Empirical Bayes estimation of gene-specific effects in micro-array research.
Funct Integr Genomics
; 5(1): 32-9, 2005 Jan.
Article
em En
| MEDLINE
| ID: mdl-15455262
ABSTRACT
Micro-array technology allows investigators the opportunity to measure expression levels of thousands of genes simultaneously. However, investigators are also faced with the challenge of simultaneous estimation of gene expression differences for thousands of genes with very small sample sizes. Traditional estimators of differences between treatment means (ordinary least squares estimators or OLS) are not the best estimators if interest is in estimation of gene expression differences for an ensemble of genes. In the case that gene expression differences are regarded as exchangeable samples from a common population, estimators are available that result in much smaller average mean-square error across the population of gene expression difference estimates. We have simulated the application of such an estimator, namely an empirical Bayes (EB) estimator of random effects in a hierarchical linear model (normal-normal). Simulation results revealed mean-square error as low as 0.05 times the mean-square error of OLS estimators (i.e., the difference between treatment means). We applied the analysis to an example dataset as a demonstration of the shrinkage of EB estimators and of the reduction in mean-square error, i.e., increase in precision, associated with EB estimators in this analysis. The method described here is available in software that is available at http//www.soph.uab.edu/ssg.asp?id=1087.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise de Sequência com Séries de Oligonucleotídeos
/
Perfilação da Expressão Gênica
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Funct Integr Genomics
Assunto da revista:
BIOLOGIA MOLECULAR
/
GENETICA
Ano de publicação:
2005
Tipo de documento:
Article
País de afiliação:
Estados Unidos