Simple Linear Regression for cars data

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.

As of September 4, 2026

Nama: Cantika Adinda NIM:2407016006 Matkul: Regresi Non Parametrik

Menginterpretasikan Regresi Polinomial

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.

Math problems of the day for 9/7/26.