Bayesian Analysis

Measuring statistical significance for full Bayesian methods in microarray analyses

Jing Cao and Song Zhang

Full-text: Open access

Abstract

Full Bayesian methods are useful tools to account for complex data structures in high-throughput data analyses. The Bayesian FDR, which is the posterior proportion of false positives relative to the total number of rejections, has been widely used to measure statistical significance for full Bayesian methods in microarray analyses. However, the Bayesian FDR is sensitive to prior specification and it is incomparable to the resampling-based FDR estimates employed by most frequentist and empirical Bayesian methods. In this paper, we propose a computationally efficient algorithm to evaluate the statistical significance for full Bayesian methods in the resampling-based framework. The resulting predictive Bayesian FDR is robust to prior specifications and it can produce a more accurate estimate of error rate. In addition, the proposed approach provides a general framework for the objective comparison of performance between full Bayesian methods and the other frequentist and empirical Bayes methods in microarray analyses, which has been an unaddressed issue. A simulation study and a real data example are presented.

Article information

Source
Bayesian Anal., Volume 5, Number 2 (2010), 413-427.

Dates
First available in Project Euclid: 20 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1340218344

Digital Object Identifier
doi:10.1214/10-BA608

Mathematical Reviews number (MathSciNet)
MR2719658

Zentralblatt MATH identifier
1330.62112

Keywords
FDR Bayesian models microarray analysis resampling method statistical significance

Citation

Cao, Jing; Zhang, Song. Measuring statistical significance for full Bayesian methods in microarray analyses. Bayesian Anal. 5 (2010), no. 2, 413--427. doi:10.1214/10-BA608. https://projecteuclid.org/euclid.ba/1340218344


Export citation