Conference Paper

Visual Analysis of Bipartite Biological Networks

DOI: 10.2312/VCBM/VCBM08/135-142 Conference: Proceedings of the Eurographics Workshop on Visual Computing for Biomedicine, VCBM 2008, Delft, The Netherlands, 2008.
Source: DBLP


In life sciences, the importance of complex network visualization is ever increasing. Yet, existing approaches for the visualization of networks are general purpose techniques that are often not suited to support the specific needs of researchers in the life sciences, or to handle the large network sizes and specific network characteristics that are prevalent in the field. Examples for such networks are biomedical ontologies and biochemical reaction networks, which are bipartite networks – a particular graph class which is rarely addressed in visualization. Our table-based approach allows to visualize large bipartite networks alongside with a multitude of attributes and hyperlinks to biological databases. To explore complex network motifs and perform intricate selections within the visualized network data, we introduce a new script-based brushing mechanism that integrates naturally with the interlinked, tabular representation. A prototype for exploring bipartite graphs, which uses the proposed visualization and interaction techniques, is also presented and used on real data sets from the application domain.

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Available from: Hans-Jörg Schulz
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    • "There are also more radical adaption approaches for the graph layout. Schulz et al. [21], for example, use two tables, one for each "side" of a bipartite network and connect the rows in the tables with edges. Each node is represented by one row and there are multiple columns for node attributes. "
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    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.
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    • "To nevertheless counter the problem of heterogeneously distributed interest values, DoI-based approaches use, for example, a diffusion of DoI values across the network [36] to even them out. Script-based approaches can be used to tailor such a diffusion process, for example, to only diffuse the values to every other node, as it makes sense for bipartite networks [34]. The same problem of a missing singular reference node also affects the representation of networks that have been reduced according to a DoI function: For the pruning of subtrees, it was always apparent where details were removed (at the bottom), yet this is no longer the case for networks in which nodes may get removed or collapsed into metanodes in various places. "
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    ABSTRACT: Large dynamic networks are targets of analysis in many fields. Tracking temporal changes at scale in these networks is challenging due in part to the fact that small changes can be missed or drowned-out by the rest of the network. For static networks, current approaches allow the identification of specific network elements within their context. However, in the case of dynamic networks, the user is left alone with finding salient local network elements and tracking them over time. In this work, we introduce a modular DoI specification to flexibly define what salient changes are and to assign them a measure of their importance in a time-varying setting. The specification takes into account neighborhood structure information, numerical attributes of nodes/edges, and their temporal evolution. A tailored visualization of the DoI specification complements our approach. Alongside a traditional node-link view of the dynamic network, it serves as an interface for the interactive definition of a DoI function. By using it to successively refine and investigate the captured details, it supports the analysis of dynamic networks from an initial view until pinpointing a user's analysis goal. We report on applying our approach to scientific co-authorship networks and give concrete results for the DBLP dataset.
    Full-text · Article · Aug 2013
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    • "An example for a technique adapting the layout to accommodate large amounts of node attributes is the table-based graph visualization technique by Schulz et al. [23], where each node corresponds to a row in a table that can have multiple columns for multiple attributes. An approach by Pretorious and van Wijk [22] uses recursive partitioning for multiple node attributes. "
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    ABSTRACT: Pathway maps are an important source of information when analyzing functional implications of experimental data on biological processes. Associating large quantities of data with nodes on a pathway map and allowing in depth-analysis at the same time, however, is a challenging task. While a wide variety of approaches for doing so exist, they either do not scale beyond a few experiments or fail to represent the pathway appropriately. To remedy this, we introduce enRoute, a new approach for interactively exploring experimental data along paths that are dynamically extracted from pathways. By showing an extracted path side-by-side with experimental data, enRoute can present large amounts of data for every pathway node. It can visualize hundreds of samples, dozens of experimental conditions, and even multiple datasets capturing different aspects of a node at the same time. Another important property of this approach is its conceptual compatibility with arbitrary forms of pathways. Most notably, enRoute works well with pathways that are manually created, as they are available in large, public pathway databases. We demonstrate enRoute with pathways from the well-established KEGG database and expression as well as copy number datasets from humans and mice with more than 1,000 experiments at the same time. We validate enRoute in case studies with domain experts, who used enRoute to explore data for glioblastoma multiforme in humans and a model of steatohepatitis in mice.
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