Hierarchical Bayes models for cDNA microarray gene expression.
Biostatistics
; 6(2): 279-91, 2005 Apr.
Article
em En
| MEDLINE
| ID: mdl-15772106
cDNA microarrays are used in many contexts to compare mRNA levels between samples of cells. Microarray experiments typically give us expression measurements on 1000-20 000 genes, but with few replicates for each gene. Traditional methods using means and standard deviations to detect differential expression are not satisfactory in this context. A handful of alternative statistics have been developed, including several empirical Bayes methods. In the present paper we present two full hierarchical Bayes models for detecting gene expression, of which one (D) describes our microarray data very well. We also compare the full Bayes and empirical Bayes approaches with respect to model assumptions, false discovery rates and computer running time. The proposed models are compared to existing empirical Bayes models in a simulation study and for a set of data (Yuen et al., 2002), where 27 genes have been categorized by quantitative real-time PCR. It turns out that the existing empirical Bayes methods have at least as good performance as the full Bayes ones.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Teorema de Bayes
/
Análise de Sequência com Séries de Oligonucleotídeos
/
Modelos Genéticos
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Revista:
Biostatistics
Ano de publicação:
2005
Tipo de documento:
Article