Title: A Comparison of Methods for Managing Type I Errors when Testing for Gene Expression Changes
Speaker: Dr. Dan Nettleton
Place: LAEB 2280; Tuesday, 4:30pm


The goal of many gene expression experiments is to determine which of several thousand genes change expression in response to a treatment. I will present results from one such experiment where researchers hope to identify rat genes that change expression in response to hypertrophy induction in muscle tissue. In this example, 8799 genes are tested for evidence of expression change using ANOVA techniques coupled with bootstrapping of residuals to determine a p-value for each gene. I will discuss three methods for using these p-values to prepare a list of genes that are believed to have changed expression in response to treatment. Selecting genes with p-values less than some significance level (e.g., 0.05) is one option that will lead to many type I errors. The use of a resampling-based method that provides approximate strong control of the probability of one or more type I errors is a second option that should lead to few type I errors (but probably many type II errors). A method controlling the false discovery rate is presented as a third option that lies somewhere between the other two methods with regard to balance between type I and type II errors.