addClusterings {clusterExperiment} | R Documentation

## Add clusterings to ClusterExperiment object

### Description

Function for adding new clusterings in form of vector (single cluster) or matrix (multiple clusterings) to an existing ClusterExperiment object

### Usage

    ## S4 method for signature 'ClusterExperiment,matrix'
    addClusterings(x, y,
      clusterTypes = "User", clusterLabels = NULL, clusterLegend = NULL)

    ## S4 method for signature 'ClusterExperiment,ClusterExperiment'
    addClusterings(x, y)

    ## S4 method for signature 'ClusterExperiment,vector'
    addClusterings(x, y,
      makePrimary = FALSE, ...)

### Arguments

`x` | a ClusterExperiment object
`y` | additional clusters to add to x. Can be a ClusterExperiment object or a matrix/vector of clusters.
`clusterTypes` | a string describing the nature of the clustering. The values 'clusterSingle', 'clusterMany', 'mergeClusters', 'makeConsensus' are reserved for the clustering coming from the package workflow and should not be used when creating a new object with the constructor.
`clusterLabels` | label(s) for the clusters being added. If `y` a matrix, the column names of that matrix will be used by default, if `clusterLabels` is not given.
`clusterLegend` | a list giving the cluster legend for the clusters added.
`makePrimary` | whether to make the added cluster the primary cluster (only relevant if `y` is a vector)
`...` | For `addClusterings`, passed to signature `ClusterExperiment,matrix`. For `[` (subsetting), passed to `SingleCellExperiment` subsetting function.

### Details

addClusterings adds y to x, and is thus not symmetric in the two arguments. In particular, the `primaryCluster`, all of the dendrogram information, `coClustering`, and `orderSamples` are all kept from the x object, even if y is a ClusterExperiment.

### Value

A `ClusterExperiment` object.

### Examples

    data(simData)

    cl1 <- clusterSingle(simData, subsample=FALSE,
    sequential=FALSE, mainClusterArgs=list(clusterArgs=list(k=3),
    clusterFunction="pam"))
    cl2 <- clusterSingle(simData, subsample=FALSE,
    sequential=FALSE, mainClusterArgs=list(clusterArgs=list(k=3),
    clusterFunction="pam"))

    addClusterings(cl1, cl2)

[Package clusterExperiment version 2.6.1 Index]