nichenetr (version 0.1.0)

evaluate_ligand_prediction_per_bin: Evaluate ligand activity predictions for different bins/groups of targets genes

Description

evaluate_ligand_prediction_per_bin: Evaluate ligand activity 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_ligand_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"

...

Additional arguments to make_discrete_ligand_target_matrix.

Value

A data.frame containing several classification evaluation metrics for ligand activity 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

# NOT RUN {
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, ligands)
# ncitations = get_ncitations_genes()
ligand_prediction_performances_target_bins_popularity = evaluate_ligand_prediction_per_bin(5,settings,ligand_target_matrix,ncitations)
# }
# NOT RUN {
# }