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J Appl Stat. 2013 Feb 1;40(2):347-357. Epub 2012 Nov 21.

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

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  • 1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.


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 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 a lung transplant microarray data, we illustrate the competitive performance of the proposed method.


Differential expression detection; Empirical Bayes modeling; Empirical null distribution; False discovery rate; Gene expression data

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