From a00c6968ca8b34e4aeb9e93ba5b52533b657b39b Mon Sep 17 00:00:00 2001 From: westbrooktm Date: Wed, 28 Jun 2023 17:01:53 -0400 Subject: [PATCH 1/4] evaluate function uses datetime to name summaries folder --- R/evaluate.R | 9 ++++++--- R/webgestalt_network.R | 2 -- man/webgestalt_network.Rd | 2 -- vignettes/GOeval.Rmd | 2 -- 4 files changed, 6 insertions(+), 9 deletions(-) diff --git a/R/evaluate.R b/R/evaluate.R index a9c01d9..6dd0c0d 100644 --- a/R/evaluate.R +++ b/R/evaluate.R @@ -40,19 +40,22 @@ evaluate <- function(network, reference_set, output_directory, network_name, edg subset_network(network, file.path(output_directory, paste0(network_name, "_subsets")), network_name, edges, num_possible_TFs) + formatted_time <- format(Sys.time(), "%Y-%m-%d-%H%M") + summaries_suffix <- paste0("_summaries_", formatted_time) + for (subset in list.files(file.path(output_directory, paste0(network_name, "_subsets")), full.names = TRUE)) { webgestalt_network(network_path = subset, reference_set = reference_set, - output_directory = file.path(output_directory, paste0(network_name, "_summaries")), + output_directory = file.path(output_directory, paste0(network_name, summaries_suffix)), # this just gets the name of each file minus the extension network_name = strsplit(basename(subset), "[.]")[[1]][1], permutations = permutations) } - metric_dfs_by_net <- mapply(get_metrics, file.path(output_directory, paste0(network_name, "_summaries")), MoreArgs=list(get_sum = get_sum, get_percent = get_percent, get_mean = get_mean, get_median = get_median, get_annotation_overlap = get_annotation_overlap, get_size = get_size, parallel = FALSE), SIMPLIFY = FALSE) + metric_dfs_by_net <- mapply(get_metrics, file.path(output_directory, paste0(network_name, summaries_suffix)), MoreArgs=list(get_sum = get_sum, get_percent = get_percent, get_mean = get_mean, get_median = get_median, get_annotation_overlap = get_annotation_overlap, get_size = get_size, parallel = FALSE), SIMPLIFY = FALSE) if (plot) { - pdf(file.path(output_directory, paste0(network_name, "_ORA_metrics_plots.pdf")), 7, 5) + pdf(file.path(output_directory, paste0(network_name, "_ORA_metrics_plots_", formatted_time, ".pdf")), 7, 5) plot_data <- plot_metrics(metric_dfs_by_net, network_name, "", perTF = (num_possible_TFs > 0), sum = get_sum, percent = get_percent, mean = get_mean, median = get_median, annotation_overlap = get_annotation_overlap, size = get_size) dev.off() } diff --git a/R/webgestalt_network.R b/R/webgestalt_network.R index 1224fe9..e40bce2 100644 --- a/R/webgestalt_network.R +++ b/R/webgestalt_network.R @@ -5,8 +5,6 @@ #' The input network file should be a tab-separated .tsv where the first column is the #' source nodes (TFs) and the second column is the target nodes (regulated genes). #' All genes must be written as their Ensembl gene ID. -#' NOTE: if using the same output_directory and network_name for a multiple runs, -#' you must first delete the contents of "webgestalt_work/" #' #' @importFrom WebGestaltR idMapping #' @importFrom WebGestaltR loadGeneSet diff --git a/man/webgestalt_network.Rd b/man/webgestalt_network.Rd index 48931aa..0658694 100644 --- a/man/webgestalt_network.Rd +++ b/man/webgestalt_network.Rd @@ -38,6 +38,4 @@ for this network and a given number of permutations. The input network file should be a tab-separated .tsv where the first column is the source nodes (TFs) and the second column is the target nodes (regulated genes). All genes must be written as their Ensembl gene ID. -NOTE: if using the same output_directory and network_name for a multiple runs, -you must first delete the contents of "webgestalt_work/" } diff --git a/vignettes/GOeval.Rmd b/vignettes/GOeval.Rmd index 3824da1..3f7667e 100644 --- a/vignettes/GOeval.Rmd +++ b/vignettes/GOeval.Rmd @@ -79,8 +79,6 @@ After a couple minutes, the directory at out_dir should contain a folder of subs The run time of the evaluate function changes with the size of the network and the number of permutations. With larger networks, it may take up to several hours. It is thus recommended to use a computing cluster when working with large networks. It is possible to speed up the process by calling each step manually as shown in the "Advanced usage" section below and running the webgestalt_network function calls in parallel. -NOTE: If you try to run the evaluate function again with the same out_dir and network_name, you must first delete the contents of "out_dir/_summaries/webgestalt_work/". To avoid having to do this, simply use a different network_name (or out_dir) for each run unless you delete/rename the previous output. - Now, let us compare with a second network. The "small_dto.tsv" network does not have edge scores, so no network subsets will be created. Generate the data for this network in a similar way as for the first one. ```{r} From 9693c2dc97a749f77b1b46ee6801740faff090e7 Mon Sep 17 00:00:00 2001 From: westbrooktm Date: Thu, 6 Jul 2023 17:38:55 -0400 Subject: [PATCH 2/4] improvements to function documentation --- R/evaluate.R | 69 +++++---- R/get_metrics.R | 137 +++++++++++------- R/get_terms.R | 16 +- R/plot_metrics.R | 62 +++++--- R/subset_network.R | 27 ++-- R/webgestalt_network.R | 33 +++-- man/evaluate.Rd | 60 +++++--- man/get_annotation_overlap.Rd | 15 +- man/get_metrics.Rd | 60 +++++--- man/get_network_metrics.Rd | 54 ++++--- man/get_terms.Rd | 13 +- man/plot_metrics.Rd | 50 ++++--- man/subset_network.Rd | 12 +- man/webgestalt_network.Rd | 24 ++- tests/testthat/data/box-test-plot_metrics.pdf | Bin 18191 -> 18288 bytes .../testthat/data/line-test-plot_metrics.pdf | Bin 16947 -> 17033 bytes vignettes/GOeval.Rmd | 2 + 17 files changed, 386 insertions(+), 248 deletions(-) diff --git a/R/evaluate.R b/R/evaluate.R index 6dd0c0d..f2f94e9 100644 --- a/R/evaluate.R +++ b/R/evaluate.R @@ -1,13 +1,12 @@ -#' evaluate + +#' Perform the whole GOeval pipeline on one network #' -#' Perform the whole GOeval pipeline on one network. Given one network, this -#' function will first make subsets with subset_network, then run ORA with -#' webgestalt_network, followed by get_metrics and plot_metrics to plot summary -#' statistics. -#' The input file should be tab-separated with three columns: source node (e.g. transcription factor), -#' target node (e.g. the regulated gene), and edge score. All genes should be written as Ensembl gene IDs. +#' @description +#' Given one network, `evaluate` will first make subsets with `subset_network`, +#' then run Over-Representation Analysis (ORA) with `webgestalt_network`, +#' followed by `get_metrics` and `plot_metrics` to plot selected summary statistics. #' -#' @param network a file containing the network to create subsets from. The file should +#' @param network path to the file containing the network to create subsets from. The file should #' be tab-separated with three columns: source node, target node, edge score #' @param reference_set path to the set of all genes possibly included in the network. Must be a .txt #' file containing exactly one column of the genes that could possibly appear in the network. @@ -17,33 +16,47 @@ #' @param edges list of total numbers of edges or average edges per TF to include in each subset #' @param num_possible_TFs if set to a number > 0, the elements of 'edges' will first be #' multiplied by this number to get the number of edges for each subset -#' @param permutations the number of randomly permuted networks to create and run ORA -#' @param get_sum bool whether to get the 'sum' metric, which is the sum of the negative -#' log base 10 of the p-value for the top term of each TF minus 3 times the total -#' number of TFs. -#' @param get_percent bool whether to get the 'percent' metric, which is the -#' percent of TFs with at least one GO term with a FDR < 0.05 -#' @param get_mean bool whether to get the 'mean' metric, which is the mean negative -#' log base 10 of the p-value for the top term of each TF -#' @param get_median bool whether to get the 'median' metric, which is the median negative -#' log base 10 of the p-value for the top term of each TF -#' @param get_annotation_overlap bool whether to get the 'annotation_overlap' metric, -#' which is the percent of TFs that are annotated to a GO term for which their -#' target genes are enriched -#' @param get_size bool whether to get the 'size' metric, which is the number of -#' TFs in the network subset that have more than one target gene with GO annotations -#' @param plot bool whether to make a pdf containing plots of the calculated metrics +#' @param permutations the number of randomly permuted networks to create and run ORA on +#' @param penalty the penalty applied to the 'sum' metric for each TF in the network +#' @param fdr_threshold the FDR threshold for a gene set term to be considered significantly +#' over-represented for the purposes of calculating the 'percent' metric +#' @param get_sum boolean whether to get and plot the 'sum' metric, which is the sum of the negative +#' log base 10 of the p-value for the top term of each source node minus 'penalty' times the total +#' number of source nodes. +#' @param get_percent boolean whether to get and plot the 'percent' metric, which is the +#' percent of source nodes with at least one term with a FDR below the 'fdr_threshold' +#' @param get_mean boolean whether to get and plot the 'mean' metric, which is the mean negative +#' log base 10 of the p-value for the top term of each source node regardless of significance +#' @param get_median boolean whether to get and plot the 'median' metric, which is the median negative +#' log base 10 of the p-value for the top term of each source node regardless of significance +#' @param get_annotation_overlap boolean whether to get and plot the 'annotation_overlap' metric, +#' which is the percent of source nodes that are annotated to at least one of the 16 GO terms for +#' which their target genes are most enriched +#' @param get_size boolean whether to get and plot the 'size' metric, which is the number of +#' source nodes in the network subset that have more than one target gene with annotations. This number is +#' used in the calculation of all other metrics. +#' @param plot boolean whether to make plots of the calculated metrics and write them to a pdf +#' +#' @details +#' The input file should be tab-separated with two or three columns: source node (e.g. transcription factor), +#' target node (e.g. the regulated gene), and, optionally, edge score. All genes must be written as Ensembl gene IDs. +#' +#' @return output of `get_metrics`. Can be used as input to `plot_metrics`. #' #' @export evaluate <- function(network, reference_set, output_directory, network_name, edges = c(512, 1024, 2048, 4096, 8192, 16384, 32768, 65536), num_possible_TFs = 0, - permutations = 3, get_sum = TRUE, get_percent = FALSE, get_mean = FALSE, get_median = FALSE, get_annotation_overlap = FALSE, get_size = TRUE, plot = TRUE) { + permutations = 3, penalty = 3, fdr_threshold = 0.05, get_sum = TRUE, get_percent = FALSE, get_mean = FALSE, get_median = FALSE, get_annotation_overlap = FALSE, get_size = TRUE, plot = TRUE) { + # first package function subset_network(network, file.path(output_directory, paste0(network_name, "_subsets")), network_name, edges, num_possible_TFs) + # for unique names to avoid overwriting formatted_time <- format(Sys.time(), "%Y-%m-%d-%H%M") summaries_suffix <- paste0("_summaries_", formatted_time) + # sequential loop over all created subsets for (subset in list.files(file.path(output_directory, paste0(network_name, "_subsets")), full.names = TRUE)) { + # second package function webgestalt_network(network_path = subset, reference_set = reference_set, output_directory = file.path(output_directory, paste0(network_name, summaries_suffix)), @@ -52,13 +65,17 @@ evaluate <- function(network, reference_set, output_directory, network_name, edg permutations = permutations) } - metric_dfs_by_net <- mapply(get_metrics, file.path(output_directory, paste0(network_name, summaries_suffix)), MoreArgs=list(get_sum = get_sum, get_percent = get_percent, get_mean = get_mean, get_median = get_median, get_annotation_overlap = get_annotation_overlap, get_size = get_size, parallel = FALSE), SIMPLIFY = FALSE) + # third package function + # mapply returns a list of length 1 containing the output of get_metrics + metric_dfs_by_net <- mapply(get_metrics, file.path(output_directory, paste0(network_name, summaries_suffix)), MoreArgs=list(get_sum = get_sum, get_percent = get_percent, get_mean = get_mean, get_median = get_median, get_annotation_overlap = get_annotation_overlap, get_size = get_size, penalty = penalty, fdr_threshold = fdr_threshold, parallel = FALSE), SIMPLIFY = FALSE) if (plot) { pdf(file.path(output_directory, paste0(network_name, "_ORA_metrics_plots_", formatted_time, ".pdf")), 7, 5) + # fourth package function plot_data <- plot_metrics(metric_dfs_by_net, network_name, "", perTF = (num_possible_TFs > 0), sum = get_sum, percent = get_percent, mean = get_mean, median = get_median, annotation_overlap = get_annotation_overlap, size = get_size) dev.off() } + # return output of get_metrics to easily re-plot with plot_metrics return(metric_dfs_by_net) } diff --git a/R/get_metrics.R b/R/get_metrics.R index cc40999..ff33d96 100644 --- a/R/get_metrics.R +++ b/R/get_metrics.R @@ -1,20 +1,27 @@ -#' calculate the percent of TFs that are annotated to a GO term for which their -#' target genes are enriched +#' Helper function for `get_metrics` #' #' @importFrom WebGestaltR idMapping #' -#' @param terms a data.frame obtained from calling get_terms on a folder of the -#' summaries output by webgestalt_network +#' @description +#' This function calculates the percent of source nodes that are annotated to a GO term +#' that either regulates or is the same as a GO term for which their target genes are enriched +#' +#' @param terms a data.frame obtained from calling `get_terms` on a folder of the +#' summaries output by `webgestalt_network` #' @param organism a string specifying the organism that the data is from, e.g. #' "hsapiens" or "scerevisiae" #' @param go_ann a data.frame of annotations of genes to GO terms. Obtain with #' WebGestaltR::loadGeneSet. #' @param go_reg a data.frame of the regulatory relationships between GO terms. #' Obtain with ontologyIndex::get_ontology. +#' +#' @return a list of source nodes +#' +#' @keywords internal get_annotation_overlap <- function(terms, organism, go_ann, go_reg) { tf_ids <- unique(terms$tfId) - # assumption of "ensembl_gene_id" is a placeholder + # assumption of "ensembl_gene_id" tf_map <- WebGestaltR::idMapping(organism = organism, inputGene = tf_ids, sourceIdType = "ensembl_gene_id", targetIdType = "entrezgene", host = "https://www.webgestalt.org/") overlap_list <- list() @@ -44,41 +51,49 @@ get_annotation_overlap <- function(terms, organism, go_ann, go_reg) { return(overlap_list) } -#' helper function to compute the desired metrics from a dataframe of the top -#' terms for each TF in a single network +#' Helper function for `get_metrics` #' #' @importFrom stats median #' +#' @description +#' This is a helper function that computes the desired metrics from a data.frame +#' of the top terms for each source node in a network. +#' #' @param full_terms a data.frame containing all the summary results for a network. -#' Use get_terms to obtain. -#' @param network_size the number of TFs in the network. May be different from -#' the number of TFs that have summary files. +#' Use `get_terms` to obtain. +#' @param network_size the number of source nodes in the network #' @param organism a string specifying the organism that the data is from, e.g. -#' "hsapiens" or "scerevisiae". Only specify if get_annotation_overlap = TRUE. -#' @param get_sum bool whether to get the 'sum' metric, which is the sum of the negative -#' log base 10 of the p-value for the top term of each TF minus 3 times the total -#' number of TFs. -#' @param get_percent bool whether to get the 'percent' metric, which is the -#' percent of TFs with at least one GO term with a FDR < 0.05 -#' @param get_mean bool whether to get the 'mean' metric, which is the mean negative -#' log base 10 of the p-value for the top term of each TF -#' @param get_median bool whether to get the 'median' metric, which is the median negative -#' log base 10 of the p-value for the top term of each TF -#' @param get_annotation_overlap bool whether to get the 'annotation_overlap' metric, -#' which is the percent of TFs that are annotated to a GO term for which their -#' target genes are enriched -#' @param get_size bool whether to get the 'size' metric, which is the number of -#' TFs in the network subset that have more than one target gene with GO annotations +#' "hsapiens" or "scerevisiae". Only required if get_annotation_overlap = TRUE. +#' @param get_sum boolean whether to get the 'sum' metric, which is the sum of the negative +#' log base 10 of the p-value for the top term of each source node minus 'penalty' times the total +#' number of source nodes. +#' @param get_percent boolean whether to get the 'percent' metric, which is the +#' percent of source nodes with at least one term with a FDR below the 'fdr_threshold' +#' @param get_mean boolean whether to get the 'mean' metric, which is the mean negative +#' log base 10 of the p-value for the top term of each source node regardless of significance +#' @param get_median boolean whether to get the 'median' metric, which is the median negative +#' log base 10 of the p-value for the top term of each source node regardless of significance +#' @param get_annotation_overlap boolean whether to get the 'annotation_overlap' metric, +#' which is the percent of source nodes that are annotated to at least one of the 16 GO terms for +#' which their target genes are most enriched +#' @param get_size boolean whether to get the 'size' metric, which is the number of +#' source nodes in the network subset that have more than one target gene with annotations. This number is +#' used in the calculation of all other metrics. +#' @param penalty the penalty applied to the 'sum' metric for each source node in the network +#' @param fdr_threshold the FDR threshold for a gene set term to be considered significantly +#' over-represented for the purposes of calculating the 'percent' metric #' @param go_ann a data.frame of annotations of genes to GO terms. Obtain with #' WebGestaltR::loadGeneSet. #' @param go_reg a data.frame of the regulatory relationships between GO terms. #' Obtain with ontologyIndex::get_ontology. -get_network_metrics <- function(full_terms, network_size, organism, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, go_ann = NULL, go_reg = NULL) { - # heuristically chosen - penalty <- 3 +#' +#' @return a list of metric values +#' +#' @keywords internal +get_network_metrics <- function(full_terms, network_size, organism, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, penalty, fdr_threshold, go_ann = NULL, go_reg = NULL) { if (!any(is.na(full_terms))) { terms <- full_terms[match(unique(full_terms$tfId), full_terms$tfId), ] - percent <- 100 * sum(terms$FDR < 0.05) / network_size + percent <- 100 * sum(terms$FDR < fdr_threshold) / network_size neglogp <- -log10(terms$pValue) # cap -logp values due to rounding to 0 for pval < 1E-16 neglogp <- ifelse(is.finite(neglogp), neglogp, 16) @@ -99,11 +114,7 @@ get_network_metrics <- function(full_terms, network_size, organism, get_sum, get )[c(get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size)]) } -#' get_metrics -#' -#' Get a data.frame that contains specified metrics for all networks that have a -#' subdirectory in the provided path including both the real and permuted networks. -#' The path should contain only directories created by webgestalt_network. +#' Get summary metrics of a network's ORA results #' #' @import stringr #' @importFrom ontologyIndex get_ontology @@ -112,29 +123,43 @@ get_network_metrics <- function(full_terms, network_size, organism, get_sum, get #' @importFrom parallel mclapply #' @importFrom parallelly availableCores #' -#' @param directory a directory containing the webgestalt_network output directory -#' for all networks of interest +#' @description +#' `get_metrics` creates a data.frame that contains specified metrics for all network subsets and their permutations +#' that have a subdirectory in the provided path. It is designed to be run on the output of the `webgestalt_network` +#' function to prepare summary metrics for plotting with the `plot_metrics` function. +#' The 'directory' path should contain only directories created by `webgestalt_network`. +#' +#' @param directory a directory containing only the directories of ORA summaries created by +#' `webgestalt_network` for all networks of interest #' @param organism a string specifying the organism that the data is from, e.g. -#' "hsapiens" or "scerevisiae". Only specify if get_annotation_overlap = TRUE. -#' @param get_sum bool whether to get the 'sum' metric, which is the sum of the negative -#' log base 10 of the p-value for the top term of each TF minus 3 times the total -#' number of TFs. -#' @param get_percent bool whether to get the 'percent' metric, which is the -#' percent of TFs with at least one GO term with a FDR < 0.05 -#' @param get_mean bool whether to get the 'mean' metric, which is the mean negative -#' log base 10 of the p-value for the top term of each TF -#' @param get_median bool whether to get the 'median' metric, which is the median negative -#' log base 10 of the p-value for the top term of each TF -#' @param get_annotation_overlap bool whether to get the 'annotation_overlap' metric, -#' which is the percent of TFs that are annotated to a GO term for which their -#' target genes are enriched -#' @param get_size bool whether to get the 'size' metric, which is the number of -#' TFs in the network subset that have more than one target gene with GO annotations -#' @param parallel bool whether to get the metrics for each network in the directory -#' in parallel or sequentially - use with caution, as this has not been adequately tested +#' "hsapiens" or "scerevisiae". Only required if get_annotation_overlap = TRUE. +#' @param get_sum boolean whether to get the 'sum' metric, which is the sum of the negative +#' log base 10 of the p-value for the top term of each source node minus 'penalty' times the total +#' number of source nodes. +#' @param get_percent boolean whether to get the 'percent' metric, which is the +#' percent of source nodes with at least one term with a FDR below the 'fdr_threshold' +#' @param get_mean boolean whether to get the 'mean' metric, which is the mean negative +#' log base 10 of the p-value for the top term of each source node regardless of significance +#' @param get_median boolean whether to get the 'median' metric, which is the median negative +#' log base 10 of the p-value for the top term of each source node regardless of significance +#' @param get_annotation_overlap boolean whether to get the 'annotation_overlap' metric, +#' which is the percent of source nodes that are annotated to at least one of the 16 GO terms for +#' which their target genes are most enriched +#' @param get_size boolean whether to get the 'size' metric, which is the number of +#' source nodes in the network subset that have more than one target gene with annotations. This number is +#' used in the calculation of all other metrics. +#' @param penalty the penalty applied to the 'sum' metric for each TF in the network +#' @param fdr_threshold the FDR threshold for a gene set term to be considered significantly +#' over-represented for the purposes of calculating the 'percent' metric +#' @param parallel boolean whether to get the metrics for each network in the directory +#' in parallel - use with caution, as this has not been adequately tested +#' +#' @return a list of data.frames, each containing the values of one metric. +#' The columns of a data.frame represent the different subset sizes, and the rows +#' represent the different network permutations. The first row is from the unpermuted networks. #' #' @export -get_metrics <- function(directory, organism = "hsapiens", get_sum = TRUE, get_percent = FALSE, get_mean = FALSE, get_median = FALSE, get_annotation_overlap = FALSE, get_size = TRUE, parallel = FALSE) { +get_metrics <- function(directory, organism = "hsapiens", get_sum = TRUE, get_percent = FALSE, get_mean = FALSE, get_median = FALSE, get_annotation_overlap = FALSE, get_size = TRUE, penalty = 3, fdr_threshold = 0.05, parallel = FALSE) { # GO regulatory relationships only needed if get_annotation_overlap = TRUE if (get_annotation_overlap) { # Load regulatory relationships between GO terms for the calculation of overlap between TF GO @@ -174,7 +199,7 @@ get_metrics <- function(directory, organism = "hsapiens", get_sum = TRUE, get_pe }) paths <- list.dirs(net, full.names = TRUE, recursive = FALSE) p_terms <- mapply(get_terms, paths, 16, SIMPLIFY = FALSE) - return(t(mapply(get_network_metrics, p_terms, network_sizes, organism, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, MoreArgs = list(go_ann = go_ann, go_reg = go_reg)))) + return(t(mapply(get_network_metrics, p_terms, network_sizes, organism, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, penalty, fdr_threshold, MoreArgs = list(go_ann = go_ann, go_reg = go_reg)))) }, mc.cores = parallelly::availableCores()) } else { # non-parallel version @@ -186,7 +211,7 @@ get_metrics <- function(directory, organism = "hsapiens", get_sum = TRUE, get_pe }) paths <- list.dirs(net, full.names = TRUE, recursive = FALSE) p_terms <- mapply(get_terms, paths, 16, SIMPLIFY = FALSE) - return(t(mapply(get_network_metrics, p_terms, network_sizes, organism, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, MoreArgs = list(go_ann = go_ann, go_reg = go_reg)))) + return(t(mapply(get_network_metrics, p_terms, network_sizes, organism, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, penalty, fdr_threshold, MoreArgs = list(go_ann = go_ann, go_reg = go_reg)))) }) } df_list <- list() diff --git a/R/get_terms.R b/R/get_terms.R index 45751ec..2d7152e 100644 --- a/R/get_terms.R +++ b/R/get_terms.R @@ -1,13 +1,16 @@ -#' get_terms -#' -#' Get ORA data for the top n terms from each TF summary file in a folder -#' output by the webgestalt_network function. +#' Gather ORA results from one folder of ORA summaries #' #' @importFrom data.table fread #' +#' @description +#' Get Over-Representation Analysis (ORA) data for the top n terms from each source node summary file +#' in a folder output by the `webgestalt_network` function. +#' #' @param path a folder containing .csv files of the ORA summaries output -#' by webgestalt_network -#' @param n the number of terms from each summary to get data for +#' by `webgestalt_network` +#' @param n the number of terms from each summary for which to get data +#' +#' @return a data.frame #' #' @export get_terms <- function(path, n) { @@ -20,6 +23,7 @@ get_terms <- function(path, n) { } combined_df <- do.call(rbind, first_rows) # extract TF ID from file name and add to row of corresponding GO term + # this is now done in `webgestalt_network` # combined_df$tfId <- rep(unlist(lapply(csv_files, function(f) {unlist(strsplit(basename(f), '_'))[1]})), each = n) return(combined_df) } else { diff --git a/R/plot_metrics.R b/R/plot_metrics.R index 94cd45c..565dbaa 100644 --- a/R/plot_metrics.R +++ b/R/plot_metrics.R @@ -1,6 +1,4 @@ -#' plot_metrics -#' -#' Plot the summary metrics from get_metrics across network subset size for one or more networks. +#' Plot the summary metrics from `get_metrics` #' #' @importFrom tidyr gather #' @importFrom tidyr separate_wider_regex @@ -8,28 +6,38 @@ #' @importFrom dplyr group_by #' @import ggplot2 #' -#' @param metric_dfs_by_net a list of outputs of get_metrics for each network to be plotted -#' @param title_text text for the title of each plot; generally the names of the networks -#' @param subtitle_text text for the subtitle of each plot -#' @param perTF bool whether the network subset sizes were specified as average target +#' @description +#' `plot_metrics` creates plots of all the summary metrics calculated by `get_metrics`. +#' By making the 'metric_dfs_by_net' argument a list of multiple outputs from `get_metrics`, +#' you can plot the metrics from multiple networks on the same graphs. +#' The x-axis is based on the network subset sizes. +#' +#' @param metric_dfs_by_net a list of outputs from `get_metrics` +#' @param title_text text for the title of every plot; generally the names of the networks +#' @param subtitle_text text for the subtitle of every plot +#' @param perTF boolean whether the network subset sizes were specified as average target #' genes per TF; changes the x-axis label -#' @param sum bool whether to plot the 'sum' metric, which is the sum of the negative -#' log base 10 of the p-value for the top term of each TF minus 3 times the total -#' number of TFs. -#' @param percent bool whether to plot the 'percent' metric, which is the -#' percent of TFs with at least one GO term with a FDR < 0.05 -#' @param mean bool whether to plot the 'mean' metric, which is the mean negative -#' log base 10 of the p-value for the top term of each TF -#' @param median bool whether to plot the 'median' metric, which is the median negative -#' log base 10 of the p-value for the top term of each TF -#' @param annotation_overlap bool whether to plot the 'annotation_overlap' metric, -#' which is the percent of TFs that are annotated to a GO term for which their -#' target genes are enriched -#' @param size bool whether to plot the 'size' metric, -#' which is the number of TFs with at least one annotated target gene +#' @param sum boolean whether to plot the 'sum' metric, which is the sum of the negative +#' log base 10 of the p-value for the top term of each source node minus the penalty times the total +#' number of source nodes. +#' @param percent boolean whether to plot the 'percent' metric, which is the +#' percent of source nodes with at least one term with a FDR below a threshold +#' @param mean boolean whether to plot the 'mean' metric, which is the mean negative +#' log base 10 of the p-value for the top term of each source node regardless of significance +#' @param median boolean whether to plot the 'median' metric, which