Statistical Engineering Division Seminar

Dr. Peihua Qiu
School of Statistics
University of Minnesota
Statistical Engineering Division Seminar
Thursday, March 20, 2008, 1:30-2:30 PM
Building 222, Room A264

Abstract

Gene microarray data are used in a large variety of applications, including pharmaceutical and clinical research. By comparing gene expression in normal and abnormal cells, microarrays can be used for identifying genes involved in particular diseases, and then these genes can be targeted by therapeutic drugs. Most gene expression data are produced from spotted microarray images. A spotted microarray image consists of thousands of spots, with individual DNA sequences first printed at each spot and then equal amounts of probes (e.g., cDNA samples) from treatment and control cells mixed and hybridized with the printed DNA sequences. To obtain gene expression data, the image needs to be segmented first to separate foregrounds from backgrounds for individual spots, and then averages of foreground pixels are used for computing the gene expression data. So, image segmentation of microarray images is related directly to the reliability of gene expression data. Several image segmentation procedures have been suggested and included in some software packages handling gene microarray data. In this talk, we discuss a new image segmentation methodology proposed recently by Qiu and Sun (2007, JASA, December issue, 1129-1144), and a post-processing procedure, both of which are based on local linear kernel smoothing. Theoretical arguments and numerical studies show that they work well in applications. This is a joint work with Dr. Jingran Sun.