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Figure 2b is the corresponding dendrogram. Each library is labeled by a library number (001 to 088), cancerous or normal indicator (can, nor), bulk tissue or cell line indicator (bk, cl), tissue type (bn = brain, bt = breast, co = colon, ki = kidney, ov = ovary, pa = pancreas, pr = prostate, sk = skin, va = vascular) and a code for the laboratory that created the library. For instance, the label 032-can-bk-bt-po corresponds to the cancerous, bulk tissue breast library whose number was 32 and was created by the laboratory labeled po.

A Methodology for Analyzing SAGE Libraries for Cancer Profiling • 47 Fig. 3. Result of OPTICS after error removal, normalization, and missing tag imputation. The first kind of tags does not help in forming clustering structures. It even produces a dilution effect to the true clusters, by disturbing the similarity and dissimilarity of libraries. Thus, these tags are removed and only the discriminating tags are kept for further analysis. ACM Transactions on Information Systems, Vol. 23, No. 1, January 2005.

1. INTRODUCTION Gene expression profiling has been studied extensively in an attempt to determine the transcriptional changes, both causative and correlative, associated with the progression of cancer [Gray and Collins 2000]. Such an approach has the potential to determine new prognostic and diagnostic biomarkers in addition to new gene targets for therapeutic intervention. 1 Background Cancers are typically classified based on macroscopic qualities such as the tissues they developed in. However, the same type of cancer can have different reactions to treatment in different people, and on the other hand, one treatment can be effective for cancers originating in different tissues.

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


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