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Extended data analysis using DeCyder EDA

wn groups, otherwise the discriminant analysis and classification might be affected.


Acknowledgment
We thank Professor Peter James, University of Lund, Sweden, for the human ovarian cancer study data.


References
1. Wold, H. Estimation of principal components and related models by iterative least squares, in Multivariate Analysis (Krishnaiah, P. R., ed.), Academic Press, New York, pp. 391–420 (1966).

2. Eriksson, I. et al. Multi- and Megavariate Data Analysis, Umetrics Academy, Umeå, Sweden (2001).

3. Everitt, B. S. et al. Cluster Analysis, 4th edition, Edward Arnold Publishers Ltd., London (2001).

4. Sokal, R. and Mitchener, C. A statistical method for evaluating systematic relationships. Univ. Kansas Sci. Bull. 38, 1409–1438 (1958).

5. Lloyd, S. Least squares quantization in pcm. IEEE Trans. Inf. Theory 28, 128–137 (1982).

6. Kohonen, T. The self-organizing map. Proc. IEEE 78, 1464–1480 (1990).

7. Tamayo, P. et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA 96, 2907–2912 (1999).

8. Hastie, T. et al. 'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biology 1, research0003.1–21 (2000). [Online.] http://genomebiology.com/2000/1/2/research/0003/

9. Dunn, J. Well separated clusters and optimal fuzzy partitions. J. Cybernetics 4, 95–104 (1974).

10. Bolshakova, N. and Azu
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