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Dynamic Visualization of Coexpression in Systems Genetics Data
September/October 2008 (vol. 14 no. 5)
pp. 1081-1095
ASCII Text
x 
 
Joshua New, Wesley Kendall, Jian Huang, Elissa Chesler, "Dynamic Visualization of Coexpression in Systems Genetics Data," IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 5, pp. 1081-1095, September/October, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2008.61,
author = {Joshua New and Wesley Kendall and Jian Huang and Elissa Chesler},
title = {Dynamic Visualization of Coexpression in Systems Genetics Data},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {14},
number = {5},
issn = {1077-2626},
year = {2008},
pages = {1081-1095},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2008.61},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Dynamic Visualization of Coexpression in Systems Genetics Data
IS - 5
SN - 1077-2626
SP1081
EP1095
EPD - 1081-1095
A1 - Joshua New,
A1 - Wesley Kendall,
A1 - Jian Huang,
A1 - Elissa Chesler,
PY - 2008
KW - Applications
KW - Multivariate visualization
KW - Visualization systems and software
VL - 14
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Joshua New, University of Tennesee, Knoxville
Wesley Kendall, University of Tennessee, Knoxville
Jian Huang, University of Tennesee, Knoxville
Elissa Chesler, Oak Ridge National Lab, Oak Ridge
Biologists hope to address grand scientific challenges by exploring the abundance of data made available through modern microarray technology and other high-throughput techniques. The impact of this data, however, is limited unless researchers can effectively assimilate such complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression require novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These tools should allow biologists to develop an intuitive understanding of the structure of biological networks and discover genes residing in critical positions in networks and pathways. By using a graph as a universal representation of correlation in gene expression, our system employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene co-expression data integrates techniques such as: graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by compound queries, dynamic level-of-detail abstraction, and template-based fuzzy classification. We demonstrate our system using a real-world workflow from a large-scale, systems genetics study of mammalian gene co-expression.

[1] J. Abello and J. Korn, “Mgv: A System for Visualizing Massive Multidigraphs,” IEEE Trans. Visualization and Computer Graphics, vol. 8, no. 1, pp. 21-38, Jan.-Mar. 2002.
[2] J. Abello, F. van Ham, and N. Krishnan, “Ask-Graphview: A Large Scale Graph Visualization System,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 5, pp. 669-677, Sept./Oct. 2006.
[3] O. Abiola et al., “The Nature and Identification of Quantitative Trait Loci: A Community's View,” Nature Rev. Genetics, vol. 4, no. 11, pp. 911-916, 2003.
[4] D. Auber, Y. Chiricota, F. Jourdan, and G. Melancon, “Multiscale Visualization of Small World Networks,” Proc. Ninth Ann. IEEE Symp. Information Visualization (InfoVis '03), pp. 75-81, 2003.
[5] Y. Bai, W.N. Gansterer, and R.C. Ward, “Block Tridiagonalization of Effectively Sparse Symmetric Matrices,” ACM Trans. Math. Software, vol. 30, no. 3, pp. 326-352, 2004.
[6] E.J. Chesler and M.A. Langston, “Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data,” Proc. Joint Ann. RECOMB '05 Satellite Workshops Systems Biology and Regulatory Genomics, pp. 150-165, 2005.
[7] E.J. Chesler, L. Lu, S. Shou, Y. Qu, J. Gu, J. Wang, H.C. Hsu, J.D. Mountz, N.E. Baldwin, M.A. Langston, J.B. Hogenesch, D.W. Threadgill, K.F. Manly, and R.W. Williams, “Complex Trait Analysis of Gene Expression Uncovers Polygenic and Pleiotropic Networks That Modulate Nervous System Function,” Nature Genetics, vol. 37, no. 3, pp. 233-242, 2005.
