ClusteredSample: A class representing a clustered FC Sample
Description
An object of class "ClusteredSample
" represents a partitioning of a sample into clusters. We model a flow cytometry sample with a mixture of cell populations where a cell population is a normally distributed cluster. An object of class "ClusteredSample
" therefore stores a list of clusters and other necessary parameters.
Creating Object
An object of class "ClusteredSample
" can be created using the following constructor
ClusteredSample(labels, centers=list(), covs=list(), sample=NULL, sample.id=NA_integer_)
labels
A vector of integers (from1:num.clusters
) indicating the cluster to which each point is allocated. This is usually obtained from a clustering algorithm.centers
A list of lengthnum.clusters
storing the centers of the clusters. The ith entry of the listcenters[[i]]
stores the center of the ith cluster. If not specified, the constructor estimatescenters
fromsample
.covs
A list of lengthnum.clusters
storing the covariance matrices of the clusters. The ith entry of the listcov[[i]]
stores the covariance matrix of the ith cluster. If not specified, the constructor estimatescov
fromsample
.
sample
A matrix, data frame of observations, or object of classflowFrame
. Rows correspond to observations and columns correspond to variables. It must be passed to the constructor if eithercenters
orcov
is unspecified; thencenters
orcov
is estimated fromsample
. sample.id
The index of the sample (relative to other samples of a cohort).
Slots
An object of class "ClusteredSample
" contains the following slots:
num.clusters
The number of clusters in the sample.
labels
A vector of integers (from range
1:num.clusters
) indicating the cluster to which each point is assigned to. For example,labels[i]=j
means that the ith element (cell) is assigned to the jth cluster.dimension
Dimensionality of the sample (number of columns in data matrix).
clusters
A list of length
num.clusters
storing the cell populations. Each cluster is stored as an object of classCluster
.size
Number of cells in the sample (summation of all cluster sizes).
sample.id
integer, denoting the index of the sample (relative to other samples of a cohort). Default is
NA_integer_
Accessors
All the slot accessor functions take an object of class ClusteredSample
. I show usage of the first accessor function. Other functions can be called similarly.
get.size
:Returns the number of cells in the sample (summation of all cluster sizes).
Usage:
get.size(object)
here
object
is aClusteredSample
object.get.num.clusters
Returns the number of clusters in the sample.
get.labels
Returns the cluster labels for each cell. For example,
labels[i]=j
means that the ith element (cell) is assigned to the jth cluster.get.dimension
Returns the dimensionality of the sample (number of columns in data matrix).
get.clusters
Returns the list of clusters in this sample. Each cluster is stored as an object of class
Cluster
.get.sample.id
Returns the index of the sample (relative to other samples of a cohort).
Methods
 show

Display details about the
ClusteredSample
object.  summary
Return descriptive summary for the
ClusteredSample
object.Usage:
summary(ClusteredSample)
 plot
We plot a sample by bivariate scatter plots where different clusters are shown in different colors.
Usage:
plot(sample, ClusteredSample, ...)
the arguments of the plot function are:
sample:
A matrix, data.frame or an object of classflowFrame
representing an FC sample.ClusteredSample:
An object of classClusteredSample
storing the clustering of the sample....
Other usual plotting related parameters.
Author(s)
Ariful Azad
See Also
Cluster
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45  ## 
## load data and retrieve a sample
## 
library(healthyFlowData)
data(hd)
sample = exprs(hd.flowSet[[1]])
## 
## cluster sample using kmeans algorithm
## 
km = kmeans(sample, centers=4, nstart=20)
cluster.labels = km$cluster
## 
## Create ClusteredSample object (Option 1 )
## without specifying centers and covs
## we need to pass FC sample for paramter estimation
## 
clustSample = ClusteredSample(labels=cluster.labels, sample=sample)
## 
## Create ClusteredSample object (Option 2)
## specifying centers and covs
## no need to pass the sample
## 
centers = list()
covs = list()
num.clusters = nrow(km$centers)
for(i in 1:num.clusters)
{
centers[[i]] = km$centers[i,]
covs[[i]] = cov(sample[cluster.labels==i,])
}
# Now we do not need to pass sample
ClusteredSample(labels=cluster.labels, centers=centers, covs=covs)
## 
## Show summary and plot a clustered sample
## 
summary(clustSample)
plot(sample, clustSample)

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