nichenetr (version 0.1.0)

calculate_fraction_top_predicted_fisher: 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.

Description

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,output = "p-value")

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

# NOT RUN {
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, 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()
# }
# NOT RUN {
# }