Microarray unsupervised

Covell DG, Wallqvist A, Rabow A et al. Molecular classification of cancer unsupervised selforganizing map analysis of gene expression microarray data. Mol Cancer Ther 2003 2 317-332. 

Exploratory methods are used not to test hypotheses but rather to get an overview of data. Various clustering methods and ordination are excellent tools for exploratory analysis of microarray data. These unsupervised methods do not require external class or group information. Clusters are generated purely based on the intrinsic similarity of the gene or sample expression profiles. No null hypothesis can be rejected, and p values are not generated to test statistical significance. Methods that... 

Boutros, P.C. and Okey, A.B. (2005) Unsupervised pattern recognition an introduction to the whys and wherefores of clustering microarray data. Brief. Bioinform. 6, 331-343. 

RNA microarray data of breast tumors have been analyzed in supervised or unsupervised manner. Supervised methods use outside information about the experimental condition (e.g., cases with metastases versus cases without) to shape the derivation of a model from the dataset. Unsupervised methods use information contained within the RNA data only and usually involve hierarchical clustering (see section on transcriptomics) to detect relationships among tumors, among genes, and connections between specific genes and specific tumors. 

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