Cluster: A class representing a cell population in FC

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Description

An object of class "Cluster" represents a cluster or a cell population. We model a cluster with a normal distribution. An object of class "Cluster" therefore represents a cluster with a mean vector, a covariance matrix and the size of the cluster.

Creating Object

An object of class Cluster is usually created when constructing an object of class ClusteredSample. Unless you know exactly what you are doing, creating an object of class "Cluster" using new or using the constructor is discouraged.

An object of class "Cluster" can be created using the following constructor

Cluster(size, center, cov, cluster.id = NA_integer_, sample.id=NA_integer_)

The arguments of the constructor bear usual meaning as described in the value section above.

Slots

An object of class "Cluster" contains the following slots:

size:

An integer denoting the number of points (cells) present in the cluster.

center:

A numeric vector denoting the center of the cluster.

cov:

A matrix denoting the covariances of the underlying normal distribution of the cluster.

cluster.id:

The index of the cluster (relative to other clusters in same sample). Default is NA_integer_ .

sample.id:

The index of sample in which the cluster belongs to. Default is NA_integer_ .

Accessors

All the slot accessor functions take an object of class Cluster. I show usage of the first accessor function. Other functions can be called similarly.

get.size:

Returns the number of cells in the cluster.

Usage: get.size(object)

here object is a Cluster object.

get.center:

Returns the center of the cluster.

get.cov:

Returns the covariances matrix of the cluster.

get.cluster.id:

Returns the index of the cluster (relative to other clusters in same sample).

get.sample.id:

Returns the index of sample in which the cluster belongs to.

sample.id<-:

Set the index of sample in which the cluster belongs to.

Methods

show

Display details about the Cluster object.

summary

Return descriptive summary for each Cluster object.

Usage: summary(Cluster)

Author(s)

Ariful Azad

See Also

ClusteredSample

Examples

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## An object of class "Cluster"" is usually created when constructing a "ClusteredSample". 
## Unless you know exactly what you are doing, creating an object of class "Cluster" 
## using new or using the constructor is discouraged.

## ------------------------------------------------
## load data and retrieve a sample
## ------------------------------------------------

library(healthyFlowData)
data(hd)
sample = exprs(hd.flowSet[[1]])

## ------------------------------------------------
## cluster sample using kmeans algorithm
## and retrive the parameters of  the first cluster
## ------------------------------------------------

km = kmeans(sample, centers=4, nstart=20)
center1 = km$centers[1,]
# compute the covariance matrix of the first cluster
cov1 = cov(sample[km$cluster==1,])
size1 = length(which(km$cluster==1))

## ------------------------------------------------
## Create an object of class "Cluster" 
## and show summary
## ------------------------------------------------

clust = Cluster(size=size1, center=center1, cov=cov1)
summary(clust)