Gene expression microarrays comprise a suite of related technologies for measuring the expression of thousands of genes simultaneously from a single biological sample. There are also numerous other high-throughput biological assays that can measure large numbers of proteins, lipids, and other biologically active compounds. In this talk, I will focus on one of the statistical challenges in the use of such data. Gene expression measurements have variances that change rapidly with the mean expression, a fact that makes it difficult to apply standard (additive) statistical analysis techniques to the raw data. Use of logarithms (or equivalently, ratios) is often suggested as a cure for this problem, but this now makes data for genes with low expression difficult to use. We introduce a data transformation that can stabilize the variance across the entire range of expression, and allow standard statistical techniques to be used. This transformation also makes normalization an easier process.