Caleydo: Design and evaluation of a visual analysis framework for gene expression data in its biological context | IEEE Conference Publication | IEEE Xplore

Caleydo: Design and evaluation of a visual analysis framework for gene expression data in its biological context


Abstract:

The goal of our work is to support experts in the process of hypotheses generation concerning the roles of genes in diseases. For a deeper understanding of the complex in...Show More

Abstract:

The goal of our work is to support experts in the process of hypotheses generation concerning the roles of genes in diseases. For a deeper understanding of the complex interdependencies between genes, it is important to bring gene expressions (measurements) into context with pathways. Pathways, which are models of biological processes, are available in online databases. In these databases, large networks are decomposed into small sub-graphs for better manageability. This simplification results in a loss of context, as pathways are interconnected and genes can occur in multiple instances scattered over the network. Our main goal is therefore to present all relevant information, i.e., gene expressions, the relations between expression and pathways and between multiple pathways in a simple, yet effective way. To achieve this we employ two different multiple-view approaches. Traditional multiple views are used for large datasets or highly interactive visualizations, while a 2.5D technique is employed to support a seamless navigation of multiple pathways which simultaneously links to the expression of the contained genes. This approach facilitates the understanding of the interconnection of pathways, and enables a non-distracting relation to gene expression data. We evaluated Caleydo with a group of users from the life science community. Users were asked to perform three tasks: pathway exploration, gene expression analysis and information comparison with and without visual links, which had to be conducted in four different conditions. Evaluation results show that the system can improve the process of understanding the complex network of pathways and the individual effects of gene expression regulation considerably. Especially the quality of the available contextual information and the spatial organization was rated good for the presented 2.5D setup.
Date of Conference: 02-05 March 2010
Date Added to IEEE Xplore: 11 March 2010
ISBN Information:

ISSN Information:

Conference Location: Taipei, Taiwan
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1 Introduction

To understand the function of genes, it is necessary to study their biological context. In which biological processes is a gene involved? Is it involved in multiple similar processes? Pathways, representations of such processes, are consulted to provide answers to these questions. Pathway data can either be presented as a large, complex network with an automated layout, or as small functional graphs, handcrafted by experts. These small pathways frequently encode meta-knowledge, such as cell localization, in the layout. Widely used pathway databases are KEGG [11] and BioCarta

http://www.biocarta.com

, which together contain about 600 pathways. Screenshot of Caleydo with open Bucket view, a parallel coordinates view, a heat map and some meta-information. The Bucket concept is an integral part of Caleydo and allows us to place views for pathway and gene expression analysis in a 2.5D setup. Relations between views are shown by means of visual links.