is the median negative +#' log base 10 of the p-value for the top term of each source node regardless of significance +#' @param annotation_overlap boolean whether to plot the 'annotation_overlap' metric, +#' which is the percent of source nodes that are annotated to at least one of the 16 GO terms for +#' which their target genes are most enriched +#' @param size boolean whether to plot the 'size' metric, which is the number of +#' source nodes in the network subset that have more than one target gene with annotations +#' +#' @return a list of data.frames each containing values of one metric. +#' The column names denote the network and subset. The first row of each data.frame +#' is from the real networks; the rest are from permuted networks. #' #' @export -plot_metrics <- function(metric_dfs_by_net, title_text, subtitle_text, perTF, sum = TRUE, percent = FALSE, mean = FALSE, median = FALSE, annotation_overlap = FALSE, size = TRUE) { +plot_metrics <- function(metric_dfs_by_net, title_text, subtitle_text = "", perTF, sum = TRUE, percent = FALSE, mean = FALSE, median = FALSE, annotation_overlap = FALSE, size = TRUE) { # determine if any of the input networks have only one size # if yes, metrics for permuted networks will be shown with box plots instead of line plots plot_with_boxes <- FALSE @@ -93,11 +101,17 @@ plot_metrics <- function(metric_dfs_by_net, title_text, subtitle_text, perTF, su plot.subtitle=ggplot2::element_text(hjust=0.5)) + ggplot2::scale_x_continuous(n.breaks = 10, trans = "log2") - # set lower limit of y axis to 0 if graphing a percent or # of TFs - if (grepl("Percent|Number", label_text[m])) { + # set lower limit of y axis to 0 if graphing a percent + if (grepl("Percent", label_text[m])) { plt <- plt + ggplot2::scale_y_continuous(limits = c(0, NA)) } + # set lower limit of y axis to 0 and ensure integer values if graphing number of source nodes + # breaks function from https://stackoverflow.com/questions/15622001/how-to-display-only-integer-values-on-an-axis-using-ggplot2 + if (grepl("Number", label_text[m])) { + plt <- plt + ggplot2::scale_y_continuous(breaks = function(x) unique(floor(pretty(seq(0, (max(x) + 1) * 1.1)))), limits = c(0, NA)) + } + # if any of the networks whose metrics are being plotted has only one size, # plotting with box plots for the permuted networks looks better if (plot_with_boxes) { diff --git a/R/subset_network.R b/R/subset_network.R index fa6a965..1836fb4 100644 --- a/R/subset_network.R +++ b/R/subset_network.R @@ -1,20 +1,18 @@ -#' subset_network -#' -#' From a network with edge scores, sorts the network in descending order of score, and -#' creates subsets using the top given numbers of edges. -#' The input file should be tab-separated with three columns: source node (e.g. transcription factor), -#' target node (e.g. the regulated gene), and edge score. -#' The output files will be tab-separated with two columns: source node, target node. -#' The size of the subsets can either be determined by the total number of edges (if 'num_possible_TFs' is not specified) -#' or by the desired average number of target nodes from each possible source node (if 'num_possible_TFs' is set to a number > 0). -#' The latter option multiplies each element of 'edges' by 'num_possible_TFs' to get the number of -#' edges in each subset. +#' Create network subsets with highest scored edges #' #' @importFrom dplyr desc #' @importFrom dplyr arrange #' @importFrom utils head #' @importFrom dplyr select #' +#' @description +#' Given a network with edge scores, `subset_network` sorts the network in descending order of score and +#' creates subsets using the top given numbers of edges. +#' The size of the subsets can either be determined by the total number of edges (if 'num_possible_TFs' is not specified) +#' or by the desired average number of target nodes from each possible source node (if 'num_possible_TFs' is set to a number > 0). +#' The latter option multiplies each element of 'edges' by 'num_possible_TFs' to get the number of +#' edges in each subset. +#' #' @param input_file a file containing the network to create subsets from. The file should #' be tab-separated with three columns: source node, target node, edge score #' @param output_directory name of the folder in which to place the created network subset files @@ -23,6 +21,13 @@ #' @param num_possible_TFs if set to a number > 0, the elements of 'edges' will first be #' multiplied by this number to get the number of edges for each subset #' +#' @details +#' The input file should be tab-separated with three columns: source node (e.g. transcription factor), +#' target node (e.g. the regulated gene), and edge score. +#' The output files will be tab-separated with two columns: source node, target node. +#' +#' @return NULL +#' #' @export subset_network <- function(input_file, output_directory, name, edges, num_possible_TFs = 0) { dir.create(output_directory, showWarnings = FALSE, recursive = TRUE) diff --git a/R/webgestalt_network.R b/R/webgestalt_network.R index e40bce2..e9e0bc1 100644 --- a/R/webgestalt_network.R +++ b/R/webgestalt_network.R @@ -1,10 +1,4 @@ -#' webgestalt_network -#' -#' Given a network or network subset, webgestalt_network will generate ORA results -#' for this network and a given number of permutations. -#' The input network file should be a tab-separated .tsv where the first column is the -#' source nodes (TFs) and the second column is the target nodes (regulated genes). -#' All genes must be written as their Ensembl gene ID. +#' Generate ORA results for a network and a given number of permutations #' #' @importFrom WebGestaltR idMapping #' @importFrom WebGestaltR loadGeneSet @@ -15,16 +9,33 @@ #' @importFrom stats xtabs #' @importFrom parallelly availableCores #' +#' @description +#' Given a network or network subset, `webgestalt_network` will generate Over-Representation Analysis (ORA) +#' results for this network and a given number of permutations. These are stored in a directory named "network_name" +#' within the specified output_directory, which contains a folder "p0" for the input network and folders "p1", "p2", +#' and so on for the different permutations of the network. Within these folders are .csv files for each source node. +#' These files contain information on the gene set terms that are the most over-represented in the source node's targets. +#' This function also creates a "webgestalt_work" directory in the output_directory, which has the same structure as +#' the "network_name" directory, but the files are .txt files containing the list of targets for each source node. +#' #' @param network_path path to the network or network subset to evaluate. Must be a tab-separated #' file where the first column is the source nodes (TFs) and the second column is the target nodes (regulated genes). #' @param reference_set path to the set of all genes possibly included in the network. Must be a .txt #' file containing exactly one column of the genes that could possibly appear in the network. -#' @param output_directory path to the folder in which output from all networks subset from the same original network should be stored -#' @param network_name the name of the folder to store the results within the output_directory. -#' It should be in the same format as the files output by subset_network: "\{name\}_\{# of edges\}" i.e. "example_8". +#' @param output_directory path to the directory in which output should be stored. It is recommended to make a dedicated +#' directory for the output from all network subsets of a single original network. +#' @param network_name the name of the directory to store the results within the output_directory. +#' It should be in the same format as the files output by `subset_network`: "\{name\}_\{# of edges\}" i.e. "example_8". #' @param organism human: "hsapiens"; yeast: "scerevisiae" #' @param database the gene set database to search for enrichment - see options with WebGestaltR::listGeneSet() -#' @param permutations the number of randomly permuted networks to create and run ORA +#' @param permutations the number of randomly permuted networks to create and run ORA on +#' +#' @details +#' The input network file should be a tab-separated .tsv where the first column is the +#' source nodes (TFs) and the second column is the target nodes (regulated genes). +#' All genes must be written as Ensembl gene IDs. +#' +#' @return NULL #' #' @export webgestalt_network <- function(network_path, reference_set, output_directory, network_name, organism = "hsapiens", database = "geneontology_Biological_Process_noRedundant", permutations = 10) { diff --git a/man/evaluate.Rd b/man/evaluate.Rd index 0049cd2..c1a4fa3 100644 --- a/man/evaluate.Rd +++ b/man/evaluate.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/evaluate.R \name{evaluate} \alias{evaluate} -\title{evaluate} +\title{Perform the whole GOeval pipeline on one network} \usage{ evaluate( network, @@ -12,6 +12,8 @@ evaluate( edges = c(512, 1024, 2048, 4096, 8192, 16384, 32768, 65536), num_possible_TFs = 0, permutations = 3, + penalty = 3, + fdr_threshold = 0.05, get_sum = TRUE, get_percent = FALSE, get_mean = FALSE, @@ -22,7 +24,7 @@ evaluate( ) } \arguments{ -\item{network}{a file containing the network to create subsets from. The file should +\item{network}{path to the file containing the network to create subsets from. The file should be tab-separated with three columns: source node, target node, edge score} \item{reference_set}{path to the set of all genes possibly included in the network. Must be a .txt @@ -38,35 +40,45 @@ ORA summaries, and plots} \item{num_possible_TFs}{if set to a number > 0, the elements of 'edges' will first be multiplied by this number to get the number of edges for each subset} -\item{permutations}{the number of randomly permuted networks to create and run ORA} +\item{permutations}{the number of randomly permuted networks to create and run ORA on} -\item{get_sum}{bool whether to get the 'sum' metric, which is the sum of the negative -log base 10 of the p-value for the top term of each TF minus 3 times the total -number of TFs.} +\item{penalty}{the penalty applied to the 'sum' metric for each TF in the network} -\item{get_percent}{bool whether to get the 'percent' metric, which is the -percent of TFs with at least one GO term with a FDR < 0.05} +\item{fdr_threshold}{the FDR threshold for a gene set term to be considered significantly +over-represented for the purposes of calculating the 'percent' metric} -\item{get_mean}{bool whether to get the 'mean' metric, which is the mean negative -log base 10 of the p-value for the top term of each TF} +\item{get_sum}{boolean whether to get and plot the 'sum' metric, which is the sum of the negative +log base 10 of the p-value for the top term of each source node minus 'penalty' times the total +number of source nodes.} -\item{get_median}{bool whether to get the 'median' metric, which is the median negative -log base 10 of the p-value for the top term of each TF} +\item{get_percent}{boolean whether to get and plot the 'percent' metric, which is the +percent of source nodes with at least one term with a FDR below the 'fdr_threshold'} -\item{get_annotation_overlap}{bool whether to get the 'annotation_overlap' metric, -which is the percent of TFs that are annotated to a GO term for which their -target genes are enriched} +\item{get_mean}{boolean whether to get and plot the 'mean' metric, which is the mean negative +log base 10 of the p-value for the top term of each source node regardless of significance} -\item{get_size}{bool whether to get the 'size' metric, which is the number of -TFs in the network subset that have more than one target gene with GO annotations} +\item{get_median}{boolean whether to get and plot the 'median' metric, which is the median negative +log base 10 of the p-value for the top term of each source node regardless of significance} -\item{plot}{bool whether to make a pdf containing plots of the calculated metrics} +\item{get_annotation_overlap}{boolean whether to get and plot the 'annotation_overlap' metric, +which is the percent of source nodes that are annotated to at least one of the 16 GO terms for +which their target genes are most enriched} + +\item{get_size}{boolean whether to get and plot the 'size' metric, which is the number of +source nodes in the network subset that have more than one target gene with annotations. This number is +used in the calculation of all other metrics.} + +\item{plot}{boolean whether to make plots of the calculated metrics and write them to a pdf} +} +\value{ +output of \code{get_metrics}. Can be used as input to \code{plot_metrics}. } \description{ -Perform the whole GOeval pipeline on one network. Given one network, this -function will first make subsets with subset_network, then run ORA with -webgestalt_network, followed by get_metrics and plot_metrics to plot summary -statistics. -The input file should be tab-separated with three columns: source node (e.g. transcription factor), -target node (e.g. the regulated gene), and edge score. All genes should be written as Ensembl gene IDs. +Given one network, \code{evaluate} will first make subsets with \code{subset_network}, +then run Over-Representation Analysis (ORA) with \code{webgestalt_network}, +followed by \code{get_metrics} and \code{plot_metrics} to plot selected summary statistics. +} +\details{ +The input file should be tab-separated with two or three columns: source node (e.g. transcription factor), +target node (e.g. the regulated gene), and, optionally, edge score. All genes must be written as Ensembl gene IDs. } diff --git a/man/get_annotation_overlap.Rd b/man/get_annotation_overlap.Rd index 9a29bc6..b0a05b7 100644 --- a/man/get_annotation_overlap.Rd +++ b/man/get_annotation_overlap.Rd @@ -2,14 +2,13 @@ % Please edit documentation in R/get_metrics.R \name{get_annotation_overlap} \alias{get_annotation_overlap} -\title{calculate the percent of TFs that are annotated to a GO term for which their -target genes are enriched} +\title{Helper function for \code{get_metrics}} \usage{ get_annotation_overlap(terms, organism, go_ann, go_reg) } \arguments{ -\item{terms}{a data.frame obtained from calling get_terms on a folder of the -summaries output by webgestalt_network} +\item{terms}{a data.frame obtained from calling \code{get_terms} on a folder of the +summaries output by \code{webgestalt_network}} \item{organism}{a string specifying the organism that the data is from, e.g. "hsapiens" or "scerevisiae"} @@ -20,7 +19,11 @@ WebGestaltR::loadGeneSet.} \item{go_reg}{a data.frame of the regulatory relationships between GO terms. Obtain with ontologyIndex::get_ontology.} } +\value{ +a list of source nodes +} \description{ -calculate the percent of TFs that are annotated to a GO term for which their -target genes are enriched +This function calculates the percent of source nodes that are annotated to a GO term +that either regulates or is the same as a GO term for which their target genes are enriched } +\keyword{internal} diff --git a/man/get_metrics.Rd b/man/get_metrics.Rd index 826cf94..99fc20d 100644 --- a/man/get_metrics.Rd +++ b/man/get_metrics.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/get_metrics.R \name{get_metrics} \alias{get_metrics} -\title{get_metrics} +\title{Get summary metrics of a network's ORA results} \usage{ get_metrics( directory, @@ -13,41 +13,55 @@ get_metrics( get_median = FALSE, get_annotation_overlap = FALSE, get_size = TRUE, + penalty = 3, + fdr_threshold = 0.05, parallel = FALSE ) } \arguments{ -\item{directory}{a directory containing the webgestalt_network output directory -for all networks of interest} +\item{directory}{a directory containing only the directories of ORA summaries created by +\code{webgestalt_network} for all networks of interest} \item{organism}{a string specifying the organism that the data is from, e.g. -"hsapiens" or "scerevisiae". Only specify if get_annotation_overlap = TRUE.} +"hsapiens" or "scerevisiae". Only required if get_annotation_overlap = TRUE.} -\item{get_sum}{bool whether to get the 'sum' metric, which is the sum of the negative -log base 10 of the p-value for the top term of each TF minus 3 times the total -number of TFs.} +\item{get_sum}{boolean whether to get the 'sum' metric, which is the sum of the negative +log base 10 of the p-value for the top term of each source node minus 'penalty' times the total +number of source nodes.} -\item{get_percent}{bool whether to get the 'percent' metric, which is the -percent of TFs with at least one GO term with a FDR < 0.05} +\item{get_percent}{boolean whether to get the 'percent' metric, which is the +percent of source nodes with at least one term with a FDR below the 'fdr_threshold'} -\item{get_mean}{bool whether to get the 'mean' metric, which is the mean negative -log base 10 of the p-value for the top term of each TF} +\item{get_mean}{boolean whether to get the 'mean' metric, which is the mean negative +log base 10 of the p-value for the top term of each source node regardless of significance} -\item{get_median}{bool whether to get the 'median' metric, which is the median negative -log base 10 of the p-value for the top term of each TF} +\item{get_median}{boolean whether to get the 'median' metric, which is the median negative +log base 10 of the p-value for the top term of each source node regardless of significance} -\item{get_annotation_overlap}{bool whether to get the 'annotation_overlap' metric, -which is the percent of TFs that are annotated to a GO term for which their -target genes are enriched} +\item{get_annotation_overlap}{boolean whether to get the 'annotation_overlap' metric, +which is the percent of source nodes that are annotated to at least one of the 16 GO terms for +which their target genes are most enriched} -\item{get_size}{bool whether to get the 'size' metric, which is the number of -TFs in the network subset that have more than one target gene with GO annotations} +\item{get_size}{boolean whether to get the 'size' metric, which is the number of +source nodes in the network subset that have more than one target gene with annotations. This number is +used in the calculation of all other metrics.} -\item{parallel}{bool whether to get the metrics for each network in the directory -in parallel or sequentially - use with caution, as this has not been adequately tested} +\item{penalty}{the penalty applied to the 'sum' metric for each TF in the network} + +\item{fdr_threshold}{the FDR threshold for a gene set term to be considered significantly +over-represented for the purposes of calculating the 'percent' metric} + +\item{parallel}{boolean whether to get the metrics for each network in the directory +in parallel - use with caution, as this has not been adequately tested} +} +\value{ +a list of data.frames, each containing the values of one metric. +The columns of a data.frame represent the different subset sizes, and the rows +represent the different network permutations. The first row is from the unpermuted networks. } \description{ -Get a data.frame that contains specified metrics for all networks that have a -subdirectory in the provided path including both the real and permuted networks. -The path should contain only directories created by webgestalt_network. +\code{get_metrics} creates a data.frame that contains specified metrics for all network subsets and their permutations +that have a subdirectory in the provided path. It is designed to be run on the output of the \code{webgestalt_network} +function to prepare summary metrics for plotting with the \code{plot_metrics} function. +The 'directory' path should contain only directories created by \code{webgestalt_network}. } diff --git a/man/get_network_metrics.Rd b/man/get_network_metrics.Rd index a4831a5..bf56ce7 100644 --- a/man/get_network_metrics.Rd +++ b/man/get_network_metrics.Rd @@ -2,8 +2,7 @@ % Please edit documentation in R/get_metrics.R \name{get_network_metrics} \alias{get_network_metrics} -\title{helper function to compute the desired metrics from a dataframe of the top -terms for each TF in a single network} +\title{Helper function for \code{get_metrics}} \usage{ get_network_metrics( full_terms, @@ -15,39 +14,46 @@ get_network_metrics( get_median, get_annotation_overlap, get_size, + penalty, + fdr_threshold, go_ann = NULL, go_reg = NULL ) } \arguments{ \item{full_terms}{a data.frame containing all the summary results for a network. -Use get_terms to obtain.} +Use \code{get_terms} to obtain.} -\item{network_size}{the number of TFs in the network. May be different from -the number of TFs that have summary files.} +\item{network_size}{the number of source nodes in the network} \item{organism}{a string specifying the organism that the data is from, e.g. -"hsapiens" or "scerevisiae". Only specify if get_annotation_overlap = TRUE.} +"hsapiens" or "scerevisiae". Only required if get_annotation_overlap = TRUE.} -\item{get_sum}{bool whether to get the 'sum' metric, which is the sum of the negative -log base 10 of the p-value for the top term of each TF minus 3 times the total -number of TFs.} +\item{get_sum}{boolean whether to get the 'sum' metric, which is the sum of the negative +log base 10 of the p-value for the top term of each source node minus 'penalty' times the total +number of source nodes.} -\item{get_percent}{bool whether to get the 'percent' metric, which is the -percent of TFs with at least one GO term with a FDR < 0.05} +\item{get_percent}{boolean whether to get the 'percent' metric, which is the +percent of source nodes with at least one term with a FDR below the 'fdr_threshold'} -\item{get_mean}{bool whether to get the 'mean' metric, which is the mean negative -log base 10 of the p-value for the top term of each TF} +\item{get_mean}{boolean whether to get the 'mean' metric, which is the mean negative +log base 10 of the p-value for the top term of each source node regardless of significance} -\item{get_median}{bool whether to get the 'median' metric, which is the median negative -log base 10 of the p-value for the top term of each TF} +\item{get_median}{boolean whether to get the 'median' metric, which is the median negative +log base 10 of the p-value for the top term of each source node regardless of significance} -\item{get_annotation_overlap}{bool whether to get the 'annotation_overlap' metric, -which is the percent of TFs that are annotated to a GO term for which their -target genes are enriched} +\item{get_annotation_overlap}{boolean whether to get the 'annotation_overlap' metric, +which is the percent of source nodes that are annotated to at least one of the 16 GO terms for +which their target genes are most enriched} -\item{get_size}{bool whether to get the 'size' metric, which is the number of -TFs in the network subset that have more than one target gene with GO annotations} +\item{get_size}{boolean whether to get the 'size' metric, which is the number of +source nodes in the network subset that have more than one target gene with annotations. This number is +used in the calculation of all other metrics.} + +\item{penalty}{the penalty applied to the 'sum' metric for each source node in the network} + +\item{fdr_threshold}{the FDR threshold for a gene set term to be considered significantly +over-represented for the purposes of calculating the 'percent' metric} \item{go_ann}{a data.frame of annotations of genes to GO terms. Obtain with WebGestaltR::loadGeneSet.} @@ -55,7 +61,11 @@ WebGestaltR::loadGeneSet.} \item{go_reg}{a data.frame of the regulatory relationships between GO terms. Obtain with ontologyIndex::get_ontology.} } +\value{ +a list of metric values +} \description{ -helper function to compute the desired metrics from a dataframe of the top -terms for each TF in a single network +This is a helper function that computes the desired metrics from a data.frame +of the top terms for each source node in a network. } +\keyword{internal} diff --git a/man/get_terms.Rd b/man/get_terms.Rd index 78b4d09..494d9ca 100644 --- a/man/get_terms.Rd +++ b/man/get_terms.Rd @@ -2,17 +2,20 @@ % Please edit documentation in R/get_terms.R \name{get_terms} \alias{get_terms} -\title{get_terms} +\title{Gather ORA results from one folder of ORA summaries} \usage{ get_terms(path, n) } \arguments{ \item{path}{a folder containing .csv files of the ORA summaries output -by webgestalt_network} +by \code{webgestalt_network}} -\item{n}{the number of terms from each summary to get data for} +\item{n}{the number of terms from each summary for which to get data} +} +\value{ +a data.frame } \description{ -Get ORA data for the top n terms from each TF summary file in a folder -output by the webgestalt_network function. +Get Over-Representation Analysis (ORA) data for the top n terms from each source node summary file +in a folder output by the \code{webgestalt_network} function. } diff --git a/man/plot_metrics.Rd b/man/plot_metrics.Rd index 9235620..abd898f 100644 --- a/man/plot_metrics.Rd +++ b/man/plot_metrics.Rd @@ -2,12 +2,12 @@ % Please edit documentation in R/plot_metrics.R \name{plot_metrics} \alias{plot_metrics} -\title{plot_metrics} +\title{Plot the summary metrics from \code{get_metrics}} \usage{ plot_metrics( metric_dfs_by_net, title_text, - subtitle_text, + subtitle_text = "", perTF, sum = TRUE, percent = FALSE, @@ -18,35 +18,43 @@ plot_metrics( ) } \arguments{ -\item{metric_dfs_by_net}{a list of outputs of get_metrics for each network to be plotted} +\item{metric_dfs_by_net}{a list of outputs from \code{get_metrics}} -\item{title_text}{text for the title of each plot; generally the names of the networks} +\item{title_text}{text for the title of every plot; generally the names of the networks} -\item{subtitle_text}{text for the subtitle of each plot} +\item{subtitle_text}{text for the subtitle of every plot} -\item{perTF}{bool whether the network subset sizes were specified as average target +\item{perTF}{boolean whether the network subset sizes were specified as average target genes per TF; changes the x-axis label} -\item{sum}{bool whether to plot the 'sum' metric, which is the sum of the negative -log base 10 of the p-value for the top term of each TF minus 3 times the total -number of TFs.} +\item{sum}{boolean whether to plot the 'sum' metric, which is the sum of the negative +log base 10 of the p-value for the top term of each source node minus the penalty times the total +number of source nodes.} -\item{percent}{bool whether to plot the 'percent' metric, which is the -percent of TFs with at least one GO term with a FDR < 0.05} +\item{percent}{boolean whether to plot the 'percent' metric, which is the +percent of source nodes with at least one term with a FDR below a threshold} -\item{mean}{bool whether to plot the 'mean' metric, which is the mean negative -log base 10 of the p-value for the top term of each TF} +\item{mean}{boolean whether to plot the 'mean' metric, which is the mean negative +log base 10 of the p-value for the top term of each source node regardless of significance} -\item{median}{bool whether to plot the 'median' metric, which is the median negative -log base 10 of the p-value for the top term of each TF} +\item{median}{boolean whether to plot the 'median' metric, which is the median negative +log base 10 of the p-value for the top term of each source node regardless of significance} -\item{annotation_overlap}{bool whether to plot the 'annotation_overlap' metric, -which is the percent of TFs that are annotated to a GO term for which their -target genes are enriched} +\item{annotation_overlap}{boolean whether to plot the 'annotation_overlap' metric, +which is the percent of source nodes that are annotated to at least one of the 16 GO terms for +which their target genes are most enriched} -\item{size}{bool whether to plot the 'size' metric, -which is the number of TFs with at least one annotated target gene} +\item{size}{boolean whether to plot the 'size' metric, which is the number of +source nodes in the network subset that have more than one target gene with annotations} +} +\value{ +a list of data.frames each containing values of one metric. +The column names denote the network and subset. The first row of each data.frame +is from the real networks; the rest are from permuted networks. } \description{ -Plot the summary metrics from get_metrics across network subset size for one or more networks. +\code{plot_metrics} creates plots of all the summary metrics calculated by \code{get_metrics}. +By making the 'metric_dfs_by_net' argument a list of multiple outputs from \code{get_metrics}, +you can plot the metrics from multiple networks on the same graphs. +The x-axis is based on the network subset sizes. } diff --git a/man/subset_network.Rd b/man/subset_network.Rd index 2ee76dd..b5912cf 100644 --- a/man/subset_network.Rd +++ b/man/subset_network.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/subset_network.R \name{subset_network} \alias{subset_network} -\title{subset_network} +\title{Create network subsets with highest scored edges} \usage{ subset_network(input_file, output_directory, name, edges, num_possible_TFs = 0) } @@ -20,13 +20,15 @@ be tab-separated with three columns: source node, target node, edge score} multiplied by this number to get the number of edges for each subset} } \description{ -From a network with edge scores, sorts the network in descending order of score, and +Given a network with edge scores, \code{subset_network} sorts the network in descending order of score and creates subsets using the top given numbers of edges. -The input file should be tab-separated with three columns: source node (e.g. transcription factor), -target node (e.g. the regulated gene), and edge score. -The output files will be tab-separated with two columns: source node, target node. The size of the subsets can either be determined by the total number of edges (if 'num_possible_TFs' is not specified) or by the desired average number of target nodes from each possible source node (if 'num_possible_TFs' is set to a number > 0). The latter option multiplies each element of 'edges' by 'num_possible_TFs' to get the number of edges in each subset. } +\details{ +The input file should be tab-separated with three columns: source node (e.g. transcription factor), +target node (e.g. the regulated gene), and edge score. +The output files will be tab-separated with two columns: source node, target node. +} diff --git a/man/webgestalt_network.Rd b/man/webgestalt_network.Rd index 0658694..2e7fb96 100644 --- a/man/webgestalt_network.Rd +++ b/man/webgestalt_network.