Levi Waldron, PhD
Associate Professor of Biostatistics
City University of New York School Graduate of Public Health and Health Policy
New York, NY, U.S.A.

Email: lwaldron.research@gmail.com
Hangouts: lwaldron.research
Skype: levi.waldron

This course will provide biologists and bioinformaticians with practical statistical and data analysis skills to perform rigorous analysis of high-throughput biological data. The course assumes some familiarity with genomics and with R programming, but does not assume prior statistical training. It covers the statistical concepts necessary to design experiments and analyze high-dimensional data generated by genomic technologies, including: exploratory data analysis, linear modeling, analysis of categorical variables, principal components analysis, and batch effects.

• Biomedical Data Science by Irizarry and Love (ePub version)
• Source repository

• http://waldronlab.io/teaching/resources/

Each day will include a hands-on lab session, that students should attempt in full.

All course materials will be available from https://github.com/waldronlab/AppStatBio/.

1. introduction
   • random variables
   • distributions
   • hypothesis testing for one or two samples (t-test, Wilcoxon test, etc)
   • data manipulation using dplyr
2. non-parametric approaches
   • hypothesis tests for categorical variables (chi-square, Fisher's Exact)
   • Monte Carlo simulation
   • permutation tests
   • bootstrap simulation
   • exploratory data analysis
3. linear modeling
   • linear and generalized linear modeling
   • model matrix and model formulae
   • multiple testing
4. unsupervised analysis
   • graphics for exploratory data analysis
   • distance in high dimensions
   • principal components analysis and multidimensional scaling
   • unsupervised clustering
   • batch effects