DrDimont (version 0.1.4)

generate_interaction_score_graphs: Computes interaction score for combined graphs

Description

Writes the input data (combined graphs for both groups in `gml` format and lists of edges adjacent to drug targets for both groups in `tsv` format) to files and calls a Python script for calculating the interaction scores. Output files written by the Python script are two graphs in `gml` format containing the interaction score as an additional `interaction_weight` edge attribute. These are loaded and returned in a named list. ATTENTION: Data exchange via files is mandatory and takes a long time for large data. Interaction score computation is expensive and slow because it involves finding all simple paths up to a certain length between source and target node of the drug target edges. Don't set the parameter `max_path_length` in drdimont_settings to a large value and only consider this step if your graphs have approximately 2 million edges or less. Computation is initiated by calculate_interaction_score. The Python script is parallelized using Ray. Use the drdimont_settings parameter `int_score_mode` to force sequential or parallel computation. Refer to the Ray documentation if you encounter problems with running the Python script in parallel. DISCLAIMER: Depending on the operating system Python comes pre-installed or has to be installed manually. Use DrDimont's install_python_dependencies to install a virtual Python or conda environment containing the required Python packages. You can use the parameter `conda` in drdimont_settings to specify if Python packages were installed with conda (`conda=TRUE`), else a virtual environment installed with pip is assumed (default: `conda=FALSE`).

Usage

generate_interaction_score_graphs(graphs, drug_target_edgelists, settings)

Value

A named list (elements `groupA` and `groupB`). Each element contains an iGraph object containing the interaction scores as interaction_weight attributes.

Arguments

graphs

[list] A named list with elements `groupA` and `groupB` containing the combined graphs of each group as iGraph object (`graphs` from output of generate_combined_graphs)

drug_target_edgelists

[list] A named list (elements `groupA` and `groupB`). Each element contains the list of edges adjacent to drug targets as a data frame (columns `from`, `to` and `weight`). `edgelists` from output of determine_drug_targets

settings

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

Examples

data(combined_graphs_example)
data(drug_target_edges_example)

example_settings <- drdimont_settings()

# \donttest{
example_interaction_score_graphs <- generate_interaction_score_graphs(
                                        graphs=combined_graphs_example$graphs,
                                        drug_target_edgelists=drug_target_edges_example$edgelists,
                                        settings=example_settings)
# }