Differential Gene Exp. Analysis and Functional Characterisation

This course will guide you through the fundamental concepts and practical steps involved in Differential Gene Expression (DGE) Analysis using RNA-Seq data. Whether you're a beginner or looking to strengthen your understanding, this module will help you master the key techniques used in gene expression studies. We begin with the purpose of DGE analysis, exploring why it is essential and the different types of comparisons performed in gene expression studies. Next, we dive into statistical testing, covering essential concepts like Student’s t-test, p-adjusted values (FDR), log2 fold change (log2FC), and visualization techniques such as Volcano and MA plots. You'll also get a step-by-step guide to performing DGE analysis in Excel.

For hands-on analysis, we introduce RNA-Seq analysis using R, where you'll learn to perform DESeq analysis, generate key plots, interpret biological significance, and handle gene ID conversions. We'll also cover clustering analysis to uncover patterns in gene expression.

Additionally, you'll explore DGE analysis with microarray data, comparing it to RNA-Seq, understanding preprocessing, and learning how to process raw data and assess quality. Throughout the course, you'll have access to Google Colab notebooks for practicing key techniques and quizzes to test your understanding at each step. By the end of this module, you'll be equipped with the skills to analyze differential gene expression data, interpret results, and apply these methods to real-world biological research