Load the package with the library function.
library(tidyverse)
library(ggplot2)
library(dce)
set.seed(42)
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()
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]]
sessionInfo()
## R version 4.2.0 RC (2022-04-21 r82226)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-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_1.5.1 graph_1.75.0
## [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_1.17.0
## [13] curatedTCGAData_1.19.0 MultiAssayExperiment_1.23.0
## [15] SummarizedExperiment_1.27.1 Biobase_2.57.0
## [17] GenomicRanges_1.49.0 GenomeInfoDb_1.33.3
## [19] IRanges_2.31.0 S4Vectors_0.35.0
## [21] BiocGenerics_0.43.0 MatrixGenerics_1.9.0
## [23] matrixStats_0.62.0 ggraph_2.0.5
## [25] ggplot2_3.3.6 BiocStyle_2.25.0
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.3 rtracklayer_1.57.0
## [3] prabclus_2.3-2 bit64_4.0.5
## [5] knitr_1.39 multcomp_1.4-19
## [7] DelayedArray_0.23.0 data.table_1.14.2
## [9] wesanderson_0.3.6 KEGGREST_1.37.0
## [11] RCurl_1.98-1.6 generics_0.1.2
## [13] metap_1.8 GenomicFeatures_1.49.1
## [15] TH.data_1.1-1 RSQLite_2.2.14
## [17] shadowtext_0.1.2 proxy_0.4-26
## [19] bit_4.0.4 tzdb_0.3.0
## [21] mutoss_0.1-12 xml2_1.3.3
## [23] lubridate_1.8.0 httpuv_1.6.5
## [25] assertthat_0.2.1 viridis_0.6.2
## [27] amap_0.8-18 xfun_0.30
## [29] hms_1.1.1 jquerylib_0.1.4
## [31] evaluate_0.15 promises_1.2.0.1
## [33] DEoptimR_1.0-11 fansi_1.0.3
## [35] restfulr_0.0.13 progress_1.2.2
## [37] dbplyr_2.1.1 readxl_1.4.0
## [39] Rgraphviz_2.41.0 igraph_1.3.1
## [41] DBI_1.1.2 tmvnsim_1.0-2
## [43] apcluster_1.4.9 RcppArmadillo_0.11.0.0.0
## [45] ellipsis_0.3.2 backports_1.4.1
## [47] bookdown_0.26 permute_0.9-7
## [49] harmonicmeanp_3.0 biomaRt_2.53.1
## [51] vctrs_0.4.1 abind_1.4-5
## [53] Linnorm_2.21.0 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_1.3-4
## [61] vegan_2.6-2 GenomicAlignments_1.33.0
## [63] pcalg_2.7-6 prettyunits_1.1.1
## [65] mclust_5.4.9 mnormt_2.0.2
## [67] cluster_2.1.3 ExperimentHub_2.5.0
## [69] GenomicDataCommons_1.21.1 crayon_1.5.1
## [71] ellipse_0.4.2 labeling_0.4.2
## [73] FMStable_0.1-2 edgeR_3.39.1
## [75] pkgconfig_2.0.3 qqconf_1.2.3
## [77] tweenr_1.0.2 nlme_3.1-157
## [79] ggm_2.5 nnet_7.3-17
## [81] rlang_1.0.2 diptest_0.76-0
## [83] lifecycle_1.0.1 sandwich_3.0-1
## [85] filelock_1.0.2 BiocFileCache_2.5.0
## [87] mathjaxr_1.6-0 modelr_0.1.8
## [89] AnnotationHub_3.5.0 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_2.65.0 blob_1.2.3
## [101] scales_1.2.0 plyr_1.8.7
## [103] memoise_2.0.1 graphite_1.43.0
## [105] magrittr_2.0.3 gdata_2.18.0
## [107] zlibbioc_1.43.0 compiler_4.2.0
## [109] BiocIO_1.7.1 clue_0.3-60
## [111] plotrix_3.8-2 Rsamtools_2.13.1
## [113] cli_3.3.0 XVector_0.37.0
## [115] MASS_7.3-57 mgcv_1.8-40
## [117] tidyselect_1.1.2 stringi_1.7.6
## [119] highr_0.9 yaml_2.3.5
## [121] locfit_1.5-9.5 ggrepel_0.9.1
## [123] grid_4.2.0 sass_0.4.1
## [125] tools_4.2.0 parallel_4.2.0
## [127] rstudioapi_0.13 snowfall_1.84-6.1
## [129] gridExtra_2.3 farver_2.1.0
## [131] Rtsne_0.16 digest_0.6.29
## [133] BiocManager_1.30.17 flexclust_1.4-1
## [135] shiny_1.7.1 mnem_1.13.0
## [137] fpc_2.2-9 ppcor_1.1
## [139] Rcpp_1.0.8.3 broom_0.8.0
## [141] BiocVersion_3.16.0 later_1.3.0
## [143] org.Hs.eg.db_3.15.0 httr_1.4.3
## [145] ggdendro_0.1.23 AnnotationDbi_1.59.0
## [147] kernlab_0.9-30 naturalsort_0.1.3
## [149] Rdpack_2.3 colorspace_2.0-3
## [151] rvest_1.0.2 XML_3.99-0.9
## [153] fs_1.5.2 splines_4.2.0
## [155] RBGL_1.73.0 statmod_1.4.36
## [157] sn_2.0.2 expm_0.999-6
## [159] graphlayouts_0.8.0 multtest_2.53.0
## [161] flexmix_2.3-17 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
## [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_1.31.3 class_7.3-20
## [177] interactiveDisplayBase_1.35.0 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_0.3.1 logger_0.2.2
## [185] numDeriv_2016.8-1.1 curl_4.3.2
## [187] gtools_3.9.2 magick_2.7.3
## [189] survival_3.3-1 limma_3.53.0
## [191] rmarkdown_2.14 fastICA_1.2-3
## [193] munsell_0.5.0 e1071_1.7-9
## [195] fastcluster_1.2.3 GenomeInfoDbData_1.2.8
## [197] reshape2_1.4.4 haven_2.5.0
## [199] gtable_0.3.0 rbibutils_2.2.8
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.