- 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.
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.
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")- Simplify Functional Enrichment Results
- Word Cloud Annotation
- A Shiny app to interactively visualize clustering results
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 of simplifyEnrichment
- Compare different similarity measures for functional terms
- Compare different partitioning methods in binary cut clustering
MIT @ Zuguang Gu