Metabolic Profiling: Disease and Xenobiotics (Issues in Toxicology, 21, Band 21) - Hardcover

 
9781849731638: Metabolic Profiling: Disease and Xenobiotics (Issues in Toxicology, 21, Band 21)
Hardcover
ISBN 10: 1849731632 ISBN 13: 9781849731638
Verlag: Royal Society of Chemistry, 2014

Inhaltsangabe

Multivariate analysis of the multi-component analytical profiles of carefully collected biofluid and/or tissue biopsy specimens can provide a 'fingerprint' of their biomolecular/metabolic status. Therefore, if applied correctly, valuable information regarding disease indicators, disease strata and sub-strata and disease activities can be obtained.

This exemplary new book highlights applications of these techniques in the areas of drug therapy and toxicology, cancer, obesity and diabetes, as well as outlining applications to cardiovascular, infectious, inflammatory and oral diseases in detail. The book gives particular reference to cautionary measures that must be applied to the diagnosis and classification of these conditions or physiological criteria. Comprehensively covering a wide range of topics, of particular interest is the focus on experimental design and 'rights and wrongs' of the techniques commonly applied by researchers, and the very recent development of powerful 'Pattern Recognition' techniques.

The book provides a detailed introduction to the area, applications and common pitfalls of the techniques discussed before moving into detailed coverage of specific disease areas, each highlighted in individual chapters. This title will provide an invaluable resource to Medicinal chemists, Biochemists and toxicologists working in industry and academia.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorinnen und Autoren

After graduating in Chemistry/Statistical Analysis at Birkbeck College, University of London in 1981, Prof. Grootveld completed his Ph.D on bioanalytical chemistry and metallodrugs in 1985 at the same institution and then conducted post-doctoral work on the analysis of 'markers' of free radical activity in biofluids at King's College, University of London. He then spent 2 1/2 years lecturing and conducting research work at the Polytechnic of North London prior to taking up a Lectureship in Clinical Chemistry at St. Bartholomews and the Royal London School of Medicine and Dentistry in 1989, where he subsequently became Senior Lecturer and then Reader in Chemical Pathology. Later, he transferred to London South Bank University where he was also Reader in Chemical Pathology, and Director of their M.Sc Forensic Science course. He is now Professor of Chemical Pathology and Biomedical Materials at the University of Bolton where he has established and now directs a Master's course in Medical and Healthcare Devices which is the first of its kind available in the UK. He was Visiting Professor of Clinical Chemistry at Queen's University Belfast from 2001-2005. Prof. Grootveld is the author of almost 100 full, refereed research publications in reputable international scientific and/or clinical journals, 20 reviews and more than 160 refereed conference contributions.



After graduating in Chemistry/Statistical Analysis at Birkbeck College, University of London in 1981, Prof. Grootveld completed his Ph.D on bioanalytical chemistry and metallodrugs in 1985 at the same institution and then conducted post-doctoral work on the analysis of 'markers' of free radical activity in biofluids at King's College, University of London. He then spent 2 1/2 years lecturing and conducting research work at the Polytechnic of North London prior to taking up a Lectureship in Clinical Chemistry at St. Bartholomews and the Royal London School of Medicine and Dentistry in 1989, where he subsequently became Senior Lecturer and then Reader in Chemical Pathology. Later, he transferred to London South Bank University where he was also Reader in Chemical Pathology, and Director of their M.Sc Forensic Science course. He is now Professor of Chemical Pathology and Biomedical Materials at the University of Bolton where he has established and now directs a Master's course in Medical and Healthcare Devices which is the first of its kind available in the UK. He was Visiting Professor of Clinical Chemistry at Queen's University Belfast from 2001-2005. Prof. Grootveld is the author of almost 100 full, refereed research publications in reputable international scientific and/or clinical journals, 20 reviews and more than 160 refereed conference contributions.

Von der hinteren Coverseite

Multivariate analysis of the multi-component analytical profiles of carefully collected biofluid and/or tissue biopsy specimens can provide a ‘fingerprint’ of their biomolecular/metabolic status. Therefore, if applied correctly, valuable information regarding disease indicators, disease strata and sub-strata, and disease activities can be obtained.

