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README.md
# Simplify Functional Enrichment Results [![R-CMD-check](https://github.com/jokergoo/simplifyEnrichment/workflows/R-CMD-check/badge.svg)](https://github.com/jokergoo/simplifyEnrichment/actions) [![bioc](http://www.bioconductor.org/shields/downloads/devel/simplifyEnrichment.svg)](https://bioconductor.org/packages/stats/bioc/simplifyEnrichment/) [![bioc](http://www.bioconductor.org/shields/years-in-bioc/simplifyEnrichment.svg)](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") ``` ### Vignette - [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) ### 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) ``` ![image](https://user-images.githubusercontent.com/449218/89673686-133c8600-d8e7-11ea-89fe-5221cb64d819.png) ### Examples - [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/) ### License MIT @ Zuguang Gu