GSO Spring Speaker 2004

High-Dimensional Semilinear Model for Analysis of Microarray Data: Theoretical Exploration and Methodological Development

Dr. Jianqing Fan
Princeton University

Venue: MATH 175

Abstract:

Normalization of microarray data is essential for coping with experimental variations and revealing meaningful biological results. We have developed a normalization procedure based on within-array replications via a Semi-Linear In-slide Model (SLIM), which adjusts objectively experimental variations without making critical biological assumptions. This semiparametric model has a number of interesting features: the parametric component and the nonparametric component that are of primary interest can be consistently estimated, the former possessing parametric rate and the latter having nonparametric rate, while the nuisance parameters can not be consistently estimated. This is an interesting extension of the partial consistent phenomena observed by Neyman and Scott (1948). The significant analysis of gene expressions is based on a newly developed weighted t-statistic, which accounts for the heteroscedasticity of the observed log-ratios of expressions, and a balanced sign permutation test. We illustrated the use of the newly developed techniques in a comparison of the expression profiles of neuroblastoma cells that were stimulated with a growth factor, macophage migration inhibitory factor.