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The most commonly applied strategies for identifying genes with a common response profile are based on clustering algorithms. These methods have no explicit rules to define the appropriate number of groups of genes. Usually the number of clusters is decided on heuristic criteria or through the application of different methods proposed to assess the number of clusters in a data set. The purpose of this paper is to compare the performance of seven of these techniques, including traditional ones, and some recently proposed. All of them produce underestimations of the true number of clusters. However, within this limitation, the  gDGC  algorithm appears to be the best. It is the only one that explicitly states a rule for cutting a dendrogram on the basis of a testing hypothesis framework, allowing the user to calibrate the sensitivity, adjusting the significance level.

How to Group Genes according to Expression Profiles?