Contents

1 Introduction

1.1 Load required packages

Load the package with the library function.

library(tidyverse)
library(ggplot2)

library(dce)

set.seed(42)

2 Pathway database overview

We provide access to the following topological pathway databases using graphite (Sales et al. 2012) in a processed format. This format looks as follows:

dce::df_pathway_statistics %>%
  arrange(desc(node_num)) %>%
  head(10) %>%
  knitr::kable()
database pathway_id pathway_name node_num edge_num
reactome R-HSA-162582 Signaling Pathways 2488 62068
reactome R-HSA-1430728 Metabolism 2047 85543
reactome R-HSA-392499 Metabolism of proteins 1894 52807
reactome R-HSA-1643685 Disease 1774 55469
reactome R-HSA-168256 Immune System 1771 58277
panther P00057 Wnt signaling pathway 1644 195344
reactome R-HSA-74160 Gene expression (Transcription) 1472 32493
reactome R-HSA-597592 Post-translational protein modification 1394 26399
kegg hsa:01100 Metabolic pathways 1343 22504
reactome R-HSA-73857 RNA Polymerase II Transcription 1339 25294

Let’s see how many pathways each database provides:

dce::df_pathway_statistics %>%
  count(database, sort = TRUE, name = "pathway_number") %>%
  knitr::kable()
database pathway_number
pathbank 48685
smpdb 48671
reactome 2406
wikipathways 640
kegg 323
panther 94
pharmgkb 90

Next, we can see how the pathway sizes are distributed for each database:

dce::df_pathway_statistics %>%
  ggplot(aes(x = node_num)) +
    geom_histogram(bins = 30) +
    facet_wrap(~ database, scales = "free") +
    theme_minimal()

3 Plotting pathways

It is easily possible to plot pathways:

pathways <- get_pathways(
  pathway_list = list(
    pathbank = c("Lactose Synthesis"),
    kegg = c("Fatty acid biosynthesis")
  )
)

lapply(pathways, function(x) {
  plot_network(
    as(x$graph, "matrix"),
    visualize_edge_weights = FALSE,
    arrow_size = 0.02,
    shadowtext = TRUE
  ) +
    ggtitle(x$pathway_name)
})
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