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Cluster Lines 3D, hclust()

The Currently selected lines and traits will be clustered according to their distance computed from markers and trait values, using the R procedure hclust() (Hierarchical cluster analysis on a set of dissimilarities). The clusters will be displayed in three dimensions calculated by Singular Value Decomposition, R procedure svd(). Analysis code Cluster4D.R. This method of clustering typically requires at least 25 lines for the execution to complete.

When you have examined the results you can select the clusters you want to use as your new Currently selected lines. Reclustering a group of lines requires at least 25 members in a cluster.

Currently selected lines: 0

Select lines or lines and trait. (Patience required for more than a few hundred lines.)

In 2009 the Toronto International Data Release Workshop agreed on a policy statement about prepublication data sharing. Accordingly, the data producers are making many of the datasets in T3 available prior to publication of a global analysis. Guidelines for appropriate sharing of these data are given in the excerpt from the Toronto Statement.

I agree to the Data Usage Policy as specified in Toronto Statement.