runCluster: Specific Clustering Method Toolkits

Description Usage Arguments Value See Also Examples

View source: R/cluster.R

Description

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

Usage

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

Arguments

object

an FSPY 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

An FSPY object with cluster

See Also

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

Examples

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if (FALSE) {
# After building an FSPY object
# Set random seed to make results reproducible

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

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

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

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

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

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

flowSpy documentation built on Nov. 8, 2020, 6:53 p.m.