Pittelkow Yvonne E, Wilson Susan R
Australian National University.
Stat Appl Genet Mol Biol. 2003;2:Article6. doi: 10.2202/1544-6115.1019. Epub 2003 Sep 4.
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.
探索微阵列数据的可视化方法对于深入了解基因表达实验数据尤为重要,例如那些与理解基因功能和相互作用相关的实验。此外,良好的可视化技术对于微阵列数据中的异常值检测、辅助结果的生物学解释以及呈现数据的总体摘要都很有用。双标图对于微阵列数据的展示特别有用,因为基因和芯片都可以同时绘制。在本文中,我们描述了几种适用于探索微阵列数据的排序技术,我们将其称为基因双标图(GE-biplot)、芯片图(Chip-plot)和基因图(Gene-plot)。该通用方法首先在根据当前微阵列数据生物学解释模拟的合成数据上进行评估。然后将其应用于两个著名的数据集,即阿隆等人(1999年)的结肠癌数据集和戈卢布等人(1999年)的白血病数据集。证明了该方法对于解释和比较同一数据的不同分析的有用性。