Search results for anything cardiovascular-related, either at the level of phenotype or disease.
res <- MultiEWCE::load_example_results()## Registered S3 method overwritten by 'ggtree':
## method from
## fortify.igraph ggnetworkres <- HPOExplorer:::annotate_phenos(phenos = res,
add_disease_data = TRUE,
add_hoverboxes = FALSE)## Getting absolute ontology level for 2,831 HPO IDs.## ℹ All local files already up-to-date!## Adding term definitions.## ℹ All local files already up-to-date!## Adding level-3 ancestor to each HPO ID.## ℹ All local files already up-to-date!## Annotating phenos with n_diseases## ℹ All local files already up-to-date!
## ℹ All local files already up-to-date!
## ℹ All local files already up-to-date!## Annotating phenos with Onset.## Annotating phenos with Disease## ℹ All local files already up-to-date!## Annotating phenos with AgeOfDeath.## Annotating phenos with Tiers.## Annotating phenos with Modifiers## Annotating phenotype frequencies.annot <- HPOExplorer::load_phenotype_to_genes(3)## ℹ All local files already up-to-date!genes <- HPOExplorer::load_phenotype_to_genes(1)## ℹ All local files already up-to-date!First, we do some initial filtering of the results to only get significant associations.
res <- res[q<0.05 & symptom.pval<0.05,]
query <- c("cardio","heart")
res <- res[grepl(paste(query,collapse = "|"),definition,ignore.case = TRUE) |
grepl(paste(query,collapse = "|"),DiseaseName,ignore.case = TRUE) |
grepl(paste(query,collapse = "|"),ancestor_name,ignore.case = TRUE),]
message(length(unique(res$DatabaseID))," diseases.")## 843 diseases.message(length(unique(res$HPO_ID))," phenotypes.")## 252 phenotypes.message(length(unique(res$HPO_ID.LinkID))," symptoms")## 5454 symptomsmessage(length(unique(res$G))," genes")## 0 genestargets <- MultiEWCE::prioritise_targets(results = res,
keep_tiers = NULL,
severity_threshold = NULL,
keep_onsets = NULL,
keep_deaths = NULL,
keep_celltypes = NULL,
symptom_p_threshold = 0.05,
keep_evidence = 6)## Prioritising gene targets.## Adding HPO IDs.## ℹ All local files already up-to-date!## Translating all phenotypes to HPO IDs.## ℹ All local files already up-to-date!## + Returning a vector of phenotypes (same order as input).## ℹ All local files already up-to-date!## Adding term definitions.## ℹ All local files already up-to-date!## Adding disease metadata: Definitions, Preferred.Label## Importing Orphanet metadata.## Importing OMIM metadata.## 830 / 1260 (65.87%) DiseaseName missing.## 448 / 843 (53.14%) Definitions missing.## Annotating phenos with MONDO metadata.## ℹ All local files already up-to-date!## 1 / 843 (0.12%) MONDO_ID missing.## 774 / 843 (91.81%) MONDO_name missing.## 832 / 843 (98.7%) MONDO_definition missing.## 441 / 843 (52.31%) Definitions missing.## Prioritised targets: step='start'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Filtering @ q-value <= 0.05## Prioritised targets: step='q_threshold'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Filtering @ fold-change >= 1## Prioritised targets: step='fold_threshold'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Prioritised targets: step='symptom_p_threshold'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Prioritised targets: step='symptom_intersection_size_threshold'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Annotating phenos with AgeOfDeath.## Prioritised targets: step='keep_deaths'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Adding level-3 ancestor to each HPO ID.## Removing remove descendants of: 'Clinical course'## Translating all phenotypes to HPO IDs.