Class discovery in the post genomic era

This chapter describes the basic concepts and application of a family of methods for class discovery, generically known as clustering, applied to microarray data. Although many clustering methods exist, only a few have been extensively used for microarray data analysis (among them I will revise hierarchical clustering, k-means, SOM, SOTA and model-based clustering). Key aspects in clustering such as the determination of the number of clusters and the reliability of the partition obtained are also discussed. Particular cases, such as the clustering of time series data, are also presented. Finally, the functional interpretation of clustering results, a key step in microarray data analysis, is also discussed.