DESeq2 Basics Explained - Differential Gene Expression Analysis - Bioinformatics 101

This introductory course explains how DESeq2 analyzes RNA-seq count data to detect differentially expressed genes. It covers count distributions, normalization with the median-of-ratios method, dispersion estimation, generalized linear models, and hypothesis testing.

Syllabus

Intro
A typical study design
Features of RNA-Seq counts data
Poisson distribution for counts data
Why is Poisson not the best model?
Negative Binomial is the way to go!
DESeq2 steps
Biases in counts data
Estimate Size Factor median of ratios method
Estimate Dispersions
Generalized Linear Models
Hypothesis testing