classification_evaluation_continuous_pred_wrapper: Assess how well classification predictions accord to the...

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

Assess how well classification predictions accord to the expected response

Description

classification_evaluation_continuous_pred_wrapper Assess how well classification predictions accord to the expected response.

Usage

classification_evaluation_continuous_pred_wrapper(response_prediction_tibble)

Arguments

response_prediction_tibble

Tibble with columns "response" and "prediction" (e.g. output of function 'assess_rf_class_probabilities')

Value

A tibble showing several classification evaluation metrics.

Examples

## Not run: 
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df)
ligands = list("TNF","BMP2","IL4")
ligand_target_matrix = construct_ligand_target_matrix(weighted_networks, ligands, ltf_cutoff = 0, algorithm = "PPR", damping_factor = 0.5, secondary_targets = FALSE)
potential_ligands = c("TNF","BMP2","IL4")
geneset = c("SOCS2","SOCS3", "IRF1")
background_expressed_genes = c("SOCS2","SOCS3","IRF1","ICAM1","ID1","ID2","ID3")
fold1_rf_prob = assess_rf_class_probabilities(round = 1,folds = 2,geneset = geneset,background_expressed_genes = background_expressed_genes ,ligands_oi = potential_ligands,ligand_target_matrix = ligand_target_matrix)

## End(Not run)

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