iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data

In the search for single-nucleotide polymorphisms which influence the observable phenotype, genome wide association studies have become an important technique for the identification of associations between genotype and phenotype of a diverse set of sequence-based data. We present a methodology for the visual assessment of single-nucleotide polymorphisms using interactive hierarchical aggregation techniques combined with methods known from traditional sequence browsers and cluster heatmaps. Our tool, the interactive Hierarchical Aggregation Table (iHAT), facilitates the visualization of multiple sequence alignments, associated metadata, and hierarchical clusterings. Different color maps and aggregation strategies as well as filtering options support the user in finding correlations between sequences and metadata. Similar to other visualizations such as parallel coordinates or heatmaps, iHAT relies on the human pattern-recognition ability for spotting patterns that might indicate correlation or anticorrelation. We demonstrate iHAT using artificial and real-world datasets for DNA and protein association studies as well as expression Quantitative Trait Locus data

CMView: Interactive contact map visualization and analysis

Summary: Contact maps are a valuable visualization tool in structural biology. They are a convenient way to display proteins in two dimensions and to quickly identify structural features such as domain architecture, secondary structure and contact clusters. We developed a tool called CMView which integrates rich contact map analysis with 3D visualization using PyMol. Our tool provides functions for contact map calculation from structure, basic editing, visualization in contact map and 3D space and structural comparison with different built-in alignment methods. A unique feature is the interactive refinement of structural alignments based on user selected substructures. Availability: CMView is freely available for Linux, Windows and MacOS. The software and a comprehensive manual can be downloaded from http://www.bioinformatics.org/cmview/. The source code is licensed under the GNU General Public License. Contact: [email protected], [email protected]

An eQTL biological data visualization challenge and approaches from the visualization community

In 2011, the IEEE VisWeek conferences inaugurated a symposium on Biological Data Visualization. Like other domain-oriented Vis symposia, this symposium's purpose was to explore the unique characteristics and requirements of visualization within the domain, and to enhance both the Visualization and Bio/Life-Sciences communities by pushing Biological data sets and domain understanding into the Visualization community, and well-informed Visualization solutions back to the Biological community. Amongst several other activities, the BioVis symposium created a data analysis and visualization contest. Unlike many contests in other venues, where the purpose is primarily to allow entrants to demonstrate tour-de-force programming skills on sample problems with known solutions, the BioVis contest was intended to whet the participants' appetites for a tremendously challenging biological domain, and simultaneously produce viable tools for a biological grand challenge domain with no extant solutions. For this purpose expression Quantitative Trait Locus (eQTL) data analysis was selected. In the BioVis 2011 contest, we provided contestants with a synthetic eQTL data set containing real biological variation, as well as a spiked-in gene expression interaction network influenced by single nucleotide polymorphism (SNP) DNA variation and a hypothetical disease model. Contestants were asked to elucidate the pattern of SNPs and interactions that predicted an individual's disease state. 9 teams competed in the contest using a mixture of methods, some analytical and others through visual exploratory methods. Independent panels of visualization and biological experts judged entries. Awards were given for each panel's favorite entry, and an overall best entry agreed upon by both panels. Three special mention awards were given for particularly innovative and useful aspects of those entries. And further recognition was given to entries that correctly answered a bonus question about how a proposed "gene therapy" change to a SNP might change an individual's disease status, which served as a calibration for each approaches' applicability to a typical domain question. In the future, BioVis will continue the data analysis and visualization contest, maintaining the philosophy of providing new challenging questions in open-ended and dramatically underserved Bio/Life Sciences domains

