Tail posterior probability for inference in pairwise and multiclass gene expression data

N. Bochkina*, S. Richardson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We consider the problem of identifying differentially expressed genes in microarray data in a Bayesian framework with a noninformative prior distribution on the parameter quantifying differential expression. We introduce a new rule, tail posterior probability, based on the posterior distribution of the standardized difference, to identify genes differentially expressed between two conditions, and we derive a frequentist estimator of the false discovery rate associated with this rule. We compare it to other Bayesian rules in the considered settings. We show how the tail posterior probability can be extended to testing a compound null hypothesis against a class of specific alternatives in multiclass data.

Original languageEnglish
Pages (from-to)1117-1125
Number of pages9
JournalBiometrics
Volume63
Issue number4
DOIs
Publication statusPublished - Dec 2007

Keywords

  • Bayesian analysis
  • compound hypothesis
  • differential expression
  • equivalence of Bayesian and frequentist inference
  • microarray gene expression
  • multiclass data
  • tail posterior probability

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