Marc Streit

Comparative Analysis of Multidimensional, Quantitative Data

When analyzing multidimensional, quantitative data, the comparison of two or more groups of dimensions is a common task. Typical sources of such data are experiments in biology, physics or engineering, which are conducted in different configurations and use replicates to ensure statistically significant results. One common way to analyze this data is to filter it using statistical methods and then run clustering algorithms to group similar values. The clustering results can be visualized using heat maps, which show differences between groups as changes in color. However, in cases where groups of dimensions have an a priori meaning, it is not desirable to cluster all dimensions combined, since a clustering algorithm can fragment continuous blocks of records. Furthermore, identifying relevant elements in heat maps becomes more difficult as the number of dimensions increases. To aid in such situations, we have developed Matchmaker, a visualization technique that allows researchers to arbitrarily arrange and compare multiple groups of dimensions at the same time. We create separate groups of dimensions which can be clustered individually, and place them in an arrangement of heat maps reminiscent of parallel coordinates. To identify relations, we render bundled curves and ribbons between related records in different groups. We then allow interactive drill-downs using enlarged detail views of the data, which enable in-depth comparisons of clusters between groups. To reduce visual clutter, we minimize crossings between the views. This paper concludes with two case studies. The first demonstrates the value of our technique for the comparison of clustering algorithms. In the second, biologists use our system to investigate why certain strains of mice develop liver disease while others remain healthy, informally showing the efficacy of our system when analyzing multidimensional data containing distinct groups of dimensions.

Context-Preserving Visual Links

Evaluating, comparing, and interpreting related pieces of information are tasks that are commonly performed during visual data analysis and in many kinds of information-intensive work. Synchronized visual highlighting of related elements is a well-known technique used to assist this task. An alternative approach, which is more invasive but also more expressive is visual linking in which line connections are rendered between related elements. In this work, we present context-preserving visual links as a new method for generating visual links. The method specifically aims to fulfill the following two goals: first, visual links should minimize the occlusion of important information; second, links should visually stand out from surrounding information by minimizing visual interference. We employ an image-based analysis of visual saliency to determine the important regions in the original representation. A consequence of the image-based approach is that our technique is application-independent and can be employed in a large number of visual data analysis scenarios in which the underlying content cannot or should not be altered. We conducted a controlled experiment that indicates that users can find linked elements in complex visualizations more quickly and with greater subjective satisfaction than in complex visualizations in which plain highlighting is used. Context-preserving visual links were perceived as visually more attractive than traditional visual links that do not account for the context information.

Caleydo: Design and evaluation of a visual analysis framework for gene expression data in its biological context

The goal of our work is to support experts in the process of hypotheses generation concerning the roles of genes in diseases. For a deeper understanding of the complex interdependencies between genes, it is important to bring gene expressions (measurements) into context with pathways. Pathways, which are models of biological processes, are available in online databases. In these databases, large networks are decomposed into small sub-graphs for better manageability. This simplification results in a loss of context, as pathways are interconnected and genes can occur in multiple instances scattered over the network. Our main goal is therefore to present all relevant information, i.e., gene expressions, the relations between expression and pathways and between multiple pathways in a simple, yet effective way. To achieve this we employ two different multiple-view approaches. Traditional multiple views are used for large datasets or highly interactive visualizations, while a 2.5D technique is employed to support a seamless navigation of multiple pathways which simultaneously links to the expression of the contained genes. This approach facilitates the understanding of the interconnection of pathways, and enables a non-distracting relation to gene expression data. We evaluated Caleydo with a group of users from the life science community. Users were asked to perform three tasks: pathway exploration, gene expression analysis and information comparison with and without visual links, which had to be conducted in four different conditions. Evaluation results show that the system can improve the process of understanding the complex network of pathways and the individual effects of gene expression regulation considerably. Especially the quality of the available contextual information and the spatial organization was rated good for the presented 2.5D setup.

