From 6ea11e25a22fb509ee9ac624efc1fad7c668ba49 Mon Sep 17 00:00:00 2001 From: Frank Koopmans Date: Tue, 30 Apr 2024 17:35:13 +0200 Subject: [PATCH] version 1.0 --- .Rbuildignore | 4 + .gitignore | 4 + DESCRIPTION | 43 +- LICENSE.note | 2 +- NAMESPACE | 15 +- NEWS.md | 5 + R/data.R | 11 +- R/download_data.R | 159 +++++ R/filter_genesets.R | 6 +- R/gene_idmapping.R | 37 +- R/{goat.R => goat-package.R} | 16 +- R/goat_nulldistributions.R | 24 +- R/load_genesets_gmtfile.R | 10 +- R/load_genesets_go.R | 62 +- R/load_genesets_syngo.R | 13 +- R/partition_genes.R | 34 +- R/plot_volcano.R | 119 ++++ R/save_genesets.R | 22 +- R/score_genesets.R | 115 +++- R/simplify_ontology.R | 48 +- R/simplify_similarity.R | 154 +++-- R/test_genesets.R | 64 +- R/test_genesets_fisherexact.R | 27 +- R/test_genesets_goat.R | 71 +- R/test_genesets_gsea.R | 22 +- R/test_genesets_hypergeometric.R | 1 + R/test_genesets_idea.R | 173 ----- R/util_stats.R | 27 +- R/utils.R | 49 +- R/validate_genelist.R | 1 + R/validate_genesets.R | 5 +- README.md | 268 ++++++-- .../compare_idea_fgsea_standalone-script.R | 92 ++- analyses/computation_time.R | 42 +- analyses/null_simulations_by_geneset_size.R | 328 ++++++--- analyses/prepare_realworld_datasets.R | 77 ++- analyses/realworld_data.R | 521 ++++++++------ analyses/roc_simulations.R | 643 ++++++++++++++++++ analyses/test_genesets_idea.R | 329 +++++++++ data/goat_example_datasets.rda | Bin 1017788 -> 0 bytes inst/CITATION | 8 + man/build_treemap.Rd | 28 - man/check_dependency.Rd | 16 - man/cluster_genesets.Rd | 3 + man/darken_color.Rd | 19 + man/dot-onUnload.Rd | 14 - man/download_genesets_goatrepo.Rd | 59 ++ man/download_goat_manuscript_data.Rd | 33 + man/dsnorm.Rd | 20 - man/edgelist_as_nested.Rd | 20 - man/edgelist_find_children_recursive.Rd | 18 - ...edgelist_find_colored_parents_recursive.Rd | 20 - man/edgelist_find_root.Rd | 16 - man/edgelist_from_ontology.Rd | 17 - man/fit_snorm_xi.Rd | 24 - man/geneset_similarity_matrix.Rd | 31 - man/genesets_as_matrix.Rd | 14 - man/gg_color_hue.Rd | 3 + man/go_find_parents.Rd | 16 - man/go_gene2go.Rd | 5 +- man/go_obo.Rd | 5 + man/goat.Rd | 21 +- man/goat_apply_precomputed_null.Rd | 19 - man/goat_example_datasets.Rd | 22 - man/goat_logo.Rd | 6 +- man/goat_nulldistribution_function.Rd | 28 - ...oat_nulldistribution_function_generator.Rd | 40 -- man/goat_nulldistribution_independent.Rd | 23 - man/goat_nulldistributions.Rd | 4 +- man/goat_print_version.Rd | 6 +- man/goat_testgene_score_matrix.Rd | 26 - man/goat_version.Rd | 3 + ...gnc_lookuptable.Rd => hgnc_idmap_table.Rd} | 8 +- man/lighten_color.Rd | 3 + man/load_genesets_gmtfile.Rd | 9 +- man/load_genesets_go_bioconductor.Rd | 5 - man/load_genesets_go_fromfile.Rd | 31 +- man/load_genesets_syngo.Rd | 10 +- man/methods_text.Rd | 18 - man/minlog10_fixzero.Rd | 7 +- man/nested_aggregate_child_stats.Rd | 16 - man/nested_find_equivalent_child_recursive.Rd | 14 - man/nested_find_level1_children.Rd | 18 - man/ontology_data_structures.Rd | 18 - man/padjust_genesets.Rd | 3 + man/parameters_prettyprint_length1.Rd | 20 - man/partition_genes.Rd | 36 +- man/plot_heatmap.Rd | 47 +- man/plot_lollipop.Rd | 66 +- man/plot_network.Rd | 8 +- man/plot_volcano.Rd | 84 +++ man/psnorm_upper_tail.Rd | 20 - man/rankscore.Rd | 7 +- man/rankscore_fixed_order.Rd | 3 + man/reduce_genesets.Rd | 3 + man/save_genesets.Rd | 3 + man/score_geneset_directionality.Rd | 57 ++ man/score_geneset_oddsratio.Rd | 10 +- man/string_trunc_right.Rd | 3 + man/symbol_to_entrez.Rd | 21 +- man/test_genesets.Rd | 29 + man/test_genesets_fisherexact.Rd | 29 +- man/test_genesets_goat.Rd | 20 - man/test_genesets_goat_bootstrap.Rd | 3 + man/test_genesets_goat_fitfunction.Rd | 3 + man/test_genesets_goat_precomputed.Rd | 55 ++ man/test_genesets_gsea.Rd | 10 +- man/test_genesets_hypergeometric.Rd | 3 + man/test_genesets_idea.Rd | 28 - man/treemap_data.Rd | 10 +- man/treemap_plot.Rd | 32 +- man/validate_genelist.Rd | 14 - man/validate_genesets.Rd | 18 - man/validate_goat_genelist.Rd | 16 - man/validate_goat_genesets.Rd | 16 - man/validate_goat_niter.Rd | 14 - man/validate_goat_scoretype.Rd | 14 - man/validate_goat_verbose.Rd | 14 - src/Makevars | 1 - src/Makevars.win | 1 - src/RcppExports.o | Bin 122662 -> 114548 bytes src/goat.dll | Bin 1186304 -> 1184256 bytes src/rcpp_gene_to_geneset_scores.cpp | 4 +- src/rcpp_gene_to_geneset_scores.o | Bin 54859 -> 54683 bytes src/rcpp_null_distributions.cpp | 12 +- src/rcpp_null_distributions.o | Bin 75141 -> 75061 bytes 126 files changed, 3407 insertions(+), 1717 deletions(-) create mode 100644 NEWS.md create mode 100644 R/download_data.R rename R/{goat.R => goat-package.R} (50%) create mode 100644 R/plot_volcano.R delete mode 100644 R/test_genesets_idea.R create mode 100644 analyses/roc_simulations.R create mode 100644 analyses/test_genesets_idea.R delete mode 100644 data/goat_example_datasets.rda create mode 100644 inst/CITATION delete mode 100644 man/build_treemap.Rd delete mode 100644 man/check_dependency.Rd create mode 100644 man/darken_color.Rd delete mode 100644 man/dot-onUnload.Rd create mode 100644 man/download_genesets_goatrepo.Rd create mode 100644 man/download_goat_manuscript_data.Rd delete mode 100644 man/dsnorm.Rd delete mode 100644 man/edgelist_as_nested.Rd delete mode 100644 man/edgelist_find_children_recursive.Rd delete mode 100644 man/edgelist_find_colored_parents_recursive.Rd delete mode 100644 man/edgelist_find_root.Rd delete mode 100644 man/edgelist_from_ontology.Rd delete mode 100644 man/fit_snorm_xi.Rd delete mode 100644 man/geneset_similarity_matrix.Rd delete mode 100644 man/genesets_as_matrix.Rd delete mode 100644 man/go_find_parents.Rd delete mode 100644 man/goat_apply_precomputed_null.Rd delete mode 100644 man/goat_example_datasets.Rd delete mode 100644 man/goat_nulldistribution_function.Rd delete mode 100644 man/goat_nulldistribution_function_generator.Rd delete mode 100644 man/goat_nulldistribution_independent.Rd delete mode 100644 man/goat_testgene_score_matrix.Rd rename man/{hgnc_lookuptable.Rd => hgnc_idmap_table.Rd} (81%) delete mode 100644 man/methods_text.Rd delete mode 100644 man/nested_aggregate_child_stats.Rd delete mode 100644 man/nested_find_equivalent_child_recursive.Rd delete mode 100644 man/nested_find_level1_children.Rd delete mode 100644 man/ontology_data_structures.Rd delete mode 100644 man/parameters_prettyprint_length1.Rd create mode 100644 man/plot_volcano.Rd delete mode 100644 man/psnorm_upper_tail.Rd create mode 100644 man/score_geneset_directionality.Rd delete mode 100644 man/test_genesets_goat.Rd delete mode 100644 man/test_genesets_idea.Rd delete mode 100644 man/validate_genelist.Rd delete mode 100644 man/validate_genesets.Rd delete mode 100644 man/validate_goat_genelist.Rd delete mode 100644 man/validate_goat_genesets.Rd delete mode 100644 man/validate_goat_niter.Rd delete mode 100644 man/validate_goat_scoretype.Rd delete mode 100644 man/validate_goat_verbose.Rd diff --git a/.Rbuildignore b/.Rbuildignore index 93f3e27..043fb2d 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -4,3 +4,7 @@ ^NOTICE$ ^analyses$ ^dev$ +^cran-comments\.md$ +^CRAN-SUBMISSION$ +^DESCRIPTION-github$ +^DESCRIPTION-cran$ diff --git a/.gitignore b/.gitignore index 2c1d394..c337065 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,8 @@ .Rhistory .RData .Ruserdata +DESCRIPTION-github +DESCRIPTION-cran +CRAN-SUBMISSION +cran-comments.md /dev/** diff --git a/DESCRIPTION b/DESCRIPTION index d86ffc1..c8f2a10 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,21 +1,23 @@ Package: goat Type: Package -Title: Geneset Ordinal Association Test (GOAT) -Version: 0.9.6 -Authors@R: +Title: Gene Set Analysis Using the Gene Set Ordinal Association Test +Version: 1.0 +Authors@R: person(given = "Frank", family = "Koopmans", role = c("aut", "cre"), - email = "frank.koopmans@vu.nl", + email = "ftwkoopmans@gmail.com", comment = c(ORCID = "0000-0002-4973-5732")) -Description: - Perform geneset enrichment analyses using the GOAT algorithm. -URL: https://github.com/ftwkoopmans/goat +Description: + Perform gene set enrichment analyses using the Gene set Ordinal + Association Test (GOAT) algorithm and visualize your + results. Koopmans, F. (2024) . +URL: https://github.com/ftwkoopmans/goat/ License: Apache License (>= 2) -Depends: +Depends: R (>= 4.1.0), dplyr (>= 1.0.3) -Imports: +Imports: tibble (>= 3.0.0), tidyselect (>= 1.2.0), tidyr (>= 1.1.2), @@ -27,27 +29,24 @@ Imports: vctrs (>= 0.3.8), MonoPoly (>= 0.3-10), ggplot2 (>= 3.3.0), - pheatmap, - treemap, + pheatmap (>= 1.0.8), + treemap (>= 2.4), igraph (>= 1.2.5), - ggraph (>= 2.0.0), + ggraph (>= 2.0.0) +Suggests: AnnotationDbi, GO.db, - org.Hs.eg.db -Suggests: + org.Hs.eg.db, fgsea, - iDEA, - future (>= 1.21.0), - future.apply (>= 1.9.0), testthat (>= 3.0.0) -Remotes: - github::ctlab/fgsea, - github::xzhoulab/iDEA -BiocViews: LinkingTo: Rcpp Encoding: UTF-8 LazyData: true LazyDataCompression: xz Roxygen: list(markdown = TRUE) -RoxygenNote: 7.2.0 +RoxygenNote: 7.3.1 Config/testthat/edition: 3 +Language: en-US +Remotes: + github::ctlab/fgsea +BiocViews: diff --git a/LICENSE.note b/LICENSE.note index c7d7307..945128d 100644 --- a/LICENSE.note +++ b/LICENSE.note @@ -5,4 +5,4 @@ All code and documentation in the goat R package as a whole is distributed under The goat R package includes the 'RunningStat' implementation of "Welford running variance" provided on John Cook's blog at https://www.johndcook.com/blog/standard_deviation/ -The goat R package bundles a number of publicly available datasets that were adopted from published scientific manuscripts. Respective PubMed identifiers are included in the dataset names. +The goat R package bundles publicly available data that were adopted from published scientific manuscripts. Respective PubMed identifiers are included in the dataset names. diff --git a/NAMESPACE b/NAMESPACE index 579cda1..0cf2ea2 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,11 +1,18 @@ # Generated by roxygen2: do not edit by hand export(cluster_genesets) +export(darken_color) +export(download_genesets_goatrepo) +export(download_goat_manuscript_data) export(filter_genesets) +export(gg_color_hue) +export(go_gene2go) +export(go_obo) export(goat_logo) export(goat_print_version) export(goat_version) -export(hgnc_lookuptable) +export(hgnc_idmap_table) +export(lighten_color) export(load_genesets_gmtfile) export(load_genesets_go_bioconductor) export(load_genesets_go_fromfile) @@ -16,9 +23,14 @@ export(partition_genes) export(plot_heatmap) export(plot_lollipop) export(plot_network) +export(plot_volcano) +export(rankscore) +export(rankscore_fixed_order) export(reduce_genesets) export(save_genesets) +export(score_geneset_directionality) export(score_geneset_oddsratio) +export(string_trunc_right) export(symbol_to_entrez) export(test_genesets) export(test_genesets_fisherexact) @@ -27,7 +39,6 @@ export(test_genesets_goat_fitfunction) export(test_genesets_goat_precomputed) export(test_genesets_gsea) export(test_genesets_hypergeometric) -export(test_genesets_idea) export(treemap_data) export(treemap_plot) import(dplyr) diff --git a/NEWS.md b/NEWS.md new file mode 100644 index 0000000..1d5bfd4 --- /dev/null +++ b/NEWS.md @@ -0,0 +1,5 @@ +# *News* + +# goat 1.0 (2024-04-28) + +First public release. diff --git a/R/data.R b/R/data.R index eaa3ff5..624fd1a 100644 --- a/R/data.R +++ b/R/data.R @@ -1,14 +1,7 @@ -#' Previously published datasets that were used as examples in the GOAT manuscript -#' -#' @format ## `goat_example_datasets` -#' a list of data.frames that represent differential expression analysis from high-throughput datasets -#' @source The name of each dataset in `goat_example_datasets` contains the respective PubMed identifier -"goat_example_datasets" - - #' Precomputed parameters used by the GOAT algorithm #' +#' @description there parameters are used by goat to efficiently perform geneset testing without bootstrapping #' @format ## `goat_nulldistributions` -#' precomputed null distribution parameters, used by goat to efficiently perform geneset testing without bootstrapping +#' a data.frame with precomputed GOAT null distribution parameters "goat_nulldistributions" diff --git a/R/download_data.R b/R/download_data.R new file mode 100644 index 0000000..7bccef9 --- /dev/null +++ b/R/download_data.R @@ -0,0 +1,159 @@ + +#' Download the datasets that were used in the GOAT manuscript +#' +#' @description +#' Downloads OMICs-based datasets that were used in the GOAT manuscript from the GOAT GitHub page. +#' This file is cached in the output directory and only needs to be downloaded once. Multiple datasets +#' are included and their names include the respective PubMed identifiers (PMID). +#' +#' If you encounter technical difficulties, try to; +#' +#' 1) download the file by copy/pasting this URL into your browser: https://github.com/ftwkoopmans/goat/raw/main/analyses/goat_manuscript_datasets.rda +#' 2) load the data in R using the following 2 lines of code, here assuming you stored the downloaded file at C:/data/goat_manuscript_datasets.rda +#' +#' `load("C:/data/goat_manuscript_datasets.rda")` +#' +#' `genelist = goat_manuscript_datasets.rda[["Wingo 2020:mass-spec:PMID32424284"]]` +#' +#' @param output_dir full path to the directory where the downloaded files should be stored. Directory is created if it does not exist. +#' e.g. `output_dir="~/data"` on unix systems, `output_dir="C:/data"` on Windows, or set to `output_dir=getwd()` to write output to the current working directory +#' @param ignore_cache boolean, set to TRUE to force re-download and ignore cached data, if any. Default: FALSE +#' @return a list of genelist data tables. The names of the list represent the datasets, +#' values in the list are data tables that can be used as a "genelist" in the GOAT R package +#' @export +download_goat_manuscript_data = function(output_dir, ignore_cache = FALSE) { + stopifnot("parameter output_dir must be a single string and represent a directory on your computer" = length(output_dir) == 1 && is.character(output_dir) && !is.na(output_dir)) + stopifnot("parameter ignore_cache must be a single boolean value" = length(ignore_cache) == 1 && ignore_cache %in% c(TRUE, FALSE)) + sprintf_template_downloadfail = "failed to download %s and store it at %s\nTry an alternative output_dir parameter or follow the download_goat_manuscript_data() function documentation to manually download the file and load it in R" + + # create dir if it does not exist + if(!dir.exists(output_dir)) { + dir.create(output_dir, recursive = TRUE) + if(!dir.exists(output_dir)) { + stop(paste0("Could not create the requested output directory: ", output_dir, "\nTry to provide an existing directory as parameter for 'output_dir'")) + } + } + + # load from cache, or download if not available + filename = paste0(output_dir, "/goat_manuscript_datasets.rda") + url = "https://github.com/ftwkoopmans/goat/raw/main/analyses/goat_manuscript_datasets.rda" + if(ignore_cache || !file.exists(filename)) { + message(paste("downloading", url, "...")) + utils::download.file(url, filename, mode = "wb") + if(!file.exists(filename)) { + stop(sprintf(sprintf_template_downloadfail, url, filename)) + } + message(paste0("downloaded data was stored at: ", filename)) + } else { + message(paste0("cached data was retrieved from: ", filename)) + } + + # load RData file into environment + e = new.env() + load(filename, envir = e) + + # validate that the expected variable is present + if(!is.list(e$goat_manuscript_datasets)) { + stop("failed to load RData file; it did not contain expected variable 'goat_manuscript_datasets'") + } + + return(e$goat_manuscript_datasets) +} + + + +#' Download and parse geneset collections from the GOAT GitHub repository +#' +#' @description while the Bioconductor respository is extensive, contains data for many species and is a part of +#' a larger infrastructure, it might contain outdated GO data when the user is not using the latest R version. +#' If users are on an R version that is a few years old, so will the GO data from Bioconductor be. +#' +#' As an alternative, we store gene2go data from NCBI (for Human genes only!) at the GOAT GitHub repository. +#' This function allows for a convenient way to download this data and then parse the genesets. +#' +#' Alternatively you can browse the file in the data branch of the GOAT GitHub repository and download these files manually, +#' then load them via the GOAT R function `load_genesets_go_fromfile()`. +#' +#' To view all available data you can open this URL in a browser; https://github.com/ftwkoopmans/goat/tree/data +#' +#' New data is automatically added biannually. The first available version is 2024-01-01, the next 2024-06-01, then 2025-01-01, and so on. +#' +#' @examples \donttest{ +#' # note: this example will download 2 files of approx 10MB in total +#' +#' # store the downloaded files in the following directory. Here, the temporary file +#' # directory is used. Alternatively, consider storing this data in a more permanent location. +#' # e.g. output_dir="~/data/go" on unix systems or output_dir="C:/data/go" on Windows +#' output_dir = tempdir() +#' +#' # download data files with GO annotations, version 2024-01-01 (default parameter) +#' # these are then parsed with the load_genesets_go_fromfile() function +#' # if the files are already available at output_dir, the download step is skipped +#' genesets_asis = download_genesets_goatrepo(output_dir) +#' +#' ### for a basic example on how to use the data obtain here, +#' ### refer to the example included at function documentation of: test_genesets() +#' } +#' @param output_dir full path to the directory where the downloaded files should be stored. Directory is created if it does not exist. +#' e.g. `output_dir="~/data"` on unix systems, `output_dir="C:/data"` on Windows, or set to `output_dir=getwd()` to write output to the current working directory +#' @param type the type of genesets to download. Currently, only "GO" is supported (default) +#' @param version the dataset version. This must be a date in format YYYY-MM-DD. Example: "2024-01-01" (default). View all available versions at https://github.com/ftwkoopmans/goat/tree/data +#' @param ignore_cache boolean, set to TRUE to force re-download and ignore cached data, if any. Default: FALSE +#' @return result from respective geneset parser function. e.g. if parameter `type` was set to"GO" (default), this function returns the result of `load_genesets_go_fromfile()`. These data returned by this function is typically used as input for `filter_genesets()`, c.f. full example at documentation for test_genesets() +#' @export +download_genesets_goatrepo = function(output_dir, type = "GO", version = "2024-01-01", ignore_cache = FALSE) { + stopifnot("parameter type must be a single string. The only supported option for now is 'GO' (default)" = length(type) == 1 && is.character(type) && !is.na(type) && type %in% c("GO")) + stopifnot("parameter version must be a single string that represents a date, see function documentation" = length(version) == 1 && is.character(version) && !is.na(version) && grepl("^\\d\\d\\d\\d\\-\\d\\d-\\d\\d$", version)) + stopifnot("parameter output_dir must be a single string and represent a directory on your computer" = length(output_dir) == 1 && is.character(output_dir) && !is.na(output_dir)) + stopifnot("parameter ignore_cache must be a single boolean value" = length(ignore_cache) == 1 && ignore_cache %in% c(TRUE, FALSE)) + + # create dir if it does not exist + if(!dir.exists(output_dir)) { + dir.create(output_dir, recursive = TRUE) + if(!dir.exists(output_dir)) { + stop(paste0("Could not create the requested output directory: ", output_dir, "\nTry to provide an existing directory as parameter for 'output_dir'")) + } + } + + if(type == "GO") { + file_g2g = sprintf("%s/gene2go_%s.gz", output_dir, version) + file_obo = sprintf("%s/go_%s.obo.gz", output_dir, version) + url_g2g = sprintf("https://github.com/ftwkoopmans/goat/raw/data/go/%s/gene2go_human_%s.gz", version, version) + url_obo = sprintf("https://github.com/ftwkoopmans/goat/raw/data/go/%s/go_%s.obo.gz", version, version) + any_download = FALSE + sprintf_template_downloadfail = "failed to download %s and store it at %s\nMost likely causes are 1) the requested file/version does not exist (try the default parameter!) and 2) Internet connection issues (try to download the here mentioned URL by copy/pasting in your browser).\nPlease refer to the download_genesets_goatrepo() function documentation to learn how you can find available versions (besides the default parameter)" + + # attempt to download if not available on disk + if(ignore_cache || !file.exists(file_g2g)) { + message(paste("downloading", url_g2g, "...")) + utils::download.file(url_g2g, file_g2g, mode = "wb") + if(!file.exists(file_g2g)) { + stop(sprintf(sprintf_template_downloadfail, url_g2g, file_g2g)) + } + any_download = TRUE + } + + # attempt to download if not available on disk + if(ignore_cache || !file.exists(file_obo)) { + message(paste("downloading", url_obo, "...")) + utils::download.file(url_obo, file_obo, mode = "wb") + if(!file.exists(file_obo)) { + stop(sprintf(sprintf_template_downloadfail, url_obo, file_obo)) + } + any_download = TRUE + } + + if(any_download) { + message(paste("downloaded geneset files were stored at:", output_dir)) + } else { + message(paste("cached geneset files were retrieved from:", output_dir)) + } + + return(load_genesets_go_fromfile(file_gene2go = file_g2g, file_goobo = file_obo)) + } + + # ... other types may be added in the future + +} + + diff --git a/R/filter_genesets.R b/R/filter_genesets.R index c964a8e..6066903 100644 --- a/R/filter_genesets.R +++ b/R/filter_genesets.R @@ -26,7 +26,7 @@ filter_genesets = function(genesets, genelist, min_overlap = 10L, max_overlap = if(!is.finite(max_size)) max_size = Inf # users can provide NA to disable filtering if(min_signif > 0) { - cat("Warning: the 'min_signif' parameter is enabled. Be careful, this is \"prefiltering\" and will affect the correcteness / calibration of estimated geneset p-values. For GOAT and GSEA, this is NOT RECOMMENDED\n") + warning("the 'min_signif' parameter is enabled. Be careful, this is \"prefiltering\" and will affect the correcteness / calibration of estimated geneset p-values. For GOAT and GSEA, this is NOT RECOMMENDED") } # settings as string @@ -78,7 +78,7 @@ filter_genesets = function(genesets, genelist, min_overlap = 10L, max_overlap = group_by(ngenes) |> # can only be a dupe if vector length is equal, so efficiently check within same-length mutate(isdupe = finddupes(genes)) |> ungroup() |> - filter(isdupe == F) |> + filter(isdupe == FALSE) |> select(-isdupe) } @@ -96,7 +96,7 @@ filter_genesets = function(genesets, genelist, min_overlap = 10L, max_overlap = } if(nrow(x) == 0) { - cat("filter_genesets() yields an empty result !\nAre the gene identifiers in your 'genesets' and 'genelist tables of the same type? e.g. both tables should contain NCBI Entrez gene IDs, or both use HGNC identifiers, or Ensembl gene IDs. Another common mistake is using different species, so double-check that both tables contain e.g. human gene identifiers\n") + warning("filter_genesets() yields an empty result !\nAre the gene identifiers in your 'genesets' and 'genelist tables of the same type? e.g. both tables should contain NCBI Entrez gene IDs, or both use HGNC identifiers, or Ensembl gene IDs. Another common mistake is using different species, so double-check that both tables contain e.g. human gene identifiers") } attr(x, "settings") <- c(attr(genesets, "settings"), settings) diff --git a/R/gene_idmapping.R b/R/gene_idmapping.R index aeb6cc8..a5c6140 100644 --- a/R/gene_idmapping.R +++ b/R/gene_idmapping.R @@ -10,15 +10,15 @@ #' table: "Total Approved Symbols" -->> "TXT" / "text file in TSV format" #' filename is typically something like non_alt_loci_set.txt #' -#' @param f full path to the downloaded table (expected to be tsv format) -#' @returns a long-format table with columns; hgnc_id, hgnc_symbol, type, value +#' @param filename full path to the downloaded table (expected to be tsv format, typically has .txt or .tsv file extension) +#' @return a long-format table with columns; hgnc_id, hgnc_symbol, type, value #' @export -hgnc_lookuptable = function(f) { +hgnc_idmap_table = function(filename) { symbol = hgnc_id = hgnc_symbol = entrez_id = prev_symbol = alias_symbol = NULL # fix invisible bindings R package NOTE - stopifnot("parameter f must be an existing file" = length(f) == 1 && is.character(f) && file.exists(f)) + stopifnot("parameter filename must be an existing file" = length(filename) == 1 && is.character(filename) && file.exists(filename)) # parse HGNC table - hgnc = data.table::fread(f, data.table = F, stringsAsFactors = F) + hgnc = data.table::fread(filename, data.table = FALSE, stringsAsFactors = FALSE) colnames(hgnc) = tolower(colnames(hgnc)) cols_expect = c("hgnc_id", "symbol", "alias_symbol", "prev_symbol", "entrez_id") cols_alt = c("HGNC ID", "Approved symbol", "Previous symbols", "Alias symbols", "NCBI Gene ID") @@ -49,7 +49,7 @@ hgnc_lookuptable = function(f) { l = strsplit(toupper(hgnc$synonyms), "[ ,;|]+") # support various delimiters map_synonym = data.frame( hgnc_id = rep(hgnc$hgnc_id, lengths(l)), - symbol = unlist(l, recursive = F, use.names = F) + symbol = unlist(l, recursive = FALSE, use.names = FALSE) ) |> filter(nchar(symbol) >= 2 & ! symbol %in% hgnc$hgnc_symbol) |> # deal with duplicate synonyms within 1 row/hgnc_id @@ -67,16 +67,20 @@ hgnc_lookuptable = function(f) { #' Map the the symbol column in a table to HGNC human gene IDs by matching official gene symbols and synonyms #' -#' @examples \dontrun{ +#' @examples #' # TODO: update the filename to your downloaded file -#' # download instructions @ `hgnc_lookuptable` -#' f = "C:/data/hgnc_complete_set.txt" -#' df = data.frame(symbol = c("vamp2", "STXBP1", "UNC18", NA, "PSD95", "NOT-A-GENE")) -#' hgnc = hgnc_lookuptable(f) -#' symbol_to_entrez(df, hgnc) -#' } +#' # download instructions in the documentation of `hgnc_idmap_table()` +#' f = "C:/DATA/HGNC/hgnc_complete_set.txt" +#' +#' if(file.exists(f)) { +#' df = data.frame(symbol = c("vamp2", "STXBP1", "UNC18", NA, "PSD95", "NOT-A-GENE")) +#' hgnc = hgnc_idmap_table(f) +#' df = symbol_to_entrez(df, hgnc) +#' print(df) +#' } #' @param x a data.table with a column symbol -#' @param hgnc HGNC lookup table from `hgnc_lookuptable()` +#' @param hgnc HGNC lookup table from `hgnc_idmap_table()` +#' @return entrez gene IDs are returned in the "gene" column of table `x`. Additionally, columns "entrez_id", "hgnc_id" and "hgnc_symbol" #' @export symbol_to_entrez = function(x, hgnc) { symbol = NULL # fix invisible bindings R package NOTE @@ -84,7 +88,7 @@ symbol_to_entrez = function(x, hgnc) { stop("x must be a data.frame with column 'symbol' (character type)") } if(!is.data.frame(hgnc) || nrow(hgnc) == 0 || !all(c("hgnc_id", "hgnc_symbol", "synonym", "entrez_id") %in% colnames(hgnc)) ) { - stop("hgnc must be a data.frame with columns 'hgnc_id', 'hgnc_symbol', 'synonym', 'entrez_id' as typically prepared using the hgnc_lookuptable() function") + stop("hgnc must be a data.frame with columns 'hgnc_id', 'hgnc_symbol', 'synonym', 'entrez_id' as typically prepared using the hgnc_idmap_table() function") } x$symbol_input = x$symbol @@ -109,6 +113,7 @@ symbol_to_entrez = function(x, hgnc) { i = match(x$hgnc_id, hgnc$hgnc_id) x$hgnc_symbol = hgnc$hgnc_symbol[i] x$entrez_id = hgnc$entrez_id[i] + x$gene = x$entrez_id # restore input symbols x$symbol = x$symbol_input @@ -118,7 +123,7 @@ symbol_to_entrez = function(x, hgnc) { tmp = x |> filter(rows_symbol_fail == FALSE) |> distinct(symbol, .keep_all = TRUE) n_input = nrow(tmp) n_fail = sum(is.na(tmp$hgnc_id)) - cat(sprintf("%d / %d (%.1f%%) unique symbols could not be mapped to a HGNC human gene ID\n", + message(sprintf("%d / %d (%.1f%%) unique symbols could not be mapped to a HGNC human gene ID", n_fail, n_input, n_fail / n_input * 100 )) return(x) diff --git a/R/goat.R b/R/goat-package.R similarity index 50% rename from R/goat.R rename to R/goat-package.R index f60bfd2..2161703 100644 --- a/R/goat.R +++ b/R/goat-package.R @@ -1,20 +1,20 @@ -#' goat: Perform geneset enrichment and overrepresentation analyses using various algorithms, including GOAT. +#' goat: Gene Set Analysis Using the Gene Set Ordinal Association Test #' #' @description -#' TODO: package description -#' -#' @docType package +#' Perform gene set enrichment analyses using the +#' Gene set Ordinal Association Test (GOAT) algorithm +#' and visualize your results. #' @name goat +#' @useDynLib goat, .registration=TRUE +#' @keywords internal #' @import dplyr #' @importFrom Rcpp sourceCpp -#' @useDynLib goat, .registration=TRUE -NULL - - +"_PACKAGE" #' cleanup Rcpp code #' #' @param libpath library path +#' @noRd .onUnload = function(libpath = NULL) { library.dynam.unload("goat", libpath) } diff --git a/R/goat_nulldistributions.R b/R/goat_nulldistributions.R index 35ef89e..23ea31c 100644 --- a/R/goat_nulldistributions.R +++ b/R/goat_nulldistributions.R @@ -1,7 +1,9 @@ #' Wrapper function for goat_nulldistribution_function, typically used to generate the Rdata file with precomputed null distribution that is bundled with GOAT #' -#' @examples \dontrun{ +#' @examples \donttest{ +#' # note that these example take a long time to compute +#' #' # example 1; precompute parameters for a huge range of genesets, 500k permuations #' goat_nulldistribution_function_generator( #' seq(from=100, to=20000, by=1), 500000, "C:/temp/", overwrite=TRUE, verbose=TRUE @@ -13,16 +15,18 @@ #' } #' @param all_geneset_sizes vector of geneset sizes; integer values larger than 10 and smaller than half the genelist length (gene_scores length) #' @param niter see `goat_nulldistribution_function` -#' @param dir_output the path to an existing directory where temporary and result files will be stored +#' @param output_dir full path to the directory where the downloaded files should be stored. Directory is created if it does not exist. +#' e.g. `output_dir="~/data"` on unix systems, `output_dir="C:/data"` on Windows, or set to `output_dir=getwd()` to write output to the current working directory #' @param overwrite should existing files be overwritten? When setting this to FALSE, be mindful that you should manually clear cached files in between analyses ! #' @param verbose see `goat_nulldistribution_function` -goat_nulldistribution_function_generator = function(all_geneset_sizes, niter, dir_output, overwrite = TRUE, verbose = FALSE) { +#' @noRd +goat_nulldistribution_function_generator = function(all_geneset_sizes, niter, output_dir, overwrite = TRUE, verbose = FALSE) { geneset_size_bins = suppressWarnings(split(all_geneset_sizes, 1:min(100, length(all_geneset_sizes)))) for(index_bin in seq_along(geneset_size_bins)) { start_time_bin = Sys.time() - file_bin = sprintf("%s/goat_null_functions_%d.rda", dir_output, index_bin) + file_bin = sprintf("%s/goat_null_functions_%d.rda", output_dir, index_bin) if(overwrite == FALSE && file.exists(file_bin)) { next } @@ -33,26 +37,26 @@ goat_nulldistribution_function_generator = function(all_geneset_sizes, niter, di genelist_fit = goat_nulldistribution_function(genelist_N, niter = niter, return_fit_objects = FALSE, verbose = (verbose && (genelist_N %in% c(100,250,500,750,1000,1500,2500,5000,10000,15000,20000))) ) result_genelists[[length(result_genelists) + 1]] = genelist_fit if(index_bin == 1) { - cat(sprintf("N:%s took %d seconds\n", genelist_N, ceiling(as.numeric(difftime(Sys.time(), start_time, units = "secs"))) )) + message(sprintf("N:%s took %d seconds", genelist_N, ceiling(as.numeric(difftime(Sys.time(), start_time, units = "secs"))) )) } } # store current batch to disk save(result_genelists, file = file_bin) - cat(sprintf("bin:%d took %.1f hours\n", index_bin, as.numeric(difftime(Sys.time(), start_time_bin, units = "hours")) )) + message(sprintf("bin:%d took %.1f hours", index_bin, as.numeric(difftime(Sys.time(), start_time_bin, units = "hours")) )) } # from cached Rdata files per 'bin' into 1 large data.frame alldata = list() - for(f in dir(dir_output, pattern = "^goat_null_functions_\\d+.rda$", full.names = TRUE)) { + for(f in dir(output_dir, pattern = "^goat_null_functions_\\d+.rda$", full.names = TRUE)) { load(f) alldata[[length(alldata) + 1]] = result_genelists } goat_nulldistributions = dplyr::bind_rows(alldata) |> mutate_all(unname) # store final result - save(goat_nulldistributions, file = paste0(dir_output, "/goat_null_alldata.rda"), compress = "xz", compression_level = 9) + save(goat_nulldistributions, file = paste0(output_dir, "/goat_null_alldata.rda"), compress = "xz", compression_level = 9) } @@ -66,6 +70,7 @@ goat_nulldistribution_function_generator = function(all_geneset_sizes, niter, di #' @param niter number of bootstrap iterations that should be performed for generating empirical null distributions. Note that setting a large number will dramatically increase the RAM usage of this function! Integer value between 10000 and 5000000 #' @param return_fit_objects should the 'fit' object, which can be used as input for `predict`, be included in results? boolean value, TRUE or FALSE, default; FALSE #' @param verbose should plots be created that describe the accuracy of fitting sd and xi? boolean value, TRUE or FALSE, default; FALSE +#' @noRd goat_nulldistribution_function = function(genelist_N, niter = 500000, return_fit_objects = FALSE, verbose = FALSE) { validate_goat_niter(niter) validate_goat_verbose(verbose) @@ -207,7 +212,8 @@ goat_nulldistribution_function = function(genelist_N, niter = 500000, return_fit #' @param gene_scores gene score vector; these values are assumed to be the 'universe' of all possible gene-level scores. These are used to compute geneset score null distributions #' @param niter number of bootstrap iterations that should be performed for generating empirical null distributions. Note that setting a large number will dramatically increase the RAM usage of this function! Integer value between 10000 and 5000000 #' @param verbose boolean, plot 10 null distribution histograms augmented with fitted skew-normal distributions -#' @returns data.frame with columns; genelist_N, size, mu, sigma, xi +#' @return data.frame with columns; genelist_N, size, mu, sigma, xi +#' @noRd goat_nulldistribution_independent = function(geneset_sizes, gene_scores, niter, verbose) { validate_goat_niter(niter) validate_goat_verbose(verbose) diff --git a/R/load_genesets_gmtfile.R b/R/load_genesets_gmtfile.R index 78f7133..0c193ee 100644 --- a/R/load_genesets_gmtfile.R +++ b/R/load_genesets_gmtfile.R @@ -6,12 +6,10 @@ #' download this data: KEGG subset of curated pathways -->> NCBI (Entrez) Gene IDs #' filename should be something like "c2.cp.kegg.v2023.1.Hs.entrez.gmt" #' -#' @examples \dontrun{ -#' genesets_asis = load_genesets_gmtfile( -#' filename = "C:/DATA/c2.cp.kegg.v2023.1.Hs.entrez.gmt", -#' label = "KEGG" -#' ) -#' } +#' @examples +#' # TODO: update the filename to your downloaded file +#' f = "C:/DATA/c2.cp.kegg.v2023.1.Hs.entrez.gmt" +#' if(file.exists(f)) genesets_asis = load_genesets_gmtfile(f, label = "KEGG") #' @param filename input file for this function should be the full path to genesets defined in GMT format #' @param label a shortname for the genesets in this file, for example "GO_CC", "KEGG", "MY_DB_V1". This will be stored in the 'source' column of the resulting table. Importantly, multiple testing correction in GOAT is grouped by this 'source' column so you probably want to use a different label for each collection-of-genesets that you load. Must not be empty, only allowed characters are; upper/lower-case letter, numbers 0-9 and underscore #' @return tibble with columns; source (character), source_version (character), id (character), name (character), genes (list), ngenes (int) diff --git a/R/load_genesets_go.R b/R/load_genesets_go.R index 7005721..f412e33 100644 --- a/R/load_genesets_go.R +++ b/R/load_genesets_go.R @@ -5,16 +5,11 @@ #' Download and import genesets from the GO database using the Bioconductor infrastructure. #' Use the `goat::load_genesets_go_fromfile` function for more fine-grained control over the GO database version that you use; it allows you to import NCBI gene2go files #' -#' #' @details #' Note that org.Hs.eg.db pulls data semi-annually from NCBI gene2go, #' but the GO database version returned by this function is tied to the version of the org.Hs.eg.db on your computer (this is controlled by the Bioconductor infrastructure). #' #' The actual GO database version that is retrieved is returned by this function in the `source_version` column. -#' -#' @examples \dontrun{ -#' genesets_asis = load_genesets_go_bioconductor() -#' } #' @param include_child_annotations boolean; include annotations against child terms? In most situations, TRUE (default) is the desired setting #' @return table with columns; source (character), source_version (character), id (character), name (character), genes (list), ngenes (int) #' @export @@ -42,14 +37,14 @@ load_genesets_go_bioconductor = function(include_child_annotations = TRUE) { ### convert Bioconductor data into a table compatible with this R package result = tibble::tibble(go_id = rep(names(go_annotations_entrez), lengths(go_annotations_entrez)), - genes = unlist(go_annotations_entrez, recursive = F, use.names = F)) |> + genes = unlist(go_annotations_entrez, recursive = FALSE, use.names = FALSE)) |> # enforce entrez gene IDs to be integers by stripping non-numeric parts # (not strictly needed atm, just a safeguard against future upstream changes, e.g. prefixing entrez IDs with 'entrez:') mutate(genes = gsub("\\D+","", genes)) |> filter(genes != "") |> mutate(genes = as.integer(genes)) |> # remove duplicate goterm*genes entries, if any - distinct(go_id, genes, .keep_all = T) |> + distinct(go_id, genes, .keep_all = TRUE) |> # back to list format tidyr::chop(cols = genes) |> # add goterm metadata; name and domain @@ -70,7 +65,7 @@ load_genesets_go_bioconductor = function(include_child_annotations = TRUE) { GOdata = as.list(GODBLINKS) # unlist the named vector per GO term into a long-format table links = dplyr::bind_rows(sapply(names(GOdata), function(n) - data.frame(child_id=n, parent_id=unname(GOdata[[n]]), relation=names(GOdata[[n]]), row.names = NULL), simplify = F, USE.NAMES = F)) + data.frame(child_id=n, parent_id=unname(GOdata[[n]]), relation=names(GOdata[[n]]), row.names = NULL), simplify = FALSE, USE.NAMES = FALSE)) links |> # remove unsupported relation types filter(relation %in% relation_accept) |> @@ -100,6 +95,8 @@ load_genesets_go_bioconductor = function(include_child_annotations = TRUE) { attr(result, "settings") <- sprintf("load_genesets_go_bioconductor(include_child_annotations=%s) = '%s'", include_child_annotations, go_annotations_metadata) + + message(paste("load_genesets_go_bioconductor(): data version =", go_annotations_metadata)) return(result) } @@ -107,17 +104,34 @@ load_genesets_go_bioconductor = function(include_child_annotations = TRUE) { #' construct a geneset table from gene2go and OBO files #' +#' @description +#' This function is used to load Gene Ontology (GO) genesets from files that you +#' manually downloaded from the links below. This enables the use of the latest data +#' from GO (in contrast, Bioconductor GO data may lag behind current data considerably). +#' To construct genesets from available raw data, download the "gene2go" file +#' (the gene annotations) from below NCBI link and download the GO OBO +#' (ontology terms and relations to respective parent/child terms) from below +#' geneontology.org link. Provide the full path to the downloaded file to this function. +#' Both "gzipped" and "uncompressed" files are supported. +#' +#' We encourage you to rename the files after your downloaded them such that +#' the date of download in incorporated; this ensures you can always keep track of +#' the GO database version that was used! For example, rename the downloaded +#' "gene2go.gz" file to "gene2go_2024-01-31.gz". +#' #' Download link for gene2go file; https://ftp.ncbi.nih.gov/gene/DATA/gene2go.gz +#' #' Download link for gene ontology OBO file; http://current.geneontology.org/ontology/go.obo #' -#' @examples \dontrun{ -#' genesets_asis = load_genesets_go_fromfile( -#' file_gene2go = "C:/DATA/download_2024-01-01/gene2go", -#' file_goobo = "C:/DATA/download_2024-01-01/go.obo" -#' ) -#' } -#' @param file_gene2go gene2go file from NCBI. Also works with the gzipped file gene2go.gz -#' @param file_goobo OBO file from geneontology.org +#' @examples +#' # TODO: update the filenames to your downloaded files +#' file_gene2go = "C:/DATA/gene2go_2024-01-01.gz" +#' file_goobo = "C:/DATA/go_2024-01-01.obo" +#' if(file.exists(file_gene2go) && file.exists(file_goobo)) { +#' genesets_asis = load_genesets_go_fromfile(file_gene2go, file_goobo) +#' } +#' @param file_gene2go full path to the gene2go file from NCBI. Also works with the gzipped file gene2go.gz +#' @param file_goobo full path to the OBO file from geneontology.org #' @param include_child_annotations boolean; include annotations against child terms? In most situations, TRUE (default) is the desired setting #' @return table with columns; source (character), source_version (character), id (character), name (character), genes (list), ngenes (int) #' @export @@ -181,10 +195,11 @@ load_genesets_go_fromfile = function(file_gene2go, file_goobo, include_child_ann #' parse gene2go file #' -#' note that it lacks parent/child relations, so from this file we only learn 'direct annotations' -#' +#' @description note that this file lacks parent/child relations, so we only learn 'direct annotations' #' @param f full path to gene2go file stored on the computer, e.g. previously downloaded from https://ftp.ncbi.nih.gov/gene/DATA/gene2go.gz #' @param taxid_filter taxonomy id, integer +#' @return a tibble with columns; source, source_version, id, name, genes, ngenes +#' @export go_gene2go = function(f, taxid_filter = 9606) { taxid = qualifier = goid = goterm = geneid = category = genes = source_version = name = ngenes = NULL # fix invisible bindings R package NOTE @@ -197,7 +212,7 @@ go_gene2go = function(f, taxid_filter = 9606) { gene2go |> # remove negative annotation. e.g. "NOT part_of" (do this after taxid filter for efficiency) - filter( ! grepl("^not ", qualifier, ignore.case = T)) |> + filter( ! grepl("^not ", qualifier, ignore.case = TRUE)) |> as_tibble() |> # retain only data we need & rename columns select(id = goid, name = goterm, genes = geneid, source = category) |> @@ -226,6 +241,7 @@ go_gene2go = function(f, taxid_filter = 9606) { #' #' @param child array of GO term IDs that represent children #' @param parent array of GO term IDs that represent respective parents +#' @noRd go_find_parents = function(child, parent) { newchild = NULL # fix invisible bindings R package NOTE stopifnot(length(child) > 0 & length(child) == length(parent) & !anyNA(child) & !anyNA(parent)) @@ -263,17 +279,21 @@ go_find_parents = function(child, parent) { #' simple vectorized parsing of GO OBO file without any dependencies (beyond dplyr/tibble/tidyr) #' -#' note that we remove links between GO terms that are across GO domains (e.g. no CC to MF relations) +#' @description note that we remove links between GO terms that are across GO domains (e.g. no CC to MF relations) +#' The only supported relations are those that match this regex; +#' `"^(is_a:|relationship: part_of|relationship: regulates|relationship: positively_regulates|relationship: negatively_regulates)"` #' #' @param f full path to go.obo file stored on the computer, e.g. previously downloaded from http://current.geneontology.org/ontology/go.obo #' @param rename_namespace boolean; rename official namespace values like 'cellular_component' to CC? (analogous for BP and MF) #' @param remove_obsolete boolean; remove obsoleted terms? +#' @return tibble with ontology terms and their relations +#' @export go_obo = function(f, rename_namespace = TRUE, remove_obsolete = TRUE) { group = lines = isterm = isid = isname = isdef = isnamespace = isobsolete = isrelationship = isparentlink = NULL # fix invisible bindings R package NOTE id = name = definition = namespace = obsolete = qc = parent_id = parent_namespace = parent = parent_id_recursive = NULL # fix invisible bindings R package NOTE go = tibble::tibble( - lines = readLines(f, warn = F), + lines = readLines(f, warn = FALSE), group = cumsum(as.integer(grepl("^\\[", lines))), isterm = lines == "[Term]", isid = grepl("^id:", lines), diff --git a/R/load_genesets_syngo.R b/R/load_genesets_syngo.R index 04cb22e..dca686d 100644 --- a/R/load_genesets_syngo.R +++ b/R/load_genesets_syngo.R @@ -6,12 +6,11 @@ #' - click "bulk download SynGO release ..." for SynGO release of interest #' - unzip #' - call this function with the full file path to the 'syngo_ontologies.xlsx' file -#' @examples \dontrun{ -#' genesets_asis = load_genesets_syngo( -#' "C:/DATA/SynGO_bulk_download_release_20210225/syngo_ontologies.xlsx" -#' ) -#' } -#' @param filename input file for this function should be the full path name for "syngo_ontologies.xlsx" from a SynGO bulk download +#' @examples +#' # TODO: update the filename to your downloaded file +#' f = "C:/DATA/SynGO_bulk_download_release_20210225/syngo_ontologies.xlsx" +#' if(file.exists(f)) genesets_asis = load_genesets_syngo(f) +#' @param filename full path to the "syngo_ontologies.xlsx" file that was extracted from a SynGO bulk download ZIP archive #' @param gene_database gene IDs to return. must be any of; "entrez" (default), "hgnc", "ensembl" #' @return table with columns; source (character), source_version (character), id (character), name (character), genes (list), ngenes (int) #' @export @@ -66,7 +65,7 @@ load_genesets_syngo = function(filename, gene_database = "entrez") { } result = result |> # no duplicate goterm*gene entries (there should be none in SynGO, but enforce in code anyway) - distinct(go_id, genes, .keep_all = T) |> + distinct(go_id, genes, .keep_all = TRUE) |> arrange(genes) |> tidyr::chop(genes) |> mutate(source = paste0("SYNGO_", go_domain), diff --git a/R/partition_genes.R b/R/partition_genes.R index 4481661..493c0f6 100644 --- a/R/partition_genes.R +++ b/R/partition_genes.R @@ -2,15 +2,32 @@ #' Classify genes into 2 groups, e.g. to define significant or topN genes, resulting in a 'signif' column with boolean values #' #' This can be convenient to prepare the significant/test/foreground set for classical ORA, -#' e.g. `test_genesets()` with parameter `method = "fisherexact"`. +#' e.g. `test_genesets()` with parameter `method = "fisherexact"`. Note that the GOAT geneset +#' enrichment algorithm does not use data in the 'signif' column of the input genelist. #' -#' @examples \dontrun{ -#' # significant hits -#' genelist = partition_genes(genelist, col="pvalue_adjust", decreasing=F, cutoff=0.01) -#' # abs(effectsize) >= 4 -#' genelist = partition_genes(genelist, col="effectsize", decreasing=T, use_abs=T, cutoff=4) -#' # top 10% 'best' p-values -#' genelist = partition_genes(genelist, col="pvalue", decreasing=F, fraction = 0.1) +#' @examples \donttest{ +#' # note: this example will download 1 files of approx 4MB +#' +#' # store the downloaded files in the following directory. Here, the temporary file +#' # directory is used. Alternatively, consider storing this data in a more permanent location. +#' # e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +#' output_dir = tempdir() +#' +#' # Download an example gene list, i.e. one of the datasets analyzed in the GOAT manuscript. +#' datasets = download_goat_manuscript_data(output_dir) +#' genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +#' +#' # example 1: significant hits +#' genelist = partition_genes(genelist, col="pvalue_adjust", decreasing=FALSE, cutoff=0.01) +#' cat(sum(genelist$signif), "/", nrow(genelist), "are signif\n") +#' +#' # example 2: abs(effectsize) >= 5 +#' genelist = partition_genes(genelist, col="effectsize", decreasing=TRUE, use_abs=TRUE, cutoff=5) +#' cat(sum(genelist$signif), "/", nrow(genelist), "are signif\n") +#' +#' # example 3: top 10% 'best' p-values +#' genelist = partition_genes(genelist, col="pvalue", decreasing=FALSE, fraction = 0.1) +#' cat(sum(genelist$signif), "/", nrow(genelist), "are signif\n") #' } #' @param genes gene tibble where each row is a unique gene, must contain column name `col` #' @param col column name in `genes` @@ -19,6 +36,7 @@ #' @param cutoff threshold for values in `col` to select (must provide exactly 1 parameter