Self Organizing

In order to have an overall measure of similarities between all GI50 data vectors we have used a self-organizing map (SOM)(1<5) to organize cellular growth inhibition data derived from the NCI60 tumor cell panels (14). The SOM algorithm identifies cluster vectors in the 60-dimensional data space by minimizing the deviation between the GI50 data vectors and the cluster vectors. Regions in the GI50-space that are dense with data vectors attract many cluster vectors and regions with few data vectors attract fewer cluster vectors, resulting in a division of response space that mimics information content.

An advantage of SOM reordered data is the ability to visualize the global clustering results in an interpretable manner. Our preferred method of display is the uniform projection of SOM clustering from high-dimensional space to a 2D-map. This mapping is both simple and retains a great deal of the original high-dimensional information. Additional details regarding the creation and access to the GI50 SOM are given in the references (14). SOM clustering of the GI50 data segregates compounds into nine major response categories: mitosis (M), membrane function (N), nucleic acid metabolism (S), metabolic stress and cell survival (Q), kinases/phosphatases and oxidative stress (P), and four unexplored regions R, F, J, and V (17,18,19). Each of these regions is further divided into a total of 80 clades [a clade is a group of clusters (nodes) that share similar cytotoxic responses] or sub-regions: M1-M8, N1-N13, P1-P8, Q1-Q7, R1-R7, S1-S13, F1-F8, J1—J8, V1-V8. The current SOM extends our previously published analysis to include the existing complement of newly screened compounds (14).

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