molnet_settings: Create global settings variable for molnet pipeline

In molnet-org/molnet: Predicting Differential Drug Response using Multi-Omics Networks

Description

Function that allows creating a global 'settings' variable used in the start_pipeline function. Default parameters can be changed within the function call.

Usage

molnet_settings(
  correlation_method = "pearson",
  print_graph_info = TRUE,
  reduction_method = "p_value",
  handling_missing_data = "all.obs",
  p_value_adjust_method = "BH",
  reduction_alpha = 0.05,
  r_squared_cutoff = 0.6,
  cut_vector = seq(0.2, 0.8, by = 0.05),
  n_threads = 1,
  parallel_chunk_size = 10^6,
  saving_path = tempdir(),
  save_individual_graphs = TRUE,
  save_combined_graphs = TRUE,
  save_drug_targets = TRUE,
  save_correlation_filename = NULL,
  python_executable = "python3",
  max_path_length = 3,
  int_score_mode = "auto",
  ...
)

Arguments

correlation_method	Correlation method used for graph generation. One of ('pearson', 'spearman', 'kendall').
print_graph_info	Boolean. Print a summary of the reduced graph to console after generation?
reduction_method	Reduction method for reducing networks. One of 'p_value', 'pickHardThreshold' or 'pickHardThreshold_alternative'. Can be a single character string if the same for all layers, else a named list mapping layer names to methods. Layers may be omitted if a method is mapped to 'default'.
handling_missing_data	Specifying the handling of missing data during correlation computation. Use "all.obs" or "pairwise.complete.obs". Argument is passed to cor. Can be a single character string if the same for all layers, else a named list mapping layer names to methods. Layers may be omitted if a method is mapped to 'default'.
p_value_adjust_method	String of the correction method applied to p-values. Passed to p.adjust. ("holm", "hochberg", "hommel", "bonferroni",
reduction_alpha	A number indicating the significance value for correlation p-values during reduction. Not-significant edges are dropped.
r_squared_cutoff	A number indicating the desired minimum scale free topology fitting index R^2 for reduction using pickHardThreshold.
cut_vector	A vector of hard threshold cuts for which the scale free topology fit indices are to be calculated during reduction with pickHardThreshold.
n_threads	Number of threads for parallel computation of p-values during p-value reduction.
parallel_chunk_size	Number of p-values in smallest work unit when computing in parallel during network reduction with method 'p_value'.
saving_path	Path to save outputs of 'molnet' functions. Default is a temporary directory.
save_individual_graphs	Boolean specifying if individual graphs should be saved during start_pipeline
save_combined_graphs	Boolean specifying if combined graphs should be saved during start_pipeline
save_drug_targets	Boolean specifying if drug targets should be saved during start_pipeline
save_correlation_filename	File name for saving correlation adjacency matrices in generate_individual_graphs.
python_executable	Path to Python executable used for computing simple paths.
max_path_length	Integer of maximum length of simple paths to include in computation.
int_score_mode	One of 'auto', 'sequential' or 'ray'. Whether to compute interaction score in parallel using the Ray python library or sequentially. When 'auto' it depends on the graph sizes.
...	Supply additional settings.

Value

Named list of settings

Examples

settings <- molnet::molnet_settings(correlation_method = "spearman", max_path_length = 3,
                                    handling_missing_data = list(
                                      default = "pairwise.complete.obs",
                                      mrna = "all.obs"
                                    ),
                                    reduction_method = "p_value"
                                   )

molnet-org/molnet documentation built on Dec. 21, 2021, 8:59 p.m.