for filtering, either `cutoff`, `fraction` or `topn`) #' @param fraction fraction of rows in `genes` tibble to select (must provide exactly 1 parameter for filtering, either `cutoff`, `fraction` or `topn`) #' @param topn number of rows in `genes` tibble to select (must provide exactly 1 parameter for filtering, either `cutoff`, `fraction` or `topn`) +#' @return input table `genes` with results in the "signif" column #' @export partition_genes = function(genes, col, decreasing = FALSE, use_abs = FALSE, cutoff = NULL, fraction = NULL, topn = NULL) { signif = NULL # fix invisible bindings R package NOTE diff --git a/R/plot_volcano.R b/R/plot_volcano.R new file mode 100644 index 0000000..7b7e5b3 --- /dev/null +++ b/R/plot_volcano.R @@ -0,0 +1,119 @@ + +#' For each provided geneset, a volcano plot of all genelist log2fc and p-values with respective geneset constituents highlighted +#' +#' @examples \donttest{ +#' # note; this example downloads data when first run, and typically takes ~60seconds +#' +#' # store the downloaded files in the following directory. Here, the temporary file +#' # directory is used. Alternatively, consider storing this data in a more permanent location. +#' # e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +#' output_dir = tempdir() +#' +#' ## first run the default example from test_genesets() to obtain geneset results +#' datasets = download_goat_manuscript_data(output_dir) +#' genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +#' genesets_asis = download_genesets_goatrepo(output_dir) +#' genesets_filtered = filter_genesets(genesets_asis, genelist) +#' result = test_genesets(genesets_filtered, genelist, method = "goat", +#' score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) +#' +#' ## example 1; select top10 GO CC terms from the geneset testing results +#' result_subset = result |> filter(source == "GO_CC") |> arrange(pvalue) |> head(n = 10) +#' pdf(paste0(output_dir, "/volcano_CC_top10.pdf"), width = 4, height = 4) +#' plot_volcano(result_subset, genelist) +#' dev.off() +#' +#' ## example 2;, select small genesets that are significant and have +#' ## near-exclusive enrichment in either up up/down-regulated genes +#' # first, add geneset directionality scores to our results +#' result = score_geneset_directionality(result, genelist) +#' # next, subset the geneset result table +#' result_subset = result |> +#' filter(signif & ngenes <= 50 & abs(score_directionality_rank) > 0.6) |> +#' arrange(pvalue_adjust) +#' # finally, create plots. Note that the genelist contains a column 'symbol' +#' # which we use here to print labels for the topN genes per plotted geneset +#' pdf(paste0(output_dir, "/volcano_signif_ngenes50_directionality06.pdf"), width = 4, height = 4) +#' plot_volcano(result_subset, genelist, topn_labels = 10) +#' dev.off() +#' } +#' @param x a subset of the results from `test_genesets` function, see example +#' @param genelist input genelist, must contain columns 'gene', 'log2fc' and 'pvalue_adjust' (not! log transformed). If parameter topn_labels is provided, also include a character column 'symbol' that contains gene names/symbols/labels +#' @param plot if `TRUE`, will directly show the plots. if `FALSE`, returns a list of ggplot objects corresponding to rows in the input `result` parameter +#' @param topn_labels for how many genes that overlap between genelist and a geneset should we plot the gene symbol? This requires a column 'symbol' in the genelist parameter (default: 0) +#' @param color_default color for genes that are not part of a geneset (default: grey) +#' @param color_highlight color used to highlight geneset constituents (default: red) +#' @param color_label provided that topn_labels is set, this is the color of the text labels (default: black) +#' @param pointsize size of the dots, this parameter is passed to geom_point (default: 2) +#' @param pointalpha alpha of the dots, this parameter is passed to geom_point (default: 0.75) +#' @param labelsize provided that topn_labels is set, this is the text size (in pt) for the labels (default: 7) +#' @return if `plot==FALSE`, a list of ggplot2 objects. Otherwise, does not return any value +#' @export +plot_volcano = function(x, genelist, plot = TRUE, topn_labels = 0, color_default = "#B0B0B0", color_highlight = "#ef5350", color_label = "#000000", pointsize = 2, pointalpha = 0.75, labelsize = 7) { + gene = log2fc = pvalue_adjust = highlight = show_label = symbol = NULL # fix invisible bindings R package NOTE + check_dependency("ggplot2", "volcano plots") + # input validation + stopifnot("parameter x must be a result from the test_genesets function, a data.frame containing columns; source(character), id(character), name(character), genes(list), ngenes(integer), pvalue_adjust(numeric)" = + is.data.frame(x) && nrow(x) > 0 && all(c("source", "id", "name", "genes", "ngenes", "pvalue_adjust") %in% colnames(x)) && is.list(x$genes) ) + stopifnot("parameter genelist must be the same genelist was was used as input for the test_genesets function AND contain columns required for creating volcano plots: gene (integer), log2fc(numeric) and pvalue_adjust(numeric)" = + is.data.frame(genelist) && nrow(genelist) > 0 && all(c("gene", "log2fc", "pvalue_adjust") %in% colnames(genelist)) ) + stopifnot("parameter plot must be a single boolean value" = length(plot) == 1 && plot %in% c(TRUE, FALSE)) + stopifnot("parameter topn_labels must be a single non-negative integer value" = length(topn_labels) == 1 && is.numeric(topn_labels) && is.finite(topn_labels) && topn_labels >= 0) + if(topn_labels != 0) { + stopifnot("if parameter topn_labels is not zero, parameter topn_labels must contain a character column 'symbol'" = "symbol" %in% colnames(genelist) && is.character(genelist$symbol)) + } + stopifnot("parameter color_default must be a single string representing a valid R color code" = length(color_default) == 1 && is.character(color_default) && nchar(color_default) > 2 && isvalid_color(color_default)) + stopifnot("parameter color_highlight must be a single string representing a valid R color code" = length(color_highlight) == 1 && is.character(color_highlight) && nchar(color_highlight) > 2 && isvalid_color(color_highlight)) + stopifnot("parameter color_label must be a single string representing a valid R color code" = length(color_label) == 1 && is.character(color_label) && nchar(color_label) > 2 && isvalid_color(color_label)) + stopifnot("parameter pointsize must be a single positive numeric value" = length(pointsize) == 1 && is.numeric(pointsize) && is.finite(pointsize) && pointsize > 0) + stopifnot("parameter pointalpha must be a single non-negative numeric value" = length(pointalpha) == 1 && is.numeric(pointalpha) && is.finite(pointalpha) && pointalpha >= 0) + stopifnot("parameter labelsize must be a single positive numeric value" = length(labelsize) == 1 && is.numeric(labelsize) && is.finite(labelsize) && labelsize > 0) + topn_labels = as.integer(topn_labels) + + plotlist = list() + for(i in 1:nrow(x)) { + plotdata = genelist |> + ungroup() |> + select(gene, log2fc, pvalue_adjust, any_of("symbol")) |> + mutate( + show_label = FALSE, + highlight = factor(gene %in% x$genes[[i]], levels = c("FALSE", "TRUE")) + ) + # if labels are to be shown, sort the genelist by p-value and then flag the topn highlighted genes + if(topn_labels != 0 && any(plotdata$highlight == TRUE)) { + plotdata = plotdata |> arrange(pvalue_adjust) + plotdata$show_label[utils::head(which(plotdata$highlight == TRUE), n = topn_labels)] = TRUE + } + # enforce plot ordering such that highlighted genes are on top + plotdata = plotdata |> arrange(highlight, desc(pvalue_adjust)) + + p = ggplot2::ggplot(plotdata, ggplot2::aes(log2fc, minlog10_fixzero(pvalue_adjust), colour = highlight)) + + ggplot2::geom_point(size = pointsize, alpha = pointalpha, stroke = 0, show.legend = FALSE) + + ggplot2::scale_colour_manual(values = c("FALSE" = color_default, "TRUE" = color_highlight)) + + ggplot2::labs(x = "log2 foldchange", y = "-log10 adjusted p-value", + title = sprintf("%s\n%s %s genes=%d adj.pvalue=%.2g", string_trunc_right(x$name[i], width = 70), + x$source[i], ifelse(x$id[i] != x$name[i], x$id[i], ""), x$ngenes[i], x$pvalue_adjust[i])) + + ggplot2::theme_bw() + + ggplot2::theme( + axis.text = ggplot2::element_text(size = 9), + axis.title = ggplot2::element_text(size = 11), + plot.title = ggplot2::element_text(size = 10), + plot.title.position = "plot" # new param in ggplot 3.2.0 , can use because GOAT requires min version 3.3.0 + ) + + if(topn_labels != 0 && any(plotdata$show_label == TRUE)) { + p = p + ggplot2::geom_text(data = plotdata |> filter(show_label == TRUE), mapping = ggplot2::aes(label = symbol), + size = labelsize / ggplot2::.pt, colour = color_label) + } + + if(plot) { + print(p) + } else { + plotlist[[i]] = p + } + } + + if(!plot) { + return(plotlist) + } +} diff --git a/R/save_genesets.R b/R/save_genesets.R index e12cb88..3b8fe60 100644 --- a/R/save_genesets.R +++ b/R/save_genesets.R @@ -22,6 +22,7 @@ #' @param genelist same as provided for `test_genesets()`. A column named 'symbol' is required, it'll be used to pretty-print gene symbols per geneset in the output table #' @param filename full path to the output file. Supported file extensions; csv, tsv, xlsx. Optionally, set to NA to not write to disk and return the result table instead #' @param arrange_genes set to TRUE (default) to arrange the genelist table by best p-value on top (if column 'pvalue' exists), alternatively by descending absolute effectsize (if no pvalue but effectize is available). Set FALSE to use sorting of the genelist table as-is +#' @return if `filename` is `NA`, returns the validated and formatted geneset result table. Otherwise, writes the table to file and does not return a value #' @export save_genesets = function(x, genelist, filename, arrange_genes = TRUE) { pvalue = effectsize = genes = genes_signif = NULL # fix invisible bindings R package NOTE @@ -42,9 +43,9 @@ save_genesets = function(x, genelist, filename, arrange_genes = TRUE) { } } - is_xlsx = grepl("\\.xlsx$", filename, ignore.case = T) - is_tsv = grepl("\\.tsv$", filename, ignore.case = T) - is_csv = grepl("\\.csv$", filename, ignore.case = T) + is_xlsx = grepl("\\.xlsx$", filename, ignore.case = TRUE) + is_tsv = grepl("\\.tsv$", filename, ignore.case = TRUE) + is_csv = grepl("\\.csv$", filename, ignore.case = TRUE) stopifnot("unknown file extension; filename should should end with '.xslx' or '.tsv' or '.csv'" = is.na(filename) || is_xlsx || is_tsv || is_csv) # note that we again repeat input validation of genesets and genelist to enforce integer ID type -> otherwise, matching/lookup might fail @@ -71,7 +72,7 @@ save_genesets = function(x, genelist, filename, arrange_genes = TRUE) { if("effectsize" %in% colnames(ref)) { ref = ref |> arrange(desc(abs(effectsize))) } else { - cat("save_genesets(); arrange_genes = TRUE, but genelist table has no pvalue nor effectsize column so no sorting is applied\n") + warning("save_genesets(); arrange_genes = TRUE, but genelist table has no pvalue nor effectsize column so no sorting is applied") } } } @@ -87,12 +88,12 @@ save_genesets = function(x, genelist, filename, arrange_genes = TRUE) { # efficiently gene symbols by unlisting, vectorized matching, and relisting gene_to_symbol = function(l, ref) { - ul = unlist(l, recursive = T, use.names = F) + ul = unlist(l, recursive = TRUE, use.names = FALSE) i = match(ul, ref$gene) stopifnot("some genes in the genesets table could not be matched to the genelist table" = !anyNA(i)) ul = ref$symbol[i] result = utils::relist(ul, skeleton = utils::as.relistable(l)) - unlist(lapply(result, paste, collapse = ","), recursive = F, use.names = F) + unlist(lapply(result, paste, collapse = ","), recursive = FALSE, use.names = FALSE) } result$genes_symbol = gene_to_symbol(result$genes, ref) @@ -102,8 +103,8 @@ save_genesets = function(x, genelist, filename, arrange_genes = TRUE) { result$genes_symbol = string_trunc_right(result$genes_symbol, width = nchar_limit) result$genes_signif_symbol = string_trunc_right(result$genes_signif_symbol, width = nchar_limit) # collapse gene identifiers as well + truncate - result$genes = string_trunc_right(unlist(lapply(result$genes, paste, collapse = ","), recursive = F, use.names = F), width = nchar_limit) - result$genes_signif = string_trunc_right(unlist(lapply(result$genes_signif, paste, collapse = ","), recursive = F, use.names = F), width = nchar_limit) + result$genes = string_trunc_right(unlist(lapply(result$genes, paste, collapse = ","), recursive = FALSE, use.names = FALSE), width = nchar_limit) + result$genes_signif = string_trunc_right(unlist(lapply(result$genes_signif, paste, collapse = ","), recursive = FALSE, use.names = FALSE), width = nchar_limit) result = result |> # remove any remaining list-type columns that the user may have added @@ -149,12 +150,13 @@ save_genesets = function(x, genelist, filename, arrange_genes = TRUE) { #' @param x see `save_genesets()` #' @param genelist see `save_genesets()` #' @param settings settings attribute attached to the genesets result table +#' @noRd methods_text = function(x, genelist, settings) { usource = unique(x$source) settings = paste(settings, collapse=" ") # extract parameters from settings string - is_genesets_go = grepl("load_genesets_go", settings, ignore.case = T) - is_genesets_syngo = grepl("syngo", settings, ignore.case = T) + is_genesets_go = grepl("load_genesets_go", settings, ignore.case = TRUE) + is_genesets_syngo = grepl("syngo", settings, ignore.case = TRUE) get_setting = function(attr, s, default = " *** unknown, check code/logfile *** ") { x = gsub(paste0(".*", attr, " *= *'*([^' ,;]+).*"), "\\1", s) diff --git a/R/score_genesets.R b/R/score_genesets.R index 9a24b31..d1e35c5 100644 --- a/R/score_genesets.R +++ b/R/score_genesets.R @@ -1,6 +1,7 @@ #' Compute odds-ratio for each geneset #' +#' @description #' gs_signif = number of significant genes in geneset G that intersect with user's genelist (i.e. foreground genes in G) #' gs_all = number of genes in geneset G that intersect with user's genelist (i.e. foreground+background genes in G) #' k_signif = total number of _significant_ genes in user's genelist @@ -11,15 +12,123 @@ #' #' oddsratio = (gs_signif/gs_all) / (k_signif/k_all) #' -#' @param genesets tibble with genesets, must contain columns 'source', 'id', 'genes' -#' @param genelist tibble with genes, must contain column 'gene', 'log2fc', 'pvalue' +#' @param genesets tibble with genesets, must contain columns 'source', 'id', 'ngenes', 'ngenes_signif' +#' @param genelist tibble with genes, must contain columns 'gene', 'signif' +#' @return input `genesets` with results in column "score_oddsratio" #' @export score_geneset_oddsratio = function(genesets, genelist) { ngenes_signif = ngenes = NULL # fix invisible bindings R package NOTE stopifnot(length(genesets) > 0 && is.data.frame(genesets) && all(c("ngenes", "ngenes_signif") %in% colnames(genesets))) stopifnot(length(genelist) > 0 && is.data.frame(genelist) && "signif" %in% colnames(genelist)) - k_all = nrow(genelist) # total number of genes in user's genelist + k_all = nrow(genelist) # total number of genes in user's genelist k_signif = sum(genelist$signif %in% TRUE) # total number of significant genes in user's genelist genesets |> mutate(score_oddsratio = (ngenes_signif/ngenes) / (k_signif/k_all)) } + + + +#' Compute a score between -1 and 1 representing the proportion of up- or down-regulated genes for each geneset, weighted by gene effectsizes +#' +#' @description +#' Importantly, the scope/utility of this score is limited to help users post-hoc filter for genesets that contain mostly up/down-regulated genes. +#' However, this might not coincide with the geneset pvalues / significance. For example, +#' genesets may exclusively contain genes with a positive effectsize but at the same time these can all be minor effects/values and thus the geneset +#' is not significant or less significant than other genesets with the exact same "directionality score". +#' For example, genesets may contain both up- and down-regulated genes but still be significant when testing with GOAT and using `score_type='effectsize'` +#' +#' The scores computed with this function can help in post-hoc interpretation of GOAT results to further narrow down all significant genesets to a +#' subset with strong directionality. For example, after `test_genesets()` we can filter the results for +#' A) significant genesets and B) that contain at most N genes and C) that are near-exclusively up/down-regulated. +#' Bringing this all together (also useful for other types of geneset testing, like ORA, score_type="pvalue", etc); +#' `result = test_genesets(genesets_filtered, genelist, method = "goat", score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05)` +#' `result = score_geneset_directionality(result, genelist)` +#' `result |> filter(signif & ngenes <= 100 & abs(score_directionality_rank) >= 0.95)` +#' +#' # score definitions; +#' geneset directionality score = weighted mean of respective genes, +#' where gene weights are 1 minus their relative rank in up/down-regulation (depending on negative/positive effectsize) +#' and the value for each gene is -1 or 1 depending on up/down-regulation (sign of effectsize). +#' +#' # pseudocode; +#' 1) gene values and weights +#' A) gene weight between 0 and 1 for the subset of upregulated genes / positive effectsizes; +#' - r = for the subset of genes with effectsize > 0, compute gene rank (1 = highest effectsize, N = smallest effectsize that is greater than zero) +#' - weight = 1 - r/max(r) +#' B) analogous to (A) for the subset of genes with negative effectsize +#' C) result per gene: value = sign of its effectsize, weight = 0 if effectsize 0, otherwise respective weights from (A) or (B) +#' +#' 2) geneset score_directionality = weighted mean over values/weights of respective genes +#' +#' @param genesets tibble with genesets, must contain columns 'source', 'id', 'genes' +#' @param genelist tibble with genes, must contain columns 'gene', 'effectsize' +#' @return input `genesets` with results in 3 columns; +#' `score_directionality_rank` is the weighted gene score where gene values are the sign of their effectsize and weights are linearly proportional to their inverse ranks in the input genelist. +#' `score_directionality_rank2` is similar, but now using rank^2 gene weights to boost the influence of most-important genes in the input genelist. +#' `score_directionality_value` uses the absolute gene effectsizes as gene weights +#' Note that the latter is least robust as it depends on the scaling of input data! +#' @export +score_geneset_directionality = function(genesets, genelist) { + effectsize = value = weight = source = id = gene = score_directionality = score_directionality_weighted = NULL # fix invisible bindings R package NOTE + stopifnot(length(genesets) > 0 && is.data.frame(genesets) && all(c("source", "id", "genes") %in% colnames(genesets)) ) + stopifnot(length(genelist) > 0 && is.data.frame(genelist) && all(c("gene", "effectsize") %in% colnames(genelist)) && all(is.numeric(genelist$effectsize) & is.finite(genelist$effectsize)) ) + + ### 1) gene scores + + # copy of the genelist + gl = data.frame( + gene = genelist$gene, + effectsize = genelist$effectsize, + value = sign(genelist$effectsize) + ) + + # compute ranks within subset of up/down + gl = gl |> arrange(effectsize) # sort by smallest effectsize first (i.e. prio down-regulated) + gl$rank_down = 1L:nrow(gl) + gl$rank_down[gl$value != -1] = NA # remove values for not-downregulated + + gl = gl |> arrange(desc(effectsize)) + gl$rank_up = 1L:nrow(gl) + gl$rank_up[gl$value != 1] = NA + + # compute weights (where 1 = most important gene, near-zero = least important) + # by rescaling ranks between 0~1 we ensure asymmetric effectsize distributions (proportion of negative/positive values) does not influence relative weights + gl$rank_down_rescaled = gl$rank_up_rescaled = NA + if(any(is.finite(gl$rank_down))) { # so we skip max() if there are no non-NA values + gl$rank_down_rescaled = gl$rank_down / max(gl$rank_down, na.rm = TRUE) + } + if(any(is.finite(gl$rank_up))) { + gl$rank_up_rescaled = gl$rank_up / max(gl$rank_up, na.rm = TRUE) + } + + gl$weight_down = 1 - gl$rank_down_rescaled + gl$weight_up = 1 - gl$rank_up_rescaled + + # combine into single weight; i.e. weight_down is only a finite number of effectsize < 0 and vice versa for weight_up + gl$weight = 0 + gl$weight[is.finite(gl$weight_down)] = gl$weight_down[is.finite(gl$weight_down)] + gl$weight[is.finite(gl$weight_up)] = gl$weight_up[is.finite(gl$weight_up)] + + + ### 2) genesets scores + # create a new data.frame with only the required data for a minor speedup + gs = data.frame(source = genesets$source, id = genesets$id, gene = genesets$genes) |> + tidyr::unchop(cols = gene) |> # convert genesets to long format + left_join(gl |> select(gene, value, weight, effectsize), by = "gene") |> # add gene-level values/weights + group_by(source, id) |> # compute geneset scores + summarise( + score_directionality_rank = stats::weighted.mean(value, weight), + score_directionality_rank2 = stats::weighted.mean(value, weight^2), + score_directionality_value = stats::weighted.mean(value, abs(effectsize)), + .groups = "drop" + ) + # scores should always be finite values, but enforce anyway + gs$score_directionality_rank[!is.finite(gs$score_directionality_rank)] = 0 + gs$score_directionality_rank2[!is.finite(gs$score_directionality_rank2)] = 0 + gs$score_directionality_value[!is.finite(gs$score_directionality_value)] = 0 + + # join results into input table + genesets |> + select(-any_of(c("score_directionality_rank", "score_directionality_rank2", "score_directionality_value"))) |> + left_join(gs, by = c("source", "id")) +} diff --git a/R/simplify_ontology.R b/R/simplify_ontology.R index 8d8d803..a62f4b2 100644 --- a/R/simplify_ontology.R +++ b/R/simplify_ontology.R @@ -2,20 +2,27 @@ #' Plot a treemap #' #' @description simple wrapper around the treemap R package. To customize this plot, copy/paste its code and tweak parameters as desired -#' @examples \dontrun{ -#' # use GOAT as per usual -#' genelist = goat_example_datasets[["Hondius 2021:mass-spec:PMID33492460"]] -#' genesets = load_genesets_go_bioconductor() -#' genesets_filtered = filter_genesets(genesets, genelist) -#' goat_result = test_genesets( -#' genesets_filtered, genelist, method = "goat", score_type = "effectsize", -#' padj_method = "bonferroni", padj_cutoff = 0.05 -#' ) +#' @examples \donttest{ +#' # note; this example downloads data when first run, and typically takes ~60seconds +#' +#' # store the downloaded files in the following directory. Here, the temporary file +#' # directory is used. Alternatively, consider storing this data in a more permanent location. +#' # e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +#' output_dir = tempdir() +#' +#' ## first run the default example from test_genesets() to obtain geneset results +#' datasets = download_goat_manuscript_data(output_dir) +#' genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +#' genesets_asis = download_genesets_goatrepo(output_dir) +#' genesets_filtered = filter_genesets(genesets_asis, genelist) +#' result = test_genesets(genesets_filtered, genelist, method = "goat", +#' score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) +#' #' # subset GO CC results -#' x = goat_result |> filter(signif & source == "GO_CC") +#' x = result |> filter(signif & source == "GO_CC") #' tm = treemap_data( #' geneset_ids = x$id, -#' genesets = genesets, +#' genesets = genesets_filtered, #' genesets_test_result = x, #' simplify = "leaf_only" # options: none/leaf_only/prune_singletons/pvalue #' ) @@ -23,6 +30,7 @@ #' } #' @param x `treemap_plotdata` data table that was computed by the `treemap_data()` function #' @param label_group set TRUE to show only group-level labels +#' @return a ggplot2 object constructed by `treemap::treemap()` #' @export treemap_plot = function(x, label_group = FALSE) { check_dependency("treemap", "plotting treemaps") @@ -63,9 +71,8 @@ treemap_plot = function(x, label_group = FALSE) { #' Construct tree and treemap data structures from geneset parent/child relations #' -#' @examples \dontrun{ -#' # refer to the goat::treemap_plot() function for a complete example -#' } +#' refer to the goat::treemap_plot() function for a complete example +#' #' @param geneset_ids vector of geneset identifiers #' @param genesets entire geneset table; typically the complete GO database #' @param genesets_test_result geneset testing results; the output from `test_genesets()` @@ -75,6 +82,7 @@ treemap_plot = function(x, label_group = FALSE) { #' "pvalue" (remove parent terms where the child term p-value is at least 4 times better) #' "none" (default; return all significant genesets that are not a "grouping term" in the treemap) #' @param toplevel_max_ngenes groups in the treemap should not have more than this many genes ('ngenes' in geneset test results). If not set, this defaults to 50% of the total number of unique genes in the geneset test results +#' @return data structure needed for `treemap_plot()` #' @export treemap_data = function(geneset_ids, genesets, genesets_test_result, simplify = "none", toplevel_max_ngenes = NA) { ods = ontology_data_structures(geneset_ids = geneset_ids, genesets = genesets, genesets_test_result = genesets_test_result) @@ -92,6 +100,7 @@ treemap_data = function(geneset_ids, genesets, genesets_test_result, simplify = #' @param p p #' @param edgelist edgelist #' @param result result +#' @noRd edgelist_find_children_recursive = function(p, edgelist, result) { children = setdiff(edgelist$child[edgelist$parent == p], result) if(length(children) == 0) return(result) @@ -109,6 +118,7 @@ edgelist_find_children_recursive = function(p, edgelist, result) { #' @param geneset_table data.frame with columns 'id' and 'parent_id' #' @param query set of all colored nodes (vector) #' @param result all colored parents, accumulated recursively (vector) +#' @noRd edgelist_find_colored_parents_recursive = function(x, geneset_table, query, result) { i = match(x, geneset_table$id) if(!is.na(i)) { @@ -135,6 +145,7 @@ edgelist_find_colored_parents_recursive = function(x, geneset_table, query, resu #' @param name element name #' @param ngenes element ngenes property #' @param edgelist data.frame with properties parent, child, child_name, child_ngenes +#' @noRd edgelist_as_nested = function(x, name, ngenes, edgelist) { l = list(id = x, name = name, ngenes = ngenes, children = list()) rows = edgelist$parent == x @@ -153,6 +164,7 @@ edgelist_as_nested = function(x, name, ngenes, edgelist) { #' #' @param x list representing a nested DAG #' @param shortlist vector of element IDs +#' @noRd nested_aggregate_child_stats = function(x, shortlist) { x$recursiveChildren = c() x$recursiveChildrenShortlist = c() @@ -175,6 +187,7 @@ nested_aggregate_child_stats = function(x, shortlist) { #' Recursively replace a DAG element by its children while the child has the same gene count as the parent #' #' @param x list representing a nested DAG +#' @noRd nested_find_equivalent_child_recursive = function(x) { if(length(x$children) > 0) { child_ngenes = unlist(lapply(x$children, "[[", "ngenes")) @@ -196,6 +209,7 @@ nested_find_equivalent_child_recursive = function(x) { #' @param obj list representing a nested DAG #' @param threshold stop if `obj$ngenes <= threshold` #' @param result list of resulting elements (when calling this function, use default empty list) +#' @noRd nested_find_level1_children = function(obj, threshold, result = list()) { # halt recursion when there are no children, or node has less than genes if(length(obj$children) == 0 || obj$ngenes <= threshold) { @@ -216,6 +230,7 @@ nested_find_level1_children = function(obj, threshold, result = list()) { #' @param ids shortlist of geneset IDs #' @param genesets importantly, genesets should be the input genesets and not the "filtered" genesets because #' only the former contains the complete ontological structure (parent/child links between genesets) +#' @noRd edgelist_from_ontology = function(ids, genesets) { if(length(ids) == 0) { return() @@ -239,6 +254,7 @@ edgelist_from_ontology = function(ids, genesets) { #' #' @param edgelist result from e.g. `edgelist_from_ontology()` #' @param genesets filtered genesets +#' @noRd edgelist_find_root = function(edgelist, genesets) { root_id = setdiff(unique(edgelist$parent), unique(edgelist$child)) stopifnot(length(root_id) == 1) @@ -255,6 +271,7 @@ edgelist_find_root = function(edgelist, genesets) { #' @param geneset_ids vector of geneset identifiers #' @param genesets entire geneset table; typically the complete GO database #' @param genesets_test_result geneset testing results; the output from `test_genesets()` +#' @noRd ontology_data_structures = function(geneset_ids, genesets, genesets_test_result) { name = ngenes = pvalue = parent_ngenes = child = NULL # fix invisible bindings R package NOTE stopifnot("parameter geneset_ids must not be empty and contain valid genesets_test_result$id values" = length(geneset_ids) > 0 && is.character(geneset_ids) && all(geneset_ids %in% genesets_test_result$id)) @@ -303,6 +320,7 @@ ontology_data_structures = function(geneset_ids, genesets, genesets_test_result) #' "pvalue" (remove parent terms where the child term p-value is at least 4 times better) #' "none" (default; return all significant genesets that are not a "grouping term" in the treemap) #' @param toplevel_max_ngenes groups in the treemap should not have more than this many genes ('ngenes' in geneset test results) +#' @noRd build_treemap = function(ods, simplify = "none", toplevel_max_ngenes = Inf) { parent = parent_pvalue = child = child_pvalue = child_name = child_ngenes = children = name = ngenes = group_ngenes = subgroup_name = NULL # fix invisible bindings R package NOTE stopifnot(length(simplify) == 1 && simplify %in% c("leaf_only", "prune_singletons", "pvalue", "none")) @@ -390,6 +408,6 @@ build_treemap = function(ods, simplify = "none", toplevel_max_ngenes = Inf) { distinct(subgroup_name, .keep_all = TRUE) } - cat(nrow(treemap_plotdata), "/", length(ods$geneset_ids), " genesets remain after simplifying ontology structure by '", simplify , "'\n", sep="") + message(paste0(nrow(treemap_plotdata), "/", length(ods$geneset_ids), " genesets remain after simplifying ontology structure by '", simplify , "'")) return(list(treemap_data = treemap_data, treemap_plotdata = treemap_plotdata)) } diff --git a/R/simplify_similarity.R b/R/simplify_similarity.R index 32c28a3..89d02f9 100644 --- a/R/simplify_similarity.R +++ b/R/simplify_similarity.R @@ -14,6 +14,7 @@ #' @param signifgenes_fraction the minimum fraction of "foreground genes" ('genes_signif' column) found across all significant genesets that should be covered by the reduced geneset collection. #' This parameter doesn't do anything if there are fewer than 5 "foreground genes" alltogether. #' Typical settings for this parameter are 0.75~0.95 (0.9 is default) +#' @return the genesets table from the `clusters` parameter, with results in column "signif_and_reduced" #' @export reduce_genesets = function(clusters, simscore_threshold = 0.9, universe_fraction = 0.25, signifgenes_fraction = 0.9) { ngenes_signif = genes_signif = rescue_count = ngenes = rescue_score = ngenes_input = NULL # fix invisible bindings R package NOTE @@ -82,6 +83,7 @@ reduce_genesets = function(clusters, simscore_threshold = 0.9, universe_fraction #' @param x results from `test_genesets()` #' @param genelist should be the same as provided to `test_genesets()` #' @param hclust_method hierarchical clustering method, any of; 'ward.D', 'ward.D2' (default), 'single', 'complete', 'average' +#' @return a list with elements genesets (param `x`), similarity, hc_row, hc_col #' @export cluster_genesets = function(x, genelist, hclust_method = "ward.D2") { x = validate_genesets(x) @@ -148,11 +150,12 @@ cluster_genesets = function(x, genelist, hclust_method = "ward.D2") { #' utility function that converts a genesets table into a sparse gene*geneset identity matrix #' #' @param genesets genesets tibble. Must contain columns; "id", "genes", "ngenes" +#' @noRd genesets_as_matrix = function(genesets) { stopifnot(c("id", "genes", "ngenes") %in% colnames(genesets)) # unlist the geneset table (row = geneset, genes are packed in a list column) ul_id = rep(genesets$id, genesets$ngenes) - ul_gene = unlist(genesets$genes, recursive = F, use.names = F) + ul_gene = unlist(genesets$genes, recursive = FALSE, use.names = FALSE) # ul_score = genelist$score_pval[match(ul_gene, genelist$gene)] # this strategy is much slower on huge datasets # tmp = aggregate(ul_score, by = list(ul_id), FUN=sum) # this strategy is much slower on huge datasets ugene = unique(ul_gene) @@ -174,6 +177,7 @@ genesets_as_matrix = function(genesets) { #' @param genelist_geneid same identifiers as `mat_row_geneid` #' @param genelist_scores a matrix where columns are various gene score types #' @param weighted boolean value +#' @noRd geneset_similarity_matrix = function(mat, mat_row_geneid, mat_col_gsid, genelist_geneid, genelist_scores, weighted = TRUE) { ### 1) find the score for each row in the gene*geneset matrix via lookup in the genelist table @@ -214,28 +218,49 @@ geneset_similarity_matrix = function(mat, mat_row_geneid, mat_col_gsid, genelist #' plot the geneset similarity matrix as a heatmap #' -#' @examples \dontrun{ -#' # try various color palettes -#' plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Viridis", rev = F)) -#' plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Inferno", rev = F)) -#' plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Lajolla", rev = T)) -#' plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Mako", rev = F)) -#' plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Turku", rev = T)) -#' plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Grays", rev = T)) +#' @examples \donttest{ +#' # note; this example downloads data when first run, and typically takes ~60seconds +#' +#' # store the downloaded files in the following directory. Here, the temporary file +#' # directory is used. Alternatively, consider storing this data in a more permanent location. +#' # e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +#' output_dir = tempdir() +#' +#' ## first run the default example from test_genesets() to obtain geneset results +#' datasets = download_goat_manuscript_data(output_dir) +#' genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +#' genesets_asis = download_genesets_goatrepo(output_dir) +#' genesets_filtered = filter_genesets(genesets_asis, genelist) +#' result = test_genesets(genesets_filtered, genelist, method = "goat", +#' score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) +#' +#' # prior to running this function, cluster the genesets +#' clusters = cluster_genesets(result, genelist) +#' +#' # use the plot heatmap function and try various color palettes +#' plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Viridis", rev = FALSE)) +#' plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Inferno", rev = FALSE)) +#' plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Lajolla", rev = TRUE)) +#' plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Mako", rev = FALSE)) +#' plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Turku", rev = TRUE)) +#' plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Grays", rev = TRUE)) #' } #' @param x result from `cluster_genesets()` -#' @param output_dir full path to output directory. Set to NA to directly show the figures instead of writing them to file -#' @param heatmap_colors a vector of 100 colors to be used for the heatmap (101 breaks are computed between 0 and the max value in the distance matrix) +#' @param output_dir set to NA to directly show the figures instead of writing them to file. +#' Otherwise, this is the full path to the directory where the downloaded files should be stored. Directory is created if it does not exist. +#' e.g. `output_dir="~/data"` on unix systems, `output_dir="C:/data"` on Windows, or set to `output_dir=getwd()` to write output to the current working directory +#' @param colors a vector of 100 colors to be used for the heatmap (101 breaks are computed between 0 and the max value in the distance matrix) #' @param fontsize parameter sent to pheatmap::pheatmap(); control the size of labels in the plot, defaults to 10. Note that you can also change the plot device size, see examples +#' @return does not return a value, plots are printed to device or files depending on `output_dir` parameter #' @export -plot_heatmap = function(x, output_dir = NA, heatmap_colors = grDevices::hcl.colors(100, "Viridis", rev = F), fontsize = 10) { +plot_heatmap = function(x, output_dir, colors = grDevices::hcl.colors(100, "Viridis", rev = FALSE), fontsize = 10) { check_dependency("pheatmap", "plotting heatmaps") stopifnot("parameter output_dir must be a full path for the output files, this directory must already exists" = length(output_dir) == 1 && (is.na(output_dir) || (is.character(output_dir) && nchar(output_dir) > 4 && dir.exists(output_dir)) ) ) - stopifnot("heatmap_colors parameter must be a vector of 100 html color-codes" = length(heatmap_colors) == 100 && is.character(heatmap_colors) && all(grepl("^#[0-9a-zA-Z]{6}$", heatmap_colors))) + stopifnot("colors parameter must be a vector of 100 html color-codes" = length(colors) == 100 && is.character(colors) && all(grepl("^#[0-9a-zA-Z]{6}$", colors))) stopifnot("fontsize parameter must be a positive number" = length(fontsize) == 1 && is.numeric(fontsize) && is.finite(fontsize) && fontsize > 0) if(length(x) == 0 || !is.list(x) || !"similarity" %in% names(x) || length(names(x$similarity)) == 0) { - cat("empty data provided to heatmap plot function\n") + warning("empty data provided to heatmap plot function") return() } stopifnot("parameter x must be a result from the cluster_genesets() function; cannot find a geneset table" = @@ -295,7 +320,7 @@ plot_heatmap = function(x, output_dir = NA, heatmap_colors = grDevices::hcl.colo mat_ngenes_breaks = pretty(c(min(c(10, mat_ngenes)), max(c(mat_ngenes, 50))), n = 8) nbreaks = length(mat_ngenes_breaks) mat_ngenes_colors = stats::setNames( - grDevices::hcl.colors(nbreaks - 1, palette = "Grays", rev = T), + grDevices::hcl.colors(nbreaks - 1, palette = "Grays", rev = TRUE), mat_ngenes_breaks[-1] ) # bin / color index (goes to nbreaks-1 max) @@ -307,7 +332,7 @@ plot_heatmap = function(x, output_dir = NA, heatmap_colors = grDevices::hcl.colo annot_row = data.frame( `gene count` = mat_ngenes_breaks[mat_ngenes_binned], row.names = rownames(hm_data), - check.names = F + check.names = FALSE ) annot_clr = list( `gene count` = mat_ngenes_colors @@ -343,7 +368,7 @@ plot_heatmap = function(x, output_dir = NA, heatmap_colors = grDevices::hcl.colo annotation_colors = annot_clr, annotation_legend = TRUE, breaks = heatmap_breaks, - color = heatmap_colors, + color = colors, cellwidth = 10, cellheight = 10, filename = f, @@ -365,30 +390,47 @@ plot_heatmap = function(x, output_dir = NA, heatmap_colors = grDevices::hcl.colo #' Lollipop chart or barplot visualization of geneset enrichment testing results #' -#' @examples \dontrun{ -#' # generate lollipop charts for each GO domain (CC/BP/MF), with geneset -log10 -#' # adjusted p-value on the x-axis and color-coding by geneset up/down-regulation -#' plot_lollipop( -#' result, output_dir = getwd(), plot_type = "lollipop", topn = 50, -#' score_xaxis = "minlogp", score_color = "updown" -#' ) +#' @examples \donttest{ +#' # note; this example downloads data when first run, and typically takes ~60seconds +#' +#' # store the downloaded files in the following directory. Here, the temporary file +#' # directory is used. Alternatively, consider storing this data in a more permanent location. +#' # e.g. output_dir="~/data/go" on unix systems or output_dir="C:/data/go" on Windows +#' output_dir = tempdir() #' -#' # alternatively, as a barplot -#' plot_lollipop( -#' result, output_dir = getwd(), plot_type = "barplot", topn = 50, -#' score_xaxis = "minlogp", score_color = "updown" -#' ) +#' ## first run the default example from test_genesets() to obtain geneset results +#' datasets = download_goat_manuscript_data(output_dir) +#' genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +#' genesets_asis = download_genesets_goatrepo(output_dir) +#' genesets_filtered = filter_genesets(genesets_asis, genelist) +#' result = test_genesets(genesets_filtered, genelist, method = "goat", +#' score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) #' -#' # alternatively, color-code genesets by enrichment of significant genes using -#' # parameter `score_color="oddsratio"` . See further `score_geneset_oddsratio` -#' # function documentation for definition/computation of this score. -#' plot_lollipop( -#' result, output_dir = getwd(), plot_type = "lollipop", topn = 50, -#' score_xaxis = "minlogp", score_color = "oddsratio" -#' ) +#' # generate lollipop charts for each GO domain (CC/BP/MF), with geneset -log10 +#' # adjusted p-value on the x-axis and color-coding by geneset up/down-regulation +#' plot_lollipop( +#' result, output_dir, plot_type = "lollipop", topn = 50, +#' score_xaxis = "minlogp", score_color = "updown" +#' ) +#' +#' # alternatively, as a barplot +#' plot_lollipop( +#' result, output_dir, plot_type = "barplot", topn = 50, +#' score_xaxis = "minlogp", score_color = "updown" +#' ) +#' +#' # alternatively, color-code genesets by enrichment of significant genes using +#' # parameter `score_color="oddsratio"` . See further `score_geneset_oddsratio` +#' # function documentation for definition/computation of this score. +#' plot_lollipop( +#' result, output_dir, plot_type = "lollipop", topn = 50, +#' score_xaxis = "minlogp", score_color = "oddsratio" +#' ) #' } #' @param x results from function `test_genesets` -#' @param output_dir full path to output directory. Set to NA to directly show the figures instead of writing them to file +#' @param output_dir set to NA to directly show the figures instead of writing them to file. +#' Otherwise, this is the full path to the directory where the downloaded files should be stored. Directory is created if it does not exist. +#' e.g. `output_dir="~/data"` on unix systems, `output_dir="C:/data"` on Windows, or set to `output_dir=getwd()` to write output to the current working directory #' @param only_reduced only show the reduced/summarized set of significant genesets. This requires that you first applied the `reduce_genesets` function #' @param plot_type Options: "barplot", "lollipop" (default) #' @param show_pvalue boolean parameter that indicates whether adjusted p-values should be shown next to each data point @@ -405,8 +447,9 @@ plot_heatmap = function(x, output_dir = NA, heatmap_colors = grDevices::hcl.colo #' @param max_ngenes only plot terms with less than N genes (quick way to get rid of large/unspecific terms) #' @param topn topn terms to show after sorting genesets by p-value. For example, this makes it easy to plot the top10 GO terms. Set to NA to ignore this filter (default) #' @param padj_cutoff adjusted pvalue cutoff for terms shown in the plot. If set to NA (default), all significant genesets are used (i.e. 