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README.md

Functional enrichment analysis of high-throughput omics data

Quick install

BiocManager::install("waldronlab/enrichOmics", dependencies = TRUE, build_vignettes = TRUE)

Workshop website

Docker image

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

Ludwig.Geistlinger@sph.cuny.edu

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)

Workshop Participation

Execution of example code and hands-on practice

R / Bioconductor packages used

  • EnrichmentBrowser
  • regioneR

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

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Functional enrichment analysis of high-throughput omics data

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