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/webgestalt_network.R \name{webgestalt_network} \alias{webgestalt_network} -\title{webgestalt_network} +\title{Generate ORA results for a network and a given number of permutations} \usage{ webgestalt_network( network_path, @@ -21,21 +21,29 @@ file where the first column is the source nodes (TFs) and the second column is t \item{reference_set}{path to the set of all genes possibly included in the network. Must be a .txt file containing exactly one column of the genes that could possibly appear in the network.} -\item{output_directory}{path to the folder in which output from all networks subset from the same original network should be stored} +\item{output_directory}{path to the directory in which output should be stored. It is recommended to make a dedicated +directory for the output from all network subsets of a single original network.} -\item{network_name}{the name of the folder to store the results within the output_directory. -It should be in the same format as the files output by subset_network: "\{name\}_\{# of edges\}" i.e. "example_8".} +\item{network_name}{the name of the directory to store the results within the output_directory. +It should be in the same format as the files output by \code{subset_network}: "\{name\}_\{# of edges\}" i.e. "example_8".} \item{organism}{human: "hsapiens"; yeast: "scerevisiae"} \item{database}{the gene set database to search for enrichment - see options with WebGestaltR::listGeneSet()} -\item{permutations}{the number of randomly permuted networks to create and run ORA} +\item{permutations}{the number of randomly permuted networks to create and run ORA on} } \description{ -Given a network or network subset, webgestalt_network will generate ORA results -for this network and a given number of permutations. +Given a network or network subset, \code{webgestalt_network} will generate Over-Representation Analysis (ORA) +results for this network and a given number of permutations. These are stored in a directory named "network_name" +within the specified output_directory, which contains a folder "p0" for the input network and folders "p1", "p2", +and so on for the different permutations of the network. Within these folders are .csv files for each source node. +These files contain information on the gene set terms that are the most over-represented in the source node's targets. +This function also creates a "webgestalt_work" directory in the output_directory, which has the same structure as +the "network_name" directory, but the files are .txt files containing the list of targets for each source node. +} +\details{ The input network file should be a tab-separated .tsv where the first column is the source nodes (TFs) and the second column is the target nodes (regulated genes). -All genes must be written as their Ensembl gene ID. +All genes must be written as Ensembl gene IDs. } diff --git a/tests/testthat/data/box-test-plot_metrics.pdf b/tests/testthat/data/box-test-plot_metrics.pdf index fde5db922ef5c23ae42d5599e2dc78a1fb87365d..f4636eafac95e36f707595bf6877bd404068905e 100644 GIT binary patch delta 2277 zcmZXTc|6mPAIHCF!!$?Oj4-(t`q^g8l_FcoeH3bvp>jovMENl17n4FI=a$NlIdfFb 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It is easiest to use either the "devtools" or "remotes" package to do so. "remotes" is a smaller package, so it might be the better option if you do not want to install all of "devtools." +Point to webgestalt website, explain possible organisms and gene ids, describe defaults. How to use different gene id. + ```{r, eval = FALSE} devtools::install_github("westbrooktm/GOeval", ref = "dev") # OR From e15b7df0296e0bc20000edeef2d6d91745dc8c61 Mon Sep 17 00:00:00 2001 From: westbrooktm Date: Fri, 7 Jul 2023 01:56:52 -0400 Subject: [PATCH 3/4] vignette updates and fix to plot_metrics --- R/evaluate.R | 18 +++++++-- R/get_metrics.R | 29 ++++++++------ R/plot_metrics.R | 6 +-- R/webgestalt_network.R | 17 +++++---- man/evaluate.Rd | 14 ++++++- man/get_annotation_overlap.Rd | 5 ++- man/get_metrics.Rd | 8 ++++ man/get_network_metrics.Rd | 6 ++- man/webgestalt_network.Rd | 8 +++- vignettes/GOeval.Rmd | 72 +++++++++++++++++++++-------------- 10 files changed, 124 insertions(+), 59 deletions(-) diff --git a/R/evaluate.R b/R/evaluate.R index f2f94e9..571dba0 100644 --- a/R/evaluate.R +++ b/R/evaluate.R @@ -13,6 +13,12 @@ #' @param output_directory path to the directory in which to store the generated network subsets, #' ORA summaries, and plots #' @param network_name short name for the network - used in file naming so may not contain spaces +#' @param organism a string specifying the organism that the data is from, e.g. +#' "hsapiens" or "scerevisiae" - see options with WebGestaltR::listOrganism() +#' @param database the gene set database to search for enrichment - see options with WebGestaltR::listGeneSet(). +#' Must be a Gene Ontology "biological process" database if get_annotation_overlap = TRUE. +#' @param gene_id the naming system used for the input genes - see options with WebGestaltR::listIdType() +#' and see webgestalt.org for examples of each type #' @param edges list of total numbers of edges or average edges per TF to include in each subset #' @param num_possible_TFs if set to a number > 0, the elements of 'edges' will first be #' multiplied by this number to get the number of edges for each subset @@ -39,13 +45,14 @@ #' #' @details #' The input file should be tab-separated with two or three columns: source node (e.g. transcription factor), -#' target node (e.g. the regulated gene), and, optionally, edge score. All genes must be written as Ensembl gene IDs. +#' target node (e.g. the regulated gene), and, optionally, edge score. #' #' @return output of `get_metrics`. Can be used as input to `plot_metrics`. #' #' @export -evaluate <- function(network, reference_set, output_directory, network_name, edges = c(512, 1024, 2048, 4096, 8192, 16384, 32768, 65536), num_possible_TFs = 0, - permutations = 3, penalty = 3, fdr_threshold = 0.05, get_sum = TRUE, get_percent = FALSE, get_mean = FALSE, get_median = FALSE, get_annotation_overlap = FALSE, get_size = TRUE, plot = TRUE) { +evaluate <- function(network, reference_set, output_directory, network_name, organism = "hsapiens", database = "geneontology_Biological_Process_noRedundant", gene_id = "ensembl_gene_id", + edges = c(512, 1024, 2048, 4096, 8192, 16384, 32768, 65536), num_possible_TFs = 0, permutations = 3, penalty = 3, fdr_threshold = 0.05, + get_sum = TRUE, get_percent = FALSE, get_mean = FALSE, get_median = FALSE, get_annotation_overlap = FALSE, get_size = TRUE, plot = TRUE) { # first package function subset_network(network, file.path(output_directory, paste0(network_name, "_subsets")), network_name, edges, num_possible_TFs) @@ -62,12 +69,15 @@ evaluate <- function(network, reference_set, output_directory, network_name, edg output_directory = file.path(output_directory, paste0(network_name, summaries_suffix)), # this just gets the name of each file minus the extension network_name = strsplit(basename(subset), "[.]")[[1]][1], + organism = organism, + database = database, + gene_id = gene_id, permutations = permutations) } # third package function # mapply returns a list of length 1 containing the output of get_metrics - metric_dfs_by_net <- mapply(get_metrics, file.path(output_directory, paste0(network_name, summaries_suffix)), MoreArgs=list(get_sum = get_sum, get_percent = get_percent, get_mean = get_mean, get_median = get_median, get_annotation_overlap = get_annotation_overlap, get_size = get_size, penalty = penalty, fdr_threshold = fdr_threshold, parallel = FALSE), SIMPLIFY = FALSE) + metric_dfs_by_net <- mapply(get_metrics, file.path(output_directory, paste0(network_name, summaries_suffix)), MoreArgs=list(organism = organism, database = database, gene_id = gene_id, get_sum = get_sum, get_percent = get_percent, get_mean = get_mean, get_median = get_median, get_annotation_overlap = get_annotation_overlap, get_size = get_size, penalty = penalty, fdr_threshold = fdr_threshold, parallel = FALSE), SIMPLIFY = FALSE) if (plot) { pdf(file.path(output_directory, paste0(network_name, "_ORA_metrics_plots_", formatted_time, ".pdf")), 7, 5) diff --git a/R/get_metrics.R b/R/get_metrics.R index ff33d96..8f0ab93 100644 --- a/R/get_metrics.R +++ b/R/get_metrics.R @@ -10,6 +10,8 @@ #' summaries output by `webgestalt_network` #' @param organism a string specifying the organism that the data is from, e.g. #' "hsapiens" or "scerevisiae" +#' @param gene_id the naming system used for the input genes - see options with WebGestaltR::listIdType() +#' and see webgestalt.org for examples of each type #' @param go_ann a data.frame of annotations of genes to GO terms. Obtain with #' WebGestaltR::loadGeneSet. #' @param go_reg a data.frame of the regulatory relationships between GO terms. @@ -18,11 +20,10 @@ #' @return a list of source nodes #' #' @keywords internal -get_annotation_overlap <- function(terms, organism, go_ann, go_reg) { +get_annotation_overlap <- function(terms, organism, gene_id, go_ann, go_reg) { tf_ids <- unique(terms$tfId) - # assumption of "ensembl_gene_id" - tf_map <- WebGestaltR::idMapping(organism = organism, inputGene = tf_ids, sourceIdType = "ensembl_gene_id", targetIdType = "entrezgene", host = "https://www.webgestalt.org/") + tf_map <- WebGestaltR::idMapping(organism = organism, inputGene = tf_ids, sourceIdType = gene_id, targetIdType = "entrezgene", host = "https://www.webgestalt.org/") overlap_list <- list() for (tf in tf_ids) { @@ -63,7 +64,9 @@ get_annotation_overlap <- function(terms, organism, go_ann, go_reg) { #' Use `get_terms` to obtain. #' @param network_size the number of source nodes in the network #' @param organism a string specifying the organism that the data is from, e.g. -#' "hsapiens" or "scerevisiae". Only required if get_annotation_overlap = TRUE. +#' "hsapiens" or "scerevisiae" +#' @param gene_id the naming system used for the input genes - see options with WebGestaltR::listIdType() +#' and see webgestalt.org for examples of each type #' @param get_sum boolean whether to get the 'sum' metric, which is the sum of the negative #' log base 10 of the p-value for the top term of each source node minus 'penalty' times the total #' number of source nodes. @@ -90,7 +93,7 @@ get_annotation_overlap <- function(terms, organism, go_ann, go_reg) { #' @return a list of metric values #' #' @keywords internal -get_network_metrics <- function(full_terms, network_size, organism, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, penalty, fdr_threshold, go_ann = NULL, go_reg = NULL) { +get_network_metrics <- function(full_terms, network_size, organism, gene_id, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, penalty, fdr_threshold, go_ann = NULL, go_reg = NULL) { if (!any(is.na(full_terms))) { terms <- full_terms[match(unique(full_terms$tfId), full_terms$tfId), ] percent <- 100 * sum(terms$FDR < fdr_threshold) / network_size @@ -99,7 +102,7 @@ get_network_metrics <- function(full_terms, network_size, organism, get_sum, get neglogp <- ifelse(is.finite(neglogp), neglogp, 16) #neglogp_zeros <- append(rep(0, network_size - length(terms$geneSet)), neglogp) if (get_annotation_overlap) { - prior_ann <- 100 * length(get_annotation_overlap(full_terms, organism, go_ann = go_ann, go_reg = go_reg)) / network_size + prior_ann <- 100 * length(get_annotation_overlap(full_terms, organism, gene_id, go_ann = go_ann, go_reg = go_reg)) / network_size } else { prior_ann <- 0 } @@ -132,7 +135,11 @@ get_network_metrics <- function(full_terms, network_size, organism, get_sum, get #' @param directory a directory containing only the directories of ORA summaries created by #' `webgestalt_network` for all networks of interest #' @param organism a string specifying the organism that the data is from, e.g. -#' "hsapiens" or "scerevisiae". Only required if get_annotation_overlap = TRUE. +#' "hsapiens" or "scerevisiae". Only required if get_annotation_overlap = TRUE. +#' @param database the gene set database to search for enrichment - see options with WebGestaltR::listGeneSet(). +#' Must be a Gene Ontology "biological process" database if get_annotation_overlap = TRUE. +#' @param gene_id the naming system used for the input genes - see options with WebGestaltR::listIdType() +#' and see webgestalt.org for examples of each type. Only required if get_annotation_overlap = TRUE. #' @param get_sum boolean whether to get the 'sum' metric, which is the sum of the negative #' log base 10 of the p-value for the top term of each source node minus 'penalty' times the total #' number of source nodes. @@ -159,7 +166,7 @@ get_network_metrics <- function(full_terms, network_size, organism, get_sum, get #' represent the different network permutations. The first row is from the unpermuted networks. #' #' @export -get_metrics <- function(directory, organism = "hsapiens", get_sum = TRUE, get_percent = FALSE, get_mean = FALSE, get_median = FALSE, get_annotation_overlap = FALSE, get_size = TRUE, penalty = 3, fdr_threshold = 0.05, parallel = FALSE) { +get_metrics <- function(directory, organism = "hsapiens", database = "geneontology_Biological_Process_noRedundant", gene_id = "ensembl_gene_id", get_sum = TRUE, get_percent = FALSE, get_mean = FALSE, get_median = FALSE, get_annotation_overlap = FALSE, get_size = TRUE, penalty = 3, fdr_threshold = 0.05, parallel = FALSE) { # GO regulatory relationships only needed if get_annotation_overlap = TRUE if (get_annotation_overlap) { # Load regulatory relationships between GO terms for the calculation of overlap between TF GO @@ -170,7 +177,7 @@ get_metrics <- function(directory, organism = "hsapiens", get_sum = TRUE, get_pe rm(go) # Load gene annotations with WebGestaltR::loadGeneSet. - suppressWarnings(go_ann <- WebGestaltR::loadGeneSet(organism = "hsapiens", enrichDatabase = "geneontology_Biological_Process_noRedundant", hostName = "https://www.webgestalt.org/")$geneSet) + suppressWarnings(go_ann <- WebGestaltR::loadGeneSet(organism = organism, enrichDatabase = database, hostName = "https://www.webgestalt.org/")$geneSet) } else { go_reg <- NULL go_ann <- NULL @@ -199,7 +206,7 @@ get_metrics <- function(directory, organism = "hsapiens", get_sum = TRUE, get_pe }) paths <- list.dirs(net, full.names = TRUE, recursive = FALSE) p_terms <- mapply(get_terms, paths, 16, SIMPLIFY = FALSE) - return(t(mapply(get_network_metrics, p_terms, network_sizes, organism, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, penalty, fdr_threshold, MoreArgs = list(go_ann = go_ann, go_reg = go_reg)))) + return(t(mapply(get_network_metrics, p_terms, network_sizes, organism, gene_id, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, penalty, fdr_threshold, MoreArgs = list(go_ann = go_ann, go_reg = go_reg)))) }, mc.cores = parallelly::availableCores()) } else { # non-parallel version @@ -211,7 +218,7 @@ get_metrics <- function(directory, organism = "hsapiens", get_sum = TRUE, get_pe }) paths <- list.dirs(net, full.names = TRUE, recursive = FALSE) p_terms <- mapply(get_terms, paths, 16, SIMPLIFY = FALSE) - return(t(mapply(get_network_metrics, p_terms, network_sizes, organism, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, penalty, fdr_threshold, MoreArgs = list(go_ann = go_ann, go_reg = go_reg)))) + return(t(mapply(get_network_metrics, p_terms, network_sizes, organism, gene_id, get_sum, get_percent, get_mean, get_median, get_annotation_overlap, get_size, penalty, fdr_threshold, MoreArgs = list(go_ann = go_ann, go_reg = go_reg)))) }) } df_list <- list() diff --git a/R/plot_metrics.R b/R/plot_metrics.R index 565dbaa..4e717f1 100644 --- a/R/plot_metrics.R +++ b/R/plot_metrics.R @@ -115,8 +115,8 @@ plot_metrics <- function(metric_dfs_by_net, title_text, subtitle_text = "", perT # if any of the networks whose metrics are being plotted has only one size, # plotting with box plots for the permuted networks looks better if (plot_with_boxes) { - plt <- plt + ggplot2::geom_boxplot(permuted, mapping=ggplot2::aes(x=size, y=metric, color = method, group = size)) + - ggplot2::labs(x = ifelse(perTF, "Edges per TF", "Edges"), y = label_text[m], color = "Network name", shape = "Network name") + plt <- plt + ggplot2::geom_boxplot(permuted, mapping=ggplot2::aes(x=size, y=metric, color = method, group = interaction(method, size))) + + ggplot2::labs(x = ifelse(perTF, "Edges per TF", "Edges"), y = label_text[m], color = "Network", shape = "Network") } else { medians = dplyr::summarise(group_by(permuted, network), median = median(metric)) medians <- tidyr::separate_wider_regex(medians, network, c(method = ".*", "_", size = ".*"), cols_remove = FALSE) @@ -126,7 +126,7 @@ plot_metrics <- function(metric_dfs_by_net, title_text, subtitle_text = "", perT for(net in unique(permuted$method)) { plt <- plt + ggplot2::geom_line(medians[medians$method == net,], mapping = ggplot2::aes(x = size, y = median, color = method, linetype = ltype), linewidth = 1.5) } - plt <- plt + ggplot2::labs(x = ifelse(perTF, "Edges per TF", "Edges"), y = label_text[m], color = "Network name", shape = "Network name", linetype = "Data") + plt <- plt + ggplot2::labs(x = ifelse(perTF, "Edges per TF", "Edges"), y = label_text[m], color = "Network", shape = "Network", linetype = "Data") } plot(plt) } diff --git a/R/webgestalt_network.R b/R/webgestalt_network.R index e9e0bc1..72f456a 100644 --- a/R/webgestalt_network.R +++ b/R/webgestalt_network.R @@ -26,23 +26,24 @@ #' directory for the output from all network subsets of a single original network. #' @param network_name the name of the directory to store the results within the output_directory. #' It should be in the same format as the files output by `subset_network`: "\{name\}_\{# of edges\}" i.e. "example_8". -#' @param organism human: "hsapiens"; yeast: "scerevisiae" +#' @param organism a string specifying the organism that the data is from, e.g. +#' "hsapiens" or "scerevisiae" - see options with WebGestaltR::listOrganism() #' @param database the gene set database to search for enrichment - see options with WebGestaltR::listGeneSet() +#' @param gene_id the naming system used for the input genes - see options with WebGestaltR::listIdType() +#' and see webgestalt.org for examples of each type #' @param permutations the number of randomly permuted networks to create and run ORA on #' #' @details #' The input network file should be a tab-separated .tsv where the first column is the #' source nodes (TFs) and the second column is the target nodes (regulated genes). -#' All genes must be written as Ensembl gene IDs. #' #' @return NULL #' #' @export -webgestalt_network <- function(network_path, reference_set, output_directory, network_name, organism = "hsapiens", database = "geneontology_Biological_Process_noRedundant", permutations = 10) { +webgestalt_network <- function(network_path, reference_set, output_directory, network_name, organism = "hsapiens", database = "geneontology_Biological_Process_noRedundant", gene_id = "ensembl_gene_id", permutations = 10) { METHOD="ORA" # ORA | GSEA | NTA - GENE_ID="ensembl_gene_id" # see options with listIdType() - set to ensembl_gene_id for now # reports are more in-depth than summaries - advisable to keep reports FALSE if not needed - REPORTS_PATH=output_directory#"GO_results" # only used if GENERATE_REPORT=TRUE + REPORTS_PATH=output_directory # only used if GENERATE_REPORT=TRUE GENERATE_REPORT=FALSE # path must exist even if GENERATE_REPORT=FALSE @@ -60,7 +61,7 @@ webgestalt_network <- function(network_path, reference_set, output_directory, ne } # finds the subset of genes in the reference set that are annotated to valid GO terms - ref_genes = WebGestaltR::idMapping(organism = organism, inputGene = read.table(reference_set)$V1, sourceIdType = GENE_ID, targetIdType = "entrezgene", host = "https://www.webgestalt.org/") + ref_genes = WebGestaltR::idMapping(organism = organism, inputGene = read.table(reference_set)$V1, sourceIdType = gene_id, targetIdType = "entrezgene", host = "https://www.webgestalt.org/") annotations = suppressWarnings(WebGestaltR::loadGeneSet(organism = organism, enrichDatabase = database)) genes_per_term = table(annotations$geneSet$geneSet) annotations_proper_size = annotations$geneSet[(genes_per_term[annotations$geneSet$geneSet] >= 10 & genes_per_term[annotations$geneSet$geneSet] <= 500),] @@ -108,9 +109,9 @@ webgestalt_network <- function(network_path, reference_set, output_directory, ne organism = organism, enrichDatabase = database, interestGeneFolder = file.path(work_dir,paste0("p",i-1)), - interestGeneType = GENE_ID, + interestGeneType = gene_id, referenceGene = annotated_ref_genes$userId, - referenceGeneType = GENE_ID, + referenceGeneType = gene_id, minNum = 10, # default 10 maxNum = 500, # default 500 reportNum = 20, # default 20 diff --git a/man/evaluate.Rd b/man/evaluate.Rd index c1a4fa3..523b93e 100644 --- a/man/evaluate.Rd +++ b/man/evaluate.Rd @@ -9,6 +9,9 @@ evaluate( reference_set, output_directory, network_name, + organism = "hsapiens", + database = "geneontology_Biological_Process_noRedundant", + gene_id = "ensembl_gene_id", edges = c(512, 1024, 2048, 4096, 8192, 16384, 32768, 65536), num_possible_TFs = 0, permutations = 3, @@ -35,6 +38,15 @@ ORA summaries, and plots} \item{network_name}{short name for the network - used in file naming so may not contain spaces} +\item{organism}{a string specifying the organism that the data is from, e.g. +"hsapiens" or "scerevisiae" - see options with WebGestaltR::listOrganism()} + +\item{database}{the gene set database to search for enrichment - see options with WebGestaltR::listGeneSet(). +Must be a Gene Ontology "biological process" database if get_annotation_overlap = TRUE.} + +\item{gene_id}{the naming system used for the input genes - see options with WebGestaltR::listIdType() +and see webgestalt.org for examples of each type} + \item{edges}{list of total numbers of edges or average edges per TF to include in each subset} \item{num_possible_TFs}{if set to a number > 0, the elements of 'edges' will first be @@ -80,5 +92,5 @@ followed by \code{get_metrics} and \code{plot_metrics} to plot selected summary } \details{ The input file should be tab-separated with two or three columns: source node (e.g. transcription factor), -target node (e.g. the regulated gene), and, optionally, edge score. All genes must be written as Ensembl gene IDs. +target node (e.g. the regulated gene), and, optionally, edge score. } diff --git a/man/get_annotation_overlap.Rd b/man/get_annotation_overlap.Rd index b0a05b7..77052dd 100644 --- a/man/get_annotation_overlap.Rd +++ b/man/get_annotation_overlap.Rd @@ -4,7 +4,7 @@ \alias{get_annotation_overlap} \title{Helper function for \code{get_metrics}} \usage{ -get_annotation_overlap(terms, organism, go_ann, go_reg) +get_annotation_overlap(terms, organism, gene_id, go_ann, go_reg) } \arguments{ \item{terms}{a data.frame obtained from calling \code{get_terms} on a folder of the @@ -13,6 +13,9 @@ summaries output by \code{webgestalt_network}} \item{organism}{a string specifying the organism that the data is from, e.g. "hsapiens" or "scerevisiae"} +\item{gene_id}{the naming system used for the input genes - see options with WebGestaltR::listIdType() +and see webgestalt.org for examples of each type} + \item{go_ann}{a data.frame of annotations of genes to GO terms. Obtain with WebGestaltR::loadGeneSet.} diff --git a/man/get_metrics.Rd b/man/get_metrics.Rd index 99fc20d..3c4156d 100644 --- a/man/get_metrics.Rd +++ b/man/get_metrics.Rd @@ -7,6 +7,8 @@ get_metrics( directory, organism = "hsapiens", + database = "geneontology_Biological_Process_noRedundant", + gene_id = "ensembl_gene_id", get_sum = TRUE, get_percent = FALSE, get_mean = FALSE, @@ -25,6 +27,12 @@ get_metrics( \item{organism}{a string specifying the organism that the data is from, e.g. "hsapiens" or "scerevisiae". Only required if get_annotation_overlap = TRUE.} +\item{database}{the gene set database to search for enrichment - see options with WebGestaltR::listGeneSet(). +Must be a Gene Ontology "biological process" database if get_annotation_overlap = TRUE.} + +\item{gene_id}{the naming system used for the input genes - see options with WebGestaltR::listIdType() +and see webgestalt.org for examples of each type. Only required if get_annotation_overlap = TRUE.} + \item{get_sum}{boolean whether to get the 'sum' metric, which is the sum of the negative log base 10 of the p-value for the top term of each source node minus 'penalty' times the total number of source nodes.} diff --git a/man/get_network_metrics.Rd b/man/get_network_metrics.Rd index bf56ce7..78340ea 100644 --- a/man/get_network_metrics.Rd +++ b/man/get_network_metrics.Rd @@ -8,6 +8,7 @@ get_network_metrics( full_terms, network_size, organism, + gene_id, get_sum, get_percent, get_mean, @@ -27,7 +28,10 @@ Use \code{get_terms} to obtain.} \item{network_size}{the number of source nodes in the network} \item{organism}{a string specifying the organism that the data is from, e.g. -"hsapiens" or "scerevisiae". Only required if get_annotation_overlap = TRUE.} +"hsapiens" or "scerevisiae"} + +\item{gene_id}{the naming system used for the input genes - see options with WebGestaltR::listIdType() +and see webgestalt.org for examples of each type} \item{get_sum}{boolean whether to get the 'sum' metric, which is the sum of the negative log base 10 of the p-value for the top term of each source node minus 'penalty' times the total diff --git a/man/webgestalt_network.Rd b/man/webgestalt_network.Rd index 2e7fb96..70df594 100644 --- a/man/webgestalt_network.Rd +++ b/man/webgestalt_network.Rd @@ -11,6 +11,7 @@ webgestalt_network( network_name, organism = "hsapiens", database = "geneontology_Biological_Process_noRedundant", + gene_id = "ensembl_gene_id", permutations = 10 ) } @@ -27,10 +28,14 @@ directory for the output from all network subsets of a single original network.} \item{network_name}{the name of the directory to store the results within the output_directory. It should be in the same format as the files output by \code{subset_network}: "\{name\}_\{# of edges\}" i.e. "example_8".