Microarray clustering method

Belacel N, Wang Q, Cuperlovic-Culf M. Clustering methods for 102. microarray gene expression data. OMICS. 2006 10 507-531. 

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... 

Figure 5.2 Demonstration of using different distance measures. The dataset used for this demonstration is the subset of genes from microarray study by Bhattachaijee et al. (2001). The clustering method used is the T-means algorithm. Different distance measures result in different clustering solutions a) Euclidean, b) city block, (c) Pearson, and (rf) cosine distances. (See color insert.)...

Mateos A, Herrero J, Tamames J, Dopazo J, Supervised neural networks for clustering conditions in DNA array data after reducing noise by clustering gene expression profiles, In Lin SM, Johnson KF, eds., Methods of Microarray Data Analysis II, Boston, Kluwer Academic Publ, pp. 91-103, 2002. 

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