Assess probability that a target gene belongs to the geneset based on a multi-ligand random forest model

assess_rf_class_probabilities Assess probability that a target gene belongs to the geneset based on a multi-ligand random forest model (with cross-validation). Target genes and background genes will be split in different groups in a stratified way.

Usage

assess_rf_class_probabilities(round,folds,geneset,background_expressed_genes,ligands_oi,ligand_target_matrix)

Arguments

round

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

folds

Integer describing how many folds should be used.

geneset

Character vector of the gene symbols of genes of which the expression is potentially affected by ligands from the interacting cell.

background_expressed_genes

Character vector of gene symbols of the background, non-affected, genes (can contain the symbols of the affected genes as well).

ligands_oi

Character vector giving the gene symbols of the ligands you want to build the multi-ligand with.

ligand_target_matrix

The NicheNet ligand-target matrix denoting regulatory potential scores between ligands and targets (ligands in columns).

Value

A tibble with columns: $gene, $response, $prediction. Response indicates whether the gene belongs to the geneset of interest, prediction gives the probability this gene belongs to the geneset according to the random forest model.

Examples

if (FALSE) { # \dontrun{
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, lr_network, 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)
} # }