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Hierarchical clustering of features

A hierarchical clustering of features is a tree presentation of the similarity in expression profiles of the features over a set of samples (or groups).
The tree structure is generated by

- letting each feature be a cluster
- calculating pairwise distances between all clusters
- joining the two closest clusters into one new cluster
- iterating 2-3 until there is only one cluster left (which will contain all samples).

To start the clustering of features:

**Toolbox** | **Expression Analysis ()**| **Feature Clustering** | **Hierarchical Clustering of Features ()**

Select at least two samples ( () or ()) or an experiment ().

**Note!** If your data contains many features, the clustering will take very long time and could make your computer unresponsive. It is recommended to perform this analysis on a subset of the data (which also makes it easier to make sense of the clustering. Typically, you will want to filter away the features that are thought to represent only noise, e.g. those with mostly low values, or with little difference between the samples). See how to create a sub-experiment in Creating sub-experiment from selection.

Clicking **Next** will display a dialog as shown in figure 22.46. The hierarchical clustering algorithm requires that you specify a distance measure and a cluster linkage. The distance measure is used specify how distances between two features should be calculated. The cluster linkage specifies how you want the distance between two clusters, each consisting of a number of features, to be calculated.

**Figure 22.46:** *Parameters for hierarchical clustering of features.*

At the top, you can choose three kinds of **Distance measures**:

**Euclidean distance**. The ordinary distance between two points - the length of the segment connecting them. If and , then the Euclidean distance between and is**1 - Pearson correlation**. The Pearson correlation coefficient between two elements and is defined as**Manhattan distance**. The Manhattan distance between two points is the distance measured along axes at right angles. If and , then the Manhattan distance between and is

Next, you can select different ways to calculate distances between clusters. The possible cluster linkage to use are:

**Single linkage**. The distance between two clusters is computed as the distance between the two closest elements in the two clusters.**Average linkage**. The distance between two clusters is computed as the average distance between objects from the first cluster and objects from the second cluster. The averaging is performed over all pairs , where is an object from the first cluster and is an object from the second cluster.**Complete linkage**. The distance between two clusters is computed as the maximal object-to-object distance , where comes from the first cluster, and comes from the second cluster. In other words, the distance between two clusters is computed as the distance between the two farthest objects in the two clusters.

At the bottom, you can select which values to cluster (see Selecting transformed and normalized values for analysis).
Click **Next** if you wish to adjust how to
handle the results. If not, click **Finish**.

**Subsections**