GitHub - jokergoo/simplifyEnrichment: Simplify functional enrichment results (original) (raw)

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")

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)

License

MIT @ Zuguang Gu