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Clustering Gene Expression DataContact: Habtom Ressom, Padma Natarajan Project Summary Clustering is a very useful and important technique for analyzing gene expression data. Most clustering methods perform well when the number of clusters is given. However, identifying the number of clusters available in gene expression data is by itself a challenging task. Hence, various validation schemes are commonly used to choose the best number of clusters. We apply a novel extension of the popular self organizing maps (SOM) known as adaptive double self-organizing map (ADSOM) to perform clustering and cluster visualization simultaneously, thereby requiring no a-priori knowledge about the number of clusters. |
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