The Visual Computer

, Volume 24, Issue 12, pp 1053–1066 | Cite as

Interactive visual analysis of time-series microarray data

  • Dong Hyun Jeong
  • Alireza Darvish
  • Kayvan Najarian
  • Jing Yang
  • William Ribarsky
Orginal Article


Estimating dynamic regulatory pathways using DNA microarray time-series can provide invaluable information about the dynamic interactions among genes and result in new methods of rational drug design. Even though several purely computational methods have been introduced for DNA pathway analysis, most of these techniques do not provide a fully interactive method to explore and analyze these dynamic interactions in detail, which is necessary to obtain a full understanding. In this paper, we present a unified modeling and visual approach focusing on visual analysis of gene regulatory pathways over time. As a preliminary step in analyzing the gene interactions, the method applies two different techniques, a clustering algorithm and an auto regressive (AR) model. This approach provides a successful prediction of the dynamic pathways involved in the biological process under study. At this level, these pure computational techniques lack the transparency required for analysis and understanding of the gene interactions. To overcome the limitations, we have designed a visual analysis method that applies several visualization techniques, including pixel-based gene representation, animation, and multi-dimensional scaling (MDS), in a new way. This visual analysis framework allows the user to quickly and thoroughly search for and find the dynamic interactions among genes, highlight interesting gene information, show the detailed annotations of the selected genes, compare regulatory behaviors for different genes, and support gene sequence analysis for the interesting genes. In order to enhance these analysis capabilities, several methods are enabled, providing a simple graph display, a pixel-based gene visualization technique, and a relation-displaying technique among gene expressions and gene regulatory pathways.


