. Nevertheless, under the permutation approach the distribution of p-values may presents systematic deviations from the theoretical null distribution which leads to biased adjusted threshold estimate, e.g. smaller than a Bonferroni or Sidak correction. We make use of parametric approximation methods based on a multivariate Normal distribution to derive stable estimates of the metabolome-wide significance level within a univariate approach based on a permutation procedure which effectively controls the maximum overall type I error rate at the α level. We illustrate the results for different model parametrizations and distributional features of the outcome measure, as well as for diverse correlation levels within the features and between the features and the phenotype in real data and simulated studies. MWSL is the open-source R software package for the empirical estimation of the metabolomic-wide significance level available at https://github.com/AlinaPeluso/MWSL.
Kamp H, Beger R, Dorne J-LCM, et al., 2018, MEtabolomics standaRds Initiative in Toxicology (MERIT), 54th Congress of the European-Societies-of-Toxicology (EUROTOX) - Toxicology Out of the Box, Publisher: ELSEVIER IRELAND LTD, Pages: S214-S214, ISSN: 0378-4274
Ye L, De Iorio M, Ebbels TMD, 2018, Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data, METABOLOMICS, Vol: 14, ISSN: 1573-3882
Posma JM, Garcia-Perez I, Ebbels TMD, et al., 2018, Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data, JOURNAL OF PROTEOME RESEARCH, Vol: 17, Pages: 1586-1595, ISSN: 1535-3893
Kaluarachchi M, Boulange CL, Karaman I, et al., 2018, A comparison of human serum and plasma metabolites using untargeted H-1 NMR spectroscopy and UPLC-MS, METABOLOMICS, Vol: 14, ISSN: 1573-3882
Harada S, Hirayama A, Chan Q, et al., 2018, Reliability of plasma polar metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry, PLOS ONE, Vol: 13, ISSN: 1932-6203
Schober D, Jacob D, Wilson M, et al., 2018, nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data, ANALYTICAL CHEMISTRY, Vol: 90, Pages: 649-656, ISSN: 0003-2700
Ebbels TMD, Rodriguez-Martinez A, Dumas ME, et al., 2018, CHAPTER 12: Advances in Computational Analysis of Metabolomic NMR Data, New Developments in NMR, Pages: 310-323
© The Royal Society of Chemistry 2018. In this chapter we discuss some of the more recent developments in preprocessing and statistical analysis of NMR spectra in metabolomics. Bayesian methods for analyzing NMR spectra are summarized and we describe one particular approach, BATMAN, in more detail. We consider techniques based on statistical associations, such as correlation spectroscopy (e.g. STOCSY and recent variants), as well as approaches that model the associations as a network and how these change under different biological conditions. The link between metabolism and genotype is explored by looking at metabolic GWAS and related techniques. Finally, we describe the relevance and current status of data standards for NMR metabolomics.
Buesen R, Chorley BN, Lima BDS, et al., 2017, Applying 'omics technologies in chemicals risk assessment: Report of an ECETOC workshop, REGULATORY TOXICOLOGY AND PHARMACOLOGY, Vol: 91, Pages: S3-S13, ISSN: 0273-2300
Kauffmann H-M, Kamp H, Fuchs R, et al., 2017, Framework for the quality assurance of 'omics technologies considering GLP requirements, REGULATORY TOXICOLOGY AND PHARMACOLOGY, Vol: 91, Pages: S27-S35, ISSN: 0273-2300
Tan LSL, Jasra A, De Iorio M, et al., 2017, BAYESIAN INFERENCE FOR MULTIPLE GAUSSIAN GRAPHICAL MODELS WITH APPLICATION TO METABOLIC ASSOCIATION NETWORKS, ANNALS OF APPLIED STATISTICS, Vol: 11, Pages: 2222-2251, ISSN: 1932-6157
Castagne R, Boulange CL, Karaman I, et al., 2017, Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted H-1 NMR Metabolic Profiling, JOURNAL OF PROTEOME RESEARCH, Vol: 16, Pages: 3623-3633, ISSN: 1535-3893
Chan Q, Loo RL, Ebbels TMD, et al., 2017, Metabolic phenotyping for discovery of urinary biomarkers of diet, xenobiotics and blood pressure in the INTERMAP Study: an overview, HYPERTENSION RESEARCH, Vol: 40, Pages: 336-345, ISSN: 0916-9636
Weber RJM, Lawson TN, Salek RM, et al., 2017, Computational tools and workflows in metabolomics: An international survey highlights the opportunity for harmonisation through Galaxy, METABOLOMICS, Vol: 13, ISSN: 1573-3882
© 2017 van Rijswijk M et al. Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.
