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

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Abstract

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.

Original languageEnglish (US)
Pages (from-to)347-357
Number of pages11
JournalJournal of Applied Statistics
Volume40
Issue number2
DOIs
StatePublished - Feb 2013

Bibliographical note

Funding Information:
This research was supported in part by NIH grant GM083345 and CA134848. We thank two anonymous referees for their constructive comments that have dramatically improved the presentation of the paper.

Keywords

  • differential expression detection
  • empirical Bayes modeling
  • empirical null distribution
  • false discovery rate
  • gene expression data

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