This exemplary new book highlights applications of these techniques in the areas of drug therapy and toxicology and cancer, as well as outlining applications to, for example, thyroid, inflammatory and oral diseases in detail. The book gives particular reference to cautionary measures that must be applied to the diagnosis and classification of these conditions or physiological criteria. Comprehensively covering a wide range of topics, of particular interest is the focus on experimental design and ‘rights and wrongs’ of the techniques commonly applied by researchers, and the very recent development of powerful ‘Pattern Recognition’ and ‘Computational Intelligence’ techniques.

The book provides a detailed introduction to the area, applications and common pitfalls of the techniques discussed before moving into detailed coverage of specific research areas (including bioenergetics and chemogenomics), each highlighted in individual chapters. This title will provide an invaluable resource to Medicinal Chemists, Biochemists and Toxicologists working in industry and academia.

Aus dem Klappentext

Multivariate analysis of the multi-component analytical profiles of carefully collected biofluid and/or tissue biopsy specimens can provide a fingerprint of their biomolecular/metabolic status. Therefore, if applied correctly, valuable information regarding disease indicators, disease strata and sub-strata, and disease activities can be obtained.

This exemplary new book highlights applications of these techniques in the areas of drug therapy and toxicology and cancer, as well as outlining applications to, for example, thyroid, inflammatory and oral diseases in detail. The book gives particular reference to cautionary measures that must be applied to the diagnosis and classification of these conditions or physiological criteria. Comprehensively covering a wide range of topics, of particular interest is the focus on experimental design and rights and wrongs of the techniques commonly applied by researchers, and the very recent development of powerful Pattern Recognition and Computational Intelligence techniques.

The book provides a detailed introduction to the area, applications and common pitfalls of the techniques discussed before moving into detailed coverage of specific research areas (including bioenergetics and chemogenomics), each highlighted in individual chapters. This title will provide an invaluable resource to Medicinal Chemists, Biochemists and Toxicologists working in industry and academia.

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Metabolic Profiling

Disease and Xenobiotics

By Martin Grootveld

The Royal Society of Chemistry

Copyright © 2015 The Royal Society of Chemistry
All rights reserved.
ISBN: 978-1-84973-163-8

Contents

Chapter 1 Introduction to the Applications of Chemometric Techniques in 'Omics' Research: Common Pitfalls, Misconceptions and 'Rights and Wrongs' Martin Grootveld, 1, 
Chapter 2 Experimental Design: Sample Collection, Sample Size, Power Calculations, Essential Assumptions and Univariate Approaches to Metabolomics Analysis Martin Grootveld and Victor Ruiz Rodado, 35, 
Chapter 3 Recent Developments in Exploratory Data Analysis and Pattern Recognition Techniques Martin Grootveld, 74, 
Chapter 4 Analysis of High-dimensional Data from Designed Metabolomics Studies Johan A. Westerhuis, Ewoud J. J. van Velzen, Jeroen J. Jansen, Huub C. J. Hoefsloot and Age K. Smilde, 117, 
Chapter 5 Current Trends in Multivariate Biomarker Discovery Darius M. Dziuda, 137, 
Chapter 6 Discovery-based Studies of Mammalian Metabolomes with the Application of Mass Spectrometry Platforms Warwick B. Dunn, Catherine L. Winder and Kathleen M. Carroll, 162, 
Chapter 7 Recent Advances in the Multivariate Chemometric Analysis of Cancer Metabolic Profiling Kenichi Yoshida and Martin Grootveld, 199, 
Chapter 8 Group-specific Internal Standard Technology (GSIST) for Mass Spectrometry-based Metabolite Profiling Jiri Adamec, 220, 
Chapter 9 18O-assisted 31P NMR and Mass Spectrometry for Phosphometabolomic Fingerprinting and Metabolic Monitoring Emirhan Nemutlu, Song Zhang, Andre Terzic and Petras Dzeja, 255, 
Chapter 10 Investigations of the Mechanisms of Action of Oral Healthcare Products using 1H NMR-based Chemometric Techniques C. J. L. Silwood and Martin Grootveld, 287, 
Chapter 11 Metabolomics Investigations of Drug-induced Hepatotoxicity Wei Tang and Qiuwei Xu, 323, 
Chapter 12 Chemogenomics Virendra S. Gomase, Akshay N. Parundekar and Archana B. Khade, 357, 
Subject Index, 379,


CHAPTER 1

Introduction to the Applications of Chemometric Techniques in 'Omics' Research: Common Pitfalls, Misconceptions and 'Rights and Wrongs'

MARTIN GROOTVELD

Leicester School of Pharmacy, Faculty of Health and Life Sciences, De Montfort University, The Gateway, Leicester LE1 9BH, UK

