kmeans_links.R

#' Function to apply kmeans clustering on the score of links
#'
#' @param score_matrix score matrix obtained from DPM or reg.DPM.
#'
#' @return a list containing
#' - the list of the links as a data frame. Each line of the data frame corresponds to a link.
#' The first and second columns are the corresponding nodes of the link and the third column is the score of the link.
#' - the threshold that all the links with scores more than it are considered as an edge.
#'
#' @examples
#' ## Load DREAM4 data (insilico_size10_1_knockouts)
#' data(dream4)
#'
#'## Run regularized DPM
#' res <- reg.dpm(data)
#'
#' ## Get the list of the links based on kmeans clustering
#' linklist <- kmeans_links(res)$LinkList
#'
#' ##Get the threshold at significance level alpha=0.05
#' kmeans_links(res)$threshold
#'
#'
#' @export

###########################################################
### Function to compute fisher z transformation pvalues ###
###########################################################
kmeans_links <- function(score_matrix){
  score_matrix <- abs(score_matrix)
  diag(score_matrix) <- NA
  score_matrix[upper.tri(score_matrix)] <- NA
  linkList <- reshape2::melt(score_matrix, na.rm=TRUE)
  colnames(linkList) <- c("Node1", "Node2", "Score")

  # do kmeans on scores
  res = kmeans(linkList$Score, centers=2)
  linkList <- linkList[res$cluster==which.max(res$centers),]

  # compute threshold
  threshold = mean(a$centers)

return(list(LinkList=linkList,threshold=threshold))
}