This course aims to teach learners the basics of differential gene expression analysis using DESeq2 in R. By the end of the course, students will understand the DESeq2 model, the steps involved in differential gene expression analysis, and the intuition behind calling differentially expressed genes. The course covers topics such as study design, RNA-Seq count data features, biases in count data, size factor estimation, dispersion estimation, generalized linear models, and hypothesis testing. The teaching method involves a video lecture format lasting 26 minutes. This course is intended for beginners in bioinformatics or genomics who are interested in learning about RNA-Seq analysis and differential gene expression.
DESeq2 Basics Explained - Differential Gene Expression Analysis - Bioinformatics 101
Overview
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
Taught by
bioinformagician