VisBricks: Multiform Visualization of Large, Inhomogeneous Data

Large volumes of real-world data often exhibit inhomogeneities: vertically in the form of correlated or independent dimensions and horizontally in the form of clustered or scattered data items. In essence, these inhomogeneities form the patterns in the data that researchers are trying to find and understand. Sophisticated statistical methods are available to reveal these patterns, however, the visualization of their outcomes is mostly still performed in a one-view-fits-all manner, In contrast, our novel visualization approach, VisBricks, acknowledges the inhomogeneity of the data and the need for different visualizations that suit the individual characteristics of the different data subsets. The overall visualization of the entire data set is patched together from smaller visualizations, there is one VisBrick for each cluster in each group of interdependent dimensions. Whereas the total impression of all VisBricks together gives a comprehensive high-level overview of the different groups of data, each VisBrick independently shows the details of the group of data it represents, State-of-the-art brushing and visual linking between all VisBricks furthermore allows the comparison of the groupings and the distribution of data items among them. In this paper, we introduce the VisBricks visualization concept, discuss its design rationale and implementation, and demonstrate its usefulness by applying it to a use case from the field of biomedicine.

Caleydo: connecting pathways and gene expression

Summary: Understanding the relationships between pathways and the altered expression of their components in disease conditions can be addressed in a visual data analysis process. Caleydo uses novel visualization techniques to support life science experts in their analysis of gene expression data in the context of pathways and functions of individual genes. Pathways and gene expression visualizations are placed in a 3D scene where selected entities (i.e. genes) are visually connected. This allows Caleydo to seamlessly integrate interactive gene expression visualization with cross-database pathway exploration. Availability: The Caleydo visualization framework is freely available on www.caleydo.org for non-commercial use. It runs on Windows and Linux and requires a 3D capable graphics card. Contact: caleydo@icg.tugraz.at; streit@icg.tugraz.at Supplementary information: Supplementary data are available at Bioinformatics online.

Model-Driven Design for the Visual Analysis of Heterogeneous Data

As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stacknflip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.

3D parallel coordinate systems—A new data visualization method in the context of microscopy-based multicolor tissue cytometry

Presentation of multiple interactions is of vital importance in the new field of cytomics. Quantitative analysis of multi‐ and polychromatic stained cells in tissue will serve as a basis for medical diagnosis and prediction of disease in forthcoming years. A major problem associated with huge interdependent data sets is visualization. Therefore, alternative and easy‐to‐handle strategies for data visualization as well as data meta‐evaluation (population analysis, cross‐correlation, co‐expression analysis) were developed.

Navigation and exploration of interconnected pathways

Visualizing pathways, i. e. models of cellular functional networks, is a challenging task in computer assisted biomedicine. Pathways are represented as large collections of interwoven graphs, with complex structures present in both the individual graphs and their interconnections. This situation requires the development of novel visualization techniques to allow efficient visual exploration. We present the Caleydo framework, which incorporates a number of approaches to handle such pathways. Navigation in the network of pathways is facilitated by a hierarchical approach which dynamically selects a working set of individual pathways for closer inspection. These pathways are interactively rendered together with visual interconnections in a 2.5D view using graphics hardware acceleration. The layout of individual graphs is not computed automatically, but taken from the KEGG and BioCarta databases, which use layouts that life scientists are familiar with. Therefore they encode essential meta‐information. While the KEGG and BioCarta pathways use a pre‐defined layout, interactions such as linking+brushing, neighborhood search or detail on demand are still fully interactive in Caleydo. We have evaluated Caleydo with pathologists working on the determination of unknown gene functions. Informal experiences confirm that Caleydo is useful in both generating and validating such hypotheses.

