View source: R/characterization_data_sources.R
evaluate_model_application | R Documentation |
evaluate_model_application
will take as input a setting of parameters (data source weights and hyperparameters) and layer-specific networks to construct a ligand-target matrix and evaluate its performance on input application settings (only target gene prediction).
evaluate_model_application(parameters_setting, lr_network, sig_network, gr_network, settings, secondary_targets = FALSE, remove_direct_links = "no", ...)
parameters_setting |
A list containing following elements: $model_name, $source_weights, $lr_sig_hub, $gr_hub, $ltf_cutoff, $algorithm, $damping_factor, $correct_topology. See |
lr_network |
A data frame / tibble containing ligand-receptor interactions (required columns: from, to, source) |
sig_network |
A data frame / tibble containing signaling interactions (required columns: from, to, source) |
gr_network |
A data frame / tibble containing gene regulatory interactions (required columns: from, to, source) |
settings |
A list of lists for which each sub-list contains the following elements: .$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. |
secondary_targets |
Indicate whether a ligand-target matrix should be returned that explicitly includes putative secondary targets of a ligand (by means of an additional matrix multiplication step considering primary targets as possible regulators). Default: FALSE |
remove_direct_links |
Indicate whether direct ligand-target and receptor-target links in the gene regulatory network should be kept or not. "no": keep links; "ligand": remove direct ligand-target links; "ligand-receptor": remove both direct ligand-target and receptor-target links. Default: "no" |
... |
Additional arguments to |
A list containing following elements: $model_name, $performances_target_prediction.
## Not run:
library(dplyr)
settings = lapply(expression_settings_validation[1:4], convert_expression_settings_evaluation)
weights_settings_loi = prepare_settings_leave_one_in_characterization(lr_network,sig_network, gr_network, source_weights_df)
weights_settings_loi = lapply(weights_settings_loi,add_hyperparameters_parameter_settings, lr_sig_hub = 0.25,gr_hub = 0.5,ltf_cutoff = 0,algorithm = "PPR",damping_factor = 0.8,correct_topology = TRUE)
doMC::registerDoMC(cores = 8)
output_characterization = parallel::mclapply(weights_settings_loi[1:3],evaluate_model_application,lr_network,sig_network, gr_network,settings, mc.cores = 3)
## End(Not run)
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