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perseus:user:activities:matrixanalysis:clusteringpca:hierarchicalcluster

Hierarchical clustering

General

Brief description

This activity performs hierarchical clustering of rows and/or columns and produces a visual heat map representation of the clustered matrix. Clustering can be performed with a choice of distances and linkages. This activity can also be used just to display your data in a heat map without performing clustering by deselecting row and column clustering.

Parameters

Row tree

If checked rows will be clustered and a tree (dendrogram) is generated (default: checked).

Distance

Selected distance that will be used for the clustering process (default: Euclidean). The distance can be selected from a predefined list:

  • Euclidean
  • L1
  • Maximum
  • Lp
  • Pearson correlation
  • Spearman correlation
  • Cosine
  • Canberra

Linkage

Selected clustering method that will be applied (default: Average). It can be selected from a predefined list:

  • Average
  • Complete
  • Single

Constraint

Selected constraint that should be preserved from the input data (default: None). The used constraint can be selected from a predefined list of constraints:

  • None
  • Preserve order
  • Preserve order (periodic)

Preprocess with k-means

Specifies, whether the data should be preprocessed using k-means before applying clustering and generating a heatmap (default: checked).

Number of clusters

This parameter is just relevant, if the parameter “Preprocess with k-means ” is checked. Defines the number of clusters that will be created by the k-means algorithm (default: 300).




Column tree

If checked columns will be clustered and a tree (dendrogram) is generated (default: checked).

Distance

Selected distance that will be used for the clustering process (default: Euclidean). The distance can be selected from a predefined list:

  • Euclidean
  • L1
  • Maximum
  • Lp
  • Pearson correlation
  • Spearman correlation

Linkage

Selected clustering method that will be applied (default: Average). It can be selected from a predefined list:

  • Average
  • Complete
  • Single

Constraint

Selected constraint that should be preserved from the input data (default: None). The used constraint can be selected from a predefined list of constraints:

  • None
  • Preserve order
  • Preserve order (periodic)
  • Preserve grouping

Preprocess with k-means

Specifies, whether the data should be preprocessed using k-means before applying clustering and generating a hetamap (default: checked).

Number of clusters

This parameter is just relevant, if the parameter “Preprocess with k-means” is checked. Defines the number of clusters that will be created by the k-means algorithm (default: 300).




Which columns to use

List of all expression/numerical columns in the data set (default: all numerical columns; the expression columns are selected see parameter “Use for clustering”).

Use for clustering

Selected expression/numerical columns that should be used for the clustering (default: all expression columns are selected).

Display in heat map but do not use for clustering

Selected expression/numerical columns that should be displayed in the output heat map, but are not used for the clustering (default: empty).



Parameter window

Perseus pop-up window: Clustering/PCA -> Hierarchical clustering

perseus/user/activities/matrixanalysis/clusteringpca/hierarchicalcluster.txt · Last modified: 2015/11/23 14:47 (external edit)