4 - Central Institute of Brackishwater Aquaculture

Natlonal Workshop-cum-Training on Bioinfwmatics and Information Management in Aquaculture
3.3.1. Hierarchical Clustering
The hierarchical clustering can be represented as a tree, or a dendrogram.
Branch lengths represent the degree of similarity between the genes.
Hierarchical clustering consists of two separate phases. The hierarchical
clustering is performed as follows; Each gene is assigned to a cluster of its own.
The closest pair of clusters is found and merged into a single cluster. The
distances (similarities) between the new clusters are computed and each of the
old clusters using either single, average or complete linkage method. Above
steps are repeated until all genes are clustered.
Fig4. An Example of Hierarchical clustering of C, carpio obtained using MeV
software
In single linkage clustering, the distance between one cluster and another is
considered to be equal to the shortest distance from any member of one cluster
to any member of the other cluster. In complete linkage clustering, the distance
between one cluster and another cluster is considered to be equal to the longest
distance from any member of one cluster to any member of the other cluster. Jn
average linkage, the distance between one cluster and another cluster is
considered to be equal to the average distance from any member of one cluster
to any member of the other cluster.
3.3.2. Self-organizing Map
Kohonen's self-organizing map (SOM) is a neural net that uses unsupervised
learning. SOM tries to learn to map similar input vectors (gene expression
profiles) to similar regions of the output array of nodes. The method maps the
multidimensional distances of the feature space to two-dimensional distances in
the output map. In SOMs, the number of clusters has to be predetermined. The
dimensions of the two-dimensional grid or array give the value. One has to