Metabolomics is the scientific study of the chemical processes in a living system, environment and nutrition. It is a relatively new omics science, but the potential applications are wide, including medicine, personalized medicine and intervention studies, food and nutrition, plants, agriculture and environmental science. The topics presented and discussed in this book are based on the European Molecular Biology Organization (EMBO) practical courses in metabolomics bioinformatics taught to those working in the field, from masters to postgraduate students, PhDs, postdoctoral and early PIs. The book covers the basics and fundamentals of data acquisition and analytical technologies, but the primary focus is data handling and data analysis. The mentioning and usage of a particular data analysis tool has been avoided; rather, the focus is on the concepts and principles of data processing and analysis. The material has been class-tested and includes lots of examples, computing and exercises. Key Features: Provides an overview of qualitative /quantitative methods in metabolomics Offers an introduction to the key concepts of metabolomics, including experimental design and technology Covers data handling, processing, analysis, data standards and sharing Contains lots of examples to illustrate the topics Includes contributions from some of the leading researchers in the field of metabolomics with extensive teaching experiences

Metabolomics is the scientific study of the chemical processes in a living system, environment and nutrition. It is a relatively new omics science, but the potential applications are wide, including medicine, personalized medicine and intervention studies, food and nutrition, plants, agriculture and environmental science. The topics presented and discussed in this book are based on the European Molecular Biology Organization (EMBO) practical courses in metabolomics bioinformatics taught to those working in the field, from masters to postgraduate students, PhDs, postdoctoral and early PIs. The book covers the basics and fundamentals of data acquisition and analytical technologies, but the primary focus is data handling and data analysis. The mentioning and usage of a particular data analysis tool has been avoided; rather, the focus is on the concepts and principles of data processing and analysis. The material has been class-tested and includes lots of examples, computing and exercises. Key Features: Provides an overview of qualitative /quantitative methods in metabolomics Offers an introduction to the key concepts of metabolomics, including experimental design and technology Covers data handling, processing, analysis, data standards and sharing Contains lots of examples to illustrate the topics Includes contributions from some of the leading researchers in the field of metabolomics with extensive teaching experiences

This book offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a comprehensive description of the general data analysis paradigm, from exploratory analysis (principal component analysis, self-organizing maps and clustering) to modeling (classification, regression) and validation (including variable selection). It also includes a special section discussing several more specific topics in the area of chemometrics, such as outlier detection, and biomarker identification. The corresponding R code is provided for all the examples in the book; and scripts, functions and data are available in a separate R package. This second revised edition features not only updates on many of the topics covered, but also several sections of new material (e.g., on handling missing values in PCA, multivariate process monitoring and batch correction).