Cluster the probability matrix with K-means

Cluster the probability matrix with K-means

Usage

clustByHood(object, ...)

# S4 method for class 'matrix'
clustByHood(object, k = 2^ncol(object) - 1, iter_max = 1000, nstart = 5)

# S4 method for class 'SpatialExperiment'
clustByHood(
  object,
  pm_cols,
  k = 0,
  iter_max = 1000,
  nstart = 5,
  algo = "Hartigan-Wong",
  val_names = "clusters"
)

Arguments

object

A probability matrix or a SpatialExperiment.

...

Ignore parameter.

k

The number of clusters. By default is 2^ncol(object)-1.

iter_max

the maximum number of iterations allowed.

nstart

how many random sets should be chosen.

pm_cols

The colnames of probability matrix. This is requires for SpatialExperiment input. Assuming that the probability is stored in the colData.

algo

Algorithm to be used. Options include Hartigan-Wong, Lloyd, and MacQueen.

val_names

Character. Column names used to store the clusters.

Value

A probability matrix or a SpatialExperiment object. For latter, the clustering results are saved in the colData of the SpatialExperiment object.

Examples

m <- matrix(abs(rnorm(1000 * 100)), 1000, 100)

clust <- clustByHood(m, k = 3)