Title
Gene Expression Analysis from DNA Microarrays
Speaker
Jennifer Bryan, Department of Biostatistics, University of California, Berkeley
Abstract
Microarrays allow researchers to capture the intensity of expression for thousands of genes at once. Often we compare expression in two tissues (for example, healthy versus cancerous or pre-treatment versus post-treatment) and attempt to identify genes that exhibit biologically meaningful expression profiles. For example, we might be interested in genes that are differentially expressed or that exhibit strong coexpression with other genes. In this talk, I describe the use of a deterministic rule, applied to the parameters of the gene expression distribution, to select a target subset of genes. The target subset is the parameter of interest, which can be estimated by applying the subset rule to observed sample statistics. I will discuss the conditions necessary for consistency of the subset estimator and will provide a sample size formula. Important features of the sampling distribution are estimated with the parametric bootstrap. The practical performance of the method is illustrated with a data analysis in breast cancer.
