Title
Experimental Design and Statistical Inference for cDNA Microarrays
Speaker
Dr. M. Kathleen Kerr, The Jackson Laboratory
Abstract
Gene expression microarrays are a tool that allows geneticists to compare the "level of activity" of thousands of genes in different cell samples. This information has the potential to produce enormous advances in genetics. For example, many genes have been identified by DNA sequence whose function is still unknown. Microarrays may help identify the function of such genes by discovering their patterns of expression. As a second example, this tool could help biologists understand, at the molecular level, what makes a cancer cell different from a normal cell.
As the potential of this technology has become apparent, many important and interesting statistical questions persist. The two-dye system is integral to the technology, and it is common to summarize the two fluorescent readings from a spot with their ratio. This reduction discards some valuable information in the data. Furthermore, one must account for multiple sources of variation in microarray data. This is commonly presented as the problem of data "normalization." I will discuss analysis of variance (ANOVA) techniques that integrate normalization into the data analysis so that it is done systematically and the degrees of freedom are explicitly acknowledged. Rather than relying on ratios, ANOVA models use the full amount of information in the data. This analytical framework allows one to consider alternative experimental designs for microarray studies that make more efficient use of scarce resources and produce more precise estimation of the quantities of interest. The underlying theme of this research is to incorporate rigorous statistical inference into microarray experiments.