Visual analysis Information visualization Microarray anaysis Bioinformatics 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrafiotis, D.K., Rassokhin, D.N., Lobanov, V.S.: Multidimensional scaling and visualization of large molecular similarity tables. J. Comput. Chem. 22(5), 488–500 (2001)Google Scholar
  2. 2.
    Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman D.J.: Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990)Google Scholar
  3. 3.
    Bederson, B.B., Hollan, J.D.: Pad++: A zooming graphical interface for exploring alternate interface physics. In: UIST ’94, pp. 17–26. ACM, Marina del Rey, CA (1994)CrossRefGoogle Scholar
  4. 4.
    Breinholt, G., Schierz, C.: Algorithm 781: Generating Hilbert’s space-filling curve by recursion. ACM Trans. Math. Software 24(2), 184–189 (1998)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Brown, J., Mcgregor, A., Braun, H.W.: Network performance visualization: insight through animation. In: PAM2000 Passive and Active Measurement Workshop, Apr, pp. 33–41. Hamilton (2000)Google Scholar
  6. 6.
    Buja, A., McDonald, J.A., Michalak, J., Stuetzle, W.: Interactive data visualization using focusing and linking. In: IEEE Conference on Visualization ’91, pp. 156–163. IEEE Computer Society, San Diego, CA (1991)Google Scholar
  7. 7.
    Cho, R.J., Campbell, M.J., Winzeler, E.A., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T., Gabrielian, A., Landsman, D., Lockhart, D., Davis, R.: A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell 2(1), 65–73 (1998)CrossRefGoogle Scholar
  8. 8.
    Chuah, M.C., Roth, S.F., Mattis, J., Kolojejchick, J.: SDM: malleable information graphics. In: Information Visualization (INFOVIS ’95), Oct, pp. 36–42. IEEE Computer Society, Atlanta, GA (1995)Google Scholar
  9. 9.
    Craig, P., Kennedy, J., Cumming, A.: Towards visualising temporal features in large scale microarray time-series data. In: Sixth International Conference on Information Visualisation, pp. 427–433. IEEE Computer Society, London (2002)CrossRefGoogle Scholar
  10. 10.
    Craig, P., Kennedy, J., Cumming, A.: Coordinated parallel views for the exploratory analysis of microarray time-course data. In: Third International Conference on Coordinated and Multiple Views in Exploratory Visualization, pp. 3–14. IEEE Computer Society, London (2005)CrossRefGoogle Scholar
  11. 11.
    Dahlquist, K.D., Salomonis, N., Vranizan, K., Lawlor, S.C., Conklin, B.R.: Genmapp, a new tool for viewing and analyzing microarray data on biological pathways. Nature Genetics 31(1), 19–20 (2002)CrossRefGoogle Scholar
  12. 12.
    Darvish, A., Hakimzadeh, R., Najarian, K.: Discovering dynamic regulatory pathway by applying an auto regressive model to time series DNA microarray data. In: 26th Annual International Conference of the IEEE/EMBS (2004), pp. 2941–2944. IEEE Computer Society, San Francisco (2004)Google Scholar
  13. 13.
    Darvish, A., Najarian, K., Jeong, D.H., Ribarsky, W.: System identification and nonlinear factor analysis for discovery and visualization of dynamic gene regulatory pathways. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 76–81. IEEE Computer Society, San Diego, CA (2005)Google Scholar
  14. 14.
    Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression. Proc. Natl. Acad. Sci. USA 95(25), 14863–14868 (1998)CrossRefGoogle Scholar
  15. 15.
    Eisenstein, M.: Microarrays: Quality control. Nature 442, 1067–1070 (2006)CrossRefGoogle Scholar
  16. 16.
    Fekete, J.D., Plaisant, C.: Excentric labeling: Dynamic neighborhood labeling for data visualization. In: Human Factors in Computing Systems (CHI’99), pp. 512–519. ACM, Pittsburgh, PA (1999)Google Scholar
  17. 17.
    Furnas, G., Bederson, B.B.: Scale space diagrams: Understanding multiscale interfaces. In: Human Factors in Computing Systems (CHI’95), pp. 234–241. ACM, Denver, CO (1995)Google Scholar
  18. 18.
    Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian Network to Analyze Expression Data. J. Comput. Biol. 7, 601–620 (2000)CrossRefGoogle Scholar
  19. 19.
    GeneSpringTM. Silicon Genetics. http://www.silicongenetics.comGoogle Scholar
  20. 20.
    Hong, J., Jeong, D.H., Shaw, C.D., Ribarsky, W., Borodovsky, M., Song, C.: Gvis: A scalable visualization framework for genomic data. In: Eurographics/IEEE VGTC Symposium on Visualization (EuroVis 2005), pp. 191–198. Eurographics, Leeds (2005)Google Scholar
  21. 21.
    de Hoon, M.J.L., Imoto, S., Miyano, S.: Statistical analysis of a small set of time-ordered gene expression data using linear splines. Bioinformatics 18(11), 1477–1485 (2002)CrossRefGoogle Scholar
  22. 22.
    Keim, D.A.: Designing pixel-oriented visualization techniques: theory and applications. IEEE Trans. Vis. Comput. Graph. 6(1), 59–78 (2000)CrossRefGoogle Scholar
  23. 23.
    Keim, D.A., Kriegel, H.-P., Seidl, T.: Visual feedback in querying large databases. In: IEEE Conference on Visualization ’93, pp. 158–165. IEEE, San Jose, CA (1993)Google Scholar
  24. 24.
    Liu, X., Minin, V., Huang, Y., Seligson, D.B., Horvath, S.: Statistical methods for analyzing tissue microarray data. J. Biopharmaceutical Stat. 14(3), 671–85 (2004)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Moser, R.J., Reverter, A., Kerr, C.A., Beh, K.J., Lehnert, S.A.: A mixed-model approach for the analysis of cDNA microarray gene expression data from extreme-performing pigs after infection with Actinobacillus pleuropneumoniae. J. Animal Sci. 82, 1261–1271 (2004)Google Scholar
