Empirical null distribution-based modeling of multi-class differential gene expression detection

In this paper, we study the multi-class differential gene expression detection for microarray data. We propose a likelihood-based approach to estimating an empirical null distribution to incorporate gene interactions and provide a more accurate false-positive control than the commonly used permutati...

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Bibliographic Details
Published in:Journal of applied statistics 2013-02, Vol.40 (2), p.347-357
Main Authors: Cao, Xiting, Wu, Baolin, Hertz, Marshall I.
Format: Article
Language:English
Subjects:
Applied statistics
differential expression detection
Empirical analysis
empirical Bayes modeling
empirical null distribution
Estimating
false discovery rate
Gene expression
gene expression data
Genes
Permutations
Simulation
Transplants
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Summary:In this paper, we study the multi-class differential gene expression detection for microarray data. We propose a likelihood-based approach to estimating an empirical null distribution to incorporate gene interactions and provide a more accurate false-positive control than the commonly used permutation or theoretical null distribution-based approach. We propose to rank important genes by p-values or local false discovery rate based on the estimated empirical null distribution. Through simulations and application to lung transplant microarray data, we illustrate the competitive performance of the proposed method.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2012.743976