'signif' column in the input geneset table) +#' @return if `output_dir` is `NA`, a list of ggplot2 objects. Otherwise, write plots to file and do not return any value #' @export -plot_lollipop = function(x, output_dir = NA, only_reduced = FALSE, plot_type = "lollipop", show_pvalue = FALSE, score_xaxis = "minlogp", score_color = ifelse(is.data.frame(x) && "score_type" %in% colnames(x) && is.character(x$score_type) && any(x$score_type %in% c("effectsize_up","effectsize_down")), "updown", "minlogp"), score_color_limits = "source", score_color_updown = c("#E57373", "#5E7ABC"), max_ngenes = NA, topn = NA, padj_cutoff = NA) { +plot_lollipop = function(x, output_dir, only_reduced = FALSE, plot_type = "lollipop", show_pvalue = FALSE, score_xaxis = "minlogp", score_color = ifelse(is.data.frame(x) && "score_type" %in% colnames(x) && is.character(x$score_type) && any(x$score_type %in% c("effectsize_up","effectsize_down")), "updown", "minlogp"), score_color_limits = "source", score_color_updown = c("#E57373", "#5E7ABC"), max_ngenes = NA, topn = NA, padj_cutoff = NA) { signif_and_reduced = ngenes = pvalue_adjust = pvalue = rank__ = name = score_oddsratio = score_clr = xvalue = clrvalue = NULL # fix invisible bindings R package NOTE check_dependency("ggplot2", "plot lollipop chart") x = validate_genesets(x) @@ -432,7 +475,7 @@ plot_lollipop = function(x, output_dir = NA, only_reduced = FALSE, plot_type = " if(score_color == "updown") { stopifnot("parameter score_color was set to 'updown' so we expect column 'score_type' in the provided data table AND it should contain 'effectsize_up' and/or 'effectsize_down' values (i.e. when using GOAT with parameter score_type='effectsize')" = "score_type" %in% colnames(x) && all(is.character(x$score_type)) ) if(!any(x$score_type %in% c("effectsize_up","effectsize_down"))) { - cat("*** warning: parameter score_color was set to 'updown' but we did not find any 'effectsize_up' or 'effectsize_down' values in column 'score_type' and thus all elements will be color-coded in grey (NA). Try another color-coding, or use GOAT with parameter score_type='effectsize'\n") + warning("parameter score_color was set to 'updown' but we did not find any 'effectsize_up' or 'effectsize_down' values in column 'score_type' and thus all elements will be color-coded in grey (NA). Try another color-coding, or use GOAT with parameter score_type='effectsize'") } } if(only_reduced) { @@ -462,7 +505,7 @@ plot_lollipop = function(x, output_dir = NA, only_reduced = FALSE, plot_type = " # return if there is no data to plot if(nrow(x) == 0) { - cat("zero genesets match your filtering criteria, cannot generate plots\n") + warning("zero genesets match your filtering criteria, cannot generate plots") return() } @@ -509,7 +552,7 @@ plot_lollipop = function(x, output_dir = NA, only_reduced = FALSE, plot_type = " # data subset for figure @ current source x_src = x |> filter(source == src) if(nrow(x_src) > 100) { - cat(src, "has more than 100 significant genesets, not creating plot. You can run this function and explicitly request to plot only the topn genesets, see documentation for 'topn' parameter\n") + warning(paste(src, "has more than 100 significant genesets, not creating plot. You can run this function and explicitly request to plot only the topn genesets, see documentation for 'topn' parameter")) next } @@ -618,7 +661,6 @@ plot_lollipop = function(x, output_dir = NA, only_reduced = FALSE, plot_type = " ) if(plot_type == "lollipop") { p = p + ggplot2::theme(legend.justification = c(1, 0.5)) - print("test align") } } @@ -664,15 +706,20 @@ plot_lollipop = function(x, output_dir = NA, only_reduced = FALSE, plot_type = " #' plot geneset distance matrix as a network #' +#' #' @param clusters result from `cluster_genesets` #' @param src source property (e.g. "GO_CC") #' @param show_clusters boolean value #' @param show_text boolean value #' @param topn_edges topN edges to retain per geneset (typically 5~8) #' @param clr_default default color for the network, used only when `show_clusters` is set to `FALSE` +#' @param node_color_palette function with 1 parameter, N, that returns N colors (default is goat function `gg_color_hue`) +#' @return a ggplot2 object #' @export -plot_network = function(clusters, src, show_clusters = TRUE, show_text = FALSE, topn_edges = 5, clr_default = "#29b6f6") { +plot_network = function(clusters, src, show_clusters = TRUE, show_text = FALSE, topn_edges = 5, clr_default = "#29b6f6", node_color_palette = goat::gg_color_hue) { source = cl = weight = genecount = cluster = label = NULL + check_dependency("igraph", "creating network plots") + check_dependency("ggraph", "creating network plots") ## input validation # TODO @@ -685,7 +732,7 @@ plot_network = function(clusters, src, show_clusters = TRUE, show_text = FALSE, ## from matrix to edgelist el = NULL for(i in 1:nrow(mat)) { - i_cols = utils::head(order(mat[i,], decreasing = T), topn_edges) + i_cols = utils::head(order(mat[i,], decreasing = TRUE), topn_edges) el = bind_rows(el, data.frame(from = rownames(mat)[i], to = colnames(mat)[i_cols], weight = mat[i,i_cols]) ) } el = el |> filter(weight > 0.1) @@ -698,7 +745,6 @@ plot_network = function(clusters, src, show_clusters = TRUE, show_text = FALSE, igraph::V(g)$label = clusters$genesets$name[match(igraph::V(g)$name, clusters$genesets$id)] ## layout - set.seed(123) gl = igraph::layout_with_graphopt(g, charge = 0.05, spring.constant = 10) # gl = igraph::layout_with_fr(g, niter = 1000) @@ -711,22 +757,26 @@ plot_network = function(clusters, src, show_clusters = TRUE, show_text = FALSE, ## color df_clusters = data.frame(cl = unique(igraph::V(g)$cluster)) |> arrange(cl) - df_clusters$clr = gg_color_hue(nrow(df_clusters)) - df_clusters$fill = lighten_color(df_clusters$clr, 0.25) + df_clusters$clr = df_clusters$fill = node_color_palette(nrow(df_clusters)) + df_clusters$fill = lighten_color(df_clusters$fill, 0.1) + df_clusters$clr = darken_color(df_clusters$clr, 0.1) + # df_clusters$clr = node_color_palette(nrow(df_clusters)) + # df_clusters$fill = lighten_color(df_clusters$clr, 0.25) + p = ggraph::ggraph(g, layout = gl) + ggraph::geom_edge_link0(ggplot2::aes(width = I(0.5 + weight / 10), - colour = I(grDevices::colorRampPalette(c("#ECEFF1", "#CFD8DC"))(10)[ cut(weight, breaks = (0:10)/10, include.lowest = T, labels=F) ]) ), - show.legend = F) + colour = I(grDevices::colorRampPalette(c("#ECEFF1", "#CFD8DC"))(10)[ cut(weight, breaks = (0:10)/10, include.lowest = TRUE, labels = FALSE) ]) ), + show.legend = FALSE) if(show_clusters) { if(show_text) { - p = p + ggraph::geom_node_point(ggplot2::aes(size = genecount, colour = I(df_clusters$fill[cluster])), show.legend = F) + p = p + ggraph::geom_node_point(ggplot2::aes(size = genecount, colour = I(df_clusters$fill[cluster])), show.legend = FALSE) } else { - p = p + ggraph::geom_node_point(ggplot2::aes(size = genecount, colour = I(df_clusters$clr[cluster]), fill = I(df_clusters$fill[cluster])), shape = 21, show.legend = F) + p = p + ggraph::geom_node_point(ggplot2::aes(size = genecount, colour = I(df_clusters$clr[cluster]), fill = I(df_clusters$fill[cluster])), shape = 21, show.legend = FALSE) } } else { - p = p + ggraph::geom_node_point(ggplot2::aes(size = genecount), colour = clr_default, show.legend = F) + p = p + ggraph::geom_node_point(ggplot2::aes(size = genecount), colour = clr_default, show.legend = FALSE) } p = p + diff --git a/R/test_genesets.R b/R/test_genesets.R index c38b713..b54fed5 100644 --- a/R/test_genesets.R +++ b/R/test_genesets.R @@ -9,6 +9,33 @@ #' #' Note that this is more rigorous than typical GO tools; hypothetically, one could split all GO_CC pathways into 1000 different databases/'sources' and then run enrichment testing. Consequently, the multiple testing burden is reduced if one doesn't adjust p-values for the number of 'sources' as we do here. #' +#' @examples \donttest{ +#'#' # note; this example downloads data when first run, and typically takes ~60seconds +#' +#' ## Basic example for a complete GOAT workflow +#' # Downloads test data to your computer and stores it at current working directory +#' # Refer to the GitHub documentation for elaborate documentation and a worked example +#' +#' # store the downloaded files in the following directory. Here, the temporary file +#' # directory is used. Alternatively, consider storing this data in a more permanent location. +#' # e.g. output_dir="~/data/go" on unix systems or output_dir="C:/data/go" on Windows +#' output_dir = tempdir() +#' +#' # download an example gene list +#' datasets = download_goat_manuscript_data(output_dir) +#' genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +#' +#' # download GO genesets +#' genesets_asis = download_genesets_goatrepo(output_dir) +#' +#' # filter genesets for sufficient overlap with the genelist, then apply GOAT +#' genesets_filtered = filter_genesets(genesets_asis, genelist) +#' result = test_genesets(genesets_filtered, genelist, method = "goat", +#' score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) +#' +#' # print first 10 rows of the result table +#' print(result |> select(source, name, ngenes, pvalue_adjust) |> utils::head(n=10)) +#' } #' @param genesets tibble with genesets, must contain columns 'source', 'source_version', 'id', 'name', 'genes', 'ngenes', 'ngenes_signif' #' @param genelist tibble with genes, must contain column 'gene' and 'test'. gene = character column, which are matched against list column 'genes' in genesets tibble. test = boolean column (you can set all to FALSE if not performing Fisher-exact or hypergeometric test downstream) #' @param method method for overrepresentation analysis. Options: "goat", "hypergeometric", "fisherexact", "fisherexact_ease", "gsea", "idea" @@ -46,7 +73,7 @@ test_genesets = function(genesets, genelist, method, padj_method = "BH", padj_so result = test_genesets_fisherexact(genesets, genelist, ...) } if(method == "fisherexact_ease") { - result = test_genesets_fisherexact(genesets, genelist, use_ease = T, ...) + result = test_genesets_fisherexact(genesets, genelist, use_ease = TRUE, ...) } if(method == "hypergeometric") { result = test_genesets_hypergeometric(genesets, genelist, ...) @@ -54,26 +81,23 @@ test_genesets = function(genesets, genelist, method, padj_method = "BH", padj_so if(method == "gsea") { result = test_genesets_gsea(genesets, genelist, ...) } - if(method == "idea") { - result = test_genesets_idea(genesets, genelist, ...) - } - # # fallthrough; guess if the user provided some custom function - # if(is.null(result)) { - # f = tryCatch(match.fun(method, descend = FALSE), error = function(...) NULL) - # if(!is.function(f)) { - # if(grepl("::", method, fixed = T)) { - # f = tryCatch(utils::getFromNamespace(gsub(".*::", "", method), gsub("::.*", "", method), envir = .GlobalEnv), error = function(...) NULL) - # } else { - # f = tryCatch(utils::getFromNamespace(algorithm, "goat", envir = .GlobalEnv), error = function(...) NULL) - # } - # } - # if(is.function(f)) { - # result = f(genesets = genesets, genelist = genelist, ...) - # stopifnot("custom geneset-test-function should return the input 'genesets' table with an additional 'pvalue' numeric column included" = - # is.data.frame(result) && "pvalue" %in% colnames(result) && is.numeric(result$pvalue)) - # } - # } + # fallthrough; guess if the user provided some custom function + if(is.null(result)) { + f = tryCatch(match.fun(method, descend = FALSE), error = function(...) NULL) + if(!is.function(f)) { + if(grepl("::", method, fixed = TRUE)) { + f = tryCatch(utils::getFromNamespace(gsub(".*::", "", method), gsub("::.*", "", method), envir = .GlobalEnv), error = function(...) NULL) + } else { + f = tryCatch(utils::getFromNamespace(algorithm, "goat", envir = .GlobalEnv), error = function(...) NULL) + } + } + if(is.function(f)) { + result = f(genesets = genesets, genelist = genelist, ...) + stopifnot("custom geneset-test-function should return the input 'genesets' table with an additional 'pvalue' numeric column included" = + is.data.frame(result) && "pvalue" %in% colnames(result) && is.numeric(result$pvalue)) + } + } # fallthrough check for valid method parameter diff --git a/R/test_genesets_fisherexact.R b/R/test_genesets_fisherexact.R index 137536a..6f166da 100644 --- a/R/test_genesets_fisherexact.R +++ b/R/test_genesets_fisherexact.R @@ -10,14 +10,24 @@ #' #' Only genesets with at least 1 significant gene are subjected to statistical testing (e.g. NA is returned for genesets without significant genes) #' -#' @examples \dontrun{ -#' ### same results between Fisher-exact and hypergeometric tests -#' # < prepare genelist table first > -#' gs = load_genesets_go_bioconductor() -#' gsf = filter_genesets(gs, genelist, min_overlap = 10L, max_overlap = 1500L) -#' x_hg = test_genesets_hypergeometric(gsf, genelist, min_signif = 3L) -#' x_fe = test_genesets_fisherexact(gsf, genelist, min_signif = 3L) -#' all.equal(x_hg$pvalue, x_fe$pvalue) +#' @examples \donttest{ +#' # note; this example downloads data when first run, and typically takes ~60seconds +#' +#' # store the downloaded files in the following directory. Here, the temporary file +#' # directory is used. Alternatively, consider storing this data in a more permanent location. +#' # e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +#' output_dir = tempdir() +#' +#' ## first run the default example from test_genesets() to obtain input data +#' datasets = download_goat_manuscript_data(output_dir) +#' genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +#' genesets_asis = download_genesets_goatrepo(output_dir) +#' genesets_filtered = filter_genesets(genesets_asis, genelist) +#' +#' ## example: same results between Fisher-exact and hypergeometric tests +#' result_hg = test_genesets_hypergeometric(genesets_filtered, genelist, require_nsignif = 3L) +#' result_fe = test_genesets_fisherexact(genesets_filtered, genelist, require_nsignif = 3L) +#' all.equal(result_hg$pvalue, result_fe$pvalue) #' } #' @param genesets tibble with genesets, must contain columns 'id', 'ngenes' and 'ngenes_signif' #' @param genelist tibble with genes, must contain column 'signif'. The number of rows in this table (where signif is not NA) @@ -25,6 +35,7 @@ #' @param require_nsignif minimum number of 'signif genes' that overlap with a geneset; `NA` pvalues are returned for genesets with `ngenes_signif <= require_nsignif`. #' This function 'prefilters' genesets, so beware that this will influence downstream multiple testing correction. Default is 1 #' @param use_ease use a more conservative score coined by DAVID online tools @ https://david.ncifcrf.gov/helps/functional_annotation.html#fisher +#' @return input `genesets` table with results in the "pvalue" column #' @seealso `test_genesets` #' @export test_genesets_fisherexact = function(genesets, genelist, require_nsignif = 1L, use_ease = FALSE) { diff --git a/R/test_genesets_goat.R b/R/test_genesets_goat.R index f1a52d6..dcee0c7 100644 --- a/R/test_genesets_goat.R +++ b/R/test_genesets_goat.R @@ -6,6 +6,49 @@ #' #' This is the canonical geneset test function for GOAT that uses precomputed null distributions that are bundled with the GOAT package #' +#' @examples \donttest{ +#' # note; this example downloads data when first run, and typically takes ~60seconds +#' +#' # store the downloaded files in the following directory. Here, the temporary file +#' # directory is used. Alternatively, consider storing this data in a more permanent location. +#' # e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +#' output_dir = tempdir() +#' +#' ## first run the default example from test_genesets() to obtain input data +#' datasets = download_goat_manuscript_data(output_dir) +#' genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +#' genesets_asis = download_genesets_goatrepo(output_dir) +#' genesets_filtered = filter_genesets(genesets_asis, genelist) +#' +#' ### we here compare GOAT with precomputed null distributions against +#' ### a GOAT function that performs bootstrapping to compute null distributions on-demand +#' +#' # apply goat with precomputed null (default) and goat with on-demand bootstrapping +#' result_precomputed = test_genesets(genesets_filtered, genelist, method = "goat", +#' score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) |> +#' # undo sorting by p-value @ test_genesets(), instead sort by stable IDs +#' arrange(source, id) +#' result_bootstrapped = test_genesets(genesets_filtered, genelist, method = "goat_bootstrap", +#' score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05, verbose = TRUE) |> +#' arrange(source, id) +#' +#' # tables should align +#' stopifnot(result_precomputed$id == result_bootstrapped$id) +#' # no missing values +#' stopifnot(is.finite(result_precomputed$pvalue) & +#' is.finite(is.finite(result_bootstrapped$pvalue))) +#' +#' # compare results +#' plot(result_precomputed$pvalue, result_bootstrapped$pvalue) +#' abline(0, 1, col=2) +#' +#' plot(minlog10_fixzero(result_precomputed$pvalue), +#' minlog10_fixzero(result_bootstrapped$pvalue)) +#' abline(0, 1, col=2) +#' +#' summary(minlog10_fixzero(result_precomputed$pvalue) - +#' minlog10_fixzero(result_bootstrapped$pvalue)) +#' } #' @param genesets genesets data.frame, must contain columns; "source", "id", "genes", "ngenes" #' @param genelist genelist data.frame, must contain columns "gene" and "pvalue"/"effectsize" (depending on parameter `score_type`) #' @param score_type how to compute gene scores? @@ -14,6 +57,14 @@ #' Option "effectsize_abs" uses values from the effectsize column in `genelist` in a one-way test for enrichment; is a geneset enriched when testing absolute effectsizes? #' Option "effectsize_up" uses values from the effectsize column in `genelist` in a one-way test for enrichment; is a geneset enriched in up-regulated genes? (i.e. positive effectsize) #' Option "effectsize_down" uses values from the effectsize column in `genelist` in a one-way test for enrichment; is a geneset enriched in down-regulated genes? (i.e. negative effectsize) +#' @return input `genesets` table with results in the "pvalue", "score_type" columns. +#' "zscore" column: +#' A standardized z-score is computed from geneset p-values + effectsize direction (up/down) if tested. +#' Importantly, we here return standardized z-scores because the GOAT geneset score (mean of gene scores) is relative to the respective geneset-size-matched null distributions (a skewed normal)! +#' In contrast, the standardized z-scores are comparable between genesets (as are the pvalues obviously). +#' +#' Only if either (or both) the effectsize-up/down was tested, the direction of regulation has been tested (effectsize_abs and pvalue score types are agnositic to up/down regulation). +#' So when score_type was set to any of effectsize/effectsize_down/effectsize_up, the z-scores are negative values in case the "score_type" output column is "effectsize_down". #' @seealso `test_genesets` #' @export test_genesets_goat_precomputed = function(genesets, genelist, score_type) { @@ -53,6 +104,7 @@ test_genesets_goat_precomputed = function(genesets, genelist, score_type) { #' @param score_type see `test_genesets_goat_precomputed` #' @param niter integer number of bootstrap iterations; at least 10000, at most 5000000 #' @param verbose boolean, create debug plots +#' @return see `test_genesets_goat_precomputed` #' @export test_genesets_goat_fitfunction = function(genesets, genelist, score_type, niter = 500000, verbose = FALSE) { validate_goat_scoretype(score_type) @@ -81,6 +133,7 @@ test_genesets_goat_fitfunction = function(genesets, genelist, score_type, niter #' @param score_type see `test_genesets_goat_precomputed` #' @param niter integer number of bootstrap iterations; at least 10000, at most 5000000 #' @param verbose boolean, create debug plots +#' @return see `test_genesets_goat_precomputed` #' @export test_genesets_goat_bootstrap = function(genesets, genelist, score_type, niter = 500000, verbose = FALSE) { validate_goat_scoretype(score_type) @@ -109,7 +162,8 @@ test_genesets_goat_bootstrap = function(genesets, genelist, score_type, niter = #' #' @param usize integer vector of geneset sizes #' @param par result from `goat_nulldistribution_function` -#' @returns data.frame with columns; size, mu, sigma, xi +#' @return data.frame with columns; size, mu, sigma, xi +#' @noRd goat_apply_precomputed_null = function(usize, par) { stopifnot(is.list(par) && length(par$sigma_0) == 1) f_xi = function(x,l) l$xi_0 + l$xi_1 * x + l$xi_2 * x^2 + l$xi_3 * x^3 + l$xi_4 * x^4 + l$xi_5 * x^5 + l$xi_6 * x^6 + l$xi_7 * x^7 + l$xi_8 * x^8 + l$xi_9 * x^9 + l$xi_10 * x^10 + l$xi_11 * x^11 @@ -140,6 +194,7 @@ goat_apply_precomputed_null = function(usize, par) { #' @param genelist see `test_genesets_goat_precomputed` #' @param score_type see `test_genesets_goat_precomputed` #' @return gene*score_type matrix where rows are aligned with the rows of the genelist table +#' @noRd goat_testgene_score_matrix = function(genelist, score_type) { has_pvalue = "pvalue" %in% colnames(genelist) has_effectsize = "effectsize" %in% colnames(genelist) @@ -197,6 +252,7 @@ goat_testgene_score_matrix = function(genelist, score_type) { #' @param genelist see `test_genesets_goat_precomputed` #' @param score_type see `test_genesets_goat_precomputed` #' @param nulldistribution_parameters result from goat_nulldistribution_independent +#' @noRd test_genesets_goat = function(genesets, genelist, score_type, nulldistribution_parameters) { stopifnot("precomputed null distributions contain non-finite values" = is.finite(nulldistribution_parameters$size) & is.finite(nulldistribution_parameters$mu) & @@ -209,7 +265,7 @@ test_genesets_goat = function(genesets, genelist, score_type, nulldistribution_p ### 2) compute geneset scores using C++ helper for fast aggregation ul_gs_index = rep(seq_len(nrow(genesets)), genesets$ngenes) - ul_gs_geneindex = match(unlist(genesets$genes, use.names = F, recursive = F), genelist$gene) + ul_gs_geneindex = match(unlist(genesets$genes, use.names = FALSE, recursive = FALSE), genelist$gene) # double-check that this is the filtered geneset input; all genes in the genesets table must match the genelist table stopifnot("all genes in the genesets table must be available in the genelist table (did you forget to apply the filter_genesets() function?)" = !anyNA(ul_gs_geneindex)) # importantly, rcpp_gene_to_geneset_scores returns sum scores not the mean ! @@ -241,6 +297,12 @@ test_genesets_goat = function(genesets, genelist, score_type, nulldistribution_p genesets$pvalue = pmin(1, genesets$pvalue * ncol(genelist_scores)) } + # A standardized z-score is computed from geneset p-values + effectsize direction (up/down) if tested. + genesets$zscore = stats::qnorm(genesets$pvalue / 2, lower.tail = FALSE) + if(any(score_type %in% c("effectsize", "effectsize_down", "effectsize_up"))) { + genesets$zscore = genesets$zscore * c(-1,1)[1L + as.integer(genesets$score_type == "effectsize_up")] + } + return(genesets) } @@ -248,6 +310,7 @@ test_genesets_goat = function(genesets, genelist, score_type, nulldistribution_p #' helper function to validate input parameters for GOAT computation functions #' @param x object to validate +#' @noRd validate_goat_scoretype = function(x) { stopifnot("score_type parameter must be any of 'pvalue', 'effectsize', 'effectsize_abs', 'effectsize_up', 'effectsize_down'" = length(x) == 1 && x %in% c("pvalue", "effectsize", "effectsize_up", "effectsize_down", "effectsize_abs")) } @@ -256,6 +319,7 @@ validate_goat_scoretype = function(x) { #' helper function to validate input parameters for GOAT computation functions #' @param x object to validate +#' @noRd validate_goat_niter = function(x) { stopifnot("niter parameter must be a single integer between 10000 and 5000000 (default setting is 100000)" = length(x) == 1 && is.numeric(x) && is.finite(x) && x >= 10000L && x <= 5000000) } @@ -264,6 +328,7 @@ validate_goat_niter = function(x) { #' helper function to validate input parameters for GOAT computation functions #' @param x object to validate +#' @noRd validate_goat_verbose = function(x) { stopifnot("verbose parameter must be a single boolean (TRUE or FALSE)" = length(x) == 1 && x %in% c(TRUE, FALSE)) } @@ -273,6 +338,7 @@ validate_goat_verbose = function(x) { #' helper function to validate input parameters for GOAT computation functions #' @param x object to validate #' @param score_type score type +#' @noRd validate_goat_genelist = function(x, score_type) { stopifnot("genelist parameter must be a non-empty data.frame/tibble and contain a 'gene' column" = length(x) > 0 && is.data.frame(x) && "gene" %in% colnames(x) ) @@ -291,6 +357,7 @@ validate_goat_genelist = function(x, score_type) { #' helper function to validate input parameters for GOAT computation functions #' @param x object to validate #' @param genelist_N genelist length +#' @noRd validate_goat_genesets = function(x, genelist_N) { stopifnot("genesets parameter must be a non-empty data.frame/tibble with columns source, id, genes, ngenes" = length(x) > 0 && is.data.frame(x) && nrow(x) > 0 && all(c("source", "id", "genes", "ngenes") %in% colnames(x)) ) diff --git a/R/test_genesets_gsea.R b/R/test_genesets_gsea.R index 2b8cf03..b478ed9 100644 --- a/R/test_genesets_gsea.R +++ b/R/test_genesets_gsea.R @@ -11,15 +11,20 @@ #' Option "custom" expects 2 additional parameters; `gsea_genelist_col` should be a column name in `genelist` to be used for fGSEA (values used as-is), `gsea_scoretype` should be the respective value for the fGSEA scoreType parameter ('pos', 'neg' or 'std) #' @param parallel_threads number of threads to use for parallel processing. Set to 0 to automatically select all available processors/cores, set to 1 to disable (default) or to N to use N processes. #' Note that multiprocessing sometimes breaks on RStudio + Windows, hence this parameter is set to 1 to disable multiprocessing by default for now -#' @param gseaParam passed to `fgsea::fgsea()`, from manual: "GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores.". default = 1 +#' @param gseaParam passed to `fgsea::fgsea()`, from manual: "GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores.". default = 1. Further comments by fGSEA author at https://github.com/ctlab/fgsea/issues/45 #' @param nPermSimple passed to `fgsea::fgsea()`, from manual: "Number of permutations in the simple fgsea implementation for preliminary estimation of P-values.". default = 50000 in this R package but 1000 by default in fGSEA v1.22.0; we observed much better accuracy in null simulations when increasing this from default 1k to 10k and further minor improvement towards 50k, hence the latter is our default #' @param gsea_genelist_col optional, only used for `score_type` "custom" #' @param gsea_scoretype optional, only used for `score_type` "custom" +#' @param random_seed the random seed that is passed to `set.seed()` in order to ensure fgsea results are reproducible. default: 123 +#' @return input `genesets` table with results in the "pvalue", "score_type" and "gsea_nes" columns #' @seealso `test_genesets` #' @export -test_genesets_gsea = function(genesets, genelist, score_type = NULL, parallel_threads = 1L, gseaParam = 1, nPermSimple = 50000, gsea_genelist_col = NULL, gsea_scoretype = NULL) { +test_genesets_gsea = function(genesets, genelist, score_type = NULL, parallel_threads = 1L, gseaParam = 1, nPermSimple = 50000, gsea_genelist_col = NULL, gsea_scoretype = NULL, random_seed = 123) { pvalue = effectsize = id_tmp = genes = NULL # fix invisible bindings R package NOTE - check_dependency("fgsea", "geneset enrichment testing with GSEA") + + if(!requireNamespace("fgsea", quietly = TRUE)) { + stop('to perform geneset testing with the GSEA algorithm, the fgsea R package is required (c.f. documentation on the GOAT GitHub page).\nSuggested installation steps;\n1) close RStudio and start it anew (do copy/paste these instructions before closing)\n2) run R command: install.packages("pak")\n3) run R command: pak::pkg_install("ctlab/fgsea", update=FALSE)\n4) test if the installation was successful by loading the R package, run R command: library(fgsea)', call. = FALSE) + } stopifnot("genesets parameter must be a non-empty data.frame/tibble with columns source (character type) id (character type), genes (list type)" = is.data.frame(genesets) && nrow(genesets) > 0 && all(c("id", "genes") %in% colnames(genesets)) && @@ -47,8 +52,15 @@ test_genesets_gsea = function(genesets, genelist, score_type = NULL, parallel_th # local helper function apply_fgsea = function(gs, gn) { pathway = pval = NES = NULL # fix invisible bindings R package NOTE - # always set.seed prior to fgsea to enforce reproducibilty of our pipeline - set.seed(123) + # always set.seed prior to this function to enforce the exact same results (on the same computer and input data), regardless + # of the prior RNG state. Motivating example for this R package where reproducibility of statistical results is essential; + # Let function A and B both use sample() to draw random numbers. + # The results from bind_rows(A(), B()) and bind_rows(B(), A()) could be different. + # Thus, if we do not set.seed() here, every upstream use-case must include a set.seed() call before each function call. + # Such mistakes are easily made, especially by users to are relatively new to (R) programming, so we enforce it here + prev_seed <- .Random.seed + on.exit(.Random.seed <- prev_seed) + set.seed(random_seed) suppressWarnings(fgsea::fgsea( pathways = stats::setNames(gs$genes, gs$id), # named geneset list stats = array(gn$score, dimnames = list(gn$gene)), # named gene score array diff --git a/R/test_genesets_hypergeometric.R b/R/test_genesets_hypergeometric.R index d2f66a3..46ad0a1 100644 --- a/R/test_genesets_hypergeometric.R +++ b/R/test_genesets_hypergeometric.R @@ -13,6 +13,7 @@ #' is assumed to be the total number of tested genes, the number of rows where signif==TRUE is assumed the total number of significant genes. #' @param require_nsignif minimum number of 'signif genes' that overlap with a geneset; `NA` pvalues are returned for genesets with `ngenes_signif <= require_nsignif`. #' This function 'prefilters' genesets, so beware that this will influence downstream multiple testing correction. Default is 1 +#' @return input `genesets` table with results in the "pvalue" column #' @seealso `test_genesets` #' @export test_genesets_hypergeometric = function(genesets, genelist, require_nsignif = 1L) { diff --git a/R/test_genesets_idea.R b/R/test_genesets_idea.R deleted file mode 100644 index 2061407..0000000 --- a/R/test_genesets_idea.R +++ /dev/null @@ -1,173 +0,0 @@ - -#' Geneset testing with the iDEA R package (closely follows the iDEA tutorial) -#' -#' @description -#' note that computation takes a long time with the iDEA method. On a high performance workstation computer with -#' 12 cores / 24 threads it takes approximately 6 hours for a genelist of ~14000 genes to analyze all (filtered) GO -#' genesets. Using a regular PC this might take a multitude of hours. -#' -#' @param genesets filtered genesets -#' @param genelist user genelist, must contain columns "gene", "log2fc" and "pvalue" -#' @param return_pvalue_louis boolean, set `TRUE` to return "louis corrected" p-values from iDEA (analogous to iDEA tutorial) -#' @returns input `genesets` with additional columns for geneset p-value and score type. Because iDEA can return 2 types of geneset p-values, -#' multiple columns are included in this function's results; `pvalue_idea` = iDEA geneset p-value, `pvalue_louis_idea` = `pvalue_louis` from iDEA, -#' `pvalue` = respective copy of either former column, depending on user-parameter `return_pvalue_louis` -#' @seealso `test_genesets` -#' @export -test_genesets_idea = function(genesets, genelist, return_pvalue_louis) { - force(genesets) # guard against issues with multithreaded code downstream; from lazy evaluation (default) to enforced - force(genelist) - check_dependency("iDEA", "geneset enrichment testing with iDEA") - check_dependency("future.apply", "geneset enrichment testing with iDEA") - # input validation - stopifnot("genesets parameter must be a non-empty data.frame/tibble with columns; source (optional, character type) id (character type), genes (list type)" = - is.data.frame(genesets) && nrow(genesets) > 0 && all(c("id", "genes") %in% colnames(genesets)) && - is.character(genesets$id) && is.list(genesets$genes)) - stopifnot("genelist parameter must be a non-empty data.frame/tibble with numeric columns that contain finite values; 'gene' (integer), 'log2fc' (numeric) and 'pvalue' (numeric)" = - is.data.frame(genelist) && nrow(genelist) > 0 && all(c("gene", "log2fc", "pvalue") %in% colnames(genelist)) && - all(is.finite(genelist$gene)) && all(is.finite(genelist$log2fc)) && all(is.finite(genelist$pvalue))) - stopifnot("return_pvalue_louis parameter must be TRUE or FALSE" = length(return_pvalue_louis) == 1 && return_pvalue_louis %in% c(TRUE, FALSE)) - - # init results - genesets$score_type = NA_character_ - genesets$pvalue = NA_real_ - - # temp geneset ID that combines source + id for compatibility with input genelists that contain the same ID across 'source' - genesets$id_tmp = genesets$id - if("source" %in% colnames(genesets)) { - genesets$id_tmp = paste(genesets$source, genesets$id) - } - - - ### genelist to iDEA format (assuming we have columns log2fc and pvalue @ input genelist) - # this code generally follows the iDEA tutorial, but with minor adaptions for edge-cases that cause zero or infinite se_beta estimates - # note; in GOAT we use numeric gene IDs. To avoid type conversion / indexing shenanigans, we prefix integer gene IDs with a string - genelist_idea = data.frame(beta = genelist$log2fc, beta_var = NA, pvalue = genelist$pvalue, row.names = paste0("g", genelist$gene)) - # limit pvalues between 10^-64 and 1. also deals with upstream data that might (due to rounding errors or whatever in user input) input pvalue=0 - genelist_idea$zscore = stats::qnorm(pmin(1, pmax(genelist_idea$pvalue, 10^-64)) / 2.0, lower.tail = FALSE) # between ~17 and 0 (zcore=0 @ pvalue=1) - genelist_idea$se_beta = abs(genelist_idea$beta / genelist_idea$zscore) # approximate the standard error of beta (log2fc) - genelist_idea$se_beta[!is.finite(genelist_idea$se_beta)] = 10 # default to high value where beta_se is missing/invalid (zcore=0 @ pvalue=1 -> beta/0 = Inf or -Inf), yields beta_var = 100 - genelist_idea$se_beta[genelist_idea$se_beta < 10^-16] = 10^-16 # guard against "zero standard error" (e.g. at log2fc=0; 0/x=0) to ensure downstream iDEA code doesn't suffer from division-by-zero issues - genelist_idea$beta_var = genelist_idea$se_beta^2 - - - ### genesets to iDEA format (gene*geneset identity matrix) - # helper function creates the identity matrix, which we can later trivially convert to a data.frame if needed - genesets_to_matrix = function(gs, ugene) { - # note; in GOAT we use numeric gene IDs. To avoid type conversion / indexing shenanigans, we prefix integer gene IDs with a string - m = matrix(0L, nrow = length(ugene), ncol = nrow(gs), dimnames = list(paste0("g", ugene), gs$id_tmp)) # ! use the temp ID for genesets - for(i in 1:nrow(gs)) { - m[,i] = as.integer(ugene %in% gs$genes[[i]]) - } - return(m) - } - genesets_idea = as.data.frame(genesets_to_matrix(genesets, genelist$gene)) - ## alternatively, to completely remove genes that have no annotations from the genelist and genesets; - # genesets_idea = as.data.frame(genesets_to_matrix(genesets, ugene = sort(unique(unlist(gs$genes, recursive = F, use.names = F))))) - # genelist_idea = genelist_idea[match(rownames(genesets_idea), rownames(genelist_idea)), ] - stopifnot(rownames(genesets_idea) == rownames(genelist_idea)) - - # setup multiprocessing; start future plan + cleanup on exit - oplan = future::plan(future::multisession) - on.exit(future::plan(oplan)) - - tryCatch({ - ### following the iDEA tutorial, perform geneset enrichment testing with default parameters - # as first parameter, select only the 2 required input columns and enforce their order (i.e. this is a hardcoded requirement in iDEA) - # we set `min_precent_annot=0` because we already filtered genesets upstream (i.e. default in tutorial is 0.0025, which amounts to 35 in a genelist of 14k genes; skipping over small genesets) - # we disabled multiprocessing here because it has issues on some of our test systems; this step is quite fast so no problem - idea = iDEA::CreateiDEAObject(genelist_idea[,c("beta", "beta_var")], genesets_idea, max_var_beta = 101, min_precent_annot = 0, num_core = 1) - - - ################ - # idea = iDEA::iDEA.fit( - # idea, - # fit_noGS = FALSE, - # init_beta = NULL, - # init_tau = c(-2,0.5), - # min_degene = 0, # defaults to 5 in the iDEA tutorial; "min_degene: the threshold for the number of detected DE genes in summary statistics. For some of extremely cases, the method does not work stably when the number of detected DE genes is 0" - # em_iter = 15, - # mcmc_iter = 1000, - # fit.tol = 1e-5, - # modelVariant = F, - # verbose = TRUE - # ) - ################ ANALOGOUS TO ABOVE CODE FROM IDEA TUTORIAL, DIRECTLY ADAPTED FROM iDEA SOURCE CODE, BUT WITH future.apply TO FIX PARALLEL COMPUTATION - # https://github.com/xzhoulab/iDEA/blob/a30fc8ec655813678049c5b4b4b0aa6a21c944f0/R/iDEASummary.R#L103 - # here setting default parameters from tutorial at: https://xzhoulab.github.io/iDEA/ - object = idea - init_tau = c(-2,0.5) - em_iter = 15 - mcmc_iter = 1000 - min_degene = 0 - #### start snippet from iDEA source - num_gene <- object@num_gene - num_annot <- length(object@annotation) - init_beta <- object@summary[,1] - # this line is the only deviation from the iDEA sourcecode; - # we use the future.apply package because the original code, using 'pbmclapply' - # 1) occasionally hangs in WSL and 2) doesn't perform well on Windows - # (simple workaround as we only use this specific iDEA configuration) - # (note that we can't print to console from within future "workers", so iDEA-specific-errors are lost / not reported) - res_idea <- future.apply::future_sapply(1:num_annot, FUN = function(x) { - set.seed(x) - Annot <- rep(0, object@num_gene) - Annot[object@annotation[[x]]] <- 1 - Annot <- Annot - mean(Annot) - Annot <- as.matrix(data.frame(rep(1, num_gene), Annot) ) - t1 <- system.time(model1 <- try( res <- iDEA::EMMCMCStepSummary(object@summary[,1], object@summary[,2], as.matrix(Annot), init_beta, init_tau, em_iter, mcmc_iter, min_degene) )) - if(!inherits(model1, "try-error")) { - rownames(res$pip) <- object@gene_id - colnames(res$pip) <- "PIP" - rownames(res$beta) <- object@gene_id - colnames(res$beta) <- "beta" - rownames(res$annot_coef) <- c("tau_1", "tau_2") - colnames(res$annot_coef) <- "annot_coef" - rownames(res$annot_var) <- c("tau_1", "tau_2") - colnames(res$annot_var) <- "annot_var" - res$converged <- TRUE - res$ctime <- t1[3] - } else { res <- NULL } - return(res) - }, future.seed = NULL, simplify = FALSE) - - names(res_idea) <- object@annot_id - object@de <- res_idea - #### end snippet from iDEA source - idea@de <- res_idea - ################ - - ### fix the the estimated geneset p-values as per default iDEA tutorial workflow - idea = iDEA::iDEA.louis(idea) - - ### map results to input geneset data.frame, from the idea object - index = match(genesets$id_tmp, idea@gsea$annot_id) # ! use the temp ID for genesets - if(any(!is.finite(idea@gsea$pvalue_louis[index]))) { - cat(sprintf("%d/%d (filtered) genesets that were tested with iDEA received a p-value via iDEA.louis(), iDEA model fitting failed for %d genesets\n", - sum(is.finite(idea@gsea$pvalue_louis[index])), nrow(genesets), sum(!is.finite(idea@gsea$pvalue_louis[index])) )) - } - genesets$ismissing_idea = !is.finite(idea@gsea$pvalue_louis[index]) - genesets$pvalue_idea = idea@gsea$pvalue[index] - genesets$pvalue_louis_idea = idea@gsea$pvalue_louis[index] - genesets$pvalue = idea@gsea$pvalue[index] - if(return_pvalue_louis) { - genesets$pvalue = idea@gsea$pvalue_louis[index] - } - # In this function we tested only valid, non-empty genesets that passed filter_genesets() upstream. - # If the geneset algorithm returned a non-finite pvalue, we consider it "tested but found not signif" - # (note that leaving NA values as-is would affect subsequent multiple testing correction, which in turn would be a - # source of bias if many "no effect whatsoever" genesets are returned as NA pvalues by the algorithm) - genesets$pvalue_idea[!is.finite(genesets$pvalue_idea)] = 1 - genesets$pvalue_louis_idea[!is.finite(genesets$pvalue_louis_idea)] = 1 - genesets$pvalue[!is.finite(genesets$pvalue)] = 1 - # score type - genesets$score_type = c("effectsize_down", "effectsize_up")[1 + (is.finite(idea@gsea$annot_coef[index]) & idea@gsea$annot_coef[index] > 0)] # if annot_coef > 0, pathway is up-regulated - }, - error = function(err) { - print(err) - }) - - # remove temp geneset ID column - genesets$id_tmp = NULL - return(genesets) -} diff --git a/R/util_stats.R b/R/util_stats.R index 571ce82..bc09fa7 100644 --- a/R/util_stats.R +++ b/R/util_stats.R @@ -9,15 +9,16 @@ #' @param sort2 numeric vector of length `nrow(x)` to sort by for breaking ties (descending order, higher value gets better result score) #' @param sort3 numeric vector of length `nrow(x)` to sort by for breaking ties (descending order, higher value gets better result score) #' @param colname name for result column in `x` (overwritten if already exists) -#' @examples \dontrun{ -#' x = data.frame(gene=c(1,2,3,4,5), pvalue=c(0.01, 1, 1, 0.1, 1), +#' @examples +#' x = data.frame(gene=c(1, 2, 3, 4, 5), +#' pvalue=c(0.01, 1, 1, 0.1, 1), #' effectsize=c(-2, 0.25, 0.5, 1, 0.25)) #' print(x, row.names = FALSE) -#' print(goat:::rankscore(x, sort1 = -1*x$pvalue, sort2 = abs(x$effectsize), +#' print(rankscore(x, sort1 = -1*x$pvalue, sort2 = abs(x$effectsize), #' sort3 = x$gene, colname="score") |> #' arrange(desc(score)), row.names = FALSE) -#' } #' @return data.frame `x` with added column `colname`, containing gene scores between 0 and 1000 +#' @export rankscore = function(x, sort1, sort2, sort3, colname) { i = NULL if(is.null(sort3)) { @@ -35,6 +36,8 @@ rankscore = function(x, sort1, sort2, sort3, colname) { #' Gene score array, from low to high scores #' #' @param n genelist length +#' @return result of `(1:n)^2 / (n^2 / 1000)` +#' @export rankscore_fixed_order = function(n) { (1:n)^2 / (n^2 / 1000) # compute scaling factor prior to application to rank^2 scores } @@ -43,18 +46,18 @@ rankscore_fixed_order = function(n) { #' -log10 transform a vector of p-values, replacing zeros with some limit/threshold #' -#' @examples \dontrun{ +#' @examples #' pval = c(0, 10^-6, 0.001, 0.01, 1, NA, -Inf, Inf, NaN) #' cbind( #' input = pval, #' # default; replace zeros with typical R machine precision for doubles #' minlog10_default = minlog10_fixzero(pval), #' # alternatively, replace zero with lowest non-zero pvalue in input -#' minlog10_limit_from_data minlog10_fixzero(pval, limit = NA) +#' minlog10_limit_from_data = minlog10_fixzero(pval, limit = NA) #' ) -#' } #' @param x p-value vector to transform to -log10 #' @param limit value to replace zero's in `x` with. Set NA to replace zero's in `x` with the smallest finite value in `x` (if there is none, defaults to 2.22e-16) +#' @return input parameter `x` transformed to -log10 #' @export minlog10_fixzero = function(x, limit = 2.22e-16) { stopifnot(length(limit) == 1 && (is.na(limit) || (is.numeric(limit) & is.finite(limit) & limit > 0))) @@ -81,6 +84,7 @@ minlog10_fixzero = function(x, limit = 2.22e-16) { #' @param method method for multiple testing correction, must be any of `stats::p.adjust.methods`, e.g. "BH" or "bonferroni" #' @param cutoff numeric cutoff value for adjusted p-value, `signif` column is set to TRUE for all values lesser-equals #' @param correct_sources apply Bonferroni adjustment to all p-values according to the number of geneset sources that were tested. Boolean parameter, set TRUE to enable (default) or FALSE to disable +#' @return updated `genesets` table #' @export padjust_genesets = function(genesets, method = "BH", cutoff = 0.01, correct_sources = TRUE) { pvalue = pvalue_adjust = NULL # fix invisible bindings R package NOTE @@ -115,6 +119,7 @@ padjust_genesets = function(genesets, method = "BH", cutoff = 0.01, correct_sour #' @param mean location parameter #' @param sd scale parameter #' @param xi skewness parameter +#' @noRd dsnorm = function(x, mean = 0, sd = 1, xi = 1) { dsnorm__std = function(x, xi) { if(length(xi) == 1) { @@ -142,6 +147,7 @@ dsnorm = function(x, mean = 0, sd = 1, xi = 1) { #' @param mean location parameter #' @param sd scale parameter #' @param xi skewness parameter +#' @noRd psnorm_upper_tail = function(q, mean = 0, sd = 1, xi = 1) { # vectorized over xi psnorm_upper_tail__std = function(q, xi) { @@ -159,9 +165,9 @@ psnorm_upper_tail = function(q, mean = 0, sd = 1, xi = 1) { XI[!flag_up] = 1 / XI[!flag_up] p = z # init as numeric vector of expected length # high precision for small pvalues - p[flag_up] = g[flag_up] * XI[flag_up] * stats::pnorm(q = z[flag_up]/XI[flag_up], lower.tail = F) + p[flag_up] = g[flag_up] * XI[flag_up] * stats::pnorm(q = z[flag_up]/XI[flag_up], lower.tail = FALSE) # precision for large p-values is capped near 1 (approx 1 - 10^-16), but doesn't matter - p[!flag_up] = 1 - g[!flag_up] * XI[!flag_up] * stats::pnorm(q = z[!flag_up]/XI[!flag_up], lower.tail = T) + p[!flag_up] = 1 - g[!flag_up] * XI[!flag_up] * stats::pnorm(q = z[!flag_up]/XI[!flag_up], lower.tail = TRUE) p[p > 1] = 1 return(p) @@ -172,7 +178,7 @@ psnorm_upper_tail = function(q, mean = 0, sd = 1, xi = 1) { ### QC code # # validate precision; compare to pnorm @ xi = 1 # q = seq(-12, 12, by = 0.1) - # stopifnot( all.equal(stats::pnorm(q, lower.tail = F), psnorm_upper_tail__std(q, xi = 1), tolerance = 10^-14) ) + # stopifnot( all.equal(stats::pnorm(q, lower.tail = FALSE), psnorm_upper_tail__std(q, xi = 1), tolerance = 10^-14) ) # # # validate correctness; compare to preexisting implementation # # here only using a limited range for q because reference lacks precision for large q (ours should be fine) @@ -192,6 +198,7 @@ psnorm_upper_tail = function(q, mean = 0, sd = 1, xi = 1) { #' @param init_xi initial value for xi (typically 1~1.25) #' @param lower lower bound for Xi #' @param upper Upper bound for Xi +#' @noRd fit_snorm_xi = function(x, mu, sd, init_xi, lower = NA, upper = NA) { ###### for reference; pure R variant (much slower than our Rcpp variant) diff --git a/R/utils.R b/R/utils.R index 613e8ee..6969e95 100644 --- a/R/utils.R +++ b/R/utils.R @@ -1,8 +1,7 @@ #' ASCII logo for this package #' -#' @examples -#' cat(goat_logo()) +#' @return package logo as a string #' @export goat_logo = function() { ' )_) @@ -17,6 +16,7 @@ goat_logo = function() { #' Return goat package version as a string #' #' simple wrapper around utils::packageVersion() +#' @return package version as a string #' @export goat_version = function() { as.character(utils::packageVersion("goat")) @@ -26,22 +26,23 @@ goat_version = function() { #' Print package version and logo to console #' -#' @examples -#' goat_print_version() +#' @return prints to console without returning a value #' @export goat_print_version = function() { - cat(sprintf("%s\nGOAT version %s\n", goat_logo(), goat_version())) + message(sprintf("%s\nGOAT version %s", goat_logo(), goat_version())) } #' return a prettyprint string of all length 1 parameters that are string/numeric/logical/NA #' -#' @examples \dontrun{ -#' parameters_prettyprint_length1(test1=1:2, test2=matrix(1:4,2,2), test3=data.frame(a=1), -#' test4=c(a=1), test5=1, test6="a", test7=NA, test8=Inf) -#' } +#' @examples +#' parameters_prettyprint_length1( +#' test1=1:2, test2=matrix(1:4,2,2), test3=data.frame(a=1), +#' test4=c(a=1), test5=1, test6="a", test7=NA, test8=Inf +#' ) #' @param ... arbitrary set of parameters +#' @noRd parameters_prettyprint_length1 = function(...) { arguments = list(...) if(length(arguments) == 0 || !is.list(arguments)) { @@ -69,6 +70,7 @@ parameters_prettyprint_length1 = function(...) { #' #' @param pkg R package name #' @param msg function name / reference for user +#' @noRd check_dependency = function(pkg, msg) { if(!requireNamespace(pkg, quietly = TRUE)) { stop(paste0( @@ -88,6 +90,8 @@ check_dependency = function(pkg, msg) { #' https://stackoverflow.com/a/8197703 #' #' @param n number of colors +#' @return a color code (string) +#' @export gg_color_hue = function(n) { hues = seq(15, 375, length = n + 1) grDevices::hcl(h = hues, l = 65, c = 100)[1:n] @@ -99,6 +103,8 @@ gg_color_hue = function(n) { #' #' @param color input colors #' @param frac fraction of white; >0 and <1 +#' @return adjusted value for input `color` +#' @export lighten_color = function(color, frac = 0.1){ stopifnot(frac > 0 & frac < 1) sapply(color, function(x) grDevices::colorRampPalette(c(x, "white"))(100)[ceiling(frac * 100)], simplify = TRUE, USE.NAMES = FALSE) @@ -106,12 +112,37 @@ lighten_color = function(color, frac = 0.1){ +#' naively darken a color by mixing in black +#' +#' @param color input colors +#' @param frac fraction of black; >0 and <1 +#' @return adjusted value for input `color` +#' @export +darken_color = function(color, frac = 0.1){ + stopifnot(frac > 0 & frac < 1) + sapply(color, function(x) grDevices::colorRampPalette(c(x, "black"))(100)[ceiling(frac * 100)], simplify = TRUE, USE.NAMES = FALSE) +} + + + +#' check if a string is a valid R color-code +#' +#' @param x input string +#' @noRd +isvalid_color = function(x) { + length(x) == 1 && !is.na(x) && is.character(x) && suppressWarnings(tryCatch(is.matrix(grDevices::col2rgb(x)), error = function(e) FALSE)) +} + + + #' simple string truncation #' #' replacement for stringr::trunc() so we don't need a package dependency for just 1 function (our code was adapter therefrom) #' @param string string that should be truncated #' @param width desired max length #' @param trim_left instead of right trunc (default), do left instead +#' @return truncated variant of input `string` +#' @export string_trunc_right = function(string, width, trim_left = FALSE) { N = nchar(string) too_long = !is.na(string) & N > 3 & N > width # hardcoded min string length @ 4 diff --git a/R/validate_genelist.R b/R/validate_genelist.R index 79af419..d804359 100644 --- a/R/validate_genelist.R +++ b/R/validate_genelist.R @@ -2,6 +2,7 @@ #' validate a table with genes (that should be tested in overrepresentation-analysis) for compatibility with this R package #' #' @param genelist gene tibble to validate +#' @noRd validate_genelist = function(genelist) { # 1) data.frame with all required columns ok = is.data.frame(genelist) && diff --git a/R/validate_genesets.R b/R/validate_genesets.R index 279d808..f245c5a 100644 --- a/R/validate_genesets.R +++ b/R/validate_genesets.R @@ -1,9 +1,10 @@ #' validate that a geneset table is compatible with this R package #' -#' @param genesets geneset tibble to validate, e.g. results from `load_genesets_go_bioconductor()` +#' @param genesets geneset tibble to validate, e.g. results from `load_genesets_go_fromfile()` #' @param require_signif should we check for "ngenes_signif" column ? #' @param check_gene_identiers optionally skip the validation of gene identifiers +#' @noRd validate_genesets = function(genesets, require_signif = TRUE, check_gene_identiers = TRUE) { # 1) data.frame with all required columns ok = is.data.frame(genesets) && @@ -54,7 +55,7 @@ validate_genesets = function(genesets, require_signif = TRUE, check_gene_identie if(check_gene_identiers) { x_aslist = utils::as.relistable(genesets$genes) # ignore warning about recursive unlisting; we validate below to enforce 'no recursive lists allowed' - x = unlist(genesets$genes, recursive = F, use.names = F) + x = unlist(genesets$genes, recursive = FALSE, use.names = FALSE) if(length(x) > 0) { ok = FALSE if(is.character(x)) { diff --git a/README.md b/README.md index 176d187..3ba366d 100644 --- a/README.md +++ b/README.md @@ -1,21 +1,30 @@ -The Geneset Ordinal Association Test (GOAT) is a parameter-free permutation-based algorithm for geneset enrichment analysis of preranked genelists. The full algorithm is computationally efficient and completes in the order of seconds and within 1 second when using precomputed null distributions. Validations using synthetic data show that estimated geneset p-values are well calibrated under the null hypothesis and invariant to geneset size. Application to various real-world proteomics and gene expression studies demonstrates that GOAT consistently identifies more significant Gene Ontology terms as compared to alternative methods. +The Gene set Ordinal Association Test (GOAT) is a parameter-free permutation-based algorithm for gene set enrichment analysis of preranked gene lists. The full algorithm is computationally efficient and completes in the order of seconds and within 1 second when using precomputed null distributions. Validations using synthetic data show that estimated gene set p-values are well calibrated under the null hypothesis and invariant to gene set size. Application to various real-world proteomics and gene expression studies demonstrates that GOAT consistently identifies more significant Gene Ontology terms as compared to alternative methods. The GOAT algorithm has not been published yet but a preprint is available, please cite it when using the early-access version of GOAT; -_Koopmans, F. (2023). GOAT: efficient and robust identification of geneset enrichment._ [https://doi.org/10.1101/2023.12.10.570979](https://doi.org/10.1101/2023.12.10.570979). +_Koopmans, F. (2023). GOAT: efficient and robust identification of gene set enrichment._ [https://doi.org/10.1101/2023.12.10.570979](https://doi.org/10.1101/2023.12.10.570979). -## Online tool +# Contents + - [Online