Select All
1.
A. Aris and B. Shneiderman. Designing semantic substrates for visual network exploration. Information Visualization, 6 ( 4 ): 281–300, 2007.
2.
M. Asslaber and K. Zatloukal. Biobanks: transnational, European and global networks. Brief Funct Genomic Prot., 6 ( 3 ): 193–201, 2007.
3.
B. Stolk Mining the human genome using virtual reality. In EGPGV'02: Proceedings of the Fourth Eurographics Workshop on Parallel Graphics and Visualization, pages 17–21, Aire-la-Ville, Switzerland, 2002.
4.
M. Q. W. Baldonado, A. Woodruff, and A. Kuchinsky. Guidelines for using multiple views in information visualization. In AVI'00: Proceedings on Advanced visual interfaces, pages 110–119, New York, NY, USA, 2000. ACM Press.
5.
T. Ball and S. G. Eick. Software visualization in the large. Computer, 29 ( 4 ): 33–43, 1996.
6.
A. Barsky, T. Munzner, J. Gardy, and R. Kincaid. Cerebral: Visualizing multiple experimental conditions on a graph with biological context. Visualization and Computer Graphics, IEEE Transactions on, 14 ( 6 ): 1253–1260, Nov.–Dec. 2008.
7.
C. Collins and S. Carpendale. Vislink: Revealing relationships amongst visualizations. IEEE Transactions on Visualization and Computer Graphics, 13 ( 6 ): 1192–1199. 2007.
8.
M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Academy of Science USA, 95 ( 25 ): 14863–14868, December 1998.
9.
B. J. J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315 ( 5814 ): 972–976, January 2007.
10.
H. Chung Arrayxpath II: mapping and visualizing microarray gene-expression data with biomedical ontologies and integrated biological pathway resources using scalable vector graphics. Nucleic Acids Res, 33(Web Server issue):W621-W626, Jul 2005.
11.
M. Kanehisa, M. Araki, S. Goto, M. Hattori, M. Hirakawa, M. Itoh, T. Katayama, S. Kawashima, S. Okuda, T. Tokimatsu, and Y. Yamanishi. Kegg for linking genomes to life and the environment. Nucleic Acids Research, 36 (Database-Issue): 480–484, 2008.
12.
C. Klukas and F. Schreiber. Dynamic exploration and editing of kegg pathway diagrams. Bioinformatics, 23 ( 3 ): 344–350, 2006.
13.
H. Lindroos and S. G. E. Andersson. Visualizing metabolic pathways: comparative genomics and expression analysis. In Proceedings of the IEEE, volume 90, pages 1793–1802, 2002.
14.
J. D. Mackinlay, G. G. Robertson, and S. K. Card. The perspective wall: detail and context smoothly integrated. In CHI 1991: Proceedings on Human factors in computing systems, pages 173–176, New York, NY, USA, 1991. ACM Press.
15.
B. Mlecnik, M. Scheideler, H. Hackl, J. Hartler, F. Sanchez-Cabo, and Z. Trajanoski. Pathwayexplorer: web service for visualizing high-throughput expression data on biological pathways. Nucleic Acids Research, 33 (Web Server issue): 633–637, July 2005.
16.
H. Mueller, R. Reihs, S. Sauer, K. Zatloukal, M. Streit, L. Alexander, B. Schlegl, and D. Schmalstieg. Connecting genes with diseases. In Sixth International Conference BioMedical Visualization, 2009.
17.
O. Rubel Pointcloudxplore: Visual analysis of 3d gene expression data using physical views and parallel coordinates. In EuroVis, pages 203–210. Eurographics Association, 2006.
18.
G. Schmidt-Gann, K. Schmid, M. Uehlein, J. Struck, A. Bergmann, D. Schmalstieg, M. Streit, A. Lex, D. G. van der Nest, M. van Griensven, and H. Redl. Gene- and protein expression profiling in liver in a sepsis-baboon model. In 32nd Annual Meeting on Shock, San Antonio, Texas, June 6–9, 2009.
19.
J. Seo and B. Shneiderman. A rank-by-feature framework for interactive exploration of multidimensional data. Information Visualization, 4 ( 2 ): 96–113, 2005.
20.
B. Shneiderman and A. Aris. Network visualization by semantic substrates. IEEE Transactions on Visualization and Computer Graphics, 12 ( 5 ): 733–740, 2006.
21.
M. Streit, M. Kalkusch, K. Kashofer, and D. Schmalstieg. Navigation and exploration of interconnected pathways. Computer Graphics Forum (EuroVis 2008), 27 ( 3 ): 951–958 ( 8 ), May 2008.
22.
M. Streit, A. Lex, H. Müller and D. Schmalstieg. Gaze-based interaction for information visualization. In Proceedings of web3DW2009 Conference, Algarve, Portugal, 2009.
23.
Y. Yang, E. S. Wurtele, C. Cruz-Neira, and J. A. Dickerson. Hierarchical visualization of metabolic networks using virtual reality. In VRCIA'06: Proceedings on Virtual reality continuum and its applications, pages 377–381, New York, NY, USA, 2006. ACM Press.
24.
B. Yost, Y. Haciahmetoglu, and C. North. Beyond visual acuity: the perceptual scalability of information visualizations for large displays. In CHI'07: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 101–110, New York, NY, USA, 2007.

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