Abstract
Exploring data sets by applying biclustering algorithms was first introduced in gene expression analysis. While the generated biclustered data grows with increasing rates due to the technological progress in measuring gene expression data, the visualization of the computed biclusters still remains an open issue. For efficiently analyzing the vast amount of gene expression data, we propose an algorithm to generate and layout biclusters with a minimal number of row and column duplications on the one hand and a visualization tool for interactively exploring the uncovered biclusters on the other hand. In this paper, we illustrate how the BiCluster Viewer may be applied to highlight detected biclusters generated from the original data set by using heatmaps and parallel coordinate plots. Many interactive features are provided such as ordering functions, color codings, zooming, details-on-demand, and the like. We illustrate the usefulness of our tool in a case study where yeast data is analyzed. Furthermore, we conducted a small user study with 4 participants to demonstrate that researchers are able to learn und use our tool to find insights in gene expression data very rapidly.
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
Shneiderman, B.: The eyes have it: A task by data type taxonomy for information visualizations. In: Proceedings of the IEEE Symposium on Visual Languages, pp. 336–343 (1996)
Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences 95, 14863–14868 (1998)
Inselberg, A., Dimsdale, B.: Parallel coordinates: A tool for visualizing multi-dimensional geometry. In: Proceedings of IEEE Visualization, pp. 361–378 (1990)
Grothaus, G., Mufti, A., Murali, T.: Automatic layout and visualization of biclusters. Algorithms for Molecular Biology 1 (2006)
Santamaria, R., Theron, R., Quintales, L.: A visual analytics approach for understanding biclustering results from microarray data. Bioinformatics 9 (2008)
Cheng, K., Law, N., Siu, W., Liew, A.C.: Biclusters visualization and detection using parallel coordinates plots. In: Proceedings of the International Symposium on Computational Models for Life Sciences (2007)
Cheng, K., Law, N., Siu, W., Lau, T.: BiVisu: Software tool for bicluster detection and visualization. BMC Bioinformatics 23, 2342–2344 (2007)
Cheng, K.O., Law, N.F., Siu, W.C., Liew, A.: Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization. BMC Bioinformatics 9, 210–238 (2008)
Rosenholtz, R., Li, Y., Mansfield, J., Jin, Z.: Feature Congestion: A Measure of Display Clutter. In: Proceedings of SIGCHI Conference on Human Factors in Computing Systems, pp. 761–770. ACM Press, New York (2005)
Dietzsch, J., Heinrich, J., Nieselt, K., Bartz, D.: SpRay: A visual analytics approach for gene expression data. In: IEEE Symposium on Visual Analytics Science and Technology, pp. 179–186 (2009)
Kaiser, S., Santamaria, R., Theorn, R., Quintales, L., Leisch, F.: Bicluster algorithms (2009), http://cran.r-project.org/web/packages/biclust/biclust.pdf
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2011) ISBN 3-900051-07-0
Barkow, S., Bleuler, S., Zitzler, E., Prelic, A., Frick, D.: BicAT: Biclustering analysis toolbox, ETH Zürich (2010), http://www.tik.ethz.ch/sop/bicat/?page=bicat.php
Luscher, A.: ExpressionView (2010), http://www2.unil.ch/cbg/index.php?title=ExpressionView
Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of International Conference on Intellegent Systems for Molecular Biology, pp. 93–103 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Heinrich, J., Seifert, R., Burch, M., Weiskopf, D. (2011). BiCluster Viewer: A Visualization Tool for Analyzing Gene Expression Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-24028-7_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24027-0
Online ISBN: 978-3-642-24028-7
eBook Packages: Computer ScienceComputer Science (R0)