Mengerjakan bab 8 Statistik Sosial secara mandiri

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.