The objective is to prepare students and scientists to analyze genomics data using empirical Bayes methods and to critically evaluate the statistical methods appearing in genomics articles. That is accomplished by providing the information needed for them to interpret p values for their current or future research.

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).

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.