We present ScaleTrotter, a conceptual framework for an interactive, multi-scale visualization of biological mesoscale data and, specifically, genome data. ScaleTrotter allows viewers to smoothly transition from the nucleus of a cell to the atomistic composition of the DNA, while bridging several orders of magnitude in scale. The challenges in creating an interactive visualization of genome data are fundamentally different in several ways from those in other domains like astronomy that require a multi-scale representation as well. First, genome data has intertwined scale levels---the DNA is an extremely long, connected molecule that manifests itself at all scale levels. Second, elements of the DNA do not disappear as one zooms out---instead the scale levels at which they are observed group these elements differently. Third, we have detailed information and thus geometry for the entire dataset and for all scale levels, posing a challenge for interactive visual exploration. Finally, the conceptual scale levels for genome data are close in scale space, requiring us to find ways to visually embed a smaller scale into a coarser one. We address these challenges by creating a new multi-scale visualization concept. We use a scale-dependent camera model that controls the visual embedding of the scales into their respective parents, the rendering of a subset of the scale hierarchy, and the location, size, and scope of the view. In traversing the scales, ScaleTrotter is roaming between 2D and 3D visual representations that are depicted in integrated visuals. We discuss, specifically, how this form of multi-scale visualization follows from the specific characteristics of the genome data and describe its implementation. Finally, we discuss the implications of our work to the general illustrative depiction of multi-scale data.
In general, as one navigates through large-scale 3D scenes, the underlying subject matter is intrinsically complex and requires appropriate interaction to aid intellection @cite_8 . The inspection of individual parts is challenging, in particular if the viewer is too far away to appreciate its visual details. Yet large, detailed datasets or procedural approaches are essential to create believable representations. To generate not only efficient but visualizations, we thus need to remove detail in Viola and Isenberg's @cite_39 visual abstraction sense. This allows us to render at interactive rates as well as to see the intended structures, which would otherwise be hidden due to cluttered views. Consequently, even most single-scale small-scale representations use some type of multi-scale approach and with it introduce abstraction. Generally we can distinguish three fundamental techniques: multi-scale representations by leaving out detail of a single data source, multi-scale techniques that actively represent preserved features at different scales, and multi-scale approaches that can also transit between representations of different scales. We discuss approaches for these three categories next.
@cite_39 @cite_8
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This paper presents a multi-scale morphology approach to the volume segmentation and visualization problem. The basis of the approach is applying morphological operations with spherical structuring elements at various sizes to create a representation of the volume data that encodes structural information at multiple scales. Through an interactive user interface, the user can effectively segment and visualize a specific feature of interest using a fast, region growing method with this multi-scale data representation. A graph representing the segmented feature is created to facilitate interactive visual inspection and refinement of the feature. We have introduced a new volume data visualization technique based on interactive segmentation rather than the traditional transfer-function based classification approach. This new technique offers the user greater power in isolating and examining volumetric features of interest. The SmoothScroll control is a multi-scale, multi-layer slider for the navigation in large one-dimensional datasets such as time series data. Presenting multiple data layers at gradually varying scales provides both detail and context information and allows for both fine-grained and coarse navigation and scrolling at different granularities. Visual data aggregation allows for multi-level navigation while the clear visual relation of the data layers aids the user in retaining a sense of both the current detail position and the immediate and global context. We describe SmoothScroll as well as related controls and discuss its application with the help of several usage examples.