Construct weighted layer-specific networks

construct_weighted_networks construct layer-specific weighted integrated networks from input source networks via weighted aggregation.

Usage

construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df, n_output_networks = 2)

Arguments

lr_network

A data frame / tibble containing ligand-receptor interactions (required columns: from, to, source)

sig_network

A data frame / tibble containing signaling interactions (required columns: from, to, source)

gr_network

A data frame / tibble containing gene regulatory interactions (required columns: from, to, source)

source_weights_df

A data frame / tibble containing the weights associated to each individual data source. Sources with higher weights will contribute more to the final model performance (required columns: source, weight). Note that only interactions described by sources included here, will be retained during model construction.

n_output_networks

The number of output networks to return: 2 (ligand-signaling and gene regulatory; default) or 3 (ligand-receptor, signaling and gene regulatory).

Value

A list containing 2 elements (lr_sig and gr) or 3 elements (lr, sig, gr): the integrated weighted ligand-signaling and gene regulatory networks or ligand-receptor, signaling and gene regulatory networks in data frame / tibble format with columns: from, to, weight.

Examples

if (FALSE) { # \dontrun{
## Generate the weighted networks from input source networks
wn <- construct_weighted_networks(lr_network, sig_network, gr_network, source_weights_df)
} # }