KEGG pathway visualization

This tutorial will cover visualizing differential expression analysis results onto KEGG pathway diagrams.


Installation

First, make sure you have PinPath installed.

library(PinPath)
library(org.Hs.eg.db)


Dataset

The example dataset we will use compares the expression of transcripts in lung cancer biopsies versus normal tissue. Differential expression analysis has already been performed, generating log2FCs and p-values for each gene.

lung_expr <- read.csv(
	system.file("extdata","data-lung-cancer.csv", package ="PinPath"), 
	stringsAsFactors = FALSE)

In the pathway, we want to show which gene is significantly differentially expressed. For this, we will use an adjusted p-value cutoff of 0.05.

lung_expr$Significant <- ifelse(lung_expr$adj.P.Value < 0.05, "Yes", "No")


Set colors

After we have loaded and prepared the differential expression analysis statistics, we need to define how to color the statistics in the pathway diagram. In this vignette, we will plot the log2FC and significance of each gene on the pathway diagram. We can start by loading the default color palette.

colorList <- PinPath::defaultColorList(lung_expr[,c("log2FC", "Significant")])

In the next step, we can adjust the default color palette to the desired color values. For instance, we want to display a green color when a gene is differentially expressed and a white color when it is not.

colorList[["Significant"]]$Color <- c(
	"Yes" = "green",
	"No" = "white")

Furthermore, we can set the minimum and maximum value of the log2FC color gradient to -1.5 and 1.5, respectively.

colorList[["log2FC"]]$ColorVal <- c(
	"MinVal" = -1.5,
	"MidVal" = 0,
	"MaxVal" = 1.5)


Plot pathway

We can now plot the differential expression statistics on the hsa05207: Chemical carcinogenesis - receptor activation pathway. If not specified otherwise, the pathway and legend image will be saved in your working directory. The pathway image will be opened by default.

# Select pathway
pathway_id <- "hsa05207"
infile <- BiocFileCache::bfcrpath(
	BiocFileCache::BiocFileCache(), 
	paste0("https://rest.kegg.jp/get/",pathway_id,"/kgml"))
  
# Plot pathway
pathVis <- PinPath::drawKGML(
	infile = infile,
	annGenes = "org.Hs.eg.db",
	inputDB = "ENSEMBL",
	featureIDs = lung_expr$GeneID,
	colorVar = lung_expr[,c("log2FC", "Significant")],
	colorList = colorList,
	nodeTable = TRUE,
	legend = TRUE)

Pathway image:

Legend image:


Plot network

You can also plot the pathway as a network. In contrast to the pathway diagram, every element (e.g., gene/protein) is represented exactly once.

pathVis <- PinPath::KGML2Network(
	infile = infile,
	annGenes = "org.Hs.eg.db",
	inputDB = "ENSEMBL",
	featureIDs = lung_expr$GeneID,
	colorVar = lung_expr[,c("log2FC", "Significant")],
	colorList = colorList,
	nodeTable = TRUE,
	legend = TRUE)

Network image:

Legend image:


Session info

sessionInfo()

    ## R version 4.5.1 (2025-06-13 ucrt)
    ## Platform: x86_64-w64-mingw32/x64
    ## Running under: Windows 11 x64 (build 26200)
    ## 
    ## Matrix products: default
    ##   LAPACK version 3.12.1
    ## 
    ## locale:
    ## [1] LC_COLLATE=English_Europe.utf8  LC_CTYPE=English_Europe.utf8   
    ## [3] LC_MONETARY=English_Europe.utf8 LC_NUMERIC=C                   
    ## [5] LC_TIME=English_Europe.utf8    
    ## 
    ## time zone: Europe/Amsterdam
    ## tzcode source: internal
    ## 
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
    ## [8] base     
    ## 
    ## other attached packages:
    ## [1] org.Hs.eg.db_3.22.0  AnnotationDbi_1.72.0 IRanges_2.44.0      
    ## [4] S4Vectors_0.48.0     Biobase_2.70.0       BiocGenerics_0.56.0 
    ## [7] generics_0.1.4       rWikiPathways_1.30.0 PinPath_0.99.0      
    ## 
    ## loaded via a namespace (and not attached):
    ##  [1] tidyr_1.3.2         xml2_1.5.2          bitops_1.0-9       
    ##  [4] shape_1.4.6.1       stringi_1.8.7       RSQLite_2.4.5      
    ##  [7] digest_0.6.39       magrittr_2.0.4      grid_4.5.1         
    ## [10] evaluate_1.0.5      fastmap_1.2.0       blob_1.3.0         
    ## [13] DBI_1.2.3           httr_1.4.7          purrr_1.2.1        
    ## [16] XML_3.99-0.20       Biostrings_2.78.0   textshaping_1.0.4  
    ## [19] cli_3.6.5           rlang_1.1.7         crayon_1.5.3       
    ## [22] XVector_0.50.0      bit64_4.6.0-1       withr_3.0.2        
    ## [25] cachem_1.1.0        yaml_2.3.12         otel_0.2.0         
    ## [28] tools_4.5.1         memoise_2.0.1       dplyr_1.1.4        
    ## [31] curl_7.0.0          vctrs_0.7.1         R6_2.6.1           
    ## [34] png_0.1-8           magick_2.9.0        lifecycle_1.0.5    
    ## [37] stringr_1.6.0       Seqinfo_1.0.0       KEGGREST_1.50.0    
    ## [40] bit_4.6.0           pkgconfig_2.0.3     pillar_1.11.1      
    ## [43] Rcpp_1.1.1          data.table_1.18.2.1 glue_1.8.0         
    ## [46] systemfonts_1.3.1   xfun_0.56           tibble_3.3.1       
    ## [49] tidyselect_1.2.1    rstudioapi_0.18.0   knitr_1.51         
    ## [52] rjson_0.2.23        htmltools_0.5.9     svglite_2.2.2      
    ## [55] rmarkdown_2.30      compiler_4.5.1      gridBase_0.4-7     
    ## [58] RCurl_1.98-1.17