#' Finds Moduless With Kmeans Clustering
#'
#' Function to get consensus modules from individual partition matrices
#'
#' @param 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.
#' @param min.module.size Optional. An integer between 1 and n genes (Default = 20)
#' @param 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)
#'
#' @return A dataframe of Gene Modules
#'
#' @importFrom magrittr %>%
#' @importFrom rlang .data
#' @export
findModules.consensusKmeans <- function(partition.adj, min.module.size = 20, usepam = FALSE){
# Input
# 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 = integer between 1 and n genes
# Output
# geneModules = n x 3 dimensional data frame with column names as Gene.ID, moduleNumber, and moduleLabel
# Error functions
if(class(partition.adj) != "matrix")
stop('partition.adjacency matrix should be of class matrix')
# Use pam based kmeans clustering to find the number of clusters
mod = fpc::pamk(partition.adj, krange = 2:30, usepam = usepam)
# Get individual clusters from the igraph community object
geneModules = data.frame(Gene.ID = names(mod$pamobject$cluster),
moduleNumber = mod$pamobject$cluster)
# Rename modules with size less than min module size to 0
filteredModules = geneModules %>%
dplyr::group_by(.data$moduleNumber) %>%
dplyr::summarise(counts = length(unique(.data$Gene.ID))) %>%
dplyr::filter(.data$counts >= min.module.size)
geneModules$moduleNumber[!(geneModules$moduleNumber %in% filteredModules$moduleNumber)] = 0
# Change cluster number to color labels
geneModules$moduleLabel = WGCNA::labels2colors(geneModules$moduleNumber)
return(geneModules)
}
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