ClusteredSample-class {flowMatch} | R Documentation

## 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 (from `1:num.clusters`) indicating the cluster to which each point is allocated. This is usually obtained from a clustering algorithm.

- `centers` A list of length `num.clusters` storing the centers of the clusters. The ith entry of the list `centers[[i]]` stores the center of the ith cluster. If not specified, the constructor estimates `centers` from `sample`.

- `covs` A list of length `num.clusters` storing the covariance matrices of the clusters. The ith entry of the list `cov[[i]]` stores the covariance matrix of the ith cluster. If not specified, the constructor estimates `cov` from `sample`.

- `sample` A matrix, data frame of observations, or object of class `flowFrame`. Rows correspond to observations and columns correspond to variables. It must be passed to the constructor if either `centers` or `cov` is unspecified; then `centers` or `cov` is estimated from `sample`.

- `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 class `Cluster`.

`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 a `ClusteredSample` 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 class `flowFrame` representing an FC sample.

  - `ClusteredSample:` An object of class `ClusteredSample` storing the clustering of the sample.

  - `...` Other usual plotting related parameters.

### Author(s)

Ariful Azad

### See Also

`Cluster`

### Examples

    ## ------------------------------------------------
    ## 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)

[Package flowMatch version 1.30.0 Index]