nichenetr (version 0.1.0)

get_active_ligand_receptor_network: Get active ligand-receptor network for cellular interaction between a sender and receiver cell.

Description

get_active_ligand_receptor_network Get active ligand-receptor network by looking for which ligands are expressed in a sender/signaling cell and which receptors are expressed in the receiver cell. Instead of looking at absolute expression, it is possible as well to extract a ligand-receptor network of differentially expressed ligands and receptors if a vector of log2 fold change values is used as input.

Usage

get_active_ligand_receptor_network(expression_sender, expression_receiver, lr_network, expression_cutoff_sender = 0, expression_cutoff_receiver = 0)

Arguments

expression_sender

A named numeric vector of gene expression levels (absolute or logfc) for the signaling cell that sends extracellular signals to the receiver cell

expression_receiver

A named numeric vector of gene expression levels (absolute or logfc) for the receiver cell that receives extracellular signals from the sender cell

lr_network

A data frame / tibble containing ligand-receptor interactions (required columns: from, to). Can be both unweighted and weighted.

expression_cutoff_sender

The cutoff on expression value for the sender cell: ligands will be considered active if their expression is higher than the cutoff. Default: 0.

expression_cutoff_receiver

The cutoff on expression value for the receiver cell: receptors will be considered active if their expression is higher than the cutoff. Default: 0.

Value

A data frame containing at least the variables from, to, sender_expression, receiver_expression. In this network, the active ligand-receptor interactions in the system of interest are shown.

Examples

# NOT RUN {
library(dplyr)
expression_vector_sender = rnorm(n = 10000, mean = 6, sd = 3)
expression_vector_receiver = rnorm(n = 10000, mean = 6, sd = 3)
names(expression_vector_sender) = sample(x = geneinfo_human$symbol,size = 10000,replace = FALSE)
names(expression_vector_receiver) = sample(x = geneinfo_human$symbol,size = 10000,replace = FALSE)
weighted_lr_network = construct_weighted_networks(lr_network, sig_network, gr_network, source_weights_df, n_output_networks = 3) %>% .$lr
sender_cell_receiver_lr_network = get_active_ligand_receptor_network(expression_vector_sender,expression_vector_receiver,weighted_lr_network,expression_cutoff_sender = 4, expression_cutoff_receiver = 4)
# }