Perform a Fisher's exact test to determine whether genes belonging to the gene set of interest are more likely to be part of the top-predicted targets.

calculate_fraction_top_predicted_fisher Performs a Fisher's exact test to determine whether genes belonging to the gene set of interest are more likely to be part of the top-predicted targets.

Usage

calculate_fraction_top_predicted_fisher(affected_gene_predictions, quantile_cutoff = 0.95, p_value_output = TRUE)

Arguments

affected_gene_predictions

Tibble with columns "gene", "prediction" and "response" (e.g. output of function `assess_rf_class_probabilities`)

quantile_cutoff

Quantile of which genes should be considered as top-predicted targets. Default: 0.95, thus considering the top 5 percent predicted genes as predicted targets.

p_value_output

Should total summary or p-value be returned as output? Default: TRUE.

Value

Summary of the Fisher's exact test or just the p-value

Examples

if (FALSE) { # \dontrun{
weighted_networks <- construct_weighted_networks(lr_network, sig_network, gr_network, source_weights_df)
ligands <- list("TNF", "BMP2", "IL4")
ligand_target_matrix <- construct_ligand_target_matrix(weighted_networks, lr_network, ligands, ltf_cutoff = 0, algorithm = "PPR", damping_factor = 0.5, secondary_targets = FALSE)
potential_ligands <- c("TNF", "BMP2", "IL4")
geneset <- c("SOCS2", "SOCS3", "IRF1")
background_expressed_genes <- c("SOCS2", "SOCS3", "IRF1", "ICAM1", "ID1", "ID2", "ID3")
gene_predictions_list <- seq(2) %>% lapply(assess_rf_class_probabilities, 2, geneset = geneset, background_expressed_genes = background_expressed_genes, ligands_oi = potential_ligands, ligand_target_matrix = ligand_target_matrix)
target_prediction_performances_fisher_pval <- gene_predictions_list %>%
  lapply(calculate_fraction_top_predicted_fisher) %>%
  unlist() %>%
  mean()
} # }