View source: R/evaluate_model_ligand_prediction.R
get_single_ligand_importances_regression | R Documentation |
get_single_ligand_importances_regression
Get ligand importance measures for ligands based on how well a single, individual, ligand can predict an observed response. Assess how well every ligand of interest is able to predict the observed transcriptional response in a particular dataset, according to the ligand-target model. It can be assumed that the ligand that best predicts the observed response, is more likely to be the true ligand. Response: continuous values associated to a gene, e.g. a log fold change value.
get_single_ligand_importances_regression(setting,ligand_target_matrix, ligands_position = "cols", known = TRUE)
setting |
A list containing the following elements: .$name: name of the setting; .$from: name(s) of the ligand(s) of which the predictve performance need to be assessed; .$response: the observed target response: indicate for a gene whether it was a target or not in the setting of interest. $ligand: NULL or the name of the ligand(s) that are known to be active in the setting of interest. |
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" |
known |
Indicate whether the true active ligand for a particular dataset is known or not. Default: TRUE. The true ligand will be extracted from the $ligand slot of the setting. |
A data.frame with for each ligand - data set combination, regression model fit metrics indicating how well the query ligand predicts the response in the particular dataset. Evaluation metrics are the same as in evaluate_target_prediction_regression
. In addition to the metrics, the name of the particular setting ($setting), the name of the query ligand($test_ligand), the name of the true active ligand (if known: $ligand).
## Not run:
settings = lapply(expression_settings_validation[1:5],convert_expression_settings_evaluation_regression)
settings_ligand_pred = convert_settings_ligand_prediction(settings, all_ligands = unlist(extract_ligands_from_settings(settings,combination = FALSE)), validation = TRUE, single = TRUE)
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network, source_weights_df)
ligands = extract_ligands_from_settings(settings_ligand_pred,combination = FALSE)
ligand_target_matrix = construct_ligand_target_matrix(weighted_networks, ligands)
ligand_importances = dplyr::bind_rows(lapply(settings_ligand_pred,get_single_ligand_importances_regression,ligand_target_matrix))
print(head(ligand_importances))
## End(Not run)
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