Data Analysis and Chemometrics for Metabolomics

• Autore: Brereton Richard G
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (435 pages)
• Soggetto topico: Chemometrics; Mass spectrometry
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Acknowledgments -- About the Companion Website -- Chapter 1 Introduction -- 1.1 Chemometrics -- 1.2 Metabolomics -- 1.3 Case Studies -- 1.4 Software -- References -- Chapter 2 Instrumental Methods -- 2.1 Introduction -- 2.2 Coupled Chromatography Mass Spectrometry -- 2.2.1 Chromatography -- 2.2.2 Ionisation and Detection -- 2.2.2.1 GCMS -- 2.2.2.2 LCMS -- 2.2.3 Data Matrices and Peak Tables -- 2.2.4 Step 1 of Creating a Peak Table: Transforming Chromatographic Data to an Aligned Matrix of Peaks -- 2.2.4.1 XCMS -- 2.2.4.2 Multivariate Curve Resolution -- 2.2.4.3 AMDIS -- 2.2.4.4 MZmine -- 2.2.4.5 Other Approaches -- 2.2.4.6 Which Approach is Most Suitable? -- 2.2.5 Step 2 of Creating a Peak Table: Manual Inspection -- 2.2.6 Step 3 of Creating a Peak Table: Identifying Metabolites and Annotating the Peaks -- 2.2.6.1 Experimental Libraries -- 2.2.6.2 Computational Mass Spectral and Retention Libraries -- 2.2.6.3 Expert Systems -- 2.3 Single Wavelength HPLC -- 2.4 Nuclear Magnetic Resonance -- 2.4.1 Fourier Transform Techniques -- 2.4.1.1 FT Principles -- 2.4.1.2 Resolution and Signal-to-Noise -- 2.4.2 Preparing the Transformed Spectra -- 2.4.3 Preparing the Data Table -- 2.4.3.1 Chemometric Approach -- 2.4.3.2 Deconvolution and Identification -- 2.4.4 Identification of Metabolites -- 2.5 Vibrational Spectroscopy -- 2.5.1 Raman Spectroscopy -- 2.5.2 Fourier Transform Infrared Spectroscopy -- Chapter 3 Case Studies -- 3.1 Introduction -- 3.2 Case Study 1: Presymptomatic Study of Humans With Rheumatoid Arthritis Using Blood Plasma and LCMS -- 3.3 Case Study 2: Diagnosis of Malaria in Human Blood Plasma of Children Using GCMS -- 3.4 Case Study 3: Measurement of Triglyclerides in Children's Blood Serum Using NMR. ; 3.5 Case Study 4: Glucose Intolerance and Diabetes in Humans as Assessed by Blood Serum Using NMR -- 3.6 Case Study 5: Metabolic Changes in Maize Due to Cold as Assessed by NMR -- 3.7 Case Study 6: Effect of Nitrates on Different Parts of Wheat Leaves as Analysed by FTIR -- 3.8 Case Study 7: Rapid Discrimination of Enterococcal Bacteria in Faecal Isolates by Raman Spectroscopy -- 3.9 Case Study 8: Effects of Salinity, Temperature and Hypoxia on Daphnia Magna Metabolism as Studied by GCMS -- 3.10 Case Study 9: Bioactivity in a Chinese Herbal Medicine Studies Using HPLC -- 3.11 Case Study 10: Diabetes in Mice Studied by LCMS -- Chapter 4 Principal Component Analysis -- 4.1 A Simple Example: Matrices, Vectors and Scalars -- 4.2 Visualising the Data Direct -- 4.3 Principal Components Analysis: Scores, Loadings and Eigenvalues -- 4.3.1 PCA -- 4.3.2 Scores -- 4.3.3 Loadings -- 4.3.4 Relationship Between Scores and Loadings -- 4.3.5 Eigenvalues -- 4.3.6 Reducing the Number of PCs -- 4.4 Exploration by PCA of Case Study 5 in Detail: NMR Study of the Effect of Temperature on Maize -- 4.4.1 Variable Plots -- 4.4.2 Scores and Loadings Plots of the Whole Standardised Data -- 4.4.3 Scores and Loadings of the Low-temperature Data -- 4.5 PCA of Different Case Studies -- 4.5.1 Case Study 1: LCMS Studies of Pre-arthritis -- 4.5.2 Case Study 4: NMR of Human Diabetes -- 4.5.3 Case Study 10: LCMS of Diabetes in Mice -- 4.5.4 Case Study 6: FTIR of Effect of Nitrates on Wheat -- 4.5.5 Case Study 3: NMR for Triglycerides in Serum -- 4.5.6 Case Study 7: Raman of Bacterial Faecal Isolates -- 4.6 Transforming the Data -- 4.6.1 Row Scaling -- 4.6.1.1 Row Scaling to Constant Total -- 4.6.1.2 Standard Normal Variates -- 4.6.1.3 Scaling to Reference Standards -- 4.6.2 Column Centring -- 4.6.3 Column Standardisation -- 4.6.4 Logarithmic Transformation -- 4.7 Common Issues. ; 4.7.1 Missing Data -- 4.7.2 Quality Control Samples -- 4.7.3 Variable Reduction -- Chapter 5 Statistical Basics -- 5.1 Use of P Values and Hypothesis Testing -- 5.2 Distributions and Significance -- 5.2.1 Simulated Case Study -- 5.2.2 The Normal (z) Distribution and p Values -- 5.2.3 t-Distribution and Degrees of Freedom -- 5.2.4 χ2-Distribution -- 5.2.5 