Abstract
We have developed a program, GENECUT, for analyzing datasets from gene expression profiling. GENECUT is based on a pairwise clustering method known as Normalized Cut [Shi and Malik, 1997]. GENECUT extracts global structures by progressively partitioning datasets into well-balanced groups, performing an intuitive k-way partitioning at each stage in contrast to commonly used 2-way partitioning schemes. By making use of the Nyström approximation, it is possible to perform clustering on very large genomic datasets.
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References
Buhmann, JM. Data Clustering and Learning. In: Arbib, MA, ed. The Handbook of Brain Theory and Neural Networks. MIT Press, 1995.
Chung, FRK. Spectral Graph Theory. American Mathematical Society (1997).
Duda, R, Hart, P. Pattern Classification and Scene Analysis. John Wiley & Sons (1973).
Eisen, MB et al. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci 95 (1998): 14863–14868.
Fowlkes, C, Belongie, S, Malik, J. Spatiotemporal grouping using the Nyström approximation. Proc. IEEE Comput. Soc. Conf. Comput. Vision and Pattern Recogn (2001).
Hughes, TR, Marton, MJ et al. Functional discovery via a compendium of expression profiles. Cell 102 (2000): 109–126.
Ripley, BD. Pattern Recognition and Neural Networks. Cambridge (1996).
Sharan, R, Shamir, R. Click: A clustering algorithm with applications to gene expression analysis. Proc. Of ISMB. AAAI Press, 2000.
Shi, J, Malik, J. Normalized cuts and image segmentation. Proc IEEE Conf. Computer Vision and pattern Recognition (1997): 731–737.
Shi, J, Malik, J. Normalized cuts and image segmentation. IEEE Trans. PAMI 22 (2000): 888–905.
Tamayo, P et al. Interpreting patterns of gene expression with self-organizing maps: Methods and applications to hematopoietic differentiation. Proc. Natl. Acad. Sci. 96 (1999): 2907–2912.
Xing, EP, Karp, RM. Cliff: Clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. Proc. Of the Nineteenth ISMB (2001).
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© 2002 Kluwer Academic Publishers
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Fowlkes, C., Shan, Q., Belongie, S., Malik, J. (2002). Extracting Global Structure from Gene Expression Profiles. In: Lin, S.M., Johnson, K.F. (eds) Methods of Microarray Data Analysis II. Springer, Boston, MA. https://doi.org/10.1007/0-306-47598-7_6
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DOI: https://doi.org/10.1007/0-306-47598-7_6
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4020-7111-9
Online ISBN: 978-0-306-47598-6
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