Guided visual exploration of genomic stratifications in cancer

To the editor: n nCancer is a heterogeneous disease, and molecular profiling of tumors from large cohorts has enabled characterization of new tumor subtypes. This is a prerequisite for improving personalized treatment and ultimately better patient outcomes. Potential tumor subtypes can be identified with methods such as unsupervised clustering1 or network-based stratification2, which assign patients to sets based on high-dimensional molecular profiles. Detailed characterization of identified sets and their interpretation, however, remain a time-consuming exploratory process. n nTo address these challenges, we combine ‘StratomeX’3, an interactive visualization tool, freely available at http://www.caleydo.org, with exploration tools to efficiently compare multiple patient stratifications, to correlate patient sets with clinical information or genomic alterations, and to view the differences between molecular profiles across patient sets. Although we focus on cancer genomics here, StratomeX can also be applied in other disease cohorts. n nThousands of patient stratifications can be derived from large cancer genomics datasets. This space of patient stratifications—which we call the ‘stratome’—contains stratifications based on, for example, clustering of mRNA, microRNA, or protein expression matrices; the mutation or copy number status of genes; or on clinical variables. Due to the size of the stratome and the heterogeneity of the underlying datasets, integration of computational and visual approaches is indispensable to the analyst in identifying biologically or clinically meaningful stratifications, as well as clinical parameters and pathways that together provide a comprehensive view of each patient set. n nStratomeX complements the network viewers, heat maps, and genome browsers typically used in cancer genomics4 (Supplementary Discussion and Supplementary Table 1). To visualize the relationships between multiple patient stratifications as well as other data (Fig. 1 and Supplementary Fig. 1), stratifications are represented as columns of stacked blocks where each block corresponds to a patient set. Blocks contain visualizations of the data associated with those patients, such as heat maps, pathway maps overlaid with expression data, or survival plots (Supplementary Fig. 2). Bands connecting the blocks show the pairwise overlap of sets in adjacent stratifications, with the width of the bands representing the size of the overlap relative to the size of the patient sets (Supplementary Fig. 3). This visualization is an efficient tool to confirm hypotheses about gene functions or subtypes defined by molecular profiles. n n n nFigure 1 n nSeamless integration of visual and computational components in the extended StratomeX tool n n n nStratomeX also integrates a computational framework for query-based guided exploration of the stratome directly into the visualization (Fig. 1), enabling discovery of novel relationships between patient sets and efficient generation and refinement of hypotheses about tumor subtypes. A ‘query wizard’ provides step-by-step instructions (Supplementary Fig. 1 and 4) for defining queries, and a range of computational methods are used to generate rankings (Supplementary Methods). Queries score stratifications, for example, based on their overlap with a particular patient set, or based on their overall similarity to a selected stratification. Furthermore, the analyst can query the collection for stratifications that contain patient sets that exhibit differences in survival or differential regulation of pathways. We use ‘LineUp’5, a multi-attribute ranking technique, to visualize the results of these queries and to show which stratifications or pathways score high (Fig. 1 and Supplementary Fig. 5). The tight integration between the StratomeX and LineUp views, as well as the dynamic computation of scores, is essential for rapid identification of meaningful relationships between stratifications, clinical parameters, and pathways. n nWe demonstrate the effectiveness of StratomeX in a case study (Supplementary Note, Supplementary Figs. 6-18, Supplementary Tables 2 and 3, and Supplementary Video 1) in which we explored molecular and clinical data to characterize tumor subtypes in a cohort of over 400 clear cell renal cell carcinoma cases reported by The Cancer Genome Atlas consortium6.

WeightLifter: Visual Weight Space Exploration for Multi-Criteria Decision Making

A common strategy in Multi-Criteria Decision Making (MCDM) is to rank alternative solutions by weighted summary scores. Weights, however, are often abstract to the decision maker and can only be set by vague intuition. While previous work supports a point-wise exploration of weight spaces, we argue that MCDM can benefit from a regional and global visual analysis of weight spaces. Our main contribution is WeightLifter, a novel interactive visualization technique for weight-based MCDM that facilitates the exploration of weight spaces with up to ten criteria. Our technique enables users to better understand the sensitivity of a decision to changes of weights, to efficiently localize weight regions where a given solution ranks high, and to filter out solutions which do not rank high enough for any plausible combination of weights. We provide a comprehensive requirement analysis for weight-based MCDM and describe an interactive workflow that meets these requirements. For evaluation, we describe a usage scenario of WeightLifter in automotive engineering and report qualitative feedback from users of a deployed version as well as preliminary feedback from decision makers in multiple domains. This feedback confirms that WeightLifter increases both the efficiency of weight-based MCDM and the awareness of uncertainty in the ultimate decisions.