Clustering Microarray Data

We begin with an example that will be used throughout the chapter.The data come from Sorlie et al. (2001). The goal of that article was to “classify breast carcinomas based on variations in gene expression derived from complementary deoxyribonucleic acid (cDNA) microarrays and to correlate tumor characteristics to clinical outcome.’’ The data consist of log fluorescence values for 456 cDNA clones measured on 85 tissue samples. Of the 85 samples, 4 are normal tissue samples, 78 are carcinomas, and 3 are fibroadenomas. Three of the four normal tissue samples were pooled normal breast samples from multiple individuals. Sorlie et al. (2001) selected the 456 genes from an initial set of 8102 genes so as to optimally identify the intrinsic characteristics of breast tumors. In Figures 4.1 and 4.2, the data are plotted as heat maps.∗ This representation assigns a color for every matrix entry, with negative (underexpressed) values being green, and positive (overexpressed) values red. The data presented in this plot were preprocessed by Sorlie et al. (2001), adjusting rows and columns to have median zero. This preprocessing was applied before selection of the subset of 456 genes, so the column (i.e., sample) medians are not precisely zero.