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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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

Yvonne E Pittelkow1 / Susan R Wilson2

1Australian National University

2Australian National University

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 2, Issue 1, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1019, September 2003

Publication History

Published Online:
2003-09-04

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.

Keywords: Microarray; gene expression; SVD; biplot; ordination; Principal Component Analysis; PCA; Euclidean distance; exploratory data analysis; EDA

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