Compiles a table with weighted ligand-target links — nichenet_ligand_target

A wrapper around nichenetr::get_weighted_ligand_target_links to compile a data frame with weighted links from the top ligands to their top targets.

Usage

nichenet_ligand_target_links(
  ligand_activities,
  ligand_target_matrix,
  genes_of_interest,
  n_top_ligands = 42,
  n_top_targets = 250
)

Arguments

ligand_activities: Ligand activity table as produced by nichenetr::predict_ligand_activities.
ligand_target_matrix: Ligand-target matrix as produced by nichenetr::construct_ligand_target_matrix or the wrapper around it in the current package: nichenet_ligand_target_matrix.
genes_of_interest: Character vector with the gene symbols of the genes of interest. These are the genes in the receiver cell population that are potentially affected by ligands expressed by interacting cells (e.g. genes differentially expressed upon cell-cell interaction).
n_top_ligands: How many of the top ligands to include in the ligand-target table.
n_top_targets: For each ligand, how many of the top targets to include in the ligand-target table.

Value

A tibble with columns ligand, target and weight (i.e. regulatory potential score).

Examples

if (FALSE) {
networks <- nichenet_networks()
expression <- nichenet_expression_data()
optimization_results <- nichenet_optimization(networks, expression)
nichenet_model <- nichenet_build_model(optimization_results, networks)
lt_matrix <- nichenet_ligand_target_matrix(
    nichenet_model$weighted_networks,
    networks$lr_network,
    nichenet_model$optimized_parameters
)
ligand_activities <- nichenet_ligand_activities(
    ligand_target_matrix = lt_matrix,
    lr_network = networks$lr_network,
    # the rest of the parameters should come
    # from your transcriptomics data:
    expressed_genes_transmitter = expressed_genes_transmitter,
    expressed_genes_receiver = expressed_genes_receiver,
    genes_of_interest = genes_of_interest
)
lt_links <- nichenet_ligand_target_links(
    ligand_activities = ligand_activities,
    ligand_target_matrix = lt_matrix,
    genes_of_interest = genes_of_interest,
    n_top_ligands = 20,
    n_top_targets = 100
)
}