Publications
171 results found
Alves AC, Li JV, Garcia-Perez I, et al., 2012, Characterization of data analysis methods for information recovery from metabolic 1H NMR spectra using artificial complex mixtures, Metabolomics, Vol: 8, Pages: 1170-1180
Veselkov KA, Vingara LK, Masson P, et al., 2011, Response to Comment on "Optimized Preprocessing of Ultra-Performance Liquid Chromatography/Mass Spectrometry Urinary Metabolic Profiles for Improved Information Recovery", ANALYTICAL CHEMISTRY, Vol: 83, Pages: 9721-9722, ISSN: 0003-2700
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- Citations: 2
Kamburov A, Cavill R, Ebbels TMD, et al., 2011, Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA, BIOINFORMATICS, Vol: 27, Pages: 2917-2918, ISSN: 1367-4803
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- Citations: 268
Valcarcel B, Wurtz P, al Basatena N-KS, et al., 2011, A Differential Network Approach to Exploring Differences between Biological States: An Application to Prediabetes, PLOS ONE, Vol: 6, ISSN: 1932-6203
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- Citations: 27
Ebbels TMD, De Iorio M, 2011, Statistical Data Analysis in Metabolomics, Handbook of Statistical Systems Biology, Editors: Stumpf, Balding, Girolami, Publisher: Wiley, ISBN: 9781119970613
8 Statistical Data Analysis in Metabolomics Timothy MD Ebbels1 and Maria De Iorio2 1Department of Surgery and Cancer, Imperial College, London, UK 2Department of Epidemiology and Biostatistics, Imperial College, London, ...
Veselkov KA, Vingara LK, Masson P, et al., 2011, Optimized Preprocessing of Ultra-Performance Liquid Chromatography/Mass Spectrometry Urinary Metabolic Profiles for Improved Information Recovery, ANALYTICAL CHEMISTRY, Vol: 83, Pages: 5864-5872, ISSN: 0003-2700
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- Citations: 211
Berk M, Ebbels T, Montana G, 2011, A statistical framework for biomarker discovery in metabolomic time course data, BIOINFORMATICS, Vol: 27, Pages: 1979-1985, ISSN: 1367-4803
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- Citations: 35
Ellis JK, Athersuch TJ, Cavill R, et al., 2011, Erratum: Metabolic response to low level toxicant exposure in a novel renal tubule epithelial cell system (Molecular BioSystems (2010) DOI:10.1039/c0mb00146e), Molecular BioSystems, Vol: 7, Pages: 2081-2086, ISSN: 1742-206X
Sands CJ, Coen M, Ebbels TMD, et al., 2011, Data-Driven Approach for Metabolite Relationship Recovery in Biological <SUP>1</SUP>H NMR Data Sets Using Iterative Statistical Total Correlation Spectroscopy, ANALYTICAL CHEMISTRY, Vol: 83, Pages: 2075-2082, ISSN: 0003-2700
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- Citations: 30
Montana G, Berk M, Ebbels TMD, 2011, Modelling Short Time Series in Metabolomics: A Functional Data Analysis Approach, Software Tools and Algorithms for Biological Systems, Editors: Arabnia, Publisher: Springer
Cavill R, Kamburov A, Ellis JK, et al., 2011, Consensus-Phenotype Integration of Transcriptomic and Metabolomic Data Implies a Role for Metabolism in the Chemosensitivity of Tumour Cells, PLOS COMPUTATIONAL BIOLOGY, Vol: 7, ISSN: 1553-734X
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- Citations: 72
Chadeau-Hyam M, Athersuch TJ, Keun HC, et al., 2011, Meeting-in-the-middle using metabolic profiling - a strategy for the identification of intermediate biomarkers in cohort studies, BIOMARKERS, Vol: 16, Pages: 83-88, ISSN: 1354-750X
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- Citations: 89
Muncey H, Jones R, De Iorio M, et al., 2011, MetAssimulo
MetAssimulo is a MATLAB-based package which simulates 1H-NMR spectra of complex mixtures such as metabolic profiles. Drawing data from a metabolite standard spectral database in conjunction with concentration information input by the user or constructed automatically from the Human Metabolome Database, MetAssimulo is able to create realistic metabolic profiles containing large numbers of metabolites with a range of user-defined properties. Current features include the simulation of two groups ('case' and 'control') specified by means and standard deviations of concentrations for each metabolite. The software also allows addition of spectral noise with a realistic autocorrelation structure at user controllable levels. A crucial feature of the algorithm is its ability to simulate both intra- and inter-metabolite correlations, the analysis of which is fundamental to many techniques in the field. Further, MetAssimulo is able to simulate shifts in NMR peak positions that result from matrix effects such as pH differences which are often observed in metabolic NMR spectra and pose serious challenges for statistical algorithms.
