Evaluate target gene predictions for different bins/groups of targets genes

evaluate_target_prediction_per_bin: Evaluate target gene predictions for different bins/groups of targets genes. Bins are constructed such that genes that are similarly frequently cited are grouped together and the different bins have similar size.

Usage

evaluate_target_prediction_per_bin(nbins,settings,ligand_target_matrix,ncitations,ligands_position = "cols")

Arguments

nbins

The number of bins the target genes should be divided in based on popularity.

settings

list of lists for which each sub-list contains the information about (expression) datasets; with minimally the following elements: name of the setting ($name), ligands (possibly) active in the setting of interest ($from).

ligand_target_matrix

A matrix of ligand-target probabilty scores (or discrete target assignments).

ncitations

A data frame denoting the number of times a gene is mentioned in the Pubmed literature. Should at least contain following variables: 'symbol' and 'ncitations'. Default: ncitations (variable contained in this package). See function get_ncitations_genes for a function that makes this data frame from current Pubmed information.

ligands_position

Indicate whether the ligands in the ligand-target matrix are in the rows ("rows") or columns ("cols"). Default: "cols"

Value

A data.frame containing several classification evaluation metrics for target gene prediction. Predictions were evaluated for n different bins of target genes. The specific bin is indicated in the variable target_bin_id. target_bin_id = 1: target genes that are least mentioned in the Pubmed literature.

Examples

if (FALSE) { # \dontrun{
library(dplyr)
weighted_networks <- construct_weighted_networks(lr_network, sig_network, gr_network, source_weights_df)
settings <- lapply(expression_settings_validation[1:10], convert_expression_settings_evaluation)
ligands <- extract_ligands_from_settings(settings)
ligand_target_matrix <- construct_ligand_target_matrix(weighted_networks, lr_network, ligands)
# ncitations = get_ncitations_genes()
performances_target_bins_popularity <- evaluate_target_prediction_per_bin(5, settings, ligand_target_matrix, ncitations)
} # }