Microarrays are one of the latest breakthroughs in experimental molecular biology, that allow monitoring of gene expression of tens of thousands of genes in parallel. Knowledge about expression levels of all or a big subset of genes from different cells may help us in almost every field of society. Amongst those fields are diagnosing diseases or finding drugs to cure them. Analysis and handling of microarray data is becoming one of the major bottlenecks in the utilization of the technology.
Microarray experiments include many stages. First, samples must be extracted from cells and microarrays should be labeled. Next, the raw microarray data are images, have to be transformed into gene expression matrices. The following stages are low and high level information analysis.
Low level analysis include normalization of the data. One of the major methods used for High level analysis is Cluster analysis. Cluster analysis is traditionally used in phylogenetic research and has been adopted to microarray analysis. The goal of cluster analysis in microarrays technology is to group genes or experiments into clusters with similar profiles.
This survey reviews microarray technology with greater emphasys on cluster analysis methods and their drawbacks. An alternative method is also presented. This survey is not meant to be treated as complete in any form, as the area is currently one of the most active, and the body of research is very large. |