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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## 
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4 Session information

sessionInfo()
## R version 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## 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       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] dce_1.13.0                  graph_1.83.0               
##  [3] cowplot_1.1.3               lubridate_1.9.3            
##  [5] forcats_1.0.0               stringr_1.5.1              
##  [7] dplyr_1.1.4                 purrr_1.0.2                
##  [9] readr_2.1.5                 tidyr_1.3.1                
## [11] tibble_3.2.1                tidyverse_2.0.0            
## [13] TCGAutils_1.25.0            curatedTCGAData_1.25.4     
## [15] MultiAssayExperiment_1.31.0 SummarizedExperiment_1.35.0
## [17] Biobase_2.65.0              GenomicRanges_1.57.0       
## [19] GenomeInfoDb_1.41.0         IRanges_2.39.0             
## [21] S4Vectors_0.43.0            BiocGenerics_0.51.0        
## [23] MatrixGenerics_1.17.0       matrixStats_1.3.0          
## [25] ggraph_2.2.1                ggplot2_3.5.1              
## [27] BiocStyle_2.33.0           
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-7              httr_1.4.7               
##   [3] GenomicDataCommons_1.29.0 prabclus_2.3-3           
##   [5] Rgraphviz_2.49.0          numDeriv_2016.8-1.1      
##   [7] tools_4.4.0               utf8_1.2.4               
##   [9] R6_2.5.1                  vegan_2.6-4              
##  [11] mgcv_1.9-1                sn_2.1.1                 
##  [13] permute_0.9-7             withr_3.0.0              
##  [15] graphite_1.51.0           gridExtra_2.3            
##  [17] flexclust_1.4-2           cli_3.6.2                
##  [19] sandwich_3.1-0            labeling_0.4.3           
##  [21] sass_0.4.9                diptest_0.77-1           
##  [23] mvtnorm_1.2-4             robustbase_0.99-2        
##  [25] proxy_0.4-27              Rsamtools_2.21.0         
##  [27] FMStable_0.1-4            Linnorm_2.29.0           
##  [29] plotrix_3.8-4             limma_3.61.0             
##  [31] RSQLite_2.3.6             generics_0.1.3           
##  [33] BiocIO_1.15.0             gtools_3.9.5             
##  [35] wesanderson_0.3.7         Matrix_1.7-0             
##  [37] fansi_1.0.6               logger_0.3.0             
##  [39] abind_1.4-5               lifecycle_1.0.4          
##  [41] multcomp_1.4-25           yaml_2.3.8               
##  [43] edgeR_4.3.0               mathjaxr_1.6-0           
##  [45] SparseArray_1.5.0         BiocFileCache_2.13.0     
##  [47] Rtsne_0.17                grid_4.4.0               
##  [49] blob_1.2.4                promises_1.3.0           
##  [51] gdata_3.0.0               ppcor_1.1                
##  [53] bdsmatrix_1.3-7           ExperimentHub_2.13.0     
##  [55] crayon_1.5.2              lattice_0.22-6           
##  [57] GenomicFeatures_1.57.0    chromote_0.2.0           
##  [59] KEGGREST_1.45.0           magick_2.8.3             
##  [61] pillar_1.9.0              knitr_1.46               
##  [63] rjson_0.2.21              fpc_2.2-12               
##  [65] corpcor_1.6.10            codetools_0.2-20         
##  [67] mutoss_0.1-13             glue_1.7.0               
##  [69] RcppArmadillo_0.12.8.2.1  data.table_1.15.4        
##  [71] vctrs_0.6.5               png_0.1-8                
##  [73] Rdpack_2.6                mnem_1.21.0              
##  [75] gtable_0.3.5              kernlab_0.9-32           
##  [77] assertthat_0.2.1          amap_0.8-19              
##  [79] cachem_1.0.8              xfun_0.43                
##  [81] mime_0.12                 rbibutils_2.2.16         
##  [83] S4Arrays_1.5.0            RcppEigen_0.3.4.0.0      
##  [85] tidygraph_1.3.1           survival_3.6-4           
##  [87] tinytex_0.50              fastICA_1.2-4            
##  [89] statmod_1.5.0             TH.data_1.1-2            
##  [91] tsne_0.1-3.1              nlme_3.1-164             
##  [93] naturalsort_0.1.3         bit64_4.0.5              
##  [95] gmodels_2.19.1            filelock_1.0.3           
##  [97] bslib_0.7.0               colorspace_2.1-0         
##  [99] DBI_1.2.2                 nnet_7.3-19              
## [101] mnormt_2.1.1              tidyselect_1.2.1         
## [103] processx_3.8.4            bit_4.0.5                
## [105] compiler_4.4.0            curl_5.2.1               
## [107] rvest_1.0.4               expm_0.999-9             
## [109] xml2_1.3.6                TFisher_0.2.0            
## [111] ggdendro_0.2.0            DelayedArray_0.31.0      
## [113] shadowtext_0.1.3          bookdown_0.39            
## [115] rtracklayer_1.65.0        harmonicmeanp_3.0.1      
## [117] sfsmisc_1.1-18            scales_1.3.0             
## [119] DEoptimR_1.1-3            RBGL_1.81.0              
## [121] rappdirs_0.3.3            apcluster_1.4.13         
## [123] digest_0.6.35             snowfall_1.84-6.3        
## [125] rmarkdown_2.26            XVector_0.45.0           
## [127] htmltools_0.5.8.1         pkgconfig_2.0.3          
## [129] highr_0.10                dbplyr_2.5.0             
## [131] fastmap_1.1.1             rlang_1.1.3              
## [133] UCSC.utils_1.1.0          farver_2.1.1             
## [135] jquerylib_0.1.4           zoo_1.8-12               
## [137] jsonlite_1.8.8            BiocParallel_1.39.0      
## [139] mclust_6.1.1              RCurl_1.98-1.14          
## [141] magrittr_2.0.3            modeltools_0.2-23        
## [143] GenomeInfoDbData_1.2.12   munsell_0.5.1            
## [145] Rcpp_1.0.12               viridis_0.6.5            
## [147] stringi_1.8.3             zlibbioc_1.51.0          
## [149] MASS_7.3-60.2             plyr_1.8.9               
## [151] AnnotationHub_3.13.0      org.Hs.eg.db_3.19.1      
## [153] flexmix_2.3-19            parallel_4.4.0           
## [155] ggrepel_0.9.5             Biostrings_2.73.0        
## [157] graphlayouts_1.1.1        splines_4.4.0            
## [159] multtest_2.61.0           hms_1.1.3                
## [161] locfit_1.5-9.9            qqconf_1.3.2             
## [163] ps_1.7.6                  igraph_2.0.3             
## [165] fastcluster_1.2.6         reshape2_1.4.4           
## [167] BiocVersion_3.20.0        XML_3.99-0.16.1          
## [169] evaluate_0.23             metap_1.10               
## [171] pcalg_2.7-11              BiocManager_1.30.22      
## [173] tzdb_0.4.0                tweenr_2.0.3             
## [175] polyclip_1.10-6           clue_0.3-65              
## [177] BiocBaseUtils_1.7.0       ggforce_0.4.2            
## [179] restfulr_0.0.15           e1071_1.7-14             
## [181] later_1.3.2               viridisLite_0.4.2        
## [183] class_7.3-22              snow_0.4-4               
## [185] websocket_1.4.1           ggm_2.5.1                
## [187] memoise_2.0.1             AnnotationDbi_1.67.0     
## [189] GenomicAlignments_1.41.0  ellipse_0.5.0            
## [191] cluster_2.1.6             timechange_0.3.0

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.