runClara: runClara

In JhuangLab/CytoTree: A Toolkit for Flow And Mass Cytometry Data

Description

Clustering a data matrix into k clusters

Usage

runClara(
  object,
  k = 25,
  metric = c("euclidean", "manhattan", "jaccard"),
  stand = FALSE,
  samples = 5,
  scale = TRUE,
  trace = 0,
  verbose = FALSE,
  ...
)

Arguments

object	a CYT object
k	numeric. The number of clusters. It is required that 0 < k < n where n is the number of observations (i.e., n = nrow(x)).
metric	character. string specifying the metric to be used for calculating dissimilarities between observations.
stand	logical. Indicating if the measurements in x are standardized before calculating the dissimilarities.
samples	numeric. Say N, the number of samples to be drawn from the dataset. The default is N = 5,
scale	logical. Whether to use scaled data in kmeans.
trace	numberic. Indicating a trace level for diagnostic output during the algorithm
verbose	logical. Whether to print calculation progress.
...	Parameters passing to clara function

Value

a CYT object with clara.id in meta.data

See Also

clara

Examples

cyt.file <- system.file("extdata/cyt.rds", package = "CytoTree")
cyt <- readRDS(file = cyt.file)

cyt <- runClara(cyt, k = 25, verbose = TRUE)

JhuangLab/CytoTree documentation built on Nov. 16, 2020, 7:23 a.m.