A Primer on the Visualization of Microarray Data

DNA microarrays represent a powerful technology offering unprecedented scope for discovery (1 ). However, the ability to measure, in parallel, the gene expression patterns for thousands of genes represents both the strength and a key weakness of microarrays. One of the central challenges of functional genomics has been to cope with the enormity of microarray data sets, and, indeed, the usefulness of microarrays has been limited by our ability to extract useful information from these data. In general terms, analyzing microarray data requires a series of numerical transformations and/or filters intended to extract from the data set the subset of represented genes that may be of interest. The resulting lists generally represent genes with large variance or periodicity within their gene expression vectors (2 ); high fold inductions over a time course (3 ); genes that are considered significant by some statistical criterion (4 ); or genes that meet some other threshold, such as exceeding a given percentile rank in the distribution of ratios (5 ,6 ). However, examining a spreadsheet of gene names and expression ratios often provides little insight into the interesting trends or patterns that may exist within the data. Rather, methods have been developed for both the classification and display of these data sets. Indeed, given the non-hypothesis-driven nature of many microarray experiments, the ability to readily visualize trends in the data assumes paramount importance.