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Syllabus: Applied Statistics for High-Throughput Biology with Applications to Single-cell Sequencing

Instructor

Levi Waldron, PhD
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

Summary

This course will provide biologists and bioinformaticians with practical statistical and data analysis skills to perform rigorous analysis of high-throughput biological data, with applications focused on single-cell sequencing. 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 other dimension reduction methods, multiple hypothesis testing, and batch effects.

Textbooks

• Primary: Biomedical Data Science by Irizarry and Love (ePub version on Leanpub)
  • Source repository
• Secondary: Modern Statistics for Modern Biology by Holmes and Huber
• Lab materials: Orchestrating Single-Cell Analysis with Bioconductor (OSCA) by Amezquita, Lun, Hicks, Gottardo, O’Callaghan

Labs

Each day will include a hands-on lab session from Orchestrating Single-Cell Analysis with Bioconductor, that students should attempt in full.

Session detail by day

Lecture materials are available in HTML format from https://waldronlab.io/AppStatBio/.

1. Introduction

• random variables
• distributions
• hypothesis testing for one or two samples (t-test, Wilcoxon test, etc)

2. Dimensionality reduction

• distances in high dimensions
• principal components analysis and singular value decomposition
• multidimensional Scaling
• t-SNE and UMAP

3. Linear modeling

• multiple linear regression
• model formulae
• generalized linear models
• multiple hypothesis testing

4. Exploratory data analysis and batch effects

• plots for exploratory data analysis
• about batch effects