evaluate_target_prediction: Evaluation of target gene prediction.

In saeyslab/nichenetr: NicheNet: Modeling Intercellular Communication by Linking Ligands to Target Genes

Evaluation of target gene prediction.

Description

evaluate_target_prediction 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). It shows several classification evaluation metrics for the prediction. Different classification metrics are calculated depending on whether the input ligand-target matrix contains probability scores for targets or discrete target assignments.

Usage

evaluate_target_prediction(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 data.frame with following variables: setting, ligand and for probabilistic predictions: auroc, aupr, aupr_corrected (aupr - aupr for random prediction), sensitivity_roc (proxy measure, inferred from ROC), specificity_roc (proxy measure, inferred from ROC), mean_rank_GST_log_pval (-log10 of p-value of mean-rank gene set test), pearson (correlation coefficient), spearman (correlation coefficient); whereas for categorical predictions: accuracy, recall, specificity, precision, F1, F0.5, F2, mcc, informedness, markedness, fisher_pval_log (which is -log10 of p-value fisher exact test), fisher odds.

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,ligand_target_matrix)
print(head(perf1))
perf2 = lapply(setting,evaluate_target_prediction,make_discrete_ligand_target_matrix(ligand_target_matrix))

## End(Not run)

saeyslab/nichenetr documentation built on March 26, 2024, 9:22 a.m.