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

Please come to the first class with the following installed:

• Bioconductor www.bioconductor.org/install
• R Studio: https://www.rstudio.com/products/rstudio/download3/

Please create an account at www.github.com, and use it to introduce yourself at https://github.com/waldronlab/AppStatBio/issues.

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)
   • hypothesis testing for categorical variables (Fisher's Test, Chi-square test)
   • data manipulation using dplyr
2. linear modeling
   • linear and generalized linear modeling
   • model matrix and model formulae
   • multiple testing
3. unsupervised analysis
   • graphics for exploratory data analysis
   • distance in high dimensions
   • principal components analysis and multidimensional scaling
   • unsupervised clustering
   • batch effects
4. multi'omic data analysis lab session
   • core data classes in Bioconductor: GRanges, SummarizedExperiment, RaggedExperiment, MultiAssayExperiment
   • creating a MultiAssayExperiment
   • subsetting, reshaping, growing, and extraction of a MultiAssayExperiment
   • lotting, correlation, and other statistical analyses
   • multi'omics lab code and html