Visualisierungstechniken für Gruppenstrukturen in Graphen

Graph visualization plays a key role analyzing relations between objects. With increasing size of the graph, it becomes difficult to understand global and local structures of the graph. Grouping objects of the graph based on their attributes or relations helps reveal global structures. Visualizing these group structures together with the graph topology can highlight central objects and reveal outliers. The ability of a visualization to help detecting these features becomes more difficult for groups that overlap or change over time. In many applications, groups cannot be interpreted as disjoint sets of objects. In fact, objects are often involved in several groups, sometimes even to different extent. With the existing types of overlapping groups, further analysis tasks arise that need to be considered for the visualization. In addition, real-world scenarios are not static but change over time and so do relations among objects. With the graph topology changing over time, the group structure changes as well. The challenge for visualizations of dynamic groups in dynamic graphs is to facilitate the analysis of group-related features not only for individual points in time but over time, showing group evolution events. This thesis presents visualization techniques for group structures in graphs that address these challenges: overlap and time dependency. As a basis, a survey of the state of the art in visualizing group structures in graphs is presented. The first part of this thesis is dedicated to the visualization of overlapping groups in static graphs, where different types of overlaps are considered. With each technique, the complexity of the groups increases. First, a visual analytics system for crisp overlapping groups in multivariate graphs is presented. This system integrates interactive filtering of large and dense networks with groupbased layouts of the resulting subnetworks and a technique to compare those subnetworks. Second, a technique that visualizes fuzzy overlapping groups in a graph based on layout strategies and further visual mappings is presented. This technique facilitates the investigation of fuzzy group memberships at different levels of detail based on a hierarchical aggregation model. In contrast to these techniques, the third visualization technique shows groups based on multivariate edge attributes rather than vertex attributes or the topology of the graph. In particular, edge-edge relations are visualized as curves that are directly integrated into the node-link diagram representing the object-relation structure. The second part is dedicated to visualization techniques for dynamic groups in dynamic graphs. Again, the complexity of the group structure rises from the first technique addressing flat groups to the second technique addressing more complex hierarchical groups. Within both techniques, the evolution of groups is encoded using a flow metaphor. The first technique visualizes the partially aggregated graphs by node-link diagrams, whereas the second technique is based on an extended adjacency matrix representation that encodes the hierarchical structure of vertices as well as changes in the graph topology. All presented techniques visualize the group structure integrated with the graph topology in a single image. Finally, the use of all techniques is demonstrated for real data sets from biology, one of the main application domains of group structures in graphs.Netzwerkvisualisierung spielt eine wichtige Rolle bei der Analyse von Relationen zwischen Objekten. Mit zunehmender Größe des Graphen wird es schwierig globale sowie lokale Strukturen des Graphen zu verstehen. Die Gruppierung von Objekten des Graphen basierend auf ihren Attributen oder Relationen hilft dabei, globale Strukturen aufzuzeigen. Durch die Visualisierung der Gruppenstruktur zusammen mit der Graphtopologie können zentrale Objekte und Ausreißer hervorgehoben werden. Die Fähigkeit einer Visualisierung, die Erkennung solcher Merkmale zu unterstützen, wird für überlappende oder sich zeitlich verändernde Gruppen noch schwieriger. In vielen Anwendungsbereichen können Gruppen nicht als disjunkte Mengen von Objekten verstanden werden. Vielmehr sind Objekte oftmals in mehrere Gruppen involviert, teilweise sogar in unterschiedlichem Umfang. Für die verschiedenen Arten überlappender Gruppen müssen weitere Analyseaufgaben für die Visualisierung in Betracht gezogen werden. Darüber hinaus sind Szenarien in der echten Welt nicht statisch, sondern ändern sich über die Zeit, weshalb auch die Relationen zwischen den Objekten dynamisch sind. Verändert sich die Graphtopologie über die Zeit, so ändert sich auch die Gruppenstruktur des Graphen. Die Herausforderung für die Visualisierung von dynamischen Gruppen in dynamischen Graphen ist es, die Analyse von gruppenbezogenen Eigenschaften nicht nur für individuelle Zeitpunkte, sondern auch über die Zeit hinweg zu ermöglichen, um Ereignisse in der Gruppenentwicklung aufzuzeigen. Diese Doktorarbeit präsentiert Visualisierungstechniken für Gruppenstrukturen in Graphen, welche folgende Herausforderungen angehen: Überlappung und Zeitabhängigkeit. Als Grundlage wird ein Überblick über den Stand existierender Techniken zur Visualisierung von Gruppenstrukturen in Graphen präsentiert. Der darauffolgende erste Teil der Doktorarbeit widmet sich der Visualisierung von überlappenden Gruppen in statischen Graphen, wobei verschiedene Arten von Überlappung betrachtet werden. Mit jeder dieser Techniken erhöht sich die Komplexität der Überlappung. Zuerst wird ein System zur visuellen Analyse von scharf überlappenden Gruppen in multivariaten Graphen präsentiert. Dieses System integriert interaktive Methoden zum Filtern großer dichter Netzwerke mit einem gruppenbasierten Layout der resultierenden Teilnetzwerke und einer Technik zum Vergleich solcher Teilnetzwerke. Darauf folgend wird eine Technik zur Visualisierung von unscharf überlappenden Gruppen in Graphen präsentiert. Diese Technik basiert auf Layout-Strategien und weiteren visuellen Abbildungen. Sie ermöglicht es, die unscharfen Gruppenzugehörigkeiten basierend auf einem hierarchischen Aggregationsmodell auf verschiedenen Detailstufen zu untersuchen. Diese ersten Techniken zeigen beide Gruppen von Objekten des Graphen. Im Gegensatz dazu widmet sich die dritte Visualisierungstechnik Gruppen von Relationen, welche von multivariaten Attributen dieser Relationen abgeleitet werden. Im Speziellen werden Relationen zwischen Kanten als Kurven visualisiert, welche direkt in das Knoten-Kanten-Diagramm des Graphen integriert werden. Der zweite Teil dieser Doktorarbeit widmet sich Visualisierungstechniken für dynamische Gruppen in dynamischen Graphen. Auch hier steigt die Komplexität der Gruppenstruktur: Die erste Technik dient der Visualisierung von flachen Gruppen, während die zweite Visualisierungstechnik für komplexere, hierarchische Gruppen entwickelt wurde. In beiden Techniken wird die Entwicklung der Gruppen mit Hilfe einer Fluss Metapher dargestellt. Während die erste Technik teilweise aggregierte Graphen als Knoten-Kanten-Diagramme darstellt, basiert die zweite Technik auf einer erweiterten Adjazenzmatrix-Darstellung, welche die hierarchische Struktur der Objekte sowie die Änderungen in der Graphtopologie kodiert. Alle Techniken, welche in dieser Doktorarbeit vorgestellt werden, visualisieren die Gruppenstruktur gemeinsam mit der Graphtopologie in einem Bild. Zuletzt wird der Nutzen aller Techniken am Beispiel von realen Datensätzen aus der Biologie, einem der Hauptanwendungsgebiete von Gruppenstrukturen in Graphen, gezeigt