[8] E.J. Chesler, J. Wang, L. Lu, Y. Qu, K.F. Manly, and R.W. Williams, “Genetic Correlates of Gene Expression in Recombinant Inbred Strains: A Relational Model System to Explore Neurobehavioral Phenotypes,” Neuroinformatics, vol. 1, no. 4, pp. 343-357, 2003.
[9] R.W. Doerge, “Mapping and Analysis of Quantitative Trait Loci in Experimental Populations,” Nature Rev. Genetics, vol. 3, no. 1, pp.43-52, 2002.
[10] T.M.J. Fruchterman and E.M. Reingold, “Graph Drawing by Force-Directed Placement,” Software—Practice and Experience, vol. 21, no. 11, pp. 1129-1164, 1991.
[11] D.H. Geschwind, “Mice, Microarrays, and the Genetic Diversity of the Brain,” Proc. Nat'l Academy of Sciences, vol. 97, no. 20, pp.10676-10678, 2000.
[12] M. Glatter, C. Mollenhour, J. Huang, and J. Gao, “Scalable Data Servers for Large Multivariate Volume Visualization,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 5, pp. 1291-1298, Sept./Oct. 2006.
[13] J.E. Grisel, J.K. Belknap, L.A. O'Toole, M.L. Helms et al., “Quantitative Trait Loci Affecting Methamphetamine Responses in BXD Recombinant Inbred Mouse Strains,” J. Neuroscience, vol. 17, no. 2, pp. 745-754, 1997.
[14] S. Hachul, and M. Junger, “The Fast Multipole Multilevel Method,” Proc. Symp. Graph Drawing (GD'04), pp. 286-293, 2004.
[15] N. Henry, J. Fekete, and M.J. McGuffin, “Nodetrix: A Hybrid Visualization of Social Networks,” IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 6, pp. 1302-1309, Nov./Dec. 2007.
[16] N. Henry and J.D. Fekete, “Matrixexplorer: A Dual-Representation System to Explore Social Networks,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 5, pp. 677-685, Sept.-Oct. 2006.
[17] Z. Hu, J. Mellor, J. Wu, and C. DeLisi, “Visant: An Online Visualization and Analysis Tool for Biological Interaction Data,” BMC Bioinformatics, pp. 5-17, 2004.
[18] R.C. Jansen and J.P. Nap, “Genetical Genomics: The Added Value from Segregation,” Trends in Genetics, vol. 17, no. 7, pp. 388-391, 2001.
[19] B.C. Jones, L.M. Tarantino, L.A. Rodriguez, C.L. Reed et al., “, Quantitative-Trait Loci Analysis of Cocaine-Related Behaviours and Neurochemistry,” Pharmacogenetics, vol. 9, no. 5, pp. 607-617, 1999.
[20] T. Kamada and S. Kawai, “An Algorithm for Drawing General Undirected Graphs,” Information Processing Letters, vol. 31, no. 1, pp. 7-15, 1989.
[21] M. Kreuseler and H. Schumann, “A Flexible Approach for Visual Data Mining,” IEEE Trans. Visualization and Computer Graphics, vol. 8, no. 1, pp. 39-51, Jan.-Mar. 2002.
[22] S.R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins, “Trawling Emerging Cyber-Communities Automatically,” Proc. Eighth Int'l World Wide Web Conf. (WWW), 1999.
[23] M.A. Langston, A.D. Perkins, A.M. Saxton, J.A. Scharff, and B.H. Voy, “Innovative Computational Methods for Transcriptomic Data Analysis,” Proc. 21st ACM Symp. Applied Computing (SAC '06), pp. 190-194, 2006.
[24] L. Linsen, J. Locherbach, M. Berth, J. Bernhardt, and D. Becher, “Differential Protein Expression Analysis via Liquid-Chromatography/Mass-Spectrometry Data Visualization,” Proc. IEEE Conf. Visualization (VIS), 2005.
