Clustering expression profiling

In one recent example of gene expression for pharmacological analysis, Scherf et al. [102] treated 60 different human cancer cell lines with 60,000 compounds. These same 60 cell lines were analyzed by expression analysis [103], and the cell lines were clustered based on expression profiles. The clusters were significantly different from those based on their response to drugs. 

In summary, we find gene expression profiling with microarrays to be an exceptionally powerful and profound analytical fool. Not only is the technique useful for global analyses, for example, of mefabolic pathways and their interrelationships, but it also has the ability to focus upon (albeit assisted by clustering) and track important singular events that would otherwise remain hidden under a genomic backdrop. 

Yu J, Wang F, Yang GH et al. Human mieroRNA clusters genomic organization and expression profile in leukemia cell lines. Biochem Biophys Res Commun 2006 349 59-68. 

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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