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Extra resources for ACM transactions on information systems (January)
In contrast to partitioning algorithms, hierarchical algorithms do not require the number of clusters to be specified a priori. Hierarchical algorithms were used in many previously published papers on the clustering of microarray data. 1, our analysis here is based on the hierarchical clustering algorithm OPTICS [Ankerst et al. 1999], and we will show both the reachability plots and (for readers more familiar with the interpretation of tree representations) the dendrograms of our clustering results.
Our analysis suggests that the SAGE technique is robust. For instance, brain cancer libraries developed in different 1A preliminary version of this work appeared as Ng et al.  ACM Transactions on Information Systems, Vol. 23, No. 1, January 2005. A Methodology for Analyzing SAGE Libraries for Cancer Profiling • 41 laboratories form a strong cluster, suggesting that results obtained in different laboratories are in fact comparable. Our analysis also indicates that brain and breast cancer have much stronger gene expression signatures than ovarian and prostate cancer do.
2000], now part of the Gene Expression Omnibus [Edgar et al. 2002]. The availability of such a resource allows for a global comparison of normal tissues, cell lines and cancer types. Such a comparison may shed light on many interesting questions, such as: r Is the SAGE method robust? Different libraries were created at different places by different groups. Is the SAGE method itself reproducible? r Do different types of cancer behave differently at the gene expression level? r Are there readily detectable subtypes of cancers?