Pathway diagram

A biological pathway outlines the sequence of molecular interactions within a cell that result in a specific outcome or change in the cell.

Table (Data format)

This is an example of a data format containing the necessary information for pathway diagram visualizations. The table includes mock data specifically generated for this purpose.

The “Description” column contains the names of the pathways and the “geneID” column corresponds to the various genes associated with each pathway.

library(data.table)
library(stringr)
library(reactable)

df <- fread("../inst/data/pathway_diagram.txt")


reactable(
  
  df, 
  defaultPageSize = 2,
  theme = reactableTheme(
    backgroundColor  = "transparent"
  )
)

Pathway diagram

Pathway diagrams visualize the interconnected molecular events.

Each black dot represents a pathway, whereas each orange dot represents the different genes associated with that pathway.

The size of each black dot corresponds to the number of connections within that pathway.

Pathway diagrams can be constructed using the geom_edge_link() and geom_node_point() functions from the ggraph package.

library(tidygraph)

library(ggplot2)
library(ggrepel)
library(ggraph)
library(ggnewscale)
library(shadowtext)

library(extrafont)


# plot
q <- df$geneID |> 
    str_split("\\/") |> 
    lapply(function(x) { data.table("to" = x) }) |> 
    rbindlist(idcol = "from")

q$from <- df[q$from]$Description 

layout <- q |>
    as_tbl_graph() |> 
    mutate(Degree = centrality_degree(mode = 'all')) |>
    create_layout(layout = 'igraph', algorithm = 'kk')

layout$Level <- ifelse(layout$name %in% q$to, "Gene", "Term")
layout$name <- layout$name |> str_wrap(width = 15)



ggraph(layout) + 
    
    geom_edge_link(color = "#97A1A7", edge_width = .3) + 
    
    geom_node_point(
        aes(size = Degree, fill = Level), shape = 21,
        stroke = .2, color = "grey96"
    ) + 
    
    scale_size_continuous(
        range = c(4, 12), 
        guide = guide_legend(
            title = "No. of connections",
            override.aes = list(color = "grey10", stroke = .35)
        )
    ) +
    
    scale_fill_manual(
        values = c(
            "Term" = "#2E2A2B",
            "Gene" = "#DC9445"
        ),
        guide = "none" 
    ) +
    

    new_scale("size") +
    
    geom_text_repel(
        aes(x, y, label = name, size = Degree),
        family = "Calibri", fontface = "bold", 
        color = "grey10", bg.color = "grey96", bg.r = 0.075,
        segment.linetype = "dotted", segment.size = .2,
        max.overlaps = Inf
    ) +

    scale_size_continuous(range = c(3, 4), guide = "none") +
    
    theme_graph(base_family = "Calibri") +
    
    theme(
        legend.position = c(.95, .1),
        legend.title.position = "top",
        plot.margin = margin(20, 20, 20, 20),
        plot.background = element_rect(fill = "transparent", color = NA)

    )