kmeans_cluster: kmeans_cluster

In Queen0044/scHiCBayes: Single cell HiC data imputation using Bayesian hierarchy model.

Description

kmeans_cluster

Usage

kmeans_cluster(
  data,
  centers,
  nstart = 50,
  iter.max = 200,
  seed = 1234,
  algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"),
  trace = FALSE
)

Arguments

data	The observed or imputed matirx, with each column being the uppertriangular of a single cell HiC matrix.
centers	Either the number of clusters, say k, or a set of initial (distinct) cluster centres. If a number, a random set of (distinct) rows in x is chosen as the initial centres.
nstart	If centers is a number, how many random sets should be chosen?
iter.max	The maximum number of iterations allowed.
seed	Random seed.
algorithm	Character: may be abbreviated. Note that "Lloyd" and "Forgy" are alternative names for one algorithm.
trace	Logical or integer number, currently only used in the default method ("Hartigan-Wong"): if positive (or true), tracing information on the progress of the algorithm is produced. Higher values may produce more tracing information.

Value

Kmeans clustering results.

Examples

data("GSE117874_chr1_wo_diag")
data("GSE117874_imp")
cluster=kmeans_cluster(GSE117874_chr1_wo_diag, centers=2, nstart=1, iter.max=1000, seed=1)

Queen0044/scHiCBayes documentation built on Dec. 18, 2021, 8:43 a.m.