R/finalClustering.R

In zhiyhu/CIDER: Meta-Clustering for Single-Cell Data Integration and Evaluation

#' @title Final clustering step for meta-clustering
#'
#' @description Merge initial clusters into final clusters based on the
#' matrix of IDEr.
#'
#' @param seu Seurat S4 object after the step of `getIDEr`. Required.
#' @param dist A list. Output of `getIDEr`. Required.
#' @param cutree.by Character. Cut the tree by which parameter, height ("h") or
#' number of clusters ("k"). (Default: h)
#' @param cutree.h Numeric between 0 and 1. The height used to cut the tree.
#' Ignored if `cutree.by = 'k`. (Default: 0.45)
#' @param cutree.k Numeric/integer. Used to cut the tree. Ignored if
#' `cutree.by = 'h`. (Default: 3)
#' @param hc.method Character. Used to choose the hierarchical
#' clustering method.
#'
#' @return Seurat S4 object with final clustering results in `CIDER_clusters`
#' of meta.data.
#'
#' @seealso \code{\link{getIDEr}}.
#'
#' @export
#' @importFrom stats hclust cutree as.dist
#' @examples
#' library(CIDER)
#' data("pancreas")
#' ider <- getIDEr(pancreas, downsampling.size = 30)
#' seu <- finalClustering(pancreas, ider)
#' head(seu$CIDER_cluster)
finalClustering <- function(seu, dist,
                            cutree.by = "h", cutree.h = 0.45, cutree.k = 3,
                            hc.method = "complete") {

  hc <- hclust(as.dist(1 - dist[[1]]) / 2, method = hc.method)

  if (cutree.by == "h") {
    hcluster <- cutree(hc, h = cutree.h)
  } else {
    hcluster <- cutree(hc, k = cutree.k)
  }

  df_merge <- data.frame(
    initial_clusters = names(hcluster),
    final_clusters = hcluster
  )

  seu$CIDER_cluster <- df_merge$final_cluster[match(
    seu$initial_cluster,
    df_merge$initial_clusters
  )]
  seu$CIDER_cluster[is.na(seu$CIDER_cluster)] <-
    seu$initial_cluster[is.na(seu$CIDER_cluster)]

  return(seu)
}