Rafael A. Irizarry, Michael I. Love
Chapman and Hall/CRC
Published July 25, 2016
Textbook - 354 Pages - 199 Color Illustrations
ISBN 9781498775670 - CAT# K29691
Chapman and Hall/CRC
Published July 27, 2017
ISBN 9781138407206 - CAT# K36058
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Introduction. Getting started. Inference. Exploratory data analysis. Robust summaries. Matrix algebra. Linear models. Inference for high dimensional data. Statistical models. Distance and dimension reduction. Statistical models. Distance and dimension reduction. Basic machine learning. Batch effects.
"In addition to the presentation of several strategies designed to handle multivariate data, the book’s strength lies in its immediate applicability. By including relevant datasets, the embedding of R code throughout, and in the open source nature of its production (it was written in R markdown), the book has encouraged reproducible research while connecting computer code to the relevant statistical concepts. Practitioners in the life sciences would seemingly be well served to use the book as a guide for their research. . .. The open-source nature of the book is a unique benefit, as it ensures that future versions can swiftly update to include new concepts, data, or coding techniques. . . The book could also function as a textbook, particularly for a course in computational biology (either advanced undergraduate or introductory graduate).
~The American Statistician, Reviews of Books and Teaching Materials
"Overall, I found that this book is excellent for researchers in the life sciences who are interested in retrieving, analyzing, and interpreting complex research data using sophisticated statistical methods and computing. The authors have effectively condensed broad and important topics into a single book. I highly recommend this book to anyone venturing into the exciting world of data analysis in many areas."
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