tool](#online-tool) + - [Installing the R package](#installing-the-r-package) + - [Quickstart](#quickstart) + - [Tutorial: using the GOAT R package](#tutorial-using-the-goat-r-package) + - [Preparing your gene list as input for GOAT](#preparing-your-gene-list-as-input-for-goat) + - [Gene set simplification](#gene-set-simplification) -This GitHub repository contains the source-code for the GOAT R package and shows how to use it. Alternatively, check out the GOAT online tool at [https://ftwkoopmans.github.io/goat](https://ftwkoopmans.github.io/goat) +# Online tool -## Installing the R package +This GitHub repository contains the source-code for the GOAT R package and shows how to use it. Alternatively, check out the user-friendly GOAT online tool at [https://ftwkoopmans.github.io/goat/](https://ftwkoopmans.github.io/goat/) -### Before installing -#### Windows +# Installing the R package + +## 1) Preparing system dependencies + +### Windows - Install R version 4.1.0 (or higher) - Install RTools, available from https://cran.r-project.org/bin/windows/Rtools/ @@ -24,7 +33,7 @@ This GitHub repository contains the source-code for the GOAT R package and shows - Reboot your computer - Install RStudio -#### MacOS +### MacOS - For Apple computers, we suggest installing R from https://mac.r-project.org - if your current R version is below 4.3, you might have to upgrade by following these instructions to install R (installation process hasn't been tested with R 4.1 or 4.2 yet on MacOS) @@ -42,7 +51,7 @@ potential issues; - `ld: library not found for -lgfortran` if this is among your error messages, the Fortran compiler is not installed (see second step in MacOS install instructions to resolve this) - `Failed to build source package ` --> either Xcode/Fortran is not installed (step 2 in above instructions), or their version doesn't line up with your current R version -#### Linux +### Linux R version 4.1.0 (or higher) and toolchains for compilation are required. Suggested installation steps that include all system dependencies that we identified, for all (recursive) R packages that GOAT depends upon; @@ -52,7 +61,7 @@ R version 4.1.0 (or higher) and toolchains for compilation are required. Suggest - add R package repository to your /etc/apt/sources.list file (per instructions in above link) - `sudo apt-get update` - `sudo apt-get install r-base r-base-dev libcurl4-openssl-dev openssl-dev libxml2-dev libgmp-dev libglpk-dev libfontconfig1-dev libfreetype6-dev libgmp3-dev` - - if your system reports "Unable to locate package openssl-dev", replace `libssl-dev` with `libssl-dev` + - if your system reports "Unable to locate package openssl-dev", replace `openssl-dev` with `libssl-dev` - (optional) install RStudio and then `install.packages("rstudioapi")` - (if prompted, install packages into a personal library) - install the R.utils package; start R (`R` on commandline), then `install.packages('R.utils')` @@ -60,91 +69,178 @@ R version 4.1.0 (or higher) and toolchains for compilation are required. Suggest if some of the above dependencies in Fedora/Ubuntu are missing, you may encounter issues such as; - `eval: make: not found` --> toolchain for compilation is missing, execute the above installation steps -- `Failed to build source package ` --> scroll up a bit to find the suggested solution. e.g. `Missing 2 system packages. You'll probably need to install them manually:` (followed by missing packaged that need to be installed via dnf/apt) +- `Failed to build source package ` --> scroll up a bit to find the suggested solution. e.g. `Missing 2 system packages. You'll probably need to install them manually:` (followed by missing packages that need to be installed via dnf/apt) + + -### GOAT R package +## 2) Install the GOAT R package -Execute the following R commands in a new RStudio session (i.e. close RStudio if currently opened, then start RStudio anew) to install GOAT; +The GOAT R package is available from GitHub (latest updates) and CRAN (most recent major release). Installation should be performed in a new RStudio session; close RStudio if currently opened, then start RStudio anew. + +### install the latest version from GitHub ``` -install.packages("pak") # first, install the package manager that we'll use to install GOAT in the next step -pak::pkg_install("ftwkoopmans/goat", upgrade = FALSE) # latter parameter makes us skip optional updates +# if needed, install the package manager that we'll use to install GOAT in the next step +if (!requireNamespace("pak", quietly = TRUE)) + install.packages("pak") -# optionally, only if you want to make use of fGSEA or iDEA via the GOAT R package, install optional dependencies -pak::pkg_install("ftwkoopmans/goat", upgrade = FALSE, dependencies = TRUE) +# install latest goat version and all optional dependencies, but skip non-essential updates from other packages +pak::pkg_install("ftwkoopmans/goat", dependencies = TRUE, upgrade = FALSE) ``` -potential issues; +### Alternatively, install the package from CRAN + +``` +# use the BiocManager tool to install optional goat dependencies from Bioconductor +if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager") +BiocManager::install(c("AnnotationDbi", "GO.db", "org.Hs.eg.db")) + +# install goat from CRAN +install.packages("goat") +``` + + + +## troubleshooting - if `pak::pkg_install("ftwkoopmans/goat", upgrade = FALSE)` yields error like `! Failed to build source package goat` then (most likely) the toolchain for compilation is missing. Follow above instructions for Windows/MacOS/Linux +- if the installation of optional dependencies (e.g. Bioconductor packages) cause problems, try to set the `pak::pkg_install` parameter `dependencies = NA` to skip these + -## Upgrading the R package +# Quickstart -Assuming you previously installed the R package (per above instructions), you can update to the latest version using the following R command; +Just show me the code! Download test data, apply GOAT, store result table. ``` -pak::pkg_install("ftwkoopmans/goat", upgrade = FALSE) # latter parameter makes us skip optional updates +library(goat) + +# TODO: change the output dir to some folder on your computer +# e.g. "~/data/goat" on unix systems, "C:/data/goat" on Windows, +# or set to getwd() to write output to the current working directory +output_dir = getwd() + +# download an example gene list +datasets = download_goat_manuscript_data(output_dir) +genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` + +# download GO gene sets +genesets_asis = download_genesets_goatrepo(output_dir) + +# filter gene sets for sufficient overlap with the gene list +genesets_filtered = filter_genesets(genesets_asis, genelist) + +# apply GOAT; score_type indicates the input gene list should be ranked by respective +# "effectsize". This is the recommended setting. Alternatively,this can be set to "pvalue" +# as well but will yield fewer genesets (c.f. GOAT manuscript, real-world benchmarks) +result = test_genesets(genesets_filtered, genelist, method = "goat", + score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) + +# print first 10 rows of the result table +print(result |> select(source, name, ngenes, pvalue_adjust) |> utils::head(n=10)) + +# store results. This function will create 2 files: +# - an output table with gene set p-values +# - a logfile that includes Methods text adapted to your settings +save_genesets(result, genelist, filename = paste0(output_dir, "/goat.xlsx")) ``` -## Quickstart +# Tutorial: using the GOAT R package -Below example assumes you've successfully installed the R package as per instructions above. +This tutorial assumes you have successfully installed the R package as per instructions above. -We'll use GOAT to identify enriched GO terms in one of the example proteomics datasets that is bundled with the GOAT R package. Besides the code comments that accompany each step in the example below, you can also check the respective R function documentation for further details (e.g. `?goat::load_genesets_go_bioconductor`). Below code will generate; +We'll use GOAT to identify enriched GO terms in an example proteomics dataset. Besides the comments that accompany each step in the example below, you can also check the respective R function documentation for further details (e.g. `?goat::load_genesets_go_fromfile`). Below code will generate; -- an output table with geneset p-values -- a logfile that includes M&M text adapted to your settings -- basic visualizations of significant genesets +- an output table with gene set p-values +- a logfile that includes Methods text adapted to your settings +- basic visualizations of significant gene sets + +## load the R package and set output directory ``` library(goat) -# optionally, set the working directory (where output files from this example will be stored) -# note that this path will be printed to console later on in the example so you can always find your results -# setwd("C:/DATA") # e.g. on Windows, this is how one would set the working directory to C:/DATA +# TODO: change the output dir to some folder on your computer +# e.g. "~/data/goat" on unix systems, "C:/data/goat" on Windows, +# or set to getwd() to write output to the current working directory +output_dir = getwd() +``` + +## gene list + +A preranked gene list is here defined as a table of gene identifiers and their respective effect sizes and/or p-values that indicate association with some experimental condition (e.g. summary statistics from an OMICS-based study). Below code downloads datasets that were analyzed in the GOAT manuscript and loads 1 as the gene list to be used in this tutorial. Documentation in the next section ("Preparing your gene list as input for GOAT") shows how to prepare your own data table / gene list. + +``` +datasets = download_goat_manuscript_data(output_dir) +genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +``` + +## gene sets + +A gene set can be any set of genes of interest; it is typically defined as a set of genes that are known members of the same biological pathway, localized to the same (sub)cellular compartment, co-expressed under certain conditions or associated with some disorder as defined in a gene set database such as GO. In the GOAT R package (and online tool) one tests for enrichment of top-ranked genes in the input gene list against each gene set from some collection/database. -# download GO genesets (only downloads once, caches data on your computer) -genesets_asis = load_genesets_go_bioconductor() -# example genelist: one of the datasets already bundled with the R package. -# later examples will show how prepare your own data table / genelist. -data("goat_example_datasets") -genelist = goat_example_datasets$`Hondius 2021:mass-spec:PMID33492460` +In the example below we'll use the GO database. To use another gene set database, refer to the section "Importing gene sets". -# importantly, we first filter all genesets to retain those relevant to your genelist -# for example, only retain genesets with at least 10 genes that are also in your genelist and remove those that contain more than 1500 genes in your genelist. Further, we remove genesets that contain more than half your genelist -# it is crucial that this function is applied for each genelist that you want to analyze because the resulting 'genesets_filtered' table is tailored to the specific genelist you are using at the moment +``` +genesets_asis = download_genesets_goatrepo(output_dir) +``` + +## filter gene sets + +Importantly, we first filter all gene sets to retain those relevant to your gene list. +In this example we use default (recommended) parameters to only retain gene sets with at least 10 genes that are also in your `genelist` and remove those that contain more than 1500 (or half the size of your gene list, whichever is smallest) genes that overlap with your gene list. + +It is crucial that this function is applied for each gene list that you want to analyze because the resulting `genesets_filtered` table is only valid for the `genelist` you are using at the moment. + +``` genesets_filtered = filter_genesets(genesets_asis, genelist, min_overlap = 10, max_overlap = 1500) +``` -# apply the GOAT algorithm, then perform multiple testing correction using the Bonferroni method and consider proteins with adjusted p-value <= 0.05 as significant -# we here test for enrichment in the 'effectsize' column of your genelist. Alternatively, to test enrichment in the gene p-value dimension, replace 'effectsize' with 'pvalue' -# to use FDR for multiple testing correction instead of the stringent Bonferroni method, set padj_method = "BH" +## gene set enrichment testing + +Apply the GOAT algorithm, then perform multiple testing correction using the Bonferroni method and consider proteins with adjusted p-value <= 0.05 as significant. We here test for enrichment in the 'effectsize' column of your gene list. Alternatively, to test enrichment in the gene p-value dimension, replace 'effectsize' with 'pvalue'. But do keep in mind that the latter setting will yield fewer genesets (c.f. GOAT manuscript, real-world benchmarks). + +To use FDR for multiple testing correction instead of the stringent Bonferroni method, set `padj_method = "BH"` + +``` result = test_genesets(genesets_filtered, genelist, method = "goat", score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) # print the significant GO term counts, per geneset 'source' (CC/BP/MF), to console print(result |> group_by(source) |> summarise(signif_count = sum(signif), .groups="drop")) -# store the results as an Excel table, and create a logfile that documents the GOAT settings you used -# this also includes M&M text that you can use in your publications. -save_genesets(result, genelist, filename = "goat.xlsx") +# print the top 25 significant genesets (for simplicity, only select key data columns) +print(result |> filter(signif) |> select(source, name, ngenes, pvalue_adjust), n = 25) +``` + +## save results + +Store the results as an Excel table, and create a logfile that documents the GOAT settings you used. +This logfile also includes Methods text tailored to your settings that is ready for use in scientific publications. -# generate lollipop charts for each GO domain (CC/BP/MF), with geneset -log10 adjusted p-value on the x-axis and color-coding by geneset up/down-regulation -# refer to the function documentation for alternative plot options (e.g. color-code by oddsratio on x-axis or create a barplot instead of a lollipop chart) -plot_lollipop(result, output_dir = getwd(), topn = 50, plot_type = "lollipop", score_xaxis = "minlogp", score_color = "updown") +``` +save_genesets(result, genelist, filename = paste0(output_dir, "goat.xlsx")) +``` + +## visualize results + +Generate lollipop charts for each GO domain (CC/BP/MF), with gene set -log10 adjusted p-value on the x-axis and color-coding by gene set up/down-regulation. +Refer to the function documentation (`?plot_lollipop`) for alternative plot options (e.g. color-code by oddsratio on x-axis or create a barplot instead of a lollipop chart). -cat("output files are available at:", getwd(), "\n") +``` +plot_lollipop(result, output_dir, topn = 50, plot_type = "lollipop", score_xaxis = "minlogp", score_color = "updown") ``` -## Preparing your genelist as input for GOAT +# Preparing your gene list as input for GOAT Gene identifiers used as input in the GOAT R package are Human NCBI Entrez gene IDs (other species currently not supported). **specification** -The expected format for your genelist is a data.frame (or 'tibble') that contains the following named columns; +The expected format for your gene list is a data.frame (or 'tibble') that contains the following named columns; - `gene` = required column with gene integer values (Human Entrez gene IDs) - `symbol` = optional column with gene symbols that will be merged into the output tables @@ -163,7 +259,7 @@ First 3 lines of a table with only pvalue data that is used for GOAT (with `scor | 335 | APOA1 | 0.09 | | 9948 | WDR1 | 1 | -You may add the required `signif` column to indicate all genes with pvalue < 0.01 are considered foreground/significant using this R statement, assuming above genelist data.frame is called 'genelist'; +You may add the required `signif` column to indicate all genes with pvalue < 0.01 are considered foreground/significant using this R statement, assuming above gene list data.frame is called 'genelist'; `genelist$signif = genelist$pvalue < 0.01` @@ -190,10 +286,9 @@ The following R code will map these genes to entrez and report the success/fail ``` # TODO: update this file path to where you stored the HGNC data table (download link in above instructions) file_hgnc = "C:/data/hgnc_complete_set.txt" -hgnc = hgnc_lookuptable(file_hgnc) +hgnc = hgnc_idmap_table(file_hgnc) genelist = symbol_to_entrez(genelist, hgnc) -# after ID mapping is done, we add a column named 'gene' to the genelist data.frame and populate it with the entrez gene IDs -genelist$gene = genelist$entrez_id +# the genelist table now contains Entrez gene IDs in the column 'gene' ``` As a next step you will need to remove failed gene mappings (i.e. no Entrez gene id was found) and remove redundant genes (same entrez_id) as shown in the next section. @@ -213,40 +308,73 @@ genelist = genelist |> ``` -## Importing genesets +# Importing gene sets + +Several options are available to import gene sets from various sources. For each, please refer to the function documentation in R (e.g. issue the R command `?load_genesets_go_fromfile`) for further explanation and suggested download links to data files. -In typical use-cases, you can automatically download GO genesets via Bioconductor using the `load_genesets_go_bioconductor` function. Example @ quickstart section above. +- function `download_genesets_goatrepo` : Automatically download GO gene sets from the GOAT GitHub repository. Data files are updated bianually. -Alternatively, several functions exist to import genesets from other sources. For each, please refer to the function documentation in R (e.g. issue the R command `?load_genesets_go_fromfile`) for download links to data files and example code. +- function `load_genesets_go_fromfile` : Load GO gene sets directly from gene2go and .OBO files stored on your computer. See the function help for download instructions. +This ensures you always use the exact same GO gene set definitions and gives you full control over the GO version used in your analyses. -- function `load_genesets_go_fromfile` : Load GO genesets directly from gene2go and .OBO files stored on your computer (e.g. to ensure you always use the exact same GO geneset definitions) +- function `load_genesets_go_bioconductor` : Automatically download GO gene sets via Bioconductor. Note that the GO database version that is obtained may lag behind current data available from GO by years ! (depending on your currently installed R/Bioconductor version). The available GO data version is printed to console, keep an eye on this. If strongly outdated, either use the canonical `load_genesets_go_fromfile` function or update your R installation to the current release. -- function `load_genesets_syngo` : Load synaptic gene ontology (SynGO) genesets obtained from www.syngoportal.org +- function `load_genesets_syngo` : Load synaptic gene ontology (SynGO) gene set database obtained from www.syngoportal.org (see the function help for download instructions) -- function `load_genesets_gmtfile` : Load genesets from GMT files, a common file format for genesets (e.g. KEGG pathways obtained via MSigDB) +- function `load_genesets_gmtfile` : Load gene sets from GMT files, a common file format (e.g. KEGG pathways obtained via MSigDB). The [documentation section](https://ftwkoopmans.github.io/goat/docs#GMTgenesets) of the GOAT online webtool elaborates on how to obtain custom gene set databases (in GMT format). -## Geneset simplification -We have implemented a basic geneset\*geneset similarity measure that can be used to cluster genesets, allowing you to quickly identify redundant results (groups of genesets that have strong overlap). +# Gene set simplification -Assuming you ran the quickstart example above, the following code will generate heatmap figures that may aid the interpretation of your GOAT results in case a large number of significant genesets were identified; +A basic geneset\*geneset similarity metric can be used to cluster gene sets, allowing you to quickly identify redundant results (groups of gene sets that have strong overlap). + +Assuming you ran the quickstart example above, the following code will generate heatmap figures that may aid the interpretation of your GOAT results in case a large number of significant gene sets were identified; ``` +## below code assumes the Quickstart example has been run (we here reuse variables 'result' and 'genelist') + # this function generates geneset*geneset similarity matrices clusters = cluster_genesets(result, genelist) -# find the subset of non-overlapping genesets. See the function documentation for tweaking these parameters using R command; ?reduce_genesets + +# find the subset of non-overlapping gene sets. See the function documentation for tweaking these parameters using R command; ?reduce_genesets # in brief, to collapse/reduce to a smaller list of genesets, lower simscore_threshold to e.g. 0.85 or 0.8 and/or signifgenes_fraction to e.g. 0.8 result = reduce_genesets(clusters, simscore_threshold = 0.9, universe_fraction = 0.25, signifgenes_fraction = 0.9) -# print signif geneset counts to console, before and after simplification + +# print signif gene set counts to console, before and after simplification print(result |> filter(signif) |> count(source)) print(result |> filter(signif_and_reduced) |> count(source)) # generate heatmaps for each GO domain (CC/BP/MF). Again, don't forget to change the output directory to an existing directory on your computer and use forward slashes in the file path -plot_heatmap(clusters, output_dir = getwd()) -# repeat the lollipop plots made before, but now only for geneset that remain after simplification. See the function documentation for tweaking the plot, e.g. plotting only a subset of genesets/results -plot_lollipop(result, output_dir = getwd(), only_reduced = TRUE, plot_type = "lollipop", score_xaxis = "minlogp", score_color = "updown") +plot_heatmap(clusters, output_dir) + +# repeat the lollipop plots made before, but now only for geneset that remain after simplification. See the function documentation for tweaking the plot, e.g. plotting only a subset of gene sets / results +plot_lollipop(result, output_dir, only_reduced = TRUE, plot_type = "lollipop", score_xaxis = "minlogp", score_color = "updown") +``` -cat("output files are available at:", getwd(), "\n") +Or try a treemap visualization of gene set testing results. Here, gene sets are grouped by their ontological structure, i.e. the parent/child relations between GO terms as described in the GO database. + +``` +## below code assumes the Quickstart example has been run (we here reuse variables 'result' and 'genesets_filtered') + +# subset only significant CC terms +treemap_input = result |> filter(signif & source == "GO_CC") + +# construct treemap data structures from gene set parent/child relations +tm = treemap_data( + geneset_ids = treemap_input$id, + genesets = genesets_filtered, + genesets_test_result = treemap_input, + ## simplify options; + # "leaf_only" (most stringent, returns only leafs in the tree structure) + # "prune_singletons" (remove parent terms that have exactly 1 child) + # "pvalue" (remove parent terms where the child term p-value is at least 4 times better) + # "none" (default; return all significant genesets that are not a "grouping term" in the treemap) + simplify = "prune_singletons" +) + +# plot the treemap +treemap_plot(tm$treemap_plotdata) ``` + diff --git a/analyses/compare_idea_fgsea_standalone-script.R b/analyses/compare_idea_fgsea_standalone-script.R index a293774..61dd26f 100644 --- a/analyses/compare_idea_fgsea_standalone-script.R +++ b/analyses/compare_idea_fgsea_standalone-script.R @@ -1,23 +1,37 @@ # Comparison between iDEA and fGSEA on previously prepared genelist (RNAseq summary stats) and genesets (GO). # This script is a minimal implementation without other dependences other than iDEA and fGSEA (and dplyr). -# Results show that iDEA yields many missing values and finds substantially fewer enriched genesets (multiple testing correction being equal). +# Results show that iDEA yields many missing values and finds substantially fewer enriched genesets +# (multiple testing correction being equal!) in an example RNAseq dataset. # # author: Frank Koopmans (frank.koopmans@vu.nl) -# date: 02-02-2024 - - +# last update: 02-04-2024 +# +### copy of output / results +# 6797/7904 genesets that were tested with iDEA received a p-value = 14.0% failure rate +# iDEA pvalue and pvalue_louis are finite value for the same genesets; TRUE (all 7904 genesets) +# iDEA; 696/7904 genesets are significant at Bonferroni alpha = 0.05 +# iDEA louis; 93/7904 genesets are significant Bonferroni alpha = 0.05 +# ignoring non-finite p-values: iDEA; 715/6797 genesets are significant Bonferroni alpha = 0.05 +# ignoring non-finite p-values: iDEA louis; 98/6797 genesets are significant Bonferroni alpha = 0.05 +# +# 7904/7904 genesets that were tested with GSEA received a p-value = 0.0% failure rate +# fGSEA; 570/7904 genesets are significant Bonferroni alpha = 0.05 +### +# ### Preparing the input RData file for this script, using the GOAT R package # library(goat) -# genelist_asis = goat::goat_example_datasets$`Sahadevan 2021:RNA-seq:PMID34021139` +# setwd("C:/DATA/") # set working dir, should contain below files goat_manuscript_datasets.rda / gene2go / GO OBO +# load("goat_manuscript_datasets.rda") +# genelist_asis = goat_manuscript_datasets$`Sahadevan 2021:RNA-seq:PMID34021139` # genesets_asis = goat::load_genesets_go_fromfile( -# file_gene2go = "C:/VU/projects/Frank - GOAT (2022)/genesets/gene2go_2024-01-01.gz", -# file_goobo = "C:/VU/projects/Frank - GOAT (2022)/genesets/go_2024-01-01.obo" +# file_gene2go = "gene2go_2024-01-01.gz", +# file_goobo = "go_2024-01-01.obo" # ) # genesets = goat::filter_genesets(genesets_asis, genelist_asis, min_overlap = 10L, max_overlap = 1500L, max_overlap_fraction = 0.5) |> select(source, id, name, genes, ngenes) # genelist = genelist_asis |> select(gene, log2fc, pvalue, pvalue_adjust) -# save(genesets, genelist, file = "C:/temp/sahadevan_pmid34021139.rda") - +# save(genesets, genelist, file = "sahadevan_pmid34021139.rda") +######################################################################################################################## require(iDEA) require(fgsea) @@ -26,28 +40,33 @@ library(dplyr) # hardcoded setting for our workstation with 24 cores PARAM_NCORE = 23 +# set working directory, assuming this is where the input file "sahadevan_pmid34021139.rda" is located +setwd("C:/DATA/") + ### load the genelist and GO genesets from RData file # we already prepared from the GO database the subset of genesets that have at least 10 genes overlapping with the input genelist load("sahadevan_pmid34021139.rda") -head(genelist) # referred to as "summary" in iDEA tutorial -head(genesets) # referred to as "annotation" in iDEA tutorial +head(genelist) # genelist = table of input gene IDs and respective log2fc and pvalue. Referred to as "summary" in iDEA tutorial +head(genesets) # genesets = table of geneset identifiers and respective gene IDs. Referred to as "annotation" in iDEA tutorial -### prepare a genelist to iDEA format +### prepare a genelist in iDEA format # this code follows the iDEA tutorial, with minor adaption that we fix "infinite se_beta estimates" following pvalue=1 in input -# note; we use numeric gene IDs. To avoid type conversion / indexing shenanigans, we prefix integer gene IDs with a string +# note; input genelist prepared for GOAT uses numeric gene IDs. To avoid type conversion / indexing shenanigans, we prefix integer gene IDs with a string when preparing data for iDEA genelist_idea = data.frame(beta = genelist$log2fc, beta_var = NA, pvalue = genelist$pvalue, row.names = paste0("gene_", genelist$gene)) genelist_idea$zscore = stats::qnorm(genelist_idea$pvalue / 2.0, lower.tail=FALSE) genelist_idea$se_beta = abs(genelist_idea$beta / genelist_idea$zscore) # approximate the standard error of beta (log2fc) -genelist_idea$se_beta[!is.finite(genelist_idea$se_beta)] = 10 # update/fix; default high value where beta_se is missing, yields beta_var = 100 -genelist_idea$se_beta[genelist_idea$se_beta < 10^-16] = 10^-16 # guard against "zero standard error" (e.g. at log2fc=0; 0/x=0) to ensure downstream iDEA code doesn't suffer from division-by-zero issues +genelist_idea$se_ismissing = !is.finite(genelist_idea$se_beta) | genelist_idea$se_beta == 0 +genelist_idea$se_beta[genelist_idea$se_ismissing] = max(genelist_idea$se_beta[!genelist_idea$se_ismissing]) # update/fix; insert high values where beta_se is missing +genelist_idea$se_beta[genelist_idea$se_beta < 10^-16] = 10^-6 # guard against "zero standard error" (e.g. at log2fc=0; 0/x=0) to ensure downstream iDEA code doesn't suffer from division-by-zero issues genelist_idea$beta_var = genelist_idea$se_beta^2 -### prepare genesets to iDEA format +### prepare genesets in iDEA format # from a data.frame with a list-column of gene IDs, to a gene*geneset identity matrix genesets_to_matrix = function(gs, ugene) { - # note; we use numeric gene IDs. To avoid type conversion / indexing shenanigans, we prefix integer gene IDs with a string + # note; input genelist prepared for GOAT uses numeric gene IDs. To avoid type conversion / indexing shenanigans, we prefix integer gene IDs with a string when preparing data for iDEA + # note; this is in sync with our preparation of genelist_idea m = matrix(0L, nrow = length(ugene), ncol = nrow(gs), dimnames = list(paste0("gene_", ugene), gs$id)) for(i in 1:nrow(gs)) { m[,i] = as.integer(ugene %in% gs$genes[[i]]) @@ -82,7 +101,7 @@ idea = iDEA::iDEA.fit( fit_noGS = FALSE, init_beta = NULL, init_tau = c(-2,0.5), - min_degene = 0, # defaults to 5 in the iDEA tutorial; "min_degene: the threshold for the number of detected DE genes in summary statistics. For some of extremely cases, the method does not work stably when the number of detected DE genes is 0" + min_degene = 0, # defaults to 5 in the iDEA tutorial, with documentation; "min_degene: the threshold for the number of detected DE genes in summary statistics. For some of extremely cases, the method does not work stably when the number of detected DE genes is 0" em_iter = 15, mcmc_iter = 1000, fit.tol = 1e-5, @@ -93,22 +112,35 @@ idea = iDEA::iDEA.fit( idea = iDEA::iDEA.louis(idea) -### map the iDEA estimated geneset p-values back to the input table -genesets$pvalue = idea@gsea$pvalue[match(genesets$id, idea@gsea$annot_id)] -genesets$pvalue_louis = idea@gsea$pvalue_louis[match(genesets$id, idea@gsea$annot_id)] -cat(sprintf("%d/%d genesets that were tested with iDEA received a p-value\n", sum(is.finite(genesets$pvalue)), nrow(genesets))) +### map the iDEA estimated geneset p-values back to the input geneset table +genesets$pvalue = genesets$pvalue_orig = idea@gsea$pvalue[match(genesets$id, idea@gsea$annot_id)] +genesets$pvalue_louis = genesets$pvalue_louis_orig = idea@gsea$pvalue_louis[match(genesets$id, idea@gsea$annot_id)] +cat(sprintf("%d/%d genesets that were tested with iDEA received a p-value = %.1f%% failure rate\n", sum(is.finite(genesets$pvalue)), nrow(genesets), sum(!is.finite(genesets$pvalue))/nrow(genesets)* 100 )) cat("iDEA pvalue and pvalue_louis are finite value for the same genesets;\n") print(table(is.finite(genesets$pvalue) == is.finite(genesets$pvalue_louis))) +# replace geneset p-values that are infinite with 1 because we did (attempt to) test these for significance +# (and don't want subsequent multiple testing correction to be affected by the unpredictable number of failures / non-finite p-values) +genesets$pvalue[!is.finite(genesets$pvalue)] = 1 +genesets$pvalue_louis[!is.finite(genesets$pvalue_louis)] = 1 + # we've tested 3 GO sources (CC, BP, MF); apply stringent Bonferroni adjustment to each genesets = genesets |> group_by(source) |> - mutate(pvalue_adjust = stats::p.adjust(pvalue, method = "bonferroni")) |> + mutate( + pvalue_adjust = stats::p.adjust(pvalue, method = "bonferroni"), + pvalue_louis_adjust = stats::p.adjust(pvalue_louis, method = "bonferroni"), + pvalue_adjust_ignoremissing = stats::p.adjust(ifelse(is.finite(pvalue_orig), pvalue_orig, NA), method = "bonferroni"), + pvalue_louis_adjust_ignoremissing = stats::p.adjust(ifelse(is.finite(pvalue_louis_orig), pvalue_louis_orig, NA), method = "bonferroni") + ) |> ungroup() # final result: print number of significant genesets per GO source -cat(sprintf("iDEA; %d/%d genesets are significant\n", sum(is.finite(genesets$pvalue_adjust) & genesets$pvalue_adjust <= 0.05), sum(is.finite(genesets$pvalue_adjust)))) -print( genesets |> filter(pvalue_adjust <= 0.05) |> count(source) |> arrange(source) ) +cat(sprintf("iDEA; %d/%d genesets are significant at Bonferroni alpha = 0.05\n", sum(is.finite(genesets$pvalue_adjust) & genesets$pvalue_adjust <= 0.05), sum(is.finite(genesets$pvalue_adjust)))) +cat(sprintf("iDEA louis; %d/%d genesets are significant Bonferroni alpha = 0.05\n", sum(is.finite(genesets$pvalue_louis_adjust) & genesets$pvalue_louis_adjust <= 0.05), sum(is.finite(genesets$pvalue_louis_adjust)))) +cat(sprintf("ignoring non-finite p-values: iDEA; %d/%d genesets are significant Bonferroni alpha = 0.05\n", sum(is.finite(genesets$pvalue_adjust_ignoremissing) & genesets$pvalue_adjust_ignoremissing <= 0.05), sum(is.finite(genesets$pvalue_adjust_ignoremissing)))) +cat(sprintf("ignoring non-finite p-values: iDEA louis; %d/%d genesets are significant Bonferroni alpha = 0.05\n", sum(is.finite(genesets$pvalue_louis_adjust_ignoremissing) & genesets$pvalue_louis_adjust_ignoremissing <= 0.05), sum(is.finite(genesets$pvalue_louis_adjust_ignoremissing)))) +# print( genesets |> filter(pvalue_adjust <= 0.05) |> count(source) |> arrange(source) ) @@ -120,8 +152,8 @@ fgsea = fgsea::fgsea( scoreType = "std", minSize = 1L, # disable filter, already done upstream maxSize = 10000L, # disable filter, already done upstream - nPermSimple = 10000, # number of permutations (default is 1000) - nproc = PARAM_NCORE + nPermSimple = 10000, # increased number of permutations (from default 1000) + nproc = PARAM_NCORE # number of threads ) |> select(id = pathway, pvalue = pval) |> # only need these 2 columns from fGSEA left_join(genesets |> select(id, source), by="id") # add the geneset "source" (GO CC/BP/MF) @@ -131,8 +163,10 @@ fgsea = fgsea |> group_by(source) |> mutate(pvalue_adjust = stats::p.adjust(pvalue, method = "bonferroni")) |> ungroup() -cat(sprintf("fGSEA; %d/%d genesets are significant\n", sum(is.finite(fgsea$pvalue_adjust) & fgsea$pvalue_adjust <= 0.05), sum(is.finite(fgsea$pvalue_adjust)))) -print( fgsea |> filter(pvalue_adjust <= 0.05) |> count(source) |> arrange(source) ) + +cat(sprintf("%d/%d genesets that were tested with GSEA received a p-value = %.1f%% failure rate\n", sum(is.finite(fgsea$pvalue)), nrow(fgsea), sum(!is.finite(fgsea$pvalue))/nrow(fgsea)* 100 )) +cat(sprintf("fGSEA; %d/%d genesets are significant Bonferroni alpha = 0.05\n", sum(is.finite(fgsea$pvalue_adjust) & fgsea$pvalue_adjust <= 0.05), sum(is.finite(fgsea$pvalue_adjust)))) +# print( fgsea |> filter(pvalue_adjust <= 0.05) |> count(source) |> arrange(source) ) diff --git a/analyses/computation_time.R b/analyses/computation_time.R index 8b2883c..b7f039a 100644 --- a/analyses/computation_time.R +++ b/analyses/computation_time.R @@ -1,13 +1,13 @@ +require(parallel) library(ggplot2) library(goat) # also loads dplyr -# output_dir = "C:/temp" -# file_gene2go = "C:/VU/projects/Frank - GOAT (2022)/genesets/gene2go_2024-01-01.gz" -# file_goobo = "C:/VU/projects/Frank - GOAT (2022)/genesets/go_2024-01-01.obo" -output_dir = "." -file_gene2go = "gene2go_2024-01-01.gz" -file_goobo = "go_2024-01-01.obo" -goat_print_version() + +# TODO: setup input/output paths +output_dir = getwd() # directory where output files should be stored +file_gene2go = "gene2go_2024-01-01.gz" # full path to NCBI gene2go file, e.g. previously downloaded from https://ftp.ncbi.nih.gov/gene/DATA/gene2go.gz +file_goobo = "go_2024-01-01.obo" # full path go GO OBO file, e.g. previously downloaded from http://current.geneontology.org/ontology/go.obo +source("test_genesets_idea.R") # load the script that implements the iDEA function; update to the full path ### parameters @@ -18,6 +18,17 @@ nthread = ifelse(tolower(.Platform$OS.type) == "windows", 1L, as.integer(min(24, cat(nthread, "thread(s) for fGSEA\n") +goat_print_version() + + + + + + + + +######################################################################################################################## + # helper function that times how many seconds a parameter function takes to evaluate (discards the respective function's result) mytime = function(f) { time_start = Sys.time() @@ -54,7 +65,7 @@ for(sim_geneset_count in c(500, 1000, 2000, 4000, 6000)) { tmp_time_idea = NA if(include_idea) { - tmp_time_idea = mytime(function(){test_genesets(iter_genesets, iter_genelist, method = "idea", return_pvalue_louis = TRUE)}) + tmp_time_idea = mytime(function(){test_genesets(iter_genesets, iter_genelist, method = "test_genesets_idea", return_pvalue_louis = TRUE)}) } # perform geneset enrichment tests and keep track of computation time @@ -80,13 +91,13 @@ for(sim_geneset_count in c(500, 1000, 2000, 4000, 6000)) { # hardcoded color-codings lbl = c( goat_pvalue = "GOAT p-value - precomputed", - goat_effectsize = "GOAT effectsize - precomputed", + goat_effectsize = "GOAT effect size - precomputed", goat_fitfunction_pvalue = "GOAT p-value - full algorithm", - goat_fitfunction_effectsize = "GOAT effectsize - full algorithm", + goat_fitfunction_effectsize = "GOAT effect size - full algorithm", gsea_pvalue_10k = "GSEA p-value - 10000 iterations", - gsea_effectsize_10k = "GSEA effectsize - 10000 iterations", + gsea_effectsize_10k = "GSEA effect size - 10000 iterations", gsea_pvalue_50k = "GSEA p-value - 50000 iterations", - gsea_effectsize_50k = "GSEA effectsize - 50000 iterations", + gsea_effectsize_50k = "GSEA effect size - 50000 iterations", idea = "iDEA"#, # hypergeometric = "hypergeometric test" ) @@ -100,7 +111,7 @@ clr = c( gsea_effectsize_10k = "#A83CFA", gsea_pvalue_50k = "#42a5f5", gsea_effectsize_50k = "#304ffe", - idea = "#8d6e63"#, + idea = "#C99D8E"#, # hypergeometric = "#69f0ae" ) @@ -116,10 +127,11 @@ tib_plot = result |> ) -myplot = function(x, p) { +myplot = function(x) { + stopifnot(x$label %in% names(lbl)) clr_values = setNames(clr, lbl) clr_values = clr_values[names(clr_values) %in% x$label] - ggplot(x, aes(geneset_count, time, colour = label, fill = label, shape = I(ifelse(grepl("effectsize",label), 25, ifelse(grepl("p-value",label), 24, 16))) )) + + ggplot(x, aes(geneset_count, time, colour = label, fill = label, shape = I(ifelse(grepl("effect.{0,1}size",label), 25, ifelse(grepl("p.{0,1}value",label), 24, 16))) )) + geom_point(size = 2.5) + geom_line(size = 0.75) + ylim(c(0, max(x$time))) + diff --git a/analyses/null_simulations_by_geneset_size.R b/analyses/null_simulations_by_geneset_size.R index 6392315..536e847 100644 --- a/analyses/null_simulations_by_geneset_size.R +++ b/analyses/null_simulations_by_geneset_size.R @@ -1,26 +1,41 @@ library(ggplot2) -library(goat) -# output_dir = "C:/temp" -# file_gene2go = "C:/VU/projects/Frank - GOAT (2022)/genesets/gene2go_2024-01-01.gz" -# file_goobo = "C:/VU/projects/Frank - GOAT (2022)/genesets/go_2024-01-01.obo" -output_dir = "." -file_gene2go = "gene2go_2024-01-01.gz" -file_goobo = "go_2024-01-01.obo" -goat_print_version() +library(goat) # also loads dplyr +# TODO: setup input/output paths +output_dir = getwd() # directory where output files should be stored +file_gene2go = "gene2go_2024-01-01.gz" # full path to NCBI gene2go file, e.g. previously downloaded from https://ftp.ncbi.nih.gov/gene/DATA/gene2go.gz +file_goobo = "go_2024-01-01.obo" # full path go GO OBO file, e.g. previously downloaded from http://current.geneontology.org/ontology/go.obo +file_datasets = "goat_manuscript_datasets.rda" # full path to the datasets prepared earlier. This RData file is available in the "analyses" directory @ github +source("test_genesets_idea.R") # load the script that implements the iDEA function; update to the full path -# hardcoded color-codings across plots/figures +# hardcoded color-codings clr = tibble::tibble( - method = c("hypergeometric", "fisherexact_ease", "gsea_pvalue", "gsea_effectsize", "goat_pvalue", "goat_effectsize", "goat_pvalue_effectsize", "goat_effectsize_up", "goat_effectsize_down", "goat_effectsize_abs"), - label = c("hypergeometric test", "Fisher-exact EASE", "GSEA p-value", "GSEA effectsize", "GOAT p-value", "GOAT effectsize", "GOAT p-value & effectsize", "GOAT effectsize up", "GOAT effectsize down", "GOAT effectsize abs"), - clr = c("#69f0ae", "#ffd600", "#42a5f5", "#304ffe", "#ff7043", "#f50057", "#d50000", "#f50057", "#f50057", "#f50057") + method = c("hypergeometric", "fisherexact_ease", "idea_default_louis", "idea_rescale_louis", "idea_beta_rescale_louis", + "gsea_pvalue", "gsea_effectsize", "gsea_pvalue_gseaParam2", "gsea_effectsize_gseaParam2", + "goat_pvalue", "goat_effectsize", "goat_pvalue_effectsize", "goat_effectsize_up", "goat_effectsize_down", "goat_effectsize_abs"), + label = c("hypergeometric test", "Fisher-exact EASE", "iDEA", "iDEA*", "iDEAb*", + "GSEA p-value", "GSEA effect size", "GSEA p-value param=2", "GSEA effectsize param=2", + "GOAT p-value", "GOAT effect size", "GOAT p-value & effect size", "GOAT effect size up", "GOAT effect size down", "GOAT effect size abs"), + clr = c("#69f0ae", "#ffd600", "#C99D8E", "#B0BEC5", "#D9A71E", + "#42a5f5", "#304ffe", "#5c6bc0", "#5e35b1", + "#ff7043", "#f50057", "#d50000", "#f50057", "#f50057", "#f50057") ) +goat_print_version() + + + + +########################################################## +### GENERATE SYNTHETIC NULL GENESETS + APPLY GOAT/GSEA ### +########################################################## + + benchmark_bin_results = function(x) { - x |> + y = x |> # 'best' pvalue on top arrange(pvalue) |> # group by geneset size ('ngenes' column) @@ -32,8 +47,18 @@ benchmark_bin_results = function(x) { frac_minlog10 = minlog10_fixzero(frac), # bin the data (within grouping) within 0.05 distance on -log10 scale (so we don't have to plot 200k datapoints later on) bin = cut(frac_minlog10, breaks = seq(from=0, to=ceiling(max(frac_minlog10)), by = 0.05), include.lowest = T, labels = F) - ) |> - # summary statistics, reducing the huge table to only information we will plot + ) + + # now that we have observed (pvalue) and expected (frac) -log10 values, we can quantify the error + # here we compute the RMSE within "ngenes" grouping + # (because function input are the geneset testing results from 1 algorithm, and here the table is grouped by "ngenes", this computes the respective error per geneset size) + err_ngenes = y |> summarise(rmse_ngenes = sqrt(mean((frac_minlog10 - pvalue_minlog10)^2)), .groups = "drop") + # analogous for overall data (i.e. over all genesets) + err_overall = sqrt(mean((y$frac_minlog10 - y$pvalue_minlog10)^2)) + # err_overall = y |> ungroup() |> summarise(rmse_overall = sqrt(mean((frac_minlog10 - pvalue_minlog10)^2)), .groups = "drop") + + # summary statistics, reducing the huge table to only information we will plot (1 value per ngenes*bin) + y = y |> group_by(ngenes, bin) |> summarise( count_genesets = n(), @@ -41,109 +66,134 @@ benchmark_bin_results = function(x) { pvalue_minlog10 = mean(pvalue_minlog10), .groups = "drop" ) + + y |> + left_join(err_ngenes, by = "ngenes") |> + mutate(rmse_overall = err_overall) } -# setup input data; GO genesets & real-world dataset that is to be tested against randomized genesets -genesets = load_genesets_go_fromfile(file_gene2go, file_goobo) -# setup input data; we'll sample from a real-world genelist -data(goat_example_datasets) -genelist = goat_example_datasets$`Sahadevan 2021:RNA-seq:PMID34021139` +file_cache = paste0(output_dir, "/null_simulations.rda") +if(file.exists(file_cache)) { + load(file_cache) +} else { + # load real-world OMICs datasets we prepared earlier from RData file + load(file_datasets) -# identifiers for the random genesets we'll generate. typically some vector of length 100k ~ 500k -# the length of this vector defines the number of random genesets we'll generate -idseq = 1L:200000L # 200k geneset permutations + # setup input data; GO genesets & real-world dataset that is to be tested against randomized genesets + genesets = load_genesets_go_fromfile(file_gene2go, file_goobo) -# ensure our results are reproducible -set.seed(123) + # setup input data; we'll sample from a real-world genelist + genelist = goat_manuscript_datasets$`Sahadevan 2021:RNA-seq:PMID34021139` -tib_plot_all = NULL -for(genelist_size in c(250L, 1000L, 4000L, 12000L)) { - stopifnot(genelist_size < nrow(genelist)) + # identifiers for the random genesets we'll generate. typically some vector of length 100k ~ 500k + # the length of this vector defines the number of random genesets we'll generate + idseq = 1L:200000L # 200k geneset permutations - cat("genelist_size =", genelist_size, "\n") + # ensure our results are reproducible + set.seed(123) - ### generate the mock data + tib_plot_all = NULL + for(genelist_size in c(250L, 1000L, 4000L, 12000L)) { + stopifnot(genelist_size < nrow(genelist)) - # subset N random genes from the genelist - mock_genelist = genelist |> slice(sample(seq_len(nrow(genelist)), size = genelist_size)) + cat("genelist_size =", genelist_size, "\n") - # generate many random genesets of size k - genesets_mock = NULL - for(mock_size in c(10L, 20L, 50L, 100L, 200L, 1000L)) { - # limit geneset sizes to at most half the genelist length (larger = go next) - if(mock_size * 2 > genelist_size) { - next - } + ### generate the mock data + + # subset N random genes from the genelist + mock_genelist = genelist |> slice(sample(seq_len(nrow(genelist)), size = genelist_size)) - # generate mock genesets; draw k random gene IDs from the genelist. Importantly, sample without replacement - genesets_mock = bind_rows( - genesets_mock, - tmp = tibble::tibble( - source = "x", - source_version = "x", - id = paste0("gs_", mock_size, "_", idseq), - name = id, - genes = sapply(idseq, function(i) sample(mock_genelist$gene, size = mock_size, replace = FALSE), simplify = FALSE, USE.NAMES = FALSE), - ngenes = mock_size, - ngenes_signif = 0L + # generate many random genesets of size k + genesets_mock = NULL + for(mock_size in c(10L, 20L, 50L, 100L, 200L, 1000L)) { + # limit geneset sizes to at most half the genelist length (larger = go next) + if(mock_size * 2 > genelist_size) { + next + } + + # generate mock genesets; draw k random gene IDs from the genelist. Importantly, sample without replacement + genesets_mock = bind_rows( + genesets_mock, + tmp = tibble::tibble( + source = "x", + source_version = "x", + id = paste0("gs_", mock_size, "_", idseq), + name = id, + genes = sapply(idseq, function(i) sample(mock_genelist$gene, size = mock_size, replace = FALSE), simplify = FALSE, USE.NAMES = FALSE), + ngenes = mock_size, + ngenes_signif = 0L + ) ) - ) + } + + + ### APPLY TEST_GENESETS + # separate statements, instead of 1 call to bind_rows, so we don't have to hold intermediate results across all algorithms in memory (i.e. R can clean up unused RAM in between) + + # GOAT + tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "goat_precomputed", score_type = "pvalue")) |> + mutate(genelist_size = genelist_size, method_name = "GOAT p-value")) + tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "goat_precomputed", score_type = "effectsize")) |> + mutate(genelist_size = genelist_size, method_name = "GOAT effect size")) + tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "goat_precomputed", score_type = "effectsize_up")) |> + mutate(genelist_size = genelist_size, method_name = "GOAT effect size up")) + tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "goat_precomputed", score_type = "effectsize_down")) |> + mutate(genelist_size = genelist_size, method_name = "GOAT effect size down")) + tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "goat_precomputed", score_type = "effectsize_abs")) |> + mutate(genelist_size = genelist_size, method_name = "GOAT effect size abs")) + # GSEA + tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "pvalue", nPermSimple = 1000L)) |> + mutate(genelist_size = genelist_size, method_name = "GSEA p-value\nniter=1000")) + tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "effectsize", nPermSimple = 1000L)) |> + mutate(genelist_size = genelist_size, method_name = "GSEA effect size\nniter=1000")) + tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "pvalue", nPermSimple = 10000L)) |> + mutate(genelist_size = genelist_size, method_name = "GSEA p-value\nniter=10000")) + tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "effectsize", nPermSimple = 10000L)) |> + mutate(genelist_size = genelist_size, method_name = "GSEA effect size\nniter=10000")) + tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "pvalue", nPermSimple = 50000L)) |> + mutate(genelist_size = genelist_size, method_name = "GSEA p-value\nniter=50000")) + tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "effectsize", nPermSimple = 50000L)) |> + mutate(genelist_size = genelist_size, method_name = "GSEA effect size\nniter=50000")) + + ### debug; apply GOAT with specific parameters, then create QQplot that splits results per geneneset size/bin + ### debug; color-code by the number of observed datapoints in each size/bin -->> be mindful that this highlights/emphasizes one-hit-wonders / extreme values + # x = test_genesets_goat_precomputed(genesets = genesets_mock, genelist = mock_genelist, score_type = "pvalue") + # y = benchmark_bin_results(x) + # p = ggplot(y, aes(frac_minlog10, pvalue_minlog10, colour = pmin(count_genesets, 500))) + + # geom_point(size = 0.3) + + # geom_abline(intercept = 0, slope = 1, colour = "red", size = 0.5) + + # facet_wrap(.