For Sonia, Sara, Agnès, Johnny, Camille
\(...\) and the “girls” who make me love the life sciences.

For Alexander

This work would not be imaginable without the R language and environment for statistical computing, the Comprehensive R Archive Network (CRAN) and the Bioconductor project. We thank everyone who has contributed to these projects. Today virtually every statistical algorithm, every imaginable interface for data handling and visualization, and many methods from all over computer science and mathematics are readily accessible through these projects.

We thank JJ Allaire and the RStudio team for making available such a powerful development environment and many useful R packages, which we have greatly enjoyed when writing this book.

We particularly thank the Bioconductor project, started by Robert Gentleman, led by Martin Morgan and powered by its amazing community of developers, for fostering interoperability, scalability and usability of R-based methods for genome-scale data, for making a vast range of biological data and annotation resources easy to work with in R, and for orchestrating collaborative, distributed development – all these aspects are essential for the complex biological data analysis workflows that you will see in this book.

We are grateful to the package developers we have worked with and whose packages play at center stage in the different chapters of this book, including Simon Anders, Ben Callahan, Michael Love, Joey McMurdie, Andrzej Oleś. Trevor Martin was a student in Stats 366 at Stanford in 2012 and co-taught the class with Susan in 2013, 2014 and 2015. As a graduate student in genetics, he brought many of the examples to life and participated in earlier versions of the material we present here. We are thankful for his help and perspective. Teaching assistants for Stats 366 who have helped develop exercises and questions include Austen Head, Haben Michael, Julia Fukuyama, Lan Huong Nguyen, Christof Seiler and Nikolaos Ignatiadis. Their enthusiasm for making interesting quizzes and lab material helped nurture students from a wide range of backgrounds on the arduous journey of approaching challenging new concepts within a computational environment that has tremendous power, yet can also be overwhelming.

Many students have provided valuable feedback over the years, and we are grateful for their many questions and quizzical looks that fed our motivation to keep evolving this course. In particular, we have received extensive feedback from Jessica Grembi, Kris Sankaran, Varun Gupta and Chao Jiang.

Mike Smith has made a neat package (msmbstyle) for rendering the aesthetic online HTML version that you are currently perusing. We are very grateful for his help and responsiveness.

We thank Lorraine Garchery for design of the cover art both for the printed and online version.

We thank David Tranah and Diana Gillooly from Cambridge University press for their constant effort helping us to make the book grammatically correct, aesthetically attractive and pedagogically coherent. Much potential for improvement remains, the responsibility for which stays with us.

We thank our family and supporters who have encouraged us and provided feedback on preliminary chapters: David Relman, Alfred Spormann, Catherine Blish, Don Knuth, Persi Diaconis, Gretchen and Barry Mazur, …..

Susan Holmes Wolfgang Huber
Stanford Heidelberg
August 2017

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