KEGGemUP

Installation

KEGGemUP can be installed from Bioconductor with the following code:

if(!requireNamespace('BiocManager', quietly = TRUE))
  install.packages('BiocManager')

BiocManager::install("KEGGemUP")

You can also install the development version of KEGGemUP from GitHub with:

# install.packages("remotes")
remotes::install_github("imbeimainz/KEGGemUP")

Load the package after installation with

library("KEGGemUP")

KEGGemUP at a glance

The KEGGemUP package allows you to:

• Retrieve and create a KEGG pathway graph, from the KGML files (with create_kegg_graph())

• Map some continuous values onto that graph (e.g. the logFoldChange, with the map_results_to_graph())

• Render that graph interactively (via render_kegg_graph(), based on visNetwork)

• Focus either on a subset or on a highlighted portion of that graph (thanks to subset_kegg_graph() and highlight_kegg_graph())

Quick start

## load an example dataset, and format the DE results
data(res_de_macro_IFNg_vs_naive, package = "KEGGemUP")

head(res_de_macro_IFNg_vs_naive)

de_results_list <- list(
  rnaseq_limma = list(
    de_table = data.frame(res_de_macro_IFNg_vs_naive),
    value_column = "logFC",
    feature_column = "ENTREZID"
  )
)

## retrieve and create the graph
kmu_cellcycle <- create_kegg_graph(pathway_id = "hsa04110")

## map the DE values to the graph
kmu_cellcycle_mapped <- map_results_to_graph(g = kmu_cellcycle, 
                                             de_results = de_results_list)

## render the graph interactively
kmu_rendered <- render_kegg_graph(g = kmu_cellcycle_mapped)

kmu_rendered

You can see more examples and a detailed usage in the package vignette

Disclaimer

KEGGemUP uses KEGG API, and therefore is provided for academic use by academic users belonging to academic institutions. See https://www.kegg.jp/kegg/rest/ for more information.