construct_ligand_target_matrix: Construct a ligand-target probability matrix for ligands of...

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

Construct a ligand-target probability matrix for ligands of interest.

Description

construct_ligand_target_matrix Convert integrated weighted networks into a matrix containg ligand-target probability scores. The higher this score, the more likely a particular ligand can induce the expression of a particular target gene.

Usage

construct_ligand_target_matrix(weighted_networks, ligands, ltf_cutoff = 0.99, algorithm = "PPR", damping_factor = 0.5, secondary_targets = FALSE,ligands_as_cols = TRUE, remove_direct_links = "no")

Arguments

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	A list of all ligands and ligand-combinations of which target gene probability scores should be calculated. Example format: list("TNF","BMP2",c("IL4","IL13")).
ltf_cutoff	Ligand-tf scores beneath the "ltf_cutoff" quantile will be set to 0. Default: 0.99 such that only the 1 percent closest tfs will be considered as possible tfs downstream of the ligand of choice.
algorithm	Selection of the algorithm to calculate ligand-tf signaling probability scores. Different options: "PPR" (personalized pagerank), "SPL" (shortest path length) and "direct"(just take weights of ligand-signaling network as ligand-tf weights). Default and recommended: PPR.
damping_factor	Only relevant when algorithm is PPR. In the PPR algorithm, the damping factor is the probability that the random walker will continue its walk on the graph; 1-damping factor is the probability that the walker will return to the seed node. Default: 0.5.
secondary_targets	Indicate whether a ligand-target matrix should be returned that explicitly includes putative secondary targets of a ligand (by means of an additional matrix multiplication step considering primary targets as possible regulators). Default: FALSE
ligands_as_cols	Indicate whether ligands should be in columns of the matrix and target genes in rows or vice versa. Default: TRUE
remove_direct_links	Indicate whether direct ligand-target and receptor-target links in the gene regulatory network should be kept or not. "no": keep links; "ligand": remove direct ligand-target links; "ligand-receptor": remove both direct ligand-target and receptor-target links. Default: "no"

Value

A matrix containing ligand-target probability scores.

Examples

 ## Not run: 
## Generate the ligand-target matrix from loaded weighted_networks
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df)
ligands = list("TNF","BMP2",c("IL4","IL13"))
ligand_target_matrix = construct_ligand_target_matrix(weighted_networks, ligands, ltf_cutoff = 0.99, algorithm = "PPR", damping_factor = 0.5, secondary_targets = FALSE, remove_direct_links = "no")

## End(Not run)

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