Abstract
This paper illustrates how the Quadratic Assignment Problem (QAP) is used as a mathematical model that helps to produce a visualization of microarray data, based on the relationships between the objects (genes or samples). The visualization method can also incorporate the result of a clustering algorithm to facilitate the process of data analysis. Specifically, we show the integration with a graph-based clustering algorithm that outperforms the results against other benchmarks, namely k −means and self-organizing maps. Even though the application uses gene expression data, the method is general and only requires a similarity function being defined between pairs of objects. The microarray dataset is based on the budding yeast (S. cerevisiae). It is composed of 79 samples taken from different experiments and 2,467 genes. The proposed method delivers an automatically generated visualization of the microarray dataset based on the integration of the relationships coming from similarity measures, a clustering result and a graph structure.
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References
Shannon, P., Markiel, A., Ozier, O., Baliga, N., Wang, J., Ramage, D., Amin, N., Schwikowski, B., Ideker, T.: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research 13, 2498–2504 (2003)
Kohler, J., Baumbach, J., Taubert, J., Specht, M., Skusa, A., Ruegg, A., Rawlings, C., Verrier, P., Philippi, S.: Graph-based analysis and visualization of experimental results with ondex. Bioinformatics 22(11), 1383–1390 (2006)
Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)
Tavazoie, S., Hughes, J., Campbell, M., Cho, R., Church, G.: Systematic determination of genetic network architecture. Nat. Genet. (22), 281–285 (1999)
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E., Golub, T.: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. (96), 2907–2912 (1999)
Burkard, R., Çela, E., Pardalos, P., Pitsoulis, L.: The quadratic assignment problem. In: Pardalos, P., Du, D. (eds.) Handbook of Combinatorial Optimization, pp. 241–338. Kluwer Academic Publishers, Dordrecht (1998)
Taillard, E.: Robust taboo search for the quadratic assignment problem. Parallel Computing 17(4-5), 443–455 (1991)
Oliveira, C., Pardalos, P., Resende, M.: Grasp with path-relinking for the quadratic assignment problem. In: Ribeiro, C.C., Martins, S.L. (eds.) WEA 2004. LNCS, vol. 3059, pp. 356–368. Springer, Heidelberg (2004)
González-Barrios, J., Quiroz, A.: A clustering procedure based on the comparison between the k nearest neighbors graph and the minimal spanning tree. Statistics & Probability Letters 62(1), 23–34 (2003)
Inostroza-Ponta, M., Berretta, R., Mendes, A., Moscato, P.: An automatic graph layout procedure to visualize correlated data. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice: Ifip 19th World Computer Congress. IFIP International Federation for Information Processing, vol. 217, pp. 179–188. Springer, Heidelberg (2006)
Shamir, R., Maron-Katz, A., Tanay, A., Linhart, C., Steinfeld, I., Sharan, R., Shiloh, Y., Elkon, R.: Expander-an integrative program suite for microarray data analysis. BMC Bioinformatics 6(232) (2005)
Gasch, A., Eisen, M.: Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology 3(11) (2002)
Handl, J., Knowles, J.: Multiobjective clustering with automatic determination of the number of clusters. Technical Report TR-COMPSYSBIO-2004-02, UMIST, Manchester, UK (2004)
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Inostroza-Ponta, M., Mendes, A., Berretta, R., Moscato, P. (2007). An Integrated QAP-Based Approach to Visualize Patterns of Gene Expression Similarity. In: Randall, M., Abbass, H.A., Wiles, J. (eds) Progress in Artificial Life. ACAL 2007. Lecture Notes in Computer Science(), vol 4828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76931-6_14
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DOI: https://doi.org/10.1007/978-3-540-76931-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76930-9
Online ISBN: 978-3-540-76931-6
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