run.clustering: Clustering the data

Description

This function takes an object of class iCellR and finds optimal number of clusters and clusters the data.

Usage

run.clustering(
  x = NULL,
  clust.method = "kmeans",
  dist.method = "euclidean",
  index.method = "silhouette",
  max.clust = 25,
  min.clust = 2,
  dims = 1:10
)

Value

An object of class iCellR.

Arguments

x: An object of class iCellR.
clust.method: the cluster analysis method to be used. This should be one of: "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid", "kmeans".
dist.method: the distance measure to be used to compute the dissimilarity matrix. This must be one of: "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski" or "NULL". By default, distance="euclidean". If the distance is "NULL", the dissimilarity matrix (diss) should be given by the user. If distance is not "NULL", the dissimilarity matrix should be "NULL".
index.method: the index to be calculated. This should be one of : "kl", "ch", "hartigan", "ccc", "scott", "marriot", "trcovw", "tracew", "friedman", "rubin", "cindex", "db", "silhouette", "duda", "pseudot2", "beale", "ratkowsky", "ball", "ptbiserial", "gap", "frey", "mcclain", "gamma", "gplus", "tau", "dunn", "hubert", "sdindex", "dindex", "sdbw", "all" (all indices except GAP, Gamma, Gplus and Tau), "alllong" (all indices with Gap, Gamma, Gplus and Tau included).
max.clust: maximal number of clusters, between 2 and (number of objects - 1), greater or equal to min.nc.
min.clust: minimum number of clusters, default = 2.
dims: PCA dimentions to be use for clustering, default = 1:10.