We present a caching framework with a novel probability-based prefetching and eviction strategy applied to atomic cache units that enables interactive rendering of gigaray light fields. Further, we describe two new use cases that are supported by our framework: panoramic light fields, including a robust imaging technique and an appropriate parameterization scheme for real-time rendering and caching; and light-field-cached volume rendering, which supports interactive exploration of large volumetric datasets using light-field rendering. We consider applications such as light-field photography and the visualization of large image stacks from modern scanning microscopes.

Advances in imaging technology leads to a continues increase of image data sets. Modern scanning microscopes, for instance, produce image stacks with a megapixel lateral resolution and many hundreds to thousands slices in axial direction. This trend will continue – resulting in very large volumetric data sets that are difficult to explore interactively, since the complexity of volume rendering is proportional to spatial and lateral resolution of the data. Light-field rendering is a fast and simple image-based rendering method that requires pre-computed image data. For volume rendering, each costly computed image is discarded after changing the viewing parameters, while it becomes idle if the viewing parameters are not changed and the visualization does not need to be updated. We combine light-field rendering and volume rendering with two goals: We recycle previously rendered images and use the idle times for filling a cached-managed light field. The final images are then composed from light-field rendering and from volume rendering – depending on the state of the light-field cache. This leads to a significant increase in rendering performance and to the ability of exploring large volumetric datasets interactively.

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.

Identification and characterization of cancer subtypes are important areas of research that are based on the integrated analysis of multiple heterogeneous genomics datasets. Since there are no tools supporting this process, much of this work is done using ad-hoc scripts and static plots, which is inefficient and limits visual exploration of the data. To address this, we have developed StratomeX, an integrative visualization tool that allows investigators to explore the relationships of candidate subtypes across multiple genomic data types such as gene expression, DNA methylation, or copy number data. StratomeX represents datasets as columns and subtypes as bricks in these columns. Ribbons between the columns connect bricks to show subtype relationships across datasets. Drill-down features enable detailed exploration. StratomeX provides insights into the functional and clinical implications of candidate subtypes by employing small multiples, which allow investigators to assess the effect of subtypes on molecular pathways or outcomes such as patient survival. As the configuration of viewing parameters in such a multi-dataset, multi-view scenario is complex, we propose a meta visualization and configuration interface for dataset dependencies and data-view relationships.
StratomeX is developed in close collaboration with domain experts. We describe case studies that illustrate how investigators used the tool to explore subtypes in large datasets and demonstrate how they efficiently replicated findings from the literature and gained new insights into the data.