Overview of pathway network databases
2022-07-17
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)
})## [[1]]
##
## [[2]]
4 Session information
sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-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] dce_1.4.99 graph_1.74.0
## [3] cowplot_1.1.1 forcats_0.5.1
## [5] stringr_1.4.0 dplyr_1.0.9
## [7] purrr_0.3.4 readr_2.1.2
## [9] tidyr_1.2.0 tibble_3.1.7
## [11] tidyverse_1.3.1 TCGAutils_1.16.0
## [13] curatedTCGAData_1.18.0 MultiAssayExperiment_1.22.0
## [15] SummarizedExperiment_1.26.1 Biobase_2.56.0
## [17] GenomicRanges_1.48.0 GenomeInfoDb_1.32.2
## [19] IRanges_2.30.0 S4Vectors_0.34.0
## [21] BiocGenerics_0.42.0 MatrixGenerics_1.8.1
## [23] matrixStats_0.62.0 ggraph_2.0.5
## [25] ggplot2_3.3.6 BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.3 rtracklayer_1.56.1
## [3] prabclus_2.3-2 bit64_4.0.5
## [5] knitr_1.39 multcomp_1.4-19
## [7] DelayedArray_0.22.0 data.table_1.14.2
## [9] wesanderson_0.3.6 KEGGREST_1.36.3
## [11] RCurl_1.98-1.7 generics_0.1.3
## [13] snow_0.4-4 metap_1.8
## [15] GenomicFeatures_1.48.3 TH.data_1.1-1
## [17] RSQLite_2.2.14 shadowtext_0.1.2
## [19] proxy_0.4-27 bit_4.0.4
## [21] tzdb_0.3.0 mutoss_0.1-12
## [23] xml2_1.3.3 lubridate_1.8.0
## [25] httpuv_1.6.5 assertthat_0.2.1
## [27] viridis_0.6.2 amap_0.8-18
## [29] xfun_0.31 hms_1.1.1
## [31] jquerylib_0.1.4 evaluate_0.15
## [33] promises_1.2.0.1 DEoptimR_1.0-11
## [35] fansi_1.0.3 restfulr_0.0.15
## [37] progress_1.2.2 dbplyr_2.2.1
## [39] readxl_1.4.0 Rgraphviz_2.40.0
## [41] igraph_1.3.3 DBI_1.1.3
## [43] apcluster_1.4.10 RcppArmadillo_0.11.2.0.0
## [45] ellipsis_0.3.2 backports_1.4.1
## [47] bookdown_0.27 permute_0.9-7
## [49] harmonicmeanp_3.0 biomaRt_2.52.0
## [51] vctrs_0.4.1 abind_1.4-5
## [53] Linnorm_2.20.0 cachem_1.0.6
## [55] RcppEigen_0.3.3.9.2 withr_2.5.0
## [57] sfsmisc_1.1-13 ggforce_0.3.3
## [59] robustbase_0.95-0 bdsmatrix_1.3-6
## [61] vegan_2.6-2 GenomicAlignments_1.32.0
## [63] pcalg_2.7-6 prettyunits_1.1.1
## [65] mclust_5.4.10 mnormt_2.1.0
## [67] cluster_2.1.3 ExperimentHub_2.4.0
## [69] GenomicDataCommons_1.20.1 crayon_1.5.1
## [71] ellipse_0.4.3 labeling_0.4.2
## [73] FMStable_0.1-4 edgeR_3.38.1
## [75] pkgconfig_2.0.3 qqconf_1.2.3
## [77] tweenr_1.0.2 nlme_3.1-158
## [79] ggm_2.5 nnet_7.3-17
## [81] rlang_1.0.4 diptest_0.76-0
## [83] lifecycle_1.0.1 sandwich_3.0-2
## [85] filelock_1.0.2 BiocFileCache_2.4.0
## [87] mathjaxr_1.6-0 modelr_0.1.8
## [89] AnnotationHub_3.4.0 cellranger_1.1.0
## [91] polyclip_1.10-0 Matrix_1.4-1
## [93] zoo_1.8-10 reprex_2.0.1
## [95] png_0.1-7 viridisLite_0.4.0
## [97] rjson_0.2.21 bitops_1.0-7
## [99] Biostrings_2.64.0 blob_1.2.3
## [101] scales_1.2.0 plyr_1.8.7
## [103] memoise_2.0.1 graphite_1.42.0
## [105] magrittr_2.0.3 gdata_2.18.0.1
## [107] zlibbioc_1.42.0 compiler_4.2.1
## [109] BiocIO_1.6.0 clue_0.3-61
## [111] plotrix_3.8-2 Rsamtools_2.12.0
## [113] cli_3.3.0 XVector_0.36.0
## [115] MASS_7.3-58 mgcv_1.8-40
## [117] tidyselect_1.1.2 stringi_1.7.8
## [119] highr_0.9 yaml_2.3.5
## [121] locfit_1.5-9.6 ggrepel_0.9.1
## [123] grid_4.2.1 sass_0.4.2
## [125] tools_4.2.1 parallel_4.2.1
## [127] rstudioapi_0.13 snowfall_1.84-6.2
## [129] gridExtra_2.3 farver_2.1.1
## [131] Rtsne_0.16 digest_0.6.29
## [133] BiocManager_1.30.18 flexclust_1.4-1
## [135] shiny_1.7.1 mnem_1.12.0
## [137] fpc_2.2-9 ppcor_1.1
## [139] Rcpp_1.0.9 broom_1.0.0
## [141] BiocVersion_3.15.2 later_1.3.0
## [143] org.Hs.eg.db_3.15.0 httr_1.4.3
## [145] ggdendro_0.1.23 AnnotationDbi_1.58.0
## [147] kernlab_0.9-31 naturalsort_0.1.3
## [149] Rdpack_2.3.1 colorspace_2.0-3
## [151] rvest_1.0.2 XML_3.99-0.10
## [153] fs_1.5.2 splines_4.2.1
## [155] RBGL_1.72.0 statmod_1.4.36
## [157] sn_2.0.2 expm_0.999-6
## [159] graphlayouts_0.8.0 multtest_2.52.0
## [161] flexmix_2.3-18 xtable_1.8-4
## [163] jsonlite_1.8.0 tidygraph_1.2.1
## [165] corpcor_1.6.10 modeltools_0.2-23
## [167] R6_2.5.1 gmodels_2.18.1.1
## [169] TFisher_0.2.0 pillar_1.7.0
## [171] htmltools_0.5.2 mime_0.12
## [173] glue_1.6.2 fastmap_1.1.0
## [175] BiocParallel_1.30.3 class_7.3-20
## [177] interactiveDisplayBase_1.34.0 codetools_0.2-18
## [179] tsne_0.1-3.1 mvtnorm_1.1-3
## [181] utf8_1.2.2 lattice_0.20-45
## [183] bslib_0.4.0 logger_0.2.2
## [185] numDeriv_2016.8-1.1 curl_4.3.2
## [187] gtools_3.9.3 magick_2.7.3
## [189] survival_3.3-1 limma_3.52.2
## [191] rmarkdown_2.14 fastICA_1.2-3
## [193] munsell_0.5.0 e1071_1.7-11
## [195] fastcluster_1.2.3 GenomeInfoDbData_1.2.8
## [197] reshape2_1.4.4 haven_2.5.0
## [199] gtable_0.3.0 rbibutils_2.2.8References
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.