[25] C. Mueller, B. Martin, and A. Lumsdaine, “A Comparison of Vertex Ordering Algorithms for Large Graph Visualization,” Proc. Sixth Int'l Asia-Pacific Symp. Visualization (APVIS '07), pp. 141-148, 2007.
[26] P. Mutton and P. Rodgers, “Spring Embedder Preprocessing for WWW Visualization,” Proc. IEEE Symp. Information Visualization (InfoVis '02), vol. 00, pp. 744-749, 2002.
[27] A. Noack, “An Energy Model for Visual Graph Clustering,” Proc. Symp. Graph Drawing (GD '04), pp. 425-436, 2004.
[28] A. Perer and B. Shneiderman, “Balancing Systematic and Flexible Exploration of Social Networks,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 5, pp. 693-700, Sept./Oct. 2006.
[29] J. Raymond, E. Gardiner, and P. Willett, “Rascal: Calculation of Graph Similarity Using Maximum Common Edge Subgraphs,” The Computer J., vol. 45, no. 6, pp. 631-644, 2002.
[30] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, second ed. Prentice Hall, 2002.
[31] P. Saraiya, C. North, and K. Duca, “An Evaluation of Microarray Visualization Tools for Biological Insight,” Proc. 10th Ann. IEEE Symp. Information Visualization (InfoVis '04), pp. 1-8, 2004.
[32] P.T. Shannon, A. Markiel, O. Ozier, N.S. Baliga, J.T. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker, “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks,” Genome Research, vol. 11, pp. 2498-2504, 2003.
[33] P.T. Shannon, D.J. Reiss, R. Bonneau, and N.S. Baliga, “The Gaggle: An Open-Source Software System for Integrating Bioinformatics Software and Data Sources,” BMC Bioinformatics, vol. 7, p. 176, 2006.
[34] Z. Shen, K.L. Ma, and T. Eliassi-Rad, “Visual Analysis of Large Heterogeneous Social Networks by Semantic and Structure,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 6, pp. 1427-1439, Nov./Dec. 2006.
[35] L. Sheng, Z.M. Ozsoyoglu, and G. Ozsoyoglu, “A Graph Query Language and Its Query Processing,” Proc. 15th Int'l Conf. Data Eng. (ICDE '99), pp. 572-581, Mar. 1999.
[36] B. Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” IEEE Visual Languages, UMCP-CSD CS-TR-3665, pp. 336-343, 1996.
[37] B. Shneiderman, “Network Visualization by Semantic Substrates,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 5, pp.733-741, Sept./Oct. 2006.
[38] M. Tarini, P. Cignoni, and C. Montani, “Ambient Occlusion and Edge Cueing for Enhancing Real Time Molecular Visualization,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 5, pp.1237-1244, Sept./Oct. 2006.
[39] F.Y. Tzeng, E.B. Lum, and K.L. Ma, “A Novel Interface for Higher-Dimensional Classification of Volume Data,” Proc. IEEE Visualization Conf. (VIS '03), pp. 505-512, 2003.
[40] F. van Ham and J. van Wijk, “Interactive Visualization of Small World Graphs,” Proc. 10th Ann. IEEE Symp. Information Visualization (InfoVis '04), pp. 199-206, 2004.
[41] J. Wang, R.W. Williams, and K.F. Manly, “Webqtl: Web-Based Complex Trait Analysis,” Neuroinformatics, vol. 1, no. 4, pp. 299-308, 2003.
[42] B. Zhang, S. Kirov, J. Snoddy, and S. Ericson, “Genetviz: A Gene Network Visualization System,” Proc. UT-ORNL-KBRIN Bioinformatics Summit, 2005.

Index Terms:
Applications, Multivariate visualization, Visualization systems and software
Citation:
Joshua New, Wesley Kendall, Jian Huang, Elissa Chesler, "Dynamic Visualization of Coexpression in Systems Genetics Data," IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 5, pp. 1081-1095, Sept.-Oct. 2008, doi:10.1109/TVCG.2008.61
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