} -\item{organism}{human: "hsapiens"; yeast: "scerevisiae"} +\item{organism}{a string specifying the organism that the data is from, e.g. +"hsapiens" or "scerevisiae" - see options with WebGestaltR::listOrganism()} \item{database}{the gene set database to search for enrichment - see options with WebGestaltR::listGeneSet()} +\item{gene_id}{the naming system used for the input genes - see options with WebGestaltR::listIdType() +and see webgestalt.org for examples of each type} + \item{permutations}{the number of randomly permuted networks to create and run ORA on} } \description{ @@ -45,5 +50,4 @@ the "network_name" directory, but the files are .txt files containing the list o \details{ The input network file should be a tab-separated .tsv where the first column is the source nodes (TFs) and the second column is the target nodes (regulated genes). -All genes must be written as Ensembl gene IDs. } diff --git a/vignettes/GOeval.Rmd b/vignettes/GOeval.Rmd index 65a7719..31adf61 100644 --- a/vignettes/GOeval.Rmd +++ b/vignettes/GOeval.Rmd @@ -16,8 +16,6 @@ knitr::opts_chunk$set( First, install and load this package. It is easiest to use either the "devtools" or "remotes" package to do so. "remotes" is a smaller package, so it might be the better option if you do not want to install all of "devtools." -Point to webgestalt website, explain possible organisms and gene ids, describe defaults. How to use different gene id. - ```{r, eval = FALSE} devtools::install_github("westbrooktm/GOeval", ref = "dev") # OR @@ -28,7 +26,7 @@ remotes::install_github("westbrooktm/GOeval", ref = "dev") library("GOeval") ``` -Now that the package is loaded, we need a network as a tab-separated .tsv file where the first column is the source nodes, the second column is the target nodes, and (if using subset_network) the third column is the edge scores. All genes should be written as their Ensembl gene ID. +Now that the package is loaded, we need a network as a tab-separated .tsv file where the first column is the source nodes, the second column is the target nodes, and the optional third column is the edge scores. To run the following examples, access the example data stored in "vignette_data.tar.gz" and extract it into a local folder. @@ -65,21 +63,23 @@ network_name <- "np3" out_dir <- "data" ``` -Run the 'evaluate' function. Look at its arguments in the documentation for ways to customize the output. - -The possible arguments for all functions can be found at the "Reference" tab at the top of this page or https://westbrooktm.github.io/GOeval/reference/index.html. +Run the `evaluate` function. ```{r} net_1_data <- evaluate(network_path, reference_path, out_dir, network_name, - # additional arguments allow for output customization - # for example, permutations = 1 allows a faster run time than the default 3 - # get_annotation_overlap = TRUE causes the computation of the annotation_overlap metric - permutations = 1, get_annotation_overlap = TRUE) + # some additional arguments + edges = c(512, 1024, 2048, 3174, 4096, 8192, 16384, 32768, 65536), + permutations = 1, + get_annotation_overlap = TRUE) ``` -After a couple minutes, the directory at out_dir should contain a folder of subsets of the original network, a folder that contains the ORA summaries and sets of target genes, and a .pdf file with plots of metrics calculated from those summaries. The evaluate function returns the data used to make the plots as a list of DataFrames. +It is possible to customize the behavior of the `evaluate` function by using arguments in addition to the four required ones. In the above example, the 'edges' argument modifies the sizes of subsets to analyze from the input network. "permutations = 1" allows a faster run time than the default "permutations = 3" at the cost of running the analyses on fewer permuted versions of the input network. "get_annotation_overlap = TRUE" indicates that the "annotation_overlap" metric should be calculated. + +The available arguments for all functions can be found at the "Reference" tab at the top of this page or https://westbrooktm.github.io/GOeval/reference/index.html. + +After `evaluate` runs for a couple minutes, the directory at out_dir should contain a folder of subsets of the original network, a folder that contains the ORA summaries and sets of target genes, and a .pdf file with plots of metrics calculated from those summaries. The evaluate function returns the data used to make the plots as a list of data.frames. -The run time of the evaluate function changes with the size of the network and the number of permutations. With larger networks, it may take up to several hours. It is thus recommended to use a computing cluster when working with large networks. It is possible to speed up the process by calling each step manually as shown in the "Advanced usage" section below and running the webgestalt_network function calls in parallel. +The run time of the evaluate function changes with the size of the network and the number of permutations. With larger networks, it may take up to several hours. It is thus recommended to use a computing cluster when working with large networks. It is possible to speed up the process if performing each step manually as shown in the "Advanced usage" section below and running the `webgestalt_network` function calls in parallel. Now, let us compare with a second network. The "small_dto.tsv" network does not have edge scores, so no network subsets will be created. Generate the data for this network in a similar way as for the first one. @@ -94,6 +94,8 @@ net_2_data <- evaluate(file.path(data_dir, "small_dto.tsv"), plot = FALSE) ``` +Use the output from both networks in the call to `plot_metrics`. + ```{r} # set main plot titles title_text <- "NP3 vs DTO" @@ -101,12 +103,26 @@ subtitle_text <- "subset for overlapping TFs" # perTF controls the x-axis label # true only if the network subset sizes were determined by - # average number of edges per TF instead of by total number of edges +# average number of edges per TF instead of by total number of edges perTF <- FALSE plot_data <- plot_metrics(c(net_1_data, net_2_data), title_text, subtitle_text, perTF, annotation_overlap = TRUE) ``` +Notice that the metrics for the "NP3" network are plotted as a line, and the metrics for the "DTO" metric are plotted as a dot at one value on the x-axis. This is because the input "NP3" network included a column of edge scores, so multiple subsets were created and analyzed. + +In the case that one of the input networks has only one size, the values for the permuted networks are shown as boxplots. They are visible in these example plots as horizontal lines, as only one permuted network was made for each network size. If every input network had multiple subsets, however, the medians of the values for the permuted networks would be plotted as a line for each input network. + +\ +\ +\ +Notes on default behavior of `evaluate`: + +By default, only the "sum" and "size" metrics are calculated by `evaluate`. Other available metrics are "percent", "mean", "median", and "annotation_overlap". Descriptions of each are available in the function documentation. + +For this function to work properly, the correct 'organism' and 'gene_id' must be specified in accordance with the genes in the input network and reference set. The default values of each are "hsapiens" (human) and "ensembl_gene_id" (for human, "ENSG" followed by 11 numbers). You can see more options for 'organism' and 'gene_id' with WebGestaltR::listOrganism() and WebGestaltR::listIdType() respectively. The exact gene ID format specified by each option given by WebGestaltR::listIdType() may not be clear. To see examples, navigate to webgestalt.org, select an "Organism of Interest", and click on the dropdown for "Gene ID Type". Hovering the cursor over an option for a few seconds will display some example gene IDs of that type. While not ideal, this may be how you have to identify which option for 'gene_id' corresponds with the format of the gene IDs in your input. + +The 'database' argument specifies which database contains the gene set terms in which to search for over-representation. The default is "geneontology_Biological_Process_noRedundant". See more options with WebGestaltR::listGeneSet(). NOTE: the "annotation_overlap" metric is only designed to work with "geneontology_Biological_Process_noRedundant" or "geneontology_Biological_Process". \ \ @@ -115,19 +131,19 @@ Advanced usage: All intermediary steps in the evaluation can be run separately with the following functions. -1. subset_network to create network subsets with the top-weighted edges +1. `subset_network` to create network subsets with the top-weighted edges -2. webgestalt_network on each of the network subsets to generate ORA results +2. `webgestalt_network` on each of the network subsets to generate ORA results -3. get_metrics on the stored ORA results to calculate summary statistics for plotting +3. `get_metrics` on the stored ORA results to calculate summary statistics for plotting -4. plot_metrics using the output of get_metrics +4. `plot_metrics` using the output of get_metrics \ \ \ We will now walk through step-by-step evaluation of the network "np3_dtoTFs.tsv", which has 56 TFs and a total of 629,832 edges. -1\. subset_network +1\. `subset_network` "np3_dtoTFs.tsv" is tab-separated and contains 3 columns: the TFs, the regulated genes, and the scores. @@ -152,9 +168,9 @@ This function sorts the input network in descending order by the values in the t The result is that now the "data/np3_dtoTFs_subsets" folder contains 10 files: "np3_8.tsv" through "np3_4096.tsv". -2. webgestalt_network +2. `webgestalt_network` -Run ORA using the webgestalt_network function on each of the network subsets created in step 1. +Run ORA using the `webgestalt_network` function on each of the network subsets created in step 1. These files are tab-separated with the first column containing the TF names and the second column containing the regulated gene names. "h1_k562_overlap_universe.txt" contains one column of the names of all genes that could appear in the network. The network_name should be different for each subset, so I choose to use the subset file names without the extension. @@ -163,7 +179,7 @@ Note organism = "hsapiens" and database = "geneontology_Biological_Process_noRed You can see more options for organism and database with WebGestaltR::listOrganism() and WebGestaltR::listGeneSet() respectively. -Also note that the run time of the below code could take well over an hour, so this vignette does not actually evaluate the code. The run time is highly dependent on the number of subsets, the number of TFs in each of those subsets, and the number of permutations. The process can be sped up (in this case by 10x) by running webgestalt_network on each subset at the same time using a computing cluster or similar. +Also note that the run time of the below code could take well over an hour, so this vignette does not actually evaluate the code. The run time is highly dependent on the number of subsets, the number of TFs in each of those subsets, and the number of permutations. The process can be sped up by running `webgestalt_network` on each subset at the same time using a computing cluster or similar. ```{r, eval = FALSE} for (subset in list.files(file.path(data_dir, "np3_dtoTFs_subsets"), full.names = TRUE)) { @@ -186,19 +202,19 @@ webgestalt_network(network_path = file.path(data_dir, "np3_dtoTFs_subsets", "np3 permutations = 1) ``` -3. get_metrics +3. `get_metrics` -Run get_metrics to calculate the desired metrics based on the output from webgestalt_network. Note the arguments 'get_sum' and 'get_size' are TRUE by default. +Run `get_metrics` to calculate the desired metrics based on the output from `webgestalt_network`. Note the arguments 'get_sum' and 'get_size' are TRUE by default. ```{r} # This would normally be in the 'out_dir' directory, but since this data was - # premade due to time constraints, it is available in 'data_dir' + # premade due to computation time constraints, it is available in 'data_dir' output_folders <- file.path(data_dir, "np3_dtoTFs_summaries") metric_dfs <- get_metrics(output_folders, get_percent = TRUE, get_mean = TRUE, get_median = TRUE, get_annotation_overlap = TRUE, parallel = FALSE) ``` -If you want to compare multiple networks, you can run get_metrics on both at the same time as shown below with the addition of the "EDN" network. When comparing multiple networks, the sizes for all must be based either on total edges or on edges per TF, but the exact subset sizes do not need to be the same. +If you want to compare multiple networks, you can run `get_metrics` on both at the same time as shown below with the addition of the "EDN" network. When comparing multiple networks, the sizes for all must be based either on total edges or on edges per TF, but the exact subset sizes do not need to be the same. ```{r, eval = FALSE} # Specify which folders contain the data for each network. @@ -209,13 +225,13 @@ If you want to compare multiple networks, you can run get_metrics on both at the metric_dfs_by_net <- mapply(get_metrics, output_folders, MoreArgs=list(get_percent = TRUE, get_mean = TRUE, get_median = TRUE, get_annotation_overlap = TRUE, parallel = FALSE), SIMPLIFY = FALSE) ``` -4. plot_metrics +4. `plot_metrics` Set a title and subtitle for the plots.\ Specify whether the subset sizes are based on total edges or edges per TF.\ -Set the metric booleans to the same values as those used in get_metrics to ensure proper plot labels.\ +Set the metric booleans to the same values as those used in `get_metrics` to ensure proper plot labels.\ \ -plot_metrics also returns a list of the DataFrames used to plot. Each DataFrame contains values for one metric across all networks, subsets, and permutations +`plot_metrics` also returns a list of the data.frames used to plot. Each data.frame contains values for one metric across all networks, subsets, and permutations. ```{r} # Specify the main titles of the output graphs From 84503a7d947886b1d01899fec6967256fdf874c7 Mon Sep 17 00:00:00 2001 From: westbrooktm Date: Fri, 7 Jul 2023 03:15:07 -0400 Subject: [PATCH 4/4] fix vignette build --- vignettes/GOeval.Rmd | 10 +- .../p1/ENSG00000068305_ORA_summary.csv | 32 +- .../p1/ENSG00000068323_ORA_summary.csv | 32 +- .../p1/ENSG00000101057_ORA_summary.csv | 32 +- .../p1/ENSG00000101412_ORA_summary.csv | 32 +- .../p1/ENSG00000120738_ORA_summary.csv | 32 +- .../p1/ENSG00000134138_ORA_summary.csv | 32 +- .../p1/ENSG00000136997_ORA_summary.csv | 32 +- .../p1/ENSG00000162772_ORA_summary.csv | 32 +- .../p1/ENSG00000163884_ORA_summary.csv | 32 +- .../p1/ENSG00000169016_ORA_summary.csv | 31 +- .../dto_3174/p1/ENSG00000068305_targets.txt | 13 +- .../dto_3174/p1/ENSG00000068323_targets.txt | 122 +- .../dto_3174/p1/ENSG00000101057_targets.txt | 1339 +- 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vignettes/data/np3_summaries/webgestalt_work/np3_3174/p1/ENSG00000169016_targets.txt delete mode 100644 vignettes/data/np3_summaries/webgestalt_work/np3_512/p1/ENSG00000068305_targets.txt create mode 100644 vignettes/data/np3_summaries/webgestalt_work/np3_512/p1/ENSG00000163884_targets.txt diff --git a/vignettes/GOeval.Rmd b/vignettes/GOeval.Rmd index 31adf61..d097ceb 100644 --- a/vignettes/GOeval.Rmd +++ b/vignettes/GOeval.Rmd @@ -65,7 +65,7 @@ out_dir <- "data" Run the `evaluate` function. -```{r} +```{r, eval = FALSE} net_1_data <- evaluate(network_path, reference_path, out_dir, network_name, # some additional arguments edges = c(512, 1024, 2048, 3174, 4096, 8192, 16384, 32768, 65536), @@ -83,7 +83,7 @@ The run time of the evaluate function changes with the size of the network and t Now, let us compare with a second network. The "small_dto.tsv" network does not have edge scores, so no network subsets will be created. Generate the data for this network in a similar way as for the first one. -```{r} +```{r, eval = FALSE} net_2_data <- evaluate(file.path(data_dir, "small_dto.tsv"), file.path(data_dir, "h1_k562_overlap_universe.txt"), out_dir, @@ -96,6 +96,12 @@ net_2_data <- evaluate(file.path(data_dir, "small_dto.tsv"), Use the output from both networks in the call to `plot_metrics`. +```{r, echo = FALSE} +net_1_data <- list(get_metrics(file.path(data_dir, "np3_summaries"), get_annotation_overlap = TRUE)) + +net_2_data <- list(get_metrics(file.path(data_dir, "dto_summaries"), get_annotation_overlap = TRUE)) +``` + ```{r} # set main plot titles title_text <- "NP3 vs DTO" diff --git a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000068305_ORA_summary.csv b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000068305_ORA_summary.csv index 6f4a737..02b620c 100644 --- a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000068305_ORA_summary.csv +++ b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000068305_ORA_summary.csv @@ -1,17 +1,17 @@ "geneSet","description","size","overlap","expect","enrichmentRatio","pValue","FDR","overlapId","userId","database" -"GO:0097193","intrinsic apoptotic signaling pathway",233,2,0.0774429426102371,25.8254649499285,0.00195680321876113,1,"6648;64782","ENSG00000112096;ENSG00000181026","geneontology_Biological_Process_noRedundant" -"GO:0070671","response to interleukin-12",38,1,0.0126301794814979,79.1754385964912,0.0125784681009865,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" -"GO:0010657","muscle cell apoptotic process",43,1,0.0142920452027476,69.968992248062,0.0142256344761132,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" -"GO:0051262","protein tetramerization",92,1,0.0305783292709949,32.7028985507246,0.03027102985761,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" -"GO:0008637","apoptotic mitochondrial changes",96,1,0.0319078218479947,31.3402777777778,0.0315731162606272,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" -"GO:0033002","muscle cell proliferation",98,1,0.0325725681364946,30.7006802721088,0.0322237221453761,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" -"GO:0010821","regulation of mitochondrion organization",150,1,0.0498559716374917,20.0577777777778,0.0490373645829694,1,"80821","ENSG00000100523","geneontology_Biological_Process_noRedundant" -"GO:0072593","reactive oxygen species metabolic process",152,1,0.0505207179259916,19.7938596491228,0.0496801240363475,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" -"GO:1901342","regulation of vasculature development",164,1,0.0545091956569909,18.3455284552846,0.0535305994596911,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" -"GO:0016042","lipid catabolic process",185,1,0.0614890316862398,16.2630630630631,0.0602438790965963,1,"80821","ENSG00000100523","geneontology_Biological_Process_noRedundant" -"GO:0042692","muscle cell differentiation",187,1,0.0621537779747396,16.0891265597148,0.0608815783254052,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" -"GO:0072331","signal transduction by p53 class mediator",192,1,0.0638156436959894,15.6701388888889,0.0624745641266515,1,"64782","ENSG00000181026","geneontology_Biological_Process_noRedundant" -"GO:0051260","protein homooligomerization",201,1,0.0668070019942389,14.9684908789386,0.0653373971366136,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" -"GO:0007568","aging",202,1,0.0671393751384888,14.8943894389439,0.0656551294661987,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" -"GO:0003013","circulatory system process",208,1,0.0691336140039885,14.4647435897436,0.0675600114041581,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" -"GO:0070997","neuron death",227,1,0.0754487037447374,13.2540381791483,0.0735750538943237,1,"6648","ENSG00000112096","geneontology_Biological_Process_noRedundant" +"GO:0097485","neuron projection guidance",141,2,0.156215377797474,12.8028368794326,0.0100440485712068,1,"2115;6712","ENSG00000173898;ENSG00000006468","geneontology_Biological_Process_noRedundant" +"GO:0031579","membrane raft organization",13,1,0.0144028362508309,69.4307692307692,0.014316938201237,1,"2319","ENSG00000132589","geneontology_Biological_Process_noRedundant" +"GO:0044091","membrane biogenesis",21,1,0.0232661200974961,42.9809523809524,0.0230354015441375,1,"2319","ENSG00000132589","geneontology_Biological_Process_noRedundant" +"GO:0034113","heterotypic cell-cell adhesion",22,1,0.0243740305783293,41.0272727272727,0.0241203150349263,1,"2319","ENSG00000132589","geneontology_Biological_Process_noRedundant" +"GO:0072348","sulfur compound transport",28,1,0.0310214934633282,32.2357142857143,0.030607056618129,1,"65010","ENSG00000225697","geneontology_Biological_Process_noRedundant" +"GO:0007586","digestion",35,1,0.0387768668291602,25.7885714285714,0.0381258571546177,1,"65010","ENSG00000225697","geneontology_Biological_Process_noRedundant" +"GO:0061564","axon development",296,2,0.327941502326612,6.09864864864865,0.0405343828762099,1,"2115;6712","ENSG00000173898;ENSG00000006468","geneontology_Biological_Process_noRedundant" +"GO:0045445","myoblast differentiation",38,1,0.0421005982716596,23.7526315789474,0.0413321016805785,1,"2319","ENSG00000132589","geneontology_Biological_Process_noRedundant" +"GO:0070671","response to interleukin-12",38,1,0.0421005982716596,23.7526315789474,0.0413321016805785,1,"6627","ENSG00000131876","geneontology_Biological_Process_noRedundant" +"GO:0042982","amyloid precursor protein metabolic process",39,1,0.0432085087524928,23.1435897435897,0.0423987103791986,1,"2319","ENSG00000132589","geneontology_Biological_Process_noRedundant" +"GO:0046717","acid secretion",40,1,0.0443164192333259,22.565,0.0434642509262342,1,"65010","ENSG00000225697","geneontology_Biological_Process_noRedundant" +"GO:0099072","regulation of postsynaptic membrane neurotransmitter receptor levels",40,1,0.0443164192333259,22.565,0.0434642509262342,1,"2319","ENSG00000132589","geneontology_Biological_Process_noRedundant" +"GO:0007422","peripheral nervous system development",41,1,0.0454243297141591,22.0146341463415,0.0445287242726327,1,"2115","ENSG00000006468","geneontology_Biological_Process_noRedundant" +"GO:0048193","Golgi vesicle transport",315,2,0.348991801462442,5.73079365079365,0.0454008207684142,1,"6712;22796","ENSG00000173898;ENSG00000135775","geneontology_Biological_Process_noRedundant" +"GO:0043631","RNA polyadenylation",46,1,0.0509638821183248,19.6217391304348,0.0498351162263958,1,"10914","ENSG00000090060","geneontology_Biological_Process_noRedundant" +"GO:0030534","adult behavior",59,1,0.0653667183691558,15.2983050847458,0.06350783285485,1,"6712","ENSG00000173898","geneontology_Biological_Process_noRedundant" diff --git a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000068323_ORA_summary.csv b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000068323_ORA_summary.csv index 29fec5d..69a7c35 100644 --- a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000068323_ORA_summary.csv +++ b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000068323_ORA_summary.csv @@ -1,17 +1,17 @@ "geneSet","description","size","overlap","expect","enrichmentRatio","pValue","FDR","overlapId","userId","database" -"GO:0045926","negative regulation of growth",142,7,1.77775315754487,3.93755453072417,0.00198386094076897,1,"604;4501;8614;10500;10501;83667;84260","ENSG00000139437;ENSG00000113739;ENSG00000167680;ENSG00000143434;ENSG00000113916;ENSG00000187193;ENSG00000130766","geneontology_Biological_Process_noRedundant" -"GO:0035270","endocrine system development",56,4,0.701085752271217,5.70543615676359,0.00518631353762666,1,"2932;3570;5170;5307","ENSG00000069011;ENSG00000160712;ENSG00000082701;ENSG00000140992","geneontology_Biological_Process_noRedundant" -"GO:0061448","connective tissue development",137,6,1.7151562153778,3.49822362896454,0.00735010076279985,1,"864;1292;4781;5307;5916;91461","ENSG00000069011;ENSG00000172819;ENSG00000142173;ENSG00000020633;ENSG00000162878;ENSG00000147862","geneontology_Biological_Process_noRedundant" -"GO:0061564","axon development",296,9,3.70573897629072,2.42866539105477,0.0117824327410532,1,"2932;4548;4781;6812;9260;10500;10501;51199;57463","ENSG00000196923;ENSG00000167680;ENSG00000143434;ENSG00000082701;ENSG00000136854;ENSG00000100503;ENSG00000181754;ENSG00000147862;ENSG00000116984","geneontology_Biological_Process_noRedundant" -"GO:0009187","cyclic nucleotide metabolic process",14,2,0.175271438067804,11.4108723135272,0.012814735869088,1,"5141;5151","ENSG00000065989;ENSG00000073417","geneontology_Biological_Process_noRedundant" -"GO:0031016","pancreas development",40,3,0.500775537336583,5.99070796460177,0.0134861965849173,1,"2932;3570;5170","ENSG00000160712;ENSG00000082701;ENSG00000140992","geneontology_Biological_Process_noRedundant" -"GO:0007272","ensheathment of neurons",74,4,0.926434744072679,4.31762736187515,0.0136917221851089,1,"4664;5916;8506;57463","ENSG00000108797;ENSG00000172819;ENSG00000138386;ENSG00000181754","geneontology_Biological_Process_noRedundant" -"GO:0016049","cell growth",305,9,3.81841347219145,2.35699985492529,0.0141130576883431,1,"604;2932;5916;10500;10501;51199;83452;83667;84260","ENSG00000139437;ENSG00000172819;ENSG00000167680;ENSG00000143434;ENSG00000082701;ENSG00000113916;ENSG00000172007;ENSG00000100503;ENSG00000130766","geneontology_Biological_Process_noRedundant" -"GO:0032102","negative regulation of response to external stimulus",158,6,1.9780633724795,3.03326985549457,0.0142314505592482,1,"4846;10500;10501;22900;26146;29937","ENSG00000117691;ENSG00000167680;ENSG00000143434;ENSG00000105483;ENSG00000164867;ENSG00000204104","geneontology_Biological_Process_noRedundant" -"GO:0007422","peripheral nervous system development",41,3,0.513294925769998,5.84459313619685,0.0144205812972097,1,"864;4664;8506","ENSG00000108797;ENSG00000138386;ENSG00000020633","geneontology_Biological_Process_noRedundant" -"GO:0032886","regulation of microtubule-based process",167,6,2.09073786838023,2.86980022256372,0.018236402755863,1,"1729;2932;26146;51199;79648;255758","ENSG00000082701;ENSG00000213123;ENSG00000147316;ENSG00000100503;ENSG00000131504;ENSG00000204104","geneontology_Biological_Process_noRedundant" -"GO:0010737","protein kinase A signaling",17,2,0.212829603368048,9.39718896408121,0.0186901982200889,1,"5141;9495","ENSG00000065989;ENSG00000179841","geneontology_Biological_Process_noRedundant" -"GO:0010959","regulation of metal ion transport",174,6,2.17837358741414,2.75434848947208,0.0218426963362333,1,"1729;4846;5170;7779;8614;57463","ENSG00000113739;ENSG00000140992;ENSG00000131504;ENSG00000170385;ENSG00000181754;ENSG00000164867","geneontology_Biological_Process_noRedundant" -"GO:0044380","protein localization to cytoskeleton",50,3,0.625969421670729,4.79256637168142,0.0244582145633063,1,"1729;2932;79648","ENSG00000082701;ENSG00000147316;ENSG00000131504","geneontology_Biological_Process_noRedundant" -"GO:0001818","negative regulation of cytokine production",133,5,1.66507866164414,3.00286113513873,0.0255327078073819,1,"604;8986;9246;22900;26146","ENSG00000113916;ENSG00000162302;ENSG00000105483;ENSG00000204104;ENSG00000156587","geneontology_Biological_Process_noRedundant" -"GO:0060560","developmental growth involved in morphogenesis",133,5,1.66507866164414,3.00286113513873,0.0255327078073819,1,"2932;5916;10500;10501;51199","ENSG00000172819;ENSG00000167680;ENSG00000143434;ENSG00000082701;ENSG00000100503","geneontology_Biological_Process_noRedundant" +"GO:0007398","ectoderm development",12,1,0.011965433192998,83.5740740740741,0.0119072481758324,1,"8324","ENSG00000155760","geneontology_Biological_Process_noRedundant" +"GO:0018200","peptidyl-glutamic acid modification",17,1,0.0169510303567472,58.9934640522876,0.0168312887479818,1,"79739","ENSG00000137941","geneontology_Biological_Process_noRedundant" +"GO:0042110","T cell activation",232,2,0.231331708397961,8.6455938697318,0.0210227670583794,1,"896;8324","ENSG00000155760;ENSG00000112576","geneontology_Biological_Process_noRedundant" +"GO:0017145","stem cell division",27,1,0.0269222246842455,37.1440329218107,0.0266139826801229,1,"8324","ENSG00000155760","geneontology_Biological_Process_noRedundant" +"GO:0007164","establishment of tissue polarity",53,1,0.0528473299357412,18.9224318658281,0.0516452814443892,1,"8324","ENSG00000155760","geneontology_Biological_Process_noRedundant" +"GO:0006360","transcription by RNA polymerase I",60,1,0.05982716596499,16.7148148148148,0.0582859516953478,1,"7343","ENSG00000108312","geneontology_Biological_Process_noRedundant" +"GO:0035051","cardiocyte differentiation",65,1,0.0648127631287392,15.4290598290598,0.0630039485689942,1,"8324","ENSG00000155760","geneontology_Biological_Process_noRedundant" +"GO:0045995","regulation of embryonic development",71,1,0.0707954797252382,14.1251956181534,0.0686378022561223,1,"8324","ENSG00000155760","geneontology_Biological_Process_noRedundant" +"GO:0006353","DNA-templated transcription, termination",73,1,0.0727897185907379,13.738203957382,0.0705090507635261,1,"7343","ENSG00000108312","geneontology_Biological_Process_noRedundant" +"GO:1904029","regulation of cyclin-dependent protein kinase activity",77,1,0.0767781963217372,13.024531024531,0.0742415251031704,1,"896","ENSG00000112576","geneontology_Biological_Process_noRedundant" +"GO:0007369","gastrulation",91,1,0.0907378683802349,11.020757020757,0.0872004944407176,1,"8324","ENSG00000155760","geneontology_Biological_Process_noRedundant" +"GO:0045165","cell fate commitment",98,1,0.0977177044094837,10.2335600907029,0.0936193066711634,1,"8324","ENSG00000155760","geneontology_Biological_Process_noRedundant" +"GO:0098727","maintenance of cell number",98,1,0.0977177044094837,10.2335600907029,0.0936193066711634,1,"8324","ENSG00000155760","geneontology_Biological_Process_noRedundant" +"GO:2000027","regulation of animal organ morphogenesis",100,1,0.0997119432749834,10.0288888888889,0.0954458682907182,1,"8324","ENSG00000155760","geneontology_Biological_Process_noRedundant" +"GO:0031099","regeneration",104,1,0.103700421005983,9.64316239316239,0.0990891786394831,1,"8324","ENSG00000155760","geneontology_Biological_Process_noRedundant" +"GO:0007219","Notch signaling pathway",117,1,0.116662973631731,8.57169990503324,0.11084002471008,1,"142678","ENSG00000197530","geneontology_Biological_Process_noRedundant" diff --git a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000101057_ORA_summary.csv b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000101057_ORA_summary.csv index 37d8c9c..f5134ff 100644 --- a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000101057_ORA_summary.csv +++ b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000101057_ORA_summary.csv @@ -1,17 +1,17 @@ "geneSet","description","size","overlap","expect","enrichmentRatio","pValue","FDR","overlapId","userId","database" -"GO:0022613","ribonucleoprotein complex biogenesis",428,97,62.0709062707733,1.56272891484546,2.42700175134836e-06,0.0019974224413597,"14;545;636;1207;1653;1736;1965;1975;2886;2971;3669;3921;4736;4927;5036;5393;6125;6128;6129;6134;6135;6136;6152;6169;6194;6202;6732;6838;8602;8607;8662;9221;9908;10171;10436;10521;10659;10856;10969;11103;11137;11171;22984;23076;23404;23405;23411;23517;23560;25819;25926;26156;26523;27292;27335;28987;29889;51042;51068;51077;51096;51118;51154;51187;51386;51491;51729;54606;55003;55052;55178;55239;55272;55646;55720;55759;55813;56342;56915;57109;57418;57647;60680;64434;79050;79066;79760;79954;81875;84128;84916;84946;114049;115416;246175;387338;550643","ENSG00000172809;ENSG00000148843;ENSG00000183520;ENSG00000092847;ENSG00000023734;ENSG00000119616;ENSG00000065268;ENSG00000127837;ENSG00000141738;ENSG00000130935;ENSG00000151746;ENSG00000136271;ENSG00000063046;ENSG00000089009;ENSG00000150990;ENSG00000146909;ENSG00000130810;ENSG00000048740;ENSG00000106263;ENSG00000242485;ENSG00000111845;ENSG00000148300;ENSG00000134001;ENSG00000160208;ENSG00000197958;ENSG00000183207;ENSG00000178982;ENSG00000100201;ENSG00000071462;ENSG00000145220;ENSG00000161082;ENSG00000130713;ENSG00000011260;ENSG00000111615;ENSG00000114391;ENSG00000120158;ENSG00000169251;ENSG00000137154;ENSG00000108651;ENSG00000074201;ENSG00000138757;ENSG00000148296;ENSG00000175054;ENSG00000184967;ENSG00000136045;ENSG00000039123;ENSG00000142252;ENSG00000147403;ENSG00000166197;ENSG00000048162;ENSG00000142937;ENSG00000151014;ENSG00000167721;ENSG00000172183;ENSG00000177971;ENSG00000168028;ENSG00000079785;ENSG00000156928;ENSG00000127804;ENSG00000134697;ENSG00000142684;ENSG00000087263;ENSG00000171861;ENSG00000126749;ENSG00000084463;ENSG00000107937;ENSG00000135521;ENSG00000171490;ENSG00000138767;ENSG00000115368;ENSG00000130826;ENSG00000122406;ENSG00000141076;ENSG00000175792;ENSG00000117481;ENSG00000096063;ENSG00000096717;ENSG00000122034;ENSG00000147604;ENSG00000204272;ENSG00000143319;ENSG00000198755;ENSG00000115761;ENSG00000053372;ENSG00000123737;ENSG00000138442;ENSG00000077348;ENSG00000100697;ENSG00000141101;ENSG00000108559;ENSG00000100129;ENSG00000170515;ENSG00000142676;ENSG00000087269;ENSG00000137876;ENSG00000117395;ENSG00000086189","geneontology_Biological_Process_noRedundant" -"GO:0016072","rRNA metabolic process",231,59,33.5008863283847,1.76114743418028,5.68410263368513e-06,0.00233900823376143,"1736;2971;3669;3921;4736;5036;5393;6125;6129;6135;6194;6202;8602;9014;9221;10171;10436;10521;10969;11103;11137;22984;23076;23404;23411;23517;23560;25926;26156;27292;28987;51077;51096;51118;51154;51729;54606;55178;55272;55646;55720;55759;55813;56915;57109;57418;57647;79050;79066;79954;81875;84128;84172;84365;84916;114049;146857;284119;387338","ENSG00000148843;ENSG00000183520;ENSG00000119616;ENSG00000065268;ENSG00000130935;ENSG00000155438;ENSG00000154760;ENSG00000136271;ENSG00000150990;ENSG00000148300;ENSG00000160208;ENSG00000100201;ENSG00000177469;ENSG00000071462;ENSG00000145220;ENSG00000130713;ENSG00000011260;ENSG00000111615;ENSG00000120158;ENSG00000137154;ENSG00000108651;ENSG00000184967;ENSG00000125630;ENSG00000136045;ENSG00000039123;ENSG00000166197;ENSG00000142937;ENSG00000167721;ENSG00000172183;ENSG00000177971;ENSG00000168028;ENSG00000127804;ENSG00000115750;ENSG00000171861;ENSG00000126749;ENSG00000084463;ENSG00000107937;ENSG00000171490;ENSG00000115368;ENSG00000130826;ENSG00000122406;ENSG00000141076;ENSG00000117481;ENSG00000096717;ENSG00000122034;ENSG00000147604;ENSG00000143319;ENSG00000198755;ENSG00000115761;ENSG00000053372;ENSG00000123737;ENSG00000138442;ENSG00000077348;ENSG00000141101;ENSG00000170515;ENSG00000142676;ENSG00000087269;ENSG00000117395;ENSG00000086189","geneontology_Biological_Process_noRedundant" -"GO:0045787","positive