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Analytical and Bioanalytical Chemistry

, Volume 407, Issue 8, pp 2047–2054 | Cite as

Optimisation of colour schemes to accurately display mass spectrometry imaging data based on human colour perception

  • Alan M. Race
  • Josephine Bunch
Review
Part of the following topical collections:
  1. Mass Spectrometry Imaging

Abstract

The choice of colour scheme used to present data can have a dramatic effect on the perceived structure present within the data. This is of particular significance in mass spectrometry imaging (MSI), where ion images that provide 2D distributions of a wide range of analytes are used to draw conclusions about the observed system. Commonly employed colour schemes are generally suboptimal for providing an accurate representation of the maximum amount of data. Rainbow-based colour schemes are extremely popular within the community, but they introduce well-documented artefacts which can be actively misleading in the interpretation of the data. In this article, we consider the suitability of colour schemes and composite image formation found in MSI literature in the context of human colour perception. We also discuss recommendations of rules for colour scheme selection for ion composites and multivariate analysis techniques such as principal component analysis (PCA).

Graphical Abstract

at Visualisation of the same data (unnormalised m/z 826 from the cerebellum region of a mouse brain) using colour schemes found in the MSI literature. Intensity spans from 0 to 100 counts. a Grayscale, b red, c green, d blue, e green to white, f cyan to white, g blue to white, h red to white, i pink to white, j copper to white, k hot, l pink hot, m green to yellow, n cyan to magenta to yellow, o double scale (blue to green, red to yellow), p temperature-based, q–t rainbow-based

Keywords

Mass spectrometry imaging Colour scheme Data visualisation 

Notes

Acknowledgements

The authors wish to thank Andrew D. Palmer, Rory T. Steven and Rian L. Griffiths for helpful discussions relating to this work. Funded by EPSRC grant EP/F50053X/1.

Supplementary material

216_2014_8404_MOESM1_ESM.pdf (5 mb)
ESM 1 (PDF 4.95 MB)

References

  1. 1.
    Moreland K (2009) Diverging color maps for scientific visualization. In: Bebis G et al. (eds) Advances in visual computing. Springer, Berlin, pp 92–103Google Scholar
  2. 2.
    Rogowitz BE, Treinish LA, Bryson S (1996) How not to lie with visualization. Comput Phys 10(3):268–273Google Scholar
  3. 3.
    Borland D, Taylor RM II (2007) Rainbow color map (still) considered harmful. IEEE Comput Graph Appl 27(2):14–17Google Scholar
  4. 4.
    Light A, Bartlein PJ (2004) The end of the rainbow? Color schemes for improved data graphics. Eos Trans Am Geophys Union 85(40):385–391Google Scholar
  5. 5.
    Stauffer R, Mayr GJ, Dabernig M, Zeileis A (2013) Somewhere over the rainbow: how to make effective use of colors in meteorological visualizations. Department of Public Finance, University of Innsbruck, InnsbruckGoogle Scholar
  6. 6.
    Tkalcic M, Tasic JF (2003) Colour spaces: perceptual, historical and applicational background. In: Eurocon 2003, Ljubljana, Slovenia, 22–24 Sept 2003, 1:304–308Google Scholar
  7. 7.
    Race AM, Styles IB, Bunch J (2012) Inclusive sharing of mass spectrometry imaging data requires a converter for all. J Proteomics 75(16):5111–5112Google Scholar
  8. 8.
    Race AM, Steven RT, Palmer AD, Styles IB, Bunch J (2013) Memory efficient principal component analysis for the dimensionality reduction of large mass spectrometry imaging data sets. Anal Chem 85(6):3071–3078Google Scholar
  9. 9.
    Dougherty B, Wade A (2014) Vischeck. http://www.vischeck.com/. Accessed 24 July 2014
  10. 10.
    Khatib-Shahidi S, Andersson M, Herman JL, Gillespie TA, Caprioli RM (2006) Direct molecular analysis of whole-body animal tissue sections by imaging MALDI mass spectrometry. Anal Chem 78(18):6448–6456Google Scholar
  11. 11.
    Adelson EH (1993) Perceptual organization and the judgment of brightness. Science 262(5142):2042–2044Google Scholar
  12. 12.
    Kimpe T, Tuytschaever T (2007) Increasing the number of gray shades in medical display systems: how much is enough? J Digit Imaging 20(4):422–432Google Scholar
  13. 13.
    Simunovic M (2009) Colour vision deficiency. Eye 24(5):747–755CrossRefGoogle Scholar
  14. 14.
    Jordan G, Deeb SS, Bosten JM, Mollon J (2010) The dimensionality of color vision in carriers of anomalous trichromacy. J Vis 10(8):1–19Google Scholar
  15. 15.
    Robichaud G, Garrard KP, Barry JA, Muddiman DC (2013) MSiReader: an open-source interface to view and analyze high resolving power MS imaging files on Matlab platform. J Am Soc Mass Spectrom 24(5):718–721Google Scholar
  16. 16.
    Parry RM, Galhena AS, Gamage CM, Bennett RV, Wang MD, Fernández FM (2013) omniSpect: an open MATLAB-based tool for visualization and analysis of matrix-assisted laser desorption/ionization and desorption electrospray ionization mass spectrometry images. J Am Soc Mass Spectrom 24(4):646–649Google Scholar
  17. 17.
    Luo MR, Cui G, Rigg B (2001) The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Res Appl 26(5):340–350Google Scholar
  18. 18.
    Sharma G, Wu W, Dalal EN (2005) The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Res Appl 30(1):21–30Google Scholar
  19. 19.
    Habekost M (2013) Which color differencing equation should be used? Int Circular Graphic Educat Res 6:20–33Google Scholar
  20. 20.
    Ware C (2004) Information visualization: perception for design. Elsevier, AmsterdamGoogle Scholar
  21. 21.
    Sugiura Y, Konishi Y, Zaima N, Kajihara S, Nakanishi H, Taguchi R, Setou M (2009) Visualization of the cell-selective distribution of PUFA-containing phosphatidylcholines in mouse brain by imaging mass spectrometry. J Lipid Res 50(9):1776–1788Google Scholar
  22. 22.
    Sodhi RN (2004) Time-of-flight secondary ion mass spectrometry (TOF-SIMS): versatility in chemical and imaging surface analysis. Analyst 129(6):483–487Google Scholar
  23. 23.
    Pachuta SJ (2004) Enhancing and automating TOF-SIMS data interpretation using principal component analysis. Appl Surf Sci 231:217–223Google Scholar
  24. 24.
    Cleveland WS, Cleveland WS (1983) A color-caused optical illusion on a statistical graph. Am Stat 37(2):101–105Google Scholar
  25. 25.
    Brewer CA, Harrower M, Sheesley B, Woodruff A, Heyman D (2014) Colorbrewer 2.0. http://colorbrewer2.org/

Copyright information

© Crown Copyright 2015

Authors and Affiliations

  1. 1.School of ChemistryUniversity of BirminghamBirminghamUK
  2. 2.Surface and NanoanalysisNational Physical LaboratoryTeddingtonUK
  3. 3.Surface and NanoanalysisNational Physical LaboratoryTeddingtonUK
  4. 4.School of PharmacyUniversity of NottinghamNottinghamUK

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