nichenetr (version 0.1.0)

evaluate_target_prediction_interprete: Evaluation of target gene prediction.

Description

evaluate_target_prediction_interprete Evaluate how well the model (i.e. the inferred ligand-target probability scores) is able to predict the observed response to a ligand (e.g. the set of DE genes after treatment of cells by a ligand; or the log fold change values). It shows several classification evaluation metrics for the prediction when response is categorical, or several regression model fit metrics when the response is continuous.

Usage

evaluate_target_prediction_interprete(setting,ligand_target_matrix, ligands_position = "cols")

Arguments

setting

A list containing the following elements: .$name: name of the setting; .$from: name(s) of the ligand(s) active in the setting of interest; .$response: named logical vector indicating whether a target is a TRUE target of the possibly active ligand(s) or a FALSE.

ligand_target_matrix

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

ligands_position

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

Value

A list with the elements $performances and $prediction_response_df. $performance is a data.frame with classification evaluation metrics if response is categorical, or regression model fit metrics if response is continuous. $prediction_response_df shows for each gene, the model prediction and the response value of the gene (e.g. whether the gene the gene is a target or not according to the observed response, or the absolute value of the log fold change of a gene).

Examples

# NOT RUN {
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network, source_weights_df)
setting = lapply(expression_settings_validation[1],convert_expression_settings_evaluation)
ligands = extract_ligands_from_settings(setting)
ligand_target_matrix = construct_ligand_target_matrix(weighted_networks, ligands)
perf1 = lapply(setting,evaluate_target_prediction_interprete,ligand_target_matrix)
setting = lapply(expression_settings_validation[1],convert_expression_settings_evaluation_regression)
perf2 = lapply(setting,evaluate_target_prediction_interprete,ligand_target_matrix)
# }