Ebbels TMD, Lindon JC, Coen M, 2011, Processing and Modeling of Nuclear Magnetic Resonance (NMR) Metabolic Profiles, METABOLIC PROFILING: METHODS AND PROTOCOLS, Editors: Metz, Publisher: HUMANA PRESS INC, Pages: 365-388, ISBN: 978-1-61737-984-0
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- Citations: 28
Ellis JK, Athersuch TJ, Cavill R, et al., 2011, Metabolic response to low-level toxicant exposure in a novel renal tubule epithelial cell system, MOLECULAR BIOSYSTEMS, Vol: 7, Pages: 247-257, ISSN: 1742-206X
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- Citations: 46
Ellis JK, Athersuch TJ, Cavill R, et al., 2011, Metabolic response to low level toxicant exposure in a novel renal tubule epithelial cell system (vol 7, pg 247, 2011), MOLECULAR BIOSYSTEMS, Vol: 7, Pages: 2081-2086, ISSN: 1742-206X
Fonville JM, Richards SE, Barton RH, et al., 2010, The Evolution of Partial Least Squares Models and Related Chemometric Approaches in Metabonomics and Metabolic Phenotyping, J. Chemometrics, Vol: 24, Pages: 636-649
Metabonomics is a key element in systems biology, and with current analytical methods, generates vast amounts ofquantitative or qualitative metabolic data. Understanding of the global function of the living organism can beachieved by integration of ‘omics’ approaches including metabonomics, genomics, transcriptomics and proteomics,increasing the complexity of the full data sets. Multivariate statistical approaches are well suited to extract thecharacterizing metabolic information associated with each level of dynamic process. In this review, we discusstechniques that have evolved from principal component analysis and partial least squares (PLS) methods with a focuson improved interpretation and modeling with respect to biomarker recovery and data visualization in the context ofmetabonomic applications. Visualization is of paramount importance to investigate complex metabolic signatures,the power and potential of which is illustrated with key papers. Recent improvements based on the removal oforthogonal variation are discussed in terms of interpretation enhancement, and are supported by relevantapplications. Flexibility of PLS methods in general and of O-PLS in particular allows implementation of derivativemethods such as O2-PLS, O-PLS-variance components, nonlinear methods, and batch modeling to improve analysis ofcomplex data sets, which facilitates extraction of information related to subtle biological processes. These approachescan be used to address issues present in complex multi-factorial data sets. Thus, we highlight the key advantages andlimitations of the different latent variable applications for top-down systems biology and assess the differencesbetween the methods available.
Yap IKS, Brown IJ, Chan Q, et al., 2010, Metabolome-Wide Association Study Identifies Multiple Biomarkers that Discriminate North and South Chinese Populations at Differing Risks of Cardiovascular Disease INTERMAP Study, JOURNAL OF PROTEOME RESEARCH, Vol: 9, Pages: 6647-6654, ISSN: 1535-3893
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- Citations: 94
Richards SE, Dumas M-E, Fonville JM, et al., 2010, Intra- and inter-omic fusion of metabolic profiling data in a systems biology framework, CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, Vol: 104, Pages: 121-131, ISSN: 0169-7439
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- Citations: 43
Muncey HJ, Jones R, De Iorio M, et al., 2010, MetAssimulo:Simulation of Realistic NMR Metabolic Profiles, BMC BIOINFORMATICS, Vol: 11, ISSN: 1471-2105
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- Citations: 17
Benton HP, Want EJ, Ebbels TMD, 2010, Correction of mass calibration gaps in liquid chromatography-mass spectrometry metabolomics data, BIOINFORMATICS, Vol: 26, Pages: 2488-2489, ISSN: 1367-4803
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- Citations: 133
Ipsen A, Want EJ, Ebbels TMD, 2010, Construction of Confidence Regions for Isotopic Abundance Patterns in LC/MS Data Sets for Rigorous Determination of Molecular Formulas, ANALYTICAL CHEMISTRY, Vol: 82, Pages: 7319-7328, ISSN: 0003-2700
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- Citations: 9
Bictash M, Ebbels TM, Chan Q, et al., 2010, Opening up the "Black Box": Metabolic phenotyping and metabolome-wide association studies in epidemiology, JOURNAL OF CLINICAL EPIDEMIOLOGY, Vol: 63, Pages: 970-979, ISSN: 0895-4356
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- Citations: 107
Chadeau-Hyam M, Ebbels TMD, Brown IJ, et al., 2010, Metabolic Profiling and the Metabolome-Wide Association Study: Significance Level For Biomarker Identification, JOURNAL OF PROTEOME RESEARCH, Vol: 9, Pages: 4620-4627, ISSN: 1535-3893
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- Citations: 88
Cavill R, Sidhu JK, Kilarski W, et al., 2010, A Combined Metabonomic and Transcriptomic Approach to Investigate Metabolism during Development in the Chick Chorioallantoic Membrane, JOURNAL OF PROTEOME RESEARCH, Vol: 9, Pages: 3126-3134, ISSN: 1535-3893
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- Citations: 11
Allen E, Moing A, Ebbels TM, et al., 2010, Correlation Network Analysis reveals a sequential reorganization of metabolic and transcriptional states during germination and gene-metabolite relationships in developing seedlings of Arabidopsis, BMC SYSTEMS BIOLOGY, Vol: 4
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- Citations: 42
Ipsen A, Want E, Lindon J, et al., 2010, Identification of parent-fragment pairs via rigorous statistical modeling of LC-MS metabolomic data, Publisher: AMER CHEMICAL SOC, ISSN: 0065-7727
Ipsen A, Want EJ, Lindon JC, et al., 2010, A statistically rigorous test for the identification of parent-fragment pairs in LC-MS datasets, Analytical Chemistry, Vol: 82, Pages: 1766-1778, ISSN: 1086-4377
Ellis JK, Chan PH, Doktorova T, et al., 2010, Effect of the Histone Deacetylase Inhibitor Trichostatin A on the Metabolome of Cultured Primary Hepatocytes, JOURNAL OF PROTEOME RESEARCH, Vol: 9, Pages: 413-419, ISSN: 1535-3893
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- Citations: 13
Bollard ME, Contel NR, Ebbels TMD, et al., 2010, NMR-Based Metabolic Profiling Identifies Biomarkers of Liver Regeneration Following Partial Hepatectomy in the Rat, JOURNAL OF PROTEOME RESEARCH, Vol: 9, Pages: 59-69, ISSN: 1535-3893
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- Citations: 71
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