~ngenes) + + # scale_colour_continuous(name = "# data points", trans = "log2", high = "#000000", low = "#BBBBBB") + + # labs(title = paste("genelist N=", genelist_size)) + + # theme_bw() + + # theme(legend.position = "bottom") + # print(p) } - ### APPLY TEST_GENESETS - # separate statements, instead of 1 call to bind_rows, so we don't have to hold intermediate results across all algorithms in memory (i.e. R can clean up unused RAM in between) - - # GOAT - tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "goat_precomputed", score_type = "pvalue")) |> - mutate(genelist_size = genelist_size, method_name = "GOAT p-value")) - tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "goat_precomputed", score_type = "effectsize")) |> - mutate(genelist_size = genelist_size, method_name = "GOAT effectsize")) - tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "goat_precomputed", score_type = "effectsize_up")) |> - mutate(genelist_size = genelist_size, method_name = "GOAT effectsize_up")) - tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "goat_precomputed", score_type = "effectsize_down")) |> - mutate(genelist_size = genelist_size, method_name = "GOAT effectsize_down")) - tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "goat_precomputed", score_type = "effectsize_abs")) |> - mutate(genelist_size = genelist_size, method_name = "GOAT effectsize_abs")) - # GSEA - tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "pvalue", nPermSimple = 1000L)) |> - mutate(genelist_size = genelist_size, method_name = "GSEA p-value\nniter=1000")) - tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "effectsize", nPermSimple = 1000L)) |> - mutate(genelist_size = genelist_size, method_name = "GSEA effectsize\nniter=1000")) - tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "pvalue", nPermSimple = 10000L)) |> - mutate(genelist_size = genelist_size, method_name = "GSEA p-value\nniter=10000")) - tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "effectsize", nPermSimple = 10000L)) |> - mutate(genelist_size = genelist_size, method_name = "GSEA effectsize\nniter=10000")) - tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "pvalue", nPermSimple = 50000L)) |> - mutate(genelist_size = genelist_size, method_name = "GSEA p-value\nniter=50000")) - tib_plot_all = bind_rows(tib_plot_all, benchmark_bin_results(test_genesets(genesets = genesets_mock, genelist = mock_genelist, method = "gsea", score_type = "effectsize", nPermSimple = 50000L)) |> - mutate(genelist_size = genelist_size, method_name = "GSEA effectsize\nniter=50000")) - - ### debug; apply GOAT with specific parameters, then create QQplot that splits results per geneneset size/bin - ### debug; color-code by the number of observed datapoints in each size/bin -->> be mindful that this highlights/emphasizes one-hit-wonders / extreme values - # x = test_genesets_goat_precomputed(genesets = genesets_mock, genelist = mock_genelist, score_type = "pvalue") - # y = benchmark_bin_results(x) - # p = ggplot(y, aes(frac_minlog10, pvalue_minlog10, colour = pmin(count_genesets, 500))) + - # geom_point(size = 0.3) + - # geom_abline(intercept = 0, slope = 1, colour = "red", size = 0.5) + - # facet_wrap(.~ngenes) + - # scale_colour_continuous(name = "# data points", trans = "log2", high = "#000000", low = "#BBBBBB") + - # labs(title = paste("genelist N=", genelist_size)) + - # theme_bw() + - # theme(legend.position = "bottom") - # print(p) + ### finally, store computations in RData file + if(!dir.exists(output_dir)) dir.create(output_dir, recursive = TRUE) + save(tib_plot_all, file = file_cache, compress = TRUE) } + + + +########################################################## +##################### CREATE FIGURES ##################### +########################################################## + + # Plot only geneset bins where we have at least 25 datapoints to prevent extreme values, # that are observed only once or twice out of 200 thousand (!) random draws, # from showing up in the plot and causing strong bias in interpretation # Outliers not supported by N observations are not indicative of an algorithm's performance. # (note that with different set.seed, those 1-out-of-200k extremes will vary while the rest should not due to averaging across bootstrap iterations) -myplot = function(x, min_datapoints = 25) { +myplot = function(x, min_datapoints = 25, show_rmse = TRUE) { y = x |> filter(count_genesets >= min_datapoints) |> # update method names @@ -159,46 +209,54 @@ myplot = function(x, min_datapoints = 25) { genelist_size_label = factor(genelist_size_label, unique(genelist_size_label)) ) - ggplot(y, aes(frac_minlog10, pvalue_minlog10, colour = ngenes)) + + p = ggplot(y, aes(frac_minlog10, pvalue_minlog10, colour = ngenes)) + geom_line(size = 0.5) + geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "black", size = 0.4) + facet_wrap(method_label ~ genelist_size_label, ncol = n_distinct(y$method_label), dir = "v") + - guides(colour = guide_legend(title = "geneset size", nrow = 1, reverse = T, override.aes = list(size = 1))) + + guides(colour = guide_legend(title = "gene set size", nrow = 1, reverse = T, override.aes = list(size = 1))) + scale_colour_discrete(direction = -1) + labs(x = "Expected -log10 p-value", y = "Observed -log10 p-value") + theme_bw() + theme( axis.text = element_text(size = 9), axis.title = element_text(size = 11), - strip.text = element_text(size = 9, margin = margin(2,1,2,1)), + strip.text = element_text(size = 8, margin = margin(2,1,2,1)), legend.position = "bottom", legend.title = element_text(size = 11), legend.text = element_text(size = 9) ) -} + if(show_rmse) { + # from input data, grab the error for each unique genelist*method pair + err = y |> + distinct(genelist_size, method_name, .keep_all = TRUE) |> + mutate(err_label = sprintf("RMSE: %.4f", rmse_overall)) + # add annotation to the plot on the top-left + p = p + + geom_label(mapping = aes(x = -0.05, y = 3.3, label = err_label, colour = I("black")), data = err, size = 7/ggplot2::.pt, hjust = 0, vjust = 1, fill = "white", label.size = NA, show.legend = FALSE) + } + + return(p) +} -if(!dir.exists(output_dir)) dir.create(output_dir, recursive = TRUE) -save(tib_plot_all, file = paste0(output_dir, "/bootstrap_genesets.rda"), compress = TRUE) -# load("C:/VU/projects/Frank - GOAT (2022)/manuscript/figures_v2_post-bugfix/bootstrap_genesets.rda") # GOAT pvalue, GOAT effectsize, GSEA 1kiter pvalue, GSEA 1kiter effectsize, GSEA 50kiter pvalue, GSEA 50kiter effectsize -plot_names = c("GOAT p-value", "GOAT effectsize", "GSEA p-value\nniter=1000", "GSEA effectsize\nniter=1000", "GSEA p-value\nniter=50000", "GSEA effectsize\nniter=50000") +plot_names = c("GOAT p-value", "GOAT effect size", "GSEA p-value\nniter=1000", "GSEA effect size\nniter=1000", "GSEA p-value\nniter=50000", "GSEA effect size\nniter=50000") p = myplot(tib_plot_all |> filter(method_name %in% plot_names) |> arrange(match(method_name, plot_names)) ) p = p + facet_grid(vars(genelist_size_label), vars(method_label)) print(p) -ggsave(p, file = paste0(output_dir, "/bootstrap_geneset_bins__figure2.pdf"), width = 6.5, height = 5) +ggsave(p, file = paste0(output_dir, "/null_simulations_bins__figure2.pdf"), width = 6.5, height = 5) # all GOAT score types p = myplot(tib_plot_all |> filter(grepl("GOAT", method_name)) ) print(p) -ggsave(p, file = paste0(output_dir, "/bootstrap_geneset_bins__GOAT-all-scoretypes.pdf"), width = 10, height = 10) +ggsave(p, file = paste0(output_dir, "/null_simulations_bins__GOAT-all-scoretypes.pdf"), width = 7, height = 7) # GSEA 1k/10k/50k iter compared p = myplot(tib_plot_all |> filter(grepl("GSEA", method_name)) ) print(p) -ggsave(p, file = paste0(output_dir, "/bootstrap_geneset_bins__GSEA-compare-niter.pdf"), width = 10, height = 10) +ggsave(p, file = paste0(output_dir, "/null_simulations_bins__GSEA-compare-niter.pdf"), width = 7, height = 7) # p = myplot(tib_plot_all |> filter(method_name == "GOAT p-value")) + # facet_wrap(method_label ~ genelist_size_label, nrow = 1) @@ -209,6 +267,66 @@ ggsave(p, file = paste0(output_dir, "/bootstrap_geneset_bins__GSEA-compare-niter p = myplot(tib_plot_all |> filter(method_name == "GOAT p-value" | method_name == "GOAT effectsize")) + facet_wrap(method_label ~ genelist_size_label, nrow = 2) print(p) -ggsave(p, file = paste0(output_dir, "/bootstrap_geneset_bins__GOAT-main-figure.pdf"), width = 5, height = 3.5) +ggsave(p, file = paste0(output_dir, "/null_simulations_bins__GOAT-pvalue-effectsize.pdf"), width = 5, height = 3.5) + + + + + + + + + +######### RMSE values for all method*genelist*ngenes combinations (same methods as null simulations main figure) + +plot_names = c("GOAT p-value", "GOAT effect size", "GSEA p-value\nniter=1000", "GSEA effect size\nniter=1000", "GSEA p-value\nniter=50000", "GSEA effect size\nniter=50000") +x = tib_plot_all |> filter(method_name %in% plot_names) |> arrange(match(method_name, plot_names)) + +y = x |> + distinct(method_name, genelist_size, ngenes, .keep_all = T) |> + select(method_name, genelist_size, ngenes, rmse_ngenes) |> + # below labels and factors are analogous to main figure plot/code + arrange(desc(ngenes)) |> + mutate(ngenes = factor(ngenes, unique(ngenes))) |> + arrange(genelist_size) |> + mutate( + genelist_size_label = paste0("N = ", genelist_size), + genelist_size_label = factor(genelist_size_label, unique(genelist_size_label)), + classification = factor(ifelse(grepl("value", method_name), "p-value", "effect size"), levels = c("p-value", "effect size")) + ) + +data_legend = y |> + distinct(method_name) |> + mutate( + method_clr_label = gsub("\n.*", "", method_name), + method_clr = clr$clr[match(gsub("_", " ", method_clr_label), clr$label)], + method_shape = ifelse(grepl("niter=1000", method_name), 15, ifelse(grepl("niter=50000", method_name), 17, 16)), + method_label = factor(method_name, levels = unique(method_name)) # follow order defined upstream + ) |> + arrange(method_label) +stopifnot(!is.na(data_legend$method_clr)) # double-check that the method name lookup succeeded + +p = ggplot(y, aes(rmse_ngenes, ngenes, colour = method_name, shape = method_name)) + + geom_point(size = 2, alpha = 0.66) + + scale_colour_manual(name = "method", values = tibble::deframe(data_legend |> select(method_name, method_clr)), breaks = data_legend$method_name, labels = data_legend$method_name) + + scale_shape_manual(name = "method", values = tibble::deframe(data_legend |> select(method_name, method_shape)), breaks = data_legend$method_name, labels = data_legend$method_name) + + facet_wrap(classification ~ genelist_size_label, ncol = 2) + + guides(colour = guide_legend(ncol = 2, byrow = TRUE), + shape = guide_legend(ncol = 2, byrow = TRUE)) + + labs(x = "RMSE", y = "Geneset size") + + theme_bw() + + theme( + axis.text = element_text(size = 9), + axis.title = element_text(size = 11), + strip.text = element_text(size = 9, margin = margin(2,1,2,1)), + legend.position = "bottom", + legend.title = element_blank(), + legend.text = element_text(size = 9) + ) +print(p) + +ggsave(p, file = paste0(output_dir, "/null_simulations_bins__rmse.pdf"), width = 3.5, height = 6.5) +### print stats; average RMSE for each method +print(y |> group_by(method_name) |> summarise(mean_RMSE = mean(rmse_ngenes), .groups = "drop")) diff --git a/analyses/prepare_realworld_datasets.R b/analyses/prepare_realworld_datasets.R index 11c519b..5df22ea 100644 --- a/analyses/prepare_realworld_datasets.R +++ b/analyses/prepare_realworld_datasets.R @@ -1,20 +1,19 @@ ### parse downloaded data into RData format +# to reproduce: download files to your computer and update respective file paths. References to papers/tables @ manuscript M&M -# to reproduce: download files to your computer and update respective file paths -# - see `hgnc_lookuptable` documentation for download instructions via genenames.org) +library(goat) # also loads dplyr -devtools::load_all() # to reproduce this code on non-development system, replace with library(goat) +# TODO: setup input/output paths +dir_input = "C:/DATA/GOAT_datasets" # directory where all datasets are stored +dir_output = "C:/DATA/GOAT_datasets" # directory where the RData file should be stored +hgnc = hgnc_idmap_table("C:/DATA/hgnc/hgnc_complete_set__2024-01-01.txt") # e.g. previously downloaded from https://ftp.ebi.ac.uk/pub/databases/genenames/hgnc/tsv/hgnc_complete_set.txt +goat_manuscript_datasets = list() -hgnc = hgnc_lookuptable("C:/DATA/hgnc/hgnc_complete_set__2024-01-01.txt") - -goat_example_datasets = list() - - -dea = tibble::as_tibble(data.table::fread("C:/VU/projects/Frank - GOAT (2022)/datasets/PMID34396684_proteomics.csv", data.table = F)) +dea = tibble::as_tibble(data.table::fread(paste0(dir_input, "/PMID34396684_proteomics.csv"), data.table = F)) genelist = dea |> filter(Test == "Compartment") |> select(symbol = ID, log2fc = logFC, effectsize = t, pvalue = P.Value, pvalue_adjust = adj.P.Val) |> @@ -28,11 +27,11 @@ genelist = dea |> arrange(pvalue) |> distinct(gene, .keep_all = T) table(genelist$signif) -goat_example_datasets[["Colameo 2021:mass-spec:PMID34396684"]] = genelist +goat_manuscript_datasets[["Colameo 2021:mass-spec:PMID34396684"]] = genelist -dea = tibble::as_tibble(data.table::fread("C:/VU/projects/Frank - GOAT (2022)/datasets/PMID34396684_transcriptomics.csv", data.table = F)) +dea = tibble::as_tibble(data.table::fread(paste0(dir_input, "/PMID34396684_transcriptomics.csv"), data.table = F)) genelist = dea |> filter(Test == "Compartment") |> select(symbol = names, log2fc = log2FC, effectsize = log2FC, pvalue = PValue, pvalue_adjust = FDR) |> @@ -46,12 +45,12 @@ genelist = dea |> arrange(pvalue) |> distinct(gene, .keep_all = T) table(genelist$signif) -goat_example_datasets[["Colameo 2021:RNA-seq:PMID34396684"]] = genelist +goat_manuscript_datasets[["Colameo 2021:RNA-seq:PMID34396684"]] = genelist # note; no multiple testing correction in GO figure @ paper, they probably didn't use a background either -dea = readxl::read_excel("C:/VU/projects/Frank - GOAT (2022)/datasets/PMID34021139_tableS8.xlsx", sheet = 4) +dea = readxl::read_excel(paste0(dir_input, "/PMID34021139_tableS8.xlsx"), sheet = 4) genelist = dea |> select(symbol = gene_name, log2fc = logFC, pvalue = PValue, pvalue_adjust = FDR) |> symbol_to_entrez(hgnc = hgnc) |> @@ -65,11 +64,11 @@ genelist = dea |> arrange(pvalue) |> distinct(gene, .keep_all = T) table(genelist$signif) -goat_example_datasets[["Sahadevan 2021:RNA-seq:PMID34021139"]] = genelist +goat_manuscript_datasets[["Sahadevan 2021:RNA-seq:PMID34021139"]] = genelist -dea = readxl::read_excel("C:/VU/projects/Frank - GOAT (2022)/datasets/PMID33087358_tableS2_orig.xlsx", skip = 6, +dea = readxl::read_excel(paste0(dir_input, "/PMID33087358_tableS2_orig.xlsx"), skip = 6, col_names = c("protein_id", "symbol", "module", "pvalue", "pvalue_adjust", "log2fc")) genelist = dea |> symbol_to_entrez(hgnc = hgnc) |> @@ -83,29 +82,30 @@ genelist = dea |> arrange(pvalue) |> distinct(gene, .keep_all = T) table(genelist$signif) -goat_example_datasets[["Higginbotham 2020:mass-spec:PMID33087358"]] = genelist +goat_manuscript_datasets[["Higginbotham 2020:mass-spec:PMID33087358"]] = genelist -dea = readxl::read_excel("C:/VU/projects/Frank - GOAT (2022)/datasets/PMID32424284_tables4_orig.xlsx", skip = 5) +dea = readxl::read_excel(paste0(dir_input, "/PMID32424284_tables4_orig.xlsx"), skip = 5) genelist = dea |> - select(Protein, log2fc = beta, effectsize = beta, pvalue = p, pvalue_adjust = `BH adjusted p`) |> + select(Protein, log2fc = beta, sd = sd_beta, pvalue = p, pvalue_adjust = `BH adjusted p`) |> mutate(symbol = toupper(gsub("[ ;,|].*", "", Protein))) |> symbol_to_entrez(hgnc = hgnc) |> mutate( symbol = hgnc_symbol, gene = entrez_id, - signif = pvalue_adjust <= 0.05 + signif = pvalue_adjust <= 0.05, + effectsize = log2fc ) |> filter(is.finite(pvalue) & is.finite(gene)) |> arrange(pvalue) |> distinct(gene, .keep_all = T) table(genelist$signif) -goat_example_datasets[["Wingo 2020:mass-spec:PMID32424284"]] = genelist +goat_manuscript_datasets[["Wingo 2020:mass-spec:PMID32424284"]] = genelist -dea = readxl::read_excel("C:/VU/projects/Frank - GOAT (2022)/datasets/PMID33492460_tables8_orig.xlsx") +dea = readxl::read_excel(paste0(dir_input, "/PMID33492460_tables8_orig.xlsx")) genelist = dea |> select(symbol = GENE, log2fc = `log2 FC Control vs Tangle`, effectsize = `log2 FC Control vs Tangle`, pvalue = `FDR Control vs Tangle`, pvalue_adjust = `FDR Control vs Tangle`) |> mutate(symbol = gsub(";.*", "", symbol)) |> @@ -119,7 +119,7 @@ genelist = dea |> arrange(pvalue) |> distinct(gene, .keep_all = T) table(genelist$signif) -goat_example_datasets[["Hondius 2021:mass-spec:PMID33492460"]] = genelist +goat_manuscript_datasets[["Hondius 2021:mass-spec:PMID33492460"]] = genelist @@ -129,8 +129,8 @@ goat_example_datasets[["Hondius 2021:mass-spec:PMID33492460"]] = genelist # load the MS-DAP R package # library(msdap) # -# dataset = import_dataset_metamorpheus(path = "C:/VU/projects/Frank - GOAT (2022)/datasets/dataset_Klaassen2018_pmid26931375", protein_qval_threshold = 0.05, collapse_peptide_by = "sequence_modified") -# dataset = import_fasta(dataset, files = c("C:/VU/projects/Frank - GOAT (2022)/datasets/dataset_Klaassen2018_pmid26931375/UP000000589_10090.fasta", "C:/VU/projects/Frank - GOAT (2022)/datasets/dataset_Klaassen2018_pmid26931375/UP000000589_10090_additional.fasta")) +# dataset = import_dataset_metamorpheus(path = paste0(dir_input, "/dataset_Klaassen2018_pmid26931375", protein_qval_threshold = 0.05, collapse_peptide_by = "sequence_modified") +# dataset = import_fasta(dataset, files = c(paste0(dir_input, "/dataset_Klaassen2018_pmid26931375/UP000000589_10090.fasta"), paste0(dir_input, "/dataset_Klaassen2018_pmid26931375/UP000000589_10090_additional.fasta"))) # dataset = remove_proteins_by_name(dataset, regular_expression = "ig \\S+ chain|keratin|GN=(krt|try|igk|igg|igkv|ighv|ighg)") # optional # dataset = sample_metadata_custom(dataset, # # a list of regular expressions to extract a short name and a group label from each full sample name @@ -157,25 +157,38 @@ goat_example_datasets[["Hondius 2021:mass-spec:PMID33492460"]] = genelist # x = protein2gene_orthologs(x, hgnc, mgi) # # print proteingroups that we cannot map to human genes # print(x |> filter(is.na(hgnc_id))) -# write.table(x, "C:/VU/projects/Frank - GOAT (2022)/datasets/pmid26931375_msdap.tsv", quote = F, sep = "\t", row.names = F) +# write.table(x, paste0(dir_input, "/pmid26931375_msdap.tsv"), quote = F, sep = "\t", row.names = F) ########## -dea = readr::read_tsv("C:/VU/projects/Frank - GOAT (2022)/datasets/pmid26931375_msdap.tsv") +dea = readr::read_tsv(paste0(dir_input, "/pmid26931375_msdap.tsv")) genelist = dea |> filter(!is.na(entrez_id)) |> distinct(entrez_id, .keep_all = T) |> mutate(gene = entrez_id) table(genelist$signif) -goat_example_datasets[["Klaassen 2016:IP mass-spec:PMID26931375"]] = genelist +goat_manuscript_datasets[["Klaassen 2016:IP mass-spec:PMID26931375"]] = genelist + + + + + +########## the list of datasets is now done + +# save RData file to disk +save(goat_manuscript_datasets, file = paste0(dir_output, "/goat_manuscript_datasets.rda"), compress = "xz") + +## unfortunately we cannot bundle a lot of data due to CRAN 5MB limit restrictions; so we only bundle the very small Klaassen et al. IP dataset (15kb compressed) +shisa6_apms_klaassen = goat_manuscript_datasets$`Klaassen 2016:IP mass-spec:PMID26931375` |> select(protein_id, log2fc, effectsize, pvalue, pvalue_adjust, signif, symbol, gene) +# uncomment line below to update R package (in dev environment) +# usethis::use_data(shisa6_apms_klaassen, overwrite = T, compress = "xz") +### alternatively, if we could fit a few hundred more kb, include the wingo et al. example +# usethis::use_data(goat_manuscript_datasets, overwrite = T, compress = "xz") +# wingo_proteomics_pmid32424284 = goat_manuscript_datasets$`Wingo 2020:mass-spec:PMID32424284` |> select(Protein, log2fc, effectsize, pvalue, pvalue_adjust, signif, symbol, gene) +# usethis::use_data(wingo_proteomics_pmid32424284, overwrite = T, compress = "xz") -########## the list of datasets is now done. Below code attaches it to our R package (provided you're in the development environment) -## to reproduce: update file path -# usethis::use_data(goat_example_datasets, overwrite = T, compress = "xz") -## store as RData file on disk as well -# save(goat_example_datasets, file = "C:/temp/goat_example_datasets.rda", compress = "xz") diff --git a/analyses/realworld_data.R b/analyses/realworld_data.R index 3635a71..ea4230d 100644 --- a/analyses/realworld_data.R +++ b/analyses/realworld_data.R @@ -1,49 +1,64 @@ library(ggplot2) library(goat) # also loads dplyr -# output_dir = "C:/temp" # to reproduce this code on non-development system, set this to your desired output dir or `getwd()` -# file_gene2go = "C:/VU/projects/Frank - GOAT (2022)/genesets/gene2go_2024-01-01.gz" -# file_goobo = "C:/VU/projects/Frank - GOAT (2022)/genesets/go_2024-01-01.obo" -output_dir = "." -file_gene2go = "gene2go_2024-01-01.gz" -file_goobo = "go_2024-01-01.obo" -goat_print_version() +# TODO: setup input/output paths +output_dir = getwd() # directory where output files should be stored +file_gene2go = "gene2go_2024-01-01.gz" # full path to NCBI gene2go file, e.g. previously downloaded from https://ftp.ncbi.nih.gov/gene/DATA/gene2go.gz +file_goobo = "go_2024-01-01.obo" # full path go GO OBO file, e.g. previously downloaded from http://current.geneontology.org/ontology/go.obo +file_datasets = "goat_manuscript_datasets.rda" # full path to the datasets prepared earlier. This RData file is available in the "analyses" directory @ github +source("test_genesets_idea.R") # load the script that implements the iDEA function; update to the full path + +# hardcoded color-codings +clr = tibble::tibble( + method = c("hypergeometric", "fisherexact_ease", "idea_default_louis", "idea_rescale_louis", "idea_beta_rescale_louis", + "gsea_pvalue", "gsea_effectsize", "gsea_pvalue_gseaParam2", "gsea_effectsize_gseaParam2", + "goat_pvalue", "goat_effectsize", "goat_pvalue_effectsize", "goat_effectsize_up", "goat_effectsize_down", "goat_effectsize_abs"), + label = c("hypergeometric test", "Fisher-exact EASE", "iDEA", "iDEA*", "iDEAb*", + "GSEA p-value", "GSEA effect size", "GSEA p-value param=2", "GSEA effectsize param=2", + "GOAT p-value", "GOAT effect size", "GOAT p-value & effect size", "GOAT effect size up", "GOAT effect size down", "GOAT effect size abs"), + clr = c("#69f0ae", "#ffd600", "#C99D8E", "#B0BEC5", "#D9A71E", + "#42a5f5", "#304ffe", "#5c6bc0", "#5e35b1", + "#ff7043", "#f50057", "#d50000", "#f50057", "#f50057", "#f50057") +) -### parameters padj_method = "bonferroni" padj_cutoff = 0.05 # optionally disable iDEA, a method that takes a LONG time to compute include_idea = TRUE -# hardcoded color-codings -clr = tibble::tibble( - method = c("hypergeometric", "fisherexact_ease", "idea", "idea_louis", "gsea_pvalue", "gsea_effectsize", "goat_pvalue", "goat_effectsize", "goat_pvalue_effectsize", "goat_effectsize_up", "goat_effectsize_down", "goat_effectsize_abs"), - label = c("hypergeometric test", "Fisher-exact EASE", "iDEA", "iDEA louis", "GSEA p-value", "GSEA effectsize", "GOAT p-value", "GOAT effectsize", "GOAT p-value & effectsize", "GOAT effectsize up", "GOAT effectsize down", "GOAT effectsize abs"), - clr = c("#69f0ae", "#ffd600", "#C99D8E", "#8d6e63", "#42a5f5", "#304ffe", "#ff7043", "#f50057", "#d50000", "#f50057", "#f50057", "#f50057") -) -# load datasets that are already bundled with the R package -data("goat_example_datasets") + + + +################################################################################################################################################################################ +################################################################################################################################################################################ +################################################################################################################################################################################ + +goat_print_version() + +# load real-world OMICs datasets we prepared earlier from RData file +load(file_datasets) # exclude the small and synapse-specific pulldown dataset from "typical OMICs datasets" for benchmarking -dataset_names_go_analysis = grep("26931375", names(goat_example_datasets), ignore.case = T, value = T, invert = T) +dataset_names_go_analysis = grep("26931375", names(goat_manuscript_datasets), ignore.case = T, value = T, invert = T) # load GO genesets from gene2go file genesets = load_genesets_go_fromfile(file_gene2go, file_goobo) results = idea_timings = NULL for(n in dataset_names_go_analysis) { - file_cache = paste0(output_dir, "/cache__", gsub("[^a-zA-Z0-9 _-]", " ", n), ".rda") + file_cache = paste0(output_dir, "/cache__realworld__", gsub("[^a-zA-Z0-9 _-]", " ", n), ".rda") n_result = n_idea_timings = NULL if(file.exists(file_cache)) { load(file_cache) } else { cat("\nDATASET: ", n, "\n\n") gc(verbose = F, full = T) - genelist = goat_example_datasets[[n]] + genelist = goat_manuscript_datasets[[n]] # filter genesets as per usual in GOAT workflow, removing genesets that have insufficient overlap with the input genelist genesets_filtered = filter_genesets(genesets, genelist, min_overlap = 10L, max_overlap = 1500L, max_overlap_fraction = 0.5, min_signif = NA, max_size = NA, dedupe = FALSE) - # apply geneset enrichment algorithms. Remove columns that are not needed downstream to reduce RAM footprint of this script (because we're applying many algorithms to many datasets) + # apply geneset enrichment algorithms, then remove columns that are not needed downstream to reduce RAM footprint of this script + # (because we're applying many algorithms to many datasets; drop columns with all the gene constituents per geneset, etc.) n_result = bind_rows( test_genesets(genesets_filtered, genelist, method = "goat", score_type = "pvalue", padj_method = padj_method, padj_cutoff = padj_cutoff) |> select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif) |> mutate(method = "goat_pvalue"), @@ -54,31 +69,44 @@ for(n in dataset_names_go_analysis) { test_genesets(genesets_filtered, genelist, method = "gsea", score_type = "pvalue", parallel_threads = 1, padj_method = padj_method, padj_cutoff = padj_cutoff) |> select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif) |> mutate(method = "gsea_pvalue"), test_genesets(genesets_filtered, genelist, method = "gsea", score_type = "effectsize", parallel_threads = 1, padj_method = padj_method, padj_cutoff = padj_cutoff) |> - select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif) |> mutate(method = "gsea_effectsize") + select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif) |> mutate(method = "gsea_effectsize"), + # alternatively, also try GSEA with alternative weights. Ref; https://github.com/ctlab/fgsea/issues/45 + test_genesets(genesets_filtered, genelist, method = "gsea", score_type = "pvalue", parallel_threads = 1, gseaParam = 2, padj_method = padj_method, padj_cutoff = padj_cutoff) |> + select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif) |> mutate(method = "gsea_pvalue_gseaParam2"), + test_genesets(genesets_filtered, genelist, method = "gsea", score_type = "effectsize", parallel_threads = 1, gseaParam = 2, padj_method = padj_method, padj_cutoff = padj_cutoff) |> + select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif) |> mutate(method = "gsea_effectsize_gseaParam2") ) + if(include_idea) { + # time one of the iDEA runs time_start = Sys.time() - tmp = test_genesets(genesets_filtered, genelist, method = "idea", return_pvalue_louis = FALSE, padj_method = padj_method, padj_cutoff = padj_cutoff) + tmp_idea1 = test_genesets(genesets_filtered, genelist, method = "test_genesets_idea", return_pvalue_louis = TRUE, idea_variant = "default", verbose = TRUE, padj_method = padj_method, padj_cutoff = padj_cutoff) |> + select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif, ismissing_idea) |> mutate(method = "idea_default_louis") time_diff = as.numeric(difftime(Sys.time(), time_start, units = "secs")) - n_result = bind_rows(n_result, - tmp |> select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif, ismissing_idea) |> mutate(method = "idea"), # because of `return_pvalue_louis = FALSE` , columns pvalue and pvalue_idea are the same - tmp |> mutate(pvalue = pvalue_louis_idea) |> - padjust_genesets(method = padj_method, cutoff = padj_cutoff) |> - select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif, ismissing_idea) |> mutate(method = "idea_louis") - ) - rm(tmp) + # analogous for iDEA variants + tmp_idea2 = test_genesets(genesets_filtered, genelist, method = "test_genesets_idea", return_pvalue_louis = TRUE, idea_variant = "rescale", verbose = TRUE, padj_method = padj_method, padj_cutoff = padj_cutoff) |> + select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif, ismissing_idea) |> mutate(method = "idea_rescale_louis") + tmp_idea3 = test_genesets(genesets_filtered, genelist, method = "test_genesets_idea", return_pvalue_louis = TRUE, idea_variant = "beta_rescale", verbose = TRUE, padj_method = padj_method, padj_cutoff = padj_cutoff) |> + select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif, ismissing_idea) |> mutate(method = "idea_beta_rescale_louis") + + n_result = bind_rows(n_result, tmp_idea1, tmp_idea2, tmp_idea3) + rm(tmp_idea1, tmp_idea2, tmp_idea3) n_idea_timings = tibble::tibble(dataset_label = n, genelist_length = nrow(genelist), geneset_count = nrow(genesets_filtered), time_sec = time_diff, time_hours = time_diff / 3600) + + ## debug; + # x = test_genesets(genesets_filtered, genelist, method = "test_genesets_idea", return_pvalue_louis = TRUE, idea_variant = "beta_rescale_center", verbose = TRUE, padj_method = padj_method, padj_cutoff = padj_cutoff) |> + # select(id, source, ngenes_input, ngenes, ngenes_signif, pvalue, pvalue_adjust, signif, ismissing_idea) |> mutate(method = "idea_beta_rescale_center_louis") } # cache results save(n, n_result, n_idea_timings, file = file_cache, compress = "xz") - # monitor progress; print |signif| per method to console - print(n_result |> filter(signif) |> count(method), n = Inf) + # monitor progress; print |signif| per method (across all GO CC/MF/BP domains) to console + print(n_result |> group_by(method) |> summarise(signif_count = sum(signif), .groups="drop"), n = Inf) # generate QC plots of the GOAT null distributions pdf(paste0(output_dir, "/realworld_goat_null-distribution_skew-normal-fit__", gsub("[^a-zA-Z0-9 _-]", " ", n), ".pdf")) - hist(goat:::rankscore_fixed_order(nrow(genelist)), xlab = "Gene score", main = "") + hist(rankscore_fixed_order(nrow(genelist)), xlab = "Gene score", main = "") x = test_genesets(genesets_filtered, genelist, method = "goat_bootstrap", score_type = "pvalue", padj_method = padj_method, padj_cutoff = padj_cutoff, verbose = TRUE) dev.off() Sys.sleep(0.5) @@ -95,20 +123,254 @@ if(include_idea) { print(idea_timings, n=Inf) } - +writexl::write_xlsx(results, paste0(output_dir, "/realworld_geneset_statistics.xlsx")) +save(results, clr, idea_timings, file = paste0(output_dir, "/realworld_data.rda"), compress = "xz") ################################################################################################################################################################################ +################################################################################################################################################################################ +################################################################################################################################################################################ -writexl::write_xlsx(results, paste0(output_dir, "/realworld_geneset_statistics.xlsx")) -save(results, clr, idea_timings, file = paste0(output_dir, "/realworld_data.rda"), compress = "xz") -################################################################################################################################################################################ +######################################################################################################################## +########################################## MAIN FIGURE: BARPLOT SIGNIF COUNTS ########################################## +######################################################################################################################## + +mybarplot = function(x) { + tib_plot = x |> + mutate( + dataset_label = sub(":", "\n", sub(":PMID\\d+", "", dataset_label, ignore.case = TRUE)), + source = gsub("_", " ", source) + ) |> + group_by(dataset_label, source, method) |> + summarise(n = sum(signif), .groups = "drop") |> + mutate(method = factor(method, levels = rev(intersect(clr$method, unique(method))) ) ) + + clr_subset = clr |> filter(method %in% tib_plot$method) + + ggplot(tib_plot, aes(x=n, y=source, fill = method, label = n)) + + geom_col(position = ggplot2::position_dodge(.9)) + + geom_text(position = ggplot2::position_dodge(.9), hjust = -0.15, size = 3) + + scale_x_continuous(expand = expansion(mult = c(0, 0.25)), n.breaks = 4) + + scale_fill_manual(values = tibble::deframe(clr_subset |> select(method, clr)), breaks = clr_subset$method, labels = clr_subset$label) + + facet_wrap(.~dataset_label, nrow = 1, scales = "free_x") + + labs(x = "significant genesets", y = "") + + theme_bw() + + theme( + legend.position = "bottom", + legend.title = element_blank(), + legend.key.size = unit(0.75, "line"), + # axis.ticks.x = element_blank(), + axis.text = element_text(size = 9), + strip.text = element_text(size = 9), + axis.title = element_text(size = 11), + panel.grid = element_blank() + ) +} + +# ORA + multiple GSEA and iDEA variants +p = mybarplot(results) +ggsave(filename = paste0(output_dir, "/realworld_barplot_signif_count - everything.pdf"), plot = p, width = 8, height = 6) + +# main figure; ORA + GOAT + updated/fixed iDEA variant + GSEA default/recommended +p = mybarplot(results |> filter(grepl("hypergeometric|goat|idea_default_louis|gsea_pvalue$|gsea_effectsize$", method))) +ggsave(filename = paste0(output_dir, "/realworld_barplot_signif_count - main figure.pdf"), plot = p, width = 8, height = 4.5) + +p = mybarplot(results |> filter( ! grepl("hypergeometric|goat|idea_default_louis|gsea_pvalue$|gsea_effectsize$", method))) +ggsave(filename = paste0(output_dir, "/realworld_barplot_signif_count - suppl figure.pdf"), plot = p, width = 8, height = 4) + + + + +######################################################################################################################## +############################################## PRINT SIGNIF INCREASE STATS ############################################# +######################################################################################################################## + +x = results |> filter(grepl("idea", method)) |> + group_by(dataset_label, method) |> summarise(n = n(), ismissing = sum(ismissing_idea %in% TRUE), pct = sprintf("%.1f", ismissing/n * 100), .groups = "drop") |> + mutate( + dataset_label = sub(":", "\n", sub(":PMID\\d+", "", dataset_label, ignore.case = TRUE)), + method = factor(method, levels = c("idea_beta_rescale_louis", "idea_rescale_louis", "idea_default_louis")) # hardcode plot order + ) |> + arrange(dataset_label, method) +print(x) + +p = ggplot(x, aes(y = method, x = ismissing / n * 100, label = pct)) + + geom_col() + + geom_text(hjust = -0.1) + + facet_wrap(.~dataset_label) + + coord_cartesian(c(0, 100)) + + labs(x = "Fraction missing (%)", y = "") + + theme_bw() + + theme( + panel.grid.major.y = element_blank() + ) +ggsave(filename = paste0(output_dir, "/realworld_idea_missingness_barplot.pdf"), plot = p, width = 5.5, height = 4) + + + + +# total number of significant GO terms (across all CC/MF/BP domains) per dataset +tmp = results |> + filter(dataset_label %in% dataset_names_go_analysis) |> + group_by(dataset_label, method) |> + summarise(n = sum(signif), .groups = "drop") +# report the % increase of GOAT significant genesets as compared to GSEA @ effectsize +cat("*** gsea_effectsize\n") +tmp |> + group_by(dataset_label) |> + mutate( + n_gsea = n[method == "gsea_effectsize"], + gain_gsea_n = n - n[method == "gsea_effectsize"], + gain_gsea_pct = round((n - n[method == "gsea_effectsize"]) / n[method == "gsea_effectsize"] * 100, 1) + ) |> + ungroup() |> + filter(method == "goat_effectsize") |> + print(n = Inf) +# report the % increase of GOAT significant genesets as compared to GSEA @ pvalues +cat("*** gsea_pvalue\n") +tmp |> + group_by(dataset_label) |> + mutate(gain_gsea = round((n - n[method == "gsea_pvalue"]) / n[method == "gsea_pvalue"] * 100, 1) ) |> + ungroup() |> + filter(method == "goat_pvalue") |> + print(n = Inf) +# report the % increase of GOAT significant genesets as compared to iDEA @ effectsize +if(include_idea) { + cat("*** idea_default_louis\n") + tmp |> + group_by(dataset_label) |> + mutate( + n_idea = n[method == "idea_default_louis"], + gain_idea = n - n[method == "idea_default_louis"], + gain_idea_pct = round((n - n[method == "idea_default_louis"]) / n[method == "idea_default_louis"] * 100, 1) + ) |> ungroup() |> + filter(method == "goat_effectsize") |> + print(n = Inf) + cat("*** idea_rescale_louis\n") + tmp |> + group_by(dataset_label) |> + mutate( + n_idea = n[method == "idea_rescale_louis"], + gain_idea = n - n[method == "idea_rescale_louis"], + gain_idea_pct = round((n - n[method == "idea_rescale_louis"]) / n[method == "idea_rescale_louis"] * 100, 1) + ) |> ungroup() |> + filter(method == "goat_effectsize") |> + print(n = Inf) + cat("*** idea_beta_rescale_louis\n") + tmp |> + group_by(dataset_label) |> + mutate( + n_idea_louis = n[method == "idea_beta_rescale_louis"], + gain_idea_louis = n - n[method == "idea_beta_rescale_louis"], + gain_idea_louis_pct = round((n - n[method == "idea_beta_rescale_louis"]) / n[method == "idea_beta_rescale_louis"] * 100, 1) + ) |> + ungroup() |> + filter(method == "goat_effectsize") |> + print(n = Inf) +} +# report the % increase of GOAT significant genesets as compared to ORA @ pvalues +cat("*** hypergeometric\n") +tmp2 = tmp |> + group_by(dataset_label) |> + mutate( + n_ora = n[method == "hypergeometric"], + gain_ora_n = n - n[method == "hypergeometric"], + gain_ora_pct = round((n - n[method == "hypergeometric"]) / n[method == "hypergeometric"] * 100, 1) + ) |> + ungroup() +tmp2 |> filter(method == "goat_pvalue") |> print(n = Inf) +tmp2 |> filter(method == "goat_effectsize") |> print(n = Inf) + + +lapply(goat_manuscript_datasets, nrow) +lapply(goat_manuscript_datasets, function(x)sum(x$signif)) +lapply(goat_manuscript_datasets, function(x)sum(x$signif)/nrow(x)) + + + + + +######################################################################################################################## +############################################# SCATTERPLOT GENESET P-VALUES ############################################# +######################################################################################################################## + +# for every dataset, plot top25 GOAT p-value datasets +# only for 2 * Colameo datasets + +pdf(paste0(output_dir, "/realworld_compare_topn_pvalmethods.pdf"), 6, 15) +for(dataset_name in grep("Colameo 2021", unique(results$dataset_label), value = T, ignore.case = T)) { + + tib_plot_filter = results |> + filter(dataset_label == dataset_name & method == "goat_pvalue") |> + mutate(dataset_label = sub(":", ":\n", sub(":PMID\\d+", "", dataset_label, ignore.case = TRUE))) |> + arrange(pvalue) |> + group_by(source) |> + mutate(myrank = 1:n()) |> + filter(myrank <= 25) |> + ungroup() + + tib_plot = results |> + filter(dataset_label == dataset_name & method %in% c("hypergeometric", "gsea_pvalue", "goat_pvalue") & id %in% tib_plot_filter$id) |> + mutate( + id = factor(id, levels = rev(tib_plot_filter$id)), + name = genesets$name[match(id, genesets$id)] + ) |> + group_by(dataset_label, method) |> + mutate(pvalue_adjust_minlog10 = minlog10_fixzero(pvalue_adjust, limit = NA)) |> + ungroup() + + clr_subset = clr |> filter(method %in% tib_plot$method) + + p = ggplot(tib_plot, aes(x = pvalue_adjust_minlog10, y = id, size = ngenes, colour = method, shape = method)) + + geom_point(alpha = 0.5) + + scale_colour_manual(name = "method", values = tibble::deframe(clr_subset |> select(method, clr)) ) + + scale_shape_discrete(name = "method") + + scale_y_discrete(breaks = levels(tib_plot$id), labels = string_trunc_right(tib_plot$name[match(levels(tib_plot$id), tib_plot$id)], width = 50)) + + scale_size(range = c(2,5), trans = "log10", name = "gene count") + + guides( + colour = guide_legend(direction = "vertical", ncol = 1, override.aes = list(size = 4)), + shape = guide_legend(direction = "vertical", ncol = 1, override.aes = list(size = 4, ncol = 1)), + size = guide_legend(direction = "vertical")) + + facet_wrap( . ~ dataset_label + source, scales = "free", ncol = 1) + + labs(x = "-log10(adjusted p-value)", y = "") + + theme_bw() + + theme( + legend.position = "bottom"#, + # legend.direction = "horizontal", + # legend.box = "horizontal" + ) + print(p) +} + +dev.off() + + + + + + + + + + + + + + + + + +######################################################################################################################## +################################################# INSPECT IDEA RESULTS ################################################# +######################################################################################################################## + + # missed geneset p-values in iDEA vs GOAT; plot distributions (density lines) across datasets (facets/panels) tmp = results |> # base data; geneset p-values from GOAT effectsize @@ -117,7 +379,7 @@ tmp = results |> # now add p-values from iDEA as separate column left_join( # remove "failed geneset p-value estimates" @ iDEA --> these will be NA in `tmp` table - results |> filter(method == "idea" & ! ismissing_idea %in% TRUE) |> select(dataset_label, source, id, pvalue_idea = pvalue), + results |> filter(method == "idea_default_louis" & ! ismissing_idea %in% TRUE) |> select(dataset_label, source, id, pvalue_idea = pvalue), by = c("dataset_label", "source", "id") ) |> group_by(dataset_label) |> @@ -129,10 +391,10 @@ tib_plot_stats = tmp |> group_by(dataset_label) |> summarise(ngeneset = n(), nge # compose plot data tib_plot = bind_rows( - tmp |> mutate(classification = "all genesets"), + tmp |> mutate(classification = "all gene sets"), tmp |> filter(is.na(pvalue_idea)) |> mutate(classification = "missing in iDEA") ) |> - mutate(classification = factor(classification, levels = c("all genesets", "missing in iDEA"))) # enforce plot order + mutate(classification = factor(classification, levels = c("all gene sets", "missing in iDEA"))) # enforce plot order # helper function to update ggplot strip titles @@ -153,7 +415,7 @@ p = ggplot(tib_plot, aes(pvalue_minlog10, fill = classification, colour = classi geom_density(alpha = 0.1) + scale_colour_manual(values = c("all genesets"="#455a64", "missing in iDEA"="#d50000"), aesthetics = c("colour", "fill")) + facet_wrap(.~dataset_label, ncol = 2, scales = "free", labeller = create_strip_title) + - labs(x = "-log10 geneset p-value @ GOAT effectsize", y = "Density") + + labs(x = "-log10 gene set p-value @ GOAT effect size", y = "Density") + theme_bw() + theme( legend.position = "bottom", @@ -171,10 +433,11 @@ ggsave(p, file = paste0(output_dir, "/realworld_characterize_idea_missing_genese ################################################################################################################################################################################ -# scatterplot fGSEA vs iDEA +# cross-compare existing methods; scatterplot fGSEA vs iDEA for(plotspec in list( - list(method="idea", lbl="iDEA"), - list(method="idea_louis", lbl="iDEA louis") + list(method="idea_default_louis", lbl="iDEA default"), + list(method="idea_rescale_louis", lbl="iDEA rescaled"), + list(method="idea_beta_rescale_louis", lbl="iDEA beta rescaled") )) { tmp = results |> filter(method %in% c(plotspec$method, "gsea_effectsize")) |> @@ -218,7 +481,7 @@ for(plotspec in list( facet_wrap(.~dataset_label, scales = "free") + guides(colour = guide_legend(title = paste("missing in", plotspec$lbl))) + scale_colour_manual(values = c("TRUE"="#d50000", "FALSE"="#455a64")) + - labs(x = "-log10 pvalue @ GSEA effectsize", y = paste("-log10 pvalue @", plotspec$lbl)) + + labs(x = "-log10 p-value @ GSEA effect size", y = paste("-log10 p-value @", plotspec$lbl)) + theme_bw() + theme( legend.position = "bottom", @@ -263,7 +526,7 @@ p = ggplot(tib_plot, aes(minlog10_fixzero(gsea_effectsize, limit = NA), minlog10 geom_point(alpha = 0.66, shape = 16) + geom_abline(intercept = 0, slope = 1, colour = "black") + facet_wrap(.~dataset_label, scales = "free") + - labs(x = "-log10 pvalue @ GSEA effectsize", y = "-log10 pvalue @ GOAT effectsize") + + labs(x = "-log10 p-value @ GSEA effect size", y = "-log10 p-value @ GOAT effect size") + theme_bw() + theme( legend.position = "bottom", @@ -278,171 +541,3 @@ ggsave(p, file = paste0(output_dir, "/realworld_scatterplot_gsea-vs-goat.pdf"), - - -################################################################################################################################################################################ - -mybarplot = function(x, fname) { - tib_plot = x |> - mutate( - dataset_label = sub(":", "\n", sub(":PMID\\d+", "", dataset_label, ignore.case = TRUE)), - source = gsub("_", " ", source) - ) |> - group_by(dataset_label, source, method) |> - summarise(n = sum(signif), .groups = "drop") |> - mutate(method = factor(method, levels = rev(intersect(clr$method, unique(method))) ) ) - - clr_subset = clr |> filter(method %in% tib_plot$method) - - p = ggplot(tib_plot, aes(x=n, y=source, fill = method, label = n)) + - geom_col(position = ggplot2::position_dodge(.9)) + - geom_text(position = ggplot2::position_dodge(.9), hjust = -0.15, size = 3) + - scale_x_continuous(expand = expansion(mult = c(0, 0.25)), n.breaks = 4) + - scale_fill_manual(values = tibble::deframe(clr_subset |> select(method, clr)), breaks = clr_subset$method, labels = clr_subset$label) + - facet_wrap(.