regulation of cell cycle",295,65,42.7825171726125,1.51931219329026,0.000265574763055554,0.0728560099982403,"301;581;595;811;894;896;899;900;983;994;1027;1111;1845;1977;1978;1981;2033;2146;2237;2260;2735;3054;3481;4085;4193;4194;4282;5037;5457;5925;6714;7015;7027;8061;8451;8766;9099;9401;9475;9493;10018;10370;10403;11113;23326;25836;25942;29899;51203;51433;51512;55367;55719;55795;55835;55904;57060;57504;64848;84967;85456;85459;115426;219736;246175","ENSG00000111087;ENSG00000240972;ENSG00000164442;ENSG00000113328;ENSG00000134318;ENSG00000114867;ENSG00000112576;ENSG00000124422;ENSG00000090097;ENSG00000160957;ENSG00000089053;ENSG00000149554;ENSG00000151247;ENSG00000089220;ENSG00000047188;ENSG00000110092;ENSG00000164190;ENSG00000106462;ENSG00000126226;ENSG00000170312;ENSG00000153094;ENSG00000119906;ENSG00000036672;ENSG00000135679;ENSG00000139687;ENSG00000139842;ENSG00000198625;ENSG00000057935;ENSG00000077782;ENSG00000168496;ENSG00000005483;ENSG00000151849;ENSG00000108861;ENSG00000165730;ENSG00000101224;ENSG00000111276;ENSG00000177595;ENSG00000167244;ENSG00000137807;ENSG00000118971;ENSG00000166004;ENSG00000175592;ENSG00000075218;ENSG00000187840;ENSG00000121957;ENSG00000197122;ENSG00000080986;ENSG00000162063;ENSG00000138767;ENSG00000152192;ENSG00000122966;ENSG00000164362;ENSG00000172534;ENSG00000103769;ENSG00000164109;ENSG00000135046;ENSG00000147854;ENSG00000169375;ENSG00000179218;ENSG00000181817;ENSG00000137804;ENSG00000198176;ENSG00000149115;ENSG00000100393;ENSG00000087088","geneontology_Biological_Process_noRedundant" -"GO:0034470","ncRNA processing",356,74,51.6290715710171,1.43330100170814,0.000653022724629082,0.134359425592434,"1653;1736;3669;3921;4736;5036;5393;6125;6129;6135;6194;6202;8602;9097;9221;10171;10436;10521;10969;11103;22984;23076;23404;23405;23411;23517;23560;25926;26156;26512;26523;27292;28987;51077;51096;51118;51154;51654;51729;54517;54606;54802;54920;54931;55178;55250;55253;55272;55646;55687;55720;55759;55813;56915;57109;57418;57647;79050;79066;79954;80324;80746;81875;84128;84881;84916;93587;114049;115708;116461;134637;283989;348180;387338","ENSG00000174177;ENSG00000148843;ENSG00000183520;ENSG00000092847;ENSG00000119616;ENSG00000065268;ENSG00000130935;ENSG00000136271;ENSG00000150990;ENSG00000165526;ENSG00000101557;ENSG00000101391;ENSG00000148300;ENSG00000160208;ENSG00000100201;ENSG00000071462;ENSG00000198860;ENSG00000145220;ENSG00000130713;ENSG00000134759;ENSG00000011260;ENSG00000111615;ENSG00000174173;ENSG00000120158;ENSG00000137154;ENSG00000108651;ENSG00000102786;ENSG00000091127;ENSG00000184967;ENSG00000166166;ENSG00000043514;ENSG00000039123;ENSG00000167264;ENSG00000166197;ENSG00000142937;ENSG00000167721;ENSG00000189007;ENSG00000172183;ENSG00000177971;ENSG00000168028;ENSG00000079785;ENSG00000127804;ENSG00000145331;ENSG00000198874;ENSG00000177192;ENSG00000171861;ENSG00000126749;ENSG00000100416;ENSG00000084463;ENSG00000107937;ENSG00000171490;ENSG00000154743;ENSG00000115368;ENSG00000130826;ENSG00000122406;ENSG00000141076;ENSG00000117481;ENSG00000096717;ENSG00000147604;ENSG00000143319;ENSG00000198755;ENSG00000115761;ENSG00000053372;ENSG00000123737;ENSG00000138442;ENSG00000077348;ENSG00000100697;ENSG00000141101;ENSG00000182173;ENSG00000170515;ENSG00000142676;ENSG00000087269;ENSG00000117395;ENSG00000086189","geneontology_Biological_Process_noRedundant" -"GO:0070972","protein localization to endoplasmic reticulum",131,32,18.9983381342788,1.68435785139871,0.00168945106013529,0.233947128421901,"3309;3921;4253;4736;6125;6128;6129;6132;6134;6135;6136;6137;6152;6156;6159;6167;6169;6170;6173;6189;6194;6202;6206;6224;6235;6449;6726;6747;54557;55176;60559;202018","ENSG00000241343;ENSG00000172809;ENSG00000145592;ENSG00000143742;ENSG00000156482;ENSG00000198918;ENSG00000129128;ENSG00000167526;ENSG00000044574;ENSG00000089009;ENSG00000104969;ENSG00000213741;ENSG00000197958;ENSG00000197860;ENSG00000008988;ENSG00000114850;ENSG00000162244;ENSG00000114391;ENSG00000137154;ENSG00000065665;ENSG00000147403;ENSG00000142937;ENSG00000112306;ENSG00000168028;ENSG00000122406;ENSG00000145425;ENSG00000147604;ENSG00000198755;ENSG00000169762;ENSG00000161016;ENSG00000142676;ENSG00000150527","geneontology_Biological_Process_noRedundant" -"GO:0006413","translational initiation",180,41,26.1045867493906,1.57060521178168,0.0017862556167455,0.233947128421901,"1653;1965;1975;1977;1978;1981;2332;3921;4736;6125;6128;6129;6132;6134;6135;6136;6137;6152;6156;6159;6167;6169;6170;6173;6189;6194;6202;6206;6224;6235;8662;8891;8892;8893;9811;22916;22927;27335;51386;54505;64410","ENSG00000241343;ENSG00000172809;ENSG00000183655;ENSG00000145592;ENSG00000114867;ENSG00000156482;ENSG00000198918;ENSG00000119718;ENSG00000167526;ENSG00000063046;ENSG00000089009;ENSG00000134030;ENSG00000130956;ENSG00000106263;ENSG00000151247;ENSG00000213741;ENSG00000134001;ENSG00000197958;ENSG00000178982;ENSG00000008988;ENSG00000162244;ENSG00000114391;ENSG00000137154;ENSG00000147403;ENSG00000145191;ENSG00000142937;ENSG00000112306;ENSG00000168028;ENSG00000079785;ENSG00000114503;ENSG00000187840;ENSG00000122406;ENSG00000102081;ENSG00000145425;ENSG00000147604;ENSG00000198755;ENSG00000161016;ENSG00000067248;ENSG00000100129;ENSG00000070785;ENSG00000142676","geneontology_Biological_Process_noRedundant" -"GO:0033028","myeloid cell apoptotic process",18,8,2.61045867493907,3.06459553518377,0.00215764266653351,0.233947128421901,"301;596;4282;5291;5293;7067;23411;23657","ENSG00000240972;ENSG00000126351;ENSG00000171791;ENSG00000171608;ENSG00000051382;ENSG00000096717;ENSG00000135046;ENSG00000151012","geneontology_Biological_Process_noRedundant" -"GO:0097191","extrinsic apoptotic signaling pathway",145,34,21.0286948814536,1.61683833407971,0.00256778923230905,0.233947128421901,"207;581;596;841;1613;2065;2260;2729;2872;2932;3717;4846;5598;6272;6478;6498;6714;7015;8737;9181;9531;10018;10038;10193;10197;11124;11337;26060;51330;55367;56616;64114;81537;204851","ENSG00000099875;ENSG00000170296;ENSG00000166484;ENSG00000134243;ENSG00000142208;ENSG00000065361;ENSG00000153094;ENSG00000151929;ENSG00000006327;ENSG00000082701;ENSG00000077782;ENSG00000171791;ENSG00000131467;ENSG00000177595;ENSG00000167657;ENSG00000096968;ENSG00000135926;ENSG00000197122;ENSG00000137275;ENSG00000185104;ENSG00000136603;ENSG00000163349;ENSG00000164362;ENSG00000184047;ENSG00000157500;ENSG00000116584;ENSG00000164867;ENSG00000126821;ENSG00000181788;ENSG00000129484;ENSG00000001084;ENSG00000181852;ENSG00000087088;ENSG00000064012","geneontology_Biological_Process_noRedundant" -"GO:0009266","response to temperature stimulus",135,32,19.578440062043,1.63445095209801,0.00282343281704733,0.233947128421901,"207;409;488;490;545;573;841;1965;2033;2288;2308;2729;2932;3309;3312;3654;4846;5037;5594;5931;6117;7067;7266;7351;8891;8892;8893;9531;10049;10294;23411;51182","ENSG00000150907;ENSG00000141480;ENSG00000126351;ENSG00000175567;ENSG00000119718;ENSG00000044574;ENSG00000089220;ENSG00000142208;ENSG00000134001;ENSG00000004478;ENSG00000174437;ENSG00000168259;ENSG00000151929;ENSG00000082701;ENSG00000187522;ENSG00000069345;ENSG00000132383;ENSG00000175054;ENSG00000145191;ENSG00000107262;ENSG00000100030;ENSG00000096717;ENSG00000105993;ENSG00000164867;ENSG00000070961;ENSG00000001084;ENSG00000109971;ENSG00000070785;ENSG00000184216;ENSG00000102054;ENSG00000100393;ENSG00000064012","geneontology_Biological_Process_noRedundant" -"GO:0071887","leukocyte apoptotic process",63,18,9.13660536228673,1.970097129761,0.00284261395409358,0.233947128421901,"207;301;384;581;4282;5291;5293;6194;8737;9693;10018;10461;23411;23657;27166;27242;57181;81704","ENSG00000240972;ENSG00000146072;ENSG00000142208;ENSG00000153094;ENSG00000153208;ENSG00000107099;ENSG00000137154;ENSG00000081181;ENSG00000171608;ENSG00000051382;ENSG00000137275;ENSG00000109756;ENSG00000096717;ENSG00000196950;ENSG00000135046;ENSG00000169230;ENSG00000151012;ENSG00000087088","geneontology_Biological_Process_noRedundant" -"GO:0038034","signal transduction in absence of ligand",45,14,6.52614668734766,2.14521687462864,0.00347168972034839,0.259745512713339,"207;581;596;1613;2065;2260;2872;2932;5598;7015;8737;9531;10018;26060","ENSG00000099875;ENSG00000166484;ENSG00000142208;ENSG00000065361;ENSG00000153094;ENSG00000151929;ENSG00000082701;ENSG00000077782;ENSG00000171791;ENSG00000167657;ENSG00000137275;ENSG00000164362;ENSG00000157500;ENSG00000087088","geneontology_Biological_Process_noRedundant" -"GO:0008544","epidermis development",145,32,21.0286948814536,1.52173019678091,0.00876855946976829,0.500202879480185,"301;488;596;865;1382;1499;2146;2260;2735;3714;4089;4665;4831;5046;5318;5493;5660;5744;6093;7027;7357;8609;9368;9475;11078;23513;25932;27152;51083;54345;55366;64434","ENSG00000141646;ENSG00000168036;ENSG00000111087;ENSG00000134318;ENSG00000109062;ENSG00000118898;ENSG00000243678;ENSG00000146909;ENSG00000148154;ENSG00000169504;ENSG00000174437;ENSG00000106462;ENSG00000118263;ENSG00000203883;ENSG00000166886;ENSG00000077782;ENSG00000205213;ENSG00000171791;ENSG00000164066;ENSG00000197746;ENSG00000069482;ENSG00000143320;ENSG00000067955;ENSG00000180900;ENSG00000087494;ENSG00000184916;ENSG00000135046;ENSG00000140479;ENSG00000198176;ENSG00000067900;ENSG00000057294;ENSG00000100106","geneontology_Biological_Process_noRedundant" -"GO:0052547","regulation of peptidase activity",225,46,32.6307334367383,1.40971394618453,0.00876924976979043,0.500202879480185,"207;317;409;581;841;843;1032;1457;1471;1476;3491;3717;4193;4898;5037;5176;5329;5447;6478;6714;6774;7035;8737;9097;9141;9475;10018;10049;10197;10541;22861;22900;23151;23204;23411;23786;27166;29843;51377;55367;55795;56616;57333;57539;64780;84168","ENSG00000142871;ENSG00000134318;ENSG00000099968;ENSG00000141480;ENSG00000160213;ENSG00000129355;ENSG00000136938;ENSG00000101557;ENSG00000078618;ENSG00000101266;ENSG00000089220;ENSG00000142208;ENSG00000142552;ENSG00000011422;ENSG00000132386;ENSG00000126226;ENSG00000153094;ENSG00000091592;ENSG00000135679;ENSG00000003400;ENSG00000170540;ENSG00000120868;ENSG00000131467;ENSG00000177595;ENSG00000096968;ENSG00000075240;ENSG00000116750;ENSG00000127948;ENSG00000003436;ENSG00000197122;ENSG00000168610;ENSG00000137275;ENSG00000184047;ENSG00000169604;ENSG00000105483;ENSG00000096717;ENSG00000118965;ENSG00000105185;ENSG00000105993;ENSG00000101439;ENSG00000181788;ENSG00000169230;ENSG00000079387;ENSG00000087088;ENSG00000064012;ENSG00000135596","geneontology_Biological_Process_noRedundant" -"GO:0044843","cell cycle G1/S phase transition",214,44,31.0354531353867,1.41773344851959,0.00923142294369939,0.500202879480185,"301;581;595;596;894;896;983;1027;1032;1977;1978;1981;2033;2146;2735;4176;4193;4194;4998;5471;5925;6117;6194;6502;7015;7027;7465;8451;8462;10106;10197;25959;29117;51512;55367;55904;57060;58190;84515;84967;85456;128239;138151;246175","ENSG00000111087;ENSG00000166483;ENSG00000172059;ENSG00000166164;ENSG00000114867;ENSG00000112576;ENSG00000144579;ENSG00000129355;ENSG00000090097;ENSG00000151247;ENSG00000145604;ENSG00000110092;ENSG00000175215;ENSG00000106462;ENSG00000170312;ENSG00000128059;ENSG00000135679;ENSG00000139687;ENSG00000139842;ENSG00000198625;ENSG00000085840;ENSG00000137154;ENSG00000132383;ENSG00000171791;ENSG00000005483;ENSG00000131467;ENSG00000197256;ENSG00000111276;ENSG00000177595;ENSG00000118971;ENSG00000075218;ENSG00000148411;ENSG00000187840;ENSG00000138767;ENSG00000125885;ENSG00000164362;ENSG00000166508;ENSG00000135046;ENSG00000181817;ENSG00000198176;ENSG00000183856;ENSG00000149115;ENSG00000100393;ENSG00000087088","geneontology_Biological_Process_noRedundant" -"GO:0009187","cyclic nucleotide metabolic process",14,6,2.03035674717483,2.95514569464149,0.00972447882343008,0.500202879480185,"109;115;271;1267;5150;5744","ENSG00000138031;ENSG00000173786;ENSG00000116337;ENSG00000162104;ENSG00000205268;ENSG00000087494","geneontology_Biological_Process_noRedundant" -"GO:0071800","podosome assembly",14,6,2.03035674717483,2.95514569464149,0.00972447882343008,0.500202879480185,"3936;6624;6714;9181;9855;285590","ENSG00000136167;ENSG00000174705;ENSG00000075618;ENSG00000197122;ENSG00000116584;ENSG00000006607","geneontology_Biological_Process_noRedundant" +"GO:0022406","membrane docking",158,4,0.70019942388655,5.7126582278481,0.00506024502313274,1,"22994;54806;55755;57488","ENSG00000136861;ENSG00000141577;ENSG00000135541;ENSG00000117868","geneontology_Biological_Process_noRedundant" +"GO:2000027","regulation of animal organ morphogenesis",100,3,0.443164192333259,6.7695,0.00968927832076527,1,"4089;8321;54806","ENSG00000157240;ENSG00000141646;ENSG00000135541","geneontology_Biological_Process_noRedundant" +"GO:0007389","pattern specification process",203,4,0.899623310436517,4.44630541871921,0.0120598070724904,1,"4089;5089;54806;100532731","ENSG00000204304;ENSG00000141646;ENSG00000135541;ENSG00000269897","geneontology_Biological_Process_noRedundant" +"GO:2001023","regulation of response to drug",38,2,0.168402393086639,11.8763157894737,0.0121729022656287,1,"5831;8648","ENSG00000183010;ENSG00000084676","geneontology_Biological_Process_noRedundant" +"GO:0007517","muscle organ development",206,4,0.912918236206514,4.38155339805825,0.012673453132066,1,"4089;4637;8321;10521","ENSG00000157240;ENSG00000141646;ENSG00000092841;ENSG00000100201","geneontology_Biological_Process_noRedundant" +"GO:0048568","embryonic organ development",227,4,1.0059827165965,3.97621145374449,0.0175349026797582,1,"8648;54806;55773;79733","ENSG00000129173;ENSG00000084676;ENSG00000036054;ENSG00000135541","geneontology_Biological_Process_noRedundant" +"GO:0016072","rRNA metabolic process",231,4,1.02370928428983,3.90735930735931,0.0185766058204675,1,"2971;6194;10521;84135","ENSG00000137154;ENSG00000122034;ENSG00000100201;ENSG00000164338","geneontology_Biological_Process_noRedundant" +"GO:0060021","roof of mouth development",48,2,0.212718812319965,9.40208333333333,0.0189965619895789,1,"4089;8321","ENSG00000157240;ENSG00000141646","geneontology_Biological_Process_noRedundant" +"GO:0006383","transcription by RNA polymerase III",51,2,0.226013738089962,8.84901960784314,0.0212941820265764,1,"2971;6617","ENSG00000122034;ENSG00000023608","geneontology_Biological_Process_noRedundant" +"GO:0003007","heart morphogenesis",136,3,0.602703301573233,4.97757352941177,0.022027548083963,1,"4089;8321;54806","ENSG00000157240;ENSG00000141646;ENSG00000135541","geneontology_Biological_Process_noRedundant" +"GO:0061458","reproductive system development",248,4,1.09904719698648,3.63951612903226,0.0234328554806591,1,"4089;6194;8648;79733","ENSG00000137154;ENSG00000129173;ENSG00000141646;ENSG00000084676","geneontology_Biological_Process_noRedundant" +"GO:0016053","organic acid biosynthetic process",249,4,1.10347883890982,3.62489959839357,0.023740506926011,1,"47;2597;5831;84706","ENSG00000131473;ENSG00000111640;ENSG00000183010;ENSG00000166123","geneontology_Biological_Process_noRedundant" +"GO:0060485","mesenchyme development",141,3,0.624861511189896,4.80106382978723,0.0241981049485545,1,"4089;10521;359845","ENSG00000141646;ENSG00000183688;ENSG00000100201","geneontology_Biological_Process_noRedundant" +"GO:0010927","cellular component assembly involved in morphogenesis",57,2,0.252603589629958,7.91754385964912,0.026215780771462,1,"4089;8506","ENSG00000108797;ENSG00000141646","geneontology_Biological_Process_noRedundant" +"GO:0030048","actin filament-based movement",62,2,0.274761799246621,7.27903225806452,0.0306347026314584,1,"4637;23327","ENSG00000092841;ENSG00000049759","geneontology_Biological_Process_noRedundant" +"GO:0006513","protein monoubiquitination",66,2,0.292488366939951,6.83787878787879,0.0343676275509706,1,"23327;100532731","ENSG00000269897;ENSG00000049759","geneontology_Biological_Process_noRedundant" diff --git a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000101412_ORA_summary.csv b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000101412_ORA_summary.csv index 3c11417..0786b70 100644 --- a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000101412_ORA_summary.csv +++ b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000101412_ORA_summary.csv @@ -1,17 +1,17 @@ "geneSet","description","size","overlap","expect","enrichmentRatio","pValue","FDR","overlapId","userId","database" -"GO:0043491","protein kinase B signaling",127,2,0.14070463106581,14.2141732283465,0.00820982755435606,1,"56917;57761","ENSG00000105419;ENSG00000101255","geneontology_Biological_Process_noRedundant" -"GO:0018200","peptidyl-glutamic acid modification",17,1,0.0188344781741635,53.0941176470588,0.0186848839671548,1,"26140","ENSG00000214021","geneontology_Biological_Process_noRedundant" -"GO:0043687","post-translational protein modification",268,2,0.296920008863284,6.73582089552239,0.0337719774848373,1,"29106;150465","ENSG00000114999;ENSG00000104112","geneontology_Biological_Process_noRedundant" -"GO:0001578","microtubule bundle formation",46,1,0.0509638821183248,19.6217391304348,0.0498351162263958,1,"26140","ENSG00000214021","geneontology_Biological_Process_noRedundant" -"GO:0097194","execution phase of apoptosis",55,1,0.0609350764458232,16.4109090909091,0.05931982582565,1,"662","ENSG00000113734","geneontology_Biological_Process_noRedundant" -"GO:0050000","chromosome localization",65,1,0.0720141812541547,13.8861538461538,0.0697583838959338,1,"150465","ENSG00000114999","geneontology_Biological_Process_noRedundant" -"GO:0015748","organophosphate ester transport",66,1,0.0731220917349878,13.6757575757576,0.0707964841259349,1,"51012","ENSG00000101166","geneontology_Biological_Process_noRedundant" -"GO:0002576","platelet degranulation",74,1,0.081985375581653,12.1972972972973,0.0790638256423364,1,"29106","ENSG00000104112","geneontology_Biological_Process_noRedundant" -"GO:0008643","carbohydrate transport",87,1,0.0963882118324839,10.3747126436782,0.0923571458725254,1,"57761","ENSG00000101255","geneontology_Biological_Process_noRedundant" -"GO:0048284","organelle fusion",95,1,0.105251495679149,9.50105263157895,0.100451561706691,1,"662","ENSG00000113734","geneontology_Biological_Process_noRedundant" -"GO:0061025","membrane fusion",101,1,0.111898958564148,8.93663366336634,0.106479674873358,1,"662","ENSG00000113734","geneontology_Biological_Process_noRedundant" -"GO:0042180","cellular ketone metabolic process",119,1,0.131841347219145,7.58487394957983,0.124346509950791,1,"57761","ENSG00000101255","geneontology_Biological_Process_noRedundant" -"GO:0045444","fat cell differentiation",126,1,0.139596720584977,7.16349206349206,0.131207427680489,1,"57761","ENSG00000101255","geneontology_Biological_Process_noRedundant" -"GO:0060560","developmental growth involved in morphogenesis",133,1,0.147352093950809,6.78646616541354,0.138019926467541,1,"150465","ENSG00000114999","geneontology_Biological_Process_noRedundant" -"GO:0016458","gene silencing",159,1,0.176157766452471,5.67672955974843,0.162904630918749,1,"29028","ENSG00000156802","geneontology_Biological_Process_noRedundant" -"GO:0048638","regulation of developmental growth",179,1,0.198315976069134,5.04245810055866,0.18160467228956,1,"150465","ENSG00000114999","geneontology_Biological_Process_noRedundant" +"GO:0008544","epidermis development",145,8,1.81531132284511,4.40695758315533,0.000451735749749993,0.266020165196415,"2146;2810;3643;4664;5660;6093;7357;51083","ENSG00000067900;ENSG00000138386;ENSG00000106462;ENSG00000171105;ENSG00000069482;ENSG00000175793;ENSG00000197746;ENSG00000148154","geneontology_Biological_Process_noRedundant" +"GO:0035270","endocrine system development",56,5,0.701085752271217,7.13179519595449,0.000646464556977921,0.266020165196415,"1028;3643;5087;5170;5307","ENSG00000129757;ENSG00000185630;ENSG00000140992;ENSG00000171105;ENSG00000069011","geneontology_Biological_Process_noRedundant" +"GO:2001023","regulation of response to drug",38,4,0.475736760469754,8.40801117838845,0.00123830243242384,0.339707633961608,"5151;5660;23204;51083","ENSG00000170540;ENSG00000073417;ENSG00000069482;ENSG00000197746","geneontology_Biological_Process_noRedundant" +"GO:0048872","homeostasis of number of cells",167,7,2.09073786838023,3.34810025965768,0.00489515923332529,0.845336864472341,"2146;2149;3815;5293;28982;54475;54892","ENSG00000171608;ENSG00000157404;ENSG00000073536;ENSG00000146918;ENSG00000106462;ENSG00000162769;ENSG00000181104","geneontology_Biological_Process_noRedundant" +"GO:0002448","mast cell mediated immunity",30,3,0.375581653002437,7.98761061946903,0.00606534582632001,0.845336864472341,"3815;5170;5293","ENSG00000171608;ENSG00000157404;ENSG00000140992","geneontology_Biological_Process_noRedundant" +"GO:0045576","mast cell activation",33,3,0.413139818302681,7.2614641995173,0.00793269371097816,0.845336864472341,"3815;5170;5293","ENSG00000171608;ENSG00000157404;ENSG00000140992","geneontology_Biological_Process_noRedundant" +"GO:0034764","positive regulation of transmembrane transport",105,5,1.31453578550853,3.80362410450906,0.0100807311084137,0.845336864472341,"2149;3643;5170;23204;51083","ENSG00000170540;ENSG00000140992;ENSG00000171105;ENSG00000069482;ENSG00000181104","geneontology_Biological_Process_noRedundant" +"GO:0030856","regulation of epithelial cell differentiation",68,4,0.851318413472191,4.6985944820406,0.010258194338129,0.845336864472341,"1028;2146;2810;6093","ENSG00000129757;ENSG00000067900;ENSG00000106462;ENSG00000175793","geneontology_Biological_Process_noRedundant" +"GO:0071900","regulation of protein serine/threonine kinase activity",347,10,4.34422778639486,2.30190507765678,0.0114979551763168,0.845336864472341,"898;1028;1856;2146;2149;2810;3643;3815;51719;51776","ENSG00000135932;ENSG00000157404;ENSG00000129757;ENSG00000106462;ENSG00000171105;ENSG00000091436;ENSG00000175793;ENSG00000004975;ENSG00000181104;ENSG00000105173","geneontology_Biological_Process_noRedundant" +"GO:0006304","DNA modification",76,4,0.951473520939508,4.20400558919422,0.0149801493877439,0.845336864472341,"2146;54890;56915;121642","ENSG00000091542;ENSG00000189046;ENSG00000106462;ENSG00000077348","geneontology_Biological_Process_noRedundant" +"GO:0043270","positive regulation of ion transport",117,5,1.46476844670951,3.4135088117389,0.0155549963909023,0.845336864472341,"2149;5026;5170;23204;51083","ENSG00000170540;ENSG00000140992;ENSG00000069482;ENSG00000083454;ENSG00000181104","geneontology_Biological_Process_noRedundant" +"GO:0034502","protein localization to chromosome",77,4,0.963992909372923,4.14940811400988,0.0156521327619988,0.845336864472341,"2146;7014;10403;157570","ENSG00000171320;ENSG00000132604;ENSG00000106462;ENSG00000080986","geneontology_Biological_Process_noRedundant" +"GO:0006401","RNA catabolic process",312,9,3.90604919122535,2.30411844792376,0.0161504006315493,0.845336864472341,"5976;6133;6173;10535;10643;26523;54890;56006;56915","ENSG00000105771;ENSG00000091542;ENSG00000241343;ENSG00000104889;ENSG00000092847;ENSG00000005007;ENSG00000136231;ENSG00000163682;ENSG00000077348","geneontology_Biological_Process_noRedundant" +"GO:0045730","respiratory burst",16,2,0.200310214934633,9.98451327433628,0.0166258488119548,0.845336864472341,"3643;5293","ENSG00000171608;ENSG00000171105","geneontology_Biological_Process_noRedundant" +"GO:0032147","activation of protein kinase activity",212,7,2.65411034788389,2.63741860076808,0.0169362661749022,0.845336864472341,"2013;2149;3643;3815;5170;51719;51776","ENSG00000135932;ENSG00000157404;ENSG00000213853;ENSG00000140992;ENSG00000171105;ENSG00000091436;ENSG00000181104","geneontology_Biological_Process_noRedundant" +"GO:0045930","negative regulation of mitotic cell cycle",215,7,2.69166851318413,2.60061741098992,0.0181546554215679,0.845336864472341,"2146;2810;10403;51776;54475;55755;56984","ENSG00000136861;ENSG00000073536;ENSG00000106462;ENSG00000080986;ENSG00000091436;ENSG00000175793;ENSG00000128789","geneontology_Biological_Process_noRedundant" diff --git a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000120738_ORA_summary.csv b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000120738_ORA_summary.csv index f6b4102..600def6 100644 --- a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000120738_ORA_summary.csv +++ b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000120738_ORA_summary.csv @@ -1,17 +1,17 @@ "geneSet","description","size","overlap","expect","enrichmentRatio","pValue","FDR","overlapId","userId","database" -"GO:0006401","RNA catabolic process",312,69,42.6900066474629,1.61630333229524,2.42508290539956e-05,0.0157493123926453,"1736;1869;1981;2107;2935;3192;3646;5515;5520;5578;6122;6129;6130;6133;6137;6138;6143;6146;6155;6156;6157;6160;6164;6165;6175;6187;6188;6191;6203;6204;6205;6207;6224;6227;6229;6234;6741;6832;7316;8531;8882;9045;9349;9811;10128;10535;10643;10921;11044;22894;23016;23049;23112;23521;23589;26019;27327;51010;55802;56006;56339;79068;79675;79727;79882;81892;87178;115752;192670","ENSG00000131914;ENSG00000165819;ENSG00000089157;ENSG00000174748;ENSG00000171858;ENSG00000148303;ENSG00000170889;ENSG00000100722;ENSG00000114867;ENSG00000109475;ENSG00000156482;ENSG00000103342;ENSG00000140718;ENSG00000182899;ENSG00000167526;ENSG00000138385;ENSG00000107371;ENSG00000134030;ENSG00000090905;ENSG00000119705;ENSG00000101412;ENSG00000008988;ENSG00000142534;ENSG00000138326;ENSG00000140988;ENSG00000113575;ENSG00000108298;ENSG00000071082;ENSG00000109917;ENSG00000156502;ENSG00000060138;ENSG00000075914;ENSG00000205937;ENSG00000149273;ENSG00000116251;ENSG00000138035;ENSG00000112941;ENSG00000124614;ENSG00000166938;ENSG00000166441;ENSG00000221914;ENSG00000188846;ENSG00000104408;ENSG00000233927;ENSG00000163682;ENSG00000125691;ENSG00000272886;ENSG00000083520;ENSG00000104889;ENSG00000131469;ENSG00000130826;ENSG00000157106;ENSG00000136231;ENSG00000100354;ENSG00000110700;ENSG00000153187;ENSG00000105771;ENSG00000147604;ENSG00000138399;ENSG00000198034;ENSG00000154229;ENSG00000142541;ENSG00000150991;ENSG00000120705;ENSG00000100316;ENSG00000134698;ENSG00000153048;ENSG00000151461;ENSG00000138095","geneontology_Biological_Process_noRedundant" -"GO:0016072","rRNA metabolic process",231,54,31.6070241524485,1.70848099269152,3.8272934125505e-05,0.0157493123926453,"1736;2091;2976;4809;4931;6129;6130;6155;6165;6187;6203;6227;6229;6234;8568;9045;9875;10607;10927;10969;22894;23016;23481;25879;26168;27043;27340;51010;51077;51118;51602;54433;54663;54680;55035;55226;55272;55636;55651;55720;55759;57109;57647;64965;79050;79954;83732;84128;84135;84365;84916;115752;134430;285855","ENSG00000183520;ENSG00000164934;ENSG00000100029;ENSG00000119616;ENSG00000171858;ENSG00000148303;ENSG00000170889;ENSG00000182899;ENSG00000120800;ENSG00000155438;ENSG00000135372;ENSG00000055044;ENSG00000107371;ENSG00000150990;ENSG00000135972;ENSG00000148300;ENSG00000138326;ENSG00000140988;ENSG00000109534;ENSG00000160214;ENSG00000143748;ENSG00000075914;ENSG00000183751;ENSG00000184967;ENSG00000166938;ENSG00000146223;ENSG00000167721;ENSG00000124784;ENSG00000133316;ENSG00000161956;ENSG00000177971;ENSG00000188846;ENSG00000198000;ENSG00000233927;ENSG00000083520;ENSG00000106723;ENSG00000134987;ENSG00000131469;ENSG00000115368;ENSG00000130826;ENSG00000141076;ENSG00000105202;ENSG00000141456;ENSG00000147604;ENSG00000100138;ENSG00000164338;ENSG00000115761;ENSG00000117174;ENSG00000138442;ENSG00000145912;ENSG00000115207;ENSG00000117395;ENSG00000142207;ENSG00000171316","geneontology_Biological_Process_noRedundant" -"GO:0070972","protein localization to endoplasmic reticulum",131,34,17.9243297141591,1.89686312080848,0.000124589905271755,0.0282158637065049,"2901;3309;6122;6129;6130;6133;6137;6138;6143;6146;6155;6156;6157;6160;6164;6165;6175;6187;6188;6191;6203;6204;6205;6207;6224;6227;6229;6234;6731;9045;9218;9349;11015;23521","ENSG00000089157;ENSG00000105737;ENSG00000174748;ENSG00000171858;ENSG00000148303;ENSG00000170889;ENSG00000109475;ENSG00000156482;ENSG00000182899;ENSG00000167526;ENSG00000044574;ENSG00000101558;ENSG00000008988;ENSG00000142534;ENSG00000138326;ENSG00000140988;ENSG00000108298;ENSG00000071082;ENSG00000149273;ENSG00000116251;ENSG00000124614;ENSG00000166441;ENSG00000188846;ENSG00000233927;ENSG00000163682;ENSG00000125691;ENSG00000131469;ENSG00000110700;ENSG00000100196;ENSG00000174780;ENSG00000147604;ENSG00000198034;ENSG00000142541;ENSG00000100316","geneontology_Biological_Process_noRedundant" -"GO:0006413","translational initiation",180,43,24.6288499889209,1.74591992802519,0.000137136640128821,0.0282158637065049,"468;1939;1974;1981;3646;5610;6122;6129;6130;6133;6137;6138;6143;6146;6155;6156;6157;6160;6164;6165;6175;6187;6188;6191;6203;6204;6205;6207;6224;6227;6229;6234;7175;8637;8664;9045;9349;9811;23521;56339;83939;220064;440275","ENSG00000165819;ENSG00000089157;ENSG00000174748;ENSG00000171858;ENSG00000128829;ENSG00000148303;ENSG00000156976;ENSG00000170889;ENSG00000114867;ENSG00000109475;ENSG00000156482;ENSG00000128272;ENSG00000182899;ENSG00000167526;ENSG00000134030;ENSG00000144895;ENSG00000008988;ENSG00000142534;ENSG00000143486;ENSG00000138326;ENSG00000140988;ENSG00000108298;ENSG00000071082;ENSG00000149273;ENSG00000116251;ENSG00000100353;ENSG00000124614;ENSG00000166441;ENSG00000188846;ENSG00000104408;ENSG00000233927;ENSG00000163682;ENSG00000125691;ENSG00000131469;ENSG00000110700;ENSG00000243056;ENSG00000147604;ENSG00000198034;ENSG00000055332;ENSG00000142541;ENSG00000149716;ENSG00000100316;ENSG00000047410","geneontology_Biological_Process_noRedundant" -"GO:0035148","tube formation",90,25,12.3144249944604,2.03013945119208,0.000311867186901238,0.0483827721712963,"25;317;1499;1855;1856;1857;2909;4524;5566;5727;5754;6497;6789;7289;7403;7408;7798;8463;8543;11019;23242;25902;54910;55081;55764","ENSG00000168036;ENSG00000125753;ENSG00000120254;ENSG00000078246;ENSG00000107404;ENSG00000097007;ENSG00000121897;ENSG00000168758;ENSG00000163913;ENSG00000004975;ENSG00000177000;ENSG00000106078;ENSG00000169641;ENSG00000120868;ENSG00000157933;ENSG00000114446;ENSG00000160007;ENSG00000161202;ENSG00000143013;ENSG00000074219;ENSG00000101109;ENSG00000112655;ENSG00000147050;ENSG00000072062;ENSG00000185920","geneontology_Biological_Process_noRedundant" -"GO:0043620","regulation of DNA-templated transcription in response to stress",66,20,9.030578329271,2.21469758311864,0.00035272980926826,0.0483827721712963,"467;468;1649;3309;4734;6197;7009;7157;7316;9531;10365;10985;22926;22933;25942;54583;83667;84181;112398;112399","ENSG00000141510;ENSG00000269858;ENSG00000128272;ENSG00000177189;ENSG00000044574;ENSG00000151929;ENSG00000089154;ENSG00000118217;ENSG00000127528;ENSG00000175197;ENSG00000068903;ENSG00000135766;ENSG00000139644;ENSG00000162772;ENSG00000124177;ENSG00000169375;ENSG00000150991;ENSG00000129521;ENSG00000069869;ENSG00000130766","geneontology_Biological_Process_noRedundant" -"GO:0022613","ribonucleoprotein