Topics

• Comparing the means of several samples by analyzing variances: the intuition behind ANOVA.
• One-way ANOVA techniques: prerequisites, omnibus F-test, post hoc tests.
• Power and sample size calculations.
• The relationship between ANOVA and linear regression (this is optional).
• Combination of effects: two-way ANOVA.
• Comparing linear regressions: Analysis of Covariance (ANCOVA).

Out of scope

This course will not teach you bioinformatics. In particular, no high-throughput sequencing data will be used because they are impractically large, and not everyone on campus is working with sequencing.

If you are interested in the statistical background of gene expression analysis with high-throughput sequencing, then please take our RNA-Seq data analysis course.

Prerequisites

Participants should have a good understanding of basic statistics. Our Statistics with R course helps you refresh your stats skills.

Basic familiarity with R is advantageous, in particular:

• Using the R Studio environment
• How to invoke R functions, pass optional/named parameters
• Some familiarity with simple plotting commands

If you have attended our R as a programming language training then you are well equipped to take this course, but this is not a strict pre-requisite.

"Bring Your Own Data"

You can bring your own data to this course and run a one-way ANOVA on it.

Please prepare a comma-separated-values (CSV) file with UNIX line endings (\n) that contains several (more than 2) columns corresponding to the groups of data whose means you would like to compare. All groups should contain the same number of observations (a "balanced" ANOVA design).

Practical information

Number of participants: minimum 5, maximum 10.

Length: The course takes one half-day, from 09:00 to 13:00 with two short breaks.