simplifyEnrichment

A new clustering algorithm, "binary cut", for clustering similarity matrices of functional terms is implemeted in this package. It also provides functions for visualizing, summarizing and comparing the clusterings.

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What simplifyEnrichment can do for you

Simplify Functional Enrichment Results

R-CMD-check
bioc
bioc

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

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Programming languages
  • R 98%
  • CSS 2%
License
</>Source code
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Participating organisations

German Cancer Research Center

Reference papers

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