Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

2012

Journal or Book Title

Statistical Applications in Genetics and Molecular Biology

Volume

11

Issue

3

First Page

12

DOI

10.1515/1544-6115.1806

Abstract

Statistical inference for microarray experiments usually involves the estimation of error variance for each gene. Because the sample size available for each gene is often low, the usual unbiased estimator of the error variance can be unreliable. Shrinkage methods, including empirical Bayes approaches that borrow information across genes to produce more stable estimates, have been developed in recent years. Because the same microarray platform is often used for at least several experiments to study similar biological systems, there is an opportunity to improve variance estimation further by borrowing information not only across genes but also across experiments. We propose a lognormal model for error variances that involves random gene effects and random experiment effects. Based on the model, we develop an empirical Bayes estimator of the error variance for each combination of gene and experiment and call this estimator BAGE because information is Borrowed Across Genes and Experiments. A permutation strategy is used to make inference about the differential expression status of each gene. Simulation studies with data generated from different probability models and real microarray data show that our method outperforms existing approaches.

Comments

This article is published as Ji, Tieming; Liu, Peng; and Nettleton, Dan (2012) "Borrowing Information Across Genes and Experiments for Improved Error Variance Estimation in Microarray Data Analysis," Statistical Applications in Genetics and Molecular Biology: Vol. 11: Iss. 3, Article 12. doi: 10.1515/1544-6115.1806. Posted with permission.

Copyright Owner

De Gruyter

Language

en

File Format

application/pdf

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