nichenetr (version 0.1.0)

get_top_predicted_genes: Find which genes were among the top-predicted targets genes in a specific cross-validation round and see whether these genes belong to the gene set of interest as well.

Description

get_top_predicted_genes Find which genes were among the top-predicted targets genes in a specific cross-validation round and see whether these genes belong to the gene set of interest as well.

Usage

get_top_predicted_genes(round,gene_prediction_list, quantile_cutoff = 0.95)

Arguments

round

Integer describing which fold of the cross-validation scheme it is.

gene_prediction_list

List with per round of cross-validation: a tibble with columns "gene", "prediction" and "response" (e.g. output of function `assess_rf_class_probabilities`)

quantile_cutoff

Quantile of which genes should be considered as top-predicted targets. Default: 0.95, thus considering the top 5 percent predicted genes as predicted targets.

Value

A tibble indicating for every gene whether it belongs to the geneset and whether it belongs to the top-predicted genes in a specific cross-validation round.

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")
gene_predictions_list = seq(2) %>% lapply(assess_rf_class_probabilities,2, geneset = geneset,background_expressed_genes = background_expressed_genes,ligands_oi = potential_ligands,ligand_target_matrix = ligand_target_matrix)
seq(length(gene_predictions_list))  %>% lapply(get_top_predicted_genes,gene_predictions_list)
# }
# NOT RUN {
# }