Visualizing genome expression and regulatory network dynamics in genomic and metabolic context

  • Authors:

  • M. A. Westenberg;S. A. F. T. van Hijum;O. P. Kuipers;J. B. T. M. Roerdink

  • Affiliations:

  • Institute for Mathematics and Computing Science, University of Groningen, Groningen, The Netherlands;Department of Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, The Netherlands and Interfacultary Centre for Functional Genomics, Ernst-Moritz ...;Department of Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, The Netherlands;Institute for Mathematics and Computing Science, University of Groningen, Groningen, The Netherlands

  • Venue:
  • EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
  • Year:
  • 2008

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Abstract

DNA microarrays are used to measure the expression levels of thousands of genes simultaneously. In a time series experiment, the gene expressions are measured as a function of time. We present an application for integrated visualization of genome expression and network dynamics in both regulatory networks and metabolic pathways. Integration of these two levels of cellular processes is necessary, since it provides the link between the measurements at the transcriptional level (gene expression levels approximated from microarray data) and the phenotype (the observable characteristics of an organism) at the functional and behavioral level. The integration requires visualization approaches besides traditional clustering and statistical analysis methods. Our application can (i) visualize the data from time series experiments in the context of a regulatory network and KEGG metabolic pathways; (ii) identify and visualize active regulatory subnetworks from the gene expression data; (iii) perform a statistical test to identify and subsequently visualize pathways that are affected by differentially expressed genes. We present a case study, which demonstrates that our approach and application both facilitates and speeds up data analysis tremendously in comparison to a more traditional approach that involves many manual, laborious, and error-prone steps.