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.

part Part I|44 pages

Modern biology and multivariate analysis

chapter 2Chapter 1|8 pages

Multi-omics and biological systems

chapter Chapter 2|8 pages

The cycle of analysis

part Part II|48 pages

mixOmics under the hood

chapter 46Chapter 5|12 pages

Projection to latent structures

chapter Chapter 6|20 pages

Visualisation for data integration

chapter Chapter 7|14 pages

Performance assessment in multivariate analyses

part Part III|190 pages

mixOmics in action

chapter 94Chapter 8|14 pages

mixOmics: Get started

chapter Chapter 9|28 pages

Principal Component Analysis (PCA)

chapter Chapter 10|40 pages

Projection to Latent Structure (PLS)

chapter Chapter 11|24 pages

Canonical Correlation Analysis (CCA)

chapter Chapter 12|32 pages

PLS-Discriminant Analysis (PLS-DA)

chapter Chapter 13|28 pages

N−data integration

chapter Chapter 14|22 pages

P−data integration