Research

I did my PhD at the Visualization Research Center Stuttgart (VISUS), University of Stuttgart, Germany. I submitted and defended my PhD thesis on Visualization Techniques for Group Structures in Graphs (supervised by Daniel Weiskopf) in 2015.

My main research focused on the development of visual analytics systems as well as information visualization techniques for the application domain of bioinformatics. I started my research in visual analytics and information visualization during my diploma thesis with the title Visualisation Toolkit for Contact Density Potentials within Amino Acid Neighbourhoods in Protein Structures, which I wrote at the Max-Planck Institute for Molecular Genetics, Berlin.

Information visualization techniques are used to derive visual representations and mappings for abstract data. The aim of visual analytics systems is to derive insights from massive, dynamic and conflicted data. The data analysis is thereby supported through adequate visualization and highly interactive interfaces. In the context of biology, these vast amounts of data are a result of experiments as well as simulations of processes of single molecules over expression values of genes up to modeling of whole populations. The analysis of such data is particularly difficult, as they are often heterogenous, high-dimensional and do contain errors. Hence, there is an urgent need for adequate, modern visualizations and analysis techniques.

During my PhD, I was particularly interested in the development of visual analytics systems and information visualization techniques for biological networks, like gene-regulatory, signal-transduction and metabolic networks. Moreover, I was interested in the diverse kinds of attributes that may be associated to the nodes and edges of the network (graph) as well as the group structure in networks. Further challenges for the visualization of biological networks or graphs in general are introduced, if the graph topology or the attributes of the graph are dynamic or fraught with uncertainty. Therefore, I also worked on visual analytics and information visualization approaches for dynamic graphs and networks in general, i.e., for graphs those topology or properties change over time.

Projects

The State of the Art in Visualizing Group Structures in Graphs

Graph visualizations encode relationships between objects. Abstracting the objects into group structures provides an overview of the data. Groups can be disjoint or overlapping, and might be organized hierarchically. However, the underlying graph still needs to be represented for analyzing the data in more depth. We surveyed research in visualizing group structures as part of graph diagrams. A particular focus was the explicit visual encoding of groups, rather than only using graph layout to implicitly indicate groups. We introduced a taxonomy of visualization techniques structuring the field into four main categories: visual node attributes vary properties of the node representation to encode the grouping, juxtaposed approaches use two separate visualizations, superimposed techniques work with two aligned visual layers, and embedded visualizations tightly integrate group and graph representation. [Literature browser]

Renodoi: Untangling Biological Networks with Degree-of-Interest Functions

RenoDoI is a visual analytics approach to untangle (lt. renodare) large and dense integrated data-knowledge networks using degree-of-interest (DoI) functions with attribute-based layouts of the resulting subnetworks. The comparison of multiple subnetworks representing different analysis facets is facilitated through an interactive super-network that integrates brushing-and-linking techniques for highlighting components across networks. [Project page] [video]

Visualizing the Evolution of Communities in Dynamic Graphs

The community structure of graphs is an important feature that gives insight into the high-level organization of objects within the graph. In real-world systems, the graph topology is oftentimes not static but changes over time and hence, also the community structure changes. Previous timeline-based approaches either visualize the dynamic graph or the dynamic community structure. In contrast, our approach combines both in a single image and therefore allows users to investigate the community structure together with the underlying dynamic graph. Our optimized ordering of vertices and selection of colors in combination with interactive highlighting techniques increases the traceability of communities along the time axis. Users can identify visual signatures, estimate the reliability of the derived community structure, and investigate whether community evolution interacts with changes in the graph topology. [Project page] [video]

iVUN: interactive Visualization of Uncertain biochemical Reaction Networks

iVUN is a visual analytics system that supports an uncertainty-aware analysis of static and dynamic attributes of biochemical reaction networks (BRNs). iVUN visualizes the model as a graph, where the statistics of the attributes are mapped to the color of edges and vertices. The graph view is combined with several linked views such as lineplots, scatterplots, and correlation matrices, to support the identification of uncertainties and the analysis of their mutual dependencies as well as their time dependencies. [Project page] iVUN will be made available Open Source on Bioinformatics.org [video]

iHAT: interactive Hierarchical Aggregation Table

iHAT is a tool for the analysis of multiple sequence alignments, e.g. nucleic acid sequences (DNA), amino acid sequences (proteins) or eQTL data, associated metadata, and hierarchical clusterings. iHAT supports the visual assessment of mutations in the sequence that are correlated with the phenotype using hierarchical aggregation techniques combined with filtering methods and data-type dependent colormaps. [Project page]

CMView: Protein Contact Map Visualisation and Analysis

CMView was developed to visualize, analyze and model protein contact maps. The contact map visualization is thereby integrated with 3D protein structure visualization using PyMol. Further functionality is supported by the Contact Geometry Analysis Plugin (CGAP). CGAP provides rich, interactive tools for analysing the geometric environment of residue-residue contacts in protein structures. CGAP can be used in an explorative way to find and understand residue interaction patterns. Furthermore, it enables users to analyze structures, clusters of residues and outliers as well as to detect possible errors within predicted structures. [Project page] CMView is available Open Source on Bioinformatics.org