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Visualisation of Gene Expression Data - the GE-biplot, the Chip-plot and the Gene-plot

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  • Pittelkow Yvonne E

    (Australian National University)

  • Wilson Susan R

    (Australian National University)

Abstract

Visualisation methods for exploring microarray data are particularly important for gaining insight into data from gene expression experiments, such as those concerned with the development of an understanding of gene function and interactions. Further, good visualisation techniques are useful for outlier detection in microarray data and for aiding biological interpretation of results, as well as for presentation of overall summaries of the data. The biplot is particularly useful for the display of microarray data as both the genes and the chips can be simultaneously plotted. In this paper we describe several ordination techniques suitable for exploring microarray data, and we call these the GE-biplot, the Chip-plot and the Gene-plot. The general method is first evaluated on synthetic data simulated in accord with current biological interpretation of microarray data. Then it is applied to two well-known data sets, namely the colon data of Alon et al. (1999) and the leukaemia data of Golub et al. (1999). The usefulness of the approach for interpreting and comparing different analyses of the same data is demonstrated.

Suggested Citation

  • Pittelkow Yvonne E & Wilson Susan R, 2003. "Visualisation of Gene Expression Data - the GE-biplot, the Chip-plot and the Gene-plot," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 2(1), pages 1-19, September.
    • Handle: RePEc:bpj:sagmbi:v:2:y:2003:i:1:n:6
    DOI: 10.2202/1544-6115.1019
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    References listed on IDEAS

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    1. Youngchao Ge & Sandrine Dudoit & Terence Speed, 2003. "Resampling-based multiple testing for microarray data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-77, June.
    2. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
    3. John Aitchison & Michael Greenacre, 2002. "Biplots of compositional data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 375-392, October.
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    1. Yvonne Pittelkow & Susan R. Wilson, 2005. "Use of Principal Component Analysis and the GE-Biplot for the Graphical Exploration of Gene Expression Data," Biometrics, The International Biometric Society, vol. 61(2), pages 630-632, June.

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