iCellR (version 1.6.1)

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
)

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.

Value

An object of class iCellR.

Examples

# NOT RUN {
demo.obj <- run.clustering(demo.obj,
                          clust.method = "kmeans",
                          dist.method = "euclidean",
                          index.method = "silhouette",
                          max.clust = 2,
                          min.clust = 2,
                          dims = 1:10)

 head([e-mail])

# }