Karaman I, Ferreira DLS, Boulange CL, et al., 2016, Workflow for Integrated Processing of Multicohort Untargeted H-1 NMR Metabolomics Data in Large-Scale Metabolic Epidemiology, JOURNAL OF PROTEOME RESEARCH, Vol: 15, Pages: 4188-4194, ISSN: 1535-3893
Tredwell GD, Bundy JG, De Iorio M, et al., 2016, Modelling the acid/base H-1 NMR chemical shift limits of metabolites in human urine, METABOLOMICS, Vol: 12, ISSN: 1573-3882
Blaise BJ, Correia G, Tin A, et al., 2016, Power Analysis and Sample Size Determination in Metabolic Phenotyping, ANALYTICAL CHEMISTRY, Vol: 88, Pages: 5179-5188, ISSN: 0003-2700
David R, Ebbels T, Gooderham N, 2016, Synergistic and Antagonistic Mutation Responses of Human MCL-5 Cells to Mixtures of Benzo[a]pyrene and 2-Amino-1-Methyl-6-Phenylimidazo[4,5-b]pyridine: Dose-Related Variation in the Joint Effects of Common Dietary Carcinogens, ENVIRONMENTAL HEALTH PERSPECTIVES, Vol: 124, Pages: 88-96, ISSN: 0091-6765
Rocca-Serra P, Salek RM, Arita M, et al., 2016, Data standards can boost metabolomics research, and if there is a will, there is a way, METABOLOMICS, Vol: 12, ISSN: 1573-3882
Griep LMO, Chekmeneva E, Stamler J, et al., 2016, Urinary hippurate and proline betaine relative to fruit intake, blood pressure, and body mass index, Publisher: CAMBRIDGE UNIV PRESS, Pages: E178-E178, ISSN: 0029-6651
Salek RM, Neumann S, Schober D, et al., 2015, Coordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access (vol 11, pg 1587, 2015), METABOLOMICS, Vol: 11, Pages: 1598-1599, ISSN: 1573-3882
Salek RM, Neumann S, Schober D, et al., 2015, COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access, METABOLOMICS, Vol: 11, Pages: 1587-1597, ISSN: 1573-3882
Roessner U, Ebbels T, 2015, One minute with the Metabolomics Society's Honorary Fellows 2015, METABOLOMICS, Vol: 11, Pages: 779-781, ISSN: 1573-3882
Salek RM, Arita M, Dayalan S, et al., 2015, Embedding standards in metabolomics: the Metabolomics Society data standards task group, METABOLOMICS, Vol: 11, Pages: 782-783, ISSN: 1573-3882
Hendrickx DM, Aerts HJWL, Caiment F, et al., 2015, diXa: a data infrastructure for chemical safety assessment, BIOINFORMATICS, Vol: 31, Pages: 1505-1507, ISSN: 1367-4803
Roessner U, Bearden DW, Ebbels T, 2015, The international Metabolomics Society in 2015: the path forward to success, METABOLOMICS, Vol: 11, Pages: 1-2, ISSN: 1573-3882
Pomyen Y, Segura M, Ebbels TMD, et al., 2015, Over-representation of correlation analysis (ORCA): a method for identifying associations between variable sets, BIOINFORMATICS, Vol: 31, Pages: 102-108, ISSN: 1367-4803
Ipsen A, Ebbels TMD, 2014, Orders of Magnitude Extension of the Effective Dynamic Range of TDC-Based TOFMS Data Through Maximum Likelihood Estimation, JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY, Vol: 25, Pages: 1824-1827, ISSN: 1044-0305
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