Microarrays produce enormous amounts of data, and the analysis of that data can be quite complex. The sheer volume of data requires special software and a database in which to store both the measurements and the results of the analyses. The exact form that the analysis takes depends on the nature of the experiment being performed. If just two samples are being directly compared (for example, gene expression in mouse heart tissue is compared with and without the administration of a drug), relatively straightforward statistical tests can be performed. If larger numbers of samples are being measured, the same tests can be performed between two samples at a time, but more sophisticated, "clustering" analyses can be performed as well.
Clustering analysis identifies groups of genes that react the same way across several different samples. For example, researchers might analyze gene expression in heart tissue from a set of mouse embryos that range in age from five to fifteen days. A clustering analysis would be able to detect a group of genes whose expression levels all increase slowly from days five to nine, peak at day ten and then fall to zero by day twelve. Only genes that have this precise pattern of expression would cluster together, in this type of analysis.
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