View source: R/application_visualization.R
get_ligand_signaling_path | R Documentation |
get_ligand_signaling_path
Extract possible signaling paths between a ligand and target gene of interest. The most highly weighted path(s) will be extracted.
get_ligand_signaling_path(ligand_tf_matrix, ligands_all, targets_all, top_n_regulators = 4, weighted_networks, ligands_position = "cols")
ligand_tf_matrix |
A matrix of ligand-regulator probability scores |
ligands_all |
A character vector of one or more ligands of interest |
targets_all |
A character vector of one or more target genes of interest |
top_n_regulators |
The number of top regulators that should be included in the ligand-target signaling network. Top regulators are regulators that score both high for being upstream of the target gene(s) and high for being downstream of the ligand. Default: 4. |
weighted_networks |
A list of two elements: lr_sig: a data frame/ tibble containg weighted ligand-receptor and signaling interactions (from, to, weight); and gr: a data frame/tibble containng weighted gene regulatory interactions (from, to, weight) |
ligands_position |
Indicate whether the ligands in the ligand-target matrix are in the rows ("rows") or columns ("cols"). Default: "cols". |
minmax_scaling |
Indicate whether the weights of both dataframes should be min-max scaled between 0.75 and 1. Default: FALSE. |
A list containing 2 elements (sig and gr): the integrated weighted ligand-signaling and gene regulatory networks data frame / tibble format with columns: from, to, weight
## Not run:
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df)
ligands = list("TNF","BMP2",c("IL4","IL13"))
ligand_tf_matrix = construct_ligand_tf_matrix(weighted_networks, ligands, ltf_cutoff = 0.99, algorithm = "PPR", damping_factor = 0.5,ligands_as_cols = TRUE)
all_ligands = c("BMP2")
all_targets = c("HEY1")
top_n_regulators = 2
ligand_target_signaling_list = get_ligand_signaling_path(ligand_tf_matrix,all_ligands,all_targets,top_n_regulators,weighted_networks)
## End(Not run)
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