~dataset_label, nrow = 1, scales = "free_x") + - labs(x = "significant genesets", y = "") + - theme_bw() + - theme( - legend.position = "bottom", - legend.title = element_blank(), - legend.key.size = unit(0.75, "line"), - # axis.ticks.x = element_blank(), - axis.text = element_text(size = 9), - strip.text = element_text(size = 9), - axis.title = element_text(size = 11), - panel.grid = element_blank() - ) - - print(p) - ggsave(p, file = fname, width = 8, height = 4.5) -} - -mybarplot(results, paste0(output_dir, "/realworld_barplot_signif_count - incl idea raw pvalues.pdf")) -mybarplot(results |> filter(!grepl("idea", method)), paste0(output_dir, "/realworld_barplot_signif_count - without idea.pdf")) -mybarplot(results |> filter(method != "idea"), paste0(output_dir, "/realworld_barplot_signif_count.pdf")) - - - - - -################################################################################################################################################################################ - -# total number of significant GO terms (across all CC/MF/BP domains) per dataset -tmp = results |> - filter(dataset_label %in% dataset_names_go_analysis) |> - group_by(dataset_label, method) |> - summarise(n = sum(signif), .groups = "drop") -# report the % increase of GOAT significant genesets as compared to GSEA @ effectsize -cat("*** gsea_effectsize\n") -tmp |> - group_by(dataset_label) |> - mutate( - n_gsea = n[method == "gsea_effectsize"], - gain_gsea_n = n - n[method == "gsea_effectsize"], - gain_gsea_pct = round((n - n[method == "gsea_effectsize"]) / n[method == "gsea_effectsize"] * 100, 1) - ) |> - ungroup() |> - filter(method == "goat_effectsize") |> - print(n = Inf) -# report the % increase of GOAT significant genesets as compared to GSEA @ pvalues -cat("*** gsea_pvalue\n") -tmp |> - group_by(dataset_label) |> - mutate(gain_gsea = round((n - n[method == "gsea_pvalue"]) / n[method == "gsea_pvalue"] * 100, 1) ) |> - ungroup() |> - filter(method == "goat_pvalue") |> - print(n = Inf) -# report the % increase of GOAT significant genesets as compared to iDEA @ effectsize -if(include_idea) { - cat("*** idea\n") - tmp |> - group_by(dataset_label) |> - mutate( - n_idea = n[method == "idea"], - gain_idea = n - n[method == "idea"], - gain_idea_pct = round((n - n[method == "idea"]) / n[method == "idea"] * 100, 1) - ) |> ungroup() |> - filter(method == "goat_effectsize") |> - print(n = Inf) - cat("*** idea louis\n") - tmp |> - group_by(dataset_label) |> - mutate( - n_idea_louis = n[method == "idea_louis"], - gain_idea_louis = n - n[method == "idea_louis"], - gain_idea_louis_pct = round((n - n[method == "idea_louis"]) / n[method == "idea_louis"] * 100, 1) - ) |> - ungroup() |> - filter(method == "goat_effectsize") |> - print(n = Inf) -} -# report the % increase of GOAT significant genesets as compared to ORA @ pvalues -cat("*** hypergeometric\n") -tmp2 = tmp |> - group_by(dataset_label) |> - mutate( - n_ora = n[method == "hypergeometric"], - gain_ora_n = n - n[method == "hypergeometric"], - gain_ora_pct = round((n - n[method == "hypergeometric"]) / n[method == "hypergeometric"] * 100, 1) - ) |> - ungroup() -tmp2 |> filter(method == "goat_pvalue") |> print(n = Inf) -tmp2 |> filter(method == "goat_effectsize") |> print(n = Inf) - - -lapply(goat_example_datasets, nrow) -lapply(goat_example_datasets, function(x)sum(x$signif)) -lapply(goat_example_datasets, function(x)sum(x$signif)/nrow(x)) - - - - - -################################################################################################################################################################################ - -# for every dataset, plot top25 GOAT p-value datasets -# only for 2 * Colameo datasets - -pdf(paste0(output_dir, "/realworld_compare_topn_pvalmethods.pdf"), 6, 15) -for(dataset_name in grep("Colameo 2021", unique(results$dataset_label), value = T, ignore.case = T)) { - - tib_plot_filter = results |> - filter(dataset_label == dataset_name & method == "goat_pvalue") |> - mutate(dataset_label = sub(":", ":\n", sub(":PMID\\d+", "", dataset_label, ignore.case = TRUE))) |> - arrange(pvalue) |> - group_by(source) |> - mutate(myrank = 1:n()) |> - filter(myrank <= 25) |> - ungroup() - - tib_plot = results |> - filter(dataset_label == dataset_name & method %in% c("hypergeometric", "gsea_pvalue", "goat_pvalue") & id %in% tib_plot_filter$id) |> - mutate( - id = factor(id, levels = rev(tib_plot_filter$id)), - name = genesets$name[match(id, genesets$id)] - ) |> - group_by(dataset_label, method) |> - mutate(pvalue_adjust_minlog10 = minlog10_fixzero(pvalue_adjust, limit = NA)) |> - ungroup() - - clr_subset = clr |> filter(method %in% tib_plot$method) - - p = ggplot(tib_plot, aes(x = pvalue_adjust_minlog10, y = id, size = ngenes, colour = method)) + - geom_point(alpha = 0.5) + - scale_fill_manual(values = tibble::deframe(clr_subset |> select(method, clr)), breaks = clr_subset$method, labels = clr_subset$label, aesthetics = c("fill", "colour")) + - scale_y_discrete(breaks = levels(tib_plot$id), labels = goat:::string_trunc_right(tib_plot$name[match(levels(tib_plot$id), tib_plot$id)], width = 50)) + - scale_size(range = c(2,5), trans = "log10", name = "gene count") + - guides(colour = guide_legend(direction = "vertical", override.aes = list(alpha=1, size = 4)), - size = guide_legend(direction = "vertical"), override.aes = list(alpha=1)) + - facet_wrap( . ~ dataset_label + source, scales = "free", ncol = 1) + - labs(x = "-log10(adjusted p-value)", y = "") + - theme_bw() + - theme( - legend.position = "bottom", - legend.direction = "horizontal", - legend.box = "horizontal" - ) - print(p) -} - -dev.off() diff --git a/analyses/roc_simulations.R b/analyses/roc_simulations.R new file mode 100644 index 0000000..c5b737c --- /dev/null +++ b/analyses/roc_simulations.R @@ -0,0 +1,643 @@ + +# 1) What do the gene-rank distributions of significant versus non-significant genesets look like in real-world data? +# - the rank values of genes from strongly enriched genesets look like a weak power-law (i.e. enrichment of top genes) +# - whereas genesets with no enrichment result in approximate uniform gene-rank distributions +# +# 2) ROC +# - generate 10k background genesets and 1k "genesets with some signal" (we explore various patterns of enrichment) +# - compute geneset p-values with GOAT, GSEA and iDEA +# - perform ROC to test their sensitivity in separating the background genesets from those that should be enriched + + + +####################################################################################################################### +############################################### compute & cache results ############################################### +####################################################################################################################### + +require(pROC) +library(ggplot2) +library(goat) # also loads dplyr + +# TODO: setup input/output paths +output_dir = getwd() # directory where output files should be stored +file_gene2go = "gene2go_2024-01-01.gz" # full path to NCBI gene2go file, e.g. previously downloaded from https://ftp.ncbi.nih.gov/gene/DATA/gene2go.gz +file_goobo = "go_2024-01-01.obo" # full path go GO OBO file, e.g. previously downloaded from http://current.geneontology.org/ontology/go.obo +file_datasets = "goat_manuscript_datasets.rda" # full path to the datasets prepared earlier. This RData file is available in the "analyses" directory @ github +source("test_genesets_idea.R") # load the script that implements the iDEA function; update to the full path + + +# real-world genesets bundled with the GOAT R package that will be used for downstream generation of synthetic genesets +dataset_names_go_analysis = c( + "Colameo 2021:RNA-seq:PMID34396684", + "Sahadevan 2021:RNA-seq:PMID34021139", + "Higginbotham 2020:mass-spec:PMID33087358", + "Wingo 2020:mass-spec:PMID32424284" +) + +# when the iDEA method is included, computations in this script will increase +# from approx. 1 hour to multiple weeks (!!) even on a fast workstation computer +include_idea = TRUE + + + +goat_print_version() + + + + +############################################ +############# helper functions ############# +############################################ + +histogram_generank_distribution = function(gs, gl, gl_col_scoretype) { + # 11 histogram breaks for 10 bins + histbreaks = seq(from = 1, to = nrow(gl), length.out = 11) + + # for each geneset, compute histogram + plotdata = NULL + for(i in 1:nrow(gs)) { + tmp = gl |> pull(!!gl_col_scoretype) + i_hist_data = tmp[match(gs$genes[[i]], genelist$gene)] + stopifnot(is.finite(i_hist_data)) + + h = hist(i_hist_data, plot = F, breaks = histbreaks) + + plotdata = bind_rows(plotdata, tibble( + id = gs$id[i], + breaks = head(histbreaks, -1), + density = h$counts / length(i_hist_data) + # density = h$density + )) + } + + return(plotdata) +} + + + +# sample k genes per genelist bin, hardcoded to enforce the gene rank distribution per generated geneset (over genelist bins) +generate_mock_genesets = function(list_genes_per_bin, genecount_per_bin, niter, geneset_id_template) { + # prior to sampling, set.seed to ensure reproducible results + set.seed(123) + + genesets = list() + for(iter in seq_len(niter)) { + # sample random genes + iter_genes = NULL + for(b in seq_along(list_genes_per_bin)) { + iter_genes = c(iter_genes, sample(list_genes_per_bin[[b]], genecount_per_bin[b], replace = FALSE)) + } + + # create the mock geneset + genesets[[length(genesets) + 1]] = tibble::tibble( + source = "x", + source_version = "x", + id = paste0(geneset_id_template, iter), + name = paste0(geneset_id_template, iter), + genes = list(iter_genes), + ngenes = length(iter_genes), + ngenes_signif = 0L # not used in these analyses, enter a default value so the mock geneset is compatible with GOAT R package input geneset validations + ) + } + + return(bind_rows(genesets)) +} + + + + +############################################ +######### check simulation designs ######### +############################################ + +### visualize various distribution designs that we may use in above simulations +### i.e. these would go into `bins_foreground_all` + +addpoints = function(x, col = 1, offset = 0) { + stopifnot(sum(x) == 100) # double-check that we always use the same number of elements in this test + lines(seq_along(x) + offset, x / sum(x), col = col) + points(seq_along(x) + offset, x / sum(x), col = col, pch = 16, cex = 0.5) +} + +plot(rep(0.1, 10), type = 'l', col = "grey", pch = 1, ylim = c(0.05, 0.2), xlab = 'gene rank "bin"', ylab = "Fraction") + +addpoints(c(15, 12, 12, 11, 10, 9, 8, 8, 8, 7), col = "red", offset = -0.1) # strong enrichment of top-ranked genes, easy +addpoints(c(14, 12, 11, 10, 10, 9, 8, 8, 9, 9), col = "purple", offset = -0.2) # relatively easy to distinguish from uniform distribution because "many" genes are sampled from bins 1 and 2 +addpoints(c(13, 11, 11, 10, 9, 10, 9, 9, 9, 9), col = "blue", offset = 0.1) # slightly more difficult, decreased number of genes from bins 1 and 2 +addpoints(c(10, 13, 11, 10, 10, 9, 9, 9, 10, 9), col = "orange", offset = 0.2) # alternatively, enrichment pre top-genes +# most challenging; compared to uniform there will be a few more topN genes +# (i.e. will not yield typical "significant genesets", but methods should be able to distinguish this from the uniform distribution) +addpoints(c(12, 11, 11, 10, 10, 9, 9, 9, 9, 10), col = "green", offset = 0.3) + + + + +############################################ +########### perform computations ########### +############################################ + +if(!dir.exists(output_dir)) { + dir.create(output_dir, showWarnings = F, recursive = T) +} + +# load real-world OMICs datasets we prepared earlier from RData file +load(file_datasets) + +# load GO genesets from gene2go file +genesets = load_genesets_go_fromfile(file_gene2go, file_goobo) + +# iterate input genelists +dataset_results = list() +for(dataset_name in dataset_names_go_analysis) { + # check if results are available from cache + output_file_prefix = paste0(output_dir, "/", gsub("[^a-zA-Z0-9 _-]", " ", dataset_name)) + file_cache = paste0(output_file_prefix, "__roc_simulations_cache.rda") + if(file.exists(file_cache)) { + load(file_cache) + } else { # no cache available, start computation + cat("\nDATASET: ", dataset_name, "\n\n") + time_start = Sys.time() + gc(verbose = F, full = T) + + # genelist used in current iteration + genelist = goat_manuscript_datasets[[dataset_name]] + + # add rank to genelist for downstream analyses + tmp = goat_testgene_score_matrix(genelist, "effectsize") + genelist$score_effectsize_up = tmp[,"effectsize_up"] + genelist$score_effectsize_down = tmp[,"effectsize_down"] + genelist = genelist |> + arrange(desc(score_effectsize_up)) |> mutate(rank_effectsize_up = 1L:n()) |> + arrange(desc(score_effectsize_down)) |> mutate(rank_effectsize_down = 1L:n()) + + # filter genesets as per usual in GOAT workflow, removing genesets that have insufficient overlap with the input genelist + genesets_filtered = filter_genesets(genesets, genelist, min_overlap = 10L, max_overlap = 1500L, max_overlap_fraction = 0.5, min_signif = NA, max_size = NA, dedupe = FALSE) + + # perform geneset testing using GSEA + result = test_genesets(genesets_filtered, genelist, method = "gsea", score_type = "effectsize") + + # now we can use these results to identify the topN and bottomN genesets and study their respective gene-rank distributions + # here we are specifically using GSEA so the resulting geneset-to-generank-distribution profiles are not biased for GOAT + + + ################################################################################################## + ################################################################################################## + + + ### iterate top/bottom-N genesets, compute histogram for each + pct_top = 10 + pct_bottom = 10 + plotdata_empirical_distributions = list() + + for(myscoretype in c("effectsize_down", "effectsize_up")) { + + # genesets to consider; filter for direction-of-effectisze + geneset size + tmp = result |> filter(score_type == myscoretype & ngenes >= 50 & ngenes <= 500) + + # foreground = top x% best genesets by respective p-value + gs_foreground = tmp |> arrange(pvalue) |> slice_head(n = floor(nrow(tmp) * (pct_top/100))) + + # background = bottom x% + gs_null = tmp |> arrange(desc(pvalue)) |> slice_head(n = floor(nrow(tmp) * (pct_bottom/100))) + + # compute histograms + plotdata_foreground = histogram_generank_distribution(gs_foreground, genelist, paste0("rank_", myscoretype)) + plotdata_null = histogram_generank_distribution(gs_null, genelist, paste0("rank_", myscoretype)) + mtitle_foreground = sprintf("top %s%% genesets by '%s' and\n50 <= N <= 500 yields %d genesets", pct_top, myscoretype, nrow(gs_foreground)) + mtitle_null = sprintf("bottom %s%% genesets by '%s' and\n50 <= N <= 500 yields %d genesets", pct_bottom, myscoretype, nrow(gs_null)) + + plotdata_empirical_distributions[[myscoretype]] = list(foreground = plotdata_foreground, null = plotdata_null, + foreground_title = mtitle_foreground, null_title = mtitle_null) + } + + + ################################################################################################## + ################################################################################################## + + + ### parameters + niter_null = 10000L # number of mock genesets to generate for background set / null distribution + niter_foreground = 1000L # number of mock genesets that should be distinct from the null + bins_null = rep.int(10L, 10L) # generating null genesets, we draw 10 genes from 10 bins (index = bin, value = number of genes) + # 5 templates for sampling genes to generate a foreground geneset + bins_foreground_all = list( + c(14, 12, 11, 10, 10, 9, 8, 8, 9, 9), # relatively easy to distinguish from uniform distribution because "many" genes are sampled from bins 1 and 2 + c(13, 11, 11, 10, 9, 10, 9, 9, 9, 9), # slightly more difficult, decreased number of genes from bins 1 and 2 + c(10, 13, 11, 10, 10, 9, 9, 9, 10, 9), # alternatively, enrichment in second bin / peak not in top-genes + c(12, 11, 11, 10, 10, 9, 9, 9, 9, 10), # most challenging + bins_null # null for suppl figure; ROC should be on diagonal + ) + + + ### prepare plot data + # split sorted input genelist into 10 bins (after sorting by effectsize) + gl = genelist |> + arrange(desc(score_effectsize_down)) |> + mutate( + rank = 1L:n(), + bin = ggplot2::cut_interval(x = 1L:n(), n = length(bins_null), labels = F) + ) + + # cache gene identifiers per "bin" in a list for downstream computational efficiency + tmp = gl |> select(gene, bin) |> tidyr::chop(cols = gene) |> arrange(bin) + list_genes_per_bin = setNames(tmp$gene, tmp$bin) + rm(tmp) + + + ### generate mock datasets + null_genesets = generate_mock_genesets(list_genes_per_bin, genecount_per_bin = bins_null, niter = niter_null, geneset_id_template = "gs_null_") + + ### for each template for mock genesets; generate synthetic genesets, then apply geneset testing with GOAT, GSEA, etc. + roc_data = list() + for(bin_index in seq_along(bins_foreground_all)) { + bins_foreground = bins_foreground_all[[bin_index]] + cat("\nDATASET: ", dataset_name, "testing geneset enrichment in bin", bin_index, "/", length(bins_foreground_all), "\n") + + # QC; ensure the geneset-frequency definitions align the number of bins and geneset-size between background and foreground + stopifnot(length(bins_null) == length(bins_foreground) && sum(bins_null) == sum(bins_foreground)) + foreground_genesets = generate_mock_genesets(list_genes_per_bin, genecount_per_bin = bins_foreground, niter = niter_foreground, geneset_id_template = "gs_diff_") + + # compute geneset enrichment p-values for all mock genesets + all_genesets = bind_rows(null_genesets, foreground_genesets) + result_goat = test_genesets(all_genesets, gl, method = "goat", score_type = "effectsize") + result_gsea = test_genesets(all_genesets, gl, method = "gsea", score_type = "effectsize") + + if(include_idea) { + result_idea_default = test_genesets(all_genesets, gl, method = "test_genesets_idea", idea_variant = "default", verbose = TRUE) + result_idea_rescaled = test_genesets(all_genesets, gl, method = "test_genesets_idea", idea_variant = "rescale", verbose = TRUE) + result_idea_beta_rescaled = test_genesets(all_genesets, gl, method = "test_genesets_idea", idea_variant = "beta_rescale", verbose = TRUE) + # if("sd" %in% colnames(gl)) { # test/proof-of-concept; when log2fc SD are present in input data, try yet another iDEA variant + # result_idea_providedsd = test_genesets(all_genesets, gl, method = "test_genesets_idea", idea_variant = "genelist_sd", verbose = TRUE) + # roc_data[[length(roc_data) + 1]] = list(foreground_genesets = foreground_genesets, results = list(GOAT = result_goat, GSEA = result_gsea, iDEA = result_idea_default, "iDEA rescaled" = result_idea_rescaled, "iDEA beta rescaled" = result_idea_beta_rescaled, "iDEA provided SD" = result_idea_providedsd)) + # } else { + roc_data[[length(roc_data) + 1]] = list(foreground_genesets = foreground_genesets, results = list(GOAT = result_goat, GSEA = result_gsea, iDEA = result_idea_default, "iDEA rescaled" = result_idea_rescaled, "iDEA beta rescaled" = result_idea_beta_rescaled)) + # } + } else { + roc_data[[length(roc_data) + 1]] = list(foreground_genesets = foreground_genesets, results = list(GOAT = result_goat, GSEA = result_gsea)) + } + } + + ### finally, save to dataset-specific cache file + save(dataset_name, output_file_prefix, gl, plotdata_empirical_distributions, bins_null, bins_foreground_all, null_genesets, roc_data, + file = file_cache, compress = "xz") + cat(sprintf("took; %.2f hours\n", as.numeric(difftime(Sys.time(), time_start, units = "hours")))) + } + + + + ### append results for the current dataset to the final/aggregate results + dataset_results[[dataset_name]] = list( + dataset_name = dataset_name, + output_file_prefix = output_file_prefix, + genelist = gl, + plotdata_empirical_distributions = plotdata_empirical_distributions, + bins_null = bins_null, + bins_foreground_all = bins_foreground_all, + null_genesets = null_genesets, + roc_data = roc_data + ) +} # end iteration over genelists + + +# finally, store all computation in a RData file +save(genesets, dataset_results, file = paste0(output_dir, "/roc_simulations.rda"), compress = "xz") + + + + + + + + + + + + + +####################################################################################################################### +############################################### plot using cached data ################################################ +####################################################################################################################### + +require(pROC) +require(patchwork) +library(ggplot2) +library(goat) # also loads dplyr + +# TODO: setup input/output paths +output_dir = getwd() # directory where output files should be stored +load(paste0(output_dir, "/roc_simulations.rda")) + + +############################################ +############# helper functions ############# +############################################ + +roc = function(l_result, legend_text_pt = 8, clr = c(GOAT = "#f50057", GSEA = "#304ffe", iDEA = "#C99D8E", "iDEA*" = "#B0BEC5", "iDEAb*" = "#D9A71E", "iDEAsd" = "#827717")) { + ### compute ROCs, storing both the pROC object and the partial area under curve at 95% specificity + # note that we order the genesets by their pvalue for ROC, i.e. multiple testing adjustment is ignored + l_roc = list() + l_roc_pauc = NULL + for(n in names(l_result)) { + l_result[[n]]$classification = c("foreground", "null")[grepl("null" ,l_result[[n]]$id) + 1] + tmp = pROC::roc(l_result[[n]]$classification, abs(l_result[[n]]$pvalue), levels = c("null", "foreground"), direction=">", partial.auc=c(1, 0.95), partial.auc.correct=F, partial.auc.focus="specificity") + l_roc_pauc = c(l_roc_pauc, as.numeric(tmp$auc)) + l_roc[[n]] = pROC::roc(l_result[[n]]$classification, abs(l_result[[n]]$pvalue), levels = c("null", "foreground"), direction=">") + } + l_roc_legend = sprintf("%s %.2f%%", names(l_roc), l_roc_pauc * 100) + + + ### ggplot + p = pROC::ggroc(l_roc, aes = "colour") + + geom_vline(xintercept = 0.95, color = "darkgrey") + + geom_segment(mapping = aes(x = 1, y = 0, xend = 0, yend = 1), data = data.frame(), color = "black", linetype = "dashed", size = 1) + + scale_colour_manual(values = clr, + breaks = names(l_result), + labels = l_roc_legend) + + coord_cartesian(xlim = 1:0, ylim = 0:1) + + # scale_x_continuous(limits = 0:1, expand = expansion(mult = c(0.05, 0.05))) + + # scale_y_continuous(limits = 0:1, expand = expansion(mult = c(0, 0.05))) + + labs(x = "Specificity", y = "Sensitivity") + + theme_bw() + + theme( + plot.background = element_blank(), + panel.grid = element_blank(), + axis.text = element_text(size = 9), + axis.title = element_text(size = 11), + legend.position = "none" + ) + + # custom legend by manually coding ggplot annotations, allowing use to use colored text (reduce legend size because we no longer need the lines) + # 8pt text, white background, no border + for(i in seq_along(l_result)) { + i_clr = clr[names(l_result)[i]] + if(is.na(i_clr)) i_clr = "grey" + p = p + annotate(geom = "label", x = 0.55, y = 0.01 + 0.1 * (i-1), label = l_roc_legend[i], size = legend_text_pt/.pt, hjust = 0, color = i_clr, fill = NA, label.size = NA) # , fill = "white" + } + + return(p) +} + + +custom_density_plot = function(g, add_normal = TRUE) { + p = g + + geom_vline(xintercept = 0, colour = "darkgrey") + + geom_line(stat = "density", adjust = 0.5) + if(add_normal) { + p = p + + geom_line(data = data.frame(x = seq(from = -5, to = 5, by = 0.1)), aes(x, dnorm(x)), inherit.aes = FALSE, color = "black") + } + p + + guides(colour = guide_legend(nrow = 2)) + + labs(y = "Density") + + theme_bw() + + theme( + legend.position = "bottom", + legend.title = element_blank(), + legend.text = element_text(size = 9), + plot.background = element_blank(), + axis.text = element_text(size = 9), + axis.title = element_text(size = 11) + ) +} + + + + +############################################ +########### generate ROC figures ########### +############################################ + +for(dataset_name in names(dataset_results)) { + x = dataset_results[[dataset_name]] + + plotlist_distr = plotlist_roc = plotlist_pval_dens = list() + plotlist_pval_dens__npanel = NULL + for(index_bins in seq_along(x$bins_foreground_all)) { + bins_foreground = x$bins_foreground_all[[index_bins]] + + ### visualize the mock geneset distributions (i.e. how many genes were sampled from each bin in the genelist) + + gl_bins = x$genelist |> distinct(bin, .keep_all = T) |> select(bin, rank) + tib_plot = tibble(bin = c(seq_along(x$bins_null), seq_along(bins_foreground)), + count = c(x$bins_null, bins_foreground), + frac = c(x$bins_null/sum(x$bins_null), bins_foreground/sum(bins_foreground)), + type = c(rep("null", length(x$bins_null)), rep("foreground", length(x$bins_null))) ) |> + mutate(generank = gl_bins$rank[match(bin, gl_bins$bin)]) + + p_distribution = ggplot(tib_plot, aes(x = generank, y = frac, colour = type, shape = type)) + + geom_line(show.legend = F) + + geom_point(size = 2, show.legend = F) + + scale_colour_manual(values = c(null = "#666666", foreground = "#4caf50")) + + scale_shape_manual(values = c(null = 16, foreground = 17)) + + scale_x_continuous(n.breaks = 4) + + coord_cartesian(ylim = c(0.05, 0.18)) + + labs(x = "Gene rank", y = "Fraction") + + theme_bw() + + theme( + plot.background = element_blank(), + axis.text = element_text(size = 9), + strip.text = element_text(size = 7), + axis.title = element_text(size = 11), + legend.title = element_text(size = 11), + legend.text = element_text(size = 9) + ) + + ### ROC based on plain geneset p-values (sans multiple testing adjustment) + l = x$roc_data[[index_bins]] + plotdata = list( + GOAT = l$results$GOAT |> select(id, pvalue), + GSEA = l$results$GSEA |> select(id, pvalue) + ) + # for iDEA, only use louis pvalues in ROC + if("iDEA" %in% names(l$results)) { + plotdata[["iDEA"]] = l$results$iDEA |> select(id, pvalue = pvalue_louis_idea) + } + if("iDEA rescaled" %in% names(l$results)) { + plotdata[["iDEA*"]] = l$results$`iDEA rescaled` |> select(id, pvalue = pvalue_louis_idea) + } + if("iDEA beta rescaled" %in% names(l$results)) { + plotdata[["iDEAb*"]] = l$results$`iDEA beta rescaled` |> select(id, pvalue = pvalue_louis_idea) + } + if("iDEA provided SD" %in% names(l$results)) { + plotdata[["iDEAsd"]] = l$results$`iDEA provided SD` |> select(id, pvalue = pvalue_louis_idea) + } + + p_roc = roc(plotdata) + + + + ### p-value distribution plots + l = x$roc_data[[index_bins]] + plotdata = bind_rows( + l$results$GOAT |> select(id, pvalue) |> mutate(method = "GOAT"), + l$results$GSEA |> select(id, pvalue) |> mutate(method = "GSEA") + ) + if("iDEA" %in% names(l$results)) { + plotdata = bind_rows( + plotdata, + l$results$iDEA |> select(id, pvalue = pvalue_idea) |> mutate(method = "iDEA as-is"), + l$results$iDEA |> select(id, pvalue = pvalue_louis_idea) |> mutate(method = "iDEA louis") + ) + } + if("iDEA rescaled" %in% names(l$results)) { + plotdata = bind_rows( + plotdata, + l$results$`iDEA rescaled` |> select(id, pvalue = pvalue_idea) |> mutate(method = "iDEA* as-is"), + l$results$`iDEA rescaled` |> select(id, pvalue = pvalue_louis_idea) |> mutate(method = "iDEA* louis") + ) + } + if("iDEA beta rescaled" %in% names(l$results)) { + plotdata = bind_rows( + plotdata, + l$results$`iDEA beta rescaled` |> select(id, pvalue = pvalue_idea) |> mutate(method = "iDEAb* as-is"), + l$results$`iDEA beta rescaled` |> select(id, pvalue = pvalue_louis_idea) |> mutate(method = "iDEAb* louis") + ) + } + if("iDEA provided SD" %in% names(l$results)) { + plotdata = bind_rows( + plotdata, + l$results$`iDEA provided SD` |> select(id, pvalue = pvalue_idea) |> mutate(method = "iDEAsd as-is"), + l$results$`iDEA provided SD` |> select(id, pvalue = pvalue_louis_idea) |> mutate(method = "iDEAsd louis") + ) + } + + plotdata = plotdata |> mutate(method = factor(method, levels = unique(method))) + p_pvaldens = ggplot(plotdata, aes(x = pvalue, colour = grepl("null", id))) + + geom_density(show.legend = F) + + scale_colour_manual(values = c("TRUE" = "#666666", "FALSE" = "#4caf50")) + # TRUE = null, FALSE = foreground + facet_wrap(.~method, scales = "free_y", ncol = 1) + + labs(x = "p-value", y = "Density") + + theme_bw() + + theme( + plot.background = element_blank(), + axis.text = element_text(size = 9), + strip.text = element_text(size = 11), + axis.title = element_text(size = 11) + ) + + + ### finally, store data + plotlist_distr[[length(plotlist_distr) + 1]] = p_distribution + plotlist_roc[[length(plotlist_roc) + 1]] = p_distribution + plotlist_roc[[length(plotlist_roc) + 1]] = p_roc + plotlist_pval_dens[[length(plotlist_pval_dens) + 1]] = p_pvaldens + plotlist_pval_dens__npanel = c(plotlist_pval_dens__npanel, length(levels(plotdata$method))) + } + + + ### empirical data + + plot_empirical = function(x) { + ggplot(x, aes(x = breaks, y = density)) + + geom_line(aes(group = id), alpha = 0.25, colour = "#777777") + + geom_smooth(formula = y ~ x, method = "loess", se = TRUE, span = 0.5, level = 0.95, colour = "#ff1744", fill = "#40c4ff", size = 1.25) + + scale_x_continuous(n.breaks = 4) + + labs(x = "Gene rank", y = "Fraction") + + theme_bw() + + theme( + plot.background = element_blank(), + axis.text = element_text(size = 9), + axis.title = element_text(size = 11), + legend.title = element_text(size = 11), + legend.text = element_text(size = 9), + plot.title = element_text(size = 11) + ) + } + + p_empirical_foreground = plot_empirical(x$plotdata_empirical_distributions$effectsize_down$foreground) + p_empirical_null = plot_empirical(x$plotdata_empirical_distributions$effectsize_down$null) + + + ### construct combined plot + + # only the first 2 templates, i.e. the easy ones + plotlist_main = list() + plotlist_main[[1]] = p_empirical_foreground + plotlist_main[[2]] = p_empirical_null + for(i in c(1:4, length(plotlist_roc) - 1, length(plotlist_roc))) plotlist_main[[length(plotlist_main) + 1]] = plotlist_roc[[i]] + + # suppl. data; show all templates + plotlist_suppl = list() + plotlist_suppl[[1]] = p_empirical_foreground + plotlist_suppl[[2]] = p_empirical_null + for(i in seq_along(plotlist_roc)) plotlist_suppl[[length(plotlist_suppl) + 1]] = plotlist_roc[[i]] + + + p_combined_main = patchwork::wrap_plots(plotlist_main, nrow = 2, byrow = FALSE) + + patchwork::plot_annotation(tag_levels = 'A') & + theme(plot.tag = element_text(size = 13)) + + p_combined_suppl = patchwork::wrap_plots(plotlist_suppl, nrow = 4, byrow = FALSE) + + patchwork::plot_annotation(tag_levels = 'A') & + theme(plot.tag = element_text(size = 13)) + + + ggsave(paste0(x$output_file_prefix, "__roc_simulations.pdf"), plot = p_combined_main, width = 8.5, height = 4) + ggsave(paste0(x$output_file_prefix, "__roc_simulations__suppl.pdf"), plot = p_combined_suppl, width = 6.5, height = 7.5) + + + + ### p-value distribution plots + + plotlist = list() + N = length(plotlist_distr) + # N = length(plotlist_distr) - 1 # optionally, skip the null distribution plot to save some horizontal space + for(i in 1:N) { + if(i == 1) plotlist[[length(plotlist) + 1]] = plotlist_distr[[i]] + theme(plot.margin = grid::unit(c(5.5, 1, 5.5, 5.5), "pt"), axis.text = element_text(size = 7)) + else plotlist[[length(plotlist) + 1]] = plotlist_distr[[i]] + labs(y = "") + theme(plot.margin = grid::unit(c(5.5, 1, 5.5, 0.5), "pt"), axis.text = element_text(size = 7)) + } + for(i in 1:N) { + if(i == 1) plotlist[[length(plotlist) + 1]] = plotlist_pval_dens[[i]] + theme(plot.margin = grid::unit(c(5.5, 1, 5.5, 5.5), "pt"), axis.text = element_text(size = 7)) + else plotlist[[length(plotlist) + 1]] = plotlist_pval_dens[[i]] + labs(y = "") + theme(plot.margin = grid::unit(c(5.5, 1, 5.5, 0.5), "pt"), axis.text = element_text(size = 7)) + } + + p_combined_pvaldens = patchwork::wrap_plots(plotlist, nrow = 2, ncol = N, byrow = TRUE, heights = c(1, max(plotlist_pval_dens__npanel))) + + patchwork::plot_annotation(tag_levels = 'A') & + theme(plot.tag = element_text(size = 13, hjust = 1)) + + ggsave(paste0(x$output_file_prefix, "__roc_simulations__pvalue-density.pdf"), plot = p_combined_pvaldens, width = 8.5, + height = ifelse(max(plotlist_pval_dens__npanel) == 2, 4.5, ifelse(max(plotlist_pval_dens__npanel) <= 4, 6, ifelse(max(plotlist_pval_dens__npanel) <= 6, 7.5, 9))) ) + +} # end iteration over genelists + + + + + + + +############################################ +#### compare iDEA input between datasets ### +############################################ + +idea_input_data = NULL +for(dataset_name in names(dataset_results)) { + genelist = goat_manuscript_datasets[[dataset_name]] + + ### below code snippet is copy/pasted from our iDEA implementation @ test_genesets_idea.R + # here we basically follow the iDEA tutorial @ https://xzhoulab.github.io/iDEA/, but with minor adaptions for edge-cases that cause zero or infinite se_beta estimates + genelist_idea = data.frame(beta = genelist$log2fc, beta_var = NA, pvalue = genelist$pvalue, row.names = paste0("g", genelist$gene)) + genelist_idea$zscore = stats::qnorm(genelist_idea$pvalue / 2.0, lower.tail=FALSE) + genelist_idea$se_beta = abs(genelist_idea$beta / genelist_idea$zscore) # approximate the standard error of beta (log2fc) + genelist_idea$se_beta[!is.finite(genelist_idea$se_beta)] = 10 # update/fix; default high value where beta_se is missing, yields beta_var = 100 + genelist_idea$se_beta[genelist_idea$se_beta < 10^-6] = 10^-6 # guard against "zero standard error" (e.g. at log2fc=0; 0/x=0) to ensure downstream iDEA code doesn't suffer from division-by-zero issues + genelist_idea$beta_var = genelist_idea$se_beta^2 + + idea_input_data = bind_rows(idea_input_data, genelist_idea |> mutate(dataset = dataset_name)) +} + +plotdata = idea_input_data |> mutate(dataset_label = sub(":", ", ", sub(":PMID.*", "", dataset))) +# input data, issue 1; beta/log2fc is not approximately normally distributed. Higginbotham-MS = outlier here, has second-most NA values @ simulations +p1 = custom_density_plot(ggplot(plotdata, aes(beta, colour = dataset_label)), add_normal = F) + + coord_cartesian(c(-2.5, 2.5)) +# input data, issue 2: beta/beta_var differ greatly between datasets. Wingo is a clear outlier +p2 = custom_density_plot(ggplot(plotdata, aes(beta / beta_var, colour = dataset_label)), add_normal = F) + + coord_cartesian(c(-50, 50)) + +p_idea_betavar_distr = patchwork::wrap_plots(p1, p2, nrow = 1, ncol = 2, byrow = TRUE, guides = "collect") + + patchwork::plot_annotation(tag_levels = 'A') & + theme(plot.tag = element_text(size = 13), legend.position = "bottom") + +ggsave(paste0(output_dir, "/idea_input_data.pdf"), plot = p_idea_betavar_distr, width = 5, height = 3.5) + + diff --git a/analyses/test_genesets_idea.R b/analyses/test_genesets_idea.R new file mode 100644 index 0000000..e87a594 --- /dev/null +++ b/analyses/test_genesets_idea.R @@ -0,0 +1,329 @@ + + +# this function is not integrated into the GOAT R package atm as it causes issues while submitting the GOAT R package to CRAN + + +#' Geneset testing with the iDEA R package +#' +#' @description +#' Follows the iDEA tutorial at https://xzhoulab.github.io/iDEA/ with minor adaptions (see `idea_variant` parameter). +#' +#' note that computation takes a long time with the iDEA method. On a high performance workstation computer with +#' 12 cores / 24 threads it takes approximately 6 hours for a genelist of ~14000 genes to analyze all (filtered) GO +#' genesets. Using a regular PC this will take much longer. +#' +#' +#' @param genesets filtered genesets +#' @param genelist user genelist, must contain columns "gene", "log2fc" and "pvalue" +#' @param return_pvalue_louis boolean, set `TRUE` (default) to return "louis corrected" p-values from iDEA (analogous to iDEA tutorial) +#' @param idea_variant single string indicating how iDEA should be used. Valid options: 'default', 'beta', 'beta_rescale', 'beta_rescale_center', 'rescale', 'rescale_center', 'genelist_sd', 'genelist_var'. +#' default = follow iDEA tutorial +#' beta = let iDEA model only the beta's (log2fc), this is equivalent to `modelVariant = TRUE` in the iDEA tutorial +#' beta_rescale = analogous to 'beta', but with beta's rescaled by their MAD (i.e. we ensure that iDEA receives input data at expected scale) +#' beta_rescale_center = analogous to 'beta_rescale', but also center to mode zero +#' rescale = rescale input such that beta_var is approximately gamma(2,0.5) distributed. This resolves the vast majority of iDEA model fitting errors that otherwise occur at default settings +#' rescale_center = analogous, but the rescaled beta/log2fc are also centered to mode zero +#' genelist_sd = use provided standard deviations (of the log2fc) in the 'sd' column of the genelist +#' genelist_var = use provided variations (of the log2fc) in the 'var' column of the genelist +#' @param verbose boolean, set `TRUE` to plot genelist beta and beta/sqrt(beta_var) distributions +#' @returns input `genesets` with additional columns for geneset p-value and score type. Because iDEA can return 2 types of geneset p-values, +#' multiple columns are included in this function's results; `pvalue_idea` = iDEA geneset p-value, `pvalue_louis_idea` = `pvalue_louis` from iDEA, +#' `pvalue` = respective copy of either former column, depending on user-parameter `return_pvalue_louis` +#' @seealso `test_genesets` +#' @return input `genesets` table with results in the "pvalue" and "score_type" columns. +#' Additional data at "ismissing_idea", "pvalue_idea" and "pvalue_louis_idea" columns +#' @export +test_genesets_idea = function(genesets, genelist, return_pvalue_louis = TRUE, idea_variant = "default", verbose = FALSE) { + x = NULL # fix invisible bindings R package NOTE + force(genesets) # guard against issues with multithreaded code downstream; enforce evaluation of input data (i.e. no lazy evaluation) + force(genelist) + if(!requireNamespace("future.apply", quietly = TRUE)) { + stop('the "future.apply" R package is required for test_genesets_idea(). Start a fresh RStudio session and issue this R statement: install.packages("future.apply)') + } + if(!requireNamespace("future", quietly = TRUE)) { + stop('the "future" R package is required for test_genesets_idea(). Start a fresh RStudio session and issue this R statement: install.packages("future)') + } + if(!requireNamespace("iDEA", quietly = TRUE)) { + stop('to perform geneset testing with the iDEA algorithm, the iDEA R package is required which is available on GitHub.\nSuggested installation steps;\n1) close RStudio and start it anew (do copy/paste these instructions before closing)\n2) run R command: install.packages("pak")\n3) run R command: pak::pkg_install("xzhoulab/iDEA", update=FALSE)\n4) test if the installation was successful by loading the R package, run R command: library(iDEA)', call. = FALSE) + } + + + # input validation + stopifnot("genesets parameter must be a non-empty data.frame/tibble with columns; source (optional, character type) id (character type), genes (list type)" = + is.data.frame(genesets) && nrow(genesets) > 0 && all(c("id", "genes") %in% colnames(genesets)) && + is.character(genesets$id) && is.list(genesets$genes)) + stopifnot("genelist parameter must be a non-empty data.frame/tibble with numeric columns that contain finite values; 'gene' (integer), 'log2fc' (numeric) and 'pvalue' (numeric)" = + is.data.frame(genelist) && nrow(genelist) > 0 && all(c("gene", "log2fc", "pvalue") %in% colnames(genelist)) && + all(is.finite(genelist$gene)) && all(is.finite(genelist$log2fc)) && all(is.finite(genelist$pvalue))) + stopifnot("return_pvalue_louis parameter must be TRUE or FALSE" = length(return_pvalue_louis) == 1 && return_pvalue_louis %in% c(TRUE, FALSE)) + stopifnot("verbose parameter must be TRUE or FALSE" = length(verbose) == 1 && verbose %in% c(TRUE, FALSE)) + stopifnot("idea_variant parameter should be a single string. Valid options: 'default', 'beta', 'beta_rescale', 'beta_rescale_center', 'rescale', 'rescale_center', 'genelist_sd', 'genelist_var'" = length(idea_variant) == 1 && idea_variant %in% c("default", "beta", "beta_rescale", "beta_rescale_center", "rescale", "rescale_center", "genelist_sd", "genelist_var")) + if(idea_variant == "genelist_sd") { + stopifnot("idea_variant parameter is genelist_sd, expect a column named 'sd' in provided genelist with only finite numeric values larger than 0" = "sd" %in% colnames(genelist) && all(is.numeric(genelist$sd) & is.finite(genelist$sd) & genelist$sd > 0)) + } + if(idea_variant == "genelist_var") { + stopifnot("idea_variant parameter is genelist_var, expect a column named 'var' in provided genelist with only finite numeric values larger than 0" = "var" %in% colnames(genelist) && all(is.numeric(genelist$var) & is.finite(genelist$var) & genelist$var > 0)) + } + + # init results + genesets$score_type = NA_character_ + genesets$pvalue = NA_real_ + + # temp geneset ID that combines source + id for compatibility with input genelists that contain the same ID across 'source' + genesets$id_tmp = genesets$id + if("source" %in% colnames(genesets)) { + genesets$id_tmp = paste(genesets$source, genesets$id) + } + + + ### genelist to iDEA format (data.frame with iDEA-required 'beta' and 'beta_var' columns) + genelist_idea = idea_prepare_genelist(genelist, idea_variant, verbose) + # this is equivalent to iDEA parameter "modelVariant". + # From iDEA tutorial; "model option to run, boolean variable, if FALSE, runing the main iDEA model, which models on z score statistics. if TRUE, runing iDEA variant model which models on beta effect size." + idea_param_ismodelvariant = idea_variant %in% c("beta", "beta_rescale", "beta_rescale_center") + cat("iDEA variant: ", idea_variant, "\n", sep = "") + + + ### genesets to iDEA format (gene*geneset identity matrix) + # helper function creates the identity matrix, which we can later trivially convert to a data.frame if needed + genesets_to_matrix = function(gs, ugene) { + # note; in GOAT we use numeric gene IDs. To avoid type conversion / indexing shenanigans, we prefix integer gene IDs with a string + m = matrix(0L, nrow = length(ugene), ncol = nrow(gs), dimnames = list(paste0("g", ugene), gs$id_tmp)) # ! use the temp ID for genesets + for(i in 1:nrow(gs)) { + m[,i] = as.integer(ugene %in% gs$genes[[i]]) + } + return(m) + } + genesets_idea = as.data.frame(genesets_to_matrix(genesets, genelist$gene)) + # double-check data structure integrity + stopifnot(rownames(genesets_idea) == rownames(genelist_idea)) + + + ### following the iDEA tutorial, perform geneset enrichment testing with default parameters + # setup multiprocessing; start future plan + cleanup on exit + oplan = future::plan(future::multisession) + on.exit(future::plan(oplan)) + + tryCatch({ + # as first parameter, select only the 2 required input columns and enforce their order (i.e. this is a hardcoded requirement in iDEA) + # we set `min_precent_annot=0` because we already filtered genesets upstream (default in tutorial is 0.0025, which amounts to 35 in a genelist of 14k genes; skipping over small genesets) + # we disabled multiprocessing here because it has issues on some of our test systems (Windows); this step is quite fast so no problem + idea = iDEA::CreateiDEAObject(genelist_idea[,c("beta", "beta_var")], genesets_idea, max_var_beta = 101, min_precent_annot = 0, num_core = 1) + + ################ + # idea = iDEA::iDEA.fit( + # idea, + # fit_noGS = FALSE, + # init_beta = NULL, + # init_tau = c(-2,0.5), + # min_degene = 0, + # em_iter = 15, + # mcmc_iter = 1000, + # fit.tol = 1e-5, + # modelVariant = F, + # verbose = TRUE + # ) + ################ + + ### ANALOGOUS TO ABOVE CODE FROM IDEA TUTORIAL, DIRECTLY ADAPTED FROM iDEA SOURCE CODE, BUT WITH future.apply TO FIX PARALLEL COMPUTATION + SWITCH VARIANT @ EMMCMCStepSummaryVariant / EMMCMCStepSummary + # https://github.com/xzhoulab/iDEA/blob/a30fc8ec655813678049c5b4b4b0aa6a21c944f0/R/iDEASummary.R#L103 + # here setting default parameters from tutorial at: https://xzhoulab.github.io/iDEA/ + object = idea + init_tau = c(-2,0.5) # initial value for the coefficient of annotations/gene sets, including the intercept in EM procedure, default is c(-2,0.5). + em_iter = 15 # maximum iteration for EM algorithm, default is 15 + mcmc_iter = 1000 # maximum iteration for MCMC algorithm, default is 1000 + min_degene = 0 # the threshold for the number of detected DE genes in summary statistics. For some of extremely cases, the method does not work stably when the number of detected DE genes is 0. + #### start snippet from iDEA source + num_gene <- object@num_gene + num_annot <- length(object@annotation) + init_beta <- object@summary[,1] + # this line is the only deviation from the iDEA sourcecode; + # we use the future.apply package because the original code, using 'pbmclapply' + # 1) occasionally hangs in WSL and 2) doesn't perform well on Windows + # (this is a simple workaround as we only use this specific iDEA configuration) + # (note that we can't print to console from within future "workers", so iDEA-specific-errors are lost / not reported) + res_idea <- future.apply::future_sapply(1:num_annot, FUN = function(x) { + set.seed(x) + Annot <- rep(0, object@num_gene) + Annot[object@annotation[[x]]] <- 1 + Annot <- Annot - mean(Annot) + Annot <- as.matrix(data.frame(rep(1, num_gene), Annot) ) + if(idea_param_ismodelvariant) { + t1 <- system.time(model1 <- try( res <- iDEA::EMMCMCStepSummaryVariant(object@summary[,1], object@summary[,2], as.matrix(Annot), init_beta, init_tau, em_iter, mcmc_iter, min_degene) )) + } else { + t1 <- system.time(model1 <- try( res <- iDEA::EMMCMCStepSummary(object@summary[,1], object@summary[,2], as.matrix(Annot), init_beta, init_tau, em_iter, mcmc_iter, min_degene) )) + } + if(!inherits(model1, "try-error")) { + rownames(res$pip) <- object@gene_id + colnames(res$pip) <- "PIP" + rownames(res$beta) <- object@gene_id + colnames(res$beta) <- "beta" + rownames(res$annot_coef) <- c("tau_1", "tau_2") + colnames(res$annot_coef) <- "annot_coef" + rownames(res$annot_var) <- c("tau_1", "tau_2") + colnames(res$annot_var) <- "annot_var" + res$converged <- TRUE + res$ctime <- t1[3] + } else { res <- NULL } + return(res) + }, future.seed = NULL, simplify = FALSE) + + names(res_idea) <- object@annot_id + object@de <- res_idea + #### end snippet from iDEA source + idea@de <- res_idea + ################ + + ### fix the the estimated geneset p-values as per default iDEA tutorial workflow + tmp = utils::capture.output(idea <- iDEA::iDEA.louis(idea)) # capture.output to silence the progress bar + + ### map results to input geneset data.frame, from the idea object + index = match(genesets$id_tmp, idea@gsea$annot_id) # ! use the temp ID for genesets + if(any(!is.finite(idea@gsea$pvalue_louis[index]))) { + cat(sprintf("%d/%d (filtered) genesets that were tested with iDEA received a p-value via iDEA.louis(), iDEA model fitting failed for %d genesets\n", + sum(is.finite(idea@gsea$pvalue_louis[index])), nrow(genesets), sum(!is.finite(idea@gsea$pvalue_louis[index])) )) + } + genesets$ismissing_idea = !is.finite(idea@gsea$pvalue_louis[index]) + genesets$pvalue_idea = idea@gsea$pvalue[index] + genesets$pvalue_louis_idea = idea@gsea$pvalue_louis[index] + genesets$pvalue = idea@gsea$pvalue[index] + if(return_pvalue_louis) { + genesets$pvalue = idea@gsea$pvalue_louis[index] + } + # In this function we tested only valid, non-empty genesets that passed filter_genesets() upstream. + # If the geneset algorithm returned a non-finite pvalue, we consider it "tested but found not significant" + # (note that leaving NA values as-is would affect subsequent multiple testing correction, which in turn would be a + # source of bias if many "no effect whatsoever" genesets were returned as NA pvalues by the algorithm) + genesets$pvalue_idea[!is.finite(genesets$pvalue_idea)] = 1 + genesets$pvalue_louis_idea[!is.finite(genesets$pvalue_louis_idea)] = 1 + genesets$pvalue[!is.finite(genesets$pvalue)] = 1 + # score type + genesets$score_type = c("effectsize_down", "effectsize_up")[1 + (is.finite(idea@gsea$annot_coef[index]) & idea@gsea$annot_coef[index] > 0)] # if annot_coef > 0, pathway is up-regulated + }, + error = function(err) { + print(err) + }) + + # remove temp geneset ID column + genesets$id_tmp = NULL + return(genesets) +} + + + +#' prepare genelist table with 'beta' and 'beta_var' variables depending on desired iDEA variant (e.g. only use beta and/or rescale input values) +#' +#' @description +#' ##### for reference, relevant snippet from iDEA tutorial @ https://xzhoulab.github.io/iDEA/ that we adopted here +#' `## Assume you have obtained the DE results from i.e. zingeR, edgeR or MAST with the data frame res_DE (column: pvalue and LogFC)` +#' `pvalue <- res_DE$pvalue #### the pvalue column` +#' `zscore <- qnorm(pvalue/2.0, lower.tail=FALSE) #### convert the pvalue to z-score` +#' `beta <- res_DE$LogFC ## effect size` +#' `se_beta <- abs(beta/zscore) ## to