network_reduction_by_pickHardThreshold: [INTERNAL] Reduces network based on WGCNA::pickHardThreshold...
In DrDimont: Drug Response Prediction from Differential Multi-Omics Networks

network_reduction_by_pickHardThreshold

R Documentation

[INTERNAL] Reduces network based on WGCNA::pickHardThreshold function

Description

[INTERNAL] This function uses pickHardThreshold.fromSimilarity to analyze scale free topology for multiple hard thresholds. A cutoff is estimated, if no cutoff is found the function terminates with an error message. All values below the cutoff will be set to NA and the reduced adjacency is returned.

Usage

network_reduction_by_pickHardThreshold(
  adjacency_matrix,
  r_squared_cutoff = 0.85,
  cut_vector = seq(0.2, 0.8, by = 0.01),
  mean_number_edges = NULL,
  edge_density = NULL
)

Arguments

`adjacency_matrix`	[matrix] Adjacency matrix of correlations computed using `cor` in `compute_correlation_matrices`
`r_squared_cutoff`	[float] A number indicating the desired minimum scale free topology fitting index R^2 for reduction using `pickHardThreshold`. (default: 0.85)
`cut_vector`	[sequence of float] A vector of hard threshold cuts for which the scale free topology fit indices are to be calculated during reduction with `pickHardThreshold`. (default: seq(0.2, 0.8, by = 0.01))
`mean_number_edges`	[int] Find a suitable edge weight cutoff employing `pickHardThreshold` to reduce the network to at most the specified mean number of edges. Attention: This parameter overwrites the 'r_squared_cutoff' and 'edge_density' parameters if not set to NULL. (default: NULL)
`edge_density`	[float] Find a suitable edge weight cutoff employing `pickHardThreshold` to reduce the network to at most the specified edge density. Attention: This parameter overwrites the 'r_squared_cutoff' parameter if not set to NULL. (default: NULL)