complex biogenesis",428,83,58.5619321958786,1.41730296265466,0.000475673148152334,0.0559255715613386,"636;1207;1736;1939;2091;3320;3326;3646;4809;4931;6122;6129;6130;6155;6165;6175;6187;6203;6204;6227;6229;6234;6433;8568;8664;9045;9875;10146;10181;10514;10607;10659;10728;10946;10969;11171;11273;22894;22907;23016;23481;23521;25879;25929;26155;26168;27043;27340;51010;51077;51118;51121;51602;54433;54475;54663;54680;55027;55035;55226;55272;55299;55636;55651;55720;55759;57109;57647;64965;79050;79760;79954;83732;83939;84128;84135;84864;84916;115752;134430;192670;220064;285855","ENSG00000073536;ENSG00000183520;ENSG00000089157;ENSG00000164934;ENSG00000100029;ENSG00000023734;ENSG00000119616;ENSG00000171858;ENSG00000132153;ENSG00000148303;ENSG00000170889;ENSG00000182899;ENSG00000120800;ENSG00000151746;ENSG00000135372;ENSG00000170854;ENSG00000055044;ENSG00000107371;ENSG00000150990;ENSG00000135972;ENSG00000048740;ENSG00000148300;ENSG00000144895;ENSG00000082516;ENSG00000168488;ENSG00000188976;ENSG00000132382;ENSG00000143486;ENSG00000138326;ENSG00000003756;ENSG00000140988;ENSG00000074201;ENSG00000183431;ENSG00000109534;ENSG00000160214;ENSG00000143748;ENSG00000075914;ENSG00000183751;ENSG00000110958;ENSG00000100353;ENSG00000184967;ENSG00000142252;ENSG00000096384;ENSG00000124614;ENSG00000166938;ENSG00000155393;ENSG00000146223;ENSG00000167721;ENSG00000124784;ENSG00000133316;ENSG00000161956;ENSG00000177971;ENSG00000188846;ENSG00000104408;ENSG00000198000;ENSG00000233927;ENSG00000113460;ENSG00000083520;ENSG00000134987;ENSG00000131469;ENSG00000115368;ENSG00000130826;ENSG00000141076;ENSG00000105202;ENSG00000141456;ENSG00000147604;ENSG00000080824;ENSG00000100138;ENSG00000164338;ENSG00000037241;ENSG00000115761;ENSG00000117174;ENSG00000138442;ENSG00000061936;ENSG00000142541;ENSG00000149716;ENSG00000100316;ENSG00000145912;ENSG00000145907;ENSG00000134698;ENSG00000117395;ENSG00000142207;ENSG00000171316","geneontology_Biological_Process_noRedundant" -"GO:0060537","muscle tissue development",193,43,26.4076002658985,1.62831910385769,0.000680773486481767,0.0699985711851977,"406;467;607;1063;1499;1938;2274;2817;3192;3475;4205;4211;4628;5307;5311;5457;5600;6497;6665;7046;7161;8324;8531;8928;9148;9794;9975;10038;10362;11149;22933;26263;51176;54583;55636;57462;57493;57538;59269;65009;79810;93185;221496","ENSG00000168036;ENSG00000069011;ENSG00000103034;ENSG00000185386;ENSG00000161021;ENSG00000064961;ENSG00000167658;ENSG00000115641;ENSG00000063660;ENSG00000155760;ENSG00000068305;ENSG00000173706;ENSG00000160973;ENSG00000127124;ENSG00000049883;ENSG00000162729;ENSG00000060138;ENSG00000167196;ENSG00000117724;ENSG00000136383;ENSG00000157933;ENSG00000068903;ENSG00000138795;ENSG00000133794;ENSG00000135766;ENSG00000152192;ENSG00000006652;ENSG00000174738;ENSG00000162772;ENSG00000153187;ENSG00000129194;ENSG00000161904;ENSG00000118762;ENSG00000106799;ENSG00000133026;ENSG00000164976;ENSG00000078900;ENSG00000112276;ENSG00000129484;ENSG00000116128;ENSG00000107954;ENSG00000143995;ENSG00000171316","geneontology_Biological_Process_noRedundant" -"GO:0090150","establishment of protein localization to membrane",270,56,36.9432749833813,1.51583745689009,0.000768995539663253,0.0699985711851977,"581;1445;1869;2810;3308;3320;4836;5366;5621;6122;6129;6130;6133;6137;6138;6143;6146;6155;6156;6157;6160;6164;6165;6175;6187;6188;6191;6203;6204;6205;6207;6224;6227;6229;6234;6731;7157;7161;9045;9349;9747;23521;25837;26517;27113;51308;51762;55750;56993;63971;84885;90427;91461;117177;161176;254887","ENSG00000198420;ENSG00000141510;ENSG00000127328;ENSG00000089157;ENSG00000174748;ENSG00000137177;ENSG00000171858;ENSG00000103653;ENSG00000148303;ENSG00000170889;ENSG00000109475;ENSG00000156482;ENSG00000182899;ENSG00000136448;ENSG00000167526;ENSG00000170606;ENSG00000006530;ENSG00000100216;ENSG00000101412;ENSG00000008988;ENSG00000142534;ENSG00000138326;ENSG00000140988;ENSG00000108298;ENSG00000071082;ENSG00000176438;ENSG00000141682;ENSG00000149273;ENSG00000116251;ENSG00000124614;ENSG00000166441;ENSG00000104081;ENSG00000188846;ENSG00000233927;ENSG00000162878;ENSG00000163682;ENSG00000167964;ENSG00000125691;ENSG00000131469;ENSG00000105327;ENSG00000175793;ENSG00000110700;ENSG00000174780;ENSG00000099800;ENSG00000147604;ENSG00000160446;ENSG00000171867;ENSG00000080824;ENSG00000198034;ENSG00000142541;ENSG00000078900;ENSG00000100316;ENSG00000132563;ENSG00000184307;ENSG00000166128;ENSG00000087088","geneontology_Biological_Process_noRedundant" -"GO:0045787","positive regulation of cell cycle",295,60,40.3639485929537,1.48647498799149,0.000850529419018198,0.0699985711851977,"25;301;581;894;990;994;995;1017;1820;1869;1981;2810;3192;3276;3326;3643;4331;4926;5087;5111;5311;5457;5566;5578;5885;5962;6604;6665;7157;7161;7175;7325;8061;8451;8877;9134;9212;9349;9700;9928;10498;10514;10615;11331;22933;23137;23476;23636;25942;26271;29127;51203;51379;55835;56257;81610;81857;84722;144455;153241","ENSG00000141510;ENSG00000161800;ENSG00000164754;ENSG00000076382;ENSG00000176390;ENSG00000114867;ENSG00000141867;ENSG00000142453;ENSG00000123374;ENSG00000097007;ENSG00000175305;ENSG00000104973;ENSG00000185630;ENSG00000146834;ENSG00000176170;ENSG00000101412;ENSG00000165891;ENSG00000132382;ENSG00000213024;ENSG00000182247;ENSG00000139842;ENSG00000132646;ENSG00000137497;ENSG00000158402;ENSG00000198887;ENSG00000151849;ENSG00000137710;ENSG00000118193;ENSG00000096384;ENSG00000101224;ENSG00000118971;ENSG00000068903;ENSG00000171105;ENSG00000125691;ENSG00000175592;ENSG00000135476;ENSG00000116017;ENSG00000152192;ENSG00000215021;ENSG00000175793;ENSG00000153187;ENSG00000135046;ENSG00000129194;ENSG00000126457;ENSG00000082014;ENSG00000169375;ENSG00000168944;ENSG00000072062;ENSG00000118762;ENSG00000112029;ENSG00000154229;ENSG00000078900;ENSG00000178999;ENSG00000137804;ENSG00000094804;ENSG00000047410;ENSG00000020426;ENSG00000134222;ENSG00000101447;ENSG00000087088","geneontology_Biological_Process_noRedundant" -"GO:0034404","nucleobase-containing small molecule biosynthetic process",130,31,17.7875027697762,1.74279663656182,0.00115875691170875,0.0835624540329989,"38;1267;1854;2023;2597;3614;3615;3643;3704;4830;4833;4860;5138;5150;5151;5213;5214;5230;7083;7084;7371;7374;7378;8604;8833;8930;10591;11164;161823;252969;283209","ENSG00000115840;ENSG00000143179;ENSG00000173786;ENSG00000125877;ENSG00000102144;ENSG00000166548;ENSG00000076248;ENSG00000128951;ENSG00000178035;ENSG00000112667;ENSG00000183696;ENSG00000167900;ENSG00000074800;ENSG00000205268;ENSG00000171105;ENSG00000152556;ENSG00000154328;ENSG00000239672;ENSG00000129071;ENSG00000111640;ENSG00000067057;ENSG00000168803;ENSG00000198805;ENSG00000103202;ENSG00000106348;ENSG00000075239;ENSG00000165609;ENSG00000163655;ENSG00000165434;ENSG00000073417;ENSG00000186642","geneontology_Biological_Process_noRedundant" -"GO:0021915","neural tube development",114,28,15.5982716596499,1.795070672633,0.0012184075922187,0.0835624540329989,"25;317;1855;1856;1857;2909;4524;5311;5566;5727;5754;6497;6789;6932;7289;7403;7408;7798;8463;8543;11019;23242;25902;26146;26227;54910;55081;55764","ENSG00000125753;ENSG00000120254;ENSG00000078246;ENSG00000107404;ENSG00000092621;ENSG00000097007;ENSG00000121897;ENSG00000168758;ENSG00000163913;ENSG00000004975;ENSG00000177000;ENSG00000106078;ENSG00000169641;ENSG00000120868;ENSG00000157933;ENSG00000114446;ENSG00000160007;ENSG00000161202;ENSG00000143013;ENSG00000074219;ENSG00000101109;ENSG00000112655;ENSG00000147050;ENSG00000081059;ENSG00000204104;ENSG00000072062;ENSG00000118762;ENSG00000185920","geneontology_Biological_Process_noRedundant" -"GO:0002181","cytoplasmic translation",89,23,12.1775980500776,1.88871400627758,0.00157371224238312,0.099628090421639,"1938;1939;1981;2107;2935;3646;6133;6138;6143;6156;6160;6165;6175;6227;6234;8531;8664;23521;51121;51611;56339;79727;132864","ENSG00000131914;ENSG00000165819;ENSG00000089157;ENSG00000174748;ENSG00000171858;ENSG00000114867;ENSG00000156482;ENSG00000103342;ENSG00000182899;ENSG00000167658;ENSG00000143486;ENSG00000108298;ENSG00000071082;ENSG00000060138;ENSG00000117543;ENSG00000100353;ENSG00000104408;ENSG00000233927;ENSG00000163682;ENSG00000037241;ENSG00000142541;ENSG00000120705;ENSG00000137449","geneontology_Biological_Process_noRedundant" -"GO:0034330","cell junction organization",175,38,23.9447152670064,1.58698901098901,0.00221909571720036,0.123237564910325,"25;389;977;1445;1495;1499;1500;1613;2017;2149;3915;5566;5578;5906;5962;6093;6801;7046;7408;7855;8861;9231;9448;9647;23114;23332;23396;25930;29841;51762;57493;57555;79778;79834;85302;91663;92140;123720","ENSG00000168036;ENSG00000125753;ENSG00000103653;ENSG00000186111;ENSG00000115808;ENSG00000135862;ENSG00000097007;ENSG00000198728;ENSG00000198561;ENSG00000071054;ENSG00000100034;ENSG00000188878;ENSG00000173706;ENSG00000076201;ENSG00000169992;ENSG00000074054;ENSG00000147649;ENSG00000085733;ENSG00000044115;ENSG00000163251;ENSG00000137710;ENSG00000177697;ENSG00000181104;ENSG00000167657;ENSG00000163531;ENSG00000164877;ENSG00000173517;ENSG00000155366;ENSG00000134317;ENSG00000072062;ENSG00000151208;ENSG00000106799;ENSG00000154229;ENSG00000067900;ENSG00000116473;ENSG00000166128;ENSG00000156232;ENSG00000179820","geneontology_Biological_Process_noRedundant" -"GO:0034470","ncRNA processing",356,68,48.7103922003102,1.39600600463995,0.00224612815753933,0.123237564910325,"1736;2091;4809;4931;6129;6130;6155;6165;6187;6203;6227;6229;6234;6301;6741;8568;9045;9875;10607;10785;10969;11044;22894;23016;23481;25879;26168;27043;27340;51010;51077;51095;51118;51602;54433;54663;54680;54888;55035;55140;55226;55272;55636;55651;55687;55720;55759;56339;56931;57109;57647;60487;64965;65080;79050;79691;79727;79954;83732;84128;84135;84267;84916;115752;134430;192670;285855;348180","ENSG00000131914;ENSG00000174177;ENSG00000165819;ENSG00000183520;ENSG00000164934;ENSG00000100029;ENSG00000119616;ENSG00000171858;ENSG00000148303;ENSG00000170889;ENSG00000182899;ENSG00000120800;ENSG00000037474;ENSG00000135372;ENSG00000055044;ENSG00000138385;ENSG00000107371;ENSG00000150990;ENSG00000135972;ENSG00000072756;ENSG00000031698;ENSG00000148300;ENSG00000151576;ENSG00000160193;ENSG00000138326;ENSG00000140988;ENSG00000066651;ENSG00000141994;ENSG00000109534;ENSG00000160214;ENSG00000143748;ENSG00000075914;ENSG00000134014;ENSG00000183751;ENSG00000184967;ENSG00000112941;ENSG00000166938;ENSG00000146223;ENSG00000167721;ENSG00000124784;ENSG00000133316;ENSG00000161956;ENSG00000177971;ENSG00000188846;ENSG00000198000;ENSG00000135900;ENSG00000233927;ENSG00000100416;ENSG00000083520;ENSG00000134987;ENSG00000131469;ENSG00000115368;ENSG00000130826;ENSG00000141076;ENSG00000105202;ENSG00000141456;ENSG00000165118;ENSG00000147604;ENSG00000100138;ENSG00000164338;ENSG00000115761;ENSG00000117174;ENSG00000138442;ENSG00000145912;ENSG00000134698;ENSG00000117395;ENSG00000142207;ENSG00000171316","geneontology_Biological_Process_noRedundant" -"GO:0051098","regulation of binding",259,51,35.4381785951695,1.43912588123114,0.00405739167128538,0.208702084091742,"25;166;348;581;1400;1499;1540;1649;1854;1855;1869;1981;2035;3148;3309;3326;3646;3925;4751;4830;4931;5347;5457;5515;5566;6093;6497;6604;6789;7009;7046;7295;7341;8664;8751;8864;9044;9212;10498;22933;23028;25942;51176;53339;54984;55355;56257;56998;81857;84893;146057","ENSG00000168036;ENSG00000166851;ENSG00000107404;ENSG00000114867;ENSG00000134452;ENSG00000142453;ENSG00000097007;ENSG00000104973;ENSG00000044574;ENSG00000254093;ENSG00000159023;ENSG00000146834;ENSG00000072832;ENSG00000095564;ENSG00000101412;ENSG00000128951;ENSG00000004487;ENSG00000164104;ENSG00000113575;ENSG00000123485;ENSG00000143537;ENSG00000132326;ENSG00000143748;ENSG00000128881;ENSG00000100353;ENSG00000096384;ENSG00000178585;ENSG00000157933;ENSG00000175197;ENSG00000064726;ENSG00000117632;ENSG00000104408;ENSG00000068903;ENSG00000138795;ENSG00000239672;ENSG00000152192;ENSG00000139644;ENSG00000116030;ENSG00000101109;ENSG00000082014;ENSG00000130203;ENSG00000169375;ENSG00000104964;ENSG00000083799;ENSG00000072062;ENSG00000106799;ENSG00000117650;ENSG00000178999;ENSG00000067900;ENSG00000136810;ENSG00000087088","geneontology_Biological_Process_noRedundant" +"GO:0007586","digestion",35,1,0.0116330600487481,85.9619047619048,0.0115892880920648,1,"29887","ENSG00000086300","geneontology_Biological_Process_noRedundant" +"GO:0031529","ruffle organization",39,1,0.0129625526257478,77.1452991452991,0.0129080480515545,1,"29887","ENSG00000086300","geneontology_Biological_Process_noRedundant" +"GO:0046717","acid secretion",40,1,0.0132949257699978,75.2166666666667,0.0132375546561878,1,"29887","ENSG00000086300","geneontology_Biological_Process_noRedundant" +"GO:0061512","protein localization to cilium",40,1,0.0132949257699978,75.2166666666667,0.0132375546561878,1,"29887","ENSG00000086300","geneontology_Biological_Process_noRedundant" +"GO:0044380","protein localization to cytoskeleton",50,1,0.0166186572124972,60.1733333333333,0.0165285884718986,1,"29887","ENSG00000086300","geneontology_Biological_Process_noRedundant" +"GO:0042476","odontogenesis",53,1,0.0176157766452471,56.7672955974843,0.0175144697503353,1,"29887","ENSG00000086300","geneontology_Biological_Process_noRedundant" +"GO:0031214","biomineral tissue development",64,1,0.0212718812319965,47.0104166666667,0.0211237306092975,1,"29887","ENSG00000086300","geneontology_Biological_Process_noRedundant" +"GO:1990823","response to leukemia inhibitory factor",70,1,0.0232661200974961,42.9809523809524,0.0230886874210309,1,"29887","ENSG00000086300","geneontology_Biological_Process_noRedundant" +"GO:0051291","protein heterooligomerization",73,1,0.024263239530246,41.2146118721461,0.0240701789625094,1,"1104","ENSG00000180198","geneontology_Biological_Process_noRedundant" +"GO:0048771","tissue remodeling",80,1,0.0265898515399956,37.6083333333333,0.026357768878943,1,"29887","ENSG00000086300","geneontology_Biological_Process_noRedundant" +"GO:0051262","protein tetramerization",92,1,0.0305783292709949,32.7028985507246,0.03027102985761,1,"1104","ENSG00000180198","geneontology_Biological_Process_noRedundant" +"GO:0006333","chromatin assembly or disassembly",112,1,0.0372257921559938,26.8630952380952,0.0367698061869052,1,"8971","ENSG00000184897","geneontology_Biological_Process_noRedundant" +"GO:1902850","microtubule cytoskeleton organization involved in mitosis",120,1,0.0398847773099934,25.0722222222222,0.0393611650519422,1,"1104","ENSG00000180198","geneontology_Biological_Process_noRedundant" +"GO:0007051","spindle organization",151,1,0.0501883447817416,19.924944812362,0.0493587805255388,1,"1104","ENSG00000180198","geneontology_Biological_Process_noRedundant" +"GO:0016458","gene silencing",159,1,0.0528473299357412,18.9224318658281,0.0519275012083062,1,"8971","ENSG00000184897","geneontology_Biological_Process_noRedundant" +"GO:0071824","protein-DNA complex subunit organization",192,1,0.0638156436959894,15.6701388888889,0.0624745641266515,1,"8971","ENSG00000184897","geneontology_Biological_Process_noRedundant" diff --git a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000134138_ORA_summary.csv b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000134138_ORA_summary.csv index bc567df..f934dfc 100644 --- a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000134138_ORA_summary.csv +++ b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000134138_ORA_summary.csv @@ -1,17 +1,17 @@ "geneSet","description","size","overlap","expect","enrichmentRatio","pValue","FDR","overlapId","userId","database" -"GO:0045103","intermediate filament-based process",28,2,0.124085973853313,16.1178571428571,0.00673035178583714,1,"3267;6647","ENSG00000142168;ENSG00000173744","geneontology_Biological_Process_noRedundant" -"GO:0055076","transition metal ion homeostasis",92,3,0.407711056946599,7.35815217391304,0.00771041198241873,1,"5621;6647;10058","ENSG00000142168;ENSG00000115657;ENSG00000171867","geneontology_Biological_Process_noRedundant" -"GO:0007050","cell cycle arrest",194,4,0.859738533126523,4.65257731958763,0.0103353101687278,1,"1028;1649;3611;5621","ENSG00000175197;ENSG00000129757;ENSG00000171867;ENSG00000166333","geneontology_Biological_Process_noRedundant" -"GO:0042116","macrophage activation",36,2,0.159539109239973,12.5361111111111,0.0109697836600284,1,"9258;9867","ENSG00000198961;ENSG00000147324","geneontology_Biological_Process_noRedundant" -"GO:0007568","aging",202,4,0.895191668513184,4.46831683168317,0.0118596171940328,1,"1028;3611;5621;6647","ENSG00000129757;ENSG00000142168;ENSG00000171867;ENSG00000166333","geneontology_Biological_Process_noRedundant" -"GO:0045445","myoblast differentiation",38,2,0.168402393086639,11.8763157894737,0.0121729022656287,1,"1649;3611","ENSG00000175197;ENSG00000166333","geneontology_Biological_Process_noRedundant" -"GO:0045088","regulation of innate immune response",208,4,0.92178152005318,4.33942307692308,0.0130935311633806,1,"661;2874;9258;9867","ENSG00000168495;ENSG00000198961;ENSG00000147324;ENSG00000132522","geneontology_Biological_Process_noRedundant" -"GO:0007422","peripheral nervous system development",41,2,0.181697318856636,11.0073170731707,0.0140807950681607,1,"3611;6647","ENSG00000142168;ENSG00000166333","geneontology_Biological_Process_noRedundant" -"GO:0043087","regulation of GTPase activity",334,5,1.48016840239309,3.37799401197605,0.0153044311183916,1,"2874;3267;5364;6647;9135","ENSG00000029725;ENSG00000142168;ENSG00000164050;ENSG00000132522;ENSG00000173744","geneontology_Biological_Process_noRedundant" -"GO:0031349","positive regulation of defense response",219,4,0.970529581209838,4.12146118721461,0.0155640359840509,1,"661;2874;9258;9867","ENSG00000168495;ENSG00000198961;ENSG00000147324;ENSG00000132522","geneontology_Biological_Process_noRedundant" -"GO:0001578","microtubule bundle formation",46,2,0.203855528473299,9.81086956521739,0.0175274235011675,1,"9055;54919","ENSG00000198901;ENSG00000164818","geneontology_Biological_Process_noRedundant" -"GO:0070997","neuron death",227,4,1.0059827165965,3.97621145374449,0.0175349026797582,1,"1649;3611;5621;6647","ENSG00000175197;ENSG00000142168;ENSG00000171867;ENSG00000166333","geneontology_Biological_Process_noRedundant" -"GO:0048285","organelle fission",350,5,1.55107467316641,3.22357142857143,0.0183921219285035,1,"1028;9055;23212;49856;79703","ENSG00000198901;ENSG00000179041;ENSG00000173715;ENSG00000129757;ENSG00000116213","geneontology_Biological_Process_noRedundant" -"GO:0043491","protein kinase B signaling",127,3,0.56281852426324,5.33031496062992,0.0184039063403225,1,"1649;3611;9258","ENSG00000175197;ENSG00000147324;ENSG00000166333","geneontology_Biological_Process_noRedundant" -"GO:0002694","regulation of leukocyte activation",251,4,1.11234212275648,3.59601593625498,0.0243632380833589,1,"5621;6647;9258;9867","ENSG00000198961;ENSG00000142168;ENSG00000171867;ENSG00000147324","geneontology_Biological_Process_noRedundant" -"GO:0010927","cellular component assembly involved in morphogenesis",57,2,0.252603589629958,7.91754385964912,0.026215780771462,1,"3267;3611","ENSG00000173744;ENSG00000166333","geneontology_Biological_Process_noRedundant" +"GO:0045787","positive regulation of cell cycle",295,70,40.3639485929537,1.73422081932341,1.58315542642118e-06,0.000660172632035716,"301;581;595;811;894;898;983;990;994;995;1869;1977;1978;1981;2033;2146;2237;2260;2735;2810;3054;3276;3326;3481;4085;4194;4331;5111;5311;5457;5566;5885;6604;6665;6714;7015;7027;7161;8451;8766;8877;9099;9134;9212;9401;9700;10370;10615;22933;23137;23326;23476;23636;25942;26271;29899;51203;51379;51512;56257;57060;64848;81857;84722;84967;85456;85459;144455;219736;246175","ENSG00000072062;ENSG00000075218;ENSG00000096384;ENSG00000176390;ENSG00000151247;ENSG00000178999;ENSG00000126457;ENSG00000175305;ENSG00000158402;ENSG00000132646;ENSG00000114867;ENSG00000134222;ENSG00000124422;ENSG00000165730;ENSG00000068903;ENSG00000078900;ENSG00000101224;ENSG00000138767;ENSG00000087088;ENSG00000141867;ENSG00000160957;ENSG00000169375;ENSG00000135046;ENSG00000170312;ENSG00000164362;ENSG00000198625;ENSG00000094804;ENSG00000118762;ENSG00000198176;ENSG00000036672;ENSG00000101412;ENSG00000181817;ENSG00000176170;ENSG00000110092;ENSG00000164442;ENSG00000198887;ENSG00000106462;ENSG00000112029;ENSG00000164109;ENSG00000197122;ENSG00000082014;ENSG00000121957;ENSG00000165891;ENSG00000076382;ENSG00000172534;ENSG00000152192;ENSG00000167244;ENSG00000168496;ENSG00000179218;ENSG00000104973;ENSG00000129194;ENSG00000090097;ENSG00000175793;ENSG00000111087;ENSG00000166004;ENSG00000139842;ENSG00000118971;ENSG00000047188;ENSG00000213024;ENSG00000187840;ENSG00000146834;ENSG00000135476;ENSG00000164754;ENSG00000100393;ENSG00000020426;ENSG00000149115;ENSG00000105173;ENSG00000137804;ENSG00000077782;ENSG00000103769","geneontology_Biological_Process_noRedundant" +"GO:0044843","cell cycle G1/S phase transition",214,55,29.2809660979393,1.87835332400015,1.60430773277209e-06,0.000660172632035716,"301;581;595;894;898;983;990;995;1032;1104;1869;1977;1978;1981;2033;2146;2735;2810;2935;3276;4174;4176;4194;4331;4863;4998;4999;5111;5311;5426;5471;5721;5932;6117;6194;7015;7027;7465;8451;8462;8882;9134;23476;25959;26271;51379;51512;56257;57060;84515;84967;85456;128239;144455;246175","ENSG00000075218;ENSG00000176390;ENSG00000151247;ENSG00000166508;ENSG00000126457;ENSG00000175305;ENSG00000158402;ENSG00000129355;ENSG00000101773;ENSG00000115942;ENSG00000132646;ENSG00000114867;ENSG00000125885;ENSG00000109917;ENSG00000138767;ENSG00000087088;ENSG00000141867;ENSG00000183856;ENSG00000135046;ENSG00000170312;ENSG00000164362;ENSG00000198625;ENSG00000137154;ENSG00000197256;ENSG00000094804;ENSG00000132383;ENSG00000118762;ENSG00000198176;ENSG00000101412;ENSG00000181817;ENSG00000110092;ENSG00000106462;ENSG00000100911;ENSG00000112029;ENSG00000165891;ENSG00000172059;ENSG00000085840;ENSG00000166483;ENSG00000100297;ENSG00000149308;ENSG00000103342;ENSG00000090097;ENSG00000175793;ENSG00000111087;ENSG00000139842;ENSG00000118971;ENSG00000187840;ENSG00000146834;ENSG00000128059;ENSG00000180198;ENSG00000100393;ENSG00000020426;ENSG00000149115;ENSG00000105173;ENSG00000177084","geneontology_Biological_Process_noRedundant" +"GO:0070972","protein localization to endoplasmic reticulum",131,34,17.9243297141591,1.89686312080848,0.000124589905271755,0.0341791640128847,"2901;3309;3921;4253;6122;6125;6130;6132;6136;6146;6152;6155;6156;6157;6159;6160;6165;6167;6170;6187;6188;6191;6194;6203;6204;6206;6227;6449;6726;6731;6747;54557;55176;202018","ENSG00000112306;ENSG00000143742;ENSG00000169762;ENSG00000131469;ENSG00000114391;ENSG00000174780;ENSG00000171858;ENSG00000044574;ENSG00000197860;ENSG00000148303;ENSG00000182899;ENSG00000137154;ENSG00000145592;ENSG00000161016;ENSG00000197958;ENSG00000116251;ENSG00000198918;ENSG00000071082;ENSG00000124614;ENSG00000104969;ENSG00000156482;ENSG00000065665;ENSG00000105737;ENSG00000170889;ENSG00000149273;ENSG00000166441;ENSG00000100316;ENSG00000122406;ENSG00000140988;ENSG00000198034;ENSG00000114850;ENSG00000150527;ENSG00000162244;ENSG00000168028","geneontology_Biological_Process_noRedundant" +"GO:0043620","regulation of DNA-templated transcription in response to stress",66,20,9.030578329271,2.21469758311864,0.00035272980926826,0.0719173291872428,"405;467;1649;2033;3309;4734;5598;6197;7009;7316;9531;10370;10985;22933;25942;25994;54583;84181;112399;219736","ENSG00000165730;ENSG00000068903;ENSG00000181061;ENSG00000175197;ENSG00000129521;ENSG00000169375;ENSG00000044574;ENSG00000166484;ENSG00000069869;ENSG00000164442;ENSG00000139644;ENSG00000151929;ENSG00000143437;ENSG00000135766;ENSG00000124177;ENSG00000162772;ENSG00000177189;ENSG00000089154;ENSG00000150991;ENSG00000100393","geneontology_Biological_Process_noRedundant" +"GO:0090150","establishment of protein localization to membrane",270,57,36.9432749833813,1.54290598290598,0.000436921805511803,0.0719173291872428,"581;841;1869;2810;3308;3320;3921;4836;5366;5584;5621;6122;6125;6130;6132;6136;6146;6152;6155;6156;6157;6159;6160;6165;6167;6170;6187;6188;6191;6194;6203;6204;6206;6227;6249;6449;6726;6731;6747;7027;7161;7532;8766;9141;23513;26519;51763;54557;55176;55750;56993;63971;84243;85377;90427;117177;254887","ENSG00000112306;ENSG00000143742;ENSG00000170606;ENSG00000131469;ENSG00000114391;ENSG00000078900;ENSG00000174780;ENSG00000171858;ENSG00000087088;ENSG00000100139;ENSG00000197860;ENSG00000132376;ENSG00000148303;ENSG00000182899;ENSG00000204160;ENSG00000137154;ENSG00000104081;ENSG00000006530;ENSG00000198176;ENSG00000145592;ENSG00000101412;ENSG00000161016;ENSG00000197958;ENSG00000116251;ENSG00000137177;ENSG00000198918;ENSG00000136448;ENSG00000134809;ENSG00000105185;ENSG00000064012;ENSG00000071082;ENSG00000124614;ENSG00000104969;ENSG00000156482;ENSG00000065665;ENSG00000080824;ENSG00000163558;ENSG00000180900;ENSG00000170889;ENSG00000149273;ENSG00000171867;ENSG00000130779;ENSG00000166441;ENSG00000175793;ENSG00000100316;ENSG00000100216;ENSG00000122406;ENSG00000140988;ENSG00000127328;ENSG00000141682;ENSG00000198034;ENSG00000184307;ENSG00000114850;ENSG00000170027;ENSG00000103769;ENSG00000162244;ENSG00000168028","geneontology_Biological_Process_noRedundant" +"GO:0044772","mitotic cell cycle phase transition",436,83,59.6565477509417,1.39129740370687,0.000859584454380835,0.117906334325905,"301;581;595;894;898;983;990;994;995;1032;1104;1869;1977;1978;1981;2033;2146;2810;2935;3276;3320;4085;4174;4176;4194;4331;4751;4863;4998;4999;5111;5311;5426;5471;5566;5721;5885;5932;6117;6152;6194;6604;7015;7027;7465;7532;8451;8462;8766;8882;9134;9212;9401;9700;9738;11064;22933;22994;22995;23476;25942;25959;26271;27338;51347;51379;51512;54801;54984;55142;55755;56257;56984;57060;57520;84131;84515;84967;85456;128239;144455;219736;246175","ENSG00000072062;ENSG00000136861;ENSG00000147874;ENSG00000075218;ENSG00000176390;ENSG00000151247;ENSG00000178999;ENSG00000166508;ENSG00000126457;ENSG00000175305;ENSG00000158402;ENSG00000129355;ENSG00000101773;ENSG00000115942;ENSG00000132646;ENSG00000114867;ENSG00000148019;ENSG00000165730;ENSG00000117650;ENSG00000068903;ENSG00000114391;ENSG00000125885;ENSG00000109917;ENSG00000101224;ENSG00000138767;ENSG00000087088;ENSG00000141867;ENSG00000160957;ENSG00000169375;ENSG00000183856;ENSG00000135046;ENSG00000170312;ENSG00000164362;ENSG00000198625;ENSG00000137154;ENSG00000197256;ENSG00000141577;ENSG00000094804;ENSG00000132383;ENSG00000118762;ENSG00000198176;ENSG00000119397;ENSG00000101412;ENSG00000181817;ENSG00000110092;ENSG00000106462;ENSG00000100911;ENSG00000112029;ENSG00000164109;ENSG00000082014;ENSG00000165891;ENSG00000172059;ENSG00000254093;ENSG00000138411;ENSG00000085840;ENSG00000166483;ENSG00000100297;ENSG00000103995;ENSG00000080824;ENSG00000149308;ENSG00000103540;ENSG00000103342;ENSG00000090097;ENSG00000175793;ENSG00000139842;ENSG00000118971;ENSG00000128789;ENSG00000187840;ENSG00000108106;ENSG00000146834;ENSG00000128059;ENSG00000135090;ENSG00000135476;ENSG00000180198;ENSG00000137814;ENSG00000164754;ENSG00000100393;ENSG00000020426;ENSG00000170027;ENSG00000149115;ENSG00000105173;ENSG00000177084;ENSG00000103769","geneontology_Biological_Process_noRedundant" +"GO:0070585","protein localization to mitochondrion",127,30,17.3770219366275,1.72641780101374,0.00161862540978697,0.165466582695703,"581;841;1649;1869;2810;3099;3308;3320;4285;4836;5366;7027;7161;7532;7855;9049;9141;9531;9776;9804;10456;10531;10953;26519;55486;55750;56993;79778;80273;90427","ENSG00000170606;ENSG00000173726;ENSG00000078900;ENSG00000087088;ENSG00000143575;ENSG00000175197;ENSG00000104081;ENSG00000006530;ENSG00000198176;ENSG00000101412;ENSG00000164877;ENSG00000175224;ENSG00000109519;ENSG00000136448;ENSG00000134809;ENSG00000151929;ENSG00000027001;ENSG00000105185;ENSG00000064012;ENSG00000163251;ENSG00000080824;ENSG00000025772;ENSG00000110711;ENSG00000159399;ENSG00000175793;ENSG00000175193;ENSG00000107959;ENSG00000100216;ENSG00000141682;ENSG00000170027","geneontology_Biological_Process_noRedundant" +"GO:0090596","sensory organ morphogenesis",100,25,13.6826944382894,1.82712550607287,0.00167091337315917,0.165466582695703,"581;1855;2048;2195;2260;3150;3249;4920;5584;5754;5916;6647;7528;7855;9368;11078;23513;24147;25987;54549;54806;112724;128637;204851;219736","ENSG00000133216;ENSG00000182704;ENSG00000163349;ENSG00000179431;ENSG00000165730;ENSG00000169071;ENSG00000087088;ENSG00000100106;ENSG00000069188;ENSG00000107404;ENSG00000172819;ENSG00000142168;ENSG00000163251;ENSG00000112655;ENSG00000160439;ENSG00000083857;ENSG00000163558;ENSG00000105707;ENSG00000125875;ENSG00000180900;ENSG00000109062;ENSG00000135541;ENSG00000205581;ENSG00000100811;ENSG00000077782","geneontology_Biological_Process_noRedundant" +"GO:0006413","translational initiation",180,39,24.6288499889209,1.58350877192982,0.00203965122977512,0.165466582695703,"1939;1974;1977;1978;1981;3921;6122;6125;6130;6132;6136;6146;6152;6155;6156;6157;6159;6160;6165;6167;6170;6187;6188;6191;6194;6203;6204;6206;6227;8637;8891;8893;22916;22927;27335;54505;64410;83939;220064","ENSG00000112306;ENSG00000151247;ENSG00000149716;ENSG00000114867;ENSG00000131469;ENSG00000114391;ENSG00000171858;ENSG00000130956;ENSG00000148303;ENSG00000182899;ENSG00000137154;ENSG00000243056;ENSG00000145592;ENSG00000161016;ENSG00000197958;ENSG00000116251;ENSG00000156976;ENSG00000178982;ENSG00000198918;ENSG00000071082;ENSG00000124614;ENSG00000070785;ENSG00000156482;ENSG00000067248;ENSG00000170889;ENSG00000149273;ENSG00000166441;ENSG00000114503;ENSG00000145191;ENSG00000144895;ENSG00000187840;ENSG00000100316;ENSG00000122406;ENSG00000140988;ENSG00000198034;ENSG00000183655;ENSG00000162244;ENSG00000143486;ENSG00000168028","geneontology_Biological_Process_noRedundant" +"GO:0032355","response to estradiol",70,19,9.57788610680257,1.98373626373626,0.002123789230561,0.165466582695703,"301;595;811;841;1788;1938;2146;3015;5111;5457;5727;5932;6812;7035;8204;8835;8930;10856;80139","ENSG00000129071;ENSG00000101773;ENSG00000132646;ENSG00000119772;ENSG00000185920;ENSG00000135046;ENSG00000003436;ENSG00000136854;ENSG00000183779;ENSG00000110092;ENSG00000106462;ENSG00000180530;ENSG00000183207;ENSG00000064012;ENSG00000152192;ENSG00000179218;ENSG00000164032;ENSG00000167658;ENSG00000120833","geneontology_Biological_Process_noRedundant" +"GO:0006605","protein