Class ClusterExperiment

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Description

ClusterExperiment is a class that extends SummarizedExperiment and is used to store the data and clustering information.

In addition to the slots of the SummarizedExperiment class, the ClusterExperiment object has the additional slots described in the Slots section.

There are several methods implemented for this class. The most important methods (e.g., clusterMany, combineMany, ...) have their own help page. Simple helper methods are described in the Methods section. For a comprehensive list of methods specific to this class see the Reference Manual.

The constructor clusterExperiment creates an object of the class ClusterExperiment. However, the typical way of creating these objects is the result of a call to clusterMany or clusterSingle.

Note that when subsetting the data, the co-clustering and dendrogram information are lost.

Usage

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clusterExperiment(se, clusters, ...)

## S4 method for signature 'matrix,ANY'
clusterExperiment(se, clusters, ...)

## S4 method for signature 'SummarizedExperiment,numeric'
clusterExperiment(se, clusters, ...)

## S4 method for signature 'SummarizedExperiment,character'
clusterExperiment(se, clusters, ...)

## S4 method for signature 'SummarizedExperiment,factor'
clusterExperiment(se, clusters, ...)

## S4 method for signature 'SummarizedExperiment,matrix'
clusterExperiment(se, clusters,
  transformation, primaryIndex = 1, clusterTypes = "User",
  clusterInfo = NULL, orderSamples = 1:ncol(se), dendro_samples = NULL,
  dendro_index = NA_real_, dendro_clusters = NULL, coClustering = NULL)

Arguments

se

a matrix or SummarizedExperiment containing the data to be clustered.

clusters

can be either a numeric or character vector, a factor, or a numeric matrix, containing the cluster labels.

...

The arguments transformation, clusterTypes and clusterInfo to be passed to the constructor for signature SummarizedExperiment,matrix.

transformation

function. A function to transform the data before performing steps that assume normal-like data (i.e. constant variance), such as the log.

primaryIndex

integer. Sets the 'primaryIndex' slot (see Slots).

clusterTypes

a string describing the nature of the clustering. The values 'clusterSingle', 'clusterMany', 'mergeClusters', 'combineMany' are reserved for the clustering coming from the package workflow and should not be used when creating a new object with the constructor.

clusterInfo

a list with information on the clustering (see Slots).

orderSamples

a vector of integers. Sets the 'orderSamples' slot (see Slots).

dendro_samples

dendrogram. Sets the 'dendro_samples' slot (see Slots).

dendro_index

numeric. Sets the dendro_index slot (see Slots).

dendro_clusters

dendrogram. Sets the 'dendro_clusters' slot (see Slots).

coClustering

matrix. Sets the 'coClustering' slot (see Slots).

Details

The clusterExperiment constructor function gives clusterLabels based on the column names of the input matrix/SummarizedExperiment. If missing, will assign labels "cluster1","cluster2", etc.

Value

A ClusterExperiment object.

Slots

transformation

function. Function to transform the data by when methods that assume normal-like data (e.g. log)

clusterMatrix

matrix. A matrix giving the integer-valued cluster ids for each sample. The rows of the matrix correspond to clusterings and columns to samples. The integer values are assigned in the order that the clusters were found, if found by setting sequential=TRUE in clusterSingle. "-1" indicates the sample was not clustered.

primaryIndex

numeric. An index that specifies the primary set of labels.

clusterInfo

list. A list with info about the clustering. If created from clusterSingle, clusterInfo will include the parameter used for the call, and the call itself. If sequential = TRUE it will also include the following components.

  • clusterInfoif sequential=TRUE and clusters were successfully found, a matrix of information regarding the algorithm behavior for each cluster (the starting and stopping K for each cluster, and the number of iterations for each cluster).

  • whyStopif sequential=TRUE and clusters were successfully found, a character string explaining what triggered the algorithm to stop.

clusterTypes

character vector with the origin of each column of clusterMatrix.

dendro_samples

dendrogram. A dendrogram containing the cluster relationship (leaves are samples; see makeDendrogram for details).

dendro_clusters

dendrogram. A dendrogram containing the cluster relationship (leaves are clusters; see makeDendrogram for details).

dendro_index

numeric. An integer giving the cluster that was used to make the dendrograms. NA_real_ value if no dendrograms are saved.

coClustering

matrix. A matrix with the cluster co-occurrence information; this can either be based on subsampling or on co-clustering across parameter sets (see clusterMany). The matrix is a square matrix with number of rows/columns equal to the number of samples.

clusterLegend

a list, one per cluster in clusterMatrix. Each element of the list is a matrix with nrows equal to the number of different clusters in the clustering, and consisting of at least two columns with the following column names: "clusterId" and "color".

orderSamples

a numeric vector (of integers) defining the order of samples to be used for plotting of samples. Usually set internally by other functions.

Examples

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se <- matrix(data=rnorm(200), ncol=10)
labels <- gl(5, 2)

cc <- clusterExperiment(se, as.numeric(labels), transformation =
function(x){x})