Visualization enables us to find structures, features, patterns and relationships in a dataset by presenting the data in various graphical forms with possible interactions. A visualization can provide a qualitative overview of large and complex datasets, can summarize data, and can assist in identifying regions of interest and appropriate parameters focused on quantitative analysis. Recent development of DNA microarray technology can be used to measure the expression levels of thousands of genes simultaneously. It has already had a significant impact on the field of bioinformatics, requiring innovative techniques to efficiently and effectively extract, analyze and visualize these fast growing data. In this paper, we present a dynamic interactive visualization environment, VizCluster, and its application on clustering gene expression data. VizCluster takes the advantage of graphical visualization methods to reveal the underlining data patterns. It combines the merits of both high dimensional projection scatter-plot and parallel coordinate plots. In its core lies a nonlinear projection which maps the n-dimensional vectors into twodimensional points. To preserves the information at different scales and yet reduces the typical problem of parallel coordinate plots being messy caused by overlapping lines, a zip zooming viewing method is proposed. Integrated with other features, VizCluster is developed to give a simple, fast, intuitive and yet powerful view of the data set. Its primary applications are on the classification of samples and evaluation of gene clusters for microarray datasets. Other applications include clustering detection and validation on low dimensional datasets. We demonstrate that VizCluster approach is promising to be used for analyzing and visualizing microarray data sets and further development is worthwhile. 1 1
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