Self-organizing maps cluster data in GeneSight. The color-coded expression level of all genes in all experiments is shown. The first step in data analysis from microarray experiments begins with categorization of expression patterns according to their similarity and existing functional annotations. Many mathematical techniques have been developed for identifying underlying patterns in complex data. Cluster analysis methods have been used to systematically group related gene expression patterns.
Hierarchical clustering is a common computational approach used for microarray data analysis.
A SOM has a set of nodes with a simple topology and a distance function on the nodes. SOMs have several features that make them particularly well suited to clustering and analysis of gene expression patterns. Combining advanced tools for microarray data analysis with the progress in hardware design of microarray spotting robots and scanning devices will enhance our understanding of biological processes at the genomic level. Kuklin, A. Using array image analysis to combat HTS bottlenecks.