Quick install
BiocManager::install("waldronlab/enrichOmics", dependencies = TRUE, build_vignettes = TRUE)Instructor(s) name(s) and contact information
Ludwig Geistlinger and Levi Waldron
CUNY School of Public Health 55 W 125th St, New York, NY 10027
Workshop Description
This workshop gives an in-depth overview of existing methods for enrichment analysis of gene expression data with regard to functional gene sets, pathways, and networks. The workshop will help participants understand the distinctions between assumptions and hypotheses of existing methods as well as the differences in objectives and interpretation of results. It will provide code and hands-on practice of all necessary steps for differential expression analysis, gene set- and network-based enrichment analysis, and identification of enriched genomic regions and regulatory elements, along with visualization and exploration of results.
Pre-requisites
Basic knowledge of R syntax
Familiarity with the SummarizedExperiment class
Familiarity with the GenomicRanges class
Familiarity with high-throughput gene expression data as obtained with microarrays and RNA-seq
Familiarity with the concept of differential expression analysis (with e.g. limma, edgeR, DESeq2)
Time outline
| Activity | Time |
|---|---|
| Background | 30m |
| Differential expression analysis | 15m |
| Gene set analysis | 30m |
| Gene network analysis | 15m |
| Genomic region analysis | 15m |
Goals and objectives
Theory
- Gene sets, pathways & regulatory networks
- Resources
- Gene set analysis vs. gene set enrichment analysis
- Underlying null: competitive vs. self-contained
- Generations: ora, fcs & topology-based
Practice:
- Data types: microarray vs. RNA-seq
- Differential expression analysis
- Defining gene sets according to GO and KEGG
- GO/KEGG overrepresentation analysis
- Functional class scoring & permutation testing
- Network-based enrichment analysis
- Genomic region enrichment analysis