F-Distribution and Hotelling's T2 -- 5.3 Multivariate Calculation of P Values and the Mahalanobis Distance -- 5.4 Discriminatory Variables -- 5.5 Conclusions -- Chapter 6 Choosing Samples -- 6.1 Motivation -- 6.2 Design of Experiments -- 6.2.1 Factors, Response and Coding -- 6.2.2 Replicates -- 6.2.3 Statistical Designs -- 6.2.3.1 Fully Crossed Designs -- 6.2.3.2 Two-level Full Factorial Designs -- 6.2.3.3 Fractional Factorial Designs -- 6.3 Sampling Designs -- 6.3.1 Simple Random Sampling -- 6.3.2 Systematic Sampling -- 6.3.3 Stratified Sampling -- 6.3.4 Cluster Sampling -- 6.3.5 Multi-stage Sampling -- Chapter 7 Determining the Provenance of a Sample -- 7.1 Pattern Recognition -- 7.2 Preliminary Processing Prior to Classification -- 7.3 Simulated Case Studies -- 7.4 Two-Class Classifiers -- 7.4.1 Linear Discriminant Analysis -- 7.4.2 Partial Least Squares Discriminant Analysis -- 7.4.2.1 PLSDA for Equal Class Sizes -- 7.4.2.2 PLSDA for Unequal Class Sizes -- 7.4.2.3 OPLS -- 7.5 One-Class Classifiers -- 7.5.1 Quadratic Discriminant Analysis -- 7.5.2 SIMCA -- 7.5.2.1 Disjoint PCA -- 7.5.2.2 D- and Q-statistics -- 7.5.2.3 Limits and Decisions -- 7.6 Multiclass Classifiers -- 7.6.1 LDA as a Multiclass Classifier -- 7.6.2 PLSDA as a Multiclass Classifier -- 7.6.2.1 One Versus All -- 7.6.2.2 One Versus One -- 7.6.2.3 PLS2DA -- 7.6.3 Multilevel PLSDA -- 7.7 Validation, Optimisation and Performance Indicators -- 7.7.1 Classification Performance -- 7.7.1.1 Two Classes -- 7.7.1.2 Multiclasses. ; 7.7.1.3 One-Class Models -- 7.7.2 Validation -- 7.7.3 Optimisation -- Chapter 8 Multivariate Calibration -- 8.1 Introduction -- 8.2 Partial Least Squares Regression -- 8.3 Training and Test Sets -- 8.4 Optimisation: Number of PLS Components -- Chapter 9 Selecting the Most Significant Variables and Markers -- 9.1 Introduction -- 9.2 Univariate Approaches -- 9.3 Loadings, Weights And Vip Scores -- 9.3.1 Principal Component Loadings -- 9.3.2 PLSDA Loadings and Weights -- 9.3.3 VIP Scores -- 9.3.4 P Values -- 9.3.5 Multilevel PLSDA -- 9.4 Selectivity Ratios -- 9.5 Volcano Plots -- Chapter 10 Which Factors are Most Significant -- 10.1 Introduction -- 10.2 Terminology and Definitions -- 10.3 Single Factor (One-Way - One-Factor) Anova Test And Regression -- 10.3.1 Balanced Design at Two Levels -- 10.3.1.1 Degrees of Freedom -- 10.3.1.2 ANOVA Test -- 10.3.1.3 Regression -- 10.3.1.4 The t-test -- 10.3.2 Unbalanced Design at Two Levels -- 10.3.3 Multiple One-Way Design with Two Levels: Multilinear Regression -- 10.3.4 Multilevel Designs -- 10.3.4.1 One-Way Multilevel ANOVA Test -- 10.3.4.2 One-Way Multilevel Regression with Dummy Variables: Unrelated Groups -- 10.3.4.3 One-Way Multilevel Multilinear Regression: Related Groups -- 10.3.4.4 Comparison and Interpretation -- 10.4 Multiple Factor (Multiway) ANOVA Test and Regression -- 10.4.1 Simulated 2 × 3 Case Study: ANOVA Test and Regression -- 10.4.1.1 ANOVA Test -- 10.4.1.2 Regression with Dummy Variables -- 10.4.2 Two-level Multiway Factorial Designs -- 10.5 ASCA -- 10.5.1 Simulated Dataset -- 10.5.2 Case Study: Environmental Effect on Daphnia -- Index -- EULA.
• Sommario/riassunto: Richard G. Brereton's 'Data Analysis and Chemometrics for Metabolomics' provides an in-depth exploration of the application of data analysis and chemometric techniques in the field of metabolomics. The book covers various instrumental methods such as mass spectrometry, nuclear magnetic resonance, and vibrational spectroscopy, emphasizing their roles in identifying and analyzing metabolites. It includes detailed case studies on diverse topics like rheumatoid arthritis, malaria diagnosis, and the effects of environmental factors on metabolism. The book also delves into statistical methods, experimental design, and pattern recognition, offering practical insights for researchers and practitioners in metabolomics. It is primarily intended for scientists and students in analytical chemistry and related fields.
• Titolo autorizzato: Data Analysis and Chemometrics for Metabolomics
• ISBN: 9781119639398; 1119639395; 9781119639374; 1119639379
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910877060303321
• Lo trovi qui: Univ. Federico II