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Multiscale Scatterplot Matrix for Visual and Interactive Exploration of Metabonomic Data

  • Fabien Jourdan
  • Alain Paris
  • Pierre-Yves Koenig
  • Guy Melançon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4370)

Abstract

We describe a method turning scatterplot matrix visualizations into malleable graphical objects facilitating interaction and selection of pixelized data elements. The method relies on density estimation techniques [1,2] applied through standard image processing. A 2D scatterplot is considered as an image and is then transformed into nested regions that can be easily selected. Based on Wattenberg and Fisher [3], and as confirmed by our experience, we believe users have a good intuition interpreting and interacting with these multiscale graphical objects. Bio-molecular data serves here as a case study for our methodology. The method was discussed and designed in collaboration with experts in metabonomics and has proven to be useful and complementary to classical statistical methods.

Keywords

Information Visualization Visual Exploration Graphical Object Drug Taking Molecule Concentration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall, Boca Raton (1986)zbMATHGoogle Scholar
  2. 2.
    Scott, D.W.: Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley Series in Probability and Statistics. Wiley-Interscience, Chichester (1992)zbMATHGoogle Scholar
  3. 3.
    Wattenberg, M., Fisher, D.: A model of multi-scale perceptual organization in information graphics. In: North, S.C., Munzner, T. (eds.) IEEE Symposium on Information Visualization, IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  4. 4.
    Keim, D.A.: Designing pixel-oriented visualization techniques: theory and applications. IEEE Transactions on Visualization and Computer Graphics 6(1), 59–78 (2000)CrossRefGoogle Scholar
  5. 5.
    Chiricota, Y., Jourdan, F., Melançon, G.: Metric-based network exploration and multiscale scatterplot. In: Ward, M., Munzner, T. (eds.) IEEE International Symposium on Information Visualization, pp. 135–142. IEEE Computer Society Press, Los Alamitos (2004)CrossRefGoogle Scholar
  6. 6.
    Becker, R.A., Cleveland, W.S.: Brushing scatterplots. Technometrics 29, 127–142 (1987)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Martin, A.R., Ward, M.O.: High dimensional brushing for interactive exploration of multivariate data. In: IEEE Conference on Visualization ’95, pp. 271–278. IEEE Computer Society Press, Los Alamitos (1995)Google Scholar
  8. 8.
    Dumas, M.E., Canlet, C., Debrauwer, L., Martin, P., Paris, A.: Selection of biomarkers by a multivariate statistical processing of composite metabonomic data sets using multiple factor analysis. Journal of Proteome Research 4(5), 1485–1492 (2005)CrossRefGoogle Scholar
  9. 9.
    Ward, M.O., Rundensteiner, E.A., Yang, J., Doshi, P.R., Rosario, G.: Interactive poster: Xmdvtool: Interactive visual data exploration system for high-dimensional data sets. In: IEEE Symposium on Information Visualization, pp. 52–53. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  10. 10.
    Russ, J.C.: The Image Processing Handbook, 3rd edn. CRC Press, Boca Raton (1998)Google Scholar
  11. 11.
    Herman, I., Marshall, M.S., Melançon, G.: Density functions for visual attributes and effective partitioning in graph visualization. In: Roth, S.F., Keim, D.A. (eds.) IEEE Symposium on Information Visualization, IEEE Computer Society, pp. 49–56. IEEE Computer Society Press, Los Alamitos (2000)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Fabien Jourdan
    • 1
  • Alain Paris
    • 1
  • Pierre-Yves Koenig
    • 2
  • Guy Melançon
    • 2
  1. 1.UMR1089 Xénobiotiques INRA-ENVT, Institut National de Recherche AgronomiqueFrance
  2. 2.Laboratoire d’Informatique, de Robotique et de Micro-électronique de Montpellier, LIRMM UMR CNRS 5506France

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