approximate the standard error of beta` +#' `beta_var = se_beta^2 ### square` +#' `summary = data.frame(beta = beta,beta_var = beta_var)` +#' `## add the gene names as the rownames of summary` +#' `rownames(summary) = rownames(res_DE) ### or the gene id column in the res_DE results` +#' +#' @param genelist see `test_genesets_idea` +#' @param idea_variant see `test_genesets_idea` +#' @param verbose see `test_genesets_idea` +#' @noRd +idea_prepare_genelist = function(genelist, idea_variant, verbose) { + genelist_idea = x = NULL + + ### genelist to iDEA format (assuming we have columns log2fc and pvalue @ input genelist) + if(idea_variant == "genelist_sd") { + ### user-provided input values + genelist_idea = data.frame(beta = genelist$log2fc, beta_var = genelist$sd^2, pvalue = genelist$pvalue, row.names = paste0("g", genelist$gene)) + } + if(idea_variant == "genelist_var") { + ### user-provided input values + genelist_idea = data.frame(beta = genelist$log2fc, beta_var = genelist$var, pvalue = genelist$pvalue, row.names = paste0("g", genelist$gene)) + } + + # rescale beta up-front @ genelist, then follow typical iDEA genelist preparation + if(idea_variant %in% c("beta_rescale", "beta_rescale_center")) { + # rescaling factor is basically the log2fc MAD. Below implementation increases robustness by ignoring zeros and extreme values + b_threshold = 3 * stats::quantile(abs(genelist$log2fc[is.finite(genelist$log2fc) & genelist$log2fc != 0]), probs = 0.9) + k = stats::mad(genelist$log2fc[is.finite(genelist$log2fc) & genelist$log2fc != 0 & abs(genelist$log2fc) < b_threshold]) + genelist$log2fc = genelist$log2fc / k + } + # note; in GOAT we use numeric gene IDs. To avoid type conversion / indexing shenanigans, we prefix integer gene IDs with a string + if(idea_variant %in% c("default", "beta", "beta_rescale", "beta_rescale_center")) { + ### here we basically follow the iDEA tutorial, but with minor adaptions for edge-cases that cause zero or infinite se_beta estimates (e.g. log2fc=0 or pvalue=1) + genelist_idea = data.frame(beta = genelist$log2fc, beta_var = NA, pvalue = genelist$pvalue, row.names = paste0("g", genelist$gene)) + genelist_idea$zscore = stats::qnorm(genelist_idea$pvalue / 2.0, lower.tail=FALSE) + genelist_idea$se_beta = abs(genelist_idea$beta / genelist_idea$zscore) # approximate the standard error of beta (log2fc) + genelist_idea$se_ismissing = !is.finite(genelist_idea$se_beta) | genelist_idea$se_beta == 0 + genelist_idea$se_beta[genelist_idea$se_ismissing] = max(genelist_idea$se_beta[!genelist_idea$se_ismissing]) # update/fix; insert high values where beta_se is missing + genelist_idea$se_beta[genelist_idea$se_beta < 10^-6] = 10^-6 # update/fix; guard against "zero standard error" (e.g. at log2fc=0; 0/x=0) + genelist_idea$beta_var = genelist_idea$se_beta^2 + } + + if(idea_variant %in% c("rescale", "rescale_center")) { + ### follow the iDEA tutorial, but additionally we standardize beta and beta_var + ## first, we follow the iDEA tutorial @ https://xzhoulab.github.io/iDEA/ + genelist_idea = data.frame(beta = genelist$log2fc, beta_var = NA, pvalue = genelist$pvalue, row.names = paste0("g", genelist$gene)) + genelist_idea$zscore = stats::qnorm(genelist_idea$pvalue / 2.0, lower.tail=FALSE) + genelist_idea$se_beta = abs(genelist_idea$beta / genelist_idea$zscore) # approximate the standard error of beta (log2fc) + genelist_idea$se_ismissing = !is.finite(genelist_idea$se_beta) | genelist_idea$se_beta == 0 + genelist_idea$se_beta[genelist_idea$se_ismissing] = max(genelist_idea$se_beta[!genelist_idea$se_ismissing]) # update/fix; insert high values where beta_se is missing + genelist_idea$beta_var = genelist_idea$se_beta^2 + + ## next; rescale such that beta_var is always on a similar scale regardless of the input data + # This will fix the many iDEA errors observed in datasets that have atypical SD distributions (when using the 'default' iDEA workflow). + # The simple scaling factor will result in roughly gamma(2, 0.5) shaped distributions + # TODO: + # we could make this more robust with a basic MLE to rescale beta_var distribution to expectation from gamma(2, 0.5), + # instead of rescaling to a constant as used here (but for a dozen datasets we tested, this works reasonably well and at least fixes all iDEA errors) + # Analogous to the threshold/non-zero filtering prior to MAD for beta_rescale, this also ignores extreme outliers (and bimodality when there are many missing SE that were capped at extreme/max value) + bv_threshold = 2 * stats::quantile(genelist_idea$beta_var[!genelist_idea$se_ismissing], probs = 0.9) + k = 0.6 / stats::mad(genelist_idea$beta_var[!genelist_idea$se_ismissing & genelist_idea$beta_var < bv_threshold]) + genelist_idea$std__var = genelist_idea$beta_var * k + genelist_idea$std__var[genelist_idea$std__var < 10^-6] = 10^-6 # guard against "zero standard error" (e.g. caused by log2fc=0) + genelist_idea$std__var[genelist_idea$std__var > 50] = 50 # after standardizing, we can cap extreme values + genelist_idea$std__beta = genelist_idea$beta * sqrt(k) # rescale beta as well, so beta/sd ratio remains unchanged + # result: new table with only the updated values + genelist_idea = data.frame(beta = genelist_idea$std__beta, beta_var = genelist_idea$std__var, pvalue = genelist_idea$pvalue, row.names = paste0("g", genelist$gene)) + } + + ## optionally, center the foldchanges to zero mode + if(idea_variant %in% c("rescale_center", "beta_rescale_center")) { + get_mode = function(x) { + density_estimate = stats::density(x, na.rm=T) + return(density_estimate$x[which.max(density_estimate$y)]) + } + m = get_mode(genelist_idea$beta) # in contrast to above code for rescaling, here we use all log2fc values (including zero's and those with pvalue=1) + stopifnot("failed to estimate mode of (centered) beta @ iDEA" = is.finite(m)) + genelist_idea$beta = genelist_idea$beta - m + } + + if(is.null(genelist_idea)) { + stop(paste("unknown option for iDEA;", idea_variant)) + } + + + if(verbose) { + mylim = function(x, qtl = c(0.0025, 0.9975)) c(-1, 1) * max(abs(stats::quantile(x[is.finite(x) & x != 0], probs = qtl, na.rm = TRUE))) + mydens = function(x, lim, adj = 0.5) stats::density(x[is.finite(x) & x >= lim[1] & x <= lim[2]], adjust = adj) + oldpar = graphics::par(mfcol = c(3,1)) + + d = mydens(genelist_idea$beta, mylim(genelist_idea$beta)) + plot(d, xlab = "beta", main = paste0("iDEA variant: ", idea_variant, "\ngrey line: standard normal")) + graphics::abline(v = 0, col = "grey") + graphics::curve(stats::dnorm(x, mean = 0, sd = 1), add = TRUE, col = "grey") + + d = mydens(genelist_idea$beta_var, c(0, stats::quantile(genelist_idea$beta_var, probs = 0.9975)) ) + plot(d, xlab = "variation", main = "grey line: gamma(shape=2, scale=0.5)") + graphics::curve(stats::dgamma(x, shape = 2, scale = 0.5), add = TRUE, col = "grey") + + d = mydens(genelist_idea$beta / sqrt(genelist_idea$beta_var), mylim(genelist_idea$beta / sqrt(genelist_idea$beta_var))) + plot(d, xlab = "beta / sd", main = "grey line: standard normal") + graphics::abline(v = 0, col = "grey") + graphics::curve(stats::dnorm(x, mean = 0, sd = 1), add = TRUE, col = "grey") + graphics::par(oldpar) + } + + return(genelist_idea) +} diff --git a/data/goat_example_datasets.rda b/data/goat_example_datasets.rda deleted file mode 100644 index 591e381bf596cd59512d77362c055346868dc874..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1017788 zcmV(jK=!}=H+ooF0004LBHlIv03iV!0000G&sfaikw)+ST>vQ&2UJ%gRpOV=m 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file mode 100644 index 28ee780..0000000 --- a/man/build_treemap.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_ontology.R -\name{build_treemap} -\alias{build_treemap} -\title{Compute treemap data structures} -\usage{ -build_treemap(ods, simplify = "none", toplevel_max_ngenes = Inf) -} -\arguments{ -\item{ods}{result from \code{ontology_data_structures()}} - -\item{simplify}{strategy for reducing the genesets returned in the treemap. Options; -"leaf_only" (most stringent, returns only leafs in the tree structure) -"prune_singletons" (remove parent terms that have exactly 1 child) -"pvalue" (remove parent terms where the child term p-value is at least 4 times better) -"none" (default; return all significant genesets that are not a "grouping term" in the treemap)} - -\item{toplevel_max_ngenes}{groups in the treemap should not have more than this many genes ('ngenes' in geneset test results)} -} -\description{ -\itemize{ -\item shortlist = genesets/DAG-nodes to plot -\item update nested DAG data structure with recursive counts of shortlist elements -\item find level-1 elements to start with (possibly further down than direct children of root) -\item collapse children of level-1 elements (either from tree structure or from greedy aggregation starting at largest element) -\item restructure treemap data into format suitable for plotting -} -} diff --git a/man/check_dependency.Rd b/man/check_dependency.Rd deleted file mode 100644 index 3d027b9..0000000 --- a/man/check_dependency.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/utils.R -\name{check_dependency} -\alias{check_dependency} -\title{throw error if R package is unavailable} -\usage{ -check_dependency(pkg, msg) -} -\arguments{ -\item{pkg}{R package name} - -\item{msg}{function name / reference for user} -} -\description{ -throw error if R package is unavailable -} diff --git a/man/cluster_genesets.Rd b/man/cluster_genesets.Rd index bd379ec..7ea3e35 100644 --- a/man/cluster_genesets.Rd +++ b/man/cluster_genesets.Rd @@ -13,6 +13,9 @@ cluster_genesets(x, genelist, hclust_method = "ward.D2") \item{hclust_method}{hierarchical clustering method, any of; 'ward.D', 'ward.D2' (default), 'single', 'complete', 'average'} } +\value{ +a list with elements genesets (param \code{x}), similarity, hc_row, hc_col +} \description{ cluster significant genesets from \code{test_genesets()} by geneset similarity (separately for each 'geneset source') } diff --git a/man/darken_color.Rd b/man/darken_color.Rd new file mode 100644 index 0000000..ee4cf67 --- /dev/null +++ b/man/darken_color.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{darken_color} +\alias{darken_color} +\title{naively darken a color by mixing in black} +\usage{ +darken_color(color, frac = 0.1) +} +\arguments{ +\item{color}{input colors} + +\item{frac}{fraction of black; >0 and <1} +} +\value{ +adjusted value for input \code{color} +} +\description{ +naively darken a color by mixing in black +} diff --git a/man/dot-onUnload.Rd b/man/dot-onUnload.Rd deleted file mode 100644 index 0544b82..0000000 --- a/man/dot-onUnload.Rd +++ /dev/null @@ -1,14 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/goat.R -\name{.onUnload} -\alias{.onUnload} -\title{cleanup Rcpp code} -\usage{ -.onUnload(libpath = NULL) -} -\arguments{ -\item{libpath}{library path} -} -\description{ -cleanup Rcpp code -} diff --git a/man/download_genesets_goatrepo.Rd b/man/download_genesets_goatrepo.Rd new file mode 100644 index 0000000..6b7fd59 --- /dev/null +++ b/man/download_genesets_goatrepo.Rd @@ -0,0 +1,59 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/download_data.R +\name{download_genesets_goatrepo} +\alias{download_genesets_goatrepo} +\title{Download and parse geneset collections from the GOAT GitHub repository} +\usage{ +download_genesets_goatrepo( + output_dir, + type = "GO", + version = "2024-01-01", + ignore_cache = FALSE +) +} +\arguments{ +\item{output_dir}{full path to the directory where the downloaded files should be stored. Directory is created if it does not exist. +e.g. \code{output_dir="~/data"} on unix systems, \code{output_dir="C:/data"} on Windows, or set to \code{output_dir=getwd()} to write output to the current working directory} + +\item{type}{the type of genesets to download. Currently, only "GO" is supported (default)} + +\item{version}{the dataset version. This must be a date in format YYYY-MM-DD. Example: "2024-01-01" (default). View all available versions at https://github.com/ftwkoopmans/goat/tree/data} + +\item{ignore_cache}{boolean, set to TRUE to force re-download and ignore cached data, if any. Default: FALSE} +} +\value{ +result from respective geneset parser function. e.g. if parameter \code{type} was set to"GO" (default), this function returns the result of \code{load_genesets_go_fromfile()}. These data returned by this function is typically used as input for \code{filter_genesets()}, c.f. full example at documentation for test_genesets() +} +\description{ +while the Bioconductor respository is extensive, contains data for many species and is a part of +a larger infrastructure, it might contain outdated GO data when the user is not using the latest R version. +If users are on an R version that is a few years old, so will the GO data from Bioconductor be. + +As an alternative, we store gene2go data from NCBI (for Human genes only!) at the GOAT GitHub repository. +This function allows for a convenient way to download this data and then parse the genesets. + +Alternatively you can browse the file in the data branch of the GOAT GitHub repository and download these files manually, +then load them via the GOAT R function \code{load_genesets_go_fromfile()}. + +To view all available data you can open this URL in a browser; https://github.com/ftwkoopmans/goat/tree/data + +New data is automatically added biannually. The first available version is 2024-01-01, the next 2024-06-01, then 2025-01-01, and so on. +} +\examples{ +\donttest{ +# note: this example will download 2 files of approx 10MB in total + +# store the downloaded files in the following directory. Here, the temporary file +# directory is used. Alternatively, consider storing this data in a more permanent location. +# e.g. output_dir="~/data/go" on unix systems or output_dir="C:/data/go" on Windows +output_dir = tempdir() + +# download data files with GO annotations, version 2024-01-01 (default parameter) +# these are then parsed with the load_genesets_go_fromfile() function +# if the files are already available at output_dir, the download step is skipped +genesets_asis = download_genesets_goatrepo(output_dir) + +### for a basic example on how to use the data obtain here, +### refer to the example included at function documentation of: test_genesets() +} +} diff --git a/man/download_goat_manuscript_data.Rd b/man/download_goat_manuscript_data.Rd new file mode 100644 index 0000000..112bce4 --- /dev/null +++ b/man/download_goat_manuscript_data.Rd @@ -0,0 +1,33 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/download_data.R +\name{download_goat_manuscript_data} +\alias{download_goat_manuscript_data} +\title{Download the datasets that were used in the GOAT manuscript} +\usage{ +download_goat_manuscript_data(output_dir, ignore_cache = FALSE) +} +\arguments{ +\item{output_dir}{full path to the directory where the downloaded files should be stored. Directory is created if it does not exist. +e.g. \code{output_dir="~/data"} on unix systems, \code{output_dir="C:/data"} on Windows, or set to \code{output_dir=getwd()} to write output to the current working directory} + +\item{ignore_cache}{boolean, set to TRUE to force re-download and ignore cached data, if any. Default: FALSE} +} +\value{ +a list of genelist data tables. The names of the list represent the datasets, +values in the list are data tables that can be used as a "genelist" in the GOAT R package +} +\description{ +Downloads OMICs-based datasets that were used in the GOAT manuscript from the GOAT GitHub page. +This file is cached in the output directory and only needs to be downloaded once. Multiple datasets +are included and their names include the respective PubMed identifiers (PMID). + +If you encounter technical difficulties, try to; +\enumerate{ +\item download the file by copy/pasting this URL into your browser: https://github.com/ftwkoopmans/goat/raw/main/analyses/goat_manuscript_datasets.rda +\item load the data in R using the following 2 lines of code, here assuming you stored the downloaded file at C:/data/goat_manuscript_datasets.rda +} + +\code{load("C:/data/goat_manuscript_datasets.rda")} + +\code{genelist = goat_manuscript_datasets.rda[["Wingo 2020:mass-spec:PMID32424284"]]} +} diff --git a/man/dsnorm.Rd b/man/dsnorm.Rd deleted file mode 100644 index 8d62fc1..0000000 --- a/man/dsnorm.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/util_stats.R -\name{dsnorm} -\alias{dsnorm} -\title{Density function for skew normal distribution} -\usage{ -dsnorm(x, mean = 0, sd = 1, xi = 1) -} -\arguments{ -\item{x}{numeric vector of values} - -\item{mean}{location parameter} - -\item{sd}{scale parameter} - -\item{xi}{skewness parameter} -} -\description{ -Density function for skew normal distribution -} diff --git a/man/edgelist_as_nested.Rd b/man/edgelist_as_nested.Rd deleted file mode 100644 index 731a39a..0000000 --- a/man/edgelist_as_nested.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_ontology.R -\name{edgelist_as_nested} -\alias{edgelist_as_nested} -\title{Convert edgelist table into a nested list structure} -\usage{ -edgelist_as_nested(x, name, ngenes, edgelist) -} -\arguments{ -\item{x}{element ID} - -\item{name}{element name} - -\item{ngenes}{element ngenes property} - -\item{edgelist}{data.frame with properties parent, child, child_name, child_ngenes} -} -\description{ -Convert edgelist table into a nested list structure -} diff --git a/man/edgelist_find_children_recursive.Rd b/man/edgelist_find_children_recursive.Rd deleted file mode 100644 index abf5d65..0000000 --- a/man/edgelist_find_children_recursive.Rd +++ /dev/null @@ -1,18 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_ontology.R -\name{edgelist_find_children_recursive} -\alias{edgelist_find_children_recursive} -\title{depth-first recursion} -\usage{ -edgelist_find_children_recursive(p, edgelist, result) -} -\arguments{ -\item{p}{p} - -\item{edgelist}{edgelist} - -\item{result}{result} -} -\description{ -depth-first recursion -} diff --git a/man/edgelist_find_colored_parents_recursive.Rd b/man/edgelist_find_colored_parents_recursive.Rd deleted file mode 100644 index 0e986a8..0000000 --- a/man/edgelist_find_colored_parents_recursive.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_ontology.R -\name{edgelist_find_colored_parents_recursive} -\alias{edgelist_find_colored_parents_recursive} -\title{Within each recursive path; halt at first colored node} -\usage{ -edgelist_find_colored_parents_recursive(x, geneset_table, query, result) -} -\arguments{ -\item{x}{geneset ID} - -\item{geneset_table}{data.frame with columns 'id' and 'parent_id'} - -\item{query}{set of all colored nodes (vector)} - -\item{result}{all colored parents, accumulated recursively (vector)} -} -\description{ -Within each recursive path; halt at first colored node -} diff --git a/man/edgelist_find_root.Rd b/man/edgelist_find_root.Rd deleted file mode 100644 index f2f365d..0000000 --- a/man/edgelist_find_root.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_ontology.R -\name{edgelist_find_root} -\alias{edgelist_find_root} -\title{Find root ID and name in edgelist} -\usage{ -edgelist_find_root(edgelist, genesets) -} -\arguments{ -\item{edgelist}{result from e.g. \code{edgelist_from_ontology()}} - -\item{genesets}{filtered genesets} -} -\description{ -throw error if there is no single root -} diff --git a/man/edgelist_from_ontology.Rd b/man/edgelist_from_ontology.Rd deleted file mode 100644 index e597a7a..0000000 --- a/man/edgelist_from_ontology.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_ontology.R -\name{edgelist_from_ontology} -\alias{edgelist_from_ontology} -\title{Construct DAG between genesets-of-interest as edgelist} -\usage{ -edgelist_from_ontology(ids, genesets) -} -\arguments{ -\item{ids}{shortlist of geneset IDs} - -\item{genesets}{importantly, genesets should be the input genesets and not the "filtered" genesets because -only the former contains the complete ontological structure (parent/child links between genesets)} -} -\description{ -this function reduces the complete DAG to a subset with only parameter genesets -} diff --git a/man/fit_snorm_xi.Rd b/man/fit_snorm_xi.Rd deleted file mode 100644 index efc4b28..0000000 --- a/man/fit_snorm_xi.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/util_stats.R -\name{fit_snorm_xi} -\alias{fit_snorm_xi} -\title{MLE estimate of parameter Xi in a skew-normal distribution} -\usage{ -fit_snorm_xi(x, mu, sd, init_xi, lower = NA, upper = NA) -} -\arguments{ -\item{x}{numeric vector} - -\item{mu}{mean value for x} - -\item{sd}{sd value for x} - -\item{init_xi}{initial value for xi (typically 1~1.25)} - -\item{lower}{lower bound for Xi} - -\item{upper}{Upper bound for Xi} -} -\description{ -MLE estimate of parameter Xi in a skew-normal distribution -} diff --git a/man/geneset_similarity_matrix.Rd b/man/geneset_similarity_matrix.Rd deleted file mode 100644 index e17e37b..0000000 --- a/man/geneset_similarity_matrix.Rd +++ /dev/null @@ -1,31 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_similarity.R -\name{geneset_similarity_matrix} -\alias{geneset_similarity_matrix} -\title{geneset similarity matrix computation} -\usage{ -geneset_similarity_matrix( - mat, - mat_row_geneid, - mat_col_gsid, - genelist_geneid, - genelist_scores, - weighted = TRUE -) -} -\arguments{ -\item{mat}{numeric matrix} - -\item{mat_row_geneid}{for mat rows; gene identifiers} - -\item{mat_col_gsid}{for mat columns; geneset identifiers} - -\item{genelist_geneid}{same identifiers as \code{mat_row_geneid}} - -\item{genelist_scores}{a matrix where columns are various gene score types} - -\item{weighted}{boolean value} -} -\description{ -importantly, if multiple genelist_scores are provided, we use the gene score type for geneset j that yields the highest sum score -} diff --git a/man/genesets_as_matrix.Rd b/man/genesets_as_matrix.Rd deleted file mode 100644 index 13a1909..0000000 --- a/man/genesets_as_matrix.Rd +++ /dev/null @@ -1,14 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_similarity.R -\name{genesets_as_matrix} -\alias{genesets_as_matrix} -\title{utility function that converts a genesets table into a sparse gene*geneset identity matrix} -\usage{ -genesets_as_matrix(genesets) -} -\arguments{ -\item{genesets}{genesets tibble. Must contain columns; "id", "genes", "ngenes"} -} -\description{ -utility function that converts a genesets table into a sparse gene*geneset identity matrix -} diff --git a/man/gg_color_hue.Rd b/man/gg_color_hue.Rd index 703f6b6..4b76028 100644 --- a/man/gg_color_hue.Rd +++ b/man/gg_color_hue.Rd @@ -9,6 +9,9 @@ gg_color_hue(n) \arguments{ \item{n}{number of colors} } +\value{ +a color code (string) +} \description{ https://stackoverflow.com/a/8197703 } diff --git a/man/go_find_parents.Rd b/man/go_find_parents.Rd deleted file mode 100644 index 748a448..0000000 --- a/man/go_find_parents.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/load_genesets_go.R -\name{go_find_parents} -\alias{go_find_parents} -\title{from a directed edgelist (child-to-parent) to a recursive lookup of the final parent term / root} -\usage{ -go_find_parents(child, parent) -} -\arguments{ -\item{child}{array of GO term IDs that represent children} - -\item{parent}{array of GO term IDs that represent respective parents} -} -\description{ -from a directed edgelist (child-to-parent) to a recursive lookup of the final parent term / root -} diff --git a/man/go_gene2go.Rd b/man/go_gene2go.Rd index cf7e5ac..d365cba 100644 --- a/man/go_gene2go.Rd +++ b/man/go_gene2go.Rd @@ -11,6 +11,9 @@ go_gene2go(f, taxid_filter = 9606) \item{taxid_filter}{taxonomy id, integer} } +\value{ +a tibble with columns; source, source_version, id, name, genes, ngenes +} \description{ -note that it lacks parent/child relations, so from this file we only learn 'direct annotations' +note that this file lacks parent/child relations, so we only learn 'direct annotations' } diff --git a/man/go_obo.Rd b/man/go_obo.Rd index a0eeea3..ee6c517 100644 --- a/man/go_obo.Rd +++ b/man/go_obo.Rd @@ -13,6 +13,11 @@ go_obo(f, rename_namespace = TRUE, remove_obsolete = TRUE) \item{remove_obsolete}{boolean; remove obsoleted terms?} } +\value{ +tibble with ontology terms and their relations +} \description{ note that we remove links between GO terms that are across GO domains (e.g. no CC to MF relations) +The only supported relations are those that match this regex; +\code{"^(is_a:|relationship: part_of|relationship: regulates|relationship: positively_regulates|relationship: negatively_regulates)"} } diff --git a/man/goat.Rd b/man/goat.Rd index 57f6fbf..c707e80 100644 --- a/man/goat.Rd +++ b/man/goat.Rd @@ -1,9 +1,24 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/goat.R +% Please edit documentation in R/goat-package.R \docType{package} \name{goat} +\alias{goat-package} \alias{goat} -\title{goat: Perform geneset enrichment and overrepresentation analyses using various algorithms, including GOAT.} +\title{goat: Gene Set Analysis Using the Gene Set Ordinal Association Test} \description{ -TODO: package description +Perform gene set enrichment analyses using the +Gene set Ordinal Association Test (GOAT) algorithm +and visualize your results. } +\seealso{ +Useful links: +\itemize{ + \item \url{https://github.com/ftwkoopmans/goat/} +} + +} +\author{ +\strong{Maintainer}: Frank Koopmans \email{ftwkoopmans@gmail.com} (\href{https://orcid.org/0000-0002-4973-5732}{ORCID}) + +} +\keyword{internal} diff --git a/man/goat_apply_precomputed_null.Rd b/man/goat_apply_precomputed_null.Rd deleted file mode 100644 index da2c8ec..0000000 --- a/man/goat_apply_precomputed_null.Rd +++ /dev/null @@ -1,19 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/test_genesets_goat.R -\name{goat_apply_precomputed_null} -\alias{goat_apply_precomputed_null} -\title{Compute the parameters of a skew-normal distribution, given a previously fitted polynomial function over geneset sizes} -\usage{ -goat_apply_precomputed_null(usize, par) -} -\arguments{ -\item{usize}{integer vector of geneset sizes} - -\item{par}{result from \code{goat_nulldistribution_function}} -} -\value{ -data.frame with columns; size, mu, sigma, xi -} -\description{ -Compute the parameters of a skew-normal distribution, given a previously fitted polynomial function over geneset sizes -} diff --git a/man/goat_example_datasets.Rd b/man/goat_example_datasets.Rd deleted file mode 100644 index af2d57c..0000000 --- a/man/goat_example_datasets.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/data.R -\docType{data} -\name{goat_example_datasets} -\alias{goat_example_datasets} -\title{Previously published datasets that were used as examples in the GOAT manuscript} -\format{ -\subsection{\code{goat_example_datasets}}{ - -a list of data.frames that represent differential expression analysis from high-throughput datasets -} -} -\source{ -The name of each dataset in \code{goat_example_datasets} contains the respective PubMed identifier -} -\usage{ -goat_example_datasets -} -\description{ -Previously published datasets that were used as examples in the GOAT manuscript -} -\keyword{datasets} diff --git a/man/goat_logo.Rd b/man/goat_logo.Rd index c42ba93..de2510e 100644 --- a/man/goat_logo.Rd +++ b/man/goat_logo.Rd @@ -6,9 +6,9 @@ \usage{ goat_logo() } +\value{ +package logo as a string +} \description{ ASCII logo for this package } -\examples{ -cat(goat_logo()) -} diff --git a/man/goat_nulldistribution_function.Rd b/man/goat_nulldistribution_function.Rd deleted file mode 100644 index 8987972..0000000 --- a/man/goat_nulldistribution_function.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/goat_nulldistributions.R -\name{goat_nulldistribution_function} -\alias{goat_nulldistribution_function} -\title{Estimate null distribution parameters for a given genelist length as a polynomial function of geneset sizes} -\usage{ -goat_nulldistribution_function( - genelist_N, - niter = 5e+05, - return_fit_objects = FALSE, - verbose = FALSE -) -} -\arguments{ -\item{genelist_N}{genelist length. Integer value between 100 and 50000} - -\item{niter}{number of bootstrap iterations that should be performed for generating empirical null distributions. Note that setting a large number will dramatically increase the RAM usage of this function! Integer value between 10000 and 5000000} - -\item{return_fit_objects}{should the 'fit' object, which can be used as input for \code{predict}, be included in results? boolean value, TRUE or FALSE, default; FALSE} - -\item{verbose}{should plots be created that describe the accuracy of fitting sd and xi? boolean value, TRUE or FALSE, default; FALSE} -} -\description{ -\enumerate{ -\item For a range of geneset sizes, from smallest (10) to largest (genelist length / 2), generate permutation-based null distributions and estimate their skew-normal parameters mean, sd, xi -\item Fit monotonic polynomial functions across geneset sizes that predict sd or xi -} -} diff --git a/man/goat_nulldistribution_function_generator.Rd b/man/goat_nulldistribution_function_generator.Rd deleted file mode 100644 index 57e1c59..0000000 --- a/man/goat_nulldistribution_function_generator.Rd +++ /dev/null @@ -1,40 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/goat_nulldistributions.R -\name{goat_nulldistribution_function_generator} -\alias{goat_nulldistribution_function_generator} -\title{Wrapper function for goat_nulldistribution_function, typically used to generate the Rdata file with precomputed null distribution that is bundled with GOAT} -\usage{ -goat_nulldistribution_function_generator( - all_geneset_sizes, - niter, - dir_output, - overwrite = TRUE, - verbose = FALSE -) -} -\arguments{ -\item{all_geneset_sizes}{vector of geneset sizes; integer values larger than 10 and smaller than half the genelist length (gene_scores length)} - -\item{niter}{see \code{goat_nulldistribution_function}} - -\item{dir_output}{the path to an existing directory where temporary and result files will be stored} - -\item{overwrite}{should existing files be overwritten? When setting this to FALSE, be mindful that you should manually clear cached files in between analyses !} - -\item{verbose}{see \code{goat_nulldistribution_function}} -} -\description{ -Wrapper function for goat_nulldistribution_function, typically used to generate the Rdata file with precomputed null distribution that is bundled with GOAT -} -\examples{ -\dontrun{ -# example 1; precompute parameters for a huge range of genesets, 500k permuations -goat_nulldistribution_function_generator( - seq(from=100, to=20000, by=1), 500000, "C:/temp/", overwrite=TRUE, verbose=TRUE -) -# example 2; 2million permutations, a few very different genelist lengths; review QC plots -goat_nulldistribution_function_generator( - c(100, 500, 1000, 5000, 15000), 2000000, "C:/temp/", overwrite=TRUE, verbose=TRUE -) -} -} diff --git a/man/goat_nulldistribution_independent.Rd b/man/goat_nulldistribution_independent.Rd deleted file mode 100644 index f537123..0000000 --- a/man/goat_nulldistribution_independent.Rd +++ /dev/null @@ -1,23 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/goat_nulldistributions.R -\name{goat_nulldistribution_independent} -\alias{goat_nulldistribution_independent} -\title{Estimate null distribution parameters for a given set of geneset sizes and an array of gene scores (from genelist)} -\usage{ -goat_nulldistribution_independent(geneset_sizes, gene_scores, niter, verbose) -} -\arguments{ -\item{geneset_sizes}{vector of geneset sizes; integer values larger than 10 and smaller than half the genelist length (gene_scores length)} - -\item{gene_scores}{gene score vector; these values are assumed to be the 'universe' of all possible gene-level scores. These are used to compute geneset score null distributions} - -\item{niter}{number of bootstrap iterations that should be performed for generating empirical null distributions. Note that setting a large number will dramatically increase the RAM usage of this function! Integer value between 10000 and 5000000} - -\item{verbose}{boolean, plot 10 null distribution histograms augmented with fitted skew-normal distributions} -} -\value{ -data.frame with columns; genelist_N, size, mu, sigma, xi -} -\description{ -Estimate null distribution parameters for a given set of geneset sizes and an array of gene scores (from genelist) -} diff --git a/man/goat_nulldistributions.Rd b/man/goat_nulldistributions.Rd index 065aad0..391e2a1 100644 --- a/man/goat_nulldistributions.Rd +++ b/man/goat_nulldistributions.Rd @@ -7,13 +7,13 @@ \format{ \subsection{\code{goat_nulldistributions}}{ -precomputed null distribution parameters, used by goat to efficiently perform geneset testing without bootstrapping +a data.frame with precomputed GOAT null distribution parameters } } \usage{ goat_nulldistributions } \description{ -Precomputed parameters used by the GOAT algorithm +there parameters are used by goat to efficiently perform geneset testing without bootstrapping } \keyword{datasets} diff --git a/man/goat_print_version.Rd b/man/goat_print_version.Rd index 4b25d85..fc58b7b 100644 --- a/man/goat_print_version.Rd +++ b/man/goat_print_version.Rd @@ -6,9 +6,9 @@ \usage{ goat_print_version() } +\value{ +prints to console without returning a value +} \description{ Print package version and logo to console } -\examples{ -goat_print_version() -} diff --git a/man/goat_testgene_score_matrix.Rd b/man/goat_testgene_score_matrix.Rd deleted file mode 100644 index 4bbee8a..0000000 --- a/man/goat_testgene_score_matrix.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/test_genesets_goat.R -\name{goat_testgene_score_matrix} -\alias{goat_testgene_score_matrix} -\title{From effectsize and pvalue in the genelist table to a matrix of 'rank transformed gene scores'} -\usage{ -goat_testgene_score_matrix(genelist, score_type) -} -\arguments{ -\item{genelist}{see \code{test_genesets_goat_precomputed}} - -\item{score_type}{see \code{test_genesets_goat_precomputed}} -} -\value{ -gene*score_type matrix where rows are aligned with the rows of the genelist table -} -\description{ -Depending on the score_type, we sort the genelist by pvalue or effectsize (up/down/absolute) and -then compute gene scores. Note that it is common for genelists to have many genes with \code{pvalue=1}, -which result in ties when sorting. We break ties for pvalue sorting by absolute effectsizes -(if available) and we break ties for (up/down/absolute) effectsizes by pvalues (if available). -Finally, we break any remaining ties by sorting the gene column to ensure stable sorting behaviour -under all circumstances. - -Technical note; after upstream input validation, we're assured this will yield a non-empty matrix with at least 1 column -} diff --git a/man/goat_version.Rd b/man/goat_version.Rd index 6bdbed1..c53596d 100644 --- a/man/goat_version.Rd +++ b/man/goat_version.Rd @@ -6,6 +6,9 @@ \usage{ goat_version() } +\value{ +package version as a string +} \description{ simple wrapper around utils::packageVersion() } diff --git a/man/hgnc_lookuptable.Rd b/man/hgnc_idmap_table.Rd similarity index 81% rename from man/hgnc_lookuptable.Rd rename to man/hgnc_idmap_table.Rd index 4d41af0..4f11562 100644 --- a/man/hgnc_lookuptable.Rd +++ b/man/hgnc_idmap_table.Rd @@ -1,13 +1,13 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/gene_idmapping.R -\name{hgnc_lookuptable} -\alias{hgnc_lookuptable} +\name{hgnc_idmap_table} +\alias{hgnc_idmap_table} \title{Parse HGNC gene identifier lookup table that was downloaded from genenames.org into a table with HGNC ID, symbol, synonym (NA if unavailable), entrez ID} \usage{ -hgnc_lookuptable(f) +hgnc_idmap_table(filename) } \arguments{ -\item{f}{full path to the downloaded table (expected to be tsv format)} +\item{filename}{full path to the downloaded table (expected to be tsv format, typically has .txt or .tsv file extension)} } \value{ a long-format table with columns; hgnc_id, hgnc_symbol, type, value diff --git a/man/lighten_color.Rd b/man/lighten_color.Rd index fa08bfa..8c84dee 100644 --- a/man/lighten_color.Rd +++ b/man/lighten_color.Rd @@ -11,6 +11,9 @@ lighten_color(color, frac = 0.1) \item{frac}{fraction of white; >0 and <1} } +\value{ +adjusted value for input \code{color} +} \description{ naively lighten a color by mixing in white } diff --git a/man/load_genesets_gmtfile.Rd b/man/load_genesets_gmtfile.Rd index 8ee75bc..f5b4c77 100644 --- a/man/load_genesets_gmtfile.Rd +++ b/man/load_genesets_gmtfile.Rd @@ -24,10 +24,7 @@ filename should be something like "c2.cp.kegg.v2023.1.Hs.entrez.gmt" } \examples{ -\dontrun{ - genesets_asis = load_genesets_gmtfile( - filename = "C:/DATA/c2.cp.kegg.v2023.1.Hs.entrez.gmt", - label = "KEGG" - ) -} + # TODO: update the filename to your downloaded file + f = "C:/DATA/c2.cp.kegg.v2023.1.Hs.entrez.gmt" + if(file.exists(f)) genesets_asis = load_genesets_gmtfile(f, label = "KEGG") } diff --git a/man/load_genesets_go_bioconductor.Rd b/man/load_genesets_go_bioconductor.Rd index df8c5db..3a3b045 100644 --- a/man/load_genesets_go_bioconductor.Rd +++ b/man/load_genesets_go_bioconductor.Rd @@ -22,8 +22,3 @@ but the GO database version returned by this function is tied to the version of The actual GO database version that is retrieved is returned by this function in the \code{source_version} column. } -\examples{ -\dontrun{ - genesets_asis = load_genesets_go_bioconductor() -} -} diff --git a/man/load_genesets_go_fromfile.Rd b/man/load_genesets_go_fromfile.Rd index 2754e0c..2f1c368 100644 --- a/man/load_genesets_go_fromfile.Rd +++ b/man/load_genesets_go_fromfile.Rd @@ -11,9 +11,9 @@ load_genesets_go_fromfile( ) } \arguments{ -\item{file_gene2go}{gene2go file from NCBI. Also works with the gzipped file gene2go.gz} +\item{file_gene2go}{full path to the gene2go file from NCBI. Also works with the gzipped file gene2go.gz} -\item{file_goobo}{OBO file from geneontology.org} +\item{file_goobo}{full path to the OBO file from geneontology.org} \item{include_child_annotations}{boolean; include annotations against child terms? In most situations, TRUE (default) is the desired setting} } @@ -21,14 +21,29 @@ load_genesets_go_fromfile( table with columns; source (character), source_version (character), id (character), name (character), genes (list), ngenes (int) } \description{ +This function is used to load Gene Ontology (GO) genesets from files that you +manually downloaded from the links below. This enables the use of the latest data +from GO (in contrast, Bioconductor GO data may lag behind current data considerably). +To construct genesets from available raw data, download the "gene2go" file +(the gene annotations) from below NCBI link and download the GO OBO +(ontology terms and relations to respective parent/child terms) from below +geneontology.org link. Provide the full path to the downloaded file to this function. +Both "gzipped" and "uncompressed" files are supported. + +We encourage you to rename the files after your downloaded them such that +the date of download in incorporated; this ensures you can always keep track of +the GO database version that was used! For example, rename the downloaded +"gene2go.gz" file to "gene2go_2024-01-31.gz". + Download link for gene2go file; https://ftp.ncbi.nih.gov/gene/DATA/gene2go.gz + Download link for gene ontology OBO file; http://current.geneontology.org/ontology/go.obo } \examples{ -\dontrun{ - genesets_asis = load_genesets_go_fromfile( - file_gene2go = "C:/DATA/download_2024-01-01/gene2go", - file_goobo = "C:/DATA/download_2024-01-01/go.obo" - ) -} + # TODO: update the filenames to your downloaded files + file_gene2go = "C:/DATA/gene2go_2024-01-01.gz" + file_goobo = "C:/DATA/go_2024-01-01.obo" + if(file.exists(file_gene2go) && file.exists(file_goobo)) { + genesets_asis = load_genesets_go_fromfile(file_gene2go, file_goobo) + } } diff --git a/man/load_genesets_syngo.Rd b/man/load_genesets_syngo.Rd index 7ce6a05..11d00b2 100644 --- a/man/load_genesets_syngo.Rd +++ b/man/load_genesets_syngo.Rd @@ -7,7 +7,7 @@ load_genesets_syngo(filename, gene_database = "entrez") } \arguments{ -\item{filename}{input file for this function should be the full path name for "syngo_ontologies.xlsx" from a SynGO bulk download} +\item{filename}{full path to the "syngo_ontologies.xlsx" file that was extracted from a SynGO bulk download ZIP archive} \item{gene_database}{gene IDs to return. must be any of; "entrez" (default), "hgnc", "ensembl"} } @@ -24,9 +24,7 @@ Workflow; } } \examples{ -\dontrun{ - genesets_asis = load_genesets_syngo( - "C:/DATA/SynGO_bulk_download_release_20210225/syngo_ontologies.xlsx" - ) -} + # TODO: update the filename to your downloaded file + f = "C:/DATA/SynGO_bulk_download_release_20210225/syngo_ontologies.xlsx" + if(file.exists(f)) genesets_asis = load_genesets_syngo(f) } diff --git a/man/methods_text.Rd b/man/methods_text.Rd deleted file mode 100644 index 490eb2f..0000000 --- a/man/methods_text.Rd +++ /dev/null @@ -1,18 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/save_genesets.R -\name{methods_text} -\alias{methods_text} -\title{create M&M text} -\usage{ -methods_text(x, genelist, settings) -} -\arguments{ -\item{x}{see \code{save_genesets()}} - -\item{genelist}{see \code{save_genesets()}} - -\item{settings}{settings attribute attached to the genesets result table} -} -\description{ -create M&M text -} diff --git a/man/minlog10_fixzero.Rd b/man/minlog10_fixzero.Rd index db2e7e4..159a7e3 100644 --- a/man/minlog10_fixzero.Rd +++ b/man/minlog10_fixzero.Rd @@ -11,18 +11,19 @@ minlog10_fixzero(x, limit = 2.22e-16) \item{limit}{value to replace zero's in \code{x} with. Set NA to replace zero's in \code{x} with the smallest finite value in \code{x} (if there is none, defaults to 2.22e-16)} } +\value{ +input parameter \code{x} transformed to -log10 +} \description{ -log10 transform a vector of p-values, replacing zeros with some limit/threshold } \examples{ -\dontrun{ pval = c(0, 10^-6, 0.001, 0.01, 1, NA, -Inf, Inf, NaN) cbind( input = pval, # default; replace zeros with typical R machine precision for doubles minlog10_default = minlog10_fixzero(pval), # alternatively, replace zero with lowest non-zero pvalue in input - minlog10_limit_from_data minlog10_fixzero(pval, limit = NA) + minlog10_limit_from_data = minlog10_fixzero(pval, limit = NA) ) } -} diff --git a/man/nested_aggregate_child_stats.Rd b/man/nested_aggregate_child_stats.Rd deleted file mode 100644 index d38703a..0000000 --- a/man/nested_aggregate_child_stats.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_ontology.R -\name{nested_aggregate_child_stats} -\alias{nested_aggregate_child_stats} -\title{For each element in a DAG, count the number of recursive child terms and those that are on shortlist} -\usage{ -nested_aggregate_child_stats(x, shortlist) -} -\arguments{ -\item{x}{list representing a nested DAG} - -\item{shortlist}{vector of element IDs} -} -\description{ -For each element in a DAG, count the number of recursive child terms and those that are on shortlist -} diff --git a/man/nested_find_equivalent_child_recursive.Rd b/man/nested_find_equivalent_child_recursive.Rd deleted file mode 100644 index 771d2ac..0000000 --- a/man/nested_find_equivalent_child_recursive.Rd +++ /dev/null @@ -1,14 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_ontology.R -\name{nested_find_equivalent_child_recursive} -\alias{nested_find_equivalent_child_recursive} -\title{Recursively replace a DAG element by its children while the child has the same gene count as the parent} -\usage{ -nested_find_equivalent_child_recursive(x) -} -\arguments{ -\item{x}{list representing a nested DAG} -} -\description{ -Recursively replace a DAG element by its children while the child has the same gene count as the parent -} diff --git a/man/nested_find_level1_children.Rd b/man/nested_find_level1_children.Rd deleted file mode 100644 index fa334b8..0000000 --- a/man/nested_find_level1_children.Rd +++ /dev/null @@ -1,18 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_ontology.R -\name{nested_find_level1_children} -\alias{nested_find_level1_children} -\title{Traverse DAG and recursively split parent term into child terms until none are larger than N genes} -\usage{ -nested_find_level1_children(obj, threshold, result = list()) -} -\arguments{ -\item{obj}{list representing a nested DAG} - -\item{threshold}{stop if \code{obj$ngenes <= threshold}} - -\item{result}{list of resulting elements (when calling this function, use default empty list)} -} -\description{ -Traverse DAG and recursively split parent term into child terms until none are larger than N genes -} diff --git a/man/ontology_data_structures.Rd b/man/ontology_data_structures.Rd deleted file mode 100644 index a24caf2..0000000 --- a/man/ontology_data_structures.Rd +++ /dev/null @@ -1,18 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/simplify_ontology.R -\name{ontology_data_structures} -\alias{ontology_data_structures} -\title{Compute tree and treemap data structures from geneset tables that include parent/child links} -\usage{ -ontology_data_structures(geneset_ids, genesets, genesets_test_result) -} -\arguments{ -\item{geneset_ids}{vector of geneset identifiers} - -\item{genesets}{entire geneset table; typically the complete GO database} - -\item{genesets_test_result}{geneset testing results; the output from \code{test_genesets()}} -} -\description{ -Compute tree and treemap data structures from geneset tables that include parent/child links -} diff --git a/man/padjust_genesets.Rd b/man/padjust_genesets.Rd index a51e187..9389bb6 100644 --- a/man/padjust_genesets.Rd +++ b/man/padjust_genesets.Rd @@ -20,6 +20,9 @@ padjust_genesets( \item{correct_sources}{apply Bonferroni adjustment to all p-values according to the number of geneset sources that were tested. Boolean parameter, set TRUE to enable (default) or FALSE to disable} } +\value{ +updated \code{genesets} table +} \description{ Adjust p-values for all genesets, grouped by 'source' then adjust for the number of 'sources' } diff --git a/man/parameters_prettyprint_length1.Rd b/man/parameters_prettyprint_length1.Rd deleted file mode 100644 index d4cea21..0000000 --- a/man/parameters_prettyprint_length1.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/utils.R -\name{parameters_prettyprint_length1} -\alias{parameters_prettyprint_length1} -\title{return a prettyprint string of all length 1 parameters that are string/numeric/logical/NA} -\usage{ -parameters_prettyprint_length1(...) -} -\arguments{ -\item{...