Overview of pathway network databases (original) (raw)
Contents
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()Let’s see how many pathways each database provides:
dce::df_pathway_statistics %>%
count(database, sort = TRUE, name = "pathway_number") %>%
knitr::kable()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)
})## [[1]]##
## [[2]]Session information
sessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## 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_1.25.1 curatedTCGAData_1.27.1
## [15] MultiAssayExperiment_1.31.5 SummarizedExperiment_1.35.5
## [17] Biobase_2.65.1 GenomicRanges_1.57.2
## [19] GenomeInfoDb_1.41.2 IRanges_2.39.2
## [21] S4Vectors_0.43.2 BiocGenerics_0.51.3
## [23] MatrixGenerics_1.17.1 matrixStats_1.4.1
## [25] ggraph_2.2.1 ggplot2_3.5.1
## [27] BiocStyle_2.33.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 httr_1.4.7
## [3] GenomicDataCommons_1.29.7 prabclus_2.3-4
## [5] Rgraphviz_2.49.1 numDeriv_2016.8-1.1
## [7] tools_4.5.0 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_3.1-1 labeling_0.4.3
## [21] sass_0.4.9 diptest_0.77-1
## [23] mvtnorm_1.3-1 robustbase_0.99-4-1
## [25] proxy_0.4-27 Rsamtools_2.21.2
## [27] FMStable_0.1-4 Linnorm_2.29.0
## [29] plotrix_3.8-4 limma_3.61.12
## [31] RSQLite_2.3.7 generics_0.1.3
## [33] BiocIO_1.15.2 gtools_3.9.5
## [35] wesanderson_0.3.7 Matrix_1.7-1
## [37] fansi_1.0.6 logger_0.4.0
## [39] abind_1.4-8 lifecycle_1.0.4
## [41] multcomp_1.4-26 yaml_2.3.10
## [43] edgeR_4.3.21 mathjaxr_1.6-0
## [45] SparseArray_1.5.45 BiocFileCache_2.13.2
## [47] Rtsne_0.17 grid_4.5.0
## [49] blob_1.2.4 promises_1.3.0
## [51] gdata_3.0.1 ppcor_1.1
## [53] bdsmatrix_1.3-7 ExperimentHub_2.13.1
## [55] crayon_1.5.3 lattice_0.22-6
## [57] GenomicFeatures_1.57.1 chromote_0.3.1
## [59] KEGGREST_1.45.1 magick_2.8.5
## [61] pillar_1.9.0 knitr_1.48
## [63] rjson_0.2.23 fpc_2.2-13
## [65] corpcor_1.6.10 codetools_0.2-20
## [67] mutoss_0.1-13 glue_1.8.0
## [69] RcppArmadillo_14.0.2-1 data.table_1.16.2
## [71] vctrs_0.6.5 png_0.1-8
## [73] Rdpack_2.6.1 mnem_1.21.0
## [75] gtable_0.3.6 kernlab_0.9-33
## [77] assertthat_0.2.1 amap_0.8-20
## [79] cachem_1.1.0 xfun_0.48
## [81] mime_0.12 rbibutils_2.3
## [83] S4Arrays_1.5.11 RcppEigen_0.3.4.0.2
## [85] tidygraph_1.3.1 survival_3.7-0
## [87] tinytex_0.53 fastICA_1.2-5.1
## [89] statmod_1.5.0 TH.data_1.1-2
## [91] tsne_0.1-3.1 nlme_3.1-166
## [93] naturalsort_0.1.3 bit64_4.5.2
## [95] gmodels_2.19.1 filelock_1.0.3
## [97] bslib_0.8.0 colorspace_2.1-1
## [99] DBI_1.2.3 nnet_7.3-19
## [101] mnormt_2.1.1 tidyselect_1.2.1
## [103] processx_3.8.4 bit_4.5.0
## [105] compiler_4.5.0 curl_5.2.3
## [107] rvest_1.0.4 expm_1.0-0
## [109] xml2_1.3.6 TFisher_0.2.0
## [111] ggdendro_0.2.0 DelayedArray_0.31.14
## [113] shadowtext_0.1.4 bookdown_0.41
## [115] rtracklayer_1.65.0 harmonicmeanp_3.0.1
## [117] sfsmisc_1.1-19 scales_1.3.0
## [119] DEoptimR_1.1-3 RBGL_1.81.0
## [121] rappdirs_0.3.3 apcluster_1.4.13
## [123] digest_0.6.37 snowfall_1.84-6.3
## [125] rmarkdown_2.28 XVector_0.45.0
## [127] htmltools_0.5.8.1 pkgconfig_2.0.3
## [129] highr_0.11 dbplyr_2.5.0
## [131] fastmap_1.2.0 rlang_1.1.4
## [133] UCSC.utils_1.1.0 farver_2.1.2
## [135] jquerylib_0.1.4 zoo_1.8-12
## [137] jsonlite_1.8.9 BiocParallel_1.39.0
## [139] mclust_6.1.1 RCurl_1.98-1.16
## [141] magrittr_2.0.3 modeltools_0.2-23
## [143] GenomeInfoDbData_1.2.13 munsell_0.5.1
## [145] Rcpp_1.0.13 viridis_0.6.5
## [147] stringi_1.8.4 zlibbioc_1.51.2
## [149] MASS_7.3-61 plyr_1.8.9
## [151] AnnotationHub_3.13.3 org.Hs.eg.db_3.20.0
## [153] flexmix_2.3-19 parallel_4.5.0
## [155] ggrepel_0.9.6 Biostrings_2.73.2
## [157] graphlayouts_1.2.0 splines_4.5.0
## [159] multtest_2.61.0 hms_1.1.3
## [161] locfit_1.5-9.10 qqconf_1.3.2
## [163] ps_1.8.0 igraph_2.1.1
## [165] fastcluster_1.2.6 reshape2_1.4.4
## [167] BiocVersion_3.20.0 XML_3.99-0.17
## [169] evaluate_1.0.1 metap_1.11
## [171] pcalg_2.7-12 BiocManager_1.30.25
## [173] tzdb_0.4.0 tweenr_2.0.3
## [175] polyclip_1.10-7 clue_0.3-65
## [177] BiocBaseUtils_1.7.3 ggforce_0.4.2
## [179] restfulr_0.0.15 e1071_1.7-16
## [181] later_1.3.2 viridisLite_0.4.2
## [183] class_7.3-22 snow_0.4-4
## [185] websocket_1.4.2 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_0.3.0References
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.