runCluster: Specific Clustering Method Toolkits

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

Description

Compute a specific clustering using the combined flow cytometry data. "som" SOM, "hclust" hclust, "clara" clara, "phenograph", "kmeans" kmeans are provided.

Usage

runCluster(
  object,
  cluster.method = c("som", "kmeans", "clara", "phenograph", "hclust", "mclust"),
  verbose = FALSE,
  ...
)

Arguments

object	a CYT object
cluster.method	character. Four clustering method are provided: som, clara, kmeans and phenograph. Clustering method "hclust" and "mclust" are not recommended because of long computing time.
verbose	logic. Whether to print calculation progress.
...	options to pass on to the clustering functions.

Value

A CYT object with cluster

See Also

SOM, hclust, clara, kmeans. You can use runSOM, runClara, runPhenotype, runKmeans, runMclust and runHclust to run clustering respectively.

Examples

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

# After building an CYT object
# Set random seed to make results reproducible

set.seed(1)
cyt <- runCluster(cyt, cluster.method = "som", xdim = 3, ydim = 3, verbose = TRUE)

# K-means clustering
cyt <- runCluster(cyt, cluster.method = "kmeans", k = 9, verbose = TRUE)

# Clara clustering
cyt <- runCluster(cyt, cluster.method = "clara", k = 9, verbose = TRUE)

# phenoGraph clustering
cyt <- runCluster(cyt, cluster.method = "phenograph", verbose = TRUE)

# hclust clustering
# not recommended for large cell size
cyt <- runCluster(cyt, cluster.method = "hclust", k = 9, verbose = TRUE)

# mclust clustering
# not recommended for large cell size
cyt <- runCluster(cyt, cluster.method = "mclust", verbose = TRUE)

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