targeting",362,69,49.5313538666076,1.39305701567987,0.00221158251476639,0.165466582695703,"51;1384;3308;3312;3320;3921;4285;4734;5584;5621;6122;6125;6130;6132;6136;6146;6152;6155;6156;6157;6159;6160;6165;6167;6170;6187;6188;6191;6194;6203;6204;6206;6227;6449;6726;6731;6747;7316;7532;7855;7879;8540;9049;9141;9146;9495;9531;9776;9804;10455;10456;10531;10953;11337;23431;26519;30849;51763;54557;55176;55486;55750;56993;79778;80273;84243;85377;117177;254887","ENSG00000112306;ENSG00000081014;ENSG00000143742;ENSG00000170606;ENSG00000131469;ENSG00000198721;ENSG00000173726;ENSG00000114391;ENSG00000174780;ENSG00000171858;ENSG00000100139;ENSG00000185359;ENSG00000143575;ENSG00000197860;ENSG00000132376;ENSG00000148303;ENSG00000182899;ENSG00000204160;ENSG00000137154;ENSG00000170296;ENSG00000006530;ENSG00000145592;ENSG00000161016;ENSG00000164877;ENSG00000175224;ENSG00000197958;ENSG00000116251;ENSG00000109519;ENSG00000069869;ENSG00000198918;ENSG00000134809;ENSG00000151929;ENSG00000027001;ENSG00000105185;ENSG00000163251;ENSG00000071082;ENSG00000124614;ENSG00000104969;ENSG00000156482;ENSG00000065665;ENSG00000080824;ENSG00000163558;ENSG00000095321;ENSG00000025772;ENSG00000110711;ENSG00000170889;ENSG00000109971;ENSG00000149273;ENSG00000171867;ENSG00000179841;ENSG00000166441;ENSG00000175193;ENSG00000150991;ENSG00000100316;ENSG00000075785;ENSG00000018510;ENSG00000196455;ENSG00000107959;ENSG00000100216;ENSG00000122406;ENSG00000140988;ENSG00000127328;ENSG00000198034;ENSG00000184307;ENSG00000114850;ENSG00000170027;ENSG00000161533;ENSG00000162244;ENSG00000168028","geneontology_Biological_Process_noRedundant" +"GO:0022613","ribonucleoprotein complex biogenesis",428,79,58.5619321958786,1.34899920541829,0.0027652452124507,0.181282039916838,"14;1207;1939;2091;2971;3320;3326;3921;4927;6122;6125;6130;6136;6152;6155;6165;6187;6194;6203;6204;6227;6433;6838;8568;8602;8607;9908;10146;10181;10607;10728;10856;10946;11103;11137;22894;23016;23212;23411;23481;23560;26156;26523;27042;27335;27340;27341;28987;51010;51042;51068;51118;51121;51154;51187;54606;55003;55027;55035;55226;55299;55646;55813;56342;57109;57647;64434;79050;79760;79954;81887;83939;84946;115416;192670;220064;246175;285855;387338","ENSG00000075914;ENSG00000096384;ENSG00000149716;ENSG00000136045;ENSG00000107937;ENSG00000117481;ENSG00000001497;ENSG00000003756;ENSG00000148296;ENSG00000135521;ENSG00000131469;ENSG00000183520;ENSG00000156928;ENSG00000114391;ENSG00000074201;ENSG00000138767;ENSG00000171858;ENSG00000117597;ENSG00000096717;ENSG00000148303;ENSG00000182899;ENSG00000130810;ENSG00000137154;ENSG00000083520;ENSG00000037241;ENSG00000134698;ENSG00000113460;ENSG00000189306;ENSG00000087269;ENSG00000197958;ENSG00000092847;ENSG00000122034;ENSG00000175792;ENSG00000178982;ENSG00000171490;ENSG00000160214;ENSG00000146223;ENSG00000111845;ENSG00000148300;ENSG00000155393;ENSG00000136271;ENSG00000183207;ENSG00000105202;ENSG00000124614;ENSG00000142684;ENSG00000141101;ENSG00000080824;ENSG00000107371;ENSG00000184967;ENSG00000170889;ENSG00000100029;ENSG00000061936;ENSG00000183751;ENSG00000138757;ENSG00000111615;ENSG00000115761;ENSG00000146909;ENSG00000110958;ENSG00000120800;ENSG00000142252;ENSG00000127837;ENSG00000144895;ENSG00000108651;ENSG00000198000;ENSG00000145220;ENSG00000100316;ENSG00000137876;ENSG00000122406;ENSG00000053372;ENSG00000140988;ENSG00000179041;ENSG00000108559;ENSG00000183431;ENSG00000145907;ENSG00000135372;ENSG00000169251;ENSG00000150990;ENSG00000143486;ENSG00000168028","geneontology_Biological_Process_noRedundant" +"GO:0006925","inflammatory cell apoptotic process",12,6,1.64192333259473,3.65425101214575,0.00286350731338869,0.181282039916838,"301;3665;5291;6950;23411;23657","ENSG00000135046;ENSG00000096717;ENSG00000120438;ENSG00000051382;ENSG00000151012;ENSG00000185507","geneontology_Biological_Process_noRedundant" +"GO:1901987","regulation of cell cycle phase transition",344,65,47.0684688677155,1.38096695226438,0.00362545061137909,0.213124703797499,"301;581;595;894;898;983;990;994;995;1032;1869;1981;2033;2146;2735;2810;3276;3320;4085;4194;4751;4998;5111;5311;5566;5721;5885;6604;7015;7027;7465;7532;8451;8766;9212;9401;9700;9738;11064;22809;22933;22976;22994;22995;23476;25942;25959;26271;51347;51512;54801;54984;55142;55755;56257;56984;57060;57520;79184;84131;84967;85456;144455;219736;246175","ENSG00000072062;ENSG00000136861;ENSG00000147874;ENSG00000075218;ENSG00000178999;ENSG00000126457;ENSG00000158402;ENSG00000129355;ENSG00000132646;ENSG00000114867;ENSG00000148019;ENSG00000165730;ENSG00000117650;ENSG00000169136;ENSG00000068903;ENSG00000101224;ENSG00000138767;ENSG00000087088;ENSG00000141867;ENSG00000160957;ENSG00000169375;ENSG00000135046;ENSG00000170312;ENSG00000164362;ENSG00000198625;ENSG00000197256;ENSG00000141577;ENSG00000094804;ENSG00000118762;ENSG00000198176;ENSG00000119397;ENSG00000101412;ENSG00000181817;ENSG00000110092;ENSG00000106462;ENSG00000100911;ENSG00000112029;ENSG00000164109;ENSG00000082014;ENSG00000165891;ENSG00000254093;ENSG00000138411;ENSG00000085840;ENSG00000166483;ENSG00000103995;ENSG00000080824;ENSG00000103540;ENSG00000090097;ENSG00000175793;ENSG00000111087;ENSG00000139842;ENSG00000118971;ENSG00000185515;ENSG00000128789;ENSG00000146834;ENSG00000135090;ENSG00000135476;ENSG00000137814;ENSG00000164754;ENSG00000100393;ENSG00000170027;ENSG00000149115;ENSG00000105173;ENSG00000157212;ENSG00000103769","geneontology_Biological_Process_noRedundant" +"GO:0061564","axon development",296,57,40.5007755373366,1.40738045738046,0.00410239386209255,0.218624866382736,"104;1267;1855;1951;2017;2048;2887;3320;3326;3475;3717;3798;3925;4548;4628;4684;4756;4781;5291;5335;5364;5457;5598;5727;6049;6152;6324;6498;6712;6714;6812;7074;7155;7408;7414;8153;8408;8766;8831;8882;9260;9798;9846;9860;10154;10381;10500;10939;23111;25987;26037;27020;51330;54910;56896;79006;200933","ENSG00000096384;ENSG00000133216;ENSG00000156642;ENSG00000116984;ENSG00000182704;ENSG00000182149;ENSG00000103260;ENSG00000155980;ENSG00000114391;ENSG00000109917;ENSG00000125753;ENSG00000143434;ENSG00000124181;ENSG00000105711;ENSG00000185920;ENSG00000147862;ENSG00000133026;ENSG00000177169;ENSG00000157851;ENSG00000085733;ENSG00000136040;ENSG00000077097;ENSG00000166484;ENSG00000197283;ENSG00000035403;ENSG00000136854;ENSG00000108830;ENSG00000141385;ENSG00000107404;ENSG00000173898;ENSG00000136603;ENSG00000008300;ENSG00000197122;ENSG00000106070;ENSG00000127870;ENSG00000096968;ENSG00000051382;ENSG00000080824;ENSG00000258947;ENSG00000152192;ENSG00000006652;ENSG00000164050;ENSG00000149294;ENSG00000117632;ENSG00000174013;ENSG00000168758;ENSG00000198799;ENSG00000173786;ENSG00000067141;ENSG00000197555;ENSG00000156299;ENSG00000033327;ENSG00000133104;ENSG00000006327;ENSG00000197381;ENSG00000196923;ENSG00000103769","geneontology_Biological_Process_noRedundant" +"GO:0070670","response to interleukin-4",25,9,3.42067360957235,2.63106072874494,0.00425030116904468,0.218624866382736,"2194;3309;3326;3615;4783;6122;6778;9817;10376","ENSG00000123416;ENSG00000096384;ENSG00000166888;ENSG00000044574;ENSG00000079999;ENSG00000178035;ENSG00000165030;ENSG00000169710;ENSG00000100316","geneontology_Biological_Process_noRedundant" diff --git a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000136997_ORA_summary.csv b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000136997_ORA_summary.csv index b5b2204..4741733 100644 --- a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000136997_ORA_summary.csv +++ b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000136997_ORA_summary.csv @@ -1,17 +1,17 @@ "geneSet","description","size","overlap","expect","enrichmentRatio","pValue","FDR","overlapId","userId","database" -"GO:0044843","cell cycle G1/S phase transition",214,2,0.213383558608464,9.37279335410176,0.0180485200236226,1,"4174;79733","ENSG00000129173;ENSG00000100297","geneontology_Biological_Process_noRedundant" -"GO:0040029","regulation of gene expression, epigenetic",216,2,0.215377797473964,9.28600823045267,0.0183691710655957,1,"3070;26147","ENSG00000119403;ENSG00000119969","geneontology_Biological_Process_noRedundant" -"GO:0006260","DNA replication",245,2,0.244294261023709,8.18684807256236,0.023292397082944,1,"4174;79733","ENSG00000129173;ENSG00000100297","geneontology_Biological_Process_noRedundant" -"GO:1903513","endoplasmic reticulum to cytosol transport",28,1,0.0279193441169953,35.8174603174603,0.0275874768634631,1,"84236","ENSG00000144468","geneontology_Biological_Process_noRedundant" -"GO:0032259","methylation",279,2,0.278196321737204,7.18916766228594,0.029691863058501,1,"26147;79828","ENSG00000119403;ENSG00000123600","geneontology_Biological_Process_noRedundant" -"GO:0035329","hippo signaling",32,1,0.0319078218479947,31.3402777777778,0.0314728051236408,1,"55841","ENSG00000047644","geneontology_Biological_Process_noRedundant" -"GO:0032527","protein exit from endoplasmic reticulum",42,1,0.041879016175493,23.8783068783069,0.0411258215655349,1,"84236","ENSG00000144468","geneontology_Biological_Process_noRedundant" -"GO:0033619","membrane protein proteolysis",42,1,0.041879016175493,23.8783068783069,0.0411258215655349,1,"84236","ENSG00000144468","geneontology_Biological_Process_noRedundant" -"GO:0016569","covalent chromatin modification",395,2,0.393862175936184,5.07791842475387,0.05609052383999,1,"3070;26147","ENSG00000119403;ENSG00000119969","geneontology_Biological_Process_noRedundant" -"GO:2000241","regulation of reproductive process",68,1,0.0678041214269887,14.7483660130719,0.0658246506082749,1,"84236","ENSG00000144468","geneontology_Biological_Process_noRedundant" -"GO:0044772","mitotic cell cycle phase transition",436,2,0.434744072678928,4.60040774719674,0.0669123315057586,1,"4174;79733","ENSG00000129173;ENSG00000100297","geneontology_Biological_Process_noRedundant" -"GO:0035265","organ growth",87,1,0.0867493906492355,11.5274584929757,0.0835144887270504,1,"55841","ENSG00000047644","geneontology_Biological_Process_noRedundant" -"GO:0061008","hepaticobiliary system development",95,1,0.0947263461112342,10.5567251461988,0.0908733167349515,1,"79733","ENSG00000129173","geneontology_Biological_Process_noRedundant" -"GO:0051302","regulation of cell division",102,1,0.101706182140483,9.8322440087146,0.0972691572361986,1,"79733","ENSG00000129173","geneontology_Biological_Process_noRedundant" -"GO:0006333","chromatin assembly or disassembly",112,1,0.111677376467981,8.95436507936508,0.106336691226969,1,"3070","ENSG00000119969","geneontology_Biological_Process_noRedundant" -"GO:0042770","signal transduction in response to DNA damage",117,1,0.116662973631731,8.57169990503324,0.11084002471008,1,"79733","ENSG00000129173","geneontology_Biological_Process_noRedundant" +"GO:0016072","rRNA metabolic process",231,62,33.5008863283847,1.85069730371487,5.37485199458843e-07,0.000442350319154627,"1736;3669;4809;4931;5036;5393;6129;6135;6165;6194;6202;6229;6234;9014;9045;9221;9875;10171;10436;10927;10969;22894;22984;23076;23212;23404;23517;25879;25926;26168;27043;27292;27341;51010;51077;51096;51602;54433;54663;54680;55178;55272;55636;55651;55720;55759;57418;57647;64965;79066;81875;83732;84128;84172;84365;84916;114049;115752;134430;146857;284119;317781","ENSG00000109534;ENSG00000147604;ENSG00000171316;ENSG00000171861;ENSG00000130826;ENSG00000117174;ENSG00000166197;ENSG00000039123;ENSG00000177469;ENSG00000065268;ENSG00000166938;ENSG00000125630;ENSG00000182899;ENSG00000137154;ENSG00000083520;ENSG00000130935;ENSG00000130713;ENSG00000164934;ENSG00000155438;ENSG00000148843;ENSG00000115368;ENSG00000138326;ENSG00000189306;ENSG00000188846;ENSG00000185163;ENSG00000133316;ENSG00000124784;ENSG00000071462;ENSG00000127804;ENSG00000138442;ENSG00000233927;ENSG00000142207;ENSG00000107371;ENSG00000123737;ENSG00000142676;ENSG00000143748;ENSG00000154760;ENSG00000100138;ENSG00000134987;ENSG00000086189;ENSG00000119616;ENSG00000117395;ENSG00000141456;ENSG00000115750;ENSG00000167721;ENSG00000106723;ENSG00000143319;ENSG00000120158;ENSG00000011260;ENSG00000055044;ENSG00000142937;ENSG00000126749;ENSG00000172183;ENSG00000177971;ENSG00000170515;ENSG00000179041;ENSG00000160208;ENSG00000145912;ENSG00000161956;ENSG00000141076;ENSG00000150990;ENSG00000135972","geneontology_Biological_Process_noRedundant" +"GO:0022613","ribonucleoprotein complex biogenesis",428,92,62.0709062707733,1.48217587799776,4.0944912740315e-05,0.0168488315926396,"545;636;1653;1736;1965;3646;3669;4809;4931;5036;5393;6128;6129;6134;6135;6165;6169;6175;6193;6194;6202;6229;6234;6732;8662;8664;9045;9221;9875;10171;10436;10514;10659;10856;10969;11171;11273;22894;22907;22984;23076;23212;23404;23405;23517;23521;25819;25879;25926;25929;26155;26168;27043;27292;27341;29889;51010;51077;51096;51386;51491;51602;54433;54475;54663;54680;55052;55178;55239;55272;55299;55636;55651;55720;55759;57418;57647;60680;64965;79066;81875;83732;84128;84864;84916;84946;114049;115752;134430;220064;317781;550643","ENSG00000109534;ENSG00000242485;ENSG00000149716;ENSG00000134697;ENSG00000147604;ENSG00000171316;ENSG00000171861;ENSG00000100353;ENSG00000130826;ENSG00000135521;ENSG00000117174;ENSG00000151746;ENSG00000087263;ENSG00000166197;ENSG00000096063;ENSG00000039123;ENSG00000132382;ENSG00000083845;ENSG00000065268;ENSG00000151014;ENSG00000166938;ENSG00000073536;ENSG00000182899;ENSG00000137154;ENSG00000083520;ENSG00000130935;ENSG00000130713;ENSG00000164934;ENSG00000148843;ENSG00000115368;ENSG00000138326;ENSG00000113460;ENSG00000189306;ENSG00000188846;ENSG00000185163;ENSG00000204272;ENSG00000133316;ENSG00000124784;ENSG00000071462;ENSG00000127804;ENSG00000100697;ENSG00000183207;ENSG00000100129;ENSG00000175054;ENSG00000132153;ENSG00000104408;ENSG00000138442;ENSG00000170854;ENSG00000233927;ENSG00000142207;ENSG00000107371;ENSG00000172809;ENSG00000048162;ENSG00000123737;ENSG00000142676;ENSG00000143748;ENSG00000089157;ENSG00000147403;ENSG00000100138;ENSG00000048740;ENSG00000134987;ENSG00000086189;ENSG00000119616;ENSG00000117395;ENSG00000141456;ENSG00000167721;ENSG00000023734;ENSG00000082516;ENSG00000143319;ENSG00000120158;ENSG00000161082;ENSG00000011260;ENSG00000055044;ENSG00000168488;ENSG00000142937;ENSG00000126749;ENSG00000172183;ENSG00000177971;ENSG00000170515;ENSG00000179041;ENSG00000142541;ENSG00000160208;ENSG00000188976;ENSG00000106263;ENSG00000079785;ENSG00000134001;ENSG00000145912;ENSG00000161956;ENSG00000089009;ENSG00000141076;ENSG00000150990;ENSG00000135972","geneontology_Biological_Process_noRedundant" +"GO:0034330","cell junction organization",175,44,25.3794593396854,1.73368547418968,0.000127107358348311,0.0348697853068866,"25;118;596;977;1445;1495;1499;1500;1845;2149;2316;3611;3691;3801;3915;4641;5578;5584;5906;5962;6714;6801;7855;8506;9231;9448;9475;9647;9693;9748;23114;23332;23396;25930;29841;51762;57555;64403;79834;84962;85302;91663;92140;123720","ENSG00000140859;ENSG00000197879;ENSG00000108797;ENSG00000135862;ENSG00000116473;ENSG00000168036;ENSG00000087274;ENSG00000108861;ENSG00000179820;ENSG00000166128;ENSG00000134317;ENSG00000173517;ENSG00000137710;ENSG00000076201;ENSG00000139880;ENSG00000188878;ENSG00000197122;ENSG00000134318;ENSG00000115808;ENSG00000163251;ENSG00000100034;ENSG00000163558;ENSG00000103653;ENSG00000177697;ENSG00000074054;ENSG00000129474;ENSG00000132470;ENSG00000181104;ENSG00000166333;ENSG00000198561;ENSG00000071054;ENSG00000065613;ENSG00000151208;ENSG00000156232;ENSG00000163531;ENSG00000186111;ENSG00000109756;ENSG00000147649;ENSG00000097007;ENSG00000169992;ENSG00000044115;ENSG00000154229;ENSG00000196924;ENSG00000171791","geneontology_Biological_Process_noRedundant" +"GO:0006413","translational initiation",180,44,26.1045867493906,1.68552754435107,0.000251553488147982,0.0443949213535838,"468;1653;1965;2332;3646;5610;6128;6129;6132;6134;6135;6137;6138;6143;6156;6159;6164;6165;6169;6175;6188;6189;6193;6194;6202;6205;6207;6224;6229;6234;6235;7175;8637;8662;8664;8891;8892;9045;9349;23521;51386;56339;220064;440275","ENSG00000149716;ENSG00000213741;ENSG00000147604;ENSG00000100353;ENSG00000110700;ENSG00000108298;ENSG00000083845;ENSG00000182899;ENSG00000165819;ENSG00000137154;ENSG00000047410;ENSG00000243056;ENSG00000174748;ENSG00000109475;ENSG00000138326;ENSG00000161016;ENSG00000188846;ENSG00000142534;ENSG00000055332;ENSG00000100129;ENSG00000070785;ENSG00000104408;ENSG00000156482;ENSG00000128829;ENSG00000233927;ENSG00000172809;ENSG00000142676;ENSG00000089157;ENSG00000147403;ENSG00000149273;ENSG00000008988;ENSG00000125691;ENSG00000119718;ENSG00000167526;ENSG00000128272;ENSG00000102081;ENSG00000142937;ENSG00000145425;ENSG00000142541;ENSG00000106263;ENSG00000079785;ENSG00000134001;ENSG00000089009;ENSG00000162244","geneontology_Biological_Process_noRedundant" +"GO:0006401","RNA catabolic process",312,68,45.2479503656105,1.5028305028305,0.000269713981492004,0.0443949213535838,"207;1736;1869;2332;3192;3646;3669;4615;5393;5520;5578;6128;6129;6132;6134;6135;6137;6138;6143;6156;6159;6164;6165;6169;6175;6188;6189;6193;6194;6202;6205;6207;6224;6229;6234;6235;6832;7316;8531;8761;8882;9045;9349;10128;10921;11044;22894;23049;23112;23404;23517;23521;23589;25819;27327;51010;55795;55802;56339;57690;79066;79675;79882;85456;87178;115752;146857;550643","ENSG00000213741;ENSG00000100354;ENSG00000147604;ENSG00000112941;ENSG00000130826;ENSG00000156502;ENSG00000110700;ENSG00000109917;ENSG00000108298;ENSG00000039123;ENSG00000083845;ENSG00000151014;ENSG00000166938;ENSG00000157106;ENSG00000182899;ENSG00000165819;ENSG00000172936;ENSG00000137154;ENSG00000083520;ENSG00000130713;ENSG00000174748;ENSG00000205937;ENSG00000060138;ENSG00000109475;ENSG00000138326;ENSG00000101412;ENSG00000161016;ENSG00000188846;ENSG00000142534;ENSG00000204272;ENSG00000127804;ENSG00000142208;ENSG00000104408;ENSG00000138399;ENSG00000156482;ENSG00000272886;ENSG00000126226;ENSG00000233927;ENSG00000107371;ENSG00000172809;ENSG00000078687;ENSG00000123737;ENSG00000142676;ENSG00000154760;ENSG00000089157;ENSG00000147403;ENSG00000149273;ENSG00000008988;ENSG00000153048;ENSG00000153187;ENSG00000125691;ENSG00000138095;ENSG00000167526;ENSG00000150991;ENSG00000102081;ENSG00000221914;ENSG00000142937;ENSG00000172183;ENSG00000145425;ENSG00000142541;ENSG00000090621;ENSG00000149115;ENSG00000090905;ENSG00000154229;ENSG00000089009;ENSG00000162244;ENSG00000100722;ENSG00000138035","geneontology_Biological_Process_noRedundant" +"GO:0034470","ncRNA processing",356,74,51.6290715710171,1.43330100170814,0.000653022724629082,0.0895729503949557,"1653;1736;3669;4809;4931;5036;5393;6129;6135;6165;6194;6202;6229;6234;6301;9045;9221;9875;10171;10436;10969;11044;22894;22984;23076;23212;23404;23405;23517;25879;25926;26168;27043;27292;27341;51010;51077;51096;51602;54433;54663;54680;54802;54920;54931;55140;55178;55250;55253;55272;55621;55636;55651;55720;55759;56339;57418;57647;64965;65080;79066;80324;80746;81875;83732;84128;84916;114049;115752;116461;134430;134637;317781;348180","ENSG00000167264;ENSG00000109534;ENSG00000031698;ENSG00000147604;ENSG00000171316;ENSG00000112941;ENSG00000171861;ENSG00000130826;ENSG00000117174;ENSG00000104907;ENSG00000166197;ENSG00000039123;ENSG00000174177;ENSG00000065268;ENSG00000174173;ENSG00000166938;ENSG00000182899;ENSG00000165819;ENSG00000137154;ENSG00000083520;ENSG00000130935;ENSG00000134759;ENSG00000130713;ENSG00000164934;ENSG00000148843;ENSG00000115368;ENSG00000138326;ENSG00000189306;ENSG00000188846;ENSG00000185163;ENSG00000133316;ENSG00000124784;ENSG00000071462;ENSG00000127804;ENSG00000198874;ENSG00000043514;ENSG00000100697;ENSG00000135900;ENSG00000138442;ENSG00000233927;ENSG00000142207;ENSG00000107371;ENSG00000154743;ENSG00000123737;ENSG00000142676;ENSG00000143748;ENSG00000198860;ENSG00000100138;ENSG00000134987;ENSG00000086189;ENSG00000119616;ENSG00000117395;ENSG00000141456;ENSG00000167721;ENSG00000143319;ENSG00000134014;ENSG00000120158;ENSG00000011260;ENSG00000055044;ENSG00000142937;ENSG00000126749;ENSG00000172183;ENSG00000177971;ENSG00000170515;ENSG00000179041;ENSG00000160208;ENSG00000079785;ENSG00000145912;ENSG00000161956;ENSG00000141076;ENSG00000189007;ENSG00000150990;ENSG00000135972;ENSG00000177192","geneontology_Biological_Process_noRedundant" +"GO:0032886","regulation of microtubule-based process",167,39,24.2192554841569,1.61028897133159,0.00140533749743499,0.165227537198428,"25;636;899;1027;1111;1499;1540;1729;2288;2932;3192;4926;5347;6188;6249;6491;7175;9126;9181;9475;10460;11186;22809;22994;23332;26146;26586;50810;50861;51199;55755;55835;57542;58526;79929;79959;84722;153241;221150","ENSG00000136861;ENSG00000168944;ENSG00000134222;ENSG00000162063;ENSG00000169136;ENSG00000168036;ENSG00000151746;ENSG00000087448;ENSG00000111276;ENSG00000165175;ENSG00000141577;ENSG00000166851;ENSG00000136108;ENSG00000047410;ENSG00000149554;ENSG00000004478;ENSG00000134318;ENSG00000108055;ENSG00000151849;ENSG00000082701;ENSG00000068028;ENSG00000166503;ENSG00000197457;ENSG00000149273;ENSG00000074054;ENSG00000137497;ENSG00000180834;ENSG00000123473;ENSG00000130779;ENSG00000101624;ENSG00000204104;ENSG00000153187;ENSG00000165480;ENSG00000100503;ENSG00000116584;ENSG00000097007;ENSG00000013810;ENSG00000131504;ENSG00000083799","geneontology_Biological_Process_noRedundant" +"GO:0090150","establishment of protein localization to membrane",270,57,39.156880124086,1.45568287921229,0.0018106484338658,0.172581742085213,"596;1445;1869;2810;3308;4641;5584;6128;6129;6132;6134;6135;6137;6138;6143;6156;6159;6164;6165;6169;6175;6188;6189;6193;6194;6202;6205;6207;6224;6229;6234;6235;6249;6745;7157;7161;9045;9349;9747;10018;23368;23513;23521;25837;26517;26519;27113;51308;51762;54471;55176;63971;65055;84885;91461;117177;161176","ENSG00000176438;ENSG00000197879;ENSG00000213741;ENSG00000147604;ENSG00000170606;ENSG00000100335;ENSG00000110700;ENSG00000078900;ENSG00000160446;ENSG00000108298;ENSG00000132563;ENSG00000083845;ENSG00000182899;ENSG00000137154;ENSG00000105327;ENSG00000166128;ENSG00000174748;ENSG00000109475;ENSG00000138326;ENSG00000101412;ENSG00000161016;ENSG00000188846;ENSG00000142534;ENSG00000137177;ENSG00000124783;ENSG00000134809;ENSG00000141510;ENSG00000099800;ENSG00000156482;ENSG00000162878;ENSG00000065665;ENSG00000163558;ENSG00000233927;ENSG00000180900;ENSG00000172809;ENSG00000167964;ENSG00000103653;ENSG00000142676;ENSG00000089157;ENSG00000147403;ENSG00000149273;ENSG00000008988;ENSG00000130779;ENSG00000175793;ENSG00000088808;ENSG00000125691;ENSG00000153094;ENSG00000167526;ENSG00000198420;ENSG00000142937;ENSG00000145425;ENSG00000127328;ENSG00000142541;ENSG00000068615;ENSG00000089009;ENSG00000171791;ENSG00000162244","geneontology_Biological_Process_noRedundant" +"GO:0031109","microtubule polymerization or depolymerization",91,24,13.1973188566364,1.81855119670246,0.00208641850514402,0.172581742085213,"25;1027;2288;4926;5300;6188;6249;7283;9181;9928;10048;10426;23332;26586;50810;50861;51199;54820;55755;55835;58526;79929;84722;221150","ENSG00000136861;ENSG00000118193;ENSG00000131462;ENSG00000072864;ENSG00000134222;ENSG00000111276;ENSG00000165175;ENSG00000136108;ENSG00000010017;ENSG00000004478;ENSG00000127445;ENSG00000126216;ENSG00000151849;ENSG00000166503;ENSG00000197457;ENSG00000149273;ENSG00000074054;ENSG00000137497;ENSG00000180834;ENSG00000130779;ENSG00000165480;ENSG00000100503;ENSG00000116584;ENSG00000097007","geneontology_Biological_Process_noRedundant" +"GO:0051961","negative regulation of nervous system development",165,38,23.9292045202748,1.58801768641341,0.00209698350042786,0.172581742085213,"348;1400;1495;1499;2288;2932;3418;3949;4038;4193;4211;4854;5108;6497;6774;7161;7289;8482;9181;9448;9682;9693;10498;10501;10509;23111;23189;23405;26146;27242;29116;55558;55755;55764;58190;84894;152559;440275","ENSG00000136861;ENSG00000134569;ENSG00000163291;ENSG00000135679;ENSG00000168036;ENSG00000078900;ENSG00000138623;ENSG00000146072;ENSG00000066135;ENSG00000144579;ENSG00000130164;ENSG00000078674;ENSG00000157933;ENSG00000130203;ENSG00000004478;ENSG00000130827;ENSG00000100697;ENSG00000185033;ENSG00000072832;ENSG00000128829;ENSG00000082701;ENSG00000168610;ENSG00000142453;ENSG00000143995;ENSG00000007944;ENSG00000169783;ENSG00000107104;ENSG00000078246;ENSG00000204104;ENSG00000167680;ENSG00000071054;ENSG00000163913;ENSG00000109756;ENSG00000116584;ENSG00000133104;ENSG00000044115;ENSG00000074181;ENSG00000182054","geneontology_Biological_Process_noRedundant" +"GO:0007059","chromosome segregation",269,56,39.0118546421449,1.43546110569946,0.00273900282014972,0.18236183051147,"1063;1104;1457;1499;3192;3835;4085;4288;4926;5347;5925;6188;6732;7175;7283;9126;9212;9493;9928;10051;10460;10636;10734;11044;11130;11331;11339;23137;23212;23310;25836;27183;29127;51433;54820;55165;55355;55719;55755;55795;56984;57082;57520;57697;64210;64682;79142;80218;81610;83852;84722;148479;150465;151246;201254;221150","ENSG00000136861;ENSG00000114999;ENSG00000079616;ENSG00000118193;ENSG00000187790;ENSG00000178999;ENSG00000131462;ENSG00000072864;ENSG00000112941;ENSG00000134222;ENSG00000168036;ENSG00000169220;ENSG00000096063;ENSG00000137807;ENSG00000148773;ENSG00000166851;ENSG00000119906;ENSG00000047410;ENSG00000215021;ENSG00000137812;ENSG00000101266;ENSG00000161800;ENSG00000198887;ENSG00000066923;ENSG00000164109;ENSG00000163535;ENSG00000108055;ENSG00000138180;ENSG00000138411;ENSG00000089053;ENSG00000113810;ENSG00000136169;ENSG00000126226;ENSG00000101447;ENSG00000164190;ENSG00000149273;ENSG00000137497;ENSG00000153187;ENSG00000121579;ENSG00000139687;ENSG00000116273;ENSG00000153107;ENSG00000165480;ENSG00000123485;ENSG00000128789;ENSG00000151503;ENSG00000117724;ENSG00000155229;ENSG00000179041;ENSG00000180198;ENSG00000132612;ENSG00000122952;ENSG00000013810;ENSG00000040633;ENSG00000169689;ENSG00000104147","geneontology_Biological_Process_noRedundant" +"GO:0042063","gliogenesis",151,35,21.8988477730999,1.59825760526968,0.00274986572390579,0.18236183051147,"25;207;664;1445;1464;1499;1869;2065;2119;2817;3074;3418;3611;3949;4665;4920;5584;5594;6497;6774;7161;8506;8891;8892;9682;10319;23224;23405;23513;25825;27242;51176;56339;79885;163175","ENSG00000153902;ENSG00000108797;ENSG00000168036;ENSG00000169071;ENSG00000078900;ENSG00000054654;ENSG00000165819;ENSG00000146072;ENSG00000066135;ENSG00000130164;ENSG00000138795;ENSG00000101412;ENSG00000157933;ENSG00000100030;ENSG00000182240;ENSG00000142208;ENSG00000100697;ENSG00000176171;ENSG00000049860;ENSG00000070785;ENSG00000173546;ENSG00000163517;ENSG00000166886;ENSG00000163558;ENSG00000168610;ENSG00000180900;ENSG00000103653;ENSG00000166333;ENSG00000119718;ENSG00000050555;ENSG00000065361;ENSG00000097007;ENSG00000244405;ENSG00000063660;ENSG00000182054","geneontology_Biological_Process_noRedundant" +"GO:0070972","protein localization to endoplasmic reticulum",131,31,18.9983381342788,1.6317216685425,0.00334697083709146,0.18236183051147,"6128;6129;6132;6134;6135;6137;6138;6143;6156;6159;6164;6165;6169;6175;6188;6189;6193;6194;6202;6205;6207;6224;6229;6234;6235;9045;9218;9349;11015;23521;55176","ENSG00000213741;ENSG00000147604;ENSG00000110700;ENSG00000108298;ENSG00000083845;ENSG00000182899;ENSG00000137154;ENSG00000174748;ENSG00000109475;ENSG00000138326;ENSG00000161016;ENSG00000188846;ENSG00000142534;ENSG00000100196;ENSG00000156482;ENSG00000065665;ENSG00000101558;ENSG00000233927;ENSG00000172809;ENSG00000142676;ENSG00000089157;ENSG00000147403;ENSG00000149273;ENSG00000008988;ENSG00000125691;ENSG00000167526;ENSG00000142937;ENSG00000145425;ENSG00000142541;ENSG00000089009;ENSG00000162244","geneontology_Biological_Process_noRedundant" +"GO:0002181","cytoplasmic translation",89,23,12.9072678927543,1.78194178590742,0.00338731182785634,0.18236183051147,"1801;2332;3646;6128;6132;6135;6138;6143;6156;6159;6165;6169;6175;6234;6235;8531;8662;8664;23521;51182;51386;56339;132864","ENSG00000137449;ENSG00000187522;ENSG00000213741;ENSG00000100353;ENSG00000108298;ENSG00000182899;ENSG00000165819;ENSG00000174748;ENSG00000060138;ENSG00000108963;ENSG00000161016;ENSG00000100129;ENSG00000104408;ENSG00000156482;ENSG00000233927;ENSG00000172809;ENSG00000142676;ENSG00000089157;ENSG00000102081;ENSG00000142541;ENSG00000106263;ENSG00000089009;ENSG00000162244","geneontology_Biological_Process_noRedundant" +"GO:0044380","protein localization to cytoskeleton",50,15,7.25127409705296,2.06860198624905,0.00373146318883721,0.18236183051147,"636;1729;2932;3192;4926;5108;6242;6491;9851;22994;26005;29887;51134;54443;81610","ENSG00000086300;ENSG00000151746;ENSG00000141577;ENSG00000078674;ENSG00000114993;ENSG00000082701;ENSG00000198920;ENSG00000101447;ENSG00000011426;ENSG00000137497;ENSG00000123473;ENSG00000153187;ENSG00000173588;ENSG00000168014;ENSG00000131504","geneontology_Biological_Process_noRedundant" +"GO:0044782","cilium organization",308,62,44.6678484378462,1.38802297778616,0.00381045075233644,0.18236183051147,"403;1540;2316;2909;5108;5116;5347;5413;7283;7802;7840;8100;8814;9657;9738;11064;11116;22994;22995;23157;23176;23216;23224;23332;25914;25930;26000;26005;26146;27241;28981;29887;49856;51134;51668;51762;54806;54820;54874;54919;55081;55142;55752;55755;55764;55779;55835;57539;79959;80127;84131;84140;84260;85302;117177;117178;125058;128637;132320;137392;153241;197335","ENSG00000136861;ENSG00000086300;ENSG00000184702;ENSG00000122507;ENSG00000138758;ENSG00000131462;ENSG00000160007;ENSG00000072864;ENSG00000148019;ENSG00000168944;ENSG00000119636;ENSG00000139437;ENSG00000173226;ENSG00000065882;ENSG00000163879;ENSG00000054654;ENSG00000137942;ENSG00000141577;ENSG00000166851;ENSG00000166128;ENSG00000160299;ENSG00000119397;ENSG00000169221;ENSG00000078674;ENSG00000116213;ENSG00000114446;ENSG00000213066;ENSG00000076201;ENSG00000151466;ENSG00000188878;ENSG00000118965;ENSG00000188343;ENSG00000151849;ENSG00000117155;ENSG00000103995;ENSG00000081870;ENSG00000125875;ENSG00000074054;ENSG00000103540;ENSG00000138175;ENSG00000116127;ENSG00000100490;ENSG00000101624;ENSG00000204104;ENSG00000122970;ENSG00000135541;ENSG00000176225;ENSG00000173588;ENSG00000164818;ENSG00000164402;ENSG00000163913;ENSG00000161996;ENSG00000167291;ENSG00000127328;ENSG00000137814;ENSG00000170264;ENSG00000168014;ENSG00000032742;ENSG00000125354;ENSG00000083799;ENSG00000206530;ENSG00000196924","geneontology_Biological_Process_noRedundant" diff --git a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000162772_ORA_summary.csv b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000162772_ORA_summary.csv index e6862e4..d1721da 100644 --- a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000162772_ORA_summary.csv +++ b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000162772_ORA_summary.csv @@ -1,17 +1,17 @@ "geneSet","description","size","overlap","expect","enrichmentRatio","pValue","FDR","overlapId","userId","database" -"GO:0031050","dsRNA