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1998] in the context of microarray data. The authors applied hierarchical pairwise average-linkage clustering (using correlation coefficient as similarity measure) to the genes on microarrays for the budding yeast genome and a human fibroblast cell line. gov/) has since then been used for different analyses of microarrays, including the clustering of genes on microarrays for normal and cancerous breast tissue [Perou et al. 1999], the clustering of different tissues on microarrays for different types of lymphoma, in order to detect possible subtypes of ACM Transactions on Information Systems, Vol.

RESULTS The following analysis is based on 88 SAGE libraries, which are publicly available on the NCBI SAGE website as of January 2001. Each of these SAGE libraries is made of a sample from one of the following tissues: brain, breast, prostate, ovary, colon, pancreas, vascular tissue, skin, or kidney. The other information consistently included with each library is whether it was made from cancerous or normal tissue, and whether it was made from bulk tissue in vivo or a cell line grown in vitro.

The other 56 libraries do not group into pronounced clusters. Note that there are several different possibilities for a similarity or dissimilarity measure between SAGE libraries, such as the Manhattan distance, the Euclidean distance, and the correlation coefficient. f. 2). We clustered the SAGE libraries using all of the mentioned functions and found that the clustering structures are typically very robust with respect to the similarity function—they differ in general only slightly. However, the correlation coefficient always resulted in the most pronounced structure, and is also consistent with previously published work.

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ACM transactions on information systems (January) by Association for Computing Machinery

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