molnet (version 0.1.0)

start_pipeline: Execute all molnet-pipeline steps sequentially

Description

This wrapper function executes all necessary steps to generate differential drug response scores from the formatted input data. The following input data is required (and detailed below): * Layers of stratified molecular data. * Additional connections between the layers. * Interactions between drugs and nodes in the network. * Settings for pipeline execution.

Usage

start_pipeline(
  layers,
  inter_layer_connections,
  drug_target_interaction,
  settings
)

Arguments

layers

Named list with different network layers containing data and identifiers for both groups. The required input format is a list with names corresponding to the content of the respective layer (e.g., "protein"). Each named element has to contain the molecular data and corresponding identifiers formatted by make_layer.

inter_layer_connections

A list with specified inter-layer connections. This list contains one or more elements defining individual inter-layer connections created by make_connection.

drug_target_interaction

A list specifying drug-target interactions for drug response score computation. The required input format of this list is created by make_drug_target. The drug response score is calculated for all drugs contained in this object.

settings

A named list containing pipeline settings. The settings list has to be initialized by molnet_settings. Items in the named list can be adjusted as desired.

Value

Data frame containing drug name and associated differential drug response score. If no target is found for a specific drug, NA is returned as a score. If Python is not installed or the interaction score computation fails for some other reason, NULL is returned instead.

Details

As this function runs through all steps of the molnet-pipeline it can take a long to complete, especially if the supplied molecular data is in large dimensions. Several prompts will be printed to supply information on how the pipeline is proceeding. Calculation of the interaction score by interaction_score requires saving large-scale graphs to file and calls a python script. This handover may take time.

Eventually a data frame is returned containing the supplied drug name and its associated differential drug response score computed by molnet.

Examples

# NOT RUN {
data(drug_gene_interactions)
data(layers_example)
inter_layer_connections = list(make_connection(from = 'mrna',
to = 'protein',
connect_on = 'gene_name'))
drug_target_interaction <- make_drug_target(target_molecules='protein',
interaction_table=drug_gene_interactions,
match_on='gene_name')
settings <- molnet_settings(handling_missing_data = list(default =
"pairwise.complete.obs",mrna = "all.obs"),
save_individual_graphs = FALSE,
save_combined_graphs = FALSE,
save_drug_targets = FALSE,
python_executable = "python3")

# }
# NOT RUN {
start_pipeline(layers_example, inter_layer_connections, drug_target_interaction, settings)
# }
# NOT RUN {
# }