University of Washington - Department of Statistics
Microarrays are part of a new class of biotechnologies that can be used to measure expression levels (DNA or RNA abundance) for thousands of genes at a time. This new technology is being applied increasingly in biological and medical research to address a wide range of problems, such as the classification of tumors or the study of host responses to bacterial infections. DNA microarray experiments raise numerous statistical questions in fields as diverse as image analysis, experimental design, hypothesis testing, cluster analysis, etc.
Microarray experiments generate large and complex datasets with very limited replication. As a consequence traditional statistical methods are not satisfactory. During this talk, I will focus on two problems: image analysis and testing for differentially expressed genes. Because of the many steps involved in the experimental process from hybridization to image analysis, cDNA microarray data often contain outliers. I will show that robust Bayesian hierarchical models are particularly well suited to obtain relevant information from such complex data sets. I will present some results using publicly available gene expression data sets.