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

BMC Bioinformatics

Research

Denis Kalkofen¹  Dieter Schmalstieg¹  Christian Partl¹  Alexander Lex²  Marc Streit³  Karl Kashofer⁴ 

[1] Graz University of Technology, Institute for Computer Graphics and Vision, Inffeldgasse 16, 8010, Graz, Austria;Graz University of Technology, Institute for Computer Graphics and Vision, Inffeldgasse 16, 8010, Graz, Austria;Harvard School of Engineering and Applied Sciences, Visual Computing Group, 33 Oxford Street, 02138, Cambridge, MA, USA;Johannes Kepler University Linz, Institute of Computer Graphics, Altenberger Straße 69, 4040, Linz, Austria;Medical University of Graz, Institute of Pathology, Auenbruggerplatz 25, 8036, Graz, Austria;

关键词: Visualization Technique;    Node Attribute;    Graph Layout;    Copy Number Data;    Cancer Cell Line Encyclopedia;   

DOI  :  10.1186/1471-2105-14-S19-S3

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Partl et al; licensee BioMed Central Ltd. 2013