nichenetr (version 0.1.0)

construct_ligand_tf_matrix: Construct a ligand-tf signaling probability matrix for ligands of interest.

Description

construct_ligand_tf_matrix Convert integrated weighted networks into a matrix containg ligand-tf probability scores. The higher this score, the more likely a particular ligand can signal to a downstream gene.

Usage

construct_ligand_tf_matrix(weighted_networks, ligands, ltf_cutoff = 0.99, algorithm = "PPR", damping_factor = 0.5, ligands_as_cols = FALSE)

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 + give the ligand itself the max score). 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.

ligands_as_cols

Indicate whether ligands should be in columns of the matrix and target genes in rows or vice versa. Default: FALSE

Value

A matrix containing ligand-target probability scores.

Examples

# NOT RUN {
 
# }
# 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_tf = construct_ligand_tf_matrix(weighted_networks, ligands, ltf_cutoff = 0.99, algorithm = "PPR", damping_factor = 0.5,ligands_as_cols = TRUE)
# }