Description Usage Arguments Value References Examples
This method clusters sample data using a twostep approach. In the first step it converts the high dimensional data into a knnnearest neighbor graph, thus connecting samples that have a similar profile. The edges in knnnearest neighbors graph are given weights according to the Jaccard similarity coefficient of the neighbors of each node. In the second step the Louvaine algorithm, which was developed to find communities in social networks, is applied to this graph in order to find samples that share similar spaces in multidimensional space.
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X 
an MbyN numeric matrix (or any object that can
be coerced to a matrix) holding N samples in columns,
where each column has M values. X can also be an object
of class 
method 
is a string that is passed to the

D 
is a dist object. If provided then 
knn 
a numeric value indicating the number of nearest neighbors to use in the initial neighborhood (default=NULL uses nclass.Sturges) 
G 
an NbyN adjacency matrix on which modularity will be maximized.
If provided 
C 
an optional vector of length N with the initial numeric cluster assignments (default is for each sample to belong to its own singleton cluster) 
repeats 
a numeric value for how many times to run the Louvain algorithm in order to find a maximal local maximum (default=50). 
verbose 
a boolean vaue indicating whether to print progress messages (default=TRUE) 
a list holding three values: C
 a numeric vector
of length N indicating the cluster number. G
 an
NbyN matrix representing the Hadamrd graph. Q

the modularity provided by G
and C
.
Levine et al, "DataDriven Phenotypic Dissection of AML Reveals Progenitorlike Cells that Correlate with Prognosis", Cell, Volume 162, Issue 1, 2 July 2015, Pages 184197
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