Finds Moduless With Kmeans Clustering — findModules.consensusKmeans • metanetwork

Function to get consensus modules from individual partition matrices

Usage

findModules.consensusKmeans(
  partition.adj,
  min.module.size = 20,
  usepam = FALSE
)

partition.adj: Required. A partition.adj = n x m adjacency matrix, where n is the number of genes and m = number of clustering methods * number of clusters in each method.
min.module.size: Optional. An integer between 1 and n genes (Default = 20)
usepam: Optional. A logical for input into pam based kmeans clustering to find the number of clusters with the function `fpc::pamk`. If TRUE, pam is used, otherwise clara (recommended for large datasets with 2,000 or more observations; dissimilarity matrices can not be used with clara). (Default = 20)

A dataframe of Gene Modules