This package aims to provide a comprehensive introduction on gene set enrichment analysis.

Topics

Topic 1: gene set resources

• Topic 1-00: Representation of gene sets in R
• Topic 1-01: GO gene sets
• Topic 1-02: Get pathways from KEGG
• Topic 1-03: Get pathways from MSigDB
• Topic 1-04: Get pathways from Reactome
• Topic 1-05: Get gene sets from UniProt Keywords
• Topic 1-06: Get GO/KEGG gene sets for other organisms
• Topic 1-07: Generate gene sets for other organisms by mapping to orthologues
• Topic 1-08: Gene ID conversion

Topic 2: ORA and GSEA

• Topic 2-01: ORA on a single gene set
• Topic 2-02: Implement ORA from scratch
• Topic 2-03: Implement GSEA from scratch
• Topic 2-04: fgsea
• Topic 2-05: Compare ORA and GSEA

Topic 3: Application in genomics

• Topic 3-01: Implement the GREAT method
• Topic 3-02: Online GREAT analysis
• Topic 3-03: Local GREAT analysis

Topic 4: Topology-based

• Topic 4-01: Centrality-based pathway enrichment analysis

Topic 5: Single-sample GSEA

• Topic 5-01: Single-sample GSEA

Topic 6: Similarity, clustering and summarization

• Topic 6-01: Similarities of terms
• Topic 6-02: Cluster and summarize

Topic 7: Visualization

• Topic 7-01: Visualization

Topic 8: GSEA framework

• Topic 8-01: GSEA framework

Install

library(devtools)
install_github("jokergoo/GSEAtopics")

To install all necessary packages for running the vignettes:

setRepositories(ind = 1:4)
install.packages(c("circlize", "reactome.db", "UniProtKeywords",
    "AnnotationHub", "Orthology.eg.db", "GSVA", "simona", "BiocManager", "CePa",
    "ggplot2", "rGREAT", "KEGGgraph", "proxyC", "sparseMatrixStats", "HilbertCurve", 
    "TxDb.Hsapiens.UCSC.hg19.knownGene", "cola", "cowplot", "eulerr", "golubEsets", 
    "hu6800.db", "microbenchmark", "preprocessCore", "simplifyEnrichment"))

There is also a simplified version

https://carpentries-incubator.github.io/bioc-rnaseq/07-gene-set-analysis.html

License

Code is released under the MIT licence. Vignettes are under the CC BY-NC-ND license.