STA250 Exercise 2: R programming exercises covering data frames and basic data analysis.

This lab introduces a practical workflow for working with public RNA-seq data in R. Using the GSE161731 whole-blood dataset from the Gene Expression Omnibus (GEO), we retrieve the raw count matrix and its sample metadata, align the two, and convert Ensembl gene identifiers into gene symbols. The lab then covers cleaning and verification of clinical metadata, descriptive statistics and the appropriate test for each variable type, the calculation and interpretation of CPM and TPM, and the gene-level expression plots and gene-set heatmaps used to inspect biologically selected marker genes. The dataset compares patients with COVID-19, influenza, seasonal coronavirus and bacterial infection against healthy controls, so the expected interferon and neutrophil signatures serve throughout as a positive control for the analysis.

In this practical session, we continue working with the GSE161731 blood RNA-seq dataset from Lab 6 to investigate a biological question: which genes are switched on or off in the blood of COVID-19 patients compared with healthy volunteers? We will first use principal component analysis (PCA) to explore similarities and differences between samples and assess whether disease status is a major source of variation in the data. We will then use DESeq2 to identify genes that are differentially expressed between COVID-19 and healthy samples, account for multiple testing across thousands of genes, and visualise the results using a volcano plot, individual gene-expression plots, and a heatmap.