coerce-methods {apcluster} | R Documentation

## Coercion of cluster hierarchies

### Description

Functions for coercing clustering object to hclust and dendrogram objects

### Usage

    ## S4 method for signature 'AggExResult'
    as.hclust(x, base=0.05)
    ## S4 method for signature 'ExClust'
    as.hclust(x, base=0.05, ...)
    ## S4 method for signature 'AggExResult'
    as.dendrogram(object, base=0.05, useNames=TRUE)
    ## S4 method for signature 'ExClust'
    as.dendrogram(object, base=0.05, useNames=TRUE, ...)

### Arguments

`x` | a clustering result object of class `APResult`, `ExClust`, or `AggExResult`
`object` | a clustering result object of class `APResult`, `ExClust`, or `AggExResult`
`base` | fraction of height used for the very first join; defaults to 0.05, i.e. the first join appears at 5% of the total height of the dendrogram (see details below).
`useNames` | if `TRUE` (default), the labels of the dendrogram are the sample/cluster names (if available); otherwise, the labels are indices.
`...` | all other arguments are passed on to `aggExCluster` (see details below).

### Details

If called for an `AggExResult` object, `as.hclust` creates an `hclust` object. The heights are transformed to the interval from `base` (height of lowest join) to 1 (height of highest join). If called for an `ExClust` or `APResult` object, `aggExCluster` is called internally to create a cluster hierarchy first. This is only possible if the pairwise similarities are included in the `sim` slot of `x` (see `aggExCluster` on how to ensure this).

If `x` is an `AggExResult` object obtained by clustering an entire data set, `as.hclust` produces a complete hierarchy. If, however, `x` is an `ExClust` (or `APResult`) object or an `AggExResult` obtained by running `aggExCluster` on an `ExClust` or `APResult` object, then `as.hclust` produces a hierarchy of clusters, not of samples.

If called for an `AggExResult` object, `as.dendrogram` creates an `dendrogram` object. Analogously to `as.hclust`, the heights are transformed to the interval ranging from `base` (height of lowest join) to 1 (height of highest join). So, any information about heights of merges is lost. If the original join heights are relevant, call `plot` on the original `AggExResult` object directly without coercing it to a `dendrogram` object first. If called for an `ExClust` or `APResult` object, `aggExCluster` is called first to create a cluster hierarchy. Again this is only possible if the pairwise similarities are included in the `sim` slot of `object`.

If `object` is an `AggExResult` object obtained by clustering an entire data set, `as.dendrogram` produces a complete dendrogram. If `object` is an `ExClust` (or `APResult`) object or an `AggExResult` obtained by previously running `aggExCluster` on an `ExClust` or `APResult` object, then `as.dendrogram` produces a complete dendrogram of all samples, too, but with the difference that entire clusters of the previous `ExClust` or `APResult` object are not further split up hierarchically. Consequently, if `x` is not a complete cluster hierarchy, but a hierarchy of clusters, `as.dendrogram(as.hclust(x))` produces a dendrogram of clusters, whereas `as.dendrogram(x)` in any case produces a dendrogram of samples (with the special property mentioned above).

### Value

see details above

### Author(s)

Ulrich Bodenhofer, Andreas Kothmeier & Johannes Palme apcluster@bioinf.jku.at

### References

http://www.bioinf.jku.at/software/apcluster

Bodenhofer, U., Kothmeier, A., and Hochreiter, S. (2011) APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463-2464. DOI: 10.1093/bioinformatics/btr406.

### See Also

`APResult`, `AggExResult`, `ExClust`, `heatmap-methods`, `apcluster`, `apclusterL`, `aggExCluster`, `cutree-methods`

### Examples

    ## create two Gaussian clouds
    cl1 <- cbind(rnorm(20, 0.2, 0.05), rnorm(20, 0.8, 0.06))
    cl2 <- cbind(rnorm(20, 0.7, 0.08), rnorm(20, 0.3, 0.05))
    x <- rbind(cl1, cl2)

    ## run affinity propagation
    apres <- apcluster(negDistMat(r=2), x, q=0.7, details=TRUE)

    ## perform agglomerative clustering of affinity propagation clusters
    aggres1 <- aggExCluster(x=apres)

    ## compute and plot dendrogram
    dend1 <- as.dendrogram(aggres1)
    dend1
    plot(dend1)

    ## compute and show dendrogram computed from hclust object
    dend2 <- as.dendrogram(as.hclust(aggres1))
    dend2
    plot(dend2)

    ## perform agglomerative clustering of whole data set
    aggres2 <- aggExCluster(negDistMat(r=2), x)

    ## compute and plot dendrogram
    dend3 <- as.dendrogram(aggres2)
    dend3
    plot(dend3)

[Package apcluster version 1.4.7 Index]