KGExplorer

Authors: Brian Schilder, Hiranyamaya Dash

README updated: Mar-08-2025

Introduction

KGExplorer is an R package designed to facilitate seamless exploration, manipulation, and visualization of biomedical knowledge graphs and ontologies. By integrating robust tools for data retrieval, filtering, mapping, and graph visualization, KGExplorer empowers researchers to work with complex biomedical datasets from resources like the the Monarch Initiative and Human Phenotype Ontology (HPO). With its modular design and extensive caching support, the package streamlines workflows by reducing redundant downloads and providing efficient tools for managing large-scale biomedical data.

Built on the foundations of powerful R libraries like tidygraph, data.table, ggplot2, and plotly, KGExplorer offers intuitive functions to manipulate and visualize knowledge graphs. Researchers can subset graphs, convert between graph representations, map between diverse ID systems, and generate interactive 3D visualizations of ontologies. KGExplorer is tailored for biomedical researchers seeking an efficient, flexible toolkit to harness the potential of knowledge graphs for scientific discovery.

KGExplorer was developed by the Neurogenomics Lab at Imperial College London, along with valuable feedback provided by the HPO team. This package is still actively evolving and growing. Community engagement is welcome and any suggestions can be submitted as an Issue or Pull Request.

Installation

Within R:

if(!require("BiocManager")) install.packages("BiocManager")

BiocManager::install("neurogenomics/KGExplorer")
library(KGExplorer)

Documentation

Website

Get started

Docker/Singularity Container

Citation

If you use KGExplorer, please cite:

Kitty B. Murphy, Robert Gordon-Smith, Jai Chapman, Momoko Otani, Brian M. Schilder, Nathan G. Skene (2023) Identification of cell type-specific gene targets underlying thousands of rare diseases and subtraits. medRxiv, https://doi.org/10.1101/2023.02.13.23285820

Contact

Neurogenomics Lab

UK Dementia Research Institute
Department of Brain Sciences
Faculty of Medicine
Imperial College London
GitHub

Session Info

utils::sessionInfo()

## R version 4.4.2 (2024-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.3.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6        jsonlite_1.9.1      renv_1.1.2         
##  [4] dplyr_1.1.4         compiler_4.4.2      BiocManager_1.30.25
##  [7] tidyselect_1.2.1    rvcheck_0.2.1       scales_1.3.0       
## [10] yaml_2.3.10         fastmap_1.2.0       here_1.0.1         
## [13] ggplot2_3.5.1       R6_2.6.1            generics_0.1.3     
## [16] knitr_1.49          yulab.utils_0.2.0   tibble_3.2.1       
## [19] desc_1.4.3          dlstats_0.1.7       munsell_0.5.1      
## [22] rprojroot_2.0.4     pillar_1.10.1       RColorBrewer_1.1-3 
## [25] rlang_1.1.5         badger_0.2.4        xfun_0.51          
## [28] fs_1.6.5            cli_3.6.4           magrittr_2.0.3     
## [31] rworkflows_1.0.6    digest_0.6.37       grid_4.4.2         
## [34] rstudioapi_0.17.1   lifecycle_1.0.4     vctrs_0.6.5        
## [37] evaluate_1.0.3      glue_1.8.0          data.table_1.17.0  
## [40] colorspace_2.1-1    rmarkdown_2.29      tools_4.4.2        
## [43] pkgconfig_2.0.3     htmltools_0.5.8.1

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