141 results found
Benton HP, Want E, Keun HC, et al., 2012, Intra- and Interlaboratory Reproducibility of Ultra Performance Liquid Chromatography-Time-of-Flight Mass Spectrometry for Urinary Metabolic Profiling, ANALYTICAL CHEMISTRY, Vol: 84, Pages: 2424-2432, ISSN: 0003-2700
Cazier J-B, Kaisaki PJ, Argoud K, et al., 2012, Untargeted Metabolome Quantitative Trait Locus Mapping Associates Variation in Urine Glycerate to Mutant Glycerate Kinase, JOURNAL OF PROTEOME RESEARCH, Vol: 11, Pages: 631-642, ISSN: 1535-3893
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
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
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
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
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
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 H-1 NMR Data Sets Using Iterative Statistical Total Correlation Spectroscopy, ANALYTICAL CHEMISTRY, Vol: 83, Pages: 2075-2082, ISSN: 0003-2700
Montana G, Berk M, Ebbels TMD, 2010, 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
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
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
Ebbels TMD, Lindon JC, Coen M, 2011, Processing and modeling of nuclear magnetic resonance (NMR) metabolic profiles., Pages: 365-388
Modern nuclear magnetic resonance (NMR) spectroscopy generates complex and information-rich metabolic profiles. These require robust, accurate, and often sophisticated statistical techniques to yield the maximum meaningful knowledge. In this chapter, we describe methods typically used to analyze such data. We begin by describing seven goals of metabolic profile analysis, ranging from production of a data table to multi-omic integration for systems biology. Methods for preprocessing and pretreatment are then presented, including issues such as instrument-level spectral processing, data reduction and deconvolution, normalization, scaling, and transformations of the data. We then discuss methods for exploratory modeling and exemplify three techniques: principal components analysis, hierarchical clustering, and self-organizing maps. Moving to predictive modeling, we focus our discussion on partial least squares regression, orthogonal partial least squares regression, and genetic algorithm approaches. A typical set of in vitro metabolic profiles is used where possible to compare and contrast the methods. The importance of validating statistical models is highlighted, and standard techniques for doing so, such as training/test set and cross-validation are described. Finally, we discuss the contributions of statistical techniques such as statistical total correlation spectroscopy, and other correlation-based methods have made to the process of structural characterization for unknown metabolites.
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.
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
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
Muncey HJ, Jones R, De Iorio M, et al., 2010, MetAssimulo:Simulation of Realistic NMR Metabolic Profiles, BMC BIOINFORMATICS, Vol: 11, ISSN: 1471-2105
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
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
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
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
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
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
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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