feature_network: Generate a co-occurence network

In tdrose/mosbi: Molecular Signature identification using Biclustering

Generate a co-occurence network

Description

The function computes a co-occurence network, based on the function full_graph. A similarity threshold is calculated using randomized biclusters (the bicluster size distribution is kept).

Usage

feature_network(
  bics,
  mat,
  n_randomizations = 5,
  n_steps = 100,
  plot_edge_dist = TRUE,
  sn_ratio = 1,
  error_threshold = 0.05,
  return_plot_data = FALSE,
  rr = 1,
  rc = 1,
  cc = 1,
  w = 0
)

Arguments

bics	A list of bicluster objects.
mat	The matrix used for biclustering.
n_randomizations	The number of randomizations for cut-off estimation. (The mean of all randomizations is used).
n_steps	Number of points where the difference between randomizations and the real data is evaluated.
plot_edge_dist	Show the plots for threshold estimation.
sn_ratio	If TRUE, the signal to noise ratio is computed, otherwise the error_threshold is used to to estimate the threshold at which only error_threshold*100 percent of the edges are estimated to be random overlaps.
error_threshold	If sn_ratio==FALSE this cut-off is used to estimate the cut-off at which only error_threshold*100 percent of the edges are estimated to be random overlaps.
return_plot_data	Please do not use outside of the package.
rr	See full_graph.
rc	See full_graph.
cc	See full_graph.
w	See parameter weighting of full_graph.

Value

An object of class cooccurrence_net.

Examples

m <- matrix(seq(1:16), nrow=4)
# m <- matrix(rnorm(10000), nrow=100)
# bics <- c(run_fabia(m), run_isa(m), run_plaid(m))
# feature_network(bics, m)

tdrose/mosbi documentation built on May 4, 2022, 3:22 p.m.