Measurement Errors and Data Transformation for Gene Expression Data, Proteomics and Metabolomics Data

Monday, September 29, 2003 - 11:30am - 11:55am
Keller 3-180
David Rocke (University of California)
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 describe an important statistical challenge in the use of such data. Using raw data, logarithms, or ratios, the variability of the measurements is strongly dependent on the level of expression, causing a failure of the assumptions of most standard methods of statistical analysis. We present a solution to this problem via a specially tuned data transformation and show how it promotes the effectiveness of simple and sophisticated analyses of the data.