## + Returning a dictionary of phenotypes (different order as input).## Prioritised targets: step='remove_descendants'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Prioritised targets: step='keep_ont_levels'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Prioritised targets: step='keep_onsets'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Prioritised targets: step='keep_tiers'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Annotating phenos with Modifiers## Prioritised targets: step='severity_threshold'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Prioritised targets: step='severity_threshold_max'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Prioritised targets: step='pheno_ndiseases_threshold'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Prioritised targets: step='pheno_frequency_threshold'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Prioritised targets: step='keep_celltypes'
## - Rows: 7,810
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39## Converting phenos to GRanges.## Loading required namespace: ensembldb## Gathering metadata for 1199 unique genes.## Loading required namespace: EnsDb.Hsapiens.v75## Prioritised targets: step='symptom_gene_overlap'
## - Rows: 22,626
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39
## - Genes: 1,199
## Prioritised targets: step='symptom_gene_overlap'
## - Rows: 22,626
## - Phenotypes: 252
## - Diseases: 843
## - Cell types: 39
## - Genes: 1,199## Filtering by gene-disease association evidence.## Annotating gene-disease associations with Evidence score## Gathering data from GenCC.## Importing cached file.## Prioritised targets: step='keep_evidence'
## - Rows: 248
## - Phenotypes: 56
## - Diseases: 33
## - Cell types: 14
## - Genes: 29## Filtering by gene size.## 29 / 29 genes kept.## Prioritised targets: step='gene_size'
## - Rows: 248
## - Phenotypes: 56
## - Diseases: 33
## - Cell types: 14
## - Genes: 29## Prioritised targets: step='keep_biotypes'
## - Rows: 248
## - Phenotypes: 56
## - Diseases: 33
## - Cell types: 14
## - Genes: 29## Annotating gene frequencies.## ℹ All local files already up-to-date!## Prioritised targets: step='gene_frequency_threshold'
## - Rows: 265
## - Phenotypes: 56
## - Diseases: 33
## - Cell types: 14
## - Genes: 29## Prioritised targets: step='keep_specificity_quantiles'
## - Rows: 265
## - Phenotypes: 56
## - Diseases: 33
## - Cell types: 14
## - Genes: 29## Prioritised targets: step='keep_mean_exp_quantiles'
## - Rows: 265
## - Phenotypes: 56
## - Diseases: 33
## - Cell types: 14
## - Genes: 29## Sorting rows.## Prioritised targets: step='end'
## - Rows: 265
## - Phenotypes: 56
## - Diseases: 33
## - Cell types: 14
## - Genes: 29vn <- MultiEWCE::prioritise_targets_network(top_targets = targets$top_targets,
submain = "Cardiovascular targets network",
save_path = here::here("networks/cardiovascular_network.html"))## Loading required namespace: pals## Loading required namespace: igraph## Loading required namespace: tidygraph## Creating network.## Loading required namespace: visNetwork## Creating plot.## Saving plot ==> /Users/schilder/Desktop/ewce/RareDiseasePrioritisation/networks/cardiovascular_network.htmlvn <- MultiEWCE::prioritise_targets_network(top_targets = targets$top_targets,
submain = "Cardiovascular targets network",
layout = "layout_with_sugiyama",
save_path = here::here("networks/cardiovascular_hierarchical_network.html"))## Creating network.## Creating plot.## Saving plot ==> /Users/schilder/Desktop/ewce/RareDiseasePrioritisation/networks/cardiovascular_hierarchical_network.htmlsessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.3 R.utils_2.12.2
