Application on breast tumor samples data. A panel of 20 different breast cancer samples . A: matrix of key aggregates computed with CLAG, with Δ = 0.2and S
(A) > 0, and zoom on the matrix. The red color scale is associated to small values and the green color scale to high values. The vast majority of values in the matrix is low and CLAG allows to distinguish them because of quantile segmentation. B: zoom on the matrix in A where the three aggregation graphs in C are indicated. C: aggregation graph produced by CLAG where three main clusters (produced by the first step of the algorithm and colored red, green and violet) are connected among each other by grey edges. Notice that the three clusters are indicated on the top of the zoomed matrix in B. Numbers labelling the nodes of the graph correspond to samples, that is columns in the matrix. D: dendrogram produced from the data clustered in A with a hierarchical clustering algorithm based closely on the average-linkage method of Sokal and Michener and developed in . Three main clusters are found. The numbers are colored as in C and they are associated to columns in the matrix in B. For each sample, we denote the presence (+) or absence (-) of factors ErbB2 and ER whose overexpression is known to vary across cancer types.