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.
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.
Rafael A. Irizarry is Professor of Applied Statistics at the Dana Farber Cancer Center and Harvard School of Public Health. In 2009 he was awarded The Presidents' Award by the Committee of Presidents of Statistical Societies (COPSS). His work has been highly cited and his open source software tools widely downloaded.
Michael I. Love is a Postdoctoral Fellow at Harvard School of Public Health. He received his Ph.D. in computational biology in 2013 from the Freie Universität Berlin.
Professors Irizarry and Love have taught seven computational biology courses on edX to hundreds of thousands of students.
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