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Prune modules

Source: R/pruneModules.R
pruneModules.Rd

Prunes the initial modules by keeping only the best targets of each transcriptional regulator.

Usage

pruneModules(
  initial_modules,
  method = c("UIK_adj", "UIK_adj_kIM", "topN"),
  consensus_network = NULL,
  min_module_size = 20L,
  max_frac_modules_lost = 0,
  exponent = 1L,
  N = 50L
)

Arguments

initial_modules

Data frame of initial modules, required columns:

regulator

Character, transcriptional regulator.

target

Character, member gene of the regulator's initial module.

weight

Numeric, consensus edge weight/adjacency, the weighted mean of replicate-wise edge weights.

method

Character, the pruning method, one of "UIK_adj", "UIK_adj_kIM", "topN".

consensus_network

igraph object, the consensus network across all species and replicates.

min_module_size

Integer, the lower threshold of module size in case of the methods "UIK_adj" and "UIK_adj_kIM" (default: 20).

max_frac_modules_lost

Numeric, the threshold for the fraction of removed modules in case of the methods "UIK_adj" and "UIK_adj_kIM" (default: 0).

exponent

Integer, the exponent the regulator-target adjacency and intramodular connectivity is raised to the power of during the cumulative sum curve calculation in case of the methods "UIK_adj" and "UIK_adj_kIM" (default: 1, i.e. the regulator-target adjacencies and intramodular connectivities stay unchanged).

N

An integer or a named integer vector specifying the desired pruned module size(s) in case of the method "topN" (default: 50).

Value

Data frame of the pruned modules with the following columns:

regulator

Character, transcriptional regulator.

module_size

Integer, the numer of genes assigned to a regulator.

target

Character, target gene of the transcriptional regulator (member of the regulator's pruned module).

weight

Numeric, consensus edge weight/adjacency, the weighted mean of replicate-wise edge weights.

Additional columns present in initial_modules will also be preserved in pruned_modules.

Details

3 methods are implemented to choose the best targets:

  • topN: Takes a fixed number of targets per regulator with the highest regulator-target adjacencies (for details see pruneModules_topN).

  • UIK_adj: Applies a dynamic stepwise pruning based on the regulator-target adjacencies (for details see pruneModules_UIK_adj).

  • UIK_adj_kIM: Applies a dynamic stepwise pruning based on the regulator-target adjacencies and intramodular connectivities (for details see pruneModules_UIK_adj_kIM).

Examples

pruned_modules <- pruneModules(initial_modules, "topN", N = 30)
pruned_modules <- pruneModules(initial_modules, "UIK_adj")
#> Step 1: filtering targets based on their adjacencies to the regulator
#> Median module size after filtering: 92.5
#> Step 2: filtering targets based on their adjacencies to the regulator
#> Median module size after filtering: 35
pruned_modules <- pruneModules(initial_modules, "UIK_adj_kIM", consensus_network)
#> Step 1: filtering targets based on their adjacencies to the regulator
#> Median module size after filtering: 92.5
#> Step 2: filtering targets based on their intramodular connectivities
#> Median module size after filtering: 33