construct_weighted_networks: Construct weighted layer-specific networks

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

Construct weighted layer-specific networks

Description

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

## Not run: 
## Generate the weighted networks from input source networks
wn = construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df)

## End(Not run)

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