The Visual Computer

, Volume 24, Issue 12, pp 1053–1066

Interactive visual analysis of time-series microarray data

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

DOI: 10.1007/s00371-007-0205-9

Cite this article as:
Jeong, D., Darvish, A., Najarian, K. et al. TVC (2008) 24: 1053. doi:10.1007/s00371-007-0205-9

Abstract

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.

Keywords

Visual analysis Information visualization Microarray anaysis Bioinformatics 

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

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