Introduction

Load required packages

Load the package with the library function.

library(tidyverse)
library(ggplot2)

library(dce)

set.seed(42)

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()

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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Session information

sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        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: Etc/UTC
## 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_1.25.1            curatedTCGAData_1.27.0     
## [15] MultiAssayExperiment_1.31.5 SummarizedExperiment_1.35.1
## [17] Biobase_2.65.1              GenomicRanges_1.57.1       
## [19] GenomeInfoDb_1.41.1         IRanges_2.39.2             
## [21] S4Vectors_0.43.2            BiocGenerics_0.51.1        
## [23] MatrixGenerics_1.17.0       matrixStats_1.3.0          
## [25] ggraph_2.2.1                ggplot2_3.5.1              
## [27] BiocStyle_2.33.1           
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-8              httr_1.4.7               
##   [3] GenomicDataCommons_1.29.4 prabclus_2.3-3           
##   [5] Rgraphviz_2.49.0          numDeriv_2016.8-1.1      
##   [7] tools_4.4.1               utf8_1.2.4               
##   [9] R6_2.5.1                  vegan_2.6-8              
##  [11] mgcv_1.9-1                sn_2.1.1                 
##  [13] permute_0.9-7             withr_3.0.1              
##  [15] graphite_1.51.0           gridExtra_2.3            
##  [17] flexclust_1.4-2           cli_3.6.3                
##  [19] sandwich_3.1-0            labeling_0.4.3           
##  [21] sass_0.4.9                diptest_0.77-1           
##  [23] mvtnorm_1.3-0             robustbase_0.99-4        
##  [25] proxy_0.4-27              Rsamtools_2.21.1         
##  [27] FMStable_0.1-4            Linnorm_2.29.0           
##  [29] plotrix_3.8-4             limma_3.61.9             
##  [31] RSQLite_2.3.7             generics_0.1.3           
##  [33] BiocIO_1.15.2             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_1.4-26           yaml_2.3.10              
##  [43] edgeR_4.3.14              mathjaxr_1.6-0           
##  [45] SparseArray_1.5.31        BiocFileCache_2.13.0     
##  [47] Rtsne_0.17                grid_4.4.1               
##  [49] blob_1.2.4                gdata_3.0.0              
##  [51] ppcor_1.1                 bdsmatrix_1.3-7          
##  [53] ExperimentHub_2.13.1      crayon_1.5.3             
##  [55] lattice_0.22-6            GenomicFeatures_1.57.0   
##  [57] KEGGREST_1.45.1           sys_3.4.2                
##  [59] maketools_1.3.0           pillar_1.9.0             
##  [61] knitr_1.48                rjson_0.2.22             
##  [63] fpc_2.2-12                corpcor_1.6.10           
##  [65] codetools_0.2-20          mutoss_0.1-13            
##  [67] glue_1.7.0                RcppArmadillo_14.0.0-1   
##  [69] data.table_1.16.0         vctrs_0.6.5              
##  [71] png_0.1-8                 Rdpack_2.6.1             
##  [73] mnem_1.21.0               gtable_0.3.5             
##  [75] kernlab_0.9-33            assertthat_0.2.1         
##  [77] amap_0.8-19               cachem_1.1.0             
##  [79] xfun_0.47                 mime_0.12                
##  [81] rbibutils_2.2.16          S4Arrays_1.5.7           
##  [83] RcppEigen_0.3.4.0.2       tidygraph_1.3.1          
##  [85] survival_3.7-0            fastICA_1.2-5.1          
##  [87] statmod_1.5.0             TH.data_1.1-2            
##  [89] nlme_3.1-166              tsne_0.1-3.1             
##  [91] naturalsort_0.1.3         bit64_4.0.5              
##  [93] gmodels_2.19.1            filelock_1.0.3           
##  [95] bslib_0.8.0               colorspace_2.1-1         
##  [97] DBI_1.2.3                 nnet_7.3-19              
##  [99] mnormt_2.1.1              tidyselect_1.2.1         
## [101] bit_4.0.5                 compiler_4.4.1           
## [103] curl_5.2.2                rvest_1.0.4              
## [105] expm_1.0-0                xml2_1.3.6               
## [107] TFisher_0.2.0             ggdendro_0.2.0           
## [109] DelayedArray_0.31.11      shadowtext_0.1.4         
## [111] rtracklayer_1.65.0        harmonicmeanp_3.0.1      
## [113] sfsmisc_1.1-19            scales_1.3.0             
## [115] DEoptimR_1.1-3            RBGL_1.81.0              
## [117] rappdirs_0.3.3            apcluster_1.4.13         
## [119] digest_0.6.37             snowfall_1.84-6.3        
## [121] rmarkdown_2.28            XVector_0.45.0           
## [123] htmltools_0.5.8.1         pkgconfig_2.0.3          
## [125] highr_0.11                dbplyr_2.5.0             
## [127] fastmap_1.2.0             rlang_1.1.4              
## [129] UCSC.utils_1.1.0          farver_2.1.2             
## [131] jquerylib_0.1.4           zoo_1.8-12               
## [133] jsonlite_1.8.8            BiocParallel_1.39.0      
## [135] mclust_6.1.1              RCurl_1.98-1.16          
## [137] magrittr_2.0.3            modeltools_0.2-23        
## [139] GenomeInfoDbData_1.2.12   munsell_0.5.1            
## [141] Rcpp_1.0.13               viridis_0.6.5            
## [143] stringi_1.8.4             zlibbioc_1.51.1          
## [145] MASS_7.3-61               plyr_1.8.9               
## [147] AnnotationHub_3.13.3      org.Hs.eg.db_3.19.1      
## [149] flexmix_2.3-19            parallel_4.4.1           
## [151] ggrepel_0.9.5             Biostrings_2.73.1        
## [153] graphlayouts_1.1.1        splines_4.4.1            
## [155] multtest_2.61.0           hms_1.1.3                
## [157] locfit_1.5-9.10           qqconf_1.3.2             
## [159] igraph_2.0.3              fastcluster_1.2.6        
## [161] buildtools_1.0.0          reshape2_1.4.4           
## [163] BiocVersion_3.20.0        XML_3.99-0.17            
## [165] evaluate_0.24.0           metap_1.11               
## [167] pcalg_2.7-11              BiocManager_1.30.25      
## [169] tzdb_0.4.0                tweenr_2.0.3             
## [171] polyclip_1.10-7           clue_0.3-65              
## [173] BiocBaseUtils_1.7.3       ggforce_0.4.2            
## [175] restfulr_0.0.15           e1071_1.7-14             
## [177] viridisLite_0.4.2         class_7.3-22             
## [179] snow_0.4-4                ggm_2.5.1                
## [181] memoise_2.0.1             AnnotationDbi_1.67.0     
## [183] GenomicAlignments_1.41.0  ellipse_0.5.0            
## [185] 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.