Applied Statistics for High-Throughput Biology
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Syllabus: Applied Statistics for High-Throughput Biology


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

Hangouts: lwaldron.research
Skype: levi.waldron

Times and Places

Classes will take place on March 6, 8, and 13:

date time place
6 Marc 2018 09:00 - 11:30 Gamma [Borgo Roma - Ca' Vignal 2]
8 Marc 2018 09:00 - 11:30 G [Borgo Roma - Ca' Vignal 2]
13 Marc 2018 09:00 - 11:30 Gamma [Borgo Roma - Ca' Vignal 2]


Please come to the first class with the following installed:

Please create an account at, and use it to introduce yourself at


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.


Related Resources


Each day will include a hands-on lab session, that students should attempt and hand in something before the following class by committing to this Github repository. You are encouraged to work together on lab exercises, but should hand in your own individual work.


A project will be handed out before the final class, that will involve analysing a genomics dataset. Each student will analyse their own dataset and prepare an individual report using R Markdown for reproducible analysis and reporting. Reports will be assessed for quality of analysis and clarity of presentation.

Session detail by day

All course materials will be available from

  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