Publikationen

A perceptually optimised Bivariate Visualisation Scheme for high-dimensional Fold-change Data

A perceptually optimised Bivariate Visualisation Scheme for high-dimensional Fold-change Data

André Müller Ulm University Ludwig Lauser Ulm University Adalbert Wilhelm Psychology and Methods Timo Ropinski Ulm University Matthias Platzer Leibniz Institute on Ageing Heiko Neumann Ulm University Hans Kestler Ulm University

Advances in Data Analysis and Classification, 2020

Abstract

Visualising 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.

Bibtex

content_copy
@article{mueller2020bivariate,
	title={A perceptually optimised Bivariate Visualisation Scheme for high-dimensional Fold-change Data},
	author={M{\"a}ller, Andr{\'e} and Lauser, Ludwig and Wilhelm, Adalbert and Ropinski, Timo and Platzer, Matthias and Neumann, Heiko and Kestler, Hans},
	year={2020},
	month={8},
	journal={Advances in Data Analysis and Classification},
	pages={1-18},
	doi={10.1007/s11634-020-00416-5},
}