runCluster {CytoTree} | R Documentation |
Compute a specific clustering using the combined flow
cytometry data. "som" SOM
, "hclust" hclust
,
"clara" clara
, "phenograph", "kmeans" kmeans
are
provided.
runCluster( object, cluster.method = c("som", "kmeans", "clara", "phenograph", "hclust", "mclust"), verbose = FALSE, ... )
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. |
A CYT object with cluster
SOM
, hclust
,
clara
, kmeans
.
You can use runSOM
, runClara
,
runPhenotype
, runKmeans
, runMclust
and
runHclust
to run clustering respectively.
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)