# Simplify Functional Enrichment Results
[](https://github.com/jokergoo/simplifyEnrichment/actions)
[](https://bioconductor.org/packages/stats/bioc/simplifyEnrichment/)
[](http://bioconductor.org/packages/devel/bioc/html/simplifyEnrichment.html)
### 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](https://doi.org/10.1016/j.gpb.2022.04.008).
### Install
`simplifyEnrichment` is available on [Bioconductor](http://www.bioconductor.org/packages/devel/bioc/html/simplifyEnrichment.html), you can install it by:
```r
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("simplifyEnrichment")
```
If you want to try the latest version, install it directly from GitHub:
```r
library(devtools)
install_github("jokergoo/simplifyEnrichment")
```
### Usage
As an example, I first generate a list of random GO IDs.
```r
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.
```r
mat = GO_similarity(go_id)
simplifyGO(mat)
```
### License
MIT @ Zuguang Gu