  26. 26.
    Nakahara, H., Nishimura, S., Inoue, M., Hori, G., Amari, S.: Gene interaction in DNA microarray data is decomposed by information geometric measure. Bioinformatics 19(9), 1124–1131 (2003)CrossRefGoogle Scholar
  27. 27.
    PathwayStudioTM. Ariadne Genomics. http://www.ariadnegenomics.comGoogle Scholar
  28. 28.
    Perlin, K., Fox, D.: Pad: An alternative approach to the computer interface. In: ACM SIGGRAPH ’93, Aug, pp. 57–64. ACM, Anaheim, CA (1993)CrossRefGoogle Scholar
  29. 29.
    Perrin, B.E., Ralaivola, L., Mazurie, A., Bottani, S., Mallet, J., D’Alche-Buc, F.: Gene networks inference using dynamic Bayesian networks. Bioinformatics 19(Suppl.2), II138–II148 (2003)Google Scholar
  30. 30.
    Peterson, B.: Dynamic Visualization of Microarray Time Series. In: white paper, (2005)Google Scholar
  31. 31.
    Pirolli, P.L., Card, S.K., Van Der Wege, M.: Visual information foraging in a focus+context visualization. In: Human Factors in Computing Systems (CHI 2001), pp. 506–513. ACM, Seattle, WA (2001)CrossRefGoogle Scholar
  32. 32.
    Reichert, J., Jabs, A., Slickers P., Suhnel J.: The IMB jena image library of biological macromolecules. Nucl. Acids Res. 28(1), 246–249 (2000)CrossRefGoogle Scholar
  33. 33.
    Robertson, G.G., Card, S.K., Mackinlay, J.D.: Information visualization using 3D interactive animation. Commun. ACM 36(4), 57–71 (1993)CrossRefGoogle Scholar
  34. 34.
    Rouchka, E.C., Mazzarella, R., States, D.J.: Computational detection of cpg islands in DNA. In: Technical Report, Washington University, Department of Computer Science, WUCS-97-39 (1997)Google Scholar
  35. 35.
    Saccharomyces Genome Database, Scholar
  36. 36.
    Sales-Pardo, M., Guimera, R., Mopeiraa, A., Widom, J., Amaral, L.A.N.: Mesoscopic modeling for nucleic acid chain dynamics. Phys. Rev. E 71, 051902 (2005)CrossRefGoogle Scholar
  37. 37.
    Saraiya, P., North, C., Duca, K.: Visualizing biological pathways: requirements analysis, systems evaluation and research agenda. J. Inform. Vis. 4(3), 191–205 (2005)CrossRefGoogle Scholar
  38. 38.
    Schulze-Wollgast, P., Tominski, C., Schumann, H.: Enhancing visual exploration by appropriate color coding. In: International Conference in Central Europe on Computer Graphics. Visualization and Computer Vision (WSCG’05), pp. 203–210. WSCG, Plzen-Bory (2005)Google Scholar
  39. 39.
    Silvescu, A., Honavar, V.: Temporal boolean network models of genetic networks and their inference from gene expression time series. Complex Systems 13(1), 54–70 (2001)MathSciNetGoogle Scholar
  40. 40.
    Speed, T.: Statistical Analysis of Gene Expression Microarray Data. Chapman & Hall/CRC Press, Barcelona, Catalonia (2003)MATHGoogle Scholar
  41. 41.
    Symeonidis, A., Tollis, I. G.: Visualization of Biological Information with Circular Drawings. In: Biological and Medical Data Analysis (ISBMDA’04), pp. 468–478 (2004)Google Scholar
  42. 42.
    Tominski, C., Schulze-Wollgast, P., Schumann, H.: 3D Information Visualization for Time Dependent Data on Maps. In: the Ninth International Conference on Information Visualisation (IV’05), pp. 175–181. IEEE, London (2005)CrossRefGoogle Scholar
  43. 43.
    Toyoda, T., Konagaya, A.: Knowledgeeditor: a new tool for interactive modeling and analyzing biological pathways based on microarray data. Bioinformatics 19(3), 433–434 (2003)CrossRefGoogle Scholar
  44. 44.
    de Waele, S., Broersen, P.M.T.: Order Selection for Vector Autoregressive models. IEEE Trans. Signal Process. 51(2), 427–433 (2003)CrossRefGoogle Scholar
  45. 45.
    van Wezel, M.C., Kosters, W.A.: Nonmetric multidimensional scaling: Neural networks versus traditional techniques. Intell. Data Anal. 8(6), 601–613 (2004)Google Scholar
  46. 46.
    Wolfsberg, T.G., Gabrielian, A.E., Campbell, M.J., Cho, R.J., Spouge, J.L., Landsman, D.: Candidate regulatory sequence elements for cell cycle-dependent transcription in saccharomyces cerevisiae. Genome Res. 9(8), 775–792 (1999)Google Scholar
  47. 47.
    Wong, P.C., Wong, K.K., Foote, H., Thomas, J.: Global visualization and alignments of whole bacterial genomes. IEEE Trans. Vis. Comput. Graph. 9(3), 361–377 (2003)CrossRefGoogle Scholar
  48. 48.
    Wright, W.: Information Animation Applications in the Capital Markets. In: IEEE Symposium on Information Visualization, pp. 19–25. IEEE, Atlanta, GA (1995)Google Scholar
  49. 49.
    Yeung, L.K., Yan, H., Liew, A.W.-C., Szeto, L.K., Yang, M., Kong, R.: Measuring correlation between microarray time-series data using dominant spectral component. Aust. Comput. J. 29, 309–314 (2004)Google Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Dong Hyun Jeong
    • 1
  • Alireza Darvish
    • 2
  • Kayvan Najarian
    • 3
  • Jing Yang
    • 1
  • William Ribarsky
    • 1
  1. 1.Charlotte Visualization CenterThe University of North Carolina at CharlotteCharlotteUSA
  2. 2.Bioinformatics & Advanced Signal Processing Lab., Dept. of Computer ScienceThe University of North Carolina at CharlotteCharlotteUSA
  3. 3.Biomedical Signal and Image Processing Lab., Dept. of Computer Science, School of EngineeringVirginia Commonwealth UniversityRichmondUSA

Personalised recommendations