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perseus:user:activities:matrixanalysis:clusteringpca:hierarchicalcluster
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Hierarchical clustering
General
- Type: Matrix Analysis
- Heading: Clustering/PCA
- Source code: not public
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/user/activities/matrixanalysis/clusteringpca/hierarchicalcluster.txt · Last modified: 2015/11/23 14:47 (external edit)
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