Hesburgh Libraries

A common task when working with transcriptomic data is the identification of differentially expressed (DE) genes or tags between groups. In this workshop participants will learn how to perform biostatistical analysis in the R programming language, which is among the most widely-used programming languages for the statistical analysis of biological data.

After completing this workshop, participants will:

1. Become comfortable working with quantified transcriptomic data.
2. Be able to replicate the statistical analysis of a published biological data set.
3. Learn what statistical comparisons are possible with the design of the experiment.
4. Conduct pairwise comparisons with edgeR to identify significantly DE genes.
5. Create different plots for exploring patterns in expression data and analysis results.

Prerequisites:

1. Participants are expected to be comfortable working in the R programming language and should be familiar with the basic concepts of variables, data types, functions, and objects.
2. Participants are expected to have the latest versions of the R software environment and RStudio desktop software installed.

Primary tools used during the workshop:

1. R software environment
2. RStudio desktop software
3. The analysis described in this workshop is part of my tutorial on Downstream Bioinformatics Analysis of Omics Data with edgeR, in which I provide a walkthrough of pairwise and analysis of variance (ANOVA) like biostatistical analysis with edgeR