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)
})
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sessionInfo()
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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_1.10.0 graph_1.80.0
## [3] cowplot_1.1.1 lubridate_1.9.3
## [5] forcats_1.0.0 stringr_1.5.0
## [7] dplyr_1.1.3 purrr_1.0.2
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## [13] TCGAutils_1.22.0 curatedTCGAData_1.23.7
## [15] MultiAssayExperiment_1.28.0 SummarizedExperiment_1.32.0
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## [23] MatrixGenerics_1.14.0 matrixStats_1.0.0
## [25] ggraph_2.1.0 ggplot2_3.4.4
## [27] BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 httr_1.4.7
## [3] GenomicDataCommons_1.26.0 prabclus_2.3-3
## [5] Rgraphviz_2.46.0 numDeriv_2016.8-1.1
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## [49] grid_4.3.1 blob_1.2.4
## [51] promises_1.2.1 gdata_3.0.0
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## [93] nlme_3.1-163 naturalsort_0.1.3
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## [97] gmodels_2.18.1.1 filelock_1.0.2
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## [101] DBI_1.1.3 nnet_7.3-19
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## [117] harmonicmeanp_3.0 sfsmisc_1.1-16
## [119] scales_1.2.1 DEoptimR_1.1-3
## [121] RBGL_1.78.0 rappdirs_0.3.3
## [123] snowfall_1.84-6.2 apcluster_1.4.11
## [125] digest_0.6.33 rmarkdown_2.25
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## [137] jsonlite_1.8.7 BiocParallel_1.36.0
## [139] mclust_6.0.0 RCurl_1.98-1.12
## [141] magrittr_2.0.3 modeltools_0.2-23
## [143] GenomeInfoDbData_1.2.11 munsell_0.5.0
## [145] Rcpp_1.0.11 viridis_0.6.4
## [147] stringi_1.7.12 zlibbioc_1.48.0
## [149] MASS_7.3-60 plyr_1.8.9
## [151] AnnotationHub_3.10.0 org.Hs.eg.db_3.18.0
## [153] flexmix_2.3-19 parallel_4.3.1
## [155] ggrepel_0.9.4 Biostrings_2.70.0
## [157] graphlayouts_1.0.1 splines_4.3.1
## [159] multtest_2.58.0 hms_1.1.3
## [161] locfit_1.5-9.8 qqconf_1.3.2
## [163] fastcluster_1.2.3 igraph_1.5.1
## [165] reshape2_1.4.4 biomaRt_2.58.0
## [167] BiocVersion_3.18.0 XML_3.99-0.14
## [169] evaluate_0.22 metap_1.9
## [171] pcalg_2.7-9 BiocManager_1.30.22
## [173] tzdb_0.4.0 tweenr_2.0.2
## [175] httpuv_1.6.12 polyclip_1.10-6
## [177] clue_0.3-65 BiocBaseUtils_1.4.0
## [179] ggforce_0.4.1 xtable_1.8-4
## [181] restfulr_0.0.15 e1071_1.7-13
## [183] later_1.3.1 viridisLite_0.4.2
## [185] class_7.3-22 snow_0.4-4
## [187] ggm_2.5 ellipse_0.5.0
## [189] memoise_2.0.1 AnnotationDbi_1.64.0
## [191] GenomicAlignments_1.38.0 cluster_2.1.4
## [193] timechange_0.2.0
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.