get_active_subnetworks: Get Active Subnetworks

In pathfindR: Enrichment Analysis Utilizing Active Subnetworks

Get Active Subnetworks

Description

Performs active subnetwork search and filters identified active subnetworks before returning final subnetworks.

Usage

get_active_subnetworks(
  significant_genes,
  network,
  score_context,
  score_quan_thr = 0.8,
  sig_gene_thr = 0.02,
  search_method = "GR",
  seed_for_stochastic_methods = 1234,
  verbose = FALSE,
  start_with_all_positives = FALSE,
  gene_init_prob = 0.1,
  sa_initial_temp = 1,
  sa_final_temp = 0.01,
  sa_iterations = 10000,
  ga_population_size = 400,
  ga_iterations = 200,
  ga_crossover_rate = 1,
  ga_mutation_rate = 0,
  gr_max_depth = 1,
  gr_search_depth = 1,
  gr_overlap_threshold = 0.5,
  gr_subnetwork_num = 1000
)

Arguments

significant_genes	vector of significant genes for the experiment
network	Prebuilt network object as returned by build_network(), built once by active_snw_enrichment_wrapper and passed to every iteration to avoid redundant PIN file I/O.
score_context	Prebuilt score context as returned by build_score_context(), built once by active_snw_enrichment_wrapper and passed to every iteration to avoid redundant Monte-Carlo calibration.
score_quan_thr	active subnetwork score quantile threshold. Must be between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
sig_gene_thr	threshold for the minimum proportion of significant genes in the subnetwork (Default = 0.02) If the number of genes to use as threshold is calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number is set to 2
search_method	algorithm to use when performing active subnetwork search. Options are greedy search (GR), simulated annealing (SA) or genetic algorithm (GA) for the search (default = 'GR').
seed_for_stochastic_methods	seed for reproducibility while running active subnetwork search
verbose	boolean value indicating whether to print messages (default=FALSE)
start_with_all_positives	if TRUE: in GA, adds an individual with all positive nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
gene_init_prob	For SA and GA, probability of adding a gene in initial solution (default = 0.1)
sa_initial_temp	Initial temperature for SA (default = 1.0)
sa_final_temp	Final temperature for SA (default = 0.01)
sa_iterations	Iteration number for SA (default = 10000)
ga_population_size	Population size for GA (default = 400)
ga_iterations	Iteration number for GA (default = 200)
ga_crossover_rate	Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
ga_mutation_rate	For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
gr_max_depth	Sets max depth in greedy search, 0 for no limit (default = 1)
gr_search_depth	Search depth in greedy search (default = 1)
gr_overlap_threshold	Overlap threshold for results of greedy search (default = 0.5)
gr_subnetwork_num	Number of subnetworks to be presented in the results (default = 1000)

Value

A list of genes in every identified active subnetwork that has a score greater than the 'score_quan_thr'th quantile and that has at least 'sig_gene_thr' affected genes.

Examples

experiment_df <- example_pathfindR_input[1:15, c(1, 3)]
colnames(experiment_df) <- c("gene", "pvalue")
pin_path <- return_pin_path("KEGG")
network <- build_network(pin_path)
score_context <- build_score_context(
  network,
  experiment_df,
  list(p_for_nonsignificant = 0.5, seed = 1234L)
)
GR_snws <- get_active_subnetworks(
  significant_genes = experiment_df$gene,
  network = network,
  score_context = score_context
)

pathfindR documentation built on July 2, 2026, 1:06 a.m.