A Perceptually Optimized Scheme for Visualizing Gene Expression Ratios with Confidence Values

  • Hans A. Kestler
  • Andre Müller
  • Malte Buchholz
  • Thomas M Gress
  • Günther Palm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4021)


Gene expression data studies are often concerned with comparing experimental versus control conditions. Ratios of gene expression values, fold changes, are therefore commonly used as biologically meaningful markers. Visual representations are inevitable for the explorative analysis of data. Fold changes alone are no reliable markers, since low signal intensities may lead to unreliable ratios and should therefore be visually marked less important than the more trustworthy ratios of larger expression values.

Methods: We propose a new visualization scheme showing ratios and their confidence together in one single diagram, enabling a more precise explorative assessment of gene expression data. Basis of the visualization scheme are near-uniform perceptible color scales improving the readability of the commonly used red-green color scale. A sub-sampling algorithm for optimizing color scales is presented. Instead of difficult to read bivariate color maps encoding two variables into a single color we propose the use of colored patches (rectangles) of different sizes representing the absolute values, while representing ratios by a univariate color map. Different pre-processing steps for visual bandwidth limitation and reliability value estimation are proposed.

Results and Conlusions: The proposed bivariate visualization scheme shows a clear perceptible order in ratio and reliability values leading to better and clearer interpretable diagrams. The proposed color scales were specifically adapted to human visual perception. Psychophysical optimized color scales are superior to traditional sRGB red-green maps. This leads to an improved explorative assessment of gene expression data.


Color Space Color Scale International Electrotechnical Commission Absolute Expression Human Visual Perception 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hans A. Kestler
    • 1
    • 2
  • Andre Müller
    • 2
  • Malte Buchholz
    • 2
  • Thomas M Gress
    • 2
    • 3
  • Günther Palm
    • 1
  1. 1.Department of Neural Information ProcessingUniversity of UlmUlmGermany
  2. 2.Department of Internal Medicine IUniversity Hospital UlmUlmGermany
  3. 3.Division of Gastroenterology and Endocrinology, Department of Internal MedicinePhilipps UniversityMarburgGermany

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