AggloClust2D: Performs Constrained 2D Agglomerative Clustering

In hicream: HI-C diffeREntial Analysis Method

Performs Constrained 2D Agglomerative Clustering

Description

This function performs a connectivity constrained 2D agglomerative clustering using scikit-learn function AgglomerativeClustering and outputs an object of class hclust that stores the hierarchy of merges and value of criterion at each merge. It also outputs the optimal level of the hierarchy with respect to the elbow heuristic.

Usage

AggloClust2D(counts, nbClust = NULL)

## S3 method for class 'res2D'
print(x, ...)

## S3 method for class 'res2D'
summary(object, ...)

## S3 method for class 'res2D'
plot(x, ...)

Arguments

counts	an object of class InteractionSet obtained from the function loadData.
nbClust	integer. Number of clusters to obtain. Set to NULL by default.
x	a res2D object to plot
...	not used
object	a res2D object to summarize

Value

An object of class res2D containing:

tree	an object of class hclust
nbClust	the number of clusters corresponding either to the value passed by the user or to the optimal level of clusters as provided by the elbow heuristic
clustering	obtained clustering

Author(s)

Élise Jorge [e-mail]
Sylvain Foissac [e-mail]
Pierre Neuvial [e-mail]
Nathalie Vialaneix [e-mail]

Examples

data("pighic")
res2D <- AggloClust2D(pighic$data)
if (!is.null(res2D)) {# in case Python or modules are not available
  clusters <- res2D$clustering
  print(res2D)
  summary(res2D)
  plot(res2D)
}

hicream documentation built on Aug. 8, 2025, 7:26 p.m.