Overview of pathway network databases
2021-05-19
Contents
1 Introduction
1.1 Load required packages
Load the package with the library function.
library(tidyverse)
library(ggplot2)
library(dce)
set.seed(42)dce::df_pathway_statistics %>%
sample_n(10) %>%
arrange(desc(node_num)) %>%
knitr::kable()| database | pathway_id | pathway_name | node_num | edge_num |
|---|---|---|---|---|
| kegg | hsa:04151 | PI3K-Akt signaling pathway | 354 | 4552 |
| kegg | hsa:04371 | Apelin signaling pathway | 134 | 942 |
| kegg | hsa:04520 | Adherens junction | 68 | 170 |
| kegg | hsa:04970 | Salivary secretion | 48 | 96 |
| kegg | hsa:05321 | Inflammatory bowel disease | 48 | 81 |
| nci | pid_4166 | Beta2 integrin cell surface interactions | 29 | 140 |
| kegg | hsa:00563 | Glycosylphosphatidylinositol (GPI)-anchor biosynthesis | 23 | 116 |
| kegg | hsa:00900 | Terpenoid backbone biosynthesis | 21 | 69 |
| biocarta | pid_10459 | rna polymerase iii transcription | 7 | 42 |
| biocarta | pid_9732 | estrogen responsive protein efp controls cell cycle and breast tumors growth | 4 | 2 |
2 Pathway database overview
We provide access to the following topological pathway databases using graphite (Sales et al. 2012):
dce::df_pathway_statistics %>%
count(database, sort = TRUE, name = "pathway_number") %>%
knitr::kable()| database | pathway_number |
|---|---|
| kegg | 317 |
| biocarta | 247 |
| nci | 212 |
| panther | 94 |
| pharmgkb | 66 |
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(
kegg = c("Citrate cycle (TCA cycle)")
)
)## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columnslapply(pathways, function(x) {
plot_network(as(x$graph, "matrix"), visualize_edge_weights = FALSE) +
ggtitle(x$pathway_name)
})## [[1]]
4 Session information
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Hs.eg.db_3.13.0 AnnotationDbi_1.54.0
## [3] dce_1.0.0 graph_1.70.0
## [5] cowplot_1.1.1 forcats_0.5.1
## [7] stringr_1.4.0 dplyr_1.0.6
## [9] purrr_0.3.4 readr_1.4.0
## [11] tidyr_1.1.3 tibble_3.1.2
## [13] tidyverse_1.3.1 TCGAutils_1.12.0
## [15] curatedTCGAData_1.13.9 MultiAssayExperiment_1.18.0
## [17] SummarizedExperiment_1.22.0 Biobase_2.52.0
## [19] GenomicRanges_1.44.0 GenomeInfoDb_1.28.0
## [21] IRanges_2.26.0 S4Vectors_0.30.0
## [23] BiocGenerics_0.38.0 MatrixGenerics_1.4.0
## [25] matrixStats_0.58.0 ggraph_2.0.5
## [27] ggplot2_3.3.3 BiocStyle_2.20.0
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.3 rtracklayer_1.52.0
## [3] prabclus_2.3-2 bit64_4.0.5
## [5] knitr_1.33 multcomp_1.4-17
## [7] DelayedArray_0.18.0 data.table_1.14.0
## [9] wesanderson_0.3.6 KEGGREST_1.32.0
## [11] RCurl_1.98-1.3 generics_0.1.0
## [13] metap_1.4 GenomicFeatures_1.44.0
## [15] TH.data_1.0-10 RSQLite_2.2.7
## [17] proxy_0.4-25 CombinePValue_1.0
## [19] bit_4.0.4 mutoss_0.1-12
## [21] xml2_1.3.2 lubridate_1.7.10
## [23] httpuv_1.6.1 assertthat_0.2.1
## [25] viridis_0.6.1 amap_0.8-18
## [27] xfun_0.23 hms_1.1.0
## [29] jquerylib_0.1.4 evaluate_0.14
## [31] promises_1.2.0.1 DEoptimR_1.0-8
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## [49] biomaRt_2.48.0 vctrs_0.3.8
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## [119] highr_0.9 yaml_2.2.1
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## [141] later_1.2.0 httr_1.4.2
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## [161] tidygraph_1.2.0 corpcor_1.6.9
## [163] modeltools_0.2-23 R6_2.5.0
## [165] gmodels_2.18.1 TFisher_0.2.0
## [167] pillar_1.6.1 htmltools_0.5.1.1
## [169] mime_0.10 glue_1.4.2
## [171] fastmap_1.1.0 BiocParallel_1.26.0
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## [181] logger_0.2.0 numDeriv_2016.8-1.1
## [183] curl_4.3.1 gtools_3.8.2
## [185] magick_2.7.2 survival_3.2-11
## [187] limma_3.48.0 rmarkdown_2.8
## [189] fastICA_1.2-2 munsell_0.5.0
## [191] e1071_1.7-6 fastcluster_1.1.25
## [193] GenomeInfoDbData_1.2.6 reshape2_1.4.4
## [195] haven_2.4.1 gtable_0.3.0
## [197] rbibutils_2.1.1References
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.