Makes a dendrogram of a set of clusters based on hclust on the medoids of the cluster.
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## S4 method for signature 'ClusterExperiment' makeDendrogram(x, whichCluster = "primaryCluster", reduceMethod = "mad", nDims = defaultNDims(x, reduceMethod), ignoreUnassignedVar = TRUE, unassignedSamples = c("outgroup", "cluster"), ...) ## S4 method for signature 'dist' makeDendrogram(x, cluster, unassignedSamples = c("outgroup", "cluster", "remove"), ...) ## S4 method for signature 'matrixOrHDF5' makeDendrogram(x, cluster, unassignedSamples = c("outgroup", "cluster", "remove"), ...)
data to define the medoids from. Matrix and
an integer index or character string that identifies which cluster should be used to make the dendrogram. Default is primaryCluster.
character A character identifying what type of
dimensionality reduction to perform before clustering. Can be either a
value stored in either of reducedDims or filterStats slot or a built-in
diminsionality reduction/filtering. The option "coCluster" will use the
co-Clustering matrix stored in the 'coClustering' slot of the
The number of dimensions to keep from
logical. Whether filtering statistics should
ignore the unassigned samples within the clustering. Only relevant if
'reduceMethod' matches one of built-in filtering statistics in
how to handle unassigned samples("-1") ; only relevant for sample clustering. See details.
for makeDendrogram, if signature
A numeric vector with cluster assignments. If x is a ClusterExperiment object, cluster is automatically the primaryCluster(x). “-1” indicates the sample was not assigned to a cluster.
The function returns two dendrograms (as a list if x is a matrix or
in the appropriate slots if x is ClusterExperiment). The cluster dendrogram
is created by applying
hclust to the medoids of each cluster.
In the sample dendrogram the clusters are again clustered, but now the
samples are also part of the resulting dendrogram. This is done by giving
each sample the value of the medoid of its cluster.
unassignedSamples governs what is done with
unassigned samples (defined by a -1 cluster value). If
unassigned=="cluster", then the dendrogram is created by hclust of the
expanded medoid data plus the original unclustered observations. If
unassignedSamples is "outgroup", then all unassigned samples are put
as an outgroup. If the
x object is a matrix, then
unassignedSamples can also be "remove", to indicate that samples
with "-1" should be discarded. This is not a permitted option, however,
x is a
ClusterExperiment object, because it would return
a dendrogram with less samples than
NCOL(x), which is not permitted
If any merge information is stored in the input object, it will be erased by a call to mergeDendrogram.
nDims is missing, it will be given a default value
depending on the value of
defaultNDims for details.
If x is a matrix, a list with two dendrograms, one in which the leaves are clusters and one in which the leaves are samples. If x is a ClusterExperiment object, the dendrograms are saved in the appropriate slots.
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data(simData) #create a clustering, for 8 clusters (truth was 3) cl <- clusterSingle(simData, subsample=FALSE, sequential=FALSE, mainClusterArgs=list(clusterFunction="pam", clusterArgs=list(k=8))) #create dendrogram of clusters: hcl <- makeDendrogram(cl) plotDendrogram(hcl) plotDendrogram(hcl, leafType="samples",plotType="colorblock")
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