Abstract
Cluster analysis is a computational method that groups together similarly-shaped patterns. It may be applied to large-scale gene expression data to form new hypotheses regarding gene function. In the present study, we clustered the temporal expression patterns of genes expressed in the rat hippocampus during normal development and after a kainate-induced seizure injury at postnatal day 25. We found that two different methods, Euclidean hierarchical and K-means clustering, produced slightly different results, and concluded that different clustering methods may he used to complement one another. We also found that certain genes cluster together both during development and after seizure injury, consistent with the idea of sets of genes that act in concert under various conditions.
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