# Simplify Functional Enrichment Results
- 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](https://doi.org/10.1016/j.gpb.2022.04.008).
`simplifyEnrichment` is available on [Bioconductor](http://www.bioconductor.org/packages/devel/bioc/html/simplifyEnrichment.html), you can install it by:
if (!requireNamespace("BiocManager", quietly=TRUE))
If you want to try the latest version, install it directly from GitHub:
- [Simplify Functional Enrichment Results](https://jokergoo.github.io/simplifyEnrichment/articles/simplifyEnrichment.html)
- [Word Cloud Annotation](https://jokergoo.github.io/simplifyEnrichment/articles/word_cloud_anno.html)
- [A Shiny app to interactively visualize clustering results](https://jokergoo.github.io/simplifyEnrichment/articles/interactive.html)
As an example, I first generate a list of random GO IDs.
go_id = random_GO(500)
#  "GO:0003283" "GO:0060032" "GO:0031334" "GO:0097476" "GO:1901222"
#  "GO:0018216"
Then generate the GO similarity matrix, split GO terms into clusters and visualize it.
mat = GO_similarity(go_id)
- [Examples of simplifyEnrichment](https://simplifyenrichment.github.io/examples/)
- [Compare different similarity measures for functional terms](https://simplifyenrichment.github.io/compare_similarity/)
- [Compare different partitioning methods in binary cut clustering](https://simplifyenrichment.github.io/test_partition_methods/)
MIT @ Zuguang Gu