DESeq2 Basics Explained - Differential Gene Expression Analysis - Bioinformatics 101

Dive into the fundamentals of differential gene expression analysis using DESeq2 in this comprehensive 26-minute video tutorial. Explore the intricacies of RNA-seq count data, starting with a typical study design and progressing through the features of RNA-Seq counts. Learn why Poisson distribution falls short for modeling this data and discover why Negative Binomial distribution is preferred. Walk through the DESeq2 steps, including handling biases in count data, estimating size factors using the median of ratios method, and calculating dispersions. Gain insights into Generalized Linear Models and hypothesis testing in the context of differential gene expression analysis. Enhance your understanding of bioinformatics and genomics with this in-depth exploration of DESeq2 basics.

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