Email: mgrootveld@dmu.ac.uk


1.1 Introduction

In this first chapter, I shall focus mainly on the two most widely employed multivariate (MV) assessment systems available in practice, specifically Principal Component Analysis (PCA) and Partial Least Squares methods, particularly Partial Least Squares-Discriminatory Analysis (PLS-DA), the first of which is an unsupervised exploratory dataset analysis (EDA) method, the second being a supervised pattern recognition technique (PRT). I have chosen to concentrate on these particular MV analysis methods here since there are numerous documented examples of the applications of these in the scientific, biomedical and/or clinical research areas in which they have sometimes been employed inappropriately, to say the least! Further details regarding the principles and modular applications of these two MV analysis approaches are provided in Appendices I and II.


1.2 Principal Component Analysis (PCA)

The applications of Principal Component Analysis (PCA) to the interpretation of MV metabolomic or chemometric datasets are manifold, and this is, perhaps, one of the most extensively applied techniques, examples of which are provided in refs 3–7, and which is sometimes employed in the first instance, if only for the detection and removal of statistical 'outlier' samples. The principles of this method involve the reduction of a large MV dataset (such as that arising from the 'bucketed' 1H NMR analysis of, say, a collection of biofluid samples, tissue biopsies or their extracts, or otherwise) to a much smaller number of 'artificial' variables known as Principal Components (PCs), which represent linear combinations of the primary (raw) dataset 'predictor' variables and, hopefully, will account for at least some, if not most, of their variance. These PCs can then, at least in principle, be employed as 'predictor' or criterion (X') variables in subsequent forms of analyses. It is clearly a valuable technique to apply when at least some level of 'redundancy' is suspected in the dataset, i.e. when some of the X variables are correlated or highly correlated (either positively or negatively) with each another. In metabolomics experiments, it is often the case that one or more (perhaps many) biofluid metabolite concentrations (or proportionately related parameters such as a resonance, signal or peak intensity) will be significantly correlated with one (or more) others, either positively or negatively. Obviously, in such situations, many of the predictor (X) variables can be rendered redundant, and this forms the basis of the PCA technique in terms of its dimensionality reduction strategy.

PCA is a procedure that converts a very large number of 'independent' variables (more realistically described as 'interdependent' variables in view of their multicorrelational status), i.e. 0.02–0.06 ppm 1H NMR spectral 'buckets' (which have variable frequency ranges if 'intelligently selected', and constant, uniform ones if not, the latter often being a pre-selected size of 0.04 or 0.05 ppm), many of which are correlated into a smaller number of uncorrelated PCs. Hence, a major objective of this form of multivariate analysis is to alleviate the dimensionality (i.e. the number of independent, possible 'predictor' variables) of the dataset whilst retaining as much of the original variance as possible. Hence, the first (primary) principal component is that which explains as much of the total variance as possible, the second as much of the remaining variance as possible, and so on with each succeeding PC until one with little or no contribution to variance is encountered; all components are, of course, orthogonal to (i.e. uncorrelated with) each other.

PCA can effectively delineate differing classifications within MV metabolomics datasets, and this is conducted according to the following procedure:

The data matrix is reduced to the much smaller number of PCs describing maximum variance within the dataset through decomposition of the X predictor variable matrix (containing the integral NMR buckets) into T score (containing class information projections of sample data onto each principal component through displacement from the origin) and P loading (describing the variables that influence the scores) matrices, such that X = t1 · p1T + ··· + tA · pAT, where the subscripted A value represents the total number of PCs, the residual information being included in a residual matrix E. The first PC should contain the maximum level of variance in the X matrix, such that the resulting deflated X matrix is then employed to seek a second component, orthogonal to the first, with the second highest variance contribution, and so on. PCA loadings with large values correspond to variables that have particularly high variance contributions towards them, and therefore they impart more to the total variance of the model system investigated.

However, there still remains much confusion regarding differences between the PCA and exploratory Factor Analysis (FA) techniques. Although similar in many respects (many of the stages followed are virtually identical), one of the most important...

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Verlag
Royal Society of Chemistry
Erscheinungsdatum
2014
Sprache
Englisch
ISBN 10 
1849731632
ISBN 13 
9781849731638
Einband
Gebundene Ausgabe
Anzahl der Seiten
395
Herausgeber
Grootveld Martin
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
preigu
mail@preigu.de

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