Getting poster data...
Albert Pritzkau (ICCAS, Leipzig, Germany)In gene expression analysis, we are facing an information explosion. Multivariate datasets with hundreds of dimensions and millions of markers are increasingly common. Hence, the exploratory visualization of gene expression datasets poses a major challenge with respect to effective algorithms and tools. Parallel coordinates have proved to be useful to support the visual exploration of large and high-dimensional datasets, but has its limitations that have been addressed by a number of extensions and adaptations of the original idea. Due to limited screen space line overdrawing leads to visual clutter hiding the inherent structure they constitute when data density is high. In order to reveal regions of high density, we enrich parallel coordinates with histogram representations. The histogram bars are attached to each axis dividing the entire range of values into uniform intervals. By aligning the histogram bars orthogonal to the plane of the parallel coordinates, an interaction space is created, which can be rotated by the user. This way the user can choose the degree of visual superimposition in an appropriate manner. Without leaving the context of parallel coordinat