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This article is part of the supplement: Highlights from the 2nd IEEE Symposium on Biological Data Visualization

Open Access Research

enRoute: dynamic path extraction from biological pathway maps for exploring heterogeneous experimental datasets

Christian Partl1*, Alexander Lex12, Marc Streit3, Denis Kalkofen1, Karl Kashofer4 and Dieter Schmalstieg1

Author Affiliations

1 Graz University of Technology, Institute for Computer Graphics and Vision, Inffeldgasse 16, 8010 Graz, Austria

2 Harvard School of Engineering and Applied Sciences, Visual Computing Group, 33 Oxford Street, MA 02138, Cambridge, US

3 Johannes Kepler University Linz, Institute of Computer Graphics, Altenberger Straße 69, 4040 Linz, Austria

4 Medical University of Graz, Institute of Pathology, Auenbruggerplatz 25, 8036 Graz, Austria

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BMC Bioinformatics 2013, 14(Suppl 19):S3  doi:10.1186/1471-2105-14-S19-S3

Published: 12 November 2013

Abstract

Jointly analyzing biological pathway maps and experimental data is critical for understanding how biological processes work in different conditions and why different samples exhibit certain characteristics. This joint analysis, however, poses a significant challenge for visualization. Current techniques are either well suited to visualize large amounts of pathway node attributes, or to represent the topology of the pathway well, but do not accomplish both at the same time. To address this we introduce enRoute, a technique that enables analysts to specify a path of interest in a pathway, extract this path into a separate, linked view, and show detailed experimental data associated with the nodes of this extracted path right next to it. This juxtaposition of the extracted path and the experimental data allows analysts to simultaneously investigate large amounts of potentially heterogeneous data, thereby solving the problem of joint analysis of topology and node attributes. As this approach does not modify the layout of pathway maps, it is compatible with arbitrary graph layouts, including those of hand-crafted, image-based pathway maps. We demonstrate the technique in context of pathways from the KEGG and the Wikipathways databases. We apply experimental data from two public databases, the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) that both contain a wide variety of genomic datasets for a large number of samples. In addition, we make use of a smaller dataset of hepatocellular carcinoma and common xenograft models. To verify the utility of enRoute, domain experts conducted two case studies where they explore data from the CCLE and the hepatocellular carcinoma datasets in the context of relevant pathways.