}{arbitrary set of parameters} -} -\description{ -return a prettyprint string of all length 1 parameters that are string/numeric/logical/NA -} -\examples{ -\dontrun{ - parameters_prettyprint_length1(test1=1:2, test2=matrix(1:4,2,2), test3=data.frame(a=1), - test4=c(a=1), test5=1, test6="a", test7=NA, test8=Inf) -} -} diff --git a/man/partition_genes.Rd b/man/partition_genes.Rd index 67d9851..62e2e68 100644 --- a/man/partition_genes.Rd +++ b/man/partition_genes.Rd @@ -29,17 +29,37 @@ partition_genes( \item{topn}{number of rows in \code{genes} tibble to select (must provide exactly 1 parameter for filtering, either \code{cutoff}, \code{fraction} or \code{topn})} } +\value{ +input table \code{genes} with results in the "signif" column +} \description{ This can be convenient to prepare the significant/test/foreground set for classical ORA, -e.g. \code{test_genesets()} with parameter \code{method = "fisherexact"}. +e.g. \code{test_genesets()} with parameter \code{method = "fisherexact"}. Note that the GOAT geneset +enrichment algorithm does not use data in the 'signif' column of the input genelist. } \examples{ -\dontrun{ - # significant hits - genelist = partition_genes(genelist, col="pvalue_adjust", decreasing=F, cutoff=0.01) - # abs(effectsize) >= 4 - genelist = partition_genes(genelist, col="effectsize", decreasing=T, use_abs=T, cutoff=4) - # top 10\% 'best' p-values - genelist = partition_genes(genelist, col="pvalue", decreasing=F, fraction = 0.1) +\donttest{ +# note: this example will download 1 files of approx 4MB + +# store the downloaded files in the following directory. Here, the temporary file +# directory is used. Alternatively, consider storing this data in a more permanent location. +# e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +output_dir = tempdir() + +# Download an example gene list, i.e. one of the datasets analyzed in the GOAT manuscript. +datasets = download_goat_manuscript_data(output_dir) +genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` + +# example 1: significant hits +genelist = partition_genes(genelist, col="pvalue_adjust", decreasing=FALSE, cutoff=0.01) +cat(sum(genelist$signif), "/", nrow(genelist), "are signif\n") + +# example 2: abs(effectsize) >= 5 +genelist = partition_genes(genelist, col="effectsize", decreasing=TRUE, use_abs=TRUE, cutoff=5) +cat(sum(genelist$signif), "/", nrow(genelist), "are signif\n") + +# example 3: top 10\% 'best' p-values +genelist = partition_genes(genelist, col="pvalue", decreasing=FALSE, fraction = 0.1) +cat(sum(genelist$signif), "/", nrow(genelist), "are signif\n") } } diff --git a/man/plot_heatmap.Rd b/man/plot_heatmap.Rd index c58c004..b161553 100644 --- a/man/plot_heatmap.Rd +++ b/man/plot_heatmap.Rd @@ -6,31 +6,54 @@ \usage{ plot_heatmap( x, - output_dir = NA, - heatmap_colors = grDevices::hcl.colors(100, "Viridis", rev = F), + output_dir, + colors = grDevices::hcl.colors(100, "Viridis", rev = FALSE), fontsize = 10 ) } \arguments{ \item{x}{result from \code{cluster_genesets()}} -\item{output_dir}{full path to output directory. Set to NA to directly show the figures instead of writing them to file} +\item{output_dir}{set to NA to directly show the figures instead of writing them to file. +Otherwise, this is the full path to the directory where the downloaded files should be stored. Directory is created if it does not exist. +e.g. \code{output_dir="~/data"} on unix systems, \code{output_dir="C:/data"} on Windows, or set to \code{output_dir=getwd()} to write output to the current working directory} -\item{heatmap_colors}{a vector of 100 colors to be used for the heatmap (101 breaks are computed between 0 and the max value in the distance matrix)} +\item{colors}{a vector of 100 colors to be used for the heatmap (101 breaks are computed between 0 and the max value in the distance matrix)} \item{fontsize}{parameter sent to pheatmap::pheatmap(); control the size of labels in the plot, defaults to 10. Note that you can also change the plot device size, see examples} } +\value{ +does not return a value, plots are printed to device or files depending on \code{output_dir} parameter +} \description{ plot the geneset similarity matrix as a heatmap } \examples{ -\dontrun{ - # try various color palettes - plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Viridis", rev = F)) - plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Inferno", rev = F)) - plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Lajolla", rev = T)) - plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Mako", rev = F)) - plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Turku", rev = T)) - plot_heatmap(plot_heatmapclusters, heatmap_colors = hcl.colors(100, "Grays", rev = T)) +\donttest{ +# note; this example downloads data when first run, and typically takes ~60seconds + +# store the downloaded files in the following directory. Here, the temporary file +# directory is used. Alternatively, consider storing this data in a more permanent location. +# e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +output_dir = tempdir() + +## first run the default example from test_genesets() to obtain geneset results +datasets = download_goat_manuscript_data(output_dir) +genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +genesets_asis = download_genesets_goatrepo(output_dir) +genesets_filtered = filter_genesets(genesets_asis, genelist) +result = test_genesets(genesets_filtered, genelist, method = "goat", + score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) + +# prior to running this function, cluster the genesets +clusters = cluster_genesets(result, genelist) + +# use the plot heatmap function and try various color palettes +plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Viridis", rev = FALSE)) +plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Inferno", rev = FALSE)) +plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Lajolla", rev = TRUE)) +plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Mako", rev = FALSE)) +plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Turku", rev = TRUE)) +plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Grays", rev = TRUE)) } } diff --git a/man/plot_lollipop.Rd b/man/plot_lollipop.Rd index 0e3809d..15a0599 100644 --- a/man/plot_lollipop.Rd +++ b/man/plot_lollipop.Rd @@ -6,7 +6,7 @@ \usage{ plot_lollipop( x, - output_dir = NA, + output_dir, only_reduced = FALSE, plot_type = "lollipop", show_pvalue = FALSE, @@ -24,7 +24,9 @@ plot_lollipop( \arguments{ \item{x}{results from function \code{test_genesets}} -\item{output_dir}{full path to output directory. Set to NA to directly show the figures instead of writing them to file} +\item{output_dir}{set to NA to directly show the figures instead of writing them to file. +Otherwise, this is the full path to the directory where the downloaded files should be stored. Directory is created if it does not exist. +e.g. \code{output_dir="~/data"} on unix systems, \code{output_dir="C:/data"} on Windows, or set to \code{output_dir=getwd()} to write output to the current working directory} \item{only_reduced}{only show the reduced/summarized set of significant genesets. This requires that you first applied the \code{reduce_genesets} function} @@ -52,30 +54,48 @@ Rows with other values are assumed NA and colored as grey.} \item{padj_cutoff}{adjusted pvalue cutoff for terms shown in the plot. If set to NA (default), all significant genesets are used (i.e. 'signif' column in the input geneset table)} } +\value{ +if \code{output_dir} is \code{NA}, a list of ggplot2 objects. Otherwise, write plots to file and do not return any value +} \description{ Lollipop chart or barplot visualization of geneset enrichment testing results } \examples{ -\dontrun{ - # generate lollipop charts for each GO domain (CC/BP/MF), with geneset -log10 - # adjusted p-value on the x-axis and color-coding by geneset up/down-regulation - plot_lollipop( - result, output_dir = getwd(), plot_type = "lollipop", topn = 50, - score_xaxis = "minlogp", score_color = "updown" - ) - - # alternatively, as a barplot - plot_lollipop( - result, output_dir = getwd(), plot_type = "barplot", topn = 50, - score_xaxis = "minlogp", score_color = "updown" - ) - - # alternatively, color-code genesets by enrichment of significant genes using - # parameter `score_color="oddsratio"` . See further `score_geneset_oddsratio` - # function documentation for definition/computation of this score. - plot_lollipop( - result, output_dir = getwd(), plot_type = "lollipop", topn = 50, - score_xaxis = "minlogp", score_color = "oddsratio" - ) +\donttest{ +# note; this example downloads data when first run, and typically takes ~60seconds + +# store the downloaded files in the following directory. Here, the temporary file +# directory is used. Alternatively, consider storing this data in a more permanent location. +# e.g. output_dir="~/data/go" on unix systems or output_dir="C:/data/go" on Windows +output_dir = tempdir() + +## first run the default example from test_genesets() to obtain geneset results +datasets = download_goat_manuscript_data(output_dir) +genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +genesets_asis = download_genesets_goatrepo(output_dir) +genesets_filtered = filter_genesets(genesets_asis, genelist) +result = test_genesets(genesets_filtered, genelist, method = "goat", + score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) + +# generate lollipop charts for each GO domain (CC/BP/MF), with geneset -log10 +# adjusted p-value on the x-axis and color-coding by geneset up/down-regulation +plot_lollipop( + result, output_dir, plot_type = "lollipop", topn = 50, + score_xaxis = "minlogp", score_color = "updown" +) + +# alternatively, as a barplot +plot_lollipop( + result, output_dir, plot_type = "barplot", topn = 50, + score_xaxis = "minlogp", score_color = "updown" +) + +# alternatively, color-code genesets by enrichment of significant genes using +# parameter `score_color="oddsratio"` . See further `score_geneset_oddsratio` +# function documentation for definition/computation of this score. +plot_lollipop( + result, output_dir, plot_type = "lollipop", topn = 50, + score_xaxis = "minlogp", score_color = "oddsratio" +) } } diff --git a/man/plot_network.Rd b/man/plot_network.Rd index 219f9ba..0a33bc0 100644 --- a/man/plot_network.Rd +++ b/man/plot_network.Rd @@ -10,7 +10,8 @@ plot_network( show_clusters = TRUE, show_text = FALSE, topn_edges = 5, - clr_default = "#29b6f6" + clr_default = "#29b6f6", + node_color_palette = goat::gg_color_hue ) } \arguments{ @@ -25,6 +26,11 @@ plot_network( \item{topn_edges}{topN edges to retain per geneset (typically 5~8)} \item{clr_default}{default color for the network, used only when \code{show_clusters} is set to \code{FALSE}} + +\item{node_color_palette}{function with 1 parameter, N, that returns N colors (default is goat function \code{gg_color_hue})} +} +\value{ +a ggplot2 object } \description{ plot geneset distance matrix as a network diff --git a/man/plot_volcano.Rd b/man/plot_volcano.Rd new file mode 100644 index 0000000..b581c94 --- /dev/null +++ b/man/plot_volcano.Rd @@ -0,0 +1,84 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plot_volcano.R +\name{plot_volcano} +\alias{plot_volcano} +\title{For each provided geneset, a volcano plot of all genelist log2fc and p-values with respective geneset constituents highlighted} +\usage{ +plot_volcano( + x, + genelist, + plot = TRUE, + topn_labels = 0, + color_default = "#B0B0B0", + color_highlight = "#ef5350", + color_label = "#000000", + pointsize = 2, + pointalpha = 0.75, + labelsize = 7 +) +} +\arguments{ +\item{x}{a subset of the results from \code{test_genesets} function, see example} + +\item{genelist}{input genelist, must contain columns 'gene', 'log2fc' and 'pvalue_adjust' (not! log transformed). If parameter topn_labels is provided, also include a character column 'symbol' that contains gene names/symbols/labels} + +\item{plot}{if \code{TRUE}, will directly show the plots. if \code{FALSE}, returns a list of ggplot objects corresponding to rows in the input \code{result} parameter} + +\item{topn_labels}{for how many genes that overlap between genelist and a geneset should we plot the gene symbol? This requires a column 'symbol' in the genelist parameter (default: 0)} + +\item{color_default}{color for genes that are not part of a geneset (default: grey)} + +\item{color_highlight}{color used to highlight geneset constituents (default: red)} + +\item{color_label}{provided that topn_labels is set, this is the color of the text labels (default: black)} + +\item{pointsize}{size of the dots, this parameter is passed to geom_point (default: 2)} + +\item{pointalpha}{alpha of the dots, this parameter is passed to geom_point (default: 0.75)} + +\item{labelsize}{provided that topn_labels is set, this is the text size (in pt) for the labels (default: 7)} +} +\value{ +if \code{plot==FALSE}, a list of ggplot2 objects. Otherwise, does not return any value +} +\description{ +For each provided geneset, a volcano plot of all genelist log2fc and p-values with respective geneset constituents highlighted +} +\examples{ +\donttest{ +# note; this example downloads data when first run, and typically takes ~60seconds + +# store the downloaded files in the following directory. Here, the temporary file +# directory is used. Alternatively, consider storing this data in a more permanent location. +# e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +output_dir = tempdir() + +## first run the default example from test_genesets() to obtain geneset results +datasets = download_goat_manuscript_data(output_dir) +genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +genesets_asis = download_genesets_goatrepo(output_dir) +genesets_filtered = filter_genesets(genesets_asis, genelist) +result = test_genesets(genesets_filtered, genelist, method = "goat", + score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) + +## example 1; select top10 GO CC terms from the geneset testing results +result_subset = result |> filter(source == "GO_CC") |> arrange(pvalue) |> head(n = 10) +pdf(paste0(output_dir, "/volcano_CC_top10.pdf"), width = 4, height = 4) +plot_volcano(result_subset, genelist) +dev.off() + +## example 2;, select small genesets that are significant and have +## near-exclusive enrichment in either up up/down-regulated genes +# first, add geneset directionality scores to our results +result = score_geneset_directionality(result, genelist) +# next, subset the geneset result table +result_subset = result |> + filter(signif & ngenes <= 50 & abs(score_directionality_rank) > 0.6) |> + arrange(pvalue_adjust) +# finally, create plots. Note that the genelist contains a column 'symbol' +# which we use here to print labels for the topN genes per plotted geneset +pdf(paste0(output_dir, "/volcano_signif_ngenes50_directionality06.pdf"), width = 4, height = 4) +plot_volcano(result_subset, genelist, topn_labels = 10) +dev.off() +} +} diff --git a/man/psnorm_upper_tail.Rd b/man/psnorm_upper_tail.Rd deleted file mode 100644 index cf7c4c6..0000000 --- a/man/psnorm_upper_tail.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/util_stats.R -\name{psnorm_upper_tail} -\alias{psnorm_upper_tail} -\title{Probability function for skew normal distribution} -\usage{ -psnorm_upper_tail(q, mean = 0, sd = 1, xi = 1) -} -\arguments{ -\item{q}{numeric vector of quantiles} - -\item{mean}{location parameter} - -\item{sd}{scale parameter} - -\item{xi}{skewness parameter} -} -\description{ -Probability function for skew normal distribution -} diff --git a/man/rankscore.Rd b/man/rankscore.Rd index 53adb95..0696c64 100644 --- a/man/rankscore.Rd +++ b/man/rankscore.Rd @@ -25,12 +25,11 @@ rank^2 can yield huge values, e.g. a large genelist of N=50000 genes would imply Thus we rescale these scores between 0~1000 so downstream applications (e.g. sum of 10000 gene scores) don't explode to huge numbers. } \examples{ -\dontrun{ - x = data.frame(gene=c(1,2,3,4,5), pvalue=c(0.01, 1, 1, 0.1, 1), + x = data.frame(gene=c(1, 2, 3, 4, 5), + pvalue=c(0.01, 1, 1, 0.1, 1), effectsize=c(-2, 0.25, 0.5, 1, 0.25)) print(x, row.names = FALSE) - print(goat:::rankscore(x, sort1 = -1*x$pvalue, sort2 = abs(x$effectsize), + print(rankscore(x, sort1 = -1*x$pvalue, sort2 = abs(x$effectsize), sort3 = x$gene, colname="score") |> arrange(desc(score)), row.names = FALSE) } -} diff --git a/man/rankscore_fixed_order.Rd b/man/rankscore_fixed_order.Rd index b8ff24b..24e8d7b 100644 --- a/man/rankscore_fixed_order.Rd +++ b/man/rankscore_fixed_order.Rd @@ -9,6 +9,9 @@ rankscore_fixed_order(n) \arguments{ \item{n}{genelist length} } +\value{ +result of \code{(1:n)^2 / (n^2 / 1000)} +} \description{ Gene score array, from low to high scores } diff --git a/man/reduce_genesets.Rd b/man/reduce_genesets.Rd index 17c395e..e68aa93 100644 --- a/man/reduce_genesets.Rd +++ b/man/reduce_genesets.Rd @@ -27,6 +27,9 @@ Typical settings for this parameter are 0.1~0.5 (0.25 is default)} This parameter doesn't do anything if there are fewer than 5 "foreground genes" alltogether. Typical settings for this parameter are 0.75~0.95 (0.9 is default)} } +\value{ +the genesets table from the \code{clusters} parameter, with results in column "signif_and_reduced" +} \description{ Analyses are performed independently per 'source' of genesets. The result of this function is the geneset table with a newly appended column 'signif_and_reduced' } diff --git a/man/save_genesets.Rd b/man/save_genesets.Rd index 29d0713..a32622a 100644 --- a/man/save_genesets.Rd +++ b/man/save_genesets.Rd @@ -15,6 +15,9 @@ save_genesets(x, genelist, filename, arrange_genes = TRUE) \item{arrange_genes}{set to TRUE (default) to arrange the genelist table by best p-value on top (if column 'pvalue' exists), alternatively by descending absolute effectsize (if no pvalue but effectize is available). Set FALSE to use sorting of the genelist table as-is} } +\value{ +if \code{filename} is \code{NA}, returns the validated and formatted geneset result table. Otherwise, writes the table to file and does not return a value +} \description{ Works for any filtered geneset table generated by this package, e.g. results from any of these functions; \code{filter_genesets()}, \code{test_genesets()} or \code{simplify_genesets()}. diff --git a/man/score_geneset_directionality.Rd b/man/score_geneset_directionality.Rd new file mode 100644 index 0000000..17af048 --- /dev/null +++ b/man/score_geneset_directionality.Rd @@ -0,0 +1,57 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/score_genesets.R +\name{score_geneset_directionality} +\alias{score_geneset_directionality} +\title{Compute a score between -1 and 1 representing the proportion of up- or down-regulated genes for each geneset, weighted by gene effectsizes} +\usage{ +score_geneset_directionality(genesets, genelist) +} +\arguments{ +\item{genesets}{tibble with genesets, must contain columns 'source', 'id', 'genes'} + +\item{genelist}{tibble with genes, must contain columns 'gene', 'effectsize'} +} +\value{ +input \code{genesets} with results in 3 columns; +\code{score_directionality_rank} is the weighted gene score where gene values are the sign of their effectsize and weights are linearly proportional to their inverse ranks in the input genelist. +\code{score_directionality_rank2} is similar, but now using rank^2 gene weights to boost the influence of most-important genes in the input genelist. +\code{score_directionality_value} uses the absolute gene effectsizes as gene weights +Note that the latter is least robust as it depends on the scaling of input data! +} +\description{ +Importantly, the scope/utility of this score is limited to help users post-hoc filter for genesets that contain mostly up/down-regulated genes. +However, this might not coincide with the geneset pvalues / significance. For example, +genesets may exclusively contain genes with a positive effectsize but at the same time these can all be minor effects/values and thus the geneset +is not significant or less significant than other genesets with the exact same "directionality score". +For example, genesets may contain both up- and down-regulated genes but still be significant when testing with GOAT and using \code{score_type='effectsize'} + +The scores computed with this function can help in post-hoc interpretation of GOAT results to further narrow down all significant genesets to a +subset with strong directionality. For example, after \code{test_genesets()} we can filter the results for +A) significant genesets and B) that contain at most N genes and C) that are near-exclusively up/down-regulated. +Bringing this all together (also useful for other types of geneset testing, like ORA, score_type="pvalue", etc); +\code{result = test_genesets(genesets_filtered, genelist, method = "goat", score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05)} +\code{result = score_geneset_directionality(result, genelist)} +\code{result |> filter(signif & ngenes <= 100 & abs(score_directionality_rank) >= 0.95)} +} +\section{score definitions;}{ +geneset directionality score = weighted mean of respective genes, +where gene weights are 1 minus their relative rank in up/down-regulation (depending on negative/positive effectsize) +and the value for each gene is -1 or 1 depending on up/down-regulation (sign of effectsize). +} + +\section{pseudocode;}{ +\enumerate{ +\item gene values and weights +A) gene weight between 0 and 1 for the subset of upregulated genes / positive effectsizes; +} +\itemize{ +\item r = for the subset of genes with effectsize > 0, compute gene rank (1 = highest effectsize, N = smallest effectsize that is greater than zero) +\item weight = 1 - r/max(r) +B) analogous to (A) for the subset of genes with negative effectsize +C) result per gene: value = sign of its effectsize, weight = 0 if effectsize 0, otherwise respective weights from (A) or (B) +} +\enumerate{ +\item geneset score_directionality = weighted mean over values/weights of respective genes +} +} + diff --git a/man/score_geneset_oddsratio.Rd b/man/score_geneset_oddsratio.Rd index c8bc518..b77ee74 100644 --- a/man/score_geneset_oddsratio.Rd +++ b/man/score_geneset_oddsratio.Rd @@ -7,17 +7,19 @@ score_geneset_oddsratio(genesets, genelist) } \arguments{ -\item{genesets}{tibble with genesets, must contain columns 'source', 'id', 'genes'} +\item{genesets}{tibble with genesets, must contain columns 'source', 'id', 'ngenes', 'ngenes_signif'} -\item{genelist}{tibble with genes, must contain column 'gene', 'log2fc', 'pvalue'} +\item{genelist}{tibble with genes, must contain columns 'gene', 'signif'} +} +\value{ +input \code{genesets} with results in column "score_oddsratio" } \description{ gs_signif = number of significant genes in geneset G that intersect with user's genelist (i.e. foreground genes in G) gs_all = number of genes in geneset G that intersect with user's genelist (i.e. foreground+background genes in G) k_signif = total number of \emph{significant} genes in user's genelist k_all = total number of genes in user's genelist -} -\details{ + gs_signif/gs_all = ratio of foreground genes in geneset G k_signif/k_all = ratio of overall foreground genes (i.e. expected value for a random geneset) diff --git a/man/string_trunc_right.Rd b/man/string_trunc_right.Rd index 0649619..1ae7963 100644 --- a/man/string_trunc_right.Rd +++ b/man/string_trunc_right.Rd @@ -13,6 +13,9 @@ string_trunc_right(string, width, trim_left = FALSE) \item{trim_left}{instead of right trunc (default), do left instead} } +\value{ +truncated variant of input \code{string} +} \description{ replacement for stringr::trunc() so we don't need a package dependency for just 1 function (our code was adapter therefrom) } diff --git a/man/symbol_to_entrez.Rd b/man/symbol_to_entrez.Rd index a93d9b5..3db225b 100644 --- a/man/symbol_to_entrez.Rd +++ b/man/symbol_to_entrez.Rd @@ -9,18 +9,23 @@ symbol_to_entrez(x, hgnc) \arguments{ \item{x}{a data.table with a column symbol} -\item{hgnc}{HGNC lookup table from \code{hgnc_lookuptable()}} +\item{hgnc}{HGNC lookup table from \code{hgnc_idmap_table()}} +} +\value{ +entrez gene IDs are returned in the "gene" column of table \code{x}. Additionally, columns "entrez_id", "hgnc_id" and "hgnc_symbol" } \description{ Map the the symbol column in a table to HGNC human gene IDs by matching official gene symbols and synonyms } \examples{ -\dontrun{ # TODO: update the filename to your downloaded file - # download instructions @ `hgnc_lookuptable` - f = "C:/data/hgnc_complete_set.txt" - df = data.frame(symbol = c("vamp2", "STXBP1", "UNC18", NA, "PSD95", "NOT-A-GENE")) - hgnc = hgnc_lookuptable(f) - symbol_to_entrez(df, hgnc) -} + # download instructions in the documentation of `hgnc_idmap_table()` + f = "C:/DATA/HGNC/hgnc_complete_set.txt" + + if(file.exists(f)) { + df = data.frame(symbol = c("vamp2", "STXBP1", "UNC18", NA, "PSD95", "NOT-A-GENE")) + hgnc = hgnc_idmap_table(f) + df = symbol_to_entrez(df, hgnc) + print(df) + } } diff --git a/man/test_genesets.Rd b/man/test_genesets.Rd index adf3491..fe4e552 100644 --- a/man/test_genesets.Rd +++ b/man/test_genesets.Rd @@ -47,3 +47,32 @@ For example, if the input is a genesets table that contains GO_CC, GO_BP and GO_ Note that this is more rigorous than typical GO tools; hypothetically, one could split all GO_CC pathways into 1000 different databases/'sources' and then run enrichment testing. Consequently, the multiple testing burden is reduced if one doesn't adjust p-values for the number of 'sources' as we do here. } +\examples{ +\donttest{ +#' # note; this example downloads data when first run, and typically takes ~60seconds + +## Basic example for a complete GOAT workflow +# Downloads test data to your computer and stores it at current working directory +# Refer to the GitHub documentation for elaborate documentation and a worked example + +# store the downloaded files in the following directory. Here, the temporary file +# directory is used. Alternatively, consider storing this data in a more permanent location. +# e.g. output_dir="~/data/go" on unix systems or output_dir="C:/data/go" on Windows +output_dir = tempdir() + +# download an example gene list +datasets = download_goat_manuscript_data(output_dir) +genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` + +# download GO genesets +genesets_asis = download_genesets_goatrepo(output_dir) + +# filter genesets for sufficient overlap with the genelist, then apply GOAT +genesets_filtered = filter_genesets(genesets_asis, genelist) +result = test_genesets(genesets_filtered, genelist, method = "goat", + score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) + +# print first 10 rows of the result table +print(result |> select(source, name, ngenes, pvalue_adjust) |> utils::head(n=10)) +} +} diff --git a/man/test_genesets_fisherexact.Rd b/man/test_genesets_fisherexact.Rd index 1a37df0..980d610 100644 --- a/man/test_genesets_fisherexact.Rd +++ b/man/test_genesets_fisherexact.Rd @@ -22,6 +22,9 @@ This function 'prefilters' genesets, so beware that this will influence downstre \item{use_ease}{use a more conservative score coined by DAVID online tools @ https://david.ncifcrf.gov/helps/functional_annotation.html#fisher} } +\value{ +input \code{genesets} table with results in the "pvalue" column +} \description{ In most cases, it's more convenient to call the more generic \code{test_genesets} function which also deals with multiple-testing correction (per geneset source) @@ -33,14 +36,24 @@ Same as hypergeometric for non-EASE, but slower because stats::fisher.test isn't Only genesets with at least 1 significant gene are subjected to statistical testing (e.g. NA is returned for genesets without significant genes) } \examples{ -\dontrun{ - ### same results between Fisher-exact and hypergeometric tests - # < prepare genelist table first > - gs = load_genesets_go_bioconductor() - gsf = filter_genesets(gs, genelist, min_overlap = 10L, max_overlap = 1500L) - x_hg = test_genesets_hypergeometric(gsf, genelist, min_signif = 3L) - x_fe = test_genesets_fisherexact(gsf, genelist, min_signif = 3L) - all.equal(x_hg$pvalue, x_fe$pvalue) +\donttest{ +# note; this example downloads data when first run, and typically takes ~60seconds + +# store the downloaded files in the following directory. Here, the temporary file +# directory is used. Alternatively, consider storing this data in a more permanent location. +# e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +output_dir = tempdir() + +## first run the default example from test_genesets() to obtain input data +datasets = download_goat_manuscript_data(output_dir) +genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +genesets_asis = download_genesets_goatrepo(output_dir) +genesets_filtered = filter_genesets(genesets_asis, genelist) + +## example: same results between Fisher-exact and hypergeometric tests +result_hg = test_genesets_hypergeometric(genesets_filtered, genelist, require_nsignif = 3L) +result_fe = test_genesets_fisherexact(genesets_filtered, genelist, require_nsignif = 3L) +all.equal(result_hg$pvalue, result_fe$pvalue) } } \seealso{ diff --git a/man/test_genesets_goat.Rd b/man/test_genesets_goat.Rd deleted file mode 100644 index dc4fe2d..0000000 --- a/man/test_genesets_goat.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/test_genesets_goat.R -\name{test_genesets_goat} -\alias{test_genesets_goat} -\title{Implementation of the GOAT algorithm} -\usage{ -test_genesets_goat(genesets, genelist, score_type, nulldistribution_parameters) -} -\arguments{ -\item{genesets}{see \code{test_genesets_goat_precomputed}} - -\item{genelist}{see \code{test_genesets_goat_precomputed}} - -\item{score_type}{see \code{test_genesets_goat_precomputed}} - -\item{nulldistribution_parameters}{result from goat_nulldistribution_independent} -} -\description{ -Implementation of the GOAT algorithm -} diff --git a/man/test_genesets_goat_bootstrap.Rd b/man/test_genesets_goat_bootstrap.Rd index 62eaf92..a3d2a18 100644 --- a/man/test_genesets_goat_bootstrap.Rd +++ b/man/test_genesets_goat_bootstrap.Rd @@ -23,6 +23,9 @@ test_genesets_goat_bootstrap( \item{verbose}{boolean, create debug plots} } +\value{ +see \code{test_genesets_goat_precomputed} +} \description{ In typical use-cases, one applies \code{test_genesets()} instead with parameter \code{method="goat"} , which in turn will use \code{test_genesets_goat_precomputed} (and not this function). diff --git a/man/test_genesets_goat_fitfunction.Rd b/man/test_genesets_goat_fitfunction.Rd index 41a2fc3..8ab91da 100644 --- a/man/test_genesets_goat_fitfunction.Rd +++ b/man/test_genesets_goat_fitfunction.Rd @@ -23,6 +23,9 @@ test_genesets_goat_fitfunction( \item{verbose}{boolean, create debug plots} } +\value{ +see \code{test_genesets_goat_precomputed} +} \description{ In typical use-cases, one applies \code{test_genesets()} instead with parameter \code{method="goat"} , which in turn will use \code{test_genesets_goat_precomputed} (and not this function). diff --git a/man/test_genesets_goat_precomputed.Rd b/man/test_genesets_goat_precomputed.Rd index d174b0e..18ccbef 100644 --- a/man/test_genesets_goat_precomputed.Rd +++ b/man/test_genesets_goat_precomputed.Rd @@ -18,11 +18,66 @@ Option "effectsize_abs" uses values from the effectsize column in \code{genelist Option "effectsize_up" uses values from the effectsize column in \code{genelist} in a one-way test for enrichment; is a geneset enriched in up-regulated genes? (i.e. positive effectsize) Option "effectsize_down" uses values from the effectsize column in \code{genelist} in a one-way test for enrichment; is a geneset enriched in down-regulated genes? (i.e. negative effectsize)} } +\value{ +input \code{genesets} table with results in the "pvalue", "score_type" columns. +"zscore" column: +A standardized z-score is computed from geneset p-values + effectsize direction (up/down) if tested. +Importantly, we here return standardized z-scores because the GOAT geneset score (mean of gene scores) is relative to the respective geneset-size-matched null distributions (a skewed normal)! +In contrast, the standardized z-scores are comparable between genesets (as are the pvalues obviously). + +Only if either (or both) the effectsize-up/down was tested, the direction of regulation has been tested (effectsize_abs and pvalue score types are agnositic to up/down regulation). +So when score_type was set to any of effectsize/effectsize_down/effectsize_up, the z-scores are negative values in case the "score_type" output column is "effectsize_down". +} \description{ In most cases, it's more convenient to call the more generic \code{test_genesets} function which also applies multiple-testing correction (per geneset source) to the geneset p-values computed by this function. This is the canonical geneset test function for GOAT that uses precomputed null distributions that are bundled with the GOAT package } +\examples{ +\donttest{ +# note; this example downloads data when first run, and typically takes ~60seconds + +# store the downloaded files in the following directory. Here, the temporary file +# directory is used. Alternatively, consider storing this data in a more permanent location. +# e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +output_dir = tempdir() + +## first run the default example from test_genesets() to obtain input data +datasets = download_goat_manuscript_data(output_dir) +genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +genesets_asis = download_genesets_goatrepo(output_dir) +genesets_filtered = filter_genesets(genesets_asis, genelist) + +### we here compare GOAT with precomputed null distributions against +### a GOAT function that performs bootstrapping to compute null distributions on-demand + +# apply goat with precomputed null (default) and goat with on-demand bootstrapping +result_precomputed = test_genesets(genesets_filtered, genelist, method = "goat", + score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) |> + # undo sorting by p-value @ test_genesets(), instead sort by stable IDs + arrange(source, id) +result_bootstrapped = test_genesets(genesets_filtered, genelist, method = "goat_bootstrap", + score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05, verbose = TRUE) |> + arrange(source, id) + +# tables should align +stopifnot(result_precomputed$id == result_bootstrapped$id) +# no missing values +stopifnot(is.finite(result_precomputed$pvalue) & + is.finite(is.finite(result_bootstrapped$pvalue))) + +# compare results +plot(result_precomputed$pvalue, result_bootstrapped$pvalue) +abline(0, 1, col=2) + +plot(minlog10_fixzero(result_precomputed$pvalue), + minlog10_fixzero(result_bootstrapped$pvalue)) +abline(0, 1, col=2) + +summary(minlog10_fixzero(result_precomputed$pvalue) - + minlog10_fixzero(result_bootstrapped$pvalue)) +} +} \seealso{ \code{test_genesets} } diff --git a/man/test_genesets_gsea.Rd b/man/test_genesets_gsea.Rd index 11cab45..29127e7 100644 --- a/man/test_genesets_gsea.Rd +++ b/man/test_genesets_gsea.Rd @@ -12,7 +12,8 @@ test_genesets_gsea( gseaParam = 1, nPermSimple = 50000, gsea_genelist_col = NULL, - gsea_scoretype = NULL + gsea_scoretype = NULL, + random_seed = 123 ) } \arguments{ @@ -28,13 +29,18 @@ Option "custom" expects 2 additional parameters; \code{gsea_genelist_col} should \item{parallel_threads}{number of threads to use for parallel processing. Set to 0 to automatically select all available processors/cores, set to 1 to disable (default) or to N to use N processes. Note that multiprocessing sometimes breaks on RStudio + Windows, hence this parameter is set to 1 to disable multiprocessing by default for now} -\item{gseaParam}{passed to \code{fgsea::fgsea()}, from manual: "GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores.". default = 1} +\item{gseaParam}{passed to \code{fgsea::fgsea()}, from manual: "GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores.". default = 1. Further comments by fGSEA author at https://github.com/ctlab/fgsea/issues/45} \item{nPermSimple}{passed to \code{fgsea::fgsea()}, from manual: "Number of permutations in the simple fgsea implementation for preliminary estimation of P-values.". default = 50000 in this R package but 1000 by default in fGSEA v1.22.0; we observed much better accuracy in null simulations when increasing this from default 1k to 10k and further minor improvement towards 50k, hence the latter is our default} \item{gsea_genelist_col}{optional, only used for \code{score_type} "custom"} \item{gsea_scoretype}{optional, only used for \code{score_type} "custom"} + +\item{random_seed}{the random seed that is passed to \code{set.seed()} in order to ensure fgsea results are reproducible. default: 123} +} +\value{ +input \code{genesets} table with results in the "pvalue", "score_type" and "gsea_nes" columns } \description{ In most cases, it's more convenient to call the more generic \code{test_genesets} function which also deals with multiple-testing correction (per geneset source) diff --git a/man/test_genesets_hypergeometric.Rd b/man/test_genesets_hypergeometric.Rd index c20e153..57c003a 100644 --- a/man/test_genesets_hypergeometric.Rd +++ b/man/test_genesets_hypergeometric.Rd @@ -15,6 +15,9 @@ is assumed to be the total number of tested genes, the number of rows where sign \item{require_nsignif}{minimum number of 'signif genes' that overlap with a geneset; \code{NA} pvalues are returned for genesets with \code{ngenes_signif <= require_nsignif}. This function 'prefilters' genesets, so beware that this will influence downstream multiple testing correction. Default is 1} } +\value{ +input \code{genesets} table with results in the "pvalue" column +} \description{ In most cases, it's more convenient to call the more generic \code{test_genesets} function which also deals with multiple-testing correction (per geneset source) diff --git a/man/test_genesets_idea.Rd b/man/test_genesets_idea.Rd deleted file mode 100644 index 206ab93..0000000 --- a/man/test_genesets_idea.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/test_genesets_idea.R -\name{test_genesets_idea} -\alias{test_genesets_idea} -\title{Geneset testing with the iDEA R package (closely follows the iDEA tutorial)} -\usage{ -test_genesets_idea(genesets, genelist, return_pvalue_louis) -} -\arguments{ -\item{genesets}{filtered genesets} - -\item{genelist}{user genelist, must contain columns "gene", "log2fc" and "pvalue"} - -\item{return_pvalue_louis}{boolean, set \code{TRUE} to return "louis corrected" p-values from iDEA (analogous to iDEA tutorial)} -} -\value{ -input \code{genesets} with additional columns for geneset p-value and score type. Because iDEA can return 2 types of geneset p-values, -multiple columns are included in this function's results; \code{pvalue_idea} = iDEA geneset p-value, \code{pvalue_louis_idea} = \code{pvalue_louis} from iDEA, -\code{pvalue} = respective copy of either former column, depending on user-parameter \code{return_pvalue_louis} -} -\description{ -note that computation takes a long time with the iDEA method. On a high performance workstation computer with -12 cores / 24 threads it takes approximately 6 hours for a genelist of ~14000 genes to analyze all (filtered) GO -genesets. Using a regular PC this might take a multitude of hours. -} -\seealso{ -\code{test_genesets} -} diff --git a/man/treemap_data.Rd b/man/treemap_data.Rd index efc0339..c42e3c3 100644 --- a/man/treemap_data.Rd +++ b/man/treemap_data.Rd @@ -27,11 +27,9 @@ treemap_data( \item{toplevel_max_ngenes}{groups in the treemap should not have more than this many genes ('ngenes' in geneset test results). If not set, this defaults to 50\% of the total number of unique genes in the geneset test results} } -\description{ -Construct tree and treemap data structures from geneset parent/child relations -} -\examples{ -\dontrun{ - # refer to the goat::treemap_plot() function for a complete example +\value{ +data structure needed for \code{treemap_plot()} } +\description{ +refer to the goat::treemap_plot() function for a complete example } diff --git a/man/treemap_plot.Rd b/man/treemap_plot.Rd index 7525baa..4862aa3 100644 --- a/man/treemap_plot.Rd +++ b/man/treemap_plot.Rd @@ -11,24 +11,34 @@ treemap_plot(x, label_group = FALSE) \item{label_group}{set TRUE to show only group-level labels} } +\value{ +a ggplot2 object constructed by \code{treemap::treemap()} +} \description{ simple wrapper around the treemap R package. To customize this plot, copy/paste its code and tweak parameters as desired } \examples{ -\dontrun{ -# use GOAT as per usual -genelist = goat_example_datasets[["Hondius 2021:mass-spec:PMID33492460"]] -genesets = load_genesets_go_bioconductor() -genesets_filtered = filter_genesets(genesets, genelist) -goat_result = test_genesets( - genesets_filtered, genelist, method = "goat", score_type = "effectsize", - padj_method = "bonferroni", padj_cutoff = 0.05 -) +\donttest{ +# note; this example downloads data when first run, and typically takes ~60seconds + +# store the downloaded files in the following directory. Here, the temporary file +# directory is used. Alternatively, consider storing this data in a more permanent location. +# e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows +output_dir = tempdir() + +## first run the default example from test_genesets() to obtain geneset results +datasets = download_goat_manuscript_data(output_dir) +genelist = datasets$`Wingo 2020:mass-spec:PMID32424284` +genesets_asis = download_genesets_goatrepo(output_dir) +genesets_filtered = filter_genesets(genesets_asis, genelist) +result = test_genesets(genesets_filtered, genelist, method = "goat", + score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05) + # subset GO CC results -x = goat_result |> filter(signif & source == "GO_CC") +x = result |> filter(signif & source == "GO_CC") tm = treemap_data( geneset_ids = x$id, - genesets = genesets, + genesets = genesets_filtered, genesets_test_result = x, simplify = "leaf_only" # options: none/leaf_only/prune_singletons/pvalue ) diff --git a/man/validate_genelist.Rd b/man/validate_genelist.Rd deleted file mode 100644 index 3414990..0000000 --- a/man/validate_genelist.Rd +++ /dev/null @@ -1,14 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/validate_genelist.R -\name{validate_genelist} -\alias{validate_genelist} -\title{validate a table with genes (that should be tested in overrepresentation-analysis) for compatibility with this R package} -\usage{ -validate_genelist(genelist) -} -\arguments{ -\item{genelist}{gene tibble to validate} -} -\description{ -validate a table with genes (that should be tested in overrepresentation-analysis) for compatibility with this R package -} diff --git a/man/validate_genesets.Rd b/man/validate_genesets.Rd deleted file mode 100644 index bc49fda..0000000 --- a/man/validate_genesets.Rd +++ /dev/null @@ -1,18 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/validate_genesets.R -\name{validate_genesets} -\alias{validate_genesets} -\title{validate that a geneset table is compatible with this R package} -\usage{ -validate_genesets(genesets, require_signif = TRUE, check_gene_identiers = TRUE) -} -\arguments{ -\item{genesets}{geneset tibble to validate, e.g. results from \code{load_genesets_go_bioconductor()}} - -\item{require_signif}{should we check for "ngenes_signif" column ?} - -\item{check_gene_identiers}{optionally skip the validation of gene identifiers} -} -\description{ -validate that a geneset table is compatible with this R package -} diff --git a/man/validate_goat_genelist.Rd b/man/validate_goat_genelist.Rd deleted file mode 100644 index 1be06fe..0000000 --- a/man/validate_goat_genelist.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/test_genesets_goat.R -\name{validate_goat_genelist} -\alias{validate_goat_genelist} -\title{helper function to validate input parameters for GOAT computation functions} -\usage{ -validate_goat_genelist(x, score_type) -} -\arguments{ -\item{x}{object to validate} - -\item{score_type}{score type} -} -\description{ -helper function to validate input parameters for GOAT computation functions -} diff --git a/man/validate_goat_genesets.Rd b/man/validate_goat_genesets.Rd deleted file mode 100644 index f3a0321..0000000 --- a/man/validate_goat_genesets.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/test_genesets_goat.R -\name{validate_goat_genesets} -\alias{validate_goat_genesets} -\title{helper function to validate input parameters for GOAT computation functions} -\usage{ -validate_goat_genesets(x, genelist_N) -} -\arguments{ -\item{x}{object to validate} - -\item{genelist_N}{genelist length} -} -\description{ -helper function to validate input parameters for GOAT computation functions -} diff --git a/man/validate_goat_niter.Rd b/man/validate_goat_niter.Rd deleted file mode 100644 index 26a6f86..0000000 --- a/man/validate_goat_niter.Rd +++ /dev/null @@ -1,14 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/test_genesets_goat.R -\name{validate_goat_niter} -\alias{validate_goat_niter} -\title{helper function to validate input parameters for GOAT computation functions} -\usage{ -validate_goat_niter(x) -} -\arguments{ -\item{x}{object to validate} -} -\description{ -helper function to validate input parameters for GOAT computation functions -} diff --git a/man/validate_goat_scoretype.Rd b/man/validate_goat_scoretype.Rd deleted file mode 100644 index f684b3b..0000000 --- a/man/validate_goat_scoretype.Rd +++ /dev/null @@ -1,14 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/test_genesets_goat.R -\name{validate_goat_scoretype} -\alias{validate_goat_scoretype} -\title{helper function to validate input parameters for GOAT computation functions} -\usage{ -validate_goat_scoretype(x) -} -\arguments{ -\item{x}{object to validate} -} -\description{ -helper function to validate input parameters for GOAT computation functions -} diff --git a/man/validate_goat_verbose.Rd b/man/validate_goat_verbose.Rd deleted file mode 100644 index afca65b..0000000 --- a/man/validate_goat_verbose.Rd +++ /dev/null @@ -1,14 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/test_genesets_goat.R -\name{validate_goat_verbose} -\alias{validate_goat_verbose} -\title{helper function to validate input parameters for GOAT computation functions} -\usage{ -validate_goat_verbose(x) -} -\arguments{ -\item{x}{object to validate} -} -\description{ -helper function to validate input parameters for GOAT computation functions -} diff --git a/src/Makevars b/src/Makevars index f6480a3..e5af248 100644 --- a/src/Makevars +++ b/src/Makevars @@ -1,3 +1,2 @@ -CXX_STD = CXX14 PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) $(SHLIB_OPENMP_CXXFLAGS) PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) diff 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cpp14 although cpp11 would do for this function -// [[Rcpp::plugins(cpp14)]] // [[Rcpp::export]] Rcpp::NumericMatrix rcpp_gene_to_geneset_scores(int n_gs, Rcpp::IntegerVector gs_index, Rcpp::IntegerVector gs_geneindex, Rcpp::NumericMatrix gene_score) { // basic input validation - if ((gs_index.size() == 0) | (gs_index.size() != gs_geneindex.size()) | (gene_score.nrow() == 0) | (gene_score.ncol() == 0)) + if ((gs_index.size() == 0) || (gs_index.size() != gs_geneindex.size()) || (gene_score.nrow() == 0) || (gene_score.ncol() == 0)) { Rcpp::stop("invalid input data"); } diff --git a/src/rcpp_gene_to_geneset_scores.o b/src/rcpp_gene_to_geneset_scores.o index ca9082c88ea453935bf97640e602503d318f6e8e..07d9cd17cf9669816e1e3df1027346bfa2f2eb46 100644 GIT binary patch delta 10020 zcmcJVdwdi{w#Ta{)1663(vuen?_p-h0|F)zkQl@aB+!EsHfT`5gjXO0nrWbUuJ!iekrV?UpJ?|eR| z``hPKovJ!jb^4Kfus!6t*FwUluX={@-&)$qP$7tx#f=)zHl@ruB+l*#N99ru~XtYBuawVQ+zQV=+aA+LbfF 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a/src/rcpp_null_distributions.cpp b/src/rcpp_null_distributions.cpp index 7fa0a79..4c1302a 100644 --- a/src/rcpp_null_distributions.cpp +++ b/src/rcpp_null_distributions.cpp @@ -20,8 +20,8 @@ #include #endif +// in previous versions we here added an additional Rcpp requirement for C++14, but this seems to be no longer needed // [[Rcpp::plugins(openmp)]] -// [[Rcpp::plugins(cpp14)]] // [[Rcpp::export(rcpp_geneset_null)]] Rcpp::NumericVector rcpp_geneset_null(Rcpp::NumericVector gene_scores, Rcpp::IntegerVector geneset_sizes, int max_geneset_size, int niter) @@ -29,15 +29,15 @@ Rcpp::NumericVector rcpp_geneset_null(Rcpp::NumericVector gene_scores, Rcpp::Int // note; we assume gene_scores have been rescaled upstream and are typically between 0 ~ 1000 // basic input validation - if ((niter < 10000) | (niter > 5000000)) + if ((niter < 10000) || (niter > 5000000)) { Rcpp::stop("rcpp_geneset_null, failed precondition; 10000 <= niter <= 5000000"); } - if ((max_geneset_size < 5) | (max_geneset_size > 50000)) + if ((max_geneset_size < 5) || (max_geneset_size > 50000)) { Rcpp::stop("rcpp_geneset_null, failed precondition; 5 <= max_geneset_size <= 50000"); } - if ((gene_scores.size() == 0) | (gene_scores.size() > 50000)) + if ((gene_scores.size() == 0) || (gene_scores.size() > 50000)) { Rcpp::stop("rcpp_geneset_null, failed precondition; 0 < gene_scores.size() <= 50000"); } @@ -45,7 +45,7 @@ Rcpp::NumericVector rcpp_geneset_null(Rcpp::NumericVector gene_scores, Rcpp::Int { Rcpp::stop("rcpp_geneset_null, failed precondition; max_geneset_size < gene_scores.size()"); } - if ((geneset_sizes.size() == 0) | (geneset_sizes.size() > 10000)) + if ((geneset_sizes.size() == 0) || (geneset_sizes.size() > 10000)) { Rcpp::stop("rcpp_geneset_null, failed precondition; 0 < geneset_sizes.size() <= 10000"); } @@ -170,7 +170,7 @@ class RunningStat -//// TODO: finish this wrapper function for bootstrapping + fit distributions +//// wrapper function for bootstrapping + fit distributions // [[Rcpp::export(rcpp_null_distributions)]] Rcpp::List rcpp_null_distributions(Rcpp::NumericVector gene_scores, Rcpp::IntegerVector geneset_sizes, int max_geneset_size, int niter) { diff --git a/src/rcpp_null_distributions.o b/src/rcpp_null_distributions.o index 04a5daea192eda7d070530f5ed861d398d30f264..5f0ed9a8a7ef6dfc42622f4ad94024a6b64f0c8d 100644 GIT binary patch delta 16876 zcmcJW30PIt{`c30vk!-HGYjH?oFg2Zg3_Dh5Gd3>c2F!eR5T4FD>MgCx={xM(%s=G zxU_5KfTd<#Qxi2UHJotB3}-S^9J&|OlDL&<8UOEE3wC(#`##TopZ9&w^Q^r;`}g~; z-NOc6T?+wV-60>TZ^rp^cG$$8$Pe0gY)0A#>XMOn;T5jP=0!#k7ktN>-mB#74BhYvn z+T+XUMvo{rgPorAh&KAz3AwV2{^1cNhXD!gZZac+%@iZo;Q2wCXEMtdKxuRZGo~%H7O(Ar+XP{gM*++DWr`dQDbogz$&@)OJNI&t6F(wZur15Qf z(KAX;1Nu?f%PiGE6+#Dl14)=|ga%rA#jPx2^f$MU5&^N~I<_h{F^uMm4p?=X)FdgI+2{k>RYBJ& zQBnpyqQn@pAh=dRe^t78&hSE3@#sc7`=k3!CB#ZoK>5$J7Munu2Bp!Zpd~qkOr^U) zwV+w_d(bsd8TAb?8;hr5Fs-3~35YVT2mY~!P7H{Ww*ouSk^l_NnS`kHW6(0tlk^N| z4QK|v2l^TG9_=1zmOITNWCKkNG#jm;qwDCLz$n=?n~*Vd1=PJjljuQEf6#062T)ip 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