July 2013

Document Type


Degree Name



Dept. of Public Health and Preventive Medicine


Oregon Health & Science University


Microarray experiments allow researchers to assess the levels of gene expression for tens of thousands of genes at a time. A frequent goal of microarray experiments is to identify genes which are differentially expressed across various biological conditions. Several methods have been developed for determining sample size for differential expression microarray experiments, but few methods have been extended to time course experiments in which gene expression is measured over a series of time points. This thesis proposes a flexible method for sample size and power analysis of time course microarray experiments using a positive false discovery rate type I error control. Because microarray data is often observed to deviate from the assumption of normality underlying the use of parametric t-tests and F-tests, and since it has been increasingly recognized that accounting for the correlation structure of gene expression data is important for accurately estimating error rate and sample size, t




School of Medicine



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.