## [3] tidyselect_1.2.0 RSQLite_2.3.1
## [5] AnnotationDbi_1.60.2 htmlwidgets_1.6.2
## [7] grid_4.2.1 BiocParallel_1.32.6
## [9] munsell_0.5.0 codetools_0.2-19
## [11] colorspace_2.1-0 Biobase_2.58.0
## [13] filelock_1.0.2 knitr_1.42
## [15] rstudioapi_0.14 orthogene_1.5.3
## [17] stats4_4.2.1 SingleCellExperiment_1.20.1
## [19] ggsignif_0.6.4 gitcreds_0.1.2
## [21] MatrixGenerics_1.10.0 httr2_0.2.2
## [23] GenomeInfoDbData_1.2.9 bit64_4.0.5
## [25] rprojroot_2.0.3 coda_0.19-4
## [27] vctrs_0.6.2 treeio_1.23.1
## [29] generics_0.1.3 xfun_0.39
## [31] timechange_0.2.0 BiocFileCache_2.6.1
## [33] R6_2.5.1 GenomeInfoDb_1.34.9
## [35] pals_1.7 AnnotationFilter_1.22.0
## [37] bitops_1.0-7 cachem_1.0.8
## [39] gridGraphics_0.5-1 DelayedArray_0.24.0
## [41] promises_1.2.0.1 BiocIO_1.8.0
## [43] scales_1.2.1 gtable_0.3.3
## [45] tidygraph_1.2.3 ontologyPlot_1.6
## [47] ensembldb_2.22.0 rlang_1.1.1
## [49] rtracklayer_1.58.0 rstatix_0.7.2
## [51] lazyeval_0.2.2 dichromat_2.0-0.1
## [53] broom_1.0.4 BiocManager_1.30.20
## [55] yaml_2.3.7 reshape2_1.4.4
## [57] HPOExplorer_0.99.9 abind_1.4-5
## [59] GenomicFeatures_1.50.4 ggnetwork_0.5.12
## [61] backports_1.4.1 httpuv_1.6.9
## [63] tools_4.2.1 ggplotify_0.1.0
## [65] statnet.common_4.8.0 ggplot2_3.4.2
## [67] ellipsis_0.3.2 gplots_3.1.3
## [69] jquerylib_0.1.4 paintmap_1.0
## [71] RColorBrewer_1.1-3 BiocGenerics_0.44.0
## [73] Rcpp_1.0.10 plyr_1.8.8
## [75] visNetwork_2.1.2 progress_1.2.2
## [77] zlibbioc_1.44.0 purrr_1.0.1
## [79] RCurl_1.98-1.12 prettyunits_1.1.1
## [81] ggpubr_0.6.0 S4Vectors_0.36.2
## [83] SummarizedExperiment_1.28.0 grr_0.9.5
## [85] here_1.0.1 fs_1.6.2
## [87] magrittr_2.0.3 data.table_1.14.8
## [89] gh_1.4.0 ProtGenerics_1.30.0
## [91] matrixStats_0.63.0 hms_1.1.3
## [93] patchwork_1.1.2 mime_0.12
## [95] evaluate_0.20 xtable_1.8-4
## [97] XML_3.99-0.14 EWCE_1.9.0
## [99] IRanges_2.32.0 MultiEWCE_0.1.4
## [101] compiler_4.2.1 biomaRt_2.54.1
## [103] maps_3.4.1 tibble_3.2.1
## [105] KernSmooth_2.23-21 crayon_1.5.2
## [107] R.oo_1.25.0 htmltools_0.5.5
## [109] ggfun_0.0.9 later_1.3.1
## [111] tidyr_1.3.0 aplot_0.1.10
## [113] lubridate_1.9.2 DBI_1.1.3
## [115] ExperimentHub_2.6.0 gprofiler2_0.2.1
## [117] dbplyr_2.3.2 rappdirs_0.3.3
## [119] EnsDb.Hsapiens.v75_2.99.0 babelgene_22.9
## [121] Matrix_1.5-4 car_3.1-2
## [123] piggyback_0.1.4 cli_3.6.1
## [125] R.methodsS3_1.8.2 igraph_1.4.2
## [127] parallel_4.2.1 GenomicRanges_1.50.2
## [129] pkgconfig_2.0.3 GenomicAlignments_1.34.1
## [131] plotly_4.10.1 xml2_1.3.4
## [133] ggtree_3.6.2 bslib_0.4.2
## [135] XVector_0.38.0 GeneOverlap_1.34.0
## [137] yulab.utils_0.0.6 stringr_1.5.0
## [139] digest_0.6.31 graph_1.76.0
## [141] Biostrings_2.66.0 rmarkdown_2.21
## [143] HGNChelper_0.8.1 tidytree_0.4.2
## [145] restfulr_0.0.15 curl_5.0.0
## [147] shiny_1.7.4 Rsamtools_2.14.0
## [149] gtools_3.9.4 rjson_0.2.21
## [151] lifecycle_1.0.3 nlme_3.1-162
## [153] jsonlite_1.8.4 carData_3.0-5
## [155] network_1.18.1 mapproj_1.2.11
## [157] viridisLite_0.4.2 limma_3.54.2
## [159] fansi_1.0.4 pillar_1.9.0
## [161] ontologyIndex_2.10 lattice_0.21-8
## [163] homologene_1.4.68.19.3.27 KEGGREST_1.38.0
## [165] fastmap_1.1.1 httr_1.4.5
## [167] interactiveDisplayBase_1.36.0 glue_1.6.2
## [169] RNOmni_1.0.1 png_0.1-8
## [171] ewceData_1.7.1 BiocVersion_3.16.0
## [173] bit_4.0.5 Rgraphviz_2.42.0
## [175] stringi_1.7.12 sass_0.4.6
## [177] blob_1.2.4 AnnotationHub_3.6.0
## [179] caTools_1.18.2 memoise_2.0.1
## [181] dplyr_1.1.2 ape_5.7-1