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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.
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.
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")
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)
MIT @ Zuguang Gu