Media Reviews
Advance praise: 'This is a gorgeous book, both visually and intellectually, superbly suited for anyone who wants to learn the nuts and bolts of modern computational biology. It can also be a practical, hands-on starting point for life scientists and students who want to break out of 'canned packages' into the more versatile world of R coding. Much richer than the typical statistics textbook, it covers a wide range of topics in machine learning and image processing. The chapter on making high-quality graphics is alone worth the price of the book.' William H. Press, University of Texas, Austin
Advance praise: 'The book is a timely, comprehensive and practical reference for anyone working with modern quantitative biotechnologies. It can be read at multiple levels. For scientists with a statistics background, it is a thorough review of key methods for design and analysis of high-throughput experiments. For life scientists with a limited exposure to statistics, it offers a series of examples with relevant data and R code. Avoiding buzzwords and hype, the book advocates appropriate statistical practice for reproducible research. I expect it to be as influential for the life sciences community as Modern Applied Statistics with S, by Venables and Ripley or Introduction to Statistical Learning, by James, Witten, Hastie and Tibshirani are for applied statistics.' Olga Vitek, Northeastern University, Boston
Advance praise: 'Navigating rich data to arrive at sensible insight requires confidence in our biological understanding, informatic ability, statistical sophistication, and skills at effective communication. Fortunately the wisdom and effort of the worldwide research community has been distilled into accessible and rich collections of R and Bioconductor software packages. Holmes and Huber provide a comprehensive guide to navigating modern statistical methods for working with complex, large, and nuanced biological data. The presentation provides a firm conceptual foundation coupled with worked practical examples, extended analysis, and refined discussion of practical and theoretical challenges facing the modern practitioner. This book provides us with the confidence and tools necessary for the analysis and comprehension of modern biological data using modern statistical methods.' Martin Morgan, Roswell Park Comprehensive Cancer Center, leader of the Bioconductor project
Advance praise: 'Holmes and Huber take an integrated approach to presenting the key statistical concepts and methods needed for the analysis of biological data. Specifically, they do a wonderful job of building these foundations in the context of modern computational tools, genuine scientific questions, and real-world datasets. The code showcases many of the newest features of R and its dynamic package ecosystem, such as using ggplot2 for visualization and dplyr for data manipulation.' Jenny Bryan, RStudio and University of British Columbia
Advance praise: 'This is a gorgeous book, both visually and intellectually, superbly suited for anyone who wants to learn the nuts and bolts of modern computational biology. It can also be a practical, hands-on starting point for life scientists and students who want to break out of 'canned packages' into the more versatile world of R coding. Much richer than the typical statistics textbook, it covers a wide range of topics in machine learning and image processing. The chapter on making high-quality graphics is alone worth the price of the book.' William H. Press, University of Texas, Austin
Advance praise: 'The book is a timely, comprehensive and practical reference for anyone working with modern quantitative biotechnologies. It can be read at multiple levels. For scientists with a statistics background, it is a thorough review of key methods for design and analysis of high-throughput experiments. For life scientists with a limited exposure to statistics, it offers a series of examples with relevant data and R code. Avoiding buzzwords and hype, the book advocates appropriate statistical practice for reproducible research. I expect it to be as influential for the life sciences community as Modern Applied Statistics with S, by Venables and Ripley or Introduction to Statistical Learning, by James, Witten, Hastie and Tibshirani are for applied statistics.' Olga Vitek, Northeastern University, Boston
Advance praise: 'Navigating rich data to arrive at sensible insight requires confidence in our biological understanding, informatic ability, statistical sophistication, and skills at effective communication. Fortunately the wisdom and effort of the worldwide research community has been distilled into accessible and rich collections of R and Bioconductor software packages. Holmes and Huber provide a comprehensive guide to navigating modern statistical methods for working with complex, large, and nuanced biological data. The presentation provides a firm conceptual foundation coupled with worked practical examples, extended analysis, and refined discussion of practical and theoretical challenges facing the modern practitioner. This book provides us with the confidence and tools necessary for the analysis and comprehension of modern biological data using modern statistical methods.' Martin Morgan, Roswell Park Comprehensive Cancer Center, leader of the Bioconductor project
Advance praise: 'Holmes and Huber take an integrated approach to presenting the key statistical concepts and methods needed for the analysis of biological data. Specifically, they do a wonderful job of building these foundations in the context of modern computational tools, genuine scientific questions, and real-world datasets. The code showcases many of the newest features of R and its dynamic package ecosystem, such as using ggplot2 for visualization and dplyr for data manipulation.' Jenny Bryan, RStudio and University of British Columbia
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