The crash course in gene enrichment analysis
Gene enrichment analysis is a systematic approach to assign ontologies, pathways, and transcription factors to gene lists usually resulted from high throughput experiments. This crash course introduces the two most frequently applied approaches to locate the common features of large gene lists, and provide opportunities to practice this analysis in the most common research scenarios.
You will learn how to perform over-representation and gene set enrichment analysis on microarray and RNA-Seq . You will understand the relevant statistical approaches which are needed to find Gene Ontology, KEGG or other pathway terms which are associated to gene lists.
Course duration: Crash course format, 1 week, expected workload 10 to 15 hours.
Suggested background knowledge: You are expected to know what high throughput gene expression experiments are, their basic goals and arrangements. You do not need special software to accomplish this course other than a web browser.
- Module 1 – Theoretical background
- Enrichment analysis: an overview
- An overview of related concepts
- Module 2 – Methodological overview
- Over-representation Analysis
- Gene Set Enrichment Analysis
- Common statistical issues
- Module 3 – Practice
- ORA – WebGestalt
- GSEA – GeneTrail