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.