Heirarchical Clustering Hierarchical clustering creates a “phylogeny� or hierarchy of the data points by employing the following algorithm: 1) 2) 3) 4)
Generate a gene similarity score for all pairs of genes Place the gene similarity scores in a matrix Join the genes that have the highest score Continue to join next similar pairs of genes
Hierarchical clustering methods include: complete-linkage clustering, average-linkage clustering, weighted pair-group averaging, and within pair-group averaging. Clustering approaches have several disadvantages, and should be used with extreme caution (if they are used at all).
Image source: http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/9 75/report/2001 Self-Organizing Maps (SOMs) SOMs are a type of neural network approach. A SOM has a set of nodes with a simple topology and a distance function on the nodes. The nodes are iteratively mapped into a k-dimensional gene expression space. The steps in assembling a SOM are as follows: