Abstract

Microarrays enable biologists to measure differences in gene expression in thousands of genes simultaneously. The data produced by microarrays present a statistical challenge, one which has been met both by new modifications of existing methods and by completely new approaches. One of the difficulties with a new approach to microarray analysis is validating the method's power and sensitivity. A simulation study could provide such validation by simulating gene expression data and investigating the method's response to changes in the data; however, due to the complex dependencies and interactions found in gene expression data, such a simulation would be complicated and time consuming. This thesis proposes a way to simulate gene expression data and validate a method by borrowing information from existing data. Analogous to the spike-in technique used to validate expression levels on an array, this simulation-based approach will add a simulated gene with known features to an existing data set. Analysis of this appended data set will reveal aspects of the method's sensitivity and power. The method and data on which this technique is illustrated come from Storey et al. (2005).

Degree

College and Department

Physical and Mathematical Sciences; Statistics

Rights

http://lib.byu.edu/about/copyright/