1st Edition

Genomics Data Analysis False Discovery Rates and Empirical Bayes Methods

By David R. Bickel Copyright 2020
    140 Pages 10 B/W Illustrations
    by CRC Press

    140 Pages 10 B/W Illustrations
    by Chapman & Hall

    140 Pages 10 B/W Illustrations
    by Chapman & Hall

    Statisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign probabilities of differential gene expression, genetic association, etc. without requiring subjective prior distributions. This book brings these methods to scientists while keeping the mathematics at an elementary level. Readers will learn the fundamental concepts behind local false discovery rates, preparing them to analyze their own genomics data and to critically evaluate published genomics research.

    Key Features:

    * dice games and exercises, including one using interactive software, for teaching the concepts in the classroom

    * examples focusing on gene expression and on genetic association data and briefly covering metabolomics data and proteomics data

    * gradual introduction to the mathematical equations needed

    * how to choose between different methods of multiple hypothesis testing

    * how to convert the output of genomics hypothesis testing software to estimates of local false discovery rates

    * guidance through the minefield of current criticisms of p values

    * material on non-Bayesian prior p values and posterior p values not previously published


    1.Basic probability and statistics, 2. Introduction to likelihood, 3. False discovery rates, 4. Simulating and analyzing gene expression data, 5. Variations in dimension and data, 6. Correcting bias in estimates of the false discovery rate, 7. The L value: An estimated local false discovery rate to replace a p value, 8. Maximum likelihood and applications, Appendix A. Generalized Bonferroni correction derived from conditional compatibility, Appendix B. How to choose a method of hypothesis testing.


    David R. Bickel is an Associate Professor in the Department of Biochemistry, Microbiology and Immunology of the University of Ottawa and a Core Member of the Ottawa Institute of Systems Biology. Since 2011, he has been teaching classes focused on the statistical analysis of genomics data. While working as a biostatistician in academia and industry, he has published new statistical methods for analyzing genomics data in leading statistics and bioinformatics journals. He is also investigating the foundations of statistical inference. For recent activity, see davidbickel.com or follow him at @DavidRBickel (Twitter).