A Bayesian determination of threshold for identifying differentially expressed genes in microarray experiments.
Stat Med
; 25(18): 3174-89, 2006 Sep 30.
Article
en En
| MEDLINE
| ID: mdl-16345048
ABSTRACT
The original definitions of false discovery rate (FDR) and false non-discovery rate (FNR) can be understood as the frequentist risks of false rejections and false non-rejections, respectively, conditional on the unknown parameter, while the Bayesian posterior FDR and posterior FNR are conditioned on the data. From a Bayesian point of view, it seems natural to take into account the uncertainties in both the parameter and the data. In this spirit, we propose averaging out the frequentist risks of false rejections and false non-rejections with respect to some prior distribution of the parameters to obtain the average FDR (AFDR) and average FNR (AFNR), respectively. A linear combination of the AFDR and AFNR, called the average Bayes error rate (ABER), is considered as an overall risk. Some useful formulas for the AFDR, AFNR and ABER are developed for normal samples with hierarchical mixture priors. The idea of finding threshold values by minimizing the ABER or controlling the AFDR is illustrated using a gene expression data set. Simulation studies show that the proposed approaches are more powerful and robust than the widely used FDR method.
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Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Teorema de Bayes
/
Análisis de Secuencia por Matrices de Oligonucleótidos
Tipo de estudio:
Prognostic_studies
Límite:
Female
/
Humans
Idioma:
En
Revista:
Stat Med
Año:
2006
Tipo del documento:
Article
País de afiliación:
Estados Unidos