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

Research output: Contribution to journalArticle

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 1 2013

Fingerprint

Empirical Distribution
Differential Expression
Null Distribution
Multi-class
Gene Expression
Microarray Data
Modeling
Gene
p-Value
Lung
False Positive
Likelihood
Permutation
Interaction
Gene expression
Microarray
Empirical distribution
Simulation
P value

Keywords

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

Cite this

Empirical null distribution-based modeling of multi-class differential gene expression detection. / Cao, Xiting; Wu, Baolin; Hertz, Marshall I.

In: Journal of Applied Statistics, Vol. 40, No. 2, 01.02.2013, p. 347-357.

Research output: Contribution to journalArticle

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