Abstract
AbstractVisualising data as diagrams using visual attributes such as colour, shape, size, and orientation is challenging. In particular, large data sets demand graphical display as an essential step in the analysis. In order to achieve comprehension often different attributes need to be displayed simultaneously. In this work a comprehensible bivariate, perceptually optimised visualisation scheme for high-dimensional data is proposed and evaluated. It can be used to show fold changes together with confidence values within a single diagram. The visualisation scheme consists of two parts: a uniform, symmetric, two-sided colour scale and a patch grid representation. Evaluation of uniformity and symmetry of the two-sided colour scale was performed in comparison to a standard RGB scale by twenty-five observers. Furthermore, the readability of the generated map was validated and compared to a bivariate heat map scheme.
Funder
Deutsche Forschungsgemeinschaft
Ministerium für Wissenschaft, Forschung und Kultur
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Statistics and Probability
Reference39 articles.
1. Augen J (2005) Bioinformatics in the post-genomic era. Addison-Wesley, Boston
2. Baldi P, Brunak S (2001) Bioinformatics: The machine learning approach. MIT Press, Cambridge
3. Bilban M, Buehler L, Head S, Desoye G, Quaranta V (2002) Defining signal thresholds in DNA microarrays: exemplary application for invasive cancer. BMC Genom 3(1):1
4. Buchholz M, Kestler HA, Bauer A, Böck W, Rau B, Leder G, Kratzer W, Bommer M, Scarpa A, Schilling MK et al (2005) Specialized DNA arrays for the differentiation of pancreatic tumors. Clin Cancer Res 11(22):8048–8054
5. Carswell CM, Wickens CD (1990) The perceptual interaction of graphical attributes: configurality, stimulus homogeneity, and object integration. Percept Psychophys 47(2):157–168. https://doi.org/10.3758/BF03205980