# Overview of pathway network databases
Author: Kim Philipp Jablonski, Martin Pirkl
Date: 2021-10-26

Kim Philipp Jablonski, Martin Pirkl

#### 2021-10-26

# Contents

# 1 Introduction

## 1.1 Load required packages

Load the package with the library function.

    library(tidyverse)
    library(ggplot2)

    library(dce)

    set.seed(42)

    dce::df_pathway_statistics %>%
      sample_n(10) %>%
      arrange(desc(node_num)) %>%
      knitr::kable()

database | pathway_id | pathway_name | node_num | edge_num
--- | --- | --- | --- | ---
kegg | hsa:04151 | PI3K-Akt signaling pathway | 354 | 4552
kegg | hsa:04371 | Apelin signaling pathway | 134 | 942
kegg | hsa:04520 | Adherens junction | 68 | 170
kegg | hsa:04970 | Salivary secretion | 48 | 96
kegg | hsa:05321 | Inflammatory bowel disease | 48 | 81
nci | pid_4166 | Beta2 integrin cell surface interactions | 29 | 140
kegg | hsa:00563 | Glycosylphosphatidylinositol (GPI)-anchor biosynthesis | 23 | 116
kegg | hsa:00900 | Terpenoid backbone biosynthesis | 21 | 69
biocarta | pid_10459 | rna polymerase iii transcription | 7 | 42
biocarta | pid_9732 | estrogen responsive protein efp controls cell cycle and breast tumors growth | 4 | 2

# 2 Pathway database overview

We provide access to the following topological pathway databases using graphite (Sales et al. 2012):

    dce::df_pathway_statistics %>%
      count(database, sort = TRUE, name = "pathway_number") %>%
      knitr::kable()

database | pathway_number
--- | ---
kegg | 317
biocarta | 247
nci | 212
panther | 94
pharmgkb | 66

    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(
        kegg = c("Citrate cycle (TCA cycle)")
      )
    )

    ## 'select()' returned 1:1 mapping between keys and columns
    ## 'select()' returned 1:1 mapping between keys and columns
    ## 'select()' returned 1:1 mapping between keys and columns
    ## 'select()' returned 1:1 mapping between keys and columns

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

    ## [[1]]

# 4 Session information

    sessionInfo()

    ## R version 4.1.1 (2021-08-10)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 20.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
    ## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_GB              LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] org.Hs.eg.db_3.14.0         AnnotationDbi_1.56.0
    ##  [3] dce_1.2.0                   graph_1.72.0
    ##  [5] cowplot_1.1.1               forcats_0.5.1
    ##  [7] stringr_1.4.0               dplyr_1.0.7
    ##  [9] purrr_0.3.4                 readr_2.0.2
    ## [11] tidyr_1.1.4                 tibble_3.1.5
    ## [13] tidyverse_1.3.1             TCGAutils_1.14.0
    ## [15] curatedTCGAData_1.15.1      MultiAssayExperiment_1.20.0
    ## [17] SummarizedExperiment_1.24.0 Biobase_2.54.0
    ## [19] GenomicRanges_1.46.0        GenomeInfoDb_1.30.0
    ## [21] IRanges_2.28.0              S4Vectors_0.32.0
    ## [23] BiocGenerics_0.40.0         MatrixGenerics_1.6.0
    ## [25] matrixStats_0.61.0          ggraph_2.0.5
    ## [27] ggplot2_3.3.5               BiocStyle_2.22.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] rappdirs_0.3.3                rtracklayer_1.54.0
    ##   [3] prabclus_2.3-2                bit64_4.0.5
    ##   [5] knitr_1.36                    multcomp_1.4-17
    ##   [7] DelayedArray_0.20.0           data.table_1.14.2
    ##   [9] wesanderson_0.3.6             KEGGREST_1.34.0
    ##  [11] RCurl_1.98-1.5                generics_0.1.1
    ##  [13] metap_1.5                     GenomicFeatures_1.46.0
    ##  [15] TH.data_1.1-0                 RSQLite_2.2.8
    ##  [17] proxy_0.4-26                  CombinePValue_1.0
    ##  [19] bit_4.0.4                     tzdb_0.1.2
    ##  [21] mutoss_0.1-12                 xml2_1.3.2
    ##  [23] lubridate_1.8.0               httpuv_1.6.3
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    ##  [33] DEoptimR_1.0-9                fansi_0.5.0
    ##  [35] restfulr_0.0.13               progress_1.2.2
    ##  [37] dbplyr_2.1.1                  readxl_1.3.1
    ##  [39] Rgraphviz_2.38.0              igraph_1.2.7
    ##  [41] DBI_1.1.1                     tmvnsim_1.0-2
    ##  [43] apcluster_1.4.8               RcppArmadillo_0.10.7.0.0
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    ##  [47] bookdown_0.24                 permute_0.9-5
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# References

Sales, Gabriele, Enrica Calura, Duccio Cavalieri, and Chiara Romualdi. 2012. “Graphite-a Bioconductor Package to Convert Pathway Topology to Gene Network.” BMC Bioinformatics 13 (1): 20.