Abstract
Microarray technology produces large amounts of information to be manipulated by analysis methods, such as biclustering algorithms, to extract new knowledge. All-purpose multivariate data visualization tools are usually not enough for studying microarray experiments. Additionally, clustering tools do not provide means of simultaneous visualization of all the biclusters obtained.
We present an interactive tool that integrates traditional visualization techniques with others related to bioinformatics, such as transcription regulatory networks and microarray heatmaps, to provide enhanced understanding of the biclustering results. Our aim is to gain insight about the structure of biological data and the behavior of different biclustering algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ashburner, M., Ball, C.A., Blake, J.A., Bolsteing, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G.: Geneontology: tool for the unification of biology. Nature Genetics 25, 25–29 (2000)
Barkow, S., Bleuer, S., Prelic, A., Zimmermann, P., Zitzler, E.: Bicat: a biclustering analysis toolbox. Bioinformatics 22(10), 1282–1283 (2006)
Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, C., Causton, H., Gaasterland, T., Glenisson, P., Holstege, F., Kim, I., Markowitz, V., Matese, J., Parkinson, H., Robinson, A., Sarkans, U., Schulze-Kremer, S., Stewart, J., Taylor, R., Vilo, J., Vingron, M.: Minimum information about a microarray experiment (miame)-toward standards for microarray data. Nat. Genet. 29(4), 365–371 (2001)
Carmona-Saez, P., Pascual-Marqui, R.D., Tirado, F., Carazo, J.M., Pascual-Montano, A.: Biclustering of gene expression data by non-smooth non-negative matrix factorization. BMC Bioinformatics 7(78) (2006)
Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proc. Int’l Conf Intell Syst Mol Biol., vol. 8, pp. 93–103 (2000)
den Bulcke, T.V., Leemput, K.V., Naudts, B., van Remortel, P., Ma, H., Verschoren, A., Moor, B.D., Marchal, K.: Syntren: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinformatics 7(43) (2006)
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. 95, 14863–14868 (1998)
Gower, J.C.: Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53(3-4), 325–338 (1966)
Kluger, Y., Basri, R., Chang, J.T., Gerstein, M.: Spectral biclustering of microarray data: Coclustering genes and conditions. Genome Research 13, 703–716 (2003)
Kruskal, J.B.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1), 1–27 (1964)
Lazzeroni, L., Owen, A.: Plaid models for gene expression data. Technical report, Standford University (2002)
Leung, Y.K., Apperley, M.D.: A review and taxonomy of distortion-oriented presentation techniques. Transactions of Computer-Human Interaction 1(2), 126–160 (1994)
Madeira, S., Oliveira, A.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions of Computational Biology and Bioinformatics 1(1), 24–45 (2004)
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002)
Prelic, A., Bleuer, S., Zimmermann, P., Wille, A., Bhlmann, P., Gruissem, W., Hennig, L., Thiele, L., Zitzler, E.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)
Rasmussen, M., Karypis, G.: gcluto: An interactive clustering, visualization and analysis system. Technical Report 04-021, University of Minnesota (2004)
Seo, J., Shneiderman, B.: A rank-by-feature framework for unsupervised multidimensional data exploration using low dimensional projections. In: IEEE Symposium on Information Visualization, pp. 65–72 (2004)
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), 1471–2105 (2005)
Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T.: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research 13, 24958–25004 (2003)
Sharan, R., Maron-Katz, A., Shamir, R.: Click and expander: a system for clustering and visualizing gene expression data. Bioinformatics 19(14), 1787–1799 (2003)
Shen-Orr, S.S., Milo, R., Mangan, S., Alon, U.: Network motifs in the transcriptional regulation network of escherichia coli. Nature Genetics 31, 64–68 (2002)
Shneiderman, B.: The eyes have it: A task by data type taxonomy for information visualizations. In: IEEE Visual Languages, number UMCP-CSD CS-TR-3665, pp. 336–343, College Park, Maryland 20742, U.S.A. (1996)
Tanay, A., Sharan, R., Shamir, R.: Biclustering algorithms: A survey. Handbook of Computational Molecular Biology (2004)
Turner, H., Bailey, T., Krzanowski, W.: Improved biclustering of microarray data demonstrated through systematic performance tests. Computational Statistics and Data Analysis 48, 235–254 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Santamaría, R., Therón, R., Quintales, L. (2007). A Framework to Analyze Biclustering Results on Microarray Experiments. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_77
Download citation
DOI: https://doi.org/10.1007/978-3-540-77226-2_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
Online ISBN: 978-3-540-77226-2
eBook Packages: Computer ScienceComputer Science (R0)