GeneTerrain: visual exploration of differential gene expression profiles organized in native biomolecular interaction networks

We propose a new network visualization technique using scattered data interpolation and surface rendering, based upon a foundation layout of a scalar field. Contours of the interpolated surfaces are generated to support multi-scale visual interaction for data exploration. Our framework visualizes quantitative attributes of nodes in a network as a continuous surface by interpolating the scalar field, therefore avoiding scalability issues typical in conventional network visualizations while also maintaining the topological properties of the original network. We applied this technique to the study of a bio-molecular interaction network integrated with gene expression data for Alzheimer's Disease (AD). In this application, differential gene expression profiles obtained from the human brain are rendered for AD patients with differing degrees of severity and compared to healthy individuals. We show that this alternative visualization technique is effective in revealing several types of molecular biomarkers, which are traditionally difficult to detect due to "noises" in data derived from DNA microarray experiments.

[1]  Cathy H. Wu,et al.  Update on genome completion and annotations: Protein Information Resource , 2004, Human Genomics.

[2]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[3]  Patrick J. F. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 2003 .

[4]  Danny Holten,et al.  Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data , 2006, IEEE Transactions on Visualization and Computer Graphics.

[5]  Allan R. Wilks,et al.  Visualizing Network Data , 1995, IEEE Trans. Vis. Comput. Graph..

[6]  Joshua M. Stuart,et al.  A Gene Expression Map for Caenorhabditis elegans , 2001, Science.

[7]  Alice J. O'Toole,et al.  DISTATIS: The Analysis of Multiple Distance Matrices , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[8]  William H. Press,et al.  Numerical recipes in C , 2002 .

[9]  D. Kell.  Metabolomics and systems biology: making sense of the soup. , 2004, Current opinion in microbiology.

[10]  Satoru Kawai,et al.  An Algorithm for Drawing General Undirected Graphs , 1989, Inf. Process. Lett..

[11]  Chaomei Chen,et al.  Visualizing evolving networks: minimum spanning trees versus pathfinder networks , 2003, IEEE Symposium on Information Visualization 2003 (IEEE Cat. No.03TH8714).

[12]  P. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 1999 .

[13]  Changyu Shen,et al.  Mining Alzheimer Disease Relevant Proteins from Integrated Protein Interactome Data , 2005, Pacific Symposium on Biocomputing.

[14]  George Michailidis,et al.  Data Visualization through Graph Drawing , 2001, Comput. Stat..

[15]  Ben Shneiderman,et al.  Balancing Systematic and Flexible Exploration of Social Networks , 2006, IEEE Transactions on Visualization and Computer Graphics.

[16]  Ioannis Xenarios,et al.  DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions , 2002, Nucleic Acids Res..

[17]  David S. Ebert,et al.  Visualization and computer graphics , 2007 .

[18]  Peter Eades,et al.  A Heuristic for Graph Drawing , 1984 .

[19]  D. Shepard.  A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[20]  Wei Wu,et al.  Genomic data visualization on the Web , 2004, Bioinform..

[21]  Tina Eliassi-Rad,et al.  Visual Analysis of Large Heterogeneous Social Networks by Semantic and Structural Abstraction , 2006 .

[22]  M. Gerstein,et al.  Genomic analysis of regulatory network dynamics reveals large topological changes , 2004, Nature.

[23]  Yehuda Koren,et al.  Graph Drawing by Stress Majorization , 2004, GD.

[24]  Steven A Carr,et al.  Protein biomarker discovery and validation: the long and uncertain path to clinical utility , 2006, Nature Biotechnology.

[25]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[26]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[27]  James J. Thomas,et al.  Visualizing the non-visual: spatial analysis and interaction with information from text documents , 1995, Proceedings of Visualization 1995 Conference.

[28]  Heinrich Müller,et al.  Image warping with scattered data interpolation , 1995, IEEE Computer Graphics and Applications.

[29]  Vladimir Batagelj,et al.  Pajek - Analysis and Visualization of Large Networks , 2004, Graph Drawing Software.

[30]  M. Tyers,et al.  Osprey: a network visualization system , 2003, Genome Biology.

[31]  Ben Shneiderman,et al.  Network Visualization by Semantic Substrates , 2006, IEEE Transactions on Visualization and Computer Graphics.

[32]  Frank van Ham,et al.  Using multilevel call matrices in large software projects , 2003, IEEE Symposium on Information Visualization 2003 (IEEE Cat. No.03TH8714).

[33]  Robert van Liere,et al.  GraphSplatting: Visualizing Graphs as Continuous Fields , 2003, IEEE Trans. Vis. Comput. Graph..

[34]  K. Goldstein,et al.  Data-driven analysis approach for biomarker discovery using molecular-profiling technologies , 2005, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.

[35]  Gary D Bader,et al.  BIND--The Biomolecular Interaction Network Database. , 2001, Nucleic acids research.

[36]  M. Vidal,et al.  Protein interaction maps for model organisms , 2001, Nature Reviews Molecular Cell Biology.

[37]  David Auber,et al.  Tulip - A Huge Graph Visualization Framework , 2004, Graph Drawing Software.

[38]  Pak Chung Wong,et al.  Graph Signatures for Visual Analytics , 2006, IEEE Transactions on Visualization and Computer Graphics.

[39]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[40]  Jake Yue Chen,et al.  ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining , 2008, BMC Bioinformatics.

[41]  Graham J. Wills.  NicheWorks—Interactive Visualization of Very Large Graphs , 1999 .

[42]  Linton C. Freeman,et al.  Carnegie Mellon: Journal of Social Structure: Visualizing Social Networks Visualizing Social Networks , 2022 .

[43]  Philippe Castagliola,et al.  A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations , 2004 .

[44]  Chris North,et al.  An Evaluation of Microarray Visualization Tools for Biological Insight , 2004 .

[45]  Andreas Noack,et al.  Visual Clustering of Graphs with Nonuniform Degrees , 2004 .

[46]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[47]  Li-Huei Tsai,et al.  Cdk5 deregulation in the pathogenesis of Alzheimer's disease. , 2004, Trends in molecular medicine.

[48]  Ying Liu,et al.  Efficient generalized matrix approximations for biomarker discovery and visualization in gene expression data. , 2006, Computational systems bioinformatics. Computational Systems Bioinformatics Conference.