K-means/medoids clustering

In a k-means or medoids clustering, features are clustered into k separate clusters. The procedures seek to find an assignment of features to clusters, for which the distances between features within the cluster is small, while distances between clusters are large.

        Toolbox | Expression Analysis (Image expressionfolder)| Feature Clustering | K-means/medoids Clustering (Image k-means)

Select at least two samples ( (Image array) or (Image rnaseq)) or an experiment (Image 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). See how to create a sub-experiment in Creating sub-experiment from selection.

Clicking Next will display a dialog as shown in figure 22.51.

Image k-means_step2
Figure 22.51: Parameters for k-means/medoids clustering.

The parameters are:

Clicking Next will display a dialog as shown in figure 22.52.

Image k-means_step3
Figure 22.52: Parameters for k-means/medoids clustering.

At the top, you can choose the Level to use. Choosing 'sample values' means that distances will be calculated using all the individual values of the samples. When 'group means' are chosen, distances are calculated using the group means.

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.