Clustering Microarray Data: Theoretical and Practical Issues

Abstract

The analysis of microarray data is a widespread functional genomics approach that allows for the monitoring of the expression of thousands of genes at once. The analysis of the great amount of data generated in a microarray experiment requires powerful statistical techniques. One of the first tasks of the analysis of microarray data is to cluster data into biologically meaningful groups according to their expression patterns. In this article, we discuss classical as well as recent clustering techniques for microarray data. We pay particular attention to both theoretical and practical issues and give some general indications that might be useful to practitioners.

Keywords:

• Distance-based clustering
• Distance measures
• Microarray data
• Model-based clustering

Acknowledgment

This work has been partially financed by MIUR (the Italian Ministry for Research) [PRIN 20077P5AWA_002].