Description Usage Arguments Details Value See Also Examples
Compute the Metric between every pair of clustering labels after merging every possible pair of clusters. Find the one that improves the Metric merging the most, merge the pair. Repeat until there is no improvement.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  Dune(clusMat, ...)
## S4 method for signature 'matrix'
Dune(
clusMat,
unclustered = NULL,
verbose = FALSE,
parallel = FALSE,
BPPARAM = BiocParallel::bpparam(),
metric = "NMI"
)
## S4 method for signature 'data.frame'
Dune(
clusMat,
unclustered = NULL,
verbose = FALSE,
parallel = FALSE,
BPPARAM = BiocParallel::bpparam(),
metric = "NMI"
)
## S4 method for signature 'SummarizedExperiment'
Dune(
clusMat,
cluster_columns,
unclustered = NULL,
verbose = FALSE,
parallel = FALSE,
BPPARAM = BiocParallel::bpparam(),
metric = "NMI"
)

clusMat 
the matrix of samples by clustering labels. 
... 
parameters including: 
unclustered 
The value assigned to unclustered cells. Default to 
verbose 
Whether or not the print cluster merging as it happens. 
parallel 
Logical, defaults to FALSE. Set to TRUE if you want to parallellize the fitting. 
BPPARAM 
object of class 
metric 
The metric that is tracked to decide which clusters to merge. For now, either ARI and NMI are accepted. Default to NMI. See details. 
cluster_columns 
if 
The Dune algorithm merges pairs of clusters in order to improve the mean adjusted Rand Index or the mean normalized mutual information with other clustering labels. It returns a list with five components.: #'
initialMat
: The initial matrix of cluster labels
currentMat
: The final matrix of cluster labels
merges
: The stepbystep detail of the merges, recapitulating
which clusters where merged in which cluster label
impMetric
: How much each merge improved the mean Metric between the
cluster label that has been merged and the other cluster labels.
metric
: The metric that was used to find the merges.
A list with four components: the initial matrix of clustering labels, the final matrix of clustering labels, the merge info matrix and the Metric improvement vector.
clusterConversion ARIImp
1 2 3 4 5 6 7 8  data("clusMat", package = "Dune")
merger < Dune(clusMat = clusMat)
# clusters 11 to 14 from cluster label 5 and 3 are subset of cluster 2 from
# other cluster labels. Designing cluster 2 as unclustered therefore means we
# do fewer merges.
merger2 < Dune(clusMat = clusMat, unclustered = 2)
merger$merges
merger2$merges

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