Multivariate Data Integration Using R : Methods and Applications with the mixOmics Package

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Author(s): Kim-Anh LeCao, Zoe Marie Welham

Large biological data, which are often noisy and high-dimensional, have become increasingly prevalent in biology and medicine. There is a real need for good training in statistics, from data exploration through to analysis and interpretation. This book provides an overview of statistical and dimension reduction methods for high-throughput biological data, with a specific focus on data integration. It starts with some biological background, key concepts underlying the multivariate methods, and then covers an array of methods implemented using the mixOmics package in R.


  • Provides a broad and accessible overview of methods for multi-omics data integration
  • Covers a wide range of multivariate methods, each designed to answer specific biological questions
  • Includes comprehensive visualisation techniques to aid in data interpretation
  • Includes many worked examples and case studies using real data
  • Includes reproducible R code for each multivariate method, using the mixOmics package

The book is suitable for researchers from a wide range of scientific disciplines wishing to apply these methods to obtain new and deeper insights into biological mechanisms and biomedical problems. The suite of tools introduced in this book will enable students and scientists to work at the interface between, and provide critical collaborative expertise to, biologists, bioinformaticians, statisticians and clinicians.

"This book was eagerly awaited both to bring together numerous research works published in recent years and to support the use of the Mixomics software which has become an essential tool for data integration and exploration when dealing with multiple types of high-dimensional biological data. It is the result of many years of research on cutting-edge developments in this domain as for sparsity. The book is very pleasant to read and well-structured around the different multivariate approaches. It is well documented with many recent references on the statistical methods and is very didactic through numerous examples accompanied by R codes and illustrations. It can be used by a large audience of statisticians and biologists to process, analyze, visualize, and interpret their multivariate microbiome and multi-omics data, but also as a basis for a course. I highly recommend this book."
- Philippe Bastien, Senior Research Associate - L'Oréal R&I

EAN: 9781000472196