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**.

In this practical session, you will work with the public GEO RNA-seq dataset GSE161731, which contains blood samples from patients with COVID-19, influenza, common-cold coronavirus infection, bacterial infection, and healthy volunteers. You will learn how to download and organise gene-count and patient metadata, ensure that samples are correctly matched, annotate Ensembl gene IDs with familiar gene symbols, and clean and explore cohort information. You will also perform basic checks using the RNA-seq data to validate sample characteristics and prepare clean, reproducible data objects for downstream analysis. By the end of the session, your dataset will be ready for PCA, quality control, and differential expression analysis in the next lab.

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.

This lab builds on the count matrix and metadata prepared in Lab 6 and moves from data handling to statistical inference. We begin with principal component analysis and sample-to-sample distances as exploratory quality control, then work through differential expression with DESeq2 and the interpretation of the results table. The differentially expressed genes are visualised with volcano, per-gene count and heatmap plots. The lab closes with pathway-level interpretation using KEGG and the MSigDB Hallmark collection, covering both over-representation analysis and gene set enrichment analysis, with attention to the choice of background set and ranking metric.

This practical lab covers a complete RNA-seq differential expression workflow in R. We begin by translating biological questions into design matrices and interpreting DESeq2 coefficients, followed by normalisation, dispersion estimation, statistical testing, and log2 fold-change shrinkage. We then perform the same analysis with edgeR and compare the results from both pipelines, focusing on differences in statistical methods and the genes identified by each approach. Finally, we perform pathway analysis using over-representation analysis (ORA) and gene set enrichment analysis (GSEA), with particular attention to appropriate background sets and gene-level ranking.

This lab introduces a practical workflow for working with public RNA-seq data in R. Using data from the Gene Expression Omnibus (GEO), we retrieve raw count data and sample metadata, organize them into a SummarizedExperiment, and explore gene annotation and identifier conversion. The lab also covers clinical metadata exploration, calculation and interpretation of CPM and TPM, and essential RNA-seq quality-control and exploratory plots, including gene-level expression plots, gene-set heatmaps, and PCA.