processing",41,1,0.0181697318856636,55.0365853658537,0.0180492835558955,1,"5511","ENSG00000117751","geneontology_Biological_Process_noRedundant" -"GO:0048013","ephrin receptor signaling pathway",56,1,0.0248171947706625,40.2946428571429,0.0245912376881525,1,"28964","ENSG00000108262","geneontology_Biological_Process_noRedundant" -"GO:0008277","regulation of G protein-coupled receptor signaling pathway",73,1,0.0323509860403279,30.9109589041096,0.0319658762844355,1,"28964","ENSG00000108262","geneontology_Biological_Process_noRedundant" -"GO:0017156","calcium ion regulated exocytosis",87,1,0.0385552847329936,25.9367816091954,0.0380076423787978,1,"28964","ENSG00000108262","geneontology_Biological_Process_noRedundant" -"GO:0051302","regulation of cell division",102,1,0.0452027476179925,22.1225490196078,0.0444495329448709,1,"28964","ENSG00000108262","geneontology_Biological_Process_noRedundant" -"GO:0099504","synaptic vesicle cycle",117,1,0.0518502105029914,19.2863247863248,0.0508590160880836,1,"28964","ENSG00000108262","geneontology_Biological_Process_noRedundant" -"GO:0099003","vesicle-mediated transport in synapse",129,1,0.0571681808109905,17.4922480620155,0.0559633424259272,1,"28964","ENSG00000108262","geneontology_Biological_Process_noRedundant" -"GO:0006836","neurotransmitter transport",130,1,0.0576113450033237,17.3576923076923,0.0563877716301863,1,"28964","ENSG00000108262","geneontology_Biological_Process_noRedundant" -"GO:0000910","cytokinesis",132,1,0.0584976733879903,17.094696969697,0.0572362006798307,1,"28964","ENSG00000108262","geneontology_Biological_Process_noRedundant" -"GO:0009266","response to temperature stimulus",135,1,0.05982716596499,16.7148148148148,0.0585077713397908,1,"81570","ENSG00000162129","geneontology_Biological_Process_noRedundant" -"GO:0008213","protein alkylation",146,1,0.0647019720806559,15.4554794520548,0.0631591950457315,1,"84787","ENSG00000133247","geneontology_Biological_Process_noRedundant" -"GO:0016458","gene silencing",159,1,0.0704631065809883,14.1918238993711,0.0686340874111081,1,"5511","ENSG00000117751","geneontology_Biological_Process_noRedundant" -"GO:0001505","regulation of neurotransmitter levels",177,1,0.0784400620429869,12.7485875706215,0.0761750491124764,1,"28964","ENSG00000108262","geneontology_Biological_Process_noRedundant" -"GO:0040029","regulation of gene expression, epigenetic",216,1,0.095723465543984,10.4467592592593,0.0923566356475761,1,"5511","ENSG00000117751","geneontology_Biological_Process_noRedundant" -"GO:0051648","vesicle localization",221,1,0.0979392865056503,10.210407239819,0.0944157168944268,1,"28964","ENSG00000108262","geneontology_Biological_Process_noRedundant" -"GO:0099177","regulation of trans-synaptic signaling",224,1,0.0992687790826501,10.0736607142857,0.0956494827885664,1,"28964","ENSG00000108262","geneontology_Biological_Process_noRedundant" +"GO:0043574","peroxisomal transport",57,1,0.0252603589629958,39.5877192982456,0.0250262025765852,1,"3295","ENSG00000133835","geneontology_Biological_Process_noRedundant" +"GO:0007031","peroxisome organization",65,1,0.0288056725016619,34.7153846153846,0.0285006867897396,1,"3295","ENSG00000133835","geneontology_Biological_Process_noRedundant" +"GO:0015748","organophosphate ester transport",66,1,0.0292488366939951,34.1893939393939,0.0289343434411002,1,"83394","ENSG00000091622","geneontology_Biological_Process_noRedundant" +"GO:1901568","fatty acid derivative metabolic process",82,1,0.0363394637713273,27.5182926829268,0.0358531250302535,1,"3295","ENSG00000133835","geneontology_Biological_Process_noRedundant" +"GO:0042445","hormone metabolic process",93,1,0.0412142698869931,24.2634408602151,0.0405882944713599,1,"3295","ENSG00000133835","geneontology_Biological_Process_noRedundant" +"GO:0033865","nucleoside bisphosphate metabolic process",97,1,0.0429869266563262,23.2628865979381,0.0423058422959168,1,"3295","ENSG00000133835","geneontology_Biological_Process_noRedundant" +"GO:0060326","cell chemotaxis",108,1,0.047861732771992,20.8935185185185,0.0470172089919979,1,"51192","ENSG00000217555","geneontology_Biological_Process_noRedundant" +"GO:0002064","epithelial cell development",125,1,0.0553955240416574,18.052,0.0542641936615964,1,"3295","ENSG00000133835","geneontology_Biological_Process_noRedundant" +"GO:0006414","translational elongation",130,1,0.0576113450033237,17.3576923076923,0.0563877716301863,1,"9801","ENSG00000115364","geneontology_Biological_Process_noRedundant" +"GO:0007548","sex differentiation",146,1,0.0647019720806559,15.4554794520548,0.0631591950457315,1,"3295","ENSG00000133835","geneontology_Biological_Process_noRedundant" +"GO:0140053","mitochondrial gene expression",159,1,0.0704631065809883,14.1918238993711,0.0686340874111081,1,"9801","ENSG00000115364","geneontology_Biological_Process_noRedundant" +"GO:0008202","steroid metabolic process",172,1,0.0762242410813206,13.1191860465116,0.0740849482151166,1,"3295","ENSG00000133835","geneontology_Biological_Process_noRedundant" +"GO:0050900","leukocyte migration",179,1,0.0793263904276535,12.6061452513966,0.0770100978953844,1,"51192","ENSG00000217555","geneontology_Biological_Process_noRedundant" +"GO:0016042","lipid catabolic process",185,1,0.081985375581653,12.1972972972973,0.0795118478838214,1,"3295","ENSG00000133835","geneontology_Biological_Process_noRedundant" +"GO:0030258","lipid modification",197,1,0.0873033458896521,11.4543147208122,0.0845000872699074,1,"3295","ENSG00000133835","geneontology_Biological_Process_noRedundant" +"GO:0010876","lipid localization",201,1,0.0890760026589852,11.226368159204,0.0861583195765496,1,"83394","ENSG00000091622","geneontology_Biological_Process_noRedundant" diff --git a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000163884_ORA_summary.csv b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000163884_ORA_summary.csv index 95ba3af..fc34306 100644 --- a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000163884_ORA_summary.csv +++ b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000163884_ORA_summary.csv @@ -1,17 +1,17 @@ "geneSet","description","size","overlap","expect","enrichmentRatio","pValue","FDR","overlapId","userId","database" -"GO:0072347","response to anesthetic",39,1,0.0172834035009971,57.8589743589744,0.0171745432445045,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:0043279","response to alkaloid",61,1,0.0270330157323288,36.9918032786885,0.026764607560501,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:0060359","response to ammonium ion",68,1,0.0301351650786616,33.1838235294118,0.0298012211836164,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:0046683","response to organophosphorus",78,1,0.0345668070019942,28.9294871794872,0.034126907117098,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:0014074","response to purine-containing compound",87,1,0.0385552847329936,25.9367816091954,0.0380076423787978,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:1901654","response to ketone",106,1,0.0469754043873255,21.2877358490566,0.0461618926775996,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:0044706","multi-multicellular organism process",108,1,0.047861732771992,20.8935185185185,0.0470172089919979,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:0009612","response to mechanical stimulus",109,1,0.0483048969643253,20.7018348623853,0.0474446513741498,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:0071241","cellular response to inorganic substance",117,1,0.0518502105029914,19.2863247863248,0.0508590160880836,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:0097305","response to alcohol",121,1,0.0536228672723244,18.6487603305785,0.0525627522361155,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:0009410","response to xenobiotic stimulus",139,1,0.0615998227343231,16.2338129496403,0.0602011974637793,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:0050890","cognition",147,1,0.0651451362729891,15.3503401360544,0.0635811954083225,1,"9993","ENSG00000070413","geneontology_Biological_Process_noRedundant" -"GO:0060491","regulation of cell projection assembly",147,1,0.0651451362729891,15.3503401360544,0.0635811954083225,1,"8814","ENSG00000100490","geneontology_Biological_Process_noRedundant" -"GO:1902115","regulation of organelle assembly",171,1,0.0757810768889874,13.1959064327485,0.0736665028183097,1,"8814","ENSG00000100490","geneontology_Biological_Process_noRedundant" -"GO:0010038","response to metal ion",209,1,0.0926213161976512,10.7966507177034,0.0894680252209313,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" -"GO:0048545","response to steroid hormone",242,1,0.107245734544649,9.32438016528926,0.103025625353262,1,"2354","ENSG00000125740","geneontology_Biological_Process_noRedundant" +"GO:0061383","trabecula morphogenesis",27,1,0.005982716596499,167.148148148148,0.00597409883298028,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" +"GO:0000959","mitochondrial RNA metabolic process",41,1,0.00908486594283182,110.073170731707,0.00906473327592261,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" +"GO:0046683","response to organophosphorus",78,1,0.0172834035009971,57.8589743589744,0.0172096737464502,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" +"GO:0048771","tissue remodeling",80,1,0.0177265676933304,56.4125,0.017648983269631,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" +"GO:0014074","response to purine-containing compound",87,1,0.0192776423664968,51.8735632183908,0.0191857932117315,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" +"GO:1990845","adaptive thermogenesis",88,1,0.0194992244626634,51.2840909090909,0.0194052392810429,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" +"GO:0002181","cytoplasmic translation",89,1,0.01972080655883,50.7078651685393,0.0196246607983216,1,"4736","ENSG00000198755","geneontology_Biological_Process_noRedundant" +"GO:0048705","skeletal system morphogenesis",111,1,0.0245956126744959,40.6576576576577,0.0244457225141527,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" +"GO:0070972","protein localization to endoplasmic reticulum",131,1,0.0290272545978285,34.4503816793893,0.0288181940383985,1,"4736","ENSG00000198755","geneontology_Biological_Process_noRedundant" +"GO:0009755","hormone-mediated signaling pathway",150,1,0.0332373144249945,30.0866666666667,0.0329629454582727,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" +"GO:0140053","mitochondrial gene expression",159,1,0.0352315532904941,28.3836477987421,0.0349231552062895,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" +"GO:0006413","translational initiation",180,1,0.0398847773099934,25.0722222222222,0.0394892440613237,1,"4736","ENSG00000198755","geneontology_Biological_Process_noRedundant" +"GO:0001503","ossification",202,1,0.0447595834256592,22.3415841584158,0.0442611526074561,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" +"GO:0030522","intracellular receptor signaling pathway",209,1,0.0463106580988256,21.5933014354067,0.045776995113532,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" +"GO:0016072","rRNA metabolic process",231,1,0.0511854642144915,19.5367965367965,0.0505332394627279,1,"4736","ENSG00000198755","geneontology_Biological_Process_noRedundant" +"GO:0048545","response to steroid hormone",242,1,0.0536228672723244,18.6487603305785,0.05290690544337,1,"133522","ENSG00000155846","geneontology_Biological_Process_noRedundant" diff --git a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000169016_ORA_summary.csv b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000169016_ORA_summary.csv index c014347..710d6a9 100644 --- a/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000169016_ORA_summary.csv +++ b/vignettes/data/dto_summaries/dto_3174/p1/ENSG00000169016_ORA_summary.csv @@ -1,17 +1,16 @@ "geneSet","description","size","overlap","expect","enrichmentRatio","pValue","FDR","overlapId","userId","database" -"GO:0050795","regulation of behavior",23,1,0.00509638821183248,196.217391304348,0.00509017654752997,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0046530","photoreceptor cell differentiation",25,1,0.00553955240416574,180.52,0.00553218679432155,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0055123","digestive system development",60,1,0.0132949257699978,75.2166666666667,0.0132514686719133,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0030856","regulation of epithelial cell differentiation",68,1,0.0150675825393308,66.3676470588235,0.0150116530085757,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0030902","hindbrain development",81,1,0.017948149789497,55.716049382716,0.0178686012031727,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0043583","ear development",90,1,0.0199423886549967,50.1444444444444,0.0198440577635676,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0090596","sensory organ morphogenesis",100,1,0.022158209616663,45.13,0.0220366770542224,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:2000027","regulation of animal organ morphogenesis",100,1,0.022158209616663,45.13,0.0220366770542224,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0043112","receptor metabolic process",122,1,0.0270330157323288,36.9918032786885,0.026851797178112,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0003007","heart morphogenesis",136,1,0.0301351650786616,33.1838235294118,0.0299097774174087,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0022406","membrane docking",158,1,0.0350099711943275,28.5632911392405,0.0347054523313076,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0006898","receptor-mediated endocytosis",159,1,0.0352315532904941,28.3836477987421,0.0349231552062895,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0001655","urogenital system development",170,1,0.0376689563483271,26.5470588235294,0.0373162663969213,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0048880","sensory system development",176,1,0.0389984489253268,25.6420454545455,0.0386203476199568,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0007389","pattern specification process",203,1,0.0449811655218258,22.2315270935961,0.0444777751929943,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" -"GO:0048568","embryonic organ development",227,1,0.0502991358298249,19.8810572687225,0.0496693516360555,1,"54806","ENSG00000135541","geneontology_Biological_Process_noRedundant" +"GO:1904837","beta-catenin-TCF complex assembly",26,1,0.0115222690006647,86.7884615384615,0.0114744773699254,1,"8295","ENSG00000196367","geneontology_Biological_Process_noRedundant" +"GO:0048857","neural nucleus development",35,1,0.0155107467316641,64.4714285714286,0.0154233094726228,1,"59345","ENSG00000114450","geneontology_Biological_Process_noRedundant" +"GO:0030901","midbrain development",49,1,0.0217150454243297,46.0510204081633,0.0215424070389262,1,"59345","ENSG00000114450","geneontology_Biological_Process_noRedundant" +"GO:0006414","translational elongation",130,1,0.0576113450033237,17.3576923076923,0.0563877716301863,1,"64978","ENSG00000204316","geneontology_Biological_Process_noRedundant" +"GO:0140053","mitochondrial gene expression",159,1,0.0704631065809883,14.1918238993711,0.0686340874111081,1,"64978","ENSG00000204316","geneontology_Biological_Process_noRedundant" +"GO:0006457","protein folding",178,1,0.0788832262353202,12.6769662921348,0.0765926442987742,1,"59345","ENSG00000114450","geneontology_Biological_Process_noRedundant" +"GO:0006413","translational initiation",180,1,0.0797695546199867,12.5361111111111,0.0774274099343156,1,"9811","ENSG00000134030","geneontology_Biological_Process_noRedundant" +"GO:0043543","protein acylation",201,1,0.0890760026589852,11.226368159204,0.0861583195765496,1,"8295","ENSG00000196367","geneontology_Biological_Process_noRedundant" +"GO:0070646","protein modification by small protein removal",242,1,0.107245734544649,9.32438016528926,0.103025625353262,1,"8295","ENSG00000196367","geneontology_Biological_Process_noRedundant" +"GO:0032984","protein-containing complex disassembly",283,1,0.125415466430312,7.97349823321555,0.119658349355158,1,"64978","ENSG00000204316","geneontology_Biological_Process_noRedundant" +"GO:0018205","peptidyl-lysine modification",306,1,0.135608242853977,7.37418300653595,0.128887010950507,1,"8295","ENSG00000196367","geneontology_Biological_Process_noRedundant" +"GO:0006401","RNA catabolic process",312,1,0.138267228007977,7.2323717948718,0.131282507909807,1,"9811","ENSG00000134030","geneontology_Biological_Process_noRedundant" +"GO:0034248","regulation of cellular amide metabolic process",333,1,0.147573676046975,6.77627627627628,0.139627858078818,1,"9811","ENSG00000134030","geneontology_Biological_Process_noRedundant" +"GO:0016569","covalent chromatin modification",395,1,0.175049855971637,5.7126582278481,0.163915958638693,1,"8295","ENSG00000196367","geneontology_Biological_Process_noRedundant" +"GO:0010608","posttranscriptional regulation of gene expression",418,1,0.185242632395302,5.39832535885168,0.17279396555165,1,"9811","ENSG00000134030","geneontology_Biological_Process_noRedundant" diff --git a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000068305_targets.txt b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000068305_targets.txt index fa6cbff..ae7504e 100644 --- a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000068305_targets.txt +++ b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000068305_targets.txt @@ -1,3 +1,10 @@ -"ENSG00000112096" -"ENSG00000181026" -"ENSG00000100523" +"ENSG00000204536" +"ENSG00000225697" +"ENSG00000173898" +"ENSG00000131876" +"ENSG00000132589" +"ENSG00000135775" +"ENSG00000006468" +"ENSG00000090060" +"ENSG00000108774" +"ENSG00000197852" diff --git a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000068323_targets.txt b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000068323_targets.txt index 68eb588..ee5c8c8 100644 --- a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000068323_targets.txt +++ b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000068323_targets.txt @@ -1,113 +1,9 @@ -"ENSG00000164916" -"ENSG00000196923" -"ENSG00000029725" -"ENSG00000083845" -"ENSG00000104907" -"ENSG00000132275" -"ENSG00000117691" -"ENSG00000119636" -"ENSG00000166483" -"ENSG00000153879" -"ENSG00000069011" -"ENSG00000139437" -"ENSG00000113739" -"ENSG00000170271" -"ENSG00000160753" -"ENSG00000108797" -"ENSG00000185624" -"ENSG00000116649" -"ENSG00000172819" -"ENSG00000126368" -"ENSG00000100335" -"ENSG00000124635" -"ENSG00000103671" -"ENSG00000206530" -"ENSG00000128294" -"ENSG00000160712" -"ENSG00000111845" -"ENSG00000142173" -"ENSG00000151576" -"ENSG00000135776" -"ENSG00000170921" -"ENSG00000138386" -"ENSG00000214827" -"ENSG00000048544" -"ENSG00000065989" -"ENSG00000124201" -"ENSG00000167680" -"ENSG00000143434" -"ENSG00000197119" -"ENSG00000082701" -"ENSG00000088970" -"ENSG00000060339" -"ENSG00000020633" -"ENSG00000165684" -"ENSG00000182240" -"ENSG00000196305" -"ENSG00000183742" -"ENSG00000140992" -"ENSG00000100359" -"ENSG00000213123" -"ENSG00000144040" -"ENSG00000090060" -"ENSG00000173548" -"ENSG00000112992" -"ENSG00000151917" -"ENSG00000173193" -"ENSG00000113916" -"ENSG00000183696" -"ENSG00000163873" -"ENSG00000177054" -"ENSG00000123179" -"ENSG00000185024" -"ENSG00000177084" -"ENSG00000136854" -"ENSG00000179841" -"ENSG00000172007" -"ENSG00000149657" -"ENSG00000147316" -"ENSG00000166123" -"ENSG00000162878" -"ENSG00000168827" -"ENSG00000073111" -"ENSG00000174840" -"ENSG00000100503" -"ENSG00000141258" -"ENSG00000165801" -"ENSG00000128512" -"ENSG00000134285" -"ENSG00000101255" -"ENSG00000162302" -"ENSG00000163961" -"ENSG00000122873" -"ENSG00000187193" -"ENSG00000131504" -"ENSG00000105483" -"ENSG00000096063" -"ENSG00000167191" -"ENSG00000170385" -"ENSG00000105173" -"ENSG00000136542" -"ENSG00000147604" -"ENSG00000124496" -"ENSG00000197905" -"ENSG00000143498" -"ENSG00000181754" -"ENSG00000083312" -"ENSG00000119655" -"ENSG00000164867" -"ENSG00000204104" -"ENSG00000069188" -"ENSG00000147862" -"ENSG00000116984" -"ENSG00000189306" -"ENSG00000001497" -"ENSG00000156587" -"ENSG00000163795" -"ENSG00000094916" -"ENSG00000163655" -"ENSG00000152620" -"ENSG00000165650" -"ENSG00000073417" -"ENSG00000130766" -"ENSG00000149150" +"ENSG00000108312" +"ENSG00000129128" +"ENSG00000074211" +"ENSG00000197530" +"ENSG00000164934" +"ENSG00000137941" +"ENSG00000155760" +"ENSG00000112576" +"ENSG00000105443" diff --git a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000101057_targets.txt b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000101057_targets.txt index 0a69a0e..6576d7a 100644 --- a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000101057_targets.txt +++ b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000101057_targets.txt @@ -1,1309 +1,40 @@ -"ENSG00000164038" -"ENSG00000108963" -"ENSG00000124535" -"ENSG00000133112" -"ENSG00000111912" -"ENSG00000188428" -"ENSG00000152291" -"ENSG00000196371" -"ENSG00000138031" -"ENSG00000074410" -"ENSG00000137812" -"ENSG00000196455" -"ENSG00000029725" -"ENSG00000142871" -"ENSG00000175602" -"ENSG00000106635" -"ENSG00000090621" -"ENSG00000023902" -"ENSG00000112096" -"ENSG00000143061" -"ENSG00000141646" -"ENSG00000139514" -"ENSG00000241343" -"ENSG00000005007" -"ENSG00000172809" -"ENSG00000150907" -"ENSG00000092841" -"ENSG00000099889" -"ENSG00000277258" -"ENSG00000182054" -"ENSG00000133226" -"ENSG00000135655" -"ENSG00000135365" -"ENSG00000132953" -"ENSG00000101577" -"ENSG00000168036" -"ENSG00000174177" -"ENSG00000075142" -"ENSG00000148843" -"ENSG00000092330" -"ENSG00000135842" -"ENSG00000064115" -"ENSG00000150753" -"ENSG00000162769" -"ENSG00000119636" -"ENSG00000183520" -"ENSG00000165661" -"ENSG00000183655" -"ENSG00000266094" -"ENSG00000111087" -"ENSG00000163535" -"ENSG00000119801" -"ENSG00000166483" -"ENSG00000090487" -"ENSG00000112137" -"ENSG00000172059" -"ENSG00000107130" -"ENSG00000240972" -"ENSG00000164442" -"ENSG00000109519" -"ENSG00000092847" -"ENSG00000140396" -"ENSG00000171475" -"ENSG00000197976" -"ENSG00000144827" -"ENSG00000198901" -"ENSG00000082212" -"ENSG00000111716" -"ENSG00000165916" -"ENSG00000242265" -"ENSG00000113328" -"ENSG00000023734" -"ENSG00000185238" -"ENSG00000103507" -"ENSG00000184226" -"ENSG00000111678" -"ENSG00000099875" -"ENSG00000119616" -"ENSG00000134318" -"ENSG00000146416" -"ENSG00000164181" -"ENSG00000107815" -"ENSG00000152104" -"ENSG00000145592" -"ENSG00000196821" -"ENSG00000103319" -"ENSG00000068028" -"ENSG00000198408" -"ENSG00000148943" -"ENSG00000107404" -"ENSG00000105176" -"ENSG00000096060" -"ENSG00000134809" -"ENSG00000175482" -"ENSG00000164683" -"ENSG00000065268" -"ENSG00000103540" -"ENSG00000170271" -"ENSG00000170296" -"ENSG00000138382" -"ENSG00000117528" -"ENSG00000166164" -"ENSG00000099968" -"ENSG00000143742" -"ENSG00000011566" -"ENSG00000141480" -"ENSG00000119899" -"ENSG00000180628" -"ENSG00000205476" -"ENSG00000185133" -"ENSG00000164897" -"ENSG00000127837" -"ENSG00000148672" -"ENSG00000100722" -"ENSG00000178038" -"ENSG00000106554" -"ENSG00000173020" -"ENSG00000167114" -"ENSG00000167371" -"ENSG00000244045" -"ENSG00000186660" -"ENSG00000075131" -"ENSG00000169756" -"ENSG00000114867" -"ENSG00000160213" -"ENSG00000070718" -"ENSG00000168228" -"ENSG00000141738" -"ENSG00000130699" -"ENSG00000126351" -"ENSG00000100320" -"ENSG00000076604" -"ENSG00000112576" -"ENSG00000156482" -"ENSG00000166484" -"ENSG00000154845" -"ENSG00000116406" -"ENSG00000131368" -"ENSG00000177169" -"ENSG00000198799" -"ENSG00000009954" -"ENSG00000160200" -"ENSG00000144579" -"ENSG00000076108" -"ENSG00000140025" -"ENSG00000269897" -"ENSG00000136982" -"ENSG00000111206" -"ENSG00000153774" -"ENSG00000182827" -"ENSG00000115808" -"ENSG00000130005" -"ENSG00000176994" -"ENSG00000139146" -"ENSG00000175567" -"ENSG00000122507" -"ENSG00000196428" -"ENSG00000115840" -"ENSG00000126003" -"ENSG00000214160" -"ENSG00000128283" -"ENSG00000129355" -"ENSG00000116649" -"ENSG00000130935" -"ENSG00000153933" -"ENSG00000198918" -"ENSG00000269190" -"ENSG00000111676" -"ENSG00000070214" -"ENSG00000164062" -"ENSG00000140057" -"ENSG00000160867" -"ENSG00000103091" -"ENSG00000109062" -"ENSG00000165752" -"ENSG00000129128" -"ENSG00000168300" -"ENSG00000155438" -"ENSG00000163596" -"ENSG00000111540" -"ENSG00000164403" -"ENSG00000058056" -"ENSG00000198055" -"ENSG00000006459" -"ENSG00000101210" -"ENSG00000133216" -"ENSG00000151746" -"ENSG00000135862" -"ENSG00000204316" -"ENSG00000198729" -"ENSG00000177463" -"ENSG00000181026" -"ENSG00000124422" -"ENSG00000154760" -"ENSG00000119718" -"ENSG00000064961" -"ENSG00000104812" -"ENSG00000139880" -"ENSG00000265491" -"ENSG00000136938" -"ENSG00000188343" -"ENSG00000121897" -"ENSG00000167526" -"ENSG00000197555" -"ENSG00000171497" -"ENSG00000196236" -"ENSG00000096433" -"ENSG00000135245" -"ENSG00000160685" -"ENSG00000085382" -"ENSG00000123297" -"ENSG00000116731" -"ENSG00000110395" -"ENSG00000120162" -"ENSG00000198380" -"ENSG00000064651" -"ENSG00000062598" -"ENSG00000111602" -"ENSG00000125249" -"ENSG00000090097" -"ENSG00000173786" -"ENSG00000136167" -"ENSG00000244274" -"ENSG00000109685" -"ENSG00000206527" -"ENSG00000083720" -"ENSG00000119682" -"ENSG00000160285" -"ENSG00000095209" -"ENSG00000136271" -"ENSG00000213066" -"ENSG00000162517" -"ENSG00000160957" -"ENSG00000196449" -"ENSG00000118898" -"ENSG00000089053" -"ENSG00000114779" -"ENSG00000165030" -"ENSG00000167658" -"ENSG00000211455" -"ENSG00000063046" -"ENSG00000174442" -"ENSG00000044574" -"ENSG00000009335" -"ENSG00000136111" -"ENSG00000109854" -"ENSG00000183048" -"ENSG00000089009" -"ENSG00000159399" -"ENSG00000083168" 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+"ENSG00000049759" +"ENSG00000132522" diff --git a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000101412_targets.txt b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000101412_targets.txt index bbcd9ad..fae0b4b 100644 --- a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000101412_targets.txt +++ b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000101412_targets.txt @@ -1,10 +1,113 @@ -"ENSG00000114999" -"ENSG00000196628" -"ENSG00000104112" -"ENSG00000101166" -"ENSG00000105419" -"ENSG00000161956" -"ENSG00000101255" -"ENSG00000214021" -"ENSG00000113734" -"ENSG00000156802" +"ENSG00000177082" +"ENSG00000170540" +"ENSG00000136861" +"ENSG00000117500" +"ENSG00000171608" +"ENSG00000135932" +"ENSG00000124795" +"ENSG00000010072" +"ENSG00000122507" +"ENSG00000111981" +"ENSG00000157404" +"ENSG00000173960" +"ENSG00000163961" +"ENSG00000145882" +"ENSG00000181274" +"ENSG00000129757" +"ENSG00000137124" +"ENSG00000205476" 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b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000120738_targets.txt index 0d355f0..c58f4a2 100644 --- a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000120738_targets.txt +++ b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000120738_targets.txt @@ -1,1235 +1,3 @@ -"ENSG00000128965" -"ENSG00000068366" -"ENSG00000184162" -"ENSG00000163041" -"ENSG00000124535" -"ENSG00000147548" -"ENSG00000138160" -"ENSG00000029725" -"ENSG00000198420" -"ENSG00000131914" -"ENSG00000106366" -"ENSG00000023902" -"ENSG00000112096" -"ENSG00000150907" -"ENSG00000213918" -"ENSG00000107816" -"ENSG00000182473" -"ENSG00000141510" -"ENSG00000137338" -"ENSG00000184575" -"ENSG00000073536" -"ENSG00000173914" -"ENSG00000101577" -"ENSG00000100410" -"ENSG00000173894" -"ENSG00000168036" -"ENSG00000146411" -"ENSG00000174177" -"ENSG00000165819" -"ENSG00000127328" -"ENSG00000164307" -"ENSG00000161800" -"ENSG00000140044" -"ENSG00000065911" -"ENSG00000143367" 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b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000136997_targets.txt index 045dad8..2c0f43f 100644 --- a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000136997_targets.txt +++ b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000136997_targets.txt @@ -1,9 +1,1309 @@ -"ENSG00000129173" +"ENSG00000153902" +"ENSG00000005810" +"ENSG00000167264" +"ENSG00000147854" +"ENSG00000109534" +"ENSG00000198911" +"ENSG00000177595" +"ENSG00000140859" +"ENSG00000116691" +"ENSG00000131368" +"ENSG00000004487" +"ENSG00000174738" +"ENSG00000136861" +"ENSG00000140090" +"ENSG00000114999" +"ENSG00000242485" +"ENSG00000118900" +"ENSG00000123416" +"ENSG00000086300" +"ENSG00000176438" +"ENSG00000147548" +"ENSG00000140057" +"ENSG00000147536" +"ENSG00000136933" +"ENSG00000143320" +"ENSG00000079616" +"ENSG00000118193" +"ENSG00000134569" +"ENSG00000184702" +"ENSG00000103202" +"ENSG00000164976" +"ENSG00000196365" +"ENSG00000129993" +"ENSG00000277258" 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b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000162772_targets.txt @@ -1,4 +1,4 @@ -"ENSG00000133247" -"ENSG00000108262" -"ENSG00000117751" -"ENSG00000162129" +"ENSG00000133835" +"ENSG00000217555" +"ENSG00000115364" +"ENSG00000091622" diff --git a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000163884_targets.txt b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000163884_targets.txt index 2ba7b51..ffa900c 100644 --- a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000163884_targets.txt +++ b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000163884_targets.txt @@ -1,4 +1,2 @@ -"ENSG00000100490" -"ENSG00000069998" -"ENSG00000125740" -"ENSG00000070413" +"ENSG00000155846" +"ENSG00000198755" diff --git a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000169016_targets.txt b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000169016_targets.txt index 2a0ea3f..fbcd3d5 100644 --- a/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000169016_targets.txt +++ b/vignettes/data/dto_summaries/webgestalt_work/dto_3174/p1/ENSG00000169016_targets.txt @@ -1,2 +1,4 @@ -"ENSG00000020577" -"ENSG00000135541" +"ENSG00000134030" +"ENSG00000114450" +"ENSG00000196367" +"ENSG00000204316" diff --git a/vignettes/data/np3_ORA_metrics_plots.pdf b/vignettes/data/np3_ORA_metrics_plots.pdf deleted file mode 100644 index b7e9521a976ef8bdd8a4462974ccfdfd203051a0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 9419 zcma)CbzGF~(nb)Fl}?dbN=jI|K}teIB%~KumL+6^SVFo%KqMrT?kZfadEfJ%^M1Ae?C+Up=AOA{|G4J5cG#aPDf0^O2?N;umi<=z2K+`$903AAejv)? zIY3$(08(^>LD04+q!I)T19B=!3i1mI@rw!y3Wy4b35sw5KpH5^|M8u!EgFuA(gvzH zLhNm9p-wScDci!)m>YnU;h5$q!JsHh*md1bXiU!# z0Jr2S12vSc3Tg8|*~ZHG;*-4|D(vVeg`3qKH^dE0Njsnvgz{g?G!ab^v@7dD?Tu&6 zrG>kNo25Bjg*0CZH8<17ip;JqcQ#L$G)E7{zC4FUyi3#XqEYa6FAwnCKW2}#I`KL= zZ4%tHgpJ)k_cAiXA>16GYT6*vXi3lvY)&p6jm`BtG!4mY92;wM9UbG*FRZ?CK1%0Y 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