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Data Analysis for the Life Sciences with R

Rafael A. Irizarry, Michael I. Love
Publication Date:
August 10, 2016
Content Details:
354 pages | 199 illustrations

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List Price:   $49.95

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  • About the Book

    Book Summary

    This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.


      • While statistics textbooks focus on mathematics, this book focuses on using a computer to perform data analysis
      • Focuses on the practical challenges faced by data analysts in the life sciences and introduces mathematics as a tool that can help us achieve scientific goals
      • All sections of this book are reproducible as they were produced by R markdown documents that include R code used to produce the figures, tables and results shown in the book


      "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

  • Contents

    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.