First step in our analysis is to cluster genes based on their expression profiles. We have used the k-means clustering algorithm, with k set to 5 and the Euclidean measure for distance.
Input of this widget are data tokens coming from the "File" widget. The output of the "K-Means Clustering" are new data tokens where genes are classified into k=5 clusters. Statistics giving the number of genes (Items) in each cluster and two quantitative measures of cluster quality are displayed.