Differential expression analysis

Keywords

Differential-expression, Statistical-model

Authors

Charlotte Soneson (@charlotte), charlottesoneson@gmail.com

Type

• Lecture

Description

This lecture covers the process from count matrix to statistical analysis results (differential expression). More specifically, it covers experimental design, normalization, statistical modeling and parameter estimation, multiple hypothesis testing and a more detailed look at some of the most common differential expression methods as well as a comparison between them.

Aims

The aim of the lecture is to introduce the audience to differential expression analysis of RNA-seq data, to point to pitfalls and best practices, and to show how to apply the most commonly used methods.

Prerequisites

• Know what a count matrix represents
• Some basic background in statistical hypothesis testing

Target audience

beginner, biologist

Learning objectives

• Recognizing the need for normalization
• Choosing an appropriate strategy for differential expression analysis
• Performing differential expression analysis
• Interpreting the output

Materials

• Lecture slides

Data

• The Bottomly data set (downloaded from ReCount) is used to create some of the slides.

Timing

Approximately half a day of lecture

Content stability

The content is relatively stable. Some parts may need to be updated if significant new differential expression methods come along.

Technical requirements

• Not applicable

Literature references

• Not applicable

Comments

• I did not check if the use of all figures is allowed or properly acknowledged.
• A license needs to be added