Tugas Bab 4 Komputasi Statistika

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.

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

Menginterpretasikan Regresi Polinomial