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The interpretation of large-scale biological data can be aided by the use of appropriate visualization tools. Heatmaps—pattern-revealing aggregate views of data—have emerged as a preferred technique for the display of genomics data, since they provide an extra dimension of information in a two-dimensional display. However, an increasing focus on the integration of data from multiple sources has created a need for the display of additional dimensions. To improve the identification of relationships between co-expressed genes identified in microarray experiments, a parallel dataset heatmap viewer has been developed for four-dimensional data display. The flexible data entry structure of the parallel heatmap viewer facilitates the display of both continuous and discrete data. Specific examples are presented for the analysis of diverse functional genomics yeast data related to gene regulation, expression, and annotation. The parallel heatmap viewer enables knowledgeable life science researchers to observe patterns and properties within high-throughput genomics data in order to rapidly identify biologically logical relationships.
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