findModules.megena: Find Modules with Megena Clustering

In Sage-Bionetworks/metanetwork: Network meta analysis

Find Modules with Megena Clustering

Description

This function finds modules from network adjacency matrix using MEGENA.

Usage

findModules.megena(
  data,
  method = "pearson",
  FDR.cutoff = 0.05,
  module.pval = 0.05,
  hub.pval = 0.05,
  doPar = TRUE,
  n.cores = NULL,
  cor.perm = 10,
  hub.perm = 100,
  min_module = 30
)

Arguments

data	Required. An n x n upper triangular adjacency in the matrix class format.
method	Optional. Method for correlation. either pearson or spearman. (Default = "pearson")
FDR.cutoff	Optional. FDR threshold to define significant correlations upon shuffling samples. (Default = 0.05)
module.pval	Optional. Module significance p-value. Recommended is 0.05. (Default = 0.05)
hub.pval	Optional. Connectivity significance p-value based random tetrahedral networks. (Default = 0.05)
doPar	Optional. If parallelization of clusters is allowed (Default =TRUE)
n.cores	Optional. The number of cores/threads to call for PCP. If NULL, n.cores = detectCores() - 1. (Default = NULL)
cor.perm	Optional. Number of permutations for calculating FDRs for all correlation pairs. (Default = 10)
hub.perm	Optional. number of permutations for calculating connectivity significance p-value. (Default = 100)
min_module	Optional. minimum number of nodes/genes allowed for filtering (Default = 30)

Value

GeneModules = n x 3 dimensional data frame with column names as Gene.ID, moduleNumber, and moduleLabel.

Sage-Bionetworks/metanetwork documentation built on April 27, 2022, 7:42 a.m.