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A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data

Author

Listed:
  • André Müller

    (Ulm University)

  • Ludwig Lausser

    (Ulm University)

  • Adalbert Wilhelm

    (Jacobs University)

  • Timo Ropinski

    (Ulm University)

  • Matthias Platzer

    (Fritz Lipmann Institut)

  • Heiko Neumann

    (Ulm University)

  • Hans A. Kestler

    (Ulm University
    Fritz Lipmann Institut)

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.

Suggested Citation

  • André Müller & Ludwig Lausser & Adalbert Wilhelm & Timo Ropinski & Matthias Platzer & Heiko Neumann & Hans A. Kestler, 2021. "A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 463-480, June.
    • Handle: RePEc:spr:advdac:v:15:y:2021:i:2:d:10.1007_s11634-020-00416-5
    DOI: 10.1007/s11634-020-00416-5
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