README.md

In jokergoo/simplifyEnrichment: Simplify Functional Enrichment Results

Simplify Functional Enrichment Results

Features

• A new method (binary cut) is proposed to efficiently cluster functional terms (e.g. GO terms) into groups from the semantic similarity matrix.
• Summaries of functional terms in each cluster are visualized by word clouds.

Citation

Zuguang Gu, et al., simplifyEnrichment: an R/Bioconductor package for Clustering and Visualizing Functional Enrichment Results, Genomics, Proteomics & Bioinformatics 2022. https://doi.org/10.1016/j.gpb.2022.04.008.

Install

simplifyEnrichment is available on Bioconductor, you can install it by:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("simplifyEnrichment")

If you want to try the latest version, install it directly from GitHub:

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

Vignette

• Simplify Functional Enrichment Results
• Word Cloud Annotation
• A Shiny app to interactively visualize clustering results

Usage

As an example, I first generate a list of random GO IDs.

library(simplifyEnrichment)
set.seed(888)
go_id = random_GO(500)
head(go_id)
# [1] "GO:0003283" "GO:0060032" "GO:0031334" "GO:0097476" "GO:1901222"
# [6] "GO:0018216"

Then generate the GO similarity matrix, split GO terms into clusters and visualize it.

mat = GO_similarity(go_id)
simplifyGO(mat)

Examples

• Examples of simplifyEnrichment
• Compare different similarity measures for functional terms
• Compare different partitioning methods in binary cut clustering

License

MIT @ Zuguang Gu

jokergoo/simplifyEnrichment documentation built on Nov. 3, 2023, 2:57 p.m.