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. How-ever, the underlying graph still needs to be represented for analyzing the data in more depth. This work surveys research in visualizing group structures as part of graph diagrams. A particular focus is the explicit visual en-coding of groups, rather than only using graph layout to implicitly indicate groups. We introduce 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, superim-posed techniques work with two aligned visual layers, and embedded visualizations tightly integrate group and graph representation. We discuss results from evaluations of those techniques as well as main areas of application. Finally, we report future challenges based on interviews we conducted with leading researchers of the field

Evaluating Partially Drawn Links for Directed Graph Edges

We investigate the readability of node-link diagrams for directed graphs when using partially drawn links instead of showing each link explicitly in its full length. Providing the complete link information between related nodes in a graph can lead to visual clutter caused by many edge crossings. To reduce visual clutter, we draw only partial links. Then, the question arises if such diagrams are still readable, understandable, and interpretable. As a step toward answering this question, we conducted a controlled user experiment with 42 participants to uncover differences in accuracy and completion time for three different tasks: identifying the existence of a direct link, the existence of an indirect connection with one intermediate node, and the node with the largest number of outgoing edges. Furthermore, we compared tapered and traditional edge representations, three different graph sizes, and six different link lengths. In all configurations, the nodes of the graph were placed according to the force-directed layout by Fruchterman and Reingold. One result of this study is that the characteristics of completion times and error rates depend on the type of task. A general observation is that partially drawn links can lead to shorter task completion times, which occurs for nearly all graph sizes, tasks, and both tapered and traditional edge representations. In contrast, there is a tendency toward higher error rates for shorter links, which in fact is task-dependent

Parallel Edge Splatting for Scalable Dynamic Graph Visualization

We present a novel dynamic graph visualization technique based on node-link diagrams. The graphs are drawn side-by-side from left to right as a sequence of narrow stripes that are placed perpendicular to the horizontal time line. The hierarchically organized vertices of the graphs are arranged on vertical, parallel lines that bound the stripes; directed edges connect these vertices from left to right. To address massive overplotting of edges in huge graphs, we employ a splatting approach that transforms the edges to a pixel-based scalar field. This field represents the edge densities in a scalable way and is depicted by non-linear color mapping. The visualization method is complemented by interaction techniques that support data exploration by aggregation, filtering, brushing, and selective data zooming. Furthermore, we formalize graph patterns so that they can be interactively highlighted on demand. A case study on software releases explores the evolution of call graphs extracted from the JUnit open source software project. In a second application, we demonstrate the scalability of our approach by applying it to a bibliography dataset containing more than 1.5 million paper titles from 60 years of research history producing a vast amount of relations between title words