Analysis: Hierarchical clustering
Hierarchical clustering, bottom-up.
The two closest points are merged and the new cluster is represented by an unweighted(median) or weighted(center of mass) average of the two points in gene expression space. This takes little RAM, allowing you to cluster a large number of genes, but the clustering results could be inferior to those obtained with e.g. average linkage. Also, clustering with absolute correlation as the distance metric does not work very well at all.