The value of the book is at least two-fold. First, it provides a compact but well-balanced introduction to the methodology of multivariate analysis in the context of omics data. Second, it instructs with the hands-on approach how the mixOmics R-package can be effectively used to perform suitable statistical analyses involving data in which several variables of different types (e.g. genes, proteins and metabolites) must be integrated into one analytic workflowThe authors not only lead through mixOmics but also provide very accurate and valuable references whenever a less well-known method or technique is discussed. Because the book is essentially a presentation of the methodology and its applications for the mixOmics project, the projects webpage http://www.mixOmics.org can be considered complementary to the book with its rich additional materiala well-written book, a properly balanced and designed mix of methodology and applications, meeting all the standards of exposition on modern computationally assisted inference methodsshould have a broad appeal to those wanting to learn dimension reduction methodology, to practitioners in omics research area who want to use them, and even to general experts in the field of high-dimensional multivariate analysisI highly recommend Multivariate Data Integration Using R to these audiences. - Krzysztof Podgórski, Lund University, Sweden; International Statistical Review, Oct 2024
"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
"The book belongs to the Computational Biology Series and presents a wide spectrum of modern methods of multivariate statistical analysis, integration and high-dimension reduction for biological data evaluated via the specialized R package. The neologism Omic is used as a root related to constellations of objects with biological information, for instance, in genomes and proteinsgenomics and proteomics (in studying proteins expressed by cells and tissues), metabolic and transcription productsmetabolomics and transcriptomics (in studying messenger RNA molecules expressed from the gens of an organism), or also in economicsReaganomics, etc.
[ . . . ] Numerous links to the internet websites related to the considered methods of multi-omics data integration are suggested, particularly, the mixOmics project is described at the link http://www.mixOmics.org, and the package is available at Install |mixOmics. The developed methods and software are suitable not only for biologists and bioinformaticians students and researchers, but can be useful for solving computational and content problems in many other fields as well." Technometrics
"This is an excellent book for computational biologists, bioinformaticians, statisticians, data scientists, and graduate students who work with high-throughput omics data. The book covers most fundamental concepts of multi-omics data integration, while focusing on their implementations through hands-on examples implemented in the mixOmics R package." - Yuehua Cui, Michigan State University, Biometrics, September 2022
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