If you want to make your entire analysis reproducible — on any machine — this workshop is for you. Building on the dataset and skills from Parts 1–3, you will assemble your R code into a scripted, end-to-end pipeline and package its computational environment using Apptainer so the analysis runs identically for collaborators regardless of operating system. Using DESeq2 to perform differential expression analysis as a working example, this session connects reproducible coding practices to containerized, portable research infrastructure.

By the end of the session, learners will be able to:

Hour 1 — DESeq2 pipeline

• Execute DESeq2 to find differentially expressed genes on a shared RNA-seq dataset, producing a results table and diagnostic plot

• Organize the analysis as a scripted, re-runnable pipeline inside an R Project

• Interpret DESeq2 output (log2 fold change, adjusted p-values) in the context of the experimental design

Hour 2 — Apptainer containerization

• Explain how containerization addresses reproducibility problems caused by software and OS differences

• Differentiate a host environment from a containerized environment, and identify when each is appropriate

• Modify a provided Apptainer definition and re-run the pipeline from inside the container to confirm

Prerequisites:

• Completion of Introduction to Programming in R Part 3.
• Github account