Abstract #300964

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JSM 2003 Abstract #300964
Activity Number: 451
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #300964
Title: Estimation of Variance Components in Mixed Linear Models: Normalizing Gene Microarray Data
Author(s): Byung S. Park*+ and Motomi Mori and Shannon K. McWeeney
Companies: Oregon Health and Science University and Portland VAMC and Oregon Health and Science University
Address: 4640 NW Buckboard Dr., Portland, Oregon, 97229-7368,
Keywords: microarray ; mixed linear models ; variance components ; unweighted means ; sufficient statistic ; UMVUE
Abstract:

Microarrays are becoming increasingly popular as a tool to monitor thousands of gene expressions simultaneously. A common objective of gene microarray experiments is to determine which genes are differentially expressed across treatments. In order to estimate the level of differential expression and its accuracy, it is essential to reflect random variations of the microarray experiment. Kerr (2001) and Wolfinger (2001) provided an approach that utilizes general linear models (LM) for normalization. Although mixed LM had been suggested for normalizing microarray experiments, properties of variance components estimators had not been explored. This study reviews properties of an estimator from various methods. In balanced mixed LM, the ANOVA estimators obtained from the usual ANOVA sum of squares are UMVUEs. In gene microarray experiments, however, unbalance is frequently encountered due to various factors. In unbalanced mixed LM, in general, variance components estimators from a certain method are not uniformly better than other methods. We believe this study has the potential to provide a better normalization strategy to investigators for the analysis of microarray.

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