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
Other contact information: https://waldronlab.io
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 on Leanpub)
- Source repository
- Modern Statistics for Modern Biology by Holmes and Huber (secondary)
- Orchestrating Single-Cell Analysis with Bioconductor (OSCA) by Amezquita, Lun, Hicks, Gottardo, O’Callaghan
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/.
- Introduction
- random variables
- distributions
- hypothesis testing for one or two samples (t-test, Wilcoxon test, etc)
- Dimensionality reduction
- Distances in high dimensions
- Principal Components Analysis and Singular Value Decomposition
- Multidimensional Scaling
- t-SNE and UMAP