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Syllabus: Applied Statistics for High-Throughput Biology

Instructor

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

Summary

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.

Textbook

Related Resources

Labs

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

Session detail